refactor: 精简仓库为 chanlun 核心与 web 分析，移除威科夫与遗留模块
删除根目录旧 Chan 模块、策略、配置、文档及 wyckoff 相关代码；更新缠论 pipeline 与笔中枢计算；补充 research 研究与 web 测试。 Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
jackyu66git
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# MacOS
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# MacOS
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.DS_Store
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.DS_Store
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# Python编译文件和缓存
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# Python
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__pycache__/
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__pycache__/
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*.py[cod]
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*.py[cod]
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*$py.class
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*$py.class
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*.pyc
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*.pyc
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*.pyo
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*.pyo
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.pytest_cache/
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# 策略文件的缓存
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# Logs & databases
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strategies/__pycache__/
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# Machine Learning / AI model files
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*_model*_xgb_model.json
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*modelchan*.json
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*.libsvm
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feature_meta
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*_model_feature_data.csv
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*.pem
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# Log files
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*.log
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*.log
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# Database files
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*.sqlite
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*.sqlite
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*.sqlite-shm
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*.sqlite-shm
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*.sqlite-wal
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*.sqlite-wal
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.DS_Store
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交易记录/~$交易规则.docx
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# Local data
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/datasvc/data
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data/
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.DS_Store
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.DS_Store
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# Local tooling
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/data_provider/data
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.DS_Store
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.DS_Store
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.DS_Store
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.DS_Store
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.DS_Store
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.DS_Store
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data_provider/._config.json
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.gstack/
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.gstack/
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# chan — Agent Entry
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本仓受 ESS 约束。不要一上来扫全库或加载全部 governance。
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## Boot
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1. `docs/PROJECT_PROFILE.md`
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2. `docs/PROJECT_RULES.md`
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3. `docs/STATE/CURRENT.md` + `docs/AGENT_MEMORY.md`
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4. 有进行中任务再读 `docs/TASKS/` / 对应 ECR / HANDOFF
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5. 角色文件：ESS 根目录 `agents/{ARCHITECT|ENGINEER|REVIEWER|RELEASE_MANAGER}.md`
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## Roles（选一）
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| 意图 | 角色 |
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|------|------|
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| 规格 / 架构 / ECR | ARCHITECT |
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| 实现 / 修 bug | ENGINEER |
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| 审阅 | REVIEWER |
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| 发版 / tag | RELEASE_MANAGER |
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## Never
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- 无 ECR 改 `config/` / `strategies/` 交易逻辑
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- 无 ADR 改缠论算法语义
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- 无 ECR 删减 `/api/analyze` 字段
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- 把聊天记录当成完成；阶段结束须落盘 `docs/`
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## Pointers
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- TRACEABILITY: `docs/TRACEABILITY.md`
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- CHANGELOG: `docs/CHANGELOG/CHANGELOG.md`
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- 人类向导：`CLAUDE.md`
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# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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缠论 (Chan Theory) technical analysis system for Freqtrade. Implements Chan Zhong Shui Chan's theory for crypto/stock trading, including fractal (分型), stroke (笔), segment (线段), pivot/center (中枢), and buy/sell point (买卖点) detection.
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## Governance
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- Agent 入口：`AGENTS.md`（boot 顺序）· `docs/PROJECT_PROFILE.md` · `docs/AGENT_MEMORY.md` · `docs/STATE/CURRENT.md`
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- ESS 文档：`docs/ECR/`、`docs/ENGINEERING_SPEC/`、`docs/TRACEABILITY.md`、`docs/CHANGELOG/`
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- **正式引擎包**：`chanlun/`；strategies / web 已用 `from chanlun import ...`
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- 根目录 `Chan*.py` / `TF_DF.py` 仍为 **兼容 shim**（旧脚本可用）
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- 变更分级：无 ECR 不改 strategies/config；无 ADR 不改缠论算法语义
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## Core Architecture
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### Chan Theory Engine (`chanlun/`)
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```text
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chanlun/
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core/ # KLU KLC BI SBI SEG ZS BIZS BSP Enum CTime
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pipeline/ # orchestrator(ChanLun) + timeframe(TF_DF) + builders/
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indicators/ # ChanMACD*
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analysis/ # Zone Classifier Pivot Heng PY Find_Trend ...
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```
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Data processing pipeline (each step feeds the next):
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1. **`chanlun.core.ChanKLU`** — Raw K-line unit with TA indicators and pattern recognition
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2. **`chanlun.core.ChanKLC`** — Combined K-line: inclusion + fractal; `.next`/`.pre` linked list
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3. **`chanlun.core.ChanBI`** — Stroke (笔)
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4. **`chanlun.core.ChanSBI`** — Special stroke → SEG
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5. **`chanlun.core.ChanSEG`** — Segment (线段)
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6. **`chanlun.core.ChanZS`** / **`ChanBIZS`** — Centers (中枢)
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7. **`chanlun.core.ChanBSP`** — Buy/Sell points
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8. **`chanlun.pipeline.orchestrator.ChanLun`** — Orchestrator
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9. **`chanlun.pipeline.timeframe.TF_DF`** — Timeframe facade；实现拆在 `pipeline/builders/`
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### Services
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- **外部 DATA_SERVICE** — 行情服务（env: `DATA_SERVICE_URL`）；本仓库可不含 data_provider 源码
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- **`web/`** — Flask UI：`create_app()` + `api/` blueprints + `services/`；前端 `static/js/app/`。默认端口见 `web/config.py`（`FLASK_PORT`，常见 8128）
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- **`strategies/`** — Freqtrade strategies（本 ECR 不改）
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- **`config/`** — Freqtrade configs（本 ECR 不改）
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### Data Flow
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```
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Exchange / DATA_SERVICE → Freqtrade Strategy / web → ChanLun → TF_DF
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→ KLU → KLC → BI → SBI → SEG → ZS → BSP
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```
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## Common Commands
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### Freqtrade Trading
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```bash
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# Live trade
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freqtrade trade -c ./user_data/Chan/config/<config>.json --strategy <StrategyName> --strategy-path ./user_data/Chan/strategies
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# Backtest
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freqtrade backtesting -c ./user_data/Chan/config/<config>.json --strategy <StrategyName> --strategy-path ./user_data/Chan/strategies --timerange=20251008-
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# Download data
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freqtrade download-data -c ./user_data/Chan/config/<config>.json -t 1m 1h 1d --pairs BTC/USDT:USDT --timerange=20240101-
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# Hyperopt
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freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy <StrategyName> --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/<config>.json -e 200 --timerange=20250201-20250901
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# Plot
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freqtrade plot-dataframe --strategy <StrategyName> --datadir user_data/data/binance -c ./user_data/Chan/config/<config>.json --timerange=20250721-
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```
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### Data Provider
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```bash
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# Docker
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cd data_provider && docker compose up -d
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# Direct
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cd data_provider && python main.py
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# With custom config
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CONFIG_PATH=./config.json python main.py
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```
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### Web UI
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```bash
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cd web && python app.py
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# or via gunicorn:
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gunicorn -w 4 -b 0.0.0.0:8123 app:app
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# Deploy scripts:
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cd web && ./deploy.sh # standard
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cd web && ./deploy_venv.sh # Ubuntu 22.04+ (venv)
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```
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### Docker (Freqtrade)
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```bash
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sudo docker compose run --rm chanlun_btc backtesting -c ./user_data/Chan/config/<config>.json --strategy <StrategyName> --strategy-path ./user_data/Chan/strategies --timerange=20250721-
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```
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## Key Conventions
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- All Chan theory classes are prefixed with `Chan` (e.g., `ChanBI`, `ChanZS`)
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- Strategies import `ChanLun` and add `sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))` to import from parent
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- MACD params: `MACD(26, 52, 9)` by default (slow period 52 instead of standard 26)
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- Enums in `ChanEnum.py` use `auto()` values
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- `ChanKLC` is a linked-list style data structure with `.next`/`.pre` pointers
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- The `TF_DF` class is the primary data container per timeframe
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- K-line direction uses `Chan_KLINE_DIR` (UP/DOWN/COMBINE/INCLUDED)
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- All text comments/commits are in Chinese
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.core.ChanBI import * # noqa: F403
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.core.ChanBIZS import * # noqa: F403
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.core.ChanBSP import * # noqa: F403
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.core.ChanCTime import * # noqa: F403
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.core.ChanEnum import * # noqa: F403
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.analysis.ChanHeng import * # noqa: F403
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.core.ChanKLC import * # noqa: F403
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.core.ChanKLU import * # noqa: F403
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.pipeline.orchestrator import ChanLun # noqa: F401
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from chanlun.pipeline.timeframe import TF_DF # noqa: F401
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.analysis.ChanLun_Classifier import * # noqa: F403
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.indicators.ChanMACD import * # noqa: F403
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.indicators.ChanMACDHistSet import * # noqa: F403
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.indicators.ChanMACDSeg import * # noqa: F403
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@@ -1,2 +0,0 @@
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.indicators.ChanMACDUnitTF import * # noqa: F403
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@@ -1,2 +0,0 @@
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.analysis.ChanPY import * # noqa: F403
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@@ -1,2 +0,0 @@
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.analysis.ChanPivotClassifier import * # noqa: F403
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@@ -1,2 +0,0 @@
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.analysis.ChanPivotMonitor import * # noqa: F403
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@@ -1,2 +0,0 @@
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.core.ChanSBI import * # noqa: F403
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@@ -1,2 +0,0 @@
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.core.ChanSEG import * # noqa: F403
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@@ -1,2 +0,0 @@
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.core.ChanZS import * # noqa: F403
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@@ -1,2 +0,0 @@
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.analysis.ChanZone import * # noqa: F403
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@@ -1,2 +0,0 @@
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.core.Chan_FX_Box import * # noqa: F403
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@@ -1,2 +0,0 @@
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.analysis.Find_Trend import * # noqa: F403
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@@ -1,2 +0,0 @@
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"""兼容 shim — 请优先 from chanlun import ..."""
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from chanlun.pipeline.timeframe import TF_DF # noqa: F401
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@@ -1,7 +0,0 @@
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"""威科夫分析（启发式）：交易区间 / 阶段 / 事件 / Volume Profile / Live。"""
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from __future__ import annotations
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from .engine import analyze_wyckoff
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from .live import execution_signal_from_wyckoff
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__all__ = ["analyze_wyckoff", "execution_signal_from_wyckoff"]
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@@ -1,196 +0,0 @@
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"""威科夫分析入口：Cycle → Phase → Event → VP + Live（MULTI-CYCLE / LIVE-STRUCTURE）。
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range.py 只产 TradingRange；Confirmed 走 events.py；Live 走 live.py。
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cycles[0]=ACTIVE；禁止 cycles[-1] 取 active。
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Execution 只消费 Confirmed（见 live.execution_signal_from_wyckoff）。
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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import pandas as pd
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from .events import build_phases, detect_bias_and_events
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from .live import analyze_live_structure
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from .range import detect_trading_ranges
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from .volume_profile import compute_volume_profile
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def _fmt_time(v) -> Optional[str]:
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if v is None:
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return None
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if hasattr(v, "isoformat"):
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try:
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return v.isoformat()
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except Exception:
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pass
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return str(v)
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def _empty(vp_bins: int) -> Dict[str, Any]:
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return {
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"cycles": [],
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"trading_range": None,
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"bias": "unknown",
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"phases": [],
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"events": [],
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"volume_profile": {"bins": [], "poc": None, "vah": None, "val": None, "bin_count": vp_bins},
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"volume_confirm": {"avg_volume": 0.0, "event_checks": {}},
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"live": None,
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}
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def _confidence_for_confirmed(
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||||||
tr: Dict[str, Any],
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||||||
phases: List[Dict[str, Any]],
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||||||
events: List[Dict[str, Any]],
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) -> Dict[str, float]:
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range_c = float(tr.get("range_confidence") or 0.5)
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|
||||||
labels = {p.get("phase") for p in phases}
|
|
||||||
phase_c = 0.35
|
|
||||||
if "A" in labels and "B" in labels:
|
|
||||||
phase_c += 0.15
|
|
||||||
if "C" in labels:
|
|
||||||
phase_c += 0.2
|
|
||||||
if "D" in labels or "E" in labels:
|
|
||||||
phase_c += 0.15
|
|
||||||
phase_c = min(0.95, phase_c)
|
|
||||||
types = {e.get("type") for e in events}
|
|
||||||
event_c = 0.25
|
|
||||||
for t in ("Spring", "UTAD", "SOS", "SOW", "LPS", "LPSY"):
|
|
||||||
if t in types:
|
|
||||||
event_c += 0.12
|
|
||||||
event_c = min(0.95, event_c)
|
|
||||||
overall = 0.4 * range_c + 0.3 * phase_c + 0.3 * event_c
|
|
||||||
return {
|
|
||||||
"range": round(range_c, 3),
|
|
||||||
"phase": round(phase_c, 3),
|
|
||||||
"event": round(event_c, 3),
|
|
||||||
"overall": round(overall, 3),
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def _build_cycle(
|
|
||||||
work: pd.DataFrame,
|
|
||||||
tr: Dict[str, Any],
|
|
||||||
cycle_id: int,
|
|
||||||
vp_bins: int,
|
|
||||||
) -> Dict[str, Any]:
|
|
||||||
bias, events, volume_confirm = detect_bias_and_events(work, tr)
|
|
||||||
phases = build_phases(work, tr, bias, events)
|
|
||||||
vp = compute_volume_profile(
|
|
||||||
work,
|
|
||||||
int(tr["abs_start_idx"]),
|
|
||||||
int(tr["abs_end_idx"]),
|
|
||||||
bin_count=vp_bins,
|
|
||||||
)
|
|
||||||
for ev in events:
|
|
||||||
ev["time"] = _fmt_time(ev.get("time"))
|
|
||||||
for ph in phases:
|
|
||||||
ph["start_time"] = _fmt_time(ph.get("start_time"))
|
|
||||||
ph["end_time"] = _fmt_time(ph.get("end_time"))
|
|
||||||
|
|
||||||
is_active = cycle_id == 0
|
|
||||||
trading_range = {
|
|
||||||
"start_time": _fmt_time(tr.get("start_time")),
|
|
||||||
"end_time": _fmt_time(tr.get("end_time")),
|
|
||||||
"high": float(tr["high"]),
|
|
||||||
"low": float(tr["low"]),
|
|
||||||
"mid": float(tr["mid"]),
|
|
||||||
"active": bool(is_active),
|
|
||||||
"bars": int(tr.get("bars", 0)),
|
|
||||||
}
|
|
||||||
conf = _confidence_for_confirmed(tr, phases, events)
|
|
||||||
|
|
||||||
# Live 层：仅 ACTIVE 周期做推演；历史周期归档为 COMPLETED
|
|
||||||
if is_active:
|
|
||||||
live = analyze_live_structure(
|
|
||||||
work, tr, confirmed_events=events, confirmed_phases=phases, bias=bias,
|
|
||||||
)
|
|
||||||
lifecycle = live.get("lifecycle") or "FORMING"
|
|
||||||
else:
|
|
||||||
live = None
|
|
||||||
lifecycle = "COMPLETED"
|
|
||||||
|
|
||||||
return {
|
|
||||||
"id": int(cycle_id),
|
|
||||||
"role": "latest" if is_active else "historical",
|
|
||||||
# MULTI-CYCLE：时间线角色
|
|
||||||
"status": "ACTIVE" if is_active else "HISTORICAL",
|
|
||||||
# LIVE-STRUCTURE：生命周期
|
|
||||||
"lifecycle": lifecycle,
|
|
||||||
"direction": "latest" if is_active else "historical",
|
|
||||||
"period": {
|
|
||||||
"start_time": _fmt_time(tr.get("start_time")),
|
|
||||||
"end_time": _fmt_time(tr.get("end_time")),
|
|
||||||
"bars": int(tr.get("bars", 0)),
|
|
||||||
},
|
|
||||||
"confidence": conf,
|
|
||||||
"trading_range": trading_range,
|
|
||||||
"bias": bias,
|
|
||||||
# 兼容旧读法：顶层 phases/events = confirmed
|
|
||||||
"phases": phases,
|
|
||||||
"events": events,
|
|
||||||
"confirmed": {
|
|
||||||
"phases": phases,
|
|
||||||
"events": events,
|
|
||||||
"volume_confirm": volume_confirm,
|
|
||||||
},
|
|
||||||
"live": live,
|
|
||||||
"volume_profile": vp,
|
|
||||||
"volume_confirm": volume_confirm,
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def analyze_wyckoff(
|
|
||||||
df: pd.DataFrame,
|
|
||||||
lookback: int = 120,
|
|
||||||
vp_bins: int = 50,
|
|
||||||
min_bars: int = 24,
|
|
||||||
atr_mult: float = 1.2,
|
|
||||||
range_start_time=None,
|
|
||||||
prefer_start_time=None,
|
|
||||||
max_cycles: int = 8,
|
|
||||||
) -> Dict[str, Any]:
|
|
||||||
"""
|
|
||||||
多周期威科夫分析。
|
|
||||||
cycles[0] = ACTIVE；顶层 phases/events 只镜像 Confirmed。
|
|
||||||
顶层 live 镜像 cycles[0].live。
|
|
||||||
"""
|
|
||||||
empty = _empty(vp_bins)
|
|
||||||
if df is None or len(df) < 30:
|
|
||||||
return empty
|
|
||||||
if not all(c in df.columns for c in ("open", "high", "low", "close")):
|
|
||||||
return empty
|
|
||||||
work = df.copy()
|
|
||||||
if "volume" not in work.columns:
|
|
||||||
work["volume"] = 1.0
|
|
||||||
|
|
||||||
trs = detect_trading_ranges(
|
|
||||||
work,
|
|
||||||
lookback=lookback,
|
|
||||||
min_bars=max(8, int(min_bars)),
|
|
||||||
atr_mult=atr_mult,
|
|
||||||
max_cycles=max(1, min(8, int(max_cycles))),
|
|
||||||
prefer_start_time=prefer_start_time,
|
|
||||||
range_start_time=range_start_time,
|
|
||||||
)
|
|
||||||
if not trs:
|
|
||||||
return empty
|
|
||||||
|
|
||||||
cycles: List[Dict[str, Any]] = []
|
|
||||||
for i, tr in enumerate(trs):
|
|
||||||
cycles.append(_build_cycle(work, tr, cycle_id=i, vp_bins=vp_bins))
|
|
||||||
|
|
||||||
active = cycles[0]
|
|
||||||
return {
|
|
||||||
"cycles": cycles,
|
|
||||||
"trading_range": active["trading_range"],
|
|
||||||
"bias": active["bias"],
|
|
||||||
"phases": active["confirmed"]["phases"],
|
|
||||||
"events": active["confirmed"]["events"],
|
|
||||||
"volume_profile": active["volume_profile"],
|
|
||||||
"volume_confirm": active["volume_confirm"],
|
|
||||||
"live": active.get("live"),
|
|
||||||
"lifecycle": active.get("lifecycle"),
|
|
||||||
}
|
|
||||||
@@ -1,369 +0,0 @@
|
|||||||
"""威科夫阶段与事件（启发式）。"""
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from typing import Any, Dict, List, Optional, Tuple
|
|
||||||
|
|
||||||
import numpy as np
|
|
||||||
import pandas as pd
|
|
||||||
|
|
||||||
|
|
||||||
def _bar_time(df: pd.DataFrame, i: int):
|
|
||||||
row = df.iloc[i]
|
|
||||||
if "date" in df.columns and pd.notna(row["date"]):
|
|
||||||
return row["date"]
|
|
||||||
if "timestamp" in df.columns:
|
|
||||||
return row["timestamp"]
|
|
||||||
return i
|
|
||||||
|
|
||||||
|
|
||||||
def _avg_vol(df: pd.DataFrame, i: int, win: int = 20) -> float:
|
|
||||||
a = max(0, i - win + 1)
|
|
||||||
v = df["volume"].astype(float).iloc[a : i + 1]
|
|
||||||
m = float(v.mean()) if len(v) else 0.0
|
|
||||||
return m if m > 0 else 1.0
|
|
||||||
|
|
||||||
|
|
||||||
def detect_bias_and_events(
|
|
||||||
df: pd.DataFrame,
|
|
||||||
tr: Dict[str, Any],
|
|
||||||
) -> Tuple[str, List[Dict[str, Any]], Dict[str, Any]]:
|
|
||||||
"""
|
|
||||||
返回 bias、events、volume_confirm。
|
|
||||||
|
|
||||||
Spring/UTAD 相对「结构高低」判定：取区间内次低/次高（剔除单根极值），
|
|
||||||
避免箱体把假破低点吃进 lo 后永远刺不破、从而无 C 阶段。
|
|
||||||
"""
|
|
||||||
hi = float(tr["high"])
|
|
||||||
lo = float(tr["low"])
|
|
||||||
mid = float(tr["mid"])
|
|
||||||
tol = float(tr.get("tol") or (hi - lo) * 0.05)
|
|
||||||
s = int(tr["abs_start_idx"])
|
|
||||||
e = int(tr["abs_end_idx"])
|
|
||||||
events: List[Dict[str, Any]] = []
|
|
||||||
|
|
||||||
# 结构边界：用次低/次高作假破参照（至少 8 根才启用）
|
|
||||||
seg = df.iloc[s : e + 1]
|
|
||||||
event_lo, event_hi = lo, hi
|
|
||||||
if len(seg) >= 8:
|
|
||||||
lows = seg["low"].astype(float)
|
|
||||||
highs = seg["high"].astype(float)
|
|
||||||
# nsmallest(2) 的较大者 = 次低；nlargest(2) 的较小者 = 次高
|
|
||||||
event_lo = float(lows.nsmallest(min(2, len(lows))).iloc[-1])
|
|
||||||
event_hi = float(highs.nlargest(min(2, len(highs))).iloc[-1])
|
|
||||||
# 勿比公布箱沿更「松」：结构带应在箱内
|
|
||||||
event_lo = max(event_lo, lo)
|
|
||||||
event_hi = min(event_hi, hi)
|
|
||||||
# 若次低仍等于极值（多根同价），略抬参照便于识别收回
|
|
||||||
if abs(event_lo - lo) < 1e-12:
|
|
||||||
event_lo = lo + max(tol * 0.35, (hi - lo) * 0.02)
|
|
||||||
if abs(event_hi - hi) < 1e-12:
|
|
||||||
event_hi = hi - max(tol * 0.35, (hi - lo) * 0.02)
|
|
||||||
|
|
||||||
# 扫描区间内及之后（含 tail_reserve）
|
|
||||||
scan_end = int(tr.get("abs_scan_end_idx", min(len(df) - 1, e + 15)))
|
|
||||||
scan_end = min(len(df) - 1, max(scan_end, e))
|
|
||||||
spring = None
|
|
||||||
utad = None
|
|
||||||
sos = None
|
|
||||||
sod = None # sign of weakness / distribution breakdown
|
|
||||||
lps = None
|
|
||||||
lpsy = None
|
|
||||||
|
|
||||||
for i in range(s + 2, scan_end + 1):
|
|
||||||
row = df.iloc[i]
|
|
||||||
low = float(row["low"])
|
|
||||||
high = float(row["high"])
|
|
||||||
close = float(row["close"])
|
|
||||||
vol = float(row["volume"]) if "volume" in df.columns else 0.0
|
|
||||||
avg_v = _avg_vol(df, i)
|
|
||||||
ratio = vol / avg_v if avg_v else 0.0
|
|
||||||
|
|
||||||
# Spring: pierce below structural support then close back
|
|
||||||
if spring is None and low < event_lo - tol * 0.35 and close >= event_lo - tol * 0.35:
|
|
||||||
vol_ok = ratio <= 1.35 or (i + 1 <= scan_end and float(df.iloc[min(i + 1, scan_end)]["volume"]) / avg_v < 1.2)
|
|
||||||
spring = {
|
|
||||||
"type": "Spring",
|
|
||||||
"time": _bar_time(df, i),
|
|
||||||
"price": low,
|
|
||||||
"note": "假破下沿后收回",
|
|
||||||
"volume_ratio": round(ratio, 3),
|
|
||||||
"volume_ok": bool(vol_ok),
|
|
||||||
"idx": i,
|
|
||||||
}
|
|
||||||
|
|
||||||
# UTAD: pierce above structural resistance then close back
|
|
||||||
if utad is None and high > event_hi + tol * 0.35 and close <= event_hi + tol * 0.35:
|
|
||||||
vol_ok = ratio >= 0.8
|
|
||||||
utad = {
|
|
||||||
"type": "UTAD",
|
|
||||||
"time": _bar_time(df, i),
|
|
||||||
"price": high,
|
|
||||||
"note": "假破上沿后跌回",
|
|
||||||
"volume_ratio": round(ratio, 3),
|
|
||||||
"volume_ok": bool(vol_ok),
|
|
||||||
"idx": i,
|
|
||||||
}
|
|
||||||
|
|
||||||
# SOS: close above high with volume
|
|
||||||
if sos is None and close > hi + tol * 0.15:
|
|
||||||
vol_ok = ratio >= 1.15
|
|
||||||
sos = {
|
|
||||||
"type": "SOS",
|
|
||||||
"time": _bar_time(df, i),
|
|
||||||
"price": close,
|
|
||||||
"note": "放量上破交易区间",
|
|
||||||
"volume_ratio": round(ratio, 3),
|
|
||||||
"volume_ok": bool(vol_ok),
|
|
||||||
"idx": i,
|
|
||||||
}
|
|
||||||
|
|
||||||
# SOW / breakdown
|
|
||||||
if sod is None and close < lo - tol * 0.15:
|
|
||||||
vol_ok = ratio >= 1.15
|
|
||||||
sod = {
|
|
||||||
"type": "SOW",
|
|
||||||
"time": _bar_time(df, i),
|
|
||||||
"price": close,
|
|
||||||
"note": "放量下破交易区间",
|
|
||||||
"volume_ratio": round(ratio, 3),
|
|
||||||
"volume_ok": bool(vol_ok),
|
|
||||||
"idx": i,
|
|
||||||
}
|
|
||||||
|
|
||||||
# LPS after SOS: pullback that holds above mid/high-band with lighter volume
|
|
||||||
if sos is not None:
|
|
||||||
si = int(sos["idx"])
|
|
||||||
for i in range(si + 1, min(len(df), si + 25)):
|
|
||||||
row = df.iloc[i]
|
|
||||||
low = float(row["low"])
|
|
||||||
close = float(row["close"])
|
|
||||||
vol = float(row["volume"]) if "volume" in df.columns else 0.0
|
|
||||||
avg_v = _avg_vol(df, i)
|
|
||||||
ratio = vol / avg_v if avg_v else 0.0
|
|
||||||
if low >= mid - tol and close >= hi - tol * 2:
|
|
||||||
vol_ok = ratio <= 1.05
|
|
||||||
lps = {
|
|
||||||
"type": "LPS",
|
|
||||||
"time": _bar_time(df, i),
|
|
||||||
"price": low,
|
|
||||||
"note": "突破后缩量回踩不破",
|
|
||||||
"volume_ratio": round(ratio, 3),
|
|
||||||
"volume_ok": bool(vol_ok),
|
|
||||||
"idx": i,
|
|
||||||
}
|
|
||||||
break
|
|
||||||
|
|
||||||
if sod is not None:
|
|
||||||
si = int(sod["idx"])
|
|
||||||
for i in range(si + 1, min(len(df), si + 25)):
|
|
||||||
row = df.iloc[i]
|
|
||||||
high = float(row["high"])
|
|
||||||
close = float(row["close"])
|
|
||||||
vol = float(row["volume"]) if "volume" in df.columns else 0.0
|
|
||||||
avg_v = _avg_vol(df, i)
|
|
||||||
ratio = vol / avg_v if avg_v else 0.0
|
|
||||||
if high <= mid + tol and close <= lo + tol * 2:
|
|
||||||
vol_ok = ratio <= 1.05
|
|
||||||
lpsy = {
|
|
||||||
"type": "LPSY",
|
|
||||||
"time": _bar_time(df, i),
|
|
||||||
"price": high,
|
|
||||||
"note": "下跌突破后缩量反抽不过",
|
|
||||||
"volume_ratio": round(ratio, 3),
|
|
||||||
"volume_ok": bool(vol_ok),
|
|
||||||
"idx": i,
|
|
||||||
}
|
|
||||||
break
|
|
||||||
|
|
||||||
# 冲突清理：已判定吸筹且有 SOS 时，丢弃更早的 UTAD（避免阶段/图面误导）
|
|
||||||
# 派发且有 SOW 时，丢弃更晚才合理的 Spring 假信号同理在偏置后再滤
|
|
||||||
keep = []
|
|
||||||
for ev in (spring, sos, lps, utad, sod, lpsy):
|
|
||||||
if not ev:
|
|
||||||
continue
|
|
||||||
keep.append(ev)
|
|
||||||
|
|
||||||
# bias（先算）
|
|
||||||
last_c = float(df["close"].iloc[-1])
|
|
||||||
bias = "unknown"
|
|
||||||
if sos and (not sod or int(sos.get("idx", 0)) >= int(sod.get("idx", 0))):
|
|
||||||
bias = "accumulation"
|
|
||||||
elif sod and (not sos or int(sod.get("idx", 0)) > int(sos.get("idx", 0))):
|
|
||||||
bias = "distribution"
|
|
||||||
elif spring and not utad:
|
|
||||||
bias = "accumulation"
|
|
||||||
elif utad and not spring:
|
|
||||||
bias = "distribution"
|
|
||||||
elif last_c >= mid:
|
|
||||||
bias = "accumulation"
|
|
||||||
else:
|
|
||||||
bias = "distribution"
|
|
||||||
|
|
||||||
filtered = []
|
|
||||||
for ev in keep:
|
|
||||||
if bias == "accumulation" and ev["type"] == "UTAD" and sos and int(ev["idx"]) <= int(sos["idx"]):
|
|
||||||
continue
|
|
||||||
if bias == "distribution" and ev["type"] == "Spring" and sod and int(ev["idx"]) <= int(sod["idx"]):
|
|
||||||
continue
|
|
||||||
filtered.append(ev)
|
|
||||||
|
|
||||||
events = [{k: v for k, v in ev.items() if k != "idx"} for ev in filtered]
|
|
||||||
|
|
||||||
avg_volume = float(df["volume"].astype(float).iloc[max(0, e - 20) : e + 1].mean()) if "volume" in df.columns else 0.0
|
|
||||||
volume_confirm = {
|
|
||||||
"avg_volume": avg_volume,
|
|
||||||
"event_checks": {ev["type"]: {"volume_ok": ev.get("volume_ok"), "volume_ratio": ev.get("volume_ratio")} for ev in events},
|
|
||||||
}
|
|
||||||
return bias, events, volume_confirm
|
|
||||||
|
|
||||||
|
|
||||||
def build_phases(
|
|
||||||
df: pd.DataFrame,
|
|
||||||
tr: Dict[str, Any],
|
|
||||||
bias: str,
|
|
||||||
events: List[Dict[str, Any]],
|
|
||||||
min_bars: int = 3,
|
|
||||||
) -> List[Dict[str, Any]]:
|
|
||||||
"""
|
|
||||||
按威科夫事件锚点切分 A–E（启发式）。
|
|
||||||
|
|
||||||
吸筹：A停止 → B筑底 → C测试(Spring) → D拉升(SOS…LPS) → E离开
|
|
||||||
派发：A停止 → B筑顶 → C测试(UTAD) → D派发(SOW…LPSY) → E离开
|
|
||||||
|
|
||||||
无 Spring/UTAD 时：若已有 SOS/SOW，用突破前末次沿带测试补 C；仍无则省略 C。
|
|
||||||
"""
|
|
||||||
s = int(tr["abs_start_idx"])
|
|
||||||
e = int(tr["abs_end_idx"])
|
|
||||||
hi = float(tr["high"])
|
|
||||||
lo = float(tr["low"])
|
|
||||||
n_last = len(df) - 1
|
|
||||||
min_span = max(2, min_bars - 1)
|
|
||||||
range_len = max(1, e - s)
|
|
||||||
|
|
||||||
def _match_idx(t) -> Optional[int]:
|
|
||||||
if t is None:
|
|
||||||
return None
|
|
||||||
lo = max(0, s - 2)
|
|
||||||
hi = min(len(df), e + 40)
|
|
||||||
for i in range(lo, hi):
|
|
||||||
if _bar_time(df, i) == t:
|
|
||||||
return i
|
|
||||||
try:
|
|
||||||
tt = pd.Timestamp(t)
|
|
||||||
sample = None
|
|
||||||
if "date" in df.columns and len(df):
|
|
||||||
sample = df["date"].iloc[min(s, n_last)]
|
|
||||||
if sample is not None and getattr(sample, "tzinfo", None) is not None and tt.tzinfo is None:
|
|
||||||
tt = tt.tz_localize(sample.tzinfo)
|
|
||||||
for i in range(lo, hi):
|
|
||||||
bt = _bar_time(df, i)
|
|
||||||
try:
|
|
||||||
if abs((pd.Timestamp(bt) - tt).total_seconds()) <= 1:
|
|
||||||
return i
|
|
||||||
except Exception:
|
|
||||||
continue
|
|
||||||
except Exception:
|
|
||||||
pass
|
|
||||||
return None
|
|
||||||
|
|
||||||
event_idx: Dict[str, int] = {}
|
|
||||||
for ev in events:
|
|
||||||
idx = _match_idx(ev.get("time"))
|
|
||||||
if idx is not None:
|
|
||||||
event_idx[str(ev.get("type"))] = idx
|
|
||||||
|
|
||||||
accum = bias != "distribution"
|
|
||||||
if accum:
|
|
||||||
c_ev = event_idx.get("Spring")
|
|
||||||
d_ev = event_idx.get("SOS")
|
|
||||||
d_tail = event_idx.get("LPS") or d_ev
|
|
||||||
else:
|
|
||||||
c_ev = event_idx.get("UTAD")
|
|
||||||
d_ev = event_idx.get("SOW")
|
|
||||||
d_tail = event_idx.get("LPSY") or d_ev
|
|
||||||
|
|
||||||
# 有 D 无明确测试事件时：用突破前最后一次触及下/上沿作为 C（次级测试）
|
|
||||||
if c_ev is None and d_ev is not None:
|
|
||||||
band = lo + (hi - lo) * 0.28 if accum else hi - (hi - lo) * 0.28
|
|
||||||
for i in range(int(d_ev) - 1, s + 1, -1):
|
|
||||||
row = df.iloc[i]
|
|
||||||
if accum and float(row["low"]) <= band:
|
|
||||||
c_ev = i
|
|
||||||
break
|
|
||||||
if not accum and float(row["high"]) >= band:
|
|
||||||
c_ev = i
|
|
||||||
break
|
|
||||||
|
|
||||||
def _lab(phase: str) -> str:
|
|
||||||
if accum:
|
|
||||||
m = {"A": "A停止下跌", "B": "B筑底", "C": "C测试", "D": "D拉升", "E": "E离开"}
|
|
||||||
else:
|
|
||||||
m = {"A": "A停止上涨", "B": "B筑顶", "C": "C测试", "D": "D派发", "E": "E离开"}
|
|
||||||
return m.get(phase, phase)
|
|
||||||
|
|
||||||
a_end = s + max(min_bars, range_len // 5)
|
|
||||||
|
|
||||||
c_start = c_end = None
|
|
||||||
if c_ev is not None:
|
|
||||||
c_start = max(s, int(c_ev) - 1)
|
|
||||||
c_end = min(n_last, int(c_ev) + 1)
|
|
||||||
|
|
||||||
if d_ev is not None:
|
|
||||||
d_start = int(d_ev)
|
|
||||||
d_end = min(n_last, max(int(d_tail or d_ev), d_start) + max(min_bars, range_len // 8))
|
|
||||||
if d_tail is not None:
|
|
||||||
d_end = max(d_end, min(n_last, int(d_tail) + 1))
|
|
||||||
else:
|
|
||||||
d_start = d_end = None
|
|
||||||
|
|
||||||
if c_start is not None:
|
|
||||||
b_end = max(a_end + 1, c_start)
|
|
||||||
elif d_start is not None:
|
|
||||||
b_end = max(a_end + 1, d_start)
|
|
||||||
else:
|
|
||||||
b_end = max(a_end + 1, e)
|
|
||||||
|
|
||||||
if d_end is not None:
|
|
||||||
e_start = min(n_last, d_end)
|
|
||||||
e_end = n_last
|
|
||||||
else:
|
|
||||||
e_start = e_end = None
|
|
||||||
|
|
||||||
raw = [("A", s, a_end), ("B", a_end, b_end)]
|
|
||||||
if c_start is not None and c_end is not None:
|
|
||||||
raw.append(("C", c_start, c_end))
|
|
||||||
if d_start is not None and d_end is not None:
|
|
||||||
raw.append(("D", d_start, d_end))
|
|
||||||
if e_start is not None and e_end is not None and e_end > e_start:
|
|
||||||
raw.append(("E", e_start, e_end))
|
|
||||||
|
|
||||||
phases: List[Dict[str, Any]] = []
|
|
||||||
cursor = s
|
|
||||||
for phase, _a, _b in raw:
|
|
||||||
if cursor >= n_last:
|
|
||||||
break
|
|
||||||
a = max(int(_a), cursor)
|
|
||||||
b = int(max(int(_b), a))
|
|
||||||
need = 1 if phase == "C" else min_span
|
|
||||||
if b < a + need:
|
|
||||||
b = min(n_last, a + need)
|
|
||||||
b = int(np.clip(b, a, n_last))
|
|
||||||
if b < a:
|
|
||||||
continue
|
|
||||||
if phases and phases[-1].get("_a") == a and phases[-1].get("_b") == b:
|
|
||||||
continue
|
|
||||||
phases.append(
|
|
||||||
{
|
|
||||||
"phase": phase,
|
|
||||||
"label": _lab(phase),
|
|
||||||
"start_time": _bar_time(df, a),
|
|
||||||
"end_time": _bar_time(df, b),
|
|
||||||
"_a": a,
|
|
||||||
"_b": b,
|
|
||||||
}
|
|
||||||
)
|
|
||||||
cursor = b
|
|
||||||
for p in phases:
|
|
||||||
p.pop("_a", None)
|
|
||||||
p.pop("_b", None)
|
|
||||||
return phases
|
|
||||||
@@ -1,258 +0,0 @@
|
|||||||
"""威科夫 Live / Developing 层（WYCKOFF-LIVE-STRUCTURE-001）。
|
|
||||||
|
|
||||||
独立于 Confirmed Engine：不修改 events 确认条件，不写入 confirmed.events。
|
|
||||||
Execution 不得消费本模块输出。
|
|
||||||
"""
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from typing import Any, Dict, List, Optional, Set
|
|
||||||
|
|
||||||
import numpy as np
|
|
||||||
import pandas as pd
|
|
||||||
|
|
||||||
|
|
||||||
def _avg_vol(df: pd.DataFrame, i: int, win: int = 20) -> float:
|
|
||||||
a = max(0, i - win + 1)
|
|
||||||
v = df["volume"].astype(float).iloc[a : i + 1]
|
|
||||||
m = float(v.mean()) if len(v) else 0.0
|
|
||||||
return m if m > 0 else 1.0
|
|
||||||
|
|
||||||
|
|
||||||
def _empty_live() -> Dict[str, Any]:
|
|
||||||
return {
|
|
||||||
"lifecycle": "UNKNOWN",
|
|
||||||
"range_formation": None,
|
|
||||||
"phase_candidate": None,
|
|
||||||
"event_candidates": [],
|
|
||||||
"next_expected": None,
|
|
||||||
"confidence": {
|
|
||||||
"cycle": 0.0,
|
|
||||||
"phase": 0.0,
|
|
||||||
"event": 0.0,
|
|
||||||
"structure": 0.0,
|
|
||||||
"volume": 0.0,
|
|
||||||
"overall": 0.0,
|
|
||||||
},
|
|
||||||
"note": "",
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def analyze_live_structure(
|
|
||||||
df: pd.DataFrame,
|
|
||||||
tr: Optional[Dict[str, Any]],
|
|
||||||
confirmed_events: Optional[List[Dict[str, Any]]] = None,
|
|
||||||
confirmed_phases: Optional[List[Dict[str, Any]]] = None,
|
|
||||||
bias: str = "unknown",
|
|
||||||
) -> Dict[str, Any]:
|
|
||||||
"""
|
|
||||||
基于当前 TradingRange 与已确认事件，推演 Live candidates。
|
|
||||||
confirmed_* 只读，用于避免重复提示已确认事件，不修改之。
|
|
||||||
"""
|
|
||||||
out = _empty_live()
|
|
||||||
if df is None or len(df) < 20 or tr is None:
|
|
||||||
out["note"] = "insufficient structure"
|
|
||||||
return out
|
|
||||||
|
|
||||||
confirmed_events = confirmed_events or []
|
|
||||||
confirmed_phases = confirmed_phases or []
|
|
||||||
confirmed_types: Set[str] = {str(e.get("type")) for e in confirmed_events if e.get("type")}
|
|
||||||
|
|
||||||
s = int(tr["abs_start_idx"])
|
|
||||||
e = int(tr["abs_end_idx"])
|
|
||||||
scan_end = int(tr.get("abs_scan_end_idx", len(df) - 1))
|
|
||||||
scan_end = min(len(df) - 1, max(scan_end, e))
|
|
||||||
hi = float(tr["high"])
|
|
||||||
lo = float(tr["low"])
|
|
||||||
mid = float(tr["mid"])
|
|
||||||
tol = float(tr.get("tol") or (hi - lo) * 0.05)
|
|
||||||
atr = float(tr.get("atr") or max((hi - lo) * 0.2, 1e-9))
|
|
||||||
|
|
||||||
seg = df.iloc[s : e + 1]
|
|
||||||
if len(seg) < 8:
|
|
||||||
out["note"] = "range too short"
|
|
||||||
return out
|
|
||||||
|
|
||||||
# —— Range Formation（横盘 / 波动收敛）——
|
|
||||||
closes = seg["close"].astype(float)
|
|
||||||
highs = seg["high"].astype(float)
|
|
||||||
lows = seg["low"].astype(float)
|
|
||||||
vols = seg["volume"].astype(float) if "volume" in seg.columns else pd.Series([1.0] * len(seg))
|
|
||||||
half = max(4, len(seg) // 2)
|
|
||||||
vol_early = float(np.std(closes.iloc[:half])) if half > 1 else 0.0
|
|
||||||
vol_late = float(np.std(closes.iloc[-half:])) if half > 1 else 0.0
|
|
||||||
width = hi - lo
|
|
||||||
width_atr = width / atr if atr > 0 else 99.0
|
|
||||||
converging = vol_early > 1e-12 and vol_late < vol_early * 0.85
|
|
||||||
range_ok = 1.2 <= width_atr <= 10.0 and len(seg) >= 16
|
|
||||||
structure_score = 0.35
|
|
||||||
if range_ok:
|
|
||||||
structure_score += 0.25
|
|
||||||
if converging:
|
|
||||||
structure_score += 0.2
|
|
||||||
if width_atr <= 6.0:
|
|
||||||
structure_score += 0.1
|
|
||||||
structure_score = float(min(0.95, structure_score))
|
|
||||||
|
|
||||||
out["range_formation"] = {
|
|
||||||
"potential_trading_range": bool(range_ok),
|
|
||||||
"converging": bool(converging),
|
|
||||||
"width_atr": round(width_atr, 3),
|
|
||||||
"bars": int(len(seg)),
|
|
||||||
}
|
|
||||||
|
|
||||||
# —— 最近 K 形态（Phase C / Event candidates）——
|
|
||||||
i = scan_end
|
|
||||||
row = df.iloc[i]
|
|
||||||
o = float(row["open"])
|
|
||||||
h = float(row["high"])
|
|
||||||
l = float(row["low"])
|
|
||||||
c = float(row["close"])
|
|
||||||
rng = max(h - l, 1e-9)
|
|
||||||
lower_wick = min(o, c) - l
|
|
||||||
upper_wick = h - max(o, c)
|
|
||||||
avg_v = _avg_vol(df, i)
|
|
||||||
vol = float(row["volume"]) if "volume" in df.columns else avg_v
|
|
||||||
vol_ratio = vol / avg_v if avg_v else 1.0
|
|
||||||
volume_score = float(np.clip(1.1 - abs(vol_ratio - 1.0) * 0.35, 0.2, 0.95))
|
|
||||||
|
|
||||||
phase_candidate = None
|
|
||||||
phase_conf = 0.0
|
|
||||||
# Phase C：测低 + 下影 + 缩量（吸筹语境）
|
|
||||||
near_lo = l <= lo + tol * 1.2
|
|
||||||
test_low = l < mid and lower_wick >= rng * 0.35
|
|
||||||
vol_contract = vol_ratio <= 1.05
|
|
||||||
if bias != "distribution" and near_lo and test_low and vol_contract:
|
|
||||||
phase_candidate = "C"
|
|
||||||
phase_conf = 0.55 + (0.1 if lower_wick >= rng * 0.5 else 0) + (0.08 if vol_ratio < 0.9 else 0)
|
|
||||||
# Phase D 候选：价格在箱上半、有上破意图但未确认 SOS
|
|
||||||
elif c >= mid and (h >= hi - tol or c > hi - tol * 0.5):
|
|
||||||
phase_candidate = "D"
|
|
||||||
phase_conf = 0.5 + (0.1 if c > mid else 0)
|
|
||||||
elif c < mid and (l <= lo + tol):
|
|
||||||
phase_candidate = "B"
|
|
||||||
phase_conf = 0.45
|
|
||||||
|
|
||||||
# 已有 confirmed phase 时，candidate 取「下一阶段」提示，不覆盖事实
|
|
||||||
confirmed_phase_set = {str(p.get("phase")) for p in confirmed_phases}
|
|
||||||
if "E" in confirmed_phase_set:
|
|
||||||
phase_candidate = phase_candidate or "E"
|
|
||||||
phase_conf = max(phase_conf, 0.7)
|
|
||||||
elif "D" in confirmed_phase_set and phase_candidate is None:
|
|
||||||
phase_candidate = "D"
|
|
||||||
phase_conf = max(phase_conf, 0.65)
|
|
||||||
|
|
||||||
out["phase_candidate"] = phase_candidate
|
|
||||||
phase_conf = float(min(0.92, phase_conf))
|
|
||||||
|
|
||||||
# —— Event candidates（仅 Spring / SOS / LPS / UTAD）——
|
|
||||||
candidates: List[Dict[str, Any]] = []
|
|
||||||
|
|
||||||
def _add(typ: str, conf: float, note: str) -> None:
|
|
||||||
if typ in confirmed_types:
|
|
||||||
return # 已确认则不再作为 candidate
|
|
||||||
candidates.append(
|
|
||||||
{
|
|
||||||
"type": typ,
|
|
||||||
"confidence": round(float(min(0.9, conf)), 3),
|
|
||||||
"confirmed": False,
|
|
||||||
"note": note,
|
|
||||||
}
|
|
||||||
)
|
|
||||||
|
|
||||||
# Spring candidate：刺破或贴近下沿，收盘收回，但未达 Confirmed 规则（或不在 confirmed）
|
|
||||||
pierce_lo = l < lo - tol * 0.15
|
|
||||||
close_back = c >= lo - tol * 0.5
|
|
||||||
if pierce_lo and close_back:
|
|
||||||
_add("Spring", 0.5 + (0.12 if vol_ratio <= 1.2 else 0) + (0.08 if close_back else 0), "假破下沿收回（未确认）")
|
|
||||||
elif l <= lo + tol * 0.35 and close_back and lower_wick >= rng * 0.4:
|
|
||||||
_add("Spring", 0.45 + (0.1 if vol_contract else 0), "测下沿长下影（未确认）")
|
|
||||||
|
|
||||||
# UTAD candidate
|
|
||||||
pierce_hi = h > hi + tol * 0.15
|
|
||||||
close_back_dn = c <= hi + tol * 0.5
|
|
||||||
if pierce_hi and close_back_dn:
|
|
||||||
_add("UTAD", 0.5 + (0.1 if vol_ratio >= 0.9 else 0), "假破上沿跌回（未确认）")
|
|
||||||
|
|
||||||
# SOS candidate：接近/轻破上沿，量能一般，未确认
|
|
||||||
if c > hi - tol * 0.4 or h >= hi:
|
|
||||||
sos_conf = 0.48 + (0.12 if c > hi else 0) + (0.1 if vol_ratio >= 1.05 else 0)
|
|
||||||
_add("SOS", sos_conf, "上破/逼近箱顶（未确认）")
|
|
||||||
|
|
||||||
# LPS candidate：站上 mid/上沿带后回踩
|
|
||||||
if c >= mid and l >= mid - tol * 1.5 and l > lo + (hi - lo) * 0.25:
|
|
||||||
_add("LPS", 0.46 + (0.1 if vol_ratio <= 1.0 else 0), "箱内上沿带回踩（未确认）")
|
|
||||||
|
|
||||||
candidates.sort(key=lambda x: x["confidence"], reverse=True)
|
|
||||||
out["event_candidates"] = candidates[:4]
|
|
||||||
|
|
||||||
event_score = float(candidates[0]["confidence"]) if candidates else 0.25
|
|
||||||
|
|
||||||
# next_expected（简规则）
|
|
||||||
next_exp = None
|
|
||||||
if "Spring" in confirmed_types and "SOS" not in confirmed_types:
|
|
||||||
next_exp = "SOS"
|
|
||||||
elif "SOS" in confirmed_types and "LPS" not in confirmed_types:
|
|
||||||
next_exp = "LPS"
|
|
||||||
elif "UTAD" in confirmed_types and "SOW" not in confirmed_types:
|
|
||||||
next_exp = "SOW"
|
|
||||||
elif any(c["type"] == "Spring" for c in candidates):
|
|
||||||
next_exp = "Test"
|
|
||||||
elif any(c["type"] == "SOS" for c in candidates):
|
|
||||||
next_exp = "LPS"
|
|
||||||
out["next_expected"] = next_exp
|
|
||||||
|
|
||||||
# —— lifecycle ——
|
|
||||||
key_confirmed = confirmed_types & {"Spring", "SOS", "UTAD", "SOW", "LPS", "LPSY"}
|
|
||||||
if key_confirmed:
|
|
||||||
lifecycle = "CONFIRMED"
|
|
||||||
elif range_ok or phase_candidate or candidates:
|
|
||||||
lifecycle = "FORMING"
|
|
||||||
else:
|
|
||||||
lifecycle = "UNKNOWN"
|
|
||||||
out["lifecycle"] = lifecycle
|
|
||||||
|
|
||||||
cycle_c = structure_score
|
|
||||||
overall = 0.35 * cycle_c + 0.25 * phase_conf + 0.25 * event_score + 0.15 * volume_score
|
|
||||||
out["confidence"] = {
|
|
||||||
"cycle": round(cycle_c, 3),
|
|
||||||
"phase": round(phase_conf, 3),
|
|
||||||
"event": round(event_score, 3),
|
|
||||||
"structure": round(structure_score, 3),
|
|
||||||
"volume": round(volume_score, 3),
|
|
||||||
"overall": round(float(overall), 3),
|
|
||||||
}
|
|
||||||
parts = []
|
|
||||||
if out["range_formation"]["potential_trading_range"]:
|
|
||||||
parts.append("Potential TR")
|
|
||||||
if phase_candidate:
|
|
||||||
parts.append(f"Phase {phase_candidate} candidate")
|
|
||||||
if candidates:
|
|
||||||
parts.append(f"{candidates[0]['type']} candidate")
|
|
||||||
out["note"] = "; ".join(parts) if parts else "observing"
|
|
||||||
return out
|
|
||||||
|
|
||||||
|
|
||||||
def execution_signal_from_wyckoff(payload: Dict[str, Any]) -> Optional[Dict[str, Any]]:
|
|
||||||
"""
|
|
||||||
Execution 边界：只允许 Confirmed。
|
|
||||||
返回 source='confirmed' 的信号描述；Live-only 时返回 None。
|
|
||||||
"""
|
|
||||||
if not payload:
|
|
||||||
return None
|
|
||||||
cycles = payload.get("cycles") or []
|
|
||||||
active = cycles[0] if cycles else None
|
|
||||||
events = []
|
|
||||||
if active and isinstance(active.get("confirmed"), dict):
|
|
||||||
events = list(active["confirmed"].get("events") or [])
|
|
||||||
if not events:
|
|
||||||
# 兼容旧顶层 events（均为 confirmed 镜像）
|
|
||||||
events = list(payload.get("events") or [])
|
|
||||||
if not events:
|
|
||||||
return None
|
|
||||||
last = events[-1]
|
|
||||||
return {
|
|
||||||
"source": "confirmed",
|
|
||||||
"type": last.get("type"),
|
|
||||||
"time": last.get("time"),
|
|
||||||
"lifecycle": (active or {}).get("lifecycle") or "CONFIRMED",
|
|
||||||
}
|
|
||||||
@@ -1,442 +0,0 @@
|
|||||||
"""交易区间检测：仅负责 TradingRange（起止/高低/结构分）。
|
|
||||||
|
|
||||||
WYCKOFF-MULTI-CYCLE-001：Phase/Event/VP 不得进入本模块。
|
|
||||||
过滤顺序固定：detect → quality → trend → overlap(<0.2) → accept → mask。
|
|
||||||
"""
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from typing import Any, Dict, List, Optional, Tuple
|
|
||||||
|
|
||||||
import numpy as np
|
|
||||||
import pandas as pd
|
|
||||||
|
|
||||||
MAX_CYCLES = 8
|
|
||||||
OVERLAP_RATIO_MAX = 0.2
|
|
||||||
|
|
||||||
|
|
||||||
def _atr(df: pd.DataFrame, period: int = 14) -> pd.Series:
|
|
||||||
high = df["high"].astype(float)
|
|
||||||
low = df["low"].astype(float)
|
|
||||||
close = df["close"].astype(float)
|
|
||||||
prev_close = close.shift(1)
|
|
||||||
tr = pd.concat(
|
|
||||||
[
|
|
||||||
(high - low).abs(),
|
|
||||||
(high - prev_close).abs(),
|
|
||||||
(low - prev_close).abs(),
|
|
||||||
],
|
|
||||||
axis=1,
|
|
||||||
).max(axis=1)
|
|
||||||
return tr.rolling(period, min_periods=max(3, period // 2)).mean()
|
|
||||||
|
|
||||||
|
|
||||||
def _robust_width(seg: pd.DataFrame) -> float:
|
|
||||||
"""用 90/10 分位估宽，避免单根影线把长窗卡死。"""
|
|
||||||
h = seg["high"].astype(float)
|
|
||||||
l = seg["low"].astype(float)
|
|
||||||
if len(seg) < 6:
|
|
||||||
return float(h.max() - l.min())
|
|
||||||
return float(np.nanpercentile(h, 90) - np.nanpercentile(l, 10))
|
|
||||||
|
|
||||||
|
|
||||||
def _score_segment(
|
|
||||||
length: int,
|
|
||||||
near_hi: int,
|
|
||||||
near_lo: int,
|
|
||||||
inside: float,
|
|
||||||
width: float,
|
|
||||||
atr: float,
|
|
||||||
) -> float:
|
|
||||||
"""结构质量分（非 Phase/Event）。"""
|
|
||||||
touch = min(near_hi, 6) + min(near_lo, 6)
|
|
||||||
width_pen = (width / atr) if atr > 0 else width
|
|
||||||
return float(touch) * 4.0 + float(inside) * 25.0 - width_pen * 3.0 + min(length / 40.0, 2.0)
|
|
||||||
|
|
||||||
|
|
||||||
def _time_col(df: pd.DataFrame) -> Optional[str]:
|
|
||||||
if "date" in df.columns:
|
|
||||||
return "date"
|
|
||||||
if "timestamp" in df.columns:
|
|
||||||
return "timestamp"
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
def _bar_index_at_or_after(work: pd.DataFrame, ts: Any) -> Optional[int]:
|
|
||||||
col = _time_col(work)
|
|
||||||
if col is None or ts is None:
|
|
||||||
return None
|
|
||||||
try:
|
|
||||||
target = pd.Timestamp(ts)
|
|
||||||
except Exception:
|
|
||||||
return None
|
|
||||||
series = pd.to_datetime(work[col], utc=True, errors="coerce")
|
|
||||||
if target.tzinfo is None:
|
|
||||||
target = target.tz_localize("UTC")
|
|
||||||
else:
|
|
||||||
target = target.tz_convert("UTC")
|
|
||||||
if series.isna().all():
|
|
||||||
return None
|
|
||||||
ge = series >= target
|
|
||||||
if ge.any():
|
|
||||||
return int(np.flatnonzero(ge.to_numpy())[0])
|
|
||||||
return 0
|
|
||||||
|
|
||||||
|
|
||||||
def _pack_range(
|
|
||||||
work: pd.DataFrame,
|
|
||||||
df: pd.DataFrame,
|
|
||||||
start_i: int,
|
|
||||||
end_i: int,
|
|
||||||
hi: float,
|
|
||||||
lo: float,
|
|
||||||
tol: float,
|
|
||||||
last_atr: float,
|
|
||||||
score: float,
|
|
||||||
n: int,
|
|
||||||
window_offset: int = 0,
|
|
||||||
) -> Dict[str, Any]:
|
|
||||||
"""组装 TradingRange（仅结构字段）。"""
|
|
||||||
mid = (hi + lo) / 2.0
|
|
||||||
last_c = float(work["close"].iloc[min(end_i, len(work) - 1)])
|
|
||||||
price_in_box = (lo - tol * 1.5) <= last_c <= (hi + tol * 1.5)
|
|
||||||
bars = int(end_i - start_i + 1)
|
|
||||||
# 结构置信：归一化 score（启发式）
|
|
||||||
range_conf = float(np.clip(score / 55.0, 0.05, 0.99))
|
|
||||||
best = {
|
|
||||||
"start_idx": int(start_i),
|
|
||||||
"end_idx": int(end_i),
|
|
||||||
"high": float(hi),
|
|
||||||
"low": float(lo),
|
|
||||||
"mid": float(mid),
|
|
||||||
"active": bool(price_in_box),
|
|
||||||
"atr": float(last_atr),
|
|
||||||
"tol": float(tol),
|
|
||||||
"bars": bars,
|
|
||||||
"score": float(score),
|
|
||||||
"quality": float(score),
|
|
||||||
"range_confidence": range_conf,
|
|
||||||
}
|
|
||||||
|
|
||||||
def _ts(row) -> Any:
|
|
||||||
col = _time_col(work)
|
|
||||||
if col and pd.notna(row[col]):
|
|
||||||
return row[col]
|
|
||||||
return None
|
|
||||||
|
|
||||||
best["start_time"] = _ts(work.iloc[best["start_idx"]])
|
|
||||||
best["end_time"] = _ts(work.iloc[best["end_idx"]])
|
|
||||||
# window_offset：slice 相对父 DataFrame 的起点；勿用 len(df)-len(work)
|
|
||||||
offset = int(window_offset)
|
|
||||||
best["abs_start_idx"] = offset + best["start_idx"]
|
|
||||||
best["abs_end_idx"] = offset + best["end_idx"]
|
|
||||||
best["abs_scan_end_idx"] = offset + n - 1
|
|
||||||
return best
|
|
||||||
|
|
||||||
|
|
||||||
def _overlap_ratio(a0: int, a1: int, b0: int, b1: int) -> float:
|
|
||||||
"""两闭区间重叠长度 / 较短区间长度。"""
|
|
||||||
lo = max(a0, b0)
|
|
||||||
hi = min(a1, b1)
|
|
||||||
if hi < lo:
|
|
||||||
return 0.0
|
|
||||||
overlap = hi - lo + 1
|
|
||||||
shorter = min(a1 - a0 + 1, b1 - b0 + 1)
|
|
||||||
if shorter <= 0:
|
|
||||||
return 0.0
|
|
||||||
return float(overlap) / float(shorter)
|
|
||||||
|
|
||||||
|
|
||||||
def _passes_quality(tr: Dict[str, Any], min_bars: int) -> bool:
|
|
||||||
if tr is None:
|
|
||||||
return False
|
|
||||||
if int(tr.get("bars") or 0) < max(8, min_bars // 2):
|
|
||||||
return False
|
|
||||||
if float(tr.get("score") or 0) < 12.0:
|
|
||||||
return False
|
|
||||||
hi = float(tr["high"])
|
|
||||||
lo = float(tr["low"])
|
|
||||||
atr = float(tr.get("atr") or 0) or 1.0
|
|
||||||
if (hi - lo) / atr > 12.0:
|
|
||||||
return False
|
|
||||||
return True
|
|
||||||
|
|
||||||
|
|
||||||
def _passes_trend_filter(work: pd.DataFrame, tr: Dict[str, Any]) -> bool:
|
|
||||||
"""趋势污染：定向位移过大则非震荡箱。"""
|
|
||||||
s = int(tr["start_idx"])
|
|
||||||
e = int(tr["end_idx"])
|
|
||||||
seg = work.iloc[s : e + 1]
|
|
||||||
if len(seg) < 8:
|
|
||||||
return False
|
|
||||||
c0 = float(seg["close"].iloc[0])
|
|
||||||
c1 = float(seg["close"].iloc[-1])
|
|
||||||
atr = float(tr.get("atr") or 0) or 1.0
|
|
||||||
drift = abs(c1 - c0) / atr
|
|
||||||
# 相对箱宽：漂移占箱宽过大 → 趋势
|
|
||||||
width = max(float(tr["high"]) - float(tr["low"]), atr)
|
|
||||||
drift_frac = abs(c1 - c0) / width
|
|
||||||
if drift > 6.0 and drift_frac > 0.55:
|
|
||||||
return False
|
|
||||||
return True
|
|
||||||
|
|
||||||
|
|
||||||
def _detect_in_window(
|
|
||||||
df: pd.DataFrame,
|
|
||||||
win_start: int,
|
|
||||||
win_end: int,
|
|
||||||
min_bars: int = 24,
|
|
||||||
atr_mult: float = 1.2,
|
|
||||||
tail_reserve: int = 12,
|
|
||||||
prefer_start_time: Any = None,
|
|
||||||
range_start_time: Any = None,
|
|
||||||
) -> Optional[Dict[str, Any]]:
|
|
||||||
"""
|
|
||||||
在 df[win_start:win_end+1] 内检测单个 TradingRange。
|
|
||||||
只返回箱体结构，不含 Phase/Event/VP。
|
|
||||||
"""
|
|
||||||
if df is None or win_end < win_start:
|
|
||||||
return None
|
|
||||||
slice_df = df.iloc[win_start : win_end + 1].reset_index(drop=True)
|
|
||||||
lookback = len(slice_df)
|
|
||||||
if lookback < min_bars + 5:
|
|
||||||
return None
|
|
||||||
|
|
||||||
work = slice_df
|
|
||||||
n = len(work)
|
|
||||||
reserve = min(tail_reserve, max(0, n - min_bars - 2))
|
|
||||||
core_end = n - reserve if reserve > 0 else n
|
|
||||||
core = work.iloc[:core_end]
|
|
||||||
if len(core) < min_bars:
|
|
||||||
core = work
|
|
||||||
core_end = n
|
|
||||||
reserve = 0
|
|
||||||
|
|
||||||
atr = _atr(work)
|
|
||||||
last_atr = float(atr.iloc[core_end - 1]) if atr.notna().iloc[:core_end].any() else float(
|
|
||||||
(core["high"] - core["low"]).mean()
|
|
||||||
)
|
|
||||||
if not np.isfinite(last_atr) or last_atr <= 0:
|
|
||||||
last_atr = float(core["close"].iloc[-1]) * 0.01
|
|
||||||
|
|
||||||
eff_atr_mult = float(atr_mult)
|
|
||||||
if lookback >= 280:
|
|
||||||
eff_atr_mult = atr_mult * 1.7
|
|
||||||
elif lookback >= 160:
|
|
||||||
eff_atr_mult = atr_mult * 1.3
|
|
||||||
width_factor = 3.8 + min(2.2, max(0.0, (lookback - 80) / 100.0))
|
|
||||||
max_width = last_atr * eff_atr_mult * width_factor
|
|
||||||
tol = last_atr * eff_atr_mult * 0.35
|
|
||||||
|
|
||||||
prefer_i = None
|
|
||||||
if prefer_start_time is not None:
|
|
||||||
prefer_i = _bar_index_at_or_after(work, prefer_start_time)
|
|
||||||
|
|
||||||
if range_start_time is not None:
|
|
||||||
start_i = _bar_index_at_or_after(work, range_start_time)
|
|
||||||
if start_i is not None and start_i <= core_end - 8:
|
|
||||||
seg = work.iloc[start_i:core_end]
|
|
||||||
hi = float(seg["high"].max())
|
|
||||||
lo = float(seg["low"].min())
|
|
||||||
rw = _robust_width(seg)
|
|
||||||
if 0 < rw <= max_width * 1.15:
|
|
||||||
near_hi = int((seg["high"] >= hi - tol).sum())
|
|
||||||
near_lo = int((seg["low"] <= lo + tol).sum())
|
|
||||||
inside = float(((seg["close"] >= lo - tol) & (seg["close"] <= hi + tol)).mean())
|
|
||||||
if near_hi >= 2 and near_lo >= 2 and inside >= 0.70:
|
|
||||||
score = _score_segment(len(seg), near_hi, near_lo, inside, rw, last_atr)
|
|
||||||
return _pack_range(
|
|
||||||
work, df, start_i, core_end - 1, hi, lo, tol, last_atr, score, n,
|
|
||||||
window_offset=win_start,
|
|
||||||
)
|
|
||||||
|
|
||||||
eff_min_bars = max(8, int(min_bars))
|
|
||||||
cn = len(core)
|
|
||||||
max_bars = min(cn, max(eff_min_bars * 2, min(96, max(eff_min_bars + 8, int(cn * 0.5)))))
|
|
||||||
cands: List[Tuple[float, int, int, int, float, float, float]] = []
|
|
||||||
|
|
||||||
def _try_seg(start_i: int, end_i: int, prefer_boost: float = 0.0) -> None:
|
|
||||||
if end_i - start_i + 1 < eff_min_bars:
|
|
||||||
return
|
|
||||||
if start_i < 0 or end_i >= cn or start_i > end_i:
|
|
||||||
return
|
|
||||||
seg = work.iloc[start_i : end_i + 1]
|
|
||||||
hi = float(seg["high"].max())
|
|
||||||
lo = float(seg["low"].min())
|
|
||||||
rw = _robust_width(seg)
|
|
||||||
if rw <= 0 or rw > max_width:
|
|
||||||
return
|
|
||||||
raw_w = hi - lo
|
|
||||||
if raw_w > max_width * 1.35:
|
|
||||||
return
|
|
||||||
near_hi = int((seg["high"] >= hi - tol).sum())
|
|
||||||
near_lo = int((seg["low"] <= lo + tol).sum())
|
|
||||||
if near_hi < 2 or near_lo < 2:
|
|
||||||
return
|
|
||||||
inside = float(((seg["close"] >= lo - tol) & (seg["close"] <= hi + tol)).mean())
|
|
||||||
if inside < 0.72:
|
|
||||||
return
|
|
||||||
length = end_i - start_i + 1
|
|
||||||
score = _score_segment(length, near_hi, near_lo, inside, rw, last_atr) + prefer_boost
|
|
||||||
cands.append((score, length, start_i, end_i, hi, lo, rw))
|
|
||||||
|
|
||||||
for length in range(min(cn, max_bars), eff_min_bars - 1, -4):
|
|
||||||
start_i = cn - length
|
|
||||||
boost = 0.0
|
|
||||||
if prefer_i is not None:
|
|
||||||
dist = abs(start_i - int(prefer_i))
|
|
||||||
if dist <= 6:
|
|
||||||
boost = 10.0
|
|
||||||
elif dist <= 14:
|
|
||||||
boost = 4.0
|
|
||||||
elif start_i > int(prefer_i) + 16:
|
|
||||||
boost = -10.0
|
|
||||||
_try_seg(start_i, cn - 1, boost)
|
|
||||||
|
|
||||||
if prefer_i is not None:
|
|
||||||
pi = int(prefer_i)
|
|
||||||
if 0 <= pi < cn:
|
|
||||||
align_max = min(cn, max(max_bars, int(cn * 0.65)))
|
|
||||||
alen = cn - pi
|
|
||||||
if eff_min_bars <= alen <= align_max:
|
|
||||||
_try_seg(pi, cn - 1, prefer_boost=18.0)
|
|
||||||
elif alen > align_max:
|
|
||||||
start_i = max(0, cn - align_max)
|
|
||||||
if start_i > pi:
|
|
||||||
start_i = pi
|
|
||||||
end_i = min(cn - 1, pi + align_max - 1)
|
|
||||||
else:
|
|
||||||
end_i = cn - 1
|
|
||||||
_try_seg(start_i, end_i, prefer_boost=12.0)
|
|
||||||
|
|
||||||
if not cands:
|
|
||||||
return None
|
|
||||||
|
|
||||||
cands.sort(key=lambda x: x[0], reverse=True)
|
|
||||||
best_score = cands[0][0]
|
|
||||||
band = max(4.0, abs(best_score) * 0.10)
|
|
||||||
near = [c for c in cands if c[0] >= best_score - band]
|
|
||||||
chosen = max(near, key=lambda x: (x[1], x[0]))
|
|
||||||
score, _length, start_i, end_i, hi, lo, _rw = chosen
|
|
||||||
return _pack_range(work, df, start_i, end_i, hi, lo, tol, last_atr, score, n, window_offset=win_start)
|
|
||||||
|
|
||||||
|
|
||||||
def detect_trading_ranges(
|
|
||||||
df: pd.DataFrame,
|
|
||||||
lookback: Optional[int] = None,
|
|
||||||
min_bars: int = 24,
|
|
||||||
atr_mult: float = 1.2,
|
|
||||||
tail_reserve: int = 12,
|
|
||||||
max_cycles: int = MAX_CYCLES,
|
|
||||||
prefer_start_time: Any = None,
|
|
||||||
range_start_time: Any = None,
|
|
||||||
) -> List[Dict[str, Any]]:
|
|
||||||
"""
|
|
||||||
倒序切多段 TradingRange（近→远）。
|
|
||||||
过滤顺序：detect → quality → trend → overlap → accept → mask。
|
|
||||||
返回列表已按时间倒序，调用方将 [0] 标为 ACTIVE。
|
|
||||||
"""
|
|
||||||
if df is None or len(df) < min_bars + 5:
|
|
||||||
return []
|
|
||||||
lb = int(lookback) if lookback is not None else len(df)
|
|
||||||
work = df.tail(lb).reset_index(drop=True)
|
|
||||||
n = len(work)
|
|
||||||
occupied: List[Dict[str, Any]] = []
|
|
||||||
accepted: List[Dict[str, Any]] = []
|
|
||||||
|
|
||||||
# 搜索右端从 n-1 往左收缩；每接受一段后右端移到该段 start 之前
|
|
||||||
search_end = n - 1
|
|
||||||
prefer = prefer_start_time
|
|
||||||
hard_start = range_start_time
|
|
||||||
|
|
||||||
while len(accepted) < max(1, int(max_cycles)) and search_end >= min_bars + 4:
|
|
||||||
# 在剩余历史内从右往左试多个右边界，避免历史箱必须贴住 search_end
|
|
||||||
# （否则中间趋势会挡住更早的真实箱）
|
|
||||||
cand = None
|
|
||||||
step = max(4, min(12, (search_end - min_bars) // 10 or 4))
|
|
||||||
for end_try in range(search_end, min_bars + 4, -step):
|
|
||||||
trial = _detect_in_window(
|
|
||||||
work,
|
|
||||||
0,
|
|
||||||
end_try,
|
|
||||||
min_bars=min_bars,
|
|
||||||
atr_mult=atr_mult,
|
|
||||||
tail_reserve=tail_reserve,
|
|
||||||
prefer_start_time=prefer if len(accepted) == 0 and end_try == search_end else None,
|
|
||||||
range_start_time=hard_start if len(accepted) == 0 and end_try == search_end else None,
|
|
||||||
)
|
|
||||||
# 1) detect
|
|
||||||
if trial is None:
|
|
||||||
continue
|
|
||||||
# 2) quality
|
|
||||||
if not _passes_quality(trial, min_bars):
|
|
||||||
continue
|
|
||||||
# 3) trend contamination
|
|
||||||
if not _passes_trend_filter(work, trial):
|
|
||||||
continue
|
|
||||||
# 4) overlap with accepted
|
|
||||||
a0, a1 = int(trial["abs_start_idx"]), int(trial["abs_end_idx"])
|
|
||||||
overlap_bad = False
|
|
||||||
for occ in occupied:
|
|
||||||
ratio = _overlap_ratio(a0, a1, int(occ["start"]), int(occ["end"]))
|
|
||||||
if ratio >= OVERLAP_RATIO_MAX:
|
|
||||||
overlap_bad = True
|
|
||||||
break
|
|
||||||
if overlap_bad:
|
|
||||||
continue
|
|
||||||
# 取最靠右的合格箱（倒序第一段）
|
|
||||||
cand = trial
|
|
||||||
break
|
|
||||||
|
|
||||||
if cand is None:
|
|
||||||
break
|
|
||||||
|
|
||||||
# 5) accept
|
|
||||||
accepted.append(cand)
|
|
||||||
a0, a1 = int(cand["abs_start_idx"]), int(cand["abs_end_idx"])
|
|
||||||
# 6) mask
|
|
||||||
occupied.append(
|
|
||||||
{
|
|
||||||
"start": a0,
|
|
||||||
"end": max(a1, int(cand.get("abs_scan_end_idx", a1))),
|
|
||||||
"quality": float(cand.get("quality") or 0),
|
|
||||||
"high": float(cand["high"]),
|
|
||||||
"low": float(cand["low"]),
|
|
||||||
}
|
|
||||||
)
|
|
||||||
# 下一轮只在更早窗口搜
|
|
||||||
search_end = int(cand["abs_start_idx"]) - 1
|
|
||||||
hard_start = None
|
|
||||||
prefer = None
|
|
||||||
|
|
||||||
# abs_* 目前相对 work；若 df 比 work 长需加 offset
|
|
||||||
offset = len(df) - len(work)
|
|
||||||
if offset:
|
|
||||||
for tr in accepted:
|
|
||||||
tr["abs_start_idx"] = int(tr["abs_start_idx"]) + offset
|
|
||||||
tr["abs_end_idx"] = int(tr["abs_end_idx"]) + offset
|
|
||||||
tr["abs_scan_end_idx"] = int(tr["abs_scan_end_idx"]) + offset
|
|
||||||
|
|
||||||
return accepted
|
|
||||||
|
|
||||||
|
|
||||||
def detect_trading_range(
|
|
||||||
df: pd.DataFrame,
|
|
||||||
lookback: int = 120,
|
|
||||||
min_bars: int = 24,
|
|
||||||
atr_mult: float = 1.2,
|
|
||||||
tail_reserve: int = 12,
|
|
||||||
range_start_time: Any = None,
|
|
||||||
prefer_start_time: Any = None,
|
|
||||||
) -> Optional[Dict[str, Any]]:
|
|
||||||
"""兼容旧接口：返回倒序列表中的第一段（ACTIVE 候选）。"""
|
|
||||||
ranges = detect_trading_ranges(
|
|
||||||
df,
|
|
||||||
lookback=lookback,
|
|
||||||
min_bars=min_bars,
|
|
||||||
atr_mult=atr_mult,
|
|
||||||
tail_reserve=tail_reserve,
|
|
||||||
max_cycles=1,
|
|
||||||
prefer_start_time=prefer_start_time,
|
|
||||||
range_start_time=range_start_time,
|
|
||||||
)
|
|
||||||
return ranges[0] if ranges else None
|
|
||||||
@@ -1,72 +0,0 @@
|
|||||||
"""区间内 Volume Profile。"""
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from typing import Any, Dict, List
|
|
||||||
|
|
||||||
import numpy as np
|
|
||||||
import pandas as pd
|
|
||||||
|
|
||||||
|
|
||||||
def compute_volume_profile(
|
|
||||||
df: pd.DataFrame,
|
|
||||||
start_idx: int,
|
|
||||||
end_idx: int,
|
|
||||||
bin_count: int = 50,
|
|
||||||
value_area_pct: float = 0.70,
|
|
||||||
) -> Dict[str, Any]:
|
|
||||||
seg = df.iloc[start_idx : end_idx + 1]
|
|
||||||
if seg.empty:
|
|
||||||
return {"bins": [], "poc": None, "vah": None, "val": None, "bin_count": bin_count}
|
|
||||||
|
|
||||||
typical = (seg["high"].astype(float) + seg["low"].astype(float) + seg["close"].astype(float)) / 3.0
|
|
||||||
vol = seg["volume"].astype(float).fillna(0.0)
|
|
||||||
lo = float(seg["low"].min())
|
|
||||||
hi = float(seg["high"].max())
|
|
||||||
if not np.isfinite(lo) or not np.isfinite(hi) or hi <= lo:
|
|
||||||
mid = float(seg["close"].iloc[-1])
|
|
||||||
return {
|
|
||||||
"bins": [{"price": mid, "volume": float(vol.sum())}],
|
|
||||||
"poc": mid,
|
|
||||||
"vah": mid,
|
|
||||||
"val": mid,
|
|
||||||
"bin_count": 1,
|
|
||||||
}
|
|
||||||
|
|
||||||
edges = np.linspace(lo, hi, bin_count + 1)
|
|
||||||
# 右开最后一桶闭合
|
|
||||||
idx = np.clip(np.digitize(typical.values, edges) - 1, 0, bin_count - 1)
|
|
||||||
vols = np.zeros(bin_count, dtype=float)
|
|
||||||
for i, v in zip(idx, vol.values):
|
|
||||||
vols[i] += float(v)
|
|
||||||
|
|
||||||
centers = (edges[:-1] + edges[1:]) / 2.0
|
|
||||||
poc_i = int(np.argmax(vols)) if vols.sum() > 0 else bin_count // 2
|
|
||||||
poc = float(centers[poc_i])
|
|
||||||
|
|
||||||
# Value Area：从 POC 向两侧扩展直到累计 >= value_area_pct
|
|
||||||
total = float(vols.sum()) or 1.0
|
|
||||||
target = total * value_area_pct
|
|
||||||
left = right = poc_i
|
|
||||||
acc = float(vols[poc_i])
|
|
||||||
while acc < target and (left > 0 or right < bin_count - 1):
|
|
||||||
left_v = vols[left - 1] if left > 0 else -1.0
|
|
||||||
right_v = vols[right + 1] if right < bin_count - 1 else -1.0
|
|
||||||
if right_v >= left_v and right < bin_count - 1:
|
|
||||||
right += 1
|
|
||||||
acc += float(vols[right])
|
|
||||||
elif left > 0:
|
|
||||||
left -= 1
|
|
||||||
acc += float(vols[left])
|
|
||||||
else:
|
|
||||||
break
|
|
||||||
|
|
||||||
bins: List[Dict[str, float]] = [
|
|
||||||
{"price": float(centers[i]), "volume": float(vols[i])} for i in range(bin_count)
|
|
||||||
]
|
|
||||||
return {
|
|
||||||
"bins": bins,
|
|
||||||
"poc": poc,
|
|
||||||
"vah": float(centers[right]),
|
|
||||||
"val": float(centers[left]),
|
|
||||||
"bin_count": bin_count,
|
|
||||||
}
|
|
||||||
@@ -467,7 +467,7 @@ class BiBuilderMixin:
|
|||||||
pre_last_bi = bi_list[-2]
|
pre_last_bi = bi_list[-2]
|
||||||
last_bi = bi_list[-1]
|
last_bi = bi_list[-1]
|
||||||
if pre_last_bi.is_sure and not last_bi.is_sure and pre_last_bi.dir == Chan_BI_DIR.UP and False:
|
if pre_last_bi.is_sure and not last_bi.is_sure and pre_last_bi.dir == Chan_BI_DIR.UP and False:
|
||||||
pre_last_bi.update_bi(klc)
|
#pre_last_bi.update_bi(klc)
|
||||||
bi_list.remove(last_bi)
|
bi_list.remove(last_bi)
|
||||||
pre_last_bi.set_next(None)
|
pre_last_bi.set_next(None)
|
||||||
#last_top.set_fx(Chan_FX_TYPE.PTOP)
|
#last_top.set_fx(Chan_FX_TYPE.PTOP)
|
||||||
@@ -585,7 +585,7 @@ class BiBuilderMixin:
|
|||||||
pre_last_bi = bi_list[-2]
|
pre_last_bi = bi_list[-2]
|
||||||
last_bi = bi_list[-1]
|
last_bi = bi_list[-1]
|
||||||
if pre_last_bi.is_sure and not last_bi.is_sure and pre_last_bi.dir == Chan_BI_DIR.DOWN and False:
|
if pre_last_bi.is_sure and not last_bi.is_sure and pre_last_bi.dir == Chan_BI_DIR.DOWN and False:
|
||||||
pre_last_bi.update_bi(klc)
|
#pre_last_bi.update_bi(klc)
|
||||||
bi_list.remove(last_bi)
|
bi_list.remove(last_bi)
|
||||||
pre_last_bi.set_next(None)
|
pre_last_bi.set_next(None)
|
||||||
last_bottom = klc
|
last_bottom = klc
|
||||||
|
|||||||
@@ -100,6 +100,21 @@ class KlineBuilderMixin:
|
|||||||
return Chan_FX_TYPE.BOTTOM
|
return Chan_FX_TYPE.BOTTOM
|
||||||
return Chan_FX_TYPE.UNKNOWN
|
return Chan_FX_TYPE.UNKNOWN
|
||||||
|
|
||||||
|
def check_fx3(self, klc):
|
||||||
|
# 右K未完成（仍在包含合并）时不分型：否则确认笔会随 next 扩区间被 check_*_fx 收回
|
||||||
|
if klc.pre and klc.next and klc.next.end_klu is not None:
|
||||||
|
next_klu = klc.next.end_klu.next
|
||||||
|
if klc.high > klc.pre.high and klc.high > klc.next.high and klc.low > klc.pre.low and klc.low > klc.next.low and klc.high > next_klu.high:
|
||||||
|
#if (klc.close > klc.ema52 or klc.next.close > klc.next.ema52) and klc.macd > 0:
|
||||||
|
klc.set_fx(Chan_FX_TYPE.TOP)
|
||||||
|
#print(klc.start_time, klc.end_time,klc.next.start_time, klc.next.end_time, klc.macd, klc.state, klc.fx, "TOP")
|
||||||
|
return Chan_FX_TYPE.TOP
|
||||||
|
elif klc.low < klc.pre.low and klc.low < klc.next.low and klc.high < klc.pre.high and klc.high < klc.next.high and klc.low < next_klu.low:
|
||||||
|
#if (klc.close < klc.ema52 or klc.next.close < klc.next.ema52) and klc.macd < 0:
|
||||||
|
klc.set_fx(Chan_FX_TYPE.BOTTOM)
|
||||||
|
#print(klc.start_time, klc.end_time,klc.next.start_time, klc.next.end_time, klc.macd, klc.state, klc.fx, "BOTTOM")
|
||||||
|
return Chan_FX_TYPE.BOTTOM
|
||||||
|
return Chan_FX_TYPE.UNKNOWN
|
||||||
def check_fx2(self, klc):
|
def check_fx2(self, klc):
|
||||||
if klc.pre and klc.next:
|
if klc.pre and klc.next:
|
||||||
if klc.high > klc.pre.close and klc.close > klc.next.close and klc.close > klc.pre.close and klc.close > klc.next.close:
|
if klc.high > klc.pre.close and klc.close > klc.next.close and klc.close > klc.pre.close and klc.close > klc.next.close:
|
||||||
|
|||||||
@@ -1,84 +0,0 @@
|
|||||||
|
|
||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.btc_chan.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 0,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"SOL/USDT:USDT",
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800,
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8882,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,98 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"strategy": "BTC_Maker_Micro_Scalper",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.btc_maker_micro_scalper.sqlite",
|
|
||||||
"dry_run_wallet": 10000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short": true,
|
|
||||||
"timeframe": "1m",
|
|
||||||
"process_only_new_candles": true,
|
|
||||||
"fee": 0.00016,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 3,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"order_types": {
|
|
||||||
"entry": "limit",
|
|
||||||
"exit": "limit",
|
|
||||||
"stoploss": "limit",
|
|
||||||
"stoploss_on_exchange": false
|
|
||||||
},
|
|
||||||
"order_time_in_force": {
|
|
||||||
"entry": "GTC",
|
|
||||||
"exit": "GTC"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "YOUR_BINANCE_API_KEY",
|
|
||||||
"secret": "YOUR_BINANCE_API_SECRET",
|
|
||||||
"ccxt_config": {
|
|
||||||
"proxies": {
|
|
||||||
"http": "http://127.0.0.1:7897",
|
|
||||||
"https": "http://127.0.0.1:7897"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"ccxt_async_config": {
|
|
||||||
"aiohttp_proxy": "http://127.0.0.1:7897"
|
|
||||||
},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "",
|
|
||||||
"chat_id": ""
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": false,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8821,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "change_me_mms_v1",
|
|
||||||
"ws_token": "change_me_mms_ws",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "BTC_Maker_Micro_Scalper",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 1
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,98 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"strategy": "BTC_Maker_Micro_Scalper_v11",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.btc_maker_micro_scalper_v11.sqlite",
|
|
||||||
"dry_run_wallet": 10000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short": true,
|
|
||||||
"timeframe": "1m",
|
|
||||||
"process_only_new_candles": true,
|
|
||||||
"fee": 0.00016,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 3,
|
|
||||||
"exit": 2,
|
|
||||||
"exit_timeout_count": 3,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"order_types": {
|
|
||||||
"entry": "limit",
|
|
||||||
"exit": "limit",
|
|
||||||
"stoploss": "limit",
|
|
||||||
"stoploss_on_exchange": false
|
|
||||||
},
|
|
||||||
"order_time_in_force": {
|
|
||||||
"entry": "GTC",
|
|
||||||
"exit": "GTC"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "YOUR_BINANCE_API_KEY",
|
|
||||||
"secret": "YOUR_BINANCE_API_SECRET",
|
|
||||||
"ccxt_config": {
|
|
||||||
"proxies": {
|
|
||||||
"http": "http://127.0.0.1:7897",
|
|
||||||
"https": "http://127.0.0.1:7897"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"ccxt_async_config": {
|
|
||||||
"aiohttp_proxy": "http://127.0.0.1:7897"
|
|
||||||
},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "",
|
|
||||||
"chat_id": ""
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": false,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8822,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "change_me_mms_v11",
|
|
||||||
"ws_token": "change_me_mms_v11_ws",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "BTC_Maker_Micro_Scalper_v11",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 1
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,83 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.btc_perpetual.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short": true,
|
|
||||||
"timeframe": "1m",
|
|
||||||
"process_only_new_candles": false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "YOUR_BINANCE_API_KEY",
|
|
||||||
"secret": "YOUR_BINANCE_API_SECRET",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": true,
|
|
||||||
"token": "YOUR_TELEGRAM_BOT_TOKEN",
|
|
||||||
"chat_id": "YOUR_TELEGRAM_CHAT_ID"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "0.0.0.0",
|
|
||||||
"listen_port": 8820,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "change_me_to_a_random_secret_key",
|
|
||||||
"ws_token": "change_me_to_a_random_ws_token",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "BTC_Perpetual_Bot",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,84 +0,0 @@
|
|||||||
|
|
||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chan.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 0,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT",
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800,
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8800,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,90 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chanlun_btc_15.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {
|
|
||||||
"proxies": {
|
|
||||||
"http": "http://127.0.0.1:7897",
|
|
||||||
"https": "http://127.0.0.1:7897"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"ccxt_async_config": {
|
|
||||||
"aiohttp_proxy": "http://127.0.0.1:7897"
|
|
||||||
},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8811,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,89 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chanlun_btc_1m.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"order_types": {
|
|
||||||
"entry": "limit",
|
|
||||||
"exit": "limit",
|
|
||||||
"stoploss": "limit",
|
|
||||||
"stoploss_on_exchange": false
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true
|
|
||||||
},
|
|
||||||
"exit_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {
|
|
||||||
"proxies": {
|
|
||||||
"http": "http://127.0.0.1:7897",
|
|
||||||
"https": "http://127.0.0.1:7897"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"ccxt_async_config": {
|
|
||||||
"aiohttp_proxy": "http://127.0.0.1:7897"
|
|
||||||
},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": true,
|
|
||||||
"token": "8197349375:AAH208JghCq8raFYF-IpnobYknCr6iGDH_0",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "0.0.0.0",
|
|
||||||
"listen_port": 8814,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 1
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,83 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chanlun_btc_15.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": true,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "0.0.0.0",
|
|
||||||
"listen_port": 8813,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,68 +0,0 @@
|
|||||||
{
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chanlun_btc_5m.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "5m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 5,
|
|
||||||
"exit": 5,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"order_types": {
|
|
||||||
"entry": "limit",
|
|
||||||
"exit": "limit",
|
|
||||||
"stoploss": "limit",
|
|
||||||
"stoploss_on_exchange": false
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true
|
|
||||||
},
|
|
||||||
"exit_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {
|
|
||||||
"proxies": {
|
|
||||||
"http": "http://127.0.0.1:7897",
|
|
||||||
"https": "http://127.0.0.1:7897"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"ccxt_async_config": {
|
|
||||||
"aiohttp_proxy": "http://127.0.0.1:7897"
|
|
||||||
},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 5
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,83 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chanlun_btc_60.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": true,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "0.0.0.0",
|
|
||||||
"listen_port": 8814,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,83 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chanlun_btc_k.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8815,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,87 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chanlun_btc_k.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short": true,
|
|
||||||
"timeframe": "1m",
|
|
||||||
"process_only_new_candles": false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8815,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@@ -1,83 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chanlun_btc_15.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "0.0.0.0",
|
|
||||||
"listen_port": 8820,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,83 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chanlun_btc_15.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": true,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "0.0.0.0",
|
|
||||||
"listen_port": 8820,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,83 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chanlun_eth_60.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"ETH/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": true,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8813,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,83 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chanlun_btc_15.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "5m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "other",
|
|
||||||
"use_order_book": false,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "other",
|
|
||||||
"use_order_book": false,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": true,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8813,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,83 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chanlun_sol.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 0,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8811,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,151 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 2,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.95,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chanlun_sol_optimized.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short": true,
|
|
||||||
"timeframe": "5m",
|
|
||||||
"process_only_new_candles": false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 2,
|
|
||||||
"exit": 2,
|
|
||||||
"exit_timeout_count": 0,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "other",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "",
|
|
||||||
"secret": "",
|
|
||||||
"ccxt_config": {
|
|
||||||
"options": {"defaultType": "swap"}
|
|
||||||
},
|
|
||||||
"ccxt_async_config": {
|
|
||||||
"enableRateLimit": true,
|
|
||||||
"rateLimit": 1000,
|
|
||||||
"timeout": 30000
|
|
||||||
},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"SOL/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": []
|
|
||||||
},
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "",
|
|
||||||
"chat_id": ""
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": false,
|
|
||||||
"listen_ip_address": "0.0.0.0",
|
|
||||||
"listen_port": 8080,
|
|
||||||
"verbosity": "error",
|
|
||||||
"jwt_secret_key": "",
|
|
||||||
"username": "",
|
|
||||||
"password": ""
|
|
||||||
},
|
|
||||||
"discord": {
|
|
||||||
"enabled": false,
|
|
||||||
"webhook": "",
|
|
||||||
"webhook_avatar": "",
|
|
||||||
"poll_delay_seconds": 10
|
|
||||||
},
|
|
||||||
"notification_settings": {
|
|
||||||
"status": "on",
|
|
||||||
"status_inactive_after": 7,
|
|
||||||
"timeframe_condition_change": "on",
|
|
||||||
"telegram": { },
|
|
||||||
"discord": { },
|
|
||||||
"notify_all": true
|
|
||||||
},
|
|
||||||
"bot_name": "SOL_Chan_Optimized",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 5
|
|
||||||
},
|
|
||||||
"edge": {
|
|
||||||
"enabled": false,
|
|
||||||
"process_throttle_secs": 3600,
|
|
||||||
"calculate_since_number_of_days": 7,
|
|
||||||
"allowed_risk": 0.01,
|
|
||||||
"stoploss_range_min": -0.01,
|
|
||||||
"stoploss_range_max": -0.007,
|
|
||||||
"stoploss_range_step": 0.001,
|
|
||||||
"minimum_winrate": 0.60,
|
|
||||||
"minimum_expectancy": 0.20,
|
|
||||||
"min_trade_number": 10,
|
|
||||||
"max_trade_duration_minute": 1440,
|
|
||||||
"remove_pumps": false
|
|
||||||
},
|
|
||||||
"order_types": {
|
|
||||||
"entry": "limit",
|
|
||||||
"exit": "market",
|
|
||||||
"emergency_exit": "market",
|
|
||||||
"force_exit": "market",
|
|
||||||
"force_entry": "market",
|
|
||||||
"stoploss": "market",
|
|
||||||
"stoploss_on_exchange": false,
|
|
||||||
"stoploss_on_exchange_interval": 60
|
|
||||||
},
|
|
||||||
"order_time_in_force": {
|
|
||||||
"entry": "GTC",
|
|
||||||
"exit": "GTC"
|
|
||||||
},
|
|
||||||
"strategy_path": "./user_data/Chan/strategies/",
|
|
||||||
"strategy": "ChanLun_SOL_Optimized",
|
|
||||||
"minimal_roi": {
|
|
||||||
"0": 0.012,
|
|
||||||
"120": 0.010,
|
|
||||||
"240": 0.007,
|
|
||||||
"360": 0.005
|
|
||||||
},
|
|
||||||
"stoploss": -0.007,
|
|
||||||
"trailing_stop": true,
|
|
||||||
"trailing_stop_positive": 0.003,
|
|
||||||
"trailing_stop_positive_offset": 0.005,
|
|
||||||
"trailing_only_offset_is_reached": true,
|
|
||||||
"use_custom_stoploss": true,
|
|
||||||
"max_open_trades_per_pair": 1,
|
|
||||||
"dry_run_wallet_refresh_time": 5,
|
|
||||||
"caches": {
|
|
||||||
"dataframe": {
|
|
||||||
"enabled": true,
|
|
||||||
"refresh_period": 60
|
|
||||||
},
|
|
||||||
"strategy": {
|
|
||||||
"enabled": true,
|
|
||||||
"refresh_period": 300
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"config": {
|
|
||||||
"pairs": ["SOL/USDT:USDT"]
|
|
||||||
}
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
@@ -1,125 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chanlun_sol.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : true,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 0,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"SOL/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"freqai": {
|
|
||||||
"enabled": true,
|
|
||||||
"purge_old_models": true,
|
|
||||||
"train_period_days": 30,
|
|
||||||
"backtest_period_days": 7,
|
|
||||||
"identifier": "chanLun1",
|
|
||||||
"live_retrain_hours": 1,
|
|
||||||
"expiration_hours": 48,
|
|
||||||
"fit_live_predictions_candles": 0,
|
|
||||||
"data_kitchen_thread_count": 4,
|
|
||||||
"save_backtest_models": true,
|
|
||||||
"save_metadata": true,
|
|
||||||
"feature_parameters": {
|
|
||||||
"include_timeframes": [
|
|
||||||
"1m",
|
|
||||||
"5m",
|
|
||||||
"15m"
|
|
||||||
],
|
|
||||||
"include_corr_pairlist": [
|
|
||||||
"BTC/USDT:USDT",
|
|
||||||
"ETH/USDT:USDT"
|
|
||||||
],
|
|
||||||
"label_period_candles": 24,
|
|
||||||
"include_shifted_candles": 2,
|
|
||||||
"indicator_periods_candles": [10, 20, 30],
|
|
||||||
"allow_duplicate_train": true
|
|
||||||
},
|
|
||||||
"data_split_parameters": {
|
|
||||||
"test_size": 0.25
|
|
||||||
},
|
|
||||||
"model_training_parameters": {
|
|
||||||
"n_estimators": 100,
|
|
||||||
"learning_rate": 0.1,
|
|
||||||
"max_depth": 5,
|
|
||||||
"subsample": 0.8,
|
|
||||||
"colsample_bytree": 0.8,
|
|
||||||
"use_label_for_weight": true,
|
|
||||||
"booster": "gbtree",
|
|
||||||
"num_class": 2
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"freqaimodel": "XGBoostClassifier",
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8811,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,103 +0,0 @@
|
|||||||
|
|
||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 3,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.btc_chan.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "30m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 0,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "ocQUqAPSD9PDhIL2lTMlMan0wMFwvvu5Fv8eYF3wUM8yPytm2jBgz51cgiHXw7J6",
|
|
||||||
"secret": "yHIc6FOnSoOI2FvygpRKKku4FKaZGI5DSwC83Ip4wRfUcxszennF6hy2vhbVuLYJ",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT",
|
|
||||||
"ETH/USDT:USDT",
|
|
||||||
"SOL/USDT:USDT",
|
|
||||||
"WIF/USDT:USDT",
|
|
||||||
"1000PEPE/USDT:USDT",
|
|
||||||
"DOGS/USDT:USDT",
|
|
||||||
"ORDI/USDT:USDT",
|
|
||||||
"AAVE/USDT:USDT",
|
|
||||||
"REEF/USDT:USDT",
|
|
||||||
"1000SATS/USDT:USDT",
|
|
||||||
"SUI/USDT:USDT",
|
|
||||||
"1INCH/USDT:USDT",
|
|
||||||
"DOGE/USDT:USDT",
|
|
||||||
"TON/USDT:USDT",
|
|
||||||
"UNI/USDT:USDT",
|
|
||||||
"XRP/USDT:USDT",
|
|
||||||
"SUN/USDT:USDT",
|
|
||||||
"NOT/USDT:USDT",
|
|
||||||
"RARE/USDT:USDT",
|
|
||||||
"RDNT/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "VolumePairList",
|
|
||||||
"number_assets": 10,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": true,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8088,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,83 +0,0 @@
|
|||||||
|
|
||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "5m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 0,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT",
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "5985766683:AAEx2Nm_4y2IC0Tj4Hhz7djVRJRso0JKaj0",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8088,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,84 +0,0 @@
|
|||||||
|
|
||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.btc_chan.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 0,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"SOL/USDT:USDT",
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800,
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": true,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": false,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8888,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,84 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"strategy": "ChanStrategy",
|
|
||||||
"db_url": "sqlite:///tradesv3.btc_chan.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 0,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"SOL/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": false,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8818,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,84 +0,0 @@
|
|||||||
|
|
||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chan.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 0,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"SOL/USDT:USDT",
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800,
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8815,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,84 +0,0 @@
|
|||||||
|
|
||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDC",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chan.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 0,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "hyperliquid",
|
|
||||||
"walletAddress": "0xA834b6d3Fa1D8A55ea8e502685ef5cbD2b2D3343",
|
|
||||||
"privateKey": "0xa399cea4c01be67c16b88e1d2121ed6e72bab6b4e8d81684ed03ce78f8b4f827",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"PURR/USDC:USDC",
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800,
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8815,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,84 +0,0 @@
|
|||||||
|
|
||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chan_sol_30.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 0,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"SOL/USDT:USDT",
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800,
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8818,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,84 +0,0 @@
|
|||||||
|
|
||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chan.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 0,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT",
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800,
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8801,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,93 +0,0 @@
|
|||||||
|
|
||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 3,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"db_url": "sqlite:///tradesv3.deepseek_trader.sqlite",
|
|
||||||
"dry_run": true,
|
|
||||||
"dry_run_wallet": 10000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 0,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT",
|
|
||||||
"ETH/USDT:USDT",
|
|
||||||
"SOL/USDT:USDT",
|
|
||||||
"WIF/USDT:USDT",
|
|
||||||
"1000PEPE/USDT:USDT",
|
|
||||||
"DOGS/USDT:USDT",
|
|
||||||
"ORDI/USDT:USDT",
|
|
||||||
"AAVE/USDT:USDT",
|
|
||||||
"REEF/USDT:USDT",
|
|
||||||
"1000SATS/USDT:USDT",
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 10,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": true,
|
|
||||||
"token": "5985766683:AAEx2Nm_4y2IC0Tj4Hhz7djVRJRso0JKaj0",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8001,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": true,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 15
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,83 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.ema26_ema52_cross.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"SOL/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": true,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "0.0.0.0",
|
|
||||||
"listen_port": 8820,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,83 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.ema_pattern.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1h",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "0.0.0.0",
|
|
||||||
"listen_port": 8888,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,70 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 2,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.elliottwave_btc.sqlite",
|
|
||||||
"dry_run_wallet": 10000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 5,
|
|
||||||
"exit": 5,
|
|
||||||
"exit_timeout_count": 3,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "",
|
|
||||||
"secret": "",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "",
|
|
||||||
"chat_id": ""
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": false,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8080,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "freqtrade_secret",
|
|
||||||
"ws_token": "freqtrade_ws",
|
|
||||||
"username": "freqtrade",
|
|
||||||
"password": "freqtrade"
|
|
||||||
},
|
|
||||||
"bot_name": "ElliottWaveBTC"
|
|
||||||
}
|
|
||||||
@@ -1,123 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.freqai_sol.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short": true,
|
|
||||||
"timeframe": "5m",
|
|
||||||
"process_only_new_candles": true,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "",
|
|
||||||
"secret": "",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"SOL/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"freqai": {
|
|
||||||
"enabled": true,
|
|
||||||
"purge_old_models": 2,
|
|
||||||
"train_period_days": 10,
|
|
||||||
"backtest_period_days": 7,
|
|
||||||
"live_retrain_hours": 1,
|
|
||||||
"identifier": "sol_futures_lgbm_v1",
|
|
||||||
"feature_parameters": {
|
|
||||||
"include_timeframes": [
|
|
||||||
"5m",
|
|
||||||
"15m"
|
|
||||||
],
|
|
||||||
"include_corr_pairlist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"label_period_candles": 12,
|
|
||||||
"include_shifted_candles": 1,
|
|
||||||
"DI_threshold": 0.9,
|
|
||||||
"weight_factor": 0.9,
|
|
||||||
"principal_component_analysis": false,
|
|
||||||
"use_SVM_to_remove_outliers": true,
|
|
||||||
"indicator_periods_candles": [
|
|
||||||
14
|
|
||||||
],
|
|
||||||
"plot_feature_importances": 0
|
|
||||||
},
|
|
||||||
"data_split_parameters": {
|
|
||||||
"test_size": 0.15,
|
|
||||||
"random_state": 42
|
|
||||||
},
|
|
||||||
"model_training_parameters": {
|
|
||||||
"n_estimators": 300,
|
|
||||||
"learning_rate": 0.05,
|
|
||||||
"max_depth": 5,
|
|
||||||
"num_leaves": 31,
|
|
||||||
"min_child_samples": 20,
|
|
||||||
"subsample": 0.8,
|
|
||||||
"colsample_bytree": 0.8,
|
|
||||||
"reg_alpha": 0.1,
|
|
||||||
"reg_lambda": 0.1,
|
|
||||||
"n_jobs": 1,
|
|
||||||
"verbosity": -1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "",
|
|
||||||
"chat_id": ""
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "0.0.0.0",
|
|
||||||
"listen_port": 8822,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqai_sol",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 5
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,83 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.heikinashi_btc.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "1m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": true,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "0.0.0.0",
|
|
||||||
"listen_port": 8814,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,83 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.ema26_ema52_cross.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"timeframe": "1m",
|
|
||||||
"can_short" : true,
|
|
||||||
"process_only_new_candles" : true,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "other",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "other",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "0.0.0.0",
|
|
||||||
"listen_port": 8821,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,91 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"strategy": "MakerEdgeProbe",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.maker_edge_probe.sqlite",
|
|
||||||
"dry_run_wallet": 10000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short": true,
|
|
||||||
"timeframe": "1m",
|
|
||||||
"process_only_new_candles": false,
|
|
||||||
"fee": 0.00016,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 3,
|
|
||||||
"exit": 2,
|
|
||||||
"exit_timeout_count": 3,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"order_types": {
|
|
||||||
"entry": "limit",
|
|
||||||
"exit": "limit",
|
|
||||||
"stoploss": "market",
|
|
||||||
"stoploss_on_exchange": false
|
|
||||||
},
|
|
||||||
"order_time_in_force": {
|
|
||||||
"entry": "GTC",
|
|
||||||
"exit": "GTC"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exit_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "YOUR_BINANCE_API_KEY",
|
|
||||||
"secret": "YOUR_BINANCE_API_SECRET",
|
|
||||||
"ccxt_config": {
|
|
||||||
"proxies": {
|
|
||||||
"http": "http://127.0.0.1:7897",
|
|
||||||
"https": "http://127.0.0.1:7897"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"ccxt_async_config": {
|
|
||||||
"aiohttp_proxy": "http://127.0.0.1:7897"
|
|
||||||
},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8823,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "maker_edge_probe_change_me",
|
|
||||||
"ws_token": "maker_edge_probe_ws",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "MakerEdgeProbe",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,81 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.sol5m.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short": true,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "other",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing": {
|
|
||||||
"price_side": "other",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"SOL/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "0.0.0.0",
|
|
||||||
"listen_port": 8822,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "SOL5m",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 5
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,83 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.chanlun_btc_15.sqlite",
|
|
||||||
"dry_run_wallet": 1000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short" : true,
|
|
||||||
"timeframe" : "15m",
|
|
||||||
"process_only_new_candles" : false,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 1,
|
|
||||||
"exit": 1,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing":{
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
|
||||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
|
||||||
"ccxt_config": {},
|
|
||||||
"ccxt_async_config": {},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"WIF/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList",
|
|
||||||
"number_assets": 1,
|
|
||||||
"sort_key": "quoteVolume",
|
|
||||||
"min_value": 0,
|
|
||||||
"refresh_period": 1800
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
|
||||||
"chat_id": "580807463"
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": true,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8811,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
|
||||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "freqtrade",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 2
|
|
||||||
}
|
|
||||||
}
|
|
||||||
Binary file not shown.
@@ -1,86 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.turtle_btc.sqlite",
|
|
||||||
"dry_run_wallet": 10000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short": true,
|
|
||||||
"timeframe": "15m",
|
|
||||||
"process_only_new_candles": true,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 15,
|
|
||||||
"exit": 15,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "",
|
|
||||||
"secret": "",
|
|
||||||
"ccxt_config": {
|
|
||||||
"proxies": {
|
|
||||||
"http": "http://127.0.0.1:7897",
|
|
||||||
"https": "http://127.0.0.1:7897"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"ccxt_async_config": {
|
|
||||||
"aiohttp_proxy": "http://127.0.0.1:7897"
|
|
||||||
},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "",
|
|
||||||
"chat_id": ""
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": false,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8822,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "turtle-btc-change-me",
|
|
||||||
"ws_token": "turtle-btc-ws-change-me",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "turtle_btc",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 5
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,86 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.wyckoff_btc.sqlite",
|
|
||||||
"dry_run_wallet": 10000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short": true,
|
|
||||||
"timeframe": "1h",
|
|
||||||
"process_only_new_candles": true,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 60,
|
|
||||||
"exit": 60,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "",
|
|
||||||
"secret": "",
|
|
||||||
"ccxt_config": {
|
|
||||||
"proxies": {
|
|
||||||
"http": "http://127.0.0.1:7897",
|
|
||||||
"https": "http://127.0.0.1:7897"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"ccxt_async_config": {
|
|
||||||
"aiohttp_proxy": "http://127.0.0.1:7897"
|
|
||||||
},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "",
|
|
||||||
"chat_id": ""
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": false,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8823,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "wyckoff-btc-change-me",
|
|
||||||
"ws_token": "wyckoff-btc-ws-change-me",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "wyckoff_btc",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 5
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,86 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.wyckoff_btc_gated.sqlite",
|
|
||||||
"dry_run_wallet": 10000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short": true,
|
|
||||||
"timeframe": "1h",
|
|
||||||
"process_only_new_candles": true,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 60,
|
|
||||||
"exit": 60,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "",
|
|
||||||
"secret": "",
|
|
||||||
"ccxt_config": {
|
|
||||||
"proxies": {
|
|
||||||
"http": "http://127.0.0.1:7897",
|
|
||||||
"https": "http://127.0.0.1:7897"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"ccxt_async_config": {
|
|
||||||
"aiohttp_proxy": "http://127.0.0.1:7897"
|
|
||||||
},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "",
|
|
||||||
"chat_id": ""
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": false,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8825,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "wyckoff-gated-change-me",
|
|
||||||
"ws_token": "wyckoff-gated-ws-change-me",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "wyckoff_btc_gated",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 5
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,86 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.wyckoff_btc_lps.sqlite",
|
|
||||||
"dry_run_wallet": 10000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short": true,
|
|
||||||
"timeframe": "1h",
|
|
||||||
"process_only_new_candles": true,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 60,
|
|
||||||
"exit": 60,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "",
|
|
||||||
"secret": "",
|
|
||||||
"ccxt_config": {
|
|
||||||
"proxies": {
|
|
||||||
"http": "http://127.0.0.1:7897",
|
|
||||||
"https": "http://127.0.0.1:7897"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"ccxt_async_config": {
|
|
||||||
"aiohttp_proxy": "http://127.0.0.1:7897"
|
|
||||||
},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "",
|
|
||||||
"chat_id": ""
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": false,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8824,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "wyckoff-lps-change-me",
|
|
||||||
"ws_token": "wyckoff-lps-ws-change-me",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "wyckoff_btc_lps",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 5
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,86 +0,0 @@
|
|||||||
{
|
|
||||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
|
||||||
"max_open_trades": 1,
|
|
||||||
"stake_currency": "USDT",
|
|
||||||
"stake_amount": "unlimited",
|
|
||||||
"tradable_balance_ratio": 0.99,
|
|
||||||
"fiat_display_currency": "USD",
|
|
||||||
"dry_run": true,
|
|
||||||
"db_url": "sqlite:///tradesv3.wyckoff_btc_v1_baseline.sqlite",
|
|
||||||
"dry_run_wallet": 10000,
|
|
||||||
"cancel_open_orders_on_exit": true,
|
|
||||||
"trading_mode": "futures",
|
|
||||||
"margin_mode": "isolated",
|
|
||||||
"can_short": true,
|
|
||||||
"timeframe": "1h",
|
|
||||||
"process_only_new_candles": true,
|
|
||||||
"unfilledtimeout": {
|
|
||||||
"entry": 60,
|
|
||||||
"exit": 60,
|
|
||||||
"exit_timeout_count": 5,
|
|
||||||
"unit": "minutes"
|
|
||||||
},
|
|
||||||
"entry_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1,
|
|
||||||
"price_last_balance": 0.0,
|
|
||||||
"check_depth_of_market": {
|
|
||||||
"enabled": false,
|
|
||||||
"bids_to_ask_delta": 1
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"exit_pricing": {
|
|
||||||
"price_side": "same",
|
|
||||||
"use_order_book": true,
|
|
||||||
"order_book_top": 1
|
|
||||||
},
|
|
||||||
"exchange": {
|
|
||||||
"name": "binance",
|
|
||||||
"key": "",
|
|
||||||
"secret": "",
|
|
||||||
"ccxt_config": {
|
|
||||||
"proxies": {
|
|
||||||
"http": "http://127.0.0.1:7897",
|
|
||||||
"https": "http://127.0.0.1:7897"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"ccxt_async_config": {
|
|
||||||
"aiohttp_proxy": "http://127.0.0.1:7897"
|
|
||||||
},
|
|
||||||
"pair_whitelist": [
|
|
||||||
"BTC/USDT:USDT"
|
|
||||||
],
|
|
||||||
"pair_blacklist": [
|
|
||||||
"BNB/.*"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"pairlists": [
|
|
||||||
{
|
|
||||||
"method": "StaticPairList"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"telegram": {
|
|
||||||
"enabled": false,
|
|
||||||
"token": "",
|
|
||||||
"chat_id": ""
|
|
||||||
},
|
|
||||||
"api_server": {
|
|
||||||
"enabled": false,
|
|
||||||
"listen_ip_address": "127.0.0.1",
|
|
||||||
"listen_port": 8823,
|
|
||||||
"verbosity": "error",
|
|
||||||
"enable_openapi": false,
|
|
||||||
"jwt_secret_key": "wyckoff-v1-baseline-change-me",
|
|
||||||
"ws_token": "wyckoff-v1-baseline-ws-change-me",
|
|
||||||
"CORS_origins": [],
|
|
||||||
"username": "freqtrader",
|
|
||||||
"password": "FreqTrade007"
|
|
||||||
},
|
|
||||||
"bot_name": "wyckoff_btc_v1_baseline",
|
|
||||||
"initial_state": "running",
|
|
||||||
"force_entry_enable": false,
|
|
||||||
"internals": {
|
|
||||||
"process_throttle_secs": 5
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,5 +0,0 @@
|
|||||||
"""crypto_wyckoff — multi-TF screener for crypto (ported from A_Share_DP Architecture v1.0)."""
|
|
||||||
|
|
||||||
from crypto_wyckoff.version import ARCHITECTURE_VERSION, WYCKOFF_ENGINE_VERSION
|
|
||||||
|
|
||||||
__all__ = ["WYCKOFF_ENGINE_VERSION", "ARCHITECTURE_VERSION"]
|
|
||||||
@@ -1,342 +0,0 @@
|
|||||||
"""Walk-forward Wyckoff phase/event annotations for chart overlay."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from datetime import date
|
|
||||||
|
|
||||||
from crypto_wyckoff.domain_models import OHLCVFrame, WyckoffCycle, WyckoffEvent, WyckoffPhase
|
|
||||||
from crypto_wyckoff.cycle import CycleEngine
|
|
||||||
from crypto_wyckoff.event import EventEngine
|
|
||||||
from crypto_wyckoff.features import FeatureEngine
|
|
||||||
from crypto_wyckoff.phase import PhaseEngine
|
|
||||||
|
|
||||||
_MIN_BARS = {"1d": 40, "1w": 26, "1M": 18}
|
|
||||||
|
|
||||||
_NOTABLE_EVENTS = {
|
|
||||||
WyckoffEvent.PS.value,
|
|
||||||
WyckoffEvent.SC.value,
|
|
||||||
WyckoffEvent.AR.value,
|
|
||||||
WyckoffEvent.ST.value,
|
|
||||||
WyckoffEvent.SPRING.value,
|
|
||||||
WyckoffEvent.TEST.value,
|
|
||||||
WyckoffEvent.SOS.value,
|
|
||||||
WyckoffEvent.LPS.value,
|
|
||||||
WyckoffEvent.JUMP.value,
|
|
||||||
WyckoffEvent.BACKUP.value,
|
|
||||||
WyckoffEvent.BC.value,
|
|
||||||
WyckoffEvent.UTAD.value,
|
|
||||||
WyckoffEvent.SOW.value,
|
|
||||||
WyckoffEvent.LPSY.value,
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def _slice_frame(frame: OHLCVFrame, end_idx: int) -> OHLCVFrame:
|
|
||||||
n = end_idx + 1
|
|
||||||
return OHLCVFrame(
|
|
||||||
ts_code=frame.ts_code,
|
|
||||||
timeframe=frame.timeframe,
|
|
||||||
trade_dates=frame.trade_dates[:n],
|
|
||||||
open=frame.open[:n],
|
|
||||||
high=frame.high[:n],
|
|
||||||
low=frame.low[:n],
|
|
||||||
close=frame.close[:n],
|
|
||||||
volume=frame.volume[:n],
|
|
||||||
amount=frame.amount[:n] if frame.amount else [],
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
def _compress_phases(points: list[tuple[str, str]]) -> list[dict]:
|
|
||||||
"""points: [(date_iso, phase), ...] → segments."""
|
|
||||||
if not points:
|
|
||||||
return []
|
|
||||||
segs: list[dict] = []
|
|
||||||
start, phase = points[0]
|
|
||||||
prev = start
|
|
||||||
for d, p in points[1:]:
|
|
||||||
if p != phase:
|
|
||||||
segs.append({"start": start, "end": prev, "phase": phase})
|
|
||||||
start, phase = d, p
|
|
||||||
prev = d
|
|
||||||
segs.append({"start": start, "end": prev, "phase": phase})
|
|
||||||
return segs
|
|
||||||
|
|
||||||
|
|
||||||
def annotate_frame(
|
|
||||||
frame: OHLCVFrame,
|
|
||||||
step: int | None = None,
|
|
||||||
*,
|
|
||||||
role: str | None = None,
|
|
||||||
) -> dict:
|
|
||||||
"""Pure annotation: phase bands + event markers + latest levels.
|
|
||||||
|
|
||||||
``role`` is the D/W/M rule alias (1d/1w/1M). Defaults to frame.timeframe.
|
|
||||||
``step`` defaults by role to keep interactive charts snappy.
|
|
||||||
"""
|
|
||||||
tf = role or frame.timeframe
|
|
||||||
min_bars = _MIN_BARS.get(tf, 30)
|
|
||||||
if step is None:
|
|
||||||
step = {"1d": 2, "1w": 1, "1M": 1}.get(tf, 2)
|
|
||||||
|
|
||||||
empty = {
|
|
||||||
"phases": [],
|
|
||||||
"events": [],
|
|
||||||
"levels": {},
|
|
||||||
"bars": len(frame),
|
|
||||||
"timeframe": tf,
|
|
||||||
}
|
|
||||||
if frame.empty or len(frame) < min_bars:
|
|
||||||
return empty
|
|
||||||
|
|
||||||
feat_eng = FeatureEngine()
|
|
||||||
cycle_eng = CycleEngine()
|
|
||||||
phase_eng = PhaseEngine()
|
|
||||||
event_eng = EventEngine()
|
|
||||||
|
|
||||||
phase_points: list[tuple[str, str]] = []
|
|
||||||
events: list[dict] = []
|
|
||||||
last_event: str | None = None
|
|
||||||
levels: dict = {}
|
|
||||||
|
|
||||||
# Ensure last bar is always evaluated
|
|
||||||
indices = list(range(min_bars - 1, len(frame), step))
|
|
||||||
if indices[-1] != len(frame) - 1:
|
|
||||||
indices.append(len(frame) - 1)
|
|
||||||
|
|
||||||
for i in indices:
|
|
||||||
sub = _slice_frame(frame, i)
|
|
||||||
f = feat_eng.run(sub, tf)
|
|
||||||
c = cycle_eng.run(f, tf)
|
|
||||||
p = phase_eng.run(c, f, tf)
|
|
||||||
e = event_eng.run(c, p, f, tf)
|
|
||||||
|
|
||||||
d = str(frame.trade_dates[i])[:10]
|
|
||||||
phase = p.payload.get("phase") or WyckoffPhase.NONE.value
|
|
||||||
phase_points.append((d, phase))
|
|
||||||
|
|
||||||
cur = e.payload.get("current_event") or WyckoffEvent.NONE.value
|
|
||||||
if cur in _NOTABLE_EVENTS and cur != last_event:
|
|
||||||
events.append({
|
|
||||||
"date": d,
|
|
||||||
"event": cur,
|
|
||||||
"price": float(frame.close[i]),
|
|
||||||
"low": float(frame.low[i]),
|
|
||||||
"high": float(frame.high[i]),
|
|
||||||
})
|
|
||||||
last_event = cur
|
|
||||||
elif cur == WyckoffEvent.NONE.value:
|
|
||||||
last_event = None
|
|
||||||
|
|
||||||
if i == len(frame) - 1 and not f.payload.get("insufficient"):
|
|
||||||
levels = {
|
|
||||||
k: f.payload.get(k)
|
|
||||||
for k in (
|
|
||||||
"range_high", "range_low", "ma20", "ma60",
|
|
||||||
"swing_high", "swing_low", "close",
|
|
||||||
)
|
|
||||||
if f.payload.get(k) is not None
|
|
||||||
}
|
|
||||||
levels["phase"] = phase
|
|
||||||
levels["cycle"] = c.payload.get("cycle")
|
|
||||||
levels["current_event"] = cur
|
|
||||||
|
|
||||||
return {
|
|
||||||
"phases": _compress_phases(phase_points),
|
|
||||||
"events": events,
|
|
||||||
"levels": levels,
|
|
||||||
"bars": len(frame),
|
|
||||||
"timeframe": tf,
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
_RANGE_CYCLES = {
|
|
||||||
WyckoffCycle.ACCUMULATION.value,
|
|
||||||
WyckoffCycle.RE_ACCUMULATION.value,
|
|
||||||
WyckoffCycle.DISTRIBUTION.value,
|
|
||||||
WyckoffCycle.RE_DISTRIBUTION.value,
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def _build_range_zones(
|
|
||||||
price_frame: OHLCVFrame,
|
|
||||||
cycle_segs: list[dict],
|
|
||||||
levels: dict | None = None,
|
|
||||||
) -> list[dict]:
|
|
||||||
"""Build price boxes (high/low × date span) for accum/distrib ranges."""
|
|
||||||
if price_frame.empty:
|
|
||||||
return []
|
|
||||||
dates = [str(d)[:10] for d in price_frame.trade_dates]
|
|
||||||
highs = price_frame.high
|
|
||||||
lows = price_frame.low
|
|
||||||
zones: list[dict] = []
|
|
||||||
|
|
||||||
for seg in cycle_segs or []:
|
|
||||||
cy = seg.get("cycle")
|
|
||||||
if cy not in _RANGE_CYCLES:
|
|
||||||
continue
|
|
||||||
start, end = seg["start"], seg["end"]
|
|
||||||
idxs = [i for i, d in enumerate(dates) if start <= d <= end]
|
|
||||||
if not idxs:
|
|
||||||
# weekly bar date may sit between daily bars — take nearest window
|
|
||||||
i0 = next((i for i, d in enumerate(dates) if d >= start), None)
|
|
||||||
if i0 is None:
|
|
||||||
continue
|
|
||||||
i1 = next((i for i, d in enumerate(dates) if d > end), len(dates)) - 1
|
|
||||||
idxs = list(range(i0, max(i0, i1) + 1))
|
|
||||||
if not idxs:
|
|
||||||
continue
|
|
||||||
# pad short weekly hits to at least ~1 week of dailies for visibility
|
|
||||||
if len(idxs) < 5 and idxs[-1] + 1 < len(dates):
|
|
||||||
extra = min(5 - len(idxs), len(dates) - 1 - idxs[-1])
|
|
||||||
idxs = list(range(idxs[0], idxs[-1] + 1 + max(0, extra)))
|
|
||||||
hi = max(highs[i] for i in idxs)
|
|
||||||
lo = min(lows[i] for i in idxs)
|
|
||||||
if hi <= lo:
|
|
||||||
continue
|
|
||||||
zones.append({
|
|
||||||
"kind": cy,
|
|
||||||
"start": dates[idxs[0]],
|
|
||||||
"end": dates[idxs[-1]],
|
|
||||||
"high": float(hi),
|
|
||||||
"low": float(lo),
|
|
||||||
"current": False,
|
|
||||||
})
|
|
||||||
|
|
||||||
# Always expose the latest trading-range box from feature snapshot
|
|
||||||
levels = levels or {}
|
|
||||||
rh, rl = levels.get("range_high"), levels.get("range_low")
|
|
||||||
if rh is not None and rl is not None and float(rh) > float(rl):
|
|
||||||
look = min(60, len(dates))
|
|
||||||
cy = levels.get("cycle") or "Unknown"
|
|
||||||
if cy not in _RANGE_CYCLES:
|
|
||||||
# Phase B/C in a range → treat as accumulation-style TR for display
|
|
||||||
ph = levels.get("phase") or ""
|
|
||||||
if ph in ("A", "B", "C"):
|
|
||||||
cy = WyckoffCycle.ACCUMULATION.value
|
|
||||||
elif ph in ("D", "E") and float(levels.get("close") or 0) < float(rh):
|
|
||||||
cy = WyckoffCycle.ACCUMULATION.value
|
|
||||||
else:
|
|
||||||
cy = "Range"
|
|
||||||
zones.append({
|
|
||||||
"kind": cy,
|
|
||||||
"start": dates[-look],
|
|
||||||
"end": dates[-1],
|
|
||||||
"high": float(rh),
|
|
||||||
"low": float(rl),
|
|
||||||
"current": True,
|
|
||||||
})
|
|
||||||
|
|
||||||
return zones
|
|
||||||
|
|
||||||
|
|
||||||
def annotate_symbol(
|
|
||||||
ts_code: str,
|
|
||||||
freq: str,
|
|
||||||
end_date: date | None = None,
|
|
||||||
lookback: int = 180,
|
|
||||||
*,
|
|
||||||
combo_id: str | None = None,
|
|
||||||
) -> dict:
|
|
||||||
"""IO + annotate for one symbol (used by API).
|
|
||||||
|
|
||||||
For the combo *low* chart, phase bands come from **mid** structure,
|
|
||||||
while event markers / levels come from the low TF.
|
|
||||||
"""
|
|
||||||
from crypto_wyckoff.combos import ROLE_HIGH, ROLE_LOW, ROLE_MID, get_combo
|
|
||||||
from crypto_wyckoff.io import load_frame
|
|
||||||
|
|
||||||
combo = get_combo(combo_id)
|
|
||||||
allowed = {combo["low"], combo["mid"], combo["high"]}
|
|
||||||
if freq not in allowed:
|
|
||||||
raise ValueError(f"freq {freq} not in combo {combo['id']} ({combo['label']})")
|
|
||||||
empty = {
|
|
||||||
"ts_code": ts_code,
|
|
||||||
"freq": freq,
|
|
||||||
"phases": [],
|
|
||||||
"events": [],
|
|
||||||
"levels": {},
|
|
||||||
"zones": [],
|
|
||||||
"bars": 0,
|
|
||||||
"phase_source": freq,
|
|
||||||
"cycles": [],
|
|
||||||
"combo_id": combo["id"],
|
|
||||||
}
|
|
||||||
_ = end_date
|
|
||||||
|
|
||||||
if freq == combo["low"]:
|
|
||||||
low = load_frame(ts_code, combo["low"], lookback)
|
|
||||||
mid = load_frame(ts_code, combo["mid"], max(60, lookback // 3))
|
|
||||||
if low is None:
|
|
||||||
return empty
|
|
||||||
d_ann = annotate_frame(low, role=ROLE_LOW)
|
|
||||||
w_ann = annotate_frame(mid, role=ROLE_MID) if mid is not None else {"phases": []}
|
|
||||||
cycles = _cycle_segments(mid, role=ROLE_MID) if mid is not None else []
|
|
||||||
levels = d_ann.get("levels") or {}
|
|
||||||
if cycles:
|
|
||||||
levels = {**levels, "cycle": cycles[-1].get("cycle") or levels.get("cycle")}
|
|
||||||
for p in reversed(w_ann.get("phases") or []):
|
|
||||||
if p.get("phase") not in (None, "None"):
|
|
||||||
levels = {**levels, "phase": p["phase"]}
|
|
||||||
break
|
|
||||||
return {
|
|
||||||
"ts_code": ts_code,
|
|
||||||
"freq": freq,
|
|
||||||
"end_date": low.trade_dates[-1].isoformat() if low.trade_dates else None,
|
|
||||||
"phases": w_ann.get("phases") or [],
|
|
||||||
"events": d_ann.get("events") or [],
|
|
||||||
"levels": d_ann.get("levels") or {},
|
|
||||||
"zones": _build_range_zones(low, cycles, levels),
|
|
||||||
"bars": d_ann.get("bars", 0),
|
|
||||||
"phase_source": combo["mid"],
|
|
||||||
"cycles": cycles,
|
|
||||||
"combo_id": combo["id"],
|
|
||||||
}
|
|
||||||
|
|
||||||
role = ROLE_MID if freq == combo["mid"] else ROLE_HIGH
|
|
||||||
frame = load_frame(ts_code, freq, lookback)
|
|
||||||
if frame is None:
|
|
||||||
return empty
|
|
||||||
out = annotate_frame(frame, role=role)
|
|
||||||
out["ts_code"] = ts_code
|
|
||||||
out["freq"] = freq
|
|
||||||
out["end_date"] = frame.trade_dates[-1].isoformat() if frame.trade_dates else None
|
|
||||||
out["phase_source"] = freq
|
|
||||||
out["cycles"] = _cycle_segments(frame, role=ROLE_HIGH if role == ROLE_HIGH else ROLE_MID)
|
|
||||||
out["zones"] = _build_range_zones(frame, out["cycles"], out.get("levels") or {})
|
|
||||||
out["combo_id"] = combo["id"]
|
|
||||||
if role == ROLE_HIGH:
|
|
||||||
if not any(p.get("phase") not in (None, "None") for p in out["phases"]):
|
|
||||||
out["phases"] = [
|
|
||||||
{"start": c["start"], "end": c["end"], "phase": c["cycle"]}
|
|
||||||
for c in out["cycles"]
|
|
||||||
if c.get("cycle") and c["cycle"] != "Unknown"
|
|
||||||
]
|
|
||||||
return out
|
|
||||||
|
|
||||||
|
|
||||||
def _cycle_segments(
|
|
||||||
frame: OHLCVFrame,
|
|
||||||
step: int | None = None,
|
|
||||||
*,
|
|
||||||
role: str | None = None,
|
|
||||||
) -> list[dict]:
|
|
||||||
"""Walk-forward cycle labels compressed to segments."""
|
|
||||||
tf = role or frame.timeframe
|
|
||||||
min_bars = _MIN_BARS.get(tf, 30)
|
|
||||||
if step is None:
|
|
||||||
step = {"1d": 3, "1w": 1, "1M": 1}.get(tf, 2)
|
|
||||||
if frame.empty or len(frame) < min_bars:
|
|
||||||
return []
|
|
||||||
|
|
||||||
feat_eng = FeatureEngine()
|
|
||||||
cycle_eng = CycleEngine()
|
|
||||||
points: list[tuple[str, str]] = []
|
|
||||||
indices = list(range(min_bars - 1, len(frame), step))
|
|
||||||
if indices[-1] != len(frame) - 1:
|
|
||||||
indices.append(len(frame) - 1)
|
|
||||||
for i in indices:
|
|
||||||
sub = _slice_frame(frame, i)
|
|
||||||
f = feat_eng.run(sub, tf)
|
|
||||||
c = cycle_eng.run(f, tf)
|
|
||||||
points.append((str(frame.trade_dates[i])[:10], c.payload.get("cycle") or "Unknown"))
|
|
||||||
segs = _compress_phases(points)
|
|
||||||
return [{"start": s["start"], "end": s["end"], "cycle": s["phase"]} for s in segs]
|
|
||||||
@@ -1,248 +0,0 @@
|
|||||||
"""Multi-timeframe combo presets for Crypto Wyckoff Screener.
|
|
||||||
|
|
||||||
Roles (engine rule aliases stay D/W/M):
|
|
||||||
high → Cycle (rules as 1M)
|
|
||||||
mid → Phase (rules as 1w)
|
|
||||||
low → Event (rules as 1d)
|
|
||||||
|
|
||||||
Actual bar TFs come from the combo (e.g. 8h/4h/1h).
|
|
||||||
"""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import json
|
|
||||||
import re
|
|
||||||
import threading
|
|
||||||
from copy import deepcopy
|
|
||||||
from pathlib import Path
|
|
||||||
from typing import Any
|
|
||||||
|
|
||||||
from crypto_wyckoff.io import DATA_DIR, ensure_dirs
|
|
||||||
|
|
||||||
ROLE_LOW = "1d"
|
|
||||||
ROLE_MID = "1w"
|
|
||||||
ROLE_HIGH = "1M"
|
|
||||||
|
|
||||||
# Minutes for ordering / validation (provider labels)
|
|
||||||
_TF_MINUTES: dict[str, int] = {
|
|
||||||
"1m": 1, "2m": 2, "3m": 3, "4m": 4, "5m": 5,
|
|
||||||
"10m": 10, "15m": 15, "20m": 20, "25m": 25, "30m": 30, "45m": 45,
|
|
||||||
"1h": 60, "2h": 120, "3h": 180, "4h": 240, "5h": 300,
|
|
||||||
"6h": 360, "7h": 420, "8h": 480, "9h": 540, "10h": 600,
|
|
||||||
"11h": 660, "12h": 720, "16h": 960, "20h": 1200,
|
|
||||||
"1d": 1440, "2d": 2880, "3d": 4320, "4d": 5760, "5d": 7200, "6d": 8640,
|
|
||||||
"1w": 10080, "2w": 20160, "3w": 30240,
|
|
||||||
"1M": 43200,
|
|
||||||
}
|
|
||||||
|
|
||||||
# TFs we allow in custom combos (provider-backed + local 1M)
|
|
||||||
ALLOWED_TFS: tuple[str, ...] = (
|
|
||||||
"1h", "2h", "3h", "4h", "6h", "8h", "12h",
|
|
||||||
"1d", "2d", "3d", "1w", "1M",
|
|
||||||
)
|
|
||||||
|
|
||||||
BUILTIN: list[dict[str, Any]] = [
|
|
||||||
{
|
|
||||||
"id": "h8_4_1",
|
|
||||||
"label": "8h / 4h / 1h",
|
|
||||||
"high": "8h",
|
|
||||||
"mid": "4h",
|
|
||||||
"low": "1h",
|
|
||||||
"builtin": True,
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"id": "d_w_m",
|
|
||||||
"label": "1d / 1w / 1M",
|
|
||||||
"high": "1M",
|
|
||||||
"mid": "1w",
|
|
||||||
"low": "1d",
|
|
||||||
"builtin": True,
|
|
||||||
},
|
|
||||||
]
|
|
||||||
|
|
||||||
_COMBOS_FILE = DATA_DIR / "combos.json"
|
|
||||||
_lock = threading.Lock()
|
|
||||||
_cache: list[dict[str, Any]] | None = None
|
|
||||||
|
|
||||||
|
|
||||||
def tf_minutes(tf: str) -> int | None:
|
|
||||||
if tf in _TF_MINUTES:
|
|
||||||
return _TF_MINUTES[tf]
|
|
||||||
# tolerate provider typo "10" → skip
|
|
||||||
m = re.fullmatch(r"(\d+)([mhdwM])", tf)
|
|
||||||
if not m:
|
|
||||||
return None
|
|
||||||
n, u = int(m.group(1)), m.group(2)
|
|
||||||
mult = {"m": 1, "h": 60, "d": 1440, "w": 10080, "M": 43200}[u]
|
|
||||||
return n * mult
|
|
||||||
|
|
||||||
|
|
||||||
def combo_id_for(high: str, mid: str, low: str) -> str:
|
|
||||||
def _tok(t: str) -> str:
|
|
||||||
return t.replace("/", "_")
|
|
||||||
|
|
||||||
return f"{_tok(high)}_{_tok(mid)}_{_tok(low)}"
|
|
||||||
|
|
||||||
|
|
||||||
def validate_combo(high: str, mid: str, low: str) -> str | None:
|
|
||||||
"""Return error message or None if ok."""
|
|
||||||
for tf in (high, mid, low):
|
|
||||||
if tf not in ALLOWED_TFS:
|
|
||||||
return f"不支持的周期: {tf}"
|
|
||||||
if len({high, mid, low}) < 3:
|
|
||||||
return "高/中/低周期必须互不相同"
|
|
||||||
hm, mm, lm = tf_minutes(high), tf_minutes(mid), tf_minutes(low)
|
|
||||||
if hm is None or mm is None or lm is None:
|
|
||||||
return "无法解析周期长度"
|
|
||||||
if not (hm > mm > lm):
|
|
||||||
return "须满足 高 > 中 > 低（例如 8h > 4h > 1h）"
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
def _normalize(row: dict[str, Any]) -> dict[str, Any] | None:
|
|
||||||
high, mid, low = row.get("high"), row.get("mid"), row.get("low")
|
|
||||||
if not high or not mid or not low:
|
|
||||||
return None
|
|
||||||
err = validate_combo(str(high), str(mid), str(low))
|
|
||||||
if err:
|
|
||||||
return None
|
|
||||||
cid = str(row.get("id") or combo_id_for(high, mid, low))
|
|
||||||
label = str(row.get("label") or f"{high} / {mid} / {low}")
|
|
||||||
return {
|
|
||||||
"id": cid,
|
|
||||||
"label": label,
|
|
||||||
"high": str(high),
|
|
||||||
"mid": str(mid),
|
|
||||||
"low": str(low),
|
|
||||||
"builtin": bool(row.get("builtin", False)),
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def _load_raw() -> list[dict[str, Any]]:
|
|
||||||
ensure_dirs()
|
|
||||||
if not _COMBOS_FILE.exists():
|
|
||||||
return deepcopy(BUILTIN)
|
|
||||||
try:
|
|
||||||
data = json.loads(_COMBOS_FILE.read_text(encoding="utf-8"))
|
|
||||||
items = data.get("combos") if isinstance(data, dict) else data
|
|
||||||
if not isinstance(items, list):
|
|
||||||
return deepcopy(BUILTIN)
|
|
||||||
except (OSError, json.JSONDecodeError):
|
|
||||||
return deepcopy(BUILTIN)
|
|
||||||
|
|
||||||
out: list[dict[str, Any]] = []
|
|
||||||
seen: set[str] = set()
|
|
||||||
for b in BUILTIN:
|
|
||||||
out.append(deepcopy(b))
|
|
||||||
seen.add(b["id"])
|
|
||||||
for row in items:
|
|
||||||
if not isinstance(row, dict):
|
|
||||||
continue
|
|
||||||
norm = _normalize(row)
|
|
||||||
if not norm or norm["id"] in seen:
|
|
||||||
continue
|
|
||||||
if norm["id"] in {b["id"] for b in BUILTIN}:
|
|
||||||
continue
|
|
||||||
norm["builtin"] = False
|
|
||||||
out.append(norm)
|
|
||||||
seen.add(norm["id"])
|
|
||||||
return out
|
|
||||||
|
|
||||||
|
|
||||||
def _save(combos: list[dict[str, Any]]) -> None:
|
|
||||||
ensure_dirs()
|
|
||||||
custom = [c for c in combos if not c.get("builtin")]
|
|
||||||
payload = {"combos": custom}
|
|
||||||
tmp = _COMBOS_FILE.with_suffix(".tmp")
|
|
||||||
tmp.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
|
|
||||||
tmp.replace(_COMBOS_FILE)
|
|
||||||
|
|
||||||
|
|
||||||
def list_combos() -> list[dict[str, Any]]:
|
|
||||||
global _cache
|
|
||||||
with _lock:
|
|
||||||
if _cache is None:
|
|
||||||
_cache = _load_raw()
|
|
||||||
return deepcopy(_cache)
|
|
||||||
|
|
||||||
|
|
||||||
def get_combo(combo_id: str | None) -> dict[str, Any]:
|
|
||||||
combos = list_combos()
|
|
||||||
if combo_id:
|
|
||||||
for c in combos:
|
|
||||||
if c["id"] == combo_id:
|
|
||||||
return deepcopy(c)
|
|
||||||
return deepcopy(combos[0])
|
|
||||||
|
|
||||||
|
|
||||||
def add_combo(high: str, mid: str, low: str, label: str | None = None) -> dict[str, Any]:
|
|
||||||
err = validate_combo(high, mid, low)
|
|
||||||
if err:
|
|
||||||
raise ValueError(err)
|
|
||||||
cid = combo_id_for(high, mid, low)
|
|
||||||
row = {
|
|
||||||
"id": cid,
|
|
||||||
"label": label or f"{high} / {mid} / {low}",
|
|
||||||
"high": high,
|
|
||||||
"mid": mid,
|
|
||||||
"low": low,
|
|
||||||
"builtin": False,
|
|
||||||
}
|
|
||||||
with _lock:
|
|
||||||
combos = _load_raw()
|
|
||||||
for c in combos:
|
|
||||||
if c["id"] == cid or (c["high"], c["mid"], c["low"]) == (high, mid, low):
|
|
||||||
_cache = combos
|
|
||||||
return deepcopy(c)
|
|
||||||
combos.append(row)
|
|
||||||
_save(combos)
|
|
||||||
_cache = combos
|
|
||||||
return deepcopy(row)
|
|
||||||
|
|
||||||
|
|
||||||
def delete_combo(combo_id: str) -> bool:
|
|
||||||
with _lock:
|
|
||||||
combos = _load_raw()
|
|
||||||
kept: list[dict[str, Any]] = []
|
|
||||||
removed = False
|
|
||||||
for c in combos:
|
|
||||||
if c["id"] == combo_id:
|
|
||||||
if c.get("builtin"):
|
|
||||||
raise ValueError("内置组合不可删除")
|
|
||||||
removed = True
|
|
||||||
continue
|
|
||||||
kept.append(c)
|
|
||||||
if removed:
|
|
||||||
_save(kept)
|
|
||||||
_cache = kept
|
|
||||||
return removed
|
|
||||||
|
|
||||||
|
|
||||||
def all_tfs_for_combos(combos: list[dict[str, Any]] | None = None) -> list[str]:
|
|
||||||
"""Unique TFs needed by active combos (stable order)."""
|
|
||||||
rows = combos if combos is not None else list_combos()
|
|
||||||
seen: list[str] = []
|
|
||||||
for c in rows:
|
|
||||||
for k in ("low", "mid", "high"):
|
|
||||||
tf = c[k]
|
|
||||||
if tf not in seen:
|
|
||||||
seen.append(tf)
|
|
||||||
return seen
|
|
||||||
|
|
||||||
|
|
||||||
def lookback_for(tf: str) -> int:
|
|
||||||
defaults = {
|
|
||||||
"1h": 500,
|
|
||||||
"2h": 400,
|
|
||||||
"3h": 350,
|
|
||||||
"4h": 300,
|
|
||||||
"6h": 280,
|
|
||||||
"8h": 250,
|
|
||||||
"12h": 220,
|
|
||||||
"1d": 250,
|
|
||||||
"2d": 200,
|
|
||||||
"3d": 180,
|
|
||||||
"1w": 104,
|
|
||||||
"1M": 60,
|
|
||||||
}
|
|
||||||
return defaults.get(tf, 200)
|
|
||||||
@@ -1,102 +0,0 @@
|
|||||||
"""Cycle Engine — monthly/weekly macro cycle via Rule Registry."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from crypto_wyckoff.domain_models import EngineResult, WyckoffCycle
|
|
||||||
from crypto_wyckoff.rules.base import RuleHit
|
|
||||||
from crypto_wyckoff.rules.registry import rule_registry
|
|
||||||
|
|
||||||
|
|
||||||
def _resolve_range_conflict(hits: list[RuleHit], features: dict) -> list[RuleHit]:
|
|
||||||
"""Accumulation vs Distribution overlap → mutually exclusive by MA120 position."""
|
|
||||||
accum = [h for h in hits if h.cycle == WyckoffCycle.ACCUMULATION.value]
|
|
||||||
dist = [h for h in hits if h.cycle == WyckoffCycle.DISTRIBUTION.value]
|
|
||||||
if not (accum and dist):
|
|
||||||
return hits
|
|
||||||
|
|
||||||
close = float(features.get("close") or 0)
|
|
||||||
ma120 = float(features.get("ma120") or close) or close
|
|
||||||
others = [
|
|
||||||
h for h in hits
|
|
||||||
if h.cycle not in (WyckoffCycle.ACCUMULATION.value, WyckoffCycle.DISTRIBUTION.value)
|
|
||||||
]
|
|
||||||
# Below MA120 → accumulation; above → distribution; equal band uses relative position
|
|
||||||
if close < ma120 * 0.995:
|
|
||||||
return others + accum
|
|
||||||
if close > ma120 * 1.005:
|
|
||||||
return others + dist
|
|
||||||
# Tight band: keep higher confidence only
|
|
||||||
best_a = max(accum, key=lambda h: h.confidence)
|
|
||||||
best_d = max(dist, key=lambda h: h.confidence)
|
|
||||||
return others + ([best_a] if best_a.confidence >= best_d.confidence else [best_d])
|
|
||||||
|
|
||||||
|
|
||||||
class CycleEngine:
|
|
||||||
name = "Cycle"
|
|
||||||
version = "1.0.0"
|
|
||||||
|
|
||||||
def run(self, feature: EngineResult, timeframe: str) -> EngineResult:
|
|
||||||
features = feature.payload
|
|
||||||
if features.get("insufficient"):
|
|
||||||
return EngineResult(
|
|
||||||
name=self.name,
|
|
||||||
version=self.version,
|
|
||||||
confidence=15.0,
|
|
||||||
score=40.0,
|
|
||||||
reasons=[f"{timeframe} 数据不足，Cycle=Unknown"],
|
|
||||||
warnings=["insufficient_features"],
|
|
||||||
payload={
|
|
||||||
"cycle": WyckoffCycle.UNKNOWN.value,
|
|
||||||
"timeframe": timeframe,
|
|
||||||
"trend_score": 40.0,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
|
|
||||||
context = {"features": features, "timeframe": timeframe}
|
|
||||||
hits: list[RuleHit] = []
|
|
||||||
for rule in rule_registry.by_category("cycle", timeframe):
|
|
||||||
hit = rule.evaluate(context)
|
|
||||||
if hit and hit.cycle:
|
|
||||||
hits.append(hit)
|
|
||||||
|
|
||||||
hits = _resolve_range_conflict(hits, features)
|
|
||||||
|
|
||||||
if not hits:
|
|
||||||
return EngineResult(
|
|
||||||
name=self.name,
|
|
||||||
version=self.version,
|
|
||||||
confidence=30.0,
|
|
||||||
score=40.0,
|
|
||||||
reasons=["无匹配周期规则，标记 Unknown"],
|
|
||||||
payload={
|
|
||||||
"cycle": WyckoffCycle.UNKNOWN.value,
|
|
||||||
"timeframe": timeframe,
|
|
||||||
"trend_score": 40.0,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
|
|
||||||
best = max(hits, key=lambda h: h.confidence)
|
|
||||||
trend_score = best.score
|
|
||||||
if best.cycle == WyckoffCycle.MARKUP.value:
|
|
||||||
trend_score = max(trend_score, 75.0)
|
|
||||||
elif best.cycle == WyckoffCycle.ACCUMULATION.value:
|
|
||||||
trend_score = max(60.0, trend_score * 0.9)
|
|
||||||
elif best.cycle == WyckoffCycle.DISTRIBUTION.value:
|
|
||||||
trend_score = min(45.0, 100 - trend_score * 0.5)
|
|
||||||
elif best.cycle == WyckoffCycle.MARKDOWN.value:
|
|
||||||
trend_score = min(30.0, 100 - trend_score)
|
|
||||||
|
|
||||||
return EngineResult(
|
|
||||||
name=self.name,
|
|
||||||
version=self.version,
|
|
||||||
confidence=best.confidence,
|
|
||||||
score=trend_score,
|
|
||||||
reasons=best.reasons,
|
|
||||||
metrics=best.metrics,
|
|
||||||
payload={
|
|
||||||
"cycle": best.cycle,
|
|
||||||
"timeframe": timeframe,
|
|
||||||
"rule_id": best.rule_id,
|
|
||||||
"trend_score": trend_score,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
@@ -1,195 +0,0 @@
|
|||||||
"""Decision Engine — multi-timeframe fusion and tradability (Architecture v1.0)."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from crypto_wyckoff.domain_models import (
|
|
||||||
DecisionSignal,
|
|
||||||
EngineResult,
|
|
||||||
RiskLevel,
|
|
||||||
WyckoffCycle,
|
|
||||||
WyckoffEvent,
|
|
||||||
WyckoffPhase,
|
|
||||||
)
|
|
||||||
|
|
||||||
BULL_CYCLES = {
|
|
||||||
WyckoffCycle.ACCUMULATION.value,
|
|
||||||
WyckoffCycle.RE_ACCUMULATION.value,
|
|
||||||
WyckoffCycle.MARKUP.value,
|
|
||||||
}
|
|
||||||
BEAR_CYCLES = {
|
|
||||||
WyckoffCycle.DISTRIBUTION.value,
|
|
||||||
WyckoffCycle.RE_DISTRIBUTION.value,
|
|
||||||
WyckoffCycle.MARKDOWN.value,
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
class DecisionEngine:
|
|
||||||
name = "Decision"
|
|
||||||
version = "1.0.0"
|
|
||||||
|
|
||||||
def run(
|
|
||||||
self,
|
|
||||||
monthly_cycle: EngineResult,
|
|
||||||
weekly_cycle: EngineResult,
|
|
||||||
weekly_phase: EngineResult,
|
|
||||||
weekly_event: EngineResult,
|
|
||||||
daily_event: EngineResult,
|
|
||||||
daily_signal: EngineResult,
|
|
||||||
) -> EngineResult:
|
|
||||||
m_cycle = monthly_cycle.payload.get("cycle", WyckoffCycle.UNKNOWN.value)
|
|
||||||
w_cycle = weekly_cycle.payload.get("cycle", WyckoffCycle.UNKNOWN.value)
|
|
||||||
w_phase = weekly_phase.payload.get("phase", WyckoffPhase.NONE.value)
|
|
||||||
w_event = weekly_event.payload.get("current_event", WyckoffEvent.NONE.value)
|
|
||||||
d_event = daily_event.payload.get("current_event", WyckoffEvent.NONE.value)
|
|
||||||
|
|
||||||
trend_score = float(monthly_cycle.payload.get("trend_score", monthly_cycle.score))
|
|
||||||
structure_score = float(weekly_phase.payload.get("structure_score", weekly_phase.score))
|
|
||||||
entry_score = float(daily_event.payload.get("entry_score", daily_event.score))
|
|
||||||
|
|
||||||
overall_score = 0.30 * trend_score + 0.30 * structure_score + 0.40 * entry_score
|
|
||||||
|
|
||||||
reasons: list[str] = []
|
|
||||||
warnings: list[str] = []
|
|
||||||
alignment = 50.0
|
|
||||||
|
|
||||||
m_bull = m_cycle in BULL_CYCLES
|
|
||||||
m_bear = m_cycle in BEAR_CYCLES
|
|
||||||
w_bull = w_cycle in BULL_CYCLES
|
|
||||||
d_bullish_event = d_event in {
|
|
||||||
WyckoffEvent.SPRING.value,
|
|
||||||
WyckoffEvent.TEST.value,
|
|
||||||
WyckoffEvent.SOS.value,
|
|
||||||
WyckoffEvent.LPS.value,
|
|
||||||
WyckoffEvent.JUMP.value,
|
|
||||||
WyckoffEvent.BACKUP.value,
|
|
||||||
}
|
|
||||||
d_bearish_event = d_event in {
|
|
||||||
WyckoffEvent.UTAD.value,
|
|
||||||
WyckoffEvent.SOW.value,
|
|
||||||
WyckoffEvent.LPSY.value,
|
|
||||||
}
|
|
||||||
|
|
||||||
# Alignment scoring
|
|
||||||
if m_bull and w_bull and d_bullish_event:
|
|
||||||
alignment = 92.0
|
|
||||||
reasons.append("✓ 月/周多头结构与日线多头事件一致")
|
|
||||||
elif m_bull and d_bullish_event:
|
|
||||||
alignment = 78.0
|
|
||||||
reasons.append("✓ 月线支持，日线有入场事件")
|
|
||||||
if not w_bull:
|
|
||||||
warnings.append("周线结构未完全确认")
|
|
||||||
alignment -= 8
|
|
||||||
elif m_bear and d_bullish_event:
|
|
||||||
alignment = 35.0
|
|
||||||
reasons.append("✗ 月线派发/下跌，日线弹簧可能只是反弹")
|
|
||||||
elif m_bear and d_bearish_event:
|
|
||||||
alignment = 85.0
|
|
||||||
reasons.append("✓ 空头多周期一致")
|
|
||||||
else:
|
|
||||||
alignment = 55.0
|
|
||||||
reasons.append("○ 多周期部分一致，需观察")
|
|
||||||
|
|
||||||
if w_phase in (WyckoffPhase.D.value, WyckoffPhase.E.value) and m_bull:
|
|
||||||
alignment = min(98.0, alignment + 6)
|
|
||||||
reasons.append(f"✓ 周线阶段 {w_phase} 结构成熟（{w_event}）")
|
|
||||||
active = daily_event.payload.get("active_events") or daily_event.payload.get("recent_events") or []
|
|
||||||
if d_event == WyckoffEvent.SPRING.value and len(active) >= 3:
|
|
||||||
alignment = min(98.0, alignment + 4)
|
|
||||||
reasons.append("✓ 日线多重事件同时确认")
|
|
||||||
|
|
||||||
# Decision signal — hard gate on monthly bear + daily spring
|
|
||||||
decision = DecisionSignal.WATCH.value
|
|
||||||
risk = RiskLevel.MEDIUM.value
|
|
||||||
|
|
||||||
if m_bear and d_event == WyckoffEvent.SPRING.value:
|
|
||||||
decision = DecisionSignal.WATCH.value
|
|
||||||
risk = RiskLevel.HIGH.value
|
|
||||||
overall_score = min(overall_score, 55.0)
|
|
||||||
reasons.append("→ 决策：观察（月线不支持，禁止追日线弹簧）")
|
|
||||||
elif m_bear and d_bullish_event:
|
|
||||||
decision = DecisionSignal.AVOID.value
|
|
||||||
risk = RiskLevel.HIGH.value
|
|
||||||
overall_score = min(overall_score, 48.0)
|
|
||||||
reasons.append("→ 决策：回避（逆大周期多头事件）")
|
|
||||||
elif (
|
|
||||||
m_bull
|
|
||||||
and w_phase in (WyckoffPhase.D.value, WyckoffPhase.E.value, WyckoffPhase.C.value)
|
|
||||||
and d_event in (WyckoffEvent.SPRING.value, WyckoffEvent.LPS.value, WyckoffEvent.SOS.value)
|
|
||||||
and alignment >= 85
|
|
||||||
and overall_score >= 80
|
|
||||||
):
|
|
||||||
decision = DecisionSignal.STRONG_BUY.value
|
|
||||||
risk = RiskLevel.LOW.value
|
|
||||||
reasons.append("→ 决策：强烈买入（三级共振）")
|
|
||||||
elif m_bull and d_bullish_event and overall_score >= 68 and alignment >= 70:
|
|
||||||
decision = DecisionSignal.BUY.value
|
|
||||||
risk = RiskLevel.LOW.value if alignment >= 80 else RiskLevel.MEDIUM.value
|
|
||||||
reasons.append("→ 决策：买入")
|
|
||||||
elif m_bear and d_bearish_event and overall_score >= 65:
|
|
||||||
decision = DecisionSignal.SELL.value
|
|
||||||
risk = RiskLevel.MEDIUM.value
|
|
||||||
reasons.append("→ 决策：卖出")
|
|
||||||
else:
|
|
||||||
decision = DecisionSignal.WATCH.value
|
|
||||||
reasons.append("→ 决策：观察")
|
|
||||||
|
|
||||||
# Stars from score + alignment
|
|
||||||
combo = 0.6 * overall_score + 0.4 * alignment
|
|
||||||
if combo >= 90:
|
|
||||||
stars = 5
|
|
||||||
elif combo >= 80:
|
|
||||||
stars = 4
|
|
||||||
elif combo >= 65:
|
|
||||||
stars = 3
|
|
||||||
elif combo >= 50:
|
|
||||||
stars = 2
|
|
||||||
else:
|
|
||||||
stars = 1
|
|
||||||
|
|
||||||
overall_confidence = (
|
|
||||||
0.25 * monthly_cycle.confidence
|
|
||||||
+ 0.25 * weekly_phase.confidence
|
|
||||||
+ 0.25 * daily_event.confidence
|
|
||||||
+ 0.25 * daily_signal.confidence
|
|
||||||
)
|
|
||||||
# Weak event pulls overall down
|
|
||||||
if daily_event.confidence < 60:
|
|
||||||
overall_confidence = min(overall_confidence, daily_event.confidence + 15)
|
|
||||||
|
|
||||||
return EngineResult(
|
|
||||||
name=self.name,
|
|
||||||
version=self.version,
|
|
||||||
confidence=overall_confidence,
|
|
||||||
score=overall_score,
|
|
||||||
reasons=reasons,
|
|
||||||
warnings=warnings,
|
|
||||||
metrics={
|
|
||||||
"trend_score": trend_score,
|
|
||||||
"structure_score": structure_score,
|
|
||||||
"entry_score": entry_score,
|
|
||||||
"alignment": alignment,
|
|
||||||
"stars": stars,
|
|
||||||
},
|
|
||||||
payload={
|
|
||||||
"decision_signal": decision,
|
|
||||||
"alignment": alignment,
|
|
||||||
"stars": stars,
|
|
||||||
"risk": risk,
|
|
||||||
"overall_score": overall_score,
|
|
||||||
"overall_confidence": overall_confidence,
|
|
||||||
"trend_score": trend_score,
|
|
||||||
"structure_score": structure_score,
|
|
||||||
"entry_score": entry_score,
|
|
||||||
"m_cycle": m_cycle,
|
|
||||||
"w_cycle": w_cycle,
|
|
||||||
"w_phase": w_phase,
|
|
||||||
"w_event": w_event,
|
|
||||||
"d_event": d_event,
|
|
||||||
# Facts preserved — never overwritten
|
|
||||||
"facts": {
|
|
||||||
"monthly": {"cycle": m_cycle},
|
|
||||||
"weekly": {"cycle": w_cycle, "phase": w_phase, "event": w_event},
|
|
||||||
"daily": {"event": d_event},
|
|
||||||
},
|
|
||||||
},
|
|
||||||
)
|
|
||||||
@@ -1,154 +0,0 @@
|
|||||||
"""Wyckoff Screener domain models — Architecture v1.0 frozen contracts."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from dataclasses import dataclass, field
|
|
||||||
from datetime import date, datetime
|
|
||||||
from enum import Enum
|
|
||||||
from typing import Any, Optional
|
|
||||||
|
|
||||||
|
|
||||||
class WyckoffCycle(str, Enum):
|
|
||||||
ACCUMULATION = "Accumulation"
|
|
||||||
RE_ACCUMULATION = "ReAccumulation"
|
|
||||||
MARKUP = "Markup"
|
|
||||||
DISTRIBUTION = "Distribution"
|
|
||||||
RE_DISTRIBUTION = "ReDistribution"
|
|
||||||
MARKDOWN = "Markdown"
|
|
||||||
UNKNOWN = "Unknown"
|
|
||||||
|
|
||||||
|
|
||||||
class WyckoffPhase(str, Enum):
|
|
||||||
A = "A"
|
|
||||||
B = "B"
|
|
||||||
C = "C"
|
|
||||||
D = "D"
|
|
||||||
E = "E"
|
|
||||||
NONE = "None"
|
|
||||||
|
|
||||||
|
|
||||||
class WyckoffEvent(str, Enum):
|
|
||||||
PS = "PS"
|
|
||||||
SC = "SC"
|
|
||||||
AR = "AR"
|
|
||||||
ST = "ST"
|
|
||||||
SPRING = "Spring"
|
|
||||||
TEST = "Test"
|
|
||||||
SOS = "SOS"
|
|
||||||
LPS = "LPS"
|
|
||||||
JUMP = "Jump"
|
|
||||||
BACKUP = "Backup"
|
|
||||||
BC = "BC"
|
|
||||||
UTAD = "UTAD"
|
|
||||||
SOW = "SOW"
|
|
||||||
LPSY = "LPSY"
|
|
||||||
NONE = "None"
|
|
||||||
|
|
||||||
|
|
||||||
class DecisionSignal(str, Enum):
|
|
||||||
STRONG_BUY = "StrongBuy"
|
|
||||||
BUY = "Buy"
|
|
||||||
WATCH = "Watch"
|
|
||||||
AVOID = "Avoid"
|
|
||||||
SELL = "Sell"
|
|
||||||
|
|
||||||
|
|
||||||
class RiskLevel(str, Enum):
|
|
||||||
LOW = "Low"
|
|
||||||
MEDIUM = "Medium"
|
|
||||||
HIGH = "High"
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class EngineResult:
|
|
||||||
"""Unified result envelope for every Wyckoff engine (v1.0 contract)."""
|
|
||||||
|
|
||||||
name: str
|
|
||||||
version: str = "1.0.0"
|
|
||||||
confidence: float = 0.0
|
|
||||||
score: float = 0.0
|
|
||||||
reasons: list[str] = field(default_factory=list)
|
|
||||||
warnings: list[str] = field(default_factory=list)
|
|
||||||
metrics: dict[str, Any] = field(default_factory=dict)
|
|
||||||
payload: dict[str, Any] = field(default_factory=dict)
|
|
||||||
|
|
||||||
def to_dict(self) -> dict[str, Any]:
|
|
||||||
return {
|
|
||||||
"name": self.name,
|
|
||||||
"version": self.version,
|
|
||||||
"confidence": self.confidence,
|
|
||||||
"score": self.score,
|
|
||||||
"reasons": self.reasons,
|
|
||||||
"warnings": self.warnings,
|
|
||||||
"metrics": self.metrics,
|
|
||||||
"payload": self.payload,
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class OHLCVFrame:
|
|
||||||
"""In-memory OHLCV for one symbol one timeframe. Engines never touch DB."""
|
|
||||||
|
|
||||||
ts_code: str
|
|
||||||
timeframe: str # "1d" | "1w" | "1M"
|
|
||||||
trade_dates: list[date]
|
|
||||||
open: list[float]
|
|
||||||
high: list[float]
|
|
||||||
low: list[float]
|
|
||||||
close: list[float]
|
|
||||||
volume: list[float]
|
|
||||||
amount: list[float] = field(default_factory=list)
|
|
||||||
|
|
||||||
def __len__(self) -> int:
|
|
||||||
return len(self.close)
|
|
||||||
|
|
||||||
@property
|
|
||||||
def empty(self) -> bool:
|
|
||||||
return len(self.close) == 0
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class WyckoffScanRow:
|
|
||||||
"""Persisted scan row for wyckoff_scan table."""
|
|
||||||
|
|
||||||
trade_date: date
|
|
||||||
ts_code: str
|
|
||||||
name: str = ""
|
|
||||||
industry: str = ""
|
|
||||||
engine_version: str = "v1.0.0"
|
|
||||||
combo_id: str = "d_w_m"
|
|
||||||
|
|
||||||
m_cycle: str = WyckoffCycle.UNKNOWN.value
|
|
||||||
cycle_confidence: float = 0.0
|
|
||||||
trend_score: float = 0.0
|
|
||||||
|
|
||||||
w_cycle: str = WyckoffCycle.UNKNOWN.value
|
|
||||||
w_phase: str = WyckoffPhase.NONE.value
|
|
||||||
w_current_event: str = WyckoffEvent.NONE.value
|
|
||||||
w_recent_events_json: str = "[]"
|
|
||||||
phase_confidence: float = 0.0
|
|
||||||
structure_score: float = 0.0
|
|
||||||
|
|
||||||
d_current_event: str = WyckoffEvent.NONE.value
|
|
||||||
d_recent_events_json: str = "[]"
|
|
||||||
event_confidence: float = 0.0
|
|
||||||
entry_score: float = 0.0
|
|
||||||
|
|
||||||
entry: Optional[float] = None
|
|
||||||
stop: Optional[float] = None
|
|
||||||
target1: Optional[float] = None
|
|
||||||
target2: Optional[float] = None
|
|
||||||
rr: Optional[float] = None
|
|
||||||
|
|
||||||
alignment: float = 0.0
|
|
||||||
stars: int = 1
|
|
||||||
decision_signal: str = DecisionSignal.WATCH.value
|
|
||||||
signal_confidence: float = 0.0
|
|
||||||
overall_confidence: float = 0.0
|
|
||||||
overall_score: float = 0.0
|
|
||||||
risk: str = RiskLevel.MEDIUM.value
|
|
||||||
reasons_json: str = "[]"
|
|
||||||
|
|
||||||
feature_snapshot_json: str = "{}"
|
|
||||||
markers_json: str = "[]"
|
|
||||||
scanned_at: datetime = field(default_factory=datetime.now)
|
|
||||||
@@ -1,149 +0,0 @@
|
|||||||
"""Event Engine — active concurrent events via Rule Registry.
|
|
||||||
|
|
||||||
Note: `active_events` are rules that fire on the latest bar snapshot,
|
|
||||||
NOT a historical SC→AR→ST timeline. Do not present as chronological chain.
|
|
||||||
"""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from crypto_wyckoff.domain_models import EngineResult, WyckoffEvent
|
|
||||||
from crypto_wyckoff.rules.registry import rule_registry
|
|
||||||
|
|
||||||
# Display order only (not temporal history)
|
|
||||||
_DISPLAY_ORDER = [
|
|
||||||
WyckoffEvent.PS.value,
|
|
||||||
WyckoffEvent.SC.value,
|
|
||||||
WyckoffEvent.AR.value,
|
|
||||||
WyckoffEvent.ST.value,
|
|
||||||
WyckoffEvent.SPRING.value,
|
|
||||||
WyckoffEvent.TEST.value,
|
|
||||||
WyckoffEvent.SOS.value,
|
|
||||||
WyckoffEvent.LPS.value,
|
|
||||||
WyckoffEvent.JUMP.value,
|
|
||||||
WyckoffEvent.BACKUP.value,
|
|
||||||
WyckoffEvent.BC.value,
|
|
||||||
WyckoffEvent.UTAD.value,
|
|
||||||
WyckoffEvent.SOW.value,
|
|
||||||
WyckoffEvent.LPSY.value,
|
|
||||||
]
|
|
||||||
|
|
||||||
# Dominant event: highest confidence wins; ties broken by this priority
|
|
||||||
_DOMINANCE_PRIORITY = [
|
|
||||||
WyckoffEvent.SOS.value,
|
|
||||||
WyckoffEvent.LPS.value,
|
|
||||||
WyckoffEvent.UTAD.value,
|
|
||||||
WyckoffEvent.SPRING.value,
|
|
||||||
WyckoffEvent.JUMP.value,
|
|
||||||
WyckoffEvent.BACKUP.value,
|
|
||||||
WyckoffEvent.TEST.value,
|
|
||||||
WyckoffEvent.SC.value,
|
|
||||||
WyckoffEvent.SOW.value,
|
|
||||||
WyckoffEvent.AR.value,
|
|
||||||
WyckoffEvent.ST.value,
|
|
||||||
]
|
|
||||||
|
|
||||||
|
|
||||||
class EventEngine:
|
|
||||||
name = "Event"
|
|
||||||
version = "1.0.0"
|
|
||||||
|
|
||||||
def run(
|
|
||||||
self,
|
|
||||||
cycle: EngineResult,
|
|
||||||
phase: EngineResult,
|
|
||||||
feature: EngineResult,
|
|
||||||
timeframe: str,
|
|
||||||
) -> EngineResult:
|
|
||||||
if feature.payload.get("insufficient"):
|
|
||||||
return EngineResult(
|
|
||||||
name=self.name,
|
|
||||||
version=self.version,
|
|
||||||
confidence=20.0,
|
|
||||||
score=30.0,
|
|
||||||
reasons=["特征不足，跳过事件识别"],
|
|
||||||
warnings=["insufficient_features"],
|
|
||||||
payload={
|
|
||||||
"current_event": WyckoffEvent.NONE.value,
|
|
||||||
"active_events": [],
|
|
||||||
"recent_events": [], # alias for DB/API compat; same as active_events
|
|
||||||
"timeframe": timeframe,
|
|
||||||
"entry_score": 30.0,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
|
|
||||||
context = {
|
|
||||||
"features": feature.payload,
|
|
||||||
"cycle": cycle.payload,
|
|
||||||
"phase": phase.payload,
|
|
||||||
"timeframe": timeframe,
|
|
||||||
}
|
|
||||||
hits = []
|
|
||||||
for rule in rule_registry.by_category("event", timeframe):
|
|
||||||
hit = rule.evaluate(context)
|
|
||||||
if hit and hit.event:
|
|
||||||
hits.append(hit)
|
|
||||||
|
|
||||||
if not hits:
|
|
||||||
return EngineResult(
|
|
||||||
name=self.name,
|
|
||||||
version=self.version,
|
|
||||||
confidence=35.0,
|
|
||||||
score=40.0,
|
|
||||||
reasons=["无显著事件"],
|
|
||||||
payload={
|
|
||||||
"current_event": WyckoffEvent.NONE.value,
|
|
||||||
"active_events": [],
|
|
||||||
"recent_events": [],
|
|
||||||
"timeframe": timeframe,
|
|
||||||
"entry_score": 40.0,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
|
|
||||||
by_event: dict[str, float] = {}
|
|
||||||
reasons: list[str] = []
|
|
||||||
metrics: dict = {}
|
|
||||||
for h in hits:
|
|
||||||
prev = by_event.get(h.event, -1.0)
|
|
||||||
if h.confidence >= prev:
|
|
||||||
by_event[h.event] = h.confidence
|
|
||||||
reasons.extend(h.reasons)
|
|
||||||
metrics.update(h.metrics)
|
|
||||||
|
|
||||||
active = [e for e in _DISPLAY_ORDER if e in by_event]
|
|
||||||
for e in by_event:
|
|
||||||
if e not in active:
|
|
||||||
active.append(e)
|
|
||||||
|
|
||||||
# Dominant = max confidence; tie-break by dominance priority index
|
|
||||||
def _dom_key(ev: str) -> tuple:
|
|
||||||
conf = by_event[ev]
|
|
||||||
try:
|
|
||||||
prio = _DOMINANCE_PRIORITY.index(ev)
|
|
||||||
except ValueError:
|
|
||||||
prio = 99
|
|
||||||
return (conf, -prio)
|
|
||||||
|
|
||||||
current = max(by_event.keys(), key=_dom_key)
|
|
||||||
event_conf = by_event[current]
|
|
||||||
co_bonus = min(12.0, max(0, len(active) - 1) * 3)
|
|
||||||
entry_score = min(98.0, event_conf + co_bonus)
|
|
||||||
if current == WyckoffEvent.SPRING.value and WyckoffEvent.TEST.value in by_event:
|
|
||||||
entry_score = min(98.0, entry_score + 5)
|
|
||||||
|
|
||||||
return EngineResult(
|
|
||||||
name=self.name,
|
|
||||||
version=self.version,
|
|
||||||
confidence=event_conf,
|
|
||||||
score=entry_score,
|
|
||||||
reasons=list(dict.fromkeys(reasons))[:8],
|
|
||||||
warnings=["active_events_are_concurrent_not_timeline"],
|
|
||||||
metrics=metrics,
|
|
||||||
payload={
|
|
||||||
"current_event": current,
|
|
||||||
"active_events": active,
|
|
||||||
"recent_events": active, # persisted column name; semantic = active
|
|
||||||
"event_scores": by_event,
|
|
||||||
"timeframe": timeframe,
|
|
||||||
"entry_score": entry_score,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
@@ -1,206 +0,0 @@
|
|||||||
"""Feature Engine — pure function over OHLCVFrame → EngineResult(FeatureSnapshot)."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from typing import Any
|
|
||||||
|
|
||||||
import numpy as np
|
|
||||||
|
|
||||||
from crypto_wyckoff.domain_models import EngineResult, OHLCVFrame
|
|
||||||
|
|
||||||
|
|
||||||
def _sma(arr: np.ndarray, n: int) -> float:
|
|
||||||
if len(arr) < n:
|
|
||||||
return float(arr[-1]) if len(arr) else 0.0
|
|
||||||
return float(np.mean(arr[-n:]))
|
|
||||||
|
|
||||||
|
|
||||||
def _atr(high: np.ndarray, low: np.ndarray, close: np.ndarray, n: int = 14) -> float:
|
|
||||||
if len(close) < 2:
|
|
||||||
return 0.0
|
|
||||||
prev_close = close[:-1]
|
|
||||||
tr = np.maximum(high[1:] - low[1:], np.maximum(np.abs(high[1:] - prev_close), np.abs(low[1:] - prev_close)))
|
|
||||||
if len(tr) < n:
|
|
||||||
return float(np.mean(tr)) if len(tr) else 0.0
|
|
||||||
return float(np.mean(tr[-n:]))
|
|
||||||
|
|
||||||
|
|
||||||
def _adx(high: np.ndarray, low: np.ndarray, close: np.ndarray, n: int = 14) -> float:
|
|
||||||
"""Simplified ADX approximation."""
|
|
||||||
if len(close) < n + 2:
|
|
||||||
return 15.0
|
|
||||||
up = high[1:] - high[:-1]
|
|
||||||
down = low[:-1] - low[1:]
|
|
||||||
plus_dm = np.where((up > down) & (up > 0), up, 0.0)
|
|
||||||
minus_dm = np.where((down > up) & (down > 0), down, 0.0)
|
|
||||||
tr = np.maximum(high[1:] - low[1:], np.maximum(np.abs(high[1:] - close[:-1]), np.abs(low[1:] - close[:-1])))
|
|
||||||
atr = np.mean(tr[-n:]) or 1e-9
|
|
||||||
plus_di = 100 * np.mean(plus_dm[-n:]) / atr
|
|
||||||
minus_di = 100 * np.mean(minus_dm[-n:]) / atr
|
|
||||||
denom = plus_di + minus_di
|
|
||||||
if denom < 1e-9:
|
|
||||||
return 10.0
|
|
||||||
dx = 100 * abs(plus_di - minus_di) / denom
|
|
||||||
return float(min(60.0, dx))
|
|
||||||
|
|
||||||
|
|
||||||
def compute_feature_snapshot(frame: OHLCVFrame) -> dict[str, Any]:
|
|
||||||
"""Compute technical snapshot dict from OHLCV (no I/O)."""
|
|
||||||
if frame.empty or len(frame) < 5:
|
|
||||||
return {"ts_code": frame.ts_code, "timeframe": frame.timeframe, "bars": len(frame)}
|
|
||||||
|
|
||||||
close = np.asarray(frame.close, dtype=float)
|
|
||||||
high = np.asarray(frame.high, dtype=float)
|
|
||||||
low = np.asarray(frame.low, dtype=float)
|
|
||||||
volume = np.asarray(frame.volume, dtype=float)
|
|
||||||
open_ = np.asarray(frame.open, dtype=float)
|
|
||||||
|
|
||||||
ma20 = _sma(close, 20)
|
|
||||||
ma60 = _sma(close, 60)
|
|
||||||
ma120 = _sma(close, min(120, len(close)))
|
|
||||||
atr = _atr(high, low, close, 14)
|
|
||||||
vol_ma20 = _sma(volume, 20) or 1e-9
|
|
||||||
volume_ratio = float(volume[-1] / vol_ma20)
|
|
||||||
|
|
||||||
look = min(60, len(close))
|
|
||||||
window_h = high[-look:]
|
|
||||||
window_l = low[-look:]
|
|
||||||
range_high = float(np.max(window_h))
|
|
||||||
range_low = float(np.min(window_l))
|
|
||||||
rng = max(range_high - range_low, 1e-9)
|
|
||||||
range_pct_60 = float(rng / close[-1]) if close[-1] else 0.0
|
|
||||||
range_position = float((close[-1] - range_low) / rng)
|
|
||||||
|
|
||||||
# Spring / UTAD hints
|
|
||||||
pierce_below = max(0.0, (range_low - low[-1]) / close[-1]) if close[-1] else 0.0
|
|
||||||
# if previous bars broke below and last close back in range
|
|
||||||
prior_low = float(np.min(low[-6:-1])) if len(low) >= 6 else float(low[-2])
|
|
||||||
pierce_below = max(pierce_below, max(0.0, (range_low - prior_low) / close[-1]))
|
|
||||||
close_back_in_range = 1.0 if close[-1] >= range_low else 0.0
|
|
||||||
reclaim_speed = 0.0
|
|
||||||
if pierce_below > 0 and close[-1] >= range_low:
|
|
||||||
reclaim_speed = min(1.0, (close[-1] - low[-1]) / max(atr, 1e-9) / 2)
|
|
||||||
|
|
||||||
pierce_above = max(0.0, (high[-1] - range_high) / close[-1])
|
|
||||||
fail_back = 1.0 if pierce_above > 0 and close[-1] <= range_high else 0.0
|
|
||||||
breakout_above = 1.0 if close[-1] > range_high and volume_ratio >= 1.0 else -1.0
|
|
||||||
|
|
||||||
# pullback hold: close near ma20 from above after being higher
|
|
||||||
pullback_hold = 0.0
|
|
||||||
if len(close) >= 5 and close[-1] > ma20 and close[-3] > close[-1] and (close[-1] - ma20) / max(atr, 1e-9) < 1.5:
|
|
||||||
pullback_hold = 0.8
|
|
||||||
|
|
||||||
ma60_prev = _sma(close[:-5], 60) if len(close) > 65 else ma60
|
|
||||||
ma60_slope = (ma60 - ma60_prev) / max(abs(ma60_prev), 1e-9)
|
|
||||||
|
|
||||||
# volume trend: recent 10 vs prior 10
|
|
||||||
if len(volume) >= 20:
|
|
||||||
volume_trend = float(np.mean(volume[-10:]) / (np.mean(volume[-20:-10]) + 1e-9) - 1.0)
|
|
||||||
else:
|
|
||||||
volume_trend = 0.0
|
|
||||||
|
|
||||||
bar_range_atr = float((high[-1] - low[-1]) / max(atr, 1e-9))
|
|
||||||
bounce_from_low = float((close[-1] - float(np.min(low[-10:]))) / close[-1]) if close[-1] else 0.0
|
|
||||||
gap_up_pct = float((open_[-1] - close[-2]) / close[-2]) if len(close) >= 2 and close[-2] else 0.0
|
|
||||||
after_strength = 0.0
|
|
||||||
if len(close) >= 4 and close[-3] > close[-4]:
|
|
||||||
after_strength = 0.7
|
|
||||||
|
|
||||||
spring_score_hint = 0.0
|
|
||||||
if pierce_below >= 0.002 and close_back_in_range:
|
|
||||||
spring_score_hint = min(90.0, 50 + pierce_below * 1500 + reclaim_speed * 20)
|
|
||||||
utad_score_hint = min(90.0, 50 + pierce_above * 1500) if pierce_above >= 0.002 and fail_back else 0.0
|
|
||||||
|
|
||||||
# swing
|
|
||||||
swing_high = float(np.max(high[-20:])) if len(high) >= 5 else float(high[-1])
|
|
||||||
swing_low = float(np.min(low[-20:])) if len(low) >= 5 else float(low[-1])
|
|
||||||
|
|
||||||
return {
|
|
||||||
"ts_code": frame.ts_code,
|
|
||||||
"timeframe": frame.timeframe,
|
|
||||||
"bars": len(frame),
|
|
||||||
"close": float(close[-1]),
|
|
||||||
"open": float(open_[-1]),
|
|
||||||
"high": float(high[-1]),
|
|
||||||
"low": float(low[-1]),
|
|
||||||
"volume": float(volume[-1]),
|
|
||||||
"ma20": ma20,
|
|
||||||
"ma60": ma60,
|
|
||||||
"ma120": ma120,
|
|
||||||
"ma60_slope": float(ma60_slope),
|
|
||||||
"atr": atr,
|
|
||||||
"adx": _adx(high, low, close),
|
|
||||||
"volume_ma20": float(vol_ma20),
|
|
||||||
"volume_ratio": volume_ratio,
|
|
||||||
"volume_trend": volume_trend,
|
|
||||||
"range_high": range_high,
|
|
||||||
"range_low": range_low,
|
|
||||||
"range_pct_60": range_pct_60,
|
|
||||||
"range_position": range_position,
|
|
||||||
"pierce_below_range": pierce_below,
|
|
||||||
"pierce_above_range": pierce_above,
|
|
||||||
"close_back_in_range": close_back_in_range,
|
|
||||||
"reclaim_speed": reclaim_speed,
|
|
||||||
"fail_back_into_range": fail_back,
|
|
||||||
"breakout_above_range": breakout_above,
|
|
||||||
"pullback_hold": pullback_hold,
|
|
||||||
"bar_range_atr": bar_range_atr,
|
|
||||||
"bounce_from_low": bounce_from_low,
|
|
||||||
"gap_up_pct": gap_up_pct,
|
|
||||||
"after_strength": after_strength,
|
|
||||||
"spring_score_hint": spring_score_hint,
|
|
||||||
"utad_score_hint": utad_score_hint,
|
|
||||||
"swing_high": swing_high,
|
|
||||||
"swing_low": swing_low,
|
|
||||||
"trade_date": str(frame.trade_dates[-1]) if frame.trade_dates else None,
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
# Minimum bars before a timeframe is considered usable (no cross-TF borrow)
|
|
||||||
_MIN_BARS = {"1d": 40, "1w": 26, "1M": 18}
|
|
||||||
|
|
||||||
|
|
||||||
class FeatureEngine:
|
|
||||||
"""Pure Feature Engine — no database access."""
|
|
||||||
|
|
||||||
name = "Feature"
|
|
||||||
version = "1.0.0"
|
|
||||||
|
|
||||||
def run(self, frame: OHLCVFrame | None, timeframe: str | None = None) -> EngineResult:
|
|
||||||
tf = timeframe or (frame.timeframe if frame else "1d")
|
|
||||||
min_bars = _MIN_BARS.get(tf, 30)
|
|
||||||
|
|
||||||
if frame is None or frame.empty or len(frame) < min_bars:
|
|
||||||
bars = 0 if frame is None or frame.empty else len(frame)
|
|
||||||
return EngineResult(
|
|
||||||
name=self.name,
|
|
||||||
version=self.version,
|
|
||||||
confidence=10.0,
|
|
||||||
score=10.0,
|
|
||||||
reasons=[f"{tf} bars={bars} < min={min_bars}，标记 insufficient"],
|
|
||||||
warnings=["insufficient_features"],
|
|
||||||
metrics={"bars": bars, "min_bars": min_bars},
|
|
||||||
payload={
|
|
||||||
"ts_code": getattr(frame, "ts_code", ""),
|
|
||||||
"timeframe": tf,
|
|
||||||
"bars": bars,
|
|
||||||
"insufficient": True,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
|
|
||||||
snap = compute_feature_snapshot(frame)
|
|
||||||
snap["insufficient"] = False
|
|
||||||
conf = 90.0 if snap.get("bars", 0) >= 60 else 50.0 + min(40.0, snap.get("bars", 0) * 0.5)
|
|
||||||
warnings = []
|
|
||||||
if snap.get("bars", 0) < 60:
|
|
||||||
warnings.append("bars偏少，特征可靠性中等")
|
|
||||||
return EngineResult(
|
|
||||||
name=self.name,
|
|
||||||
version=self.version,
|
|
||||||
confidence=conf,
|
|
||||||
score=conf,
|
|
||||||
reasons=[f"computed {snap.get('bars', 0)} bars {tf}"],
|
|
||||||
warnings=warnings,
|
|
||||||
metrics={"bars": snap.get("bars", 0)},
|
|
||||||
payload=snap,
|
|
||||||
)
|
|
||||||
@@ -1,363 +0,0 @@
|
|||||||
"""Paths + OHLCV cache + DATA_SERVICE fetch (crypto continuous calendar)."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import json
|
|
||||||
import logging
|
|
||||||
import os
|
|
||||||
import sqlite3
|
|
||||||
import time
|
|
||||||
from datetime import date, datetime, timezone
|
|
||||||
from pathlib import Path
|
|
||||||
from typing import Iterable
|
|
||||||
|
|
||||||
import requests
|
|
||||||
|
|
||||||
from crypto_wyckoff.domain_models import OHLCVFrame
|
|
||||||
|
|
||||||
logger = logging.getLogger(__name__)
|
|
||||||
|
|
||||||
_REPO_ROOT = Path(__file__).resolve().parents[1]
|
|
||||||
DATA_DIR = Path(os.environ.get("CRYPTO_WYCKOFF_DATA", str(_REPO_ROOT / "data" / "crypto_wyckoff")))
|
|
||||||
BARS_DB = DATA_DIR / "bars.sqlite"
|
|
||||||
SCAN_DB = DATA_DIR / "scan.sqlite"
|
|
||||||
|
|
||||||
DATA_SERVICE_URL = os.environ.get(
|
|
||||||
"DATA_SERVICE_URL",
|
|
||||||
os.environ.get("DATASVC_URL", "https://provider.jackyu66.com"),
|
|
||||||
).rstrip("/")
|
|
||||||
|
|
||||||
# Continuous crypto: bar counts (not A-share weekend-padded calendar multipliers)
|
|
||||||
# Provider has many TFs; 1M is resampled locally from daily UTC months.
|
|
||||||
LOOKBACK = {
|
|
||||||
"1h": 500,
|
|
||||||
"2h": 400,
|
|
||||||
"4h": 300,
|
|
||||||
"6h": 280,
|
|
||||||
"8h": 250,
|
|
||||||
"12h": 220,
|
|
||||||
"1d": 250,
|
|
||||||
"1w": 104,
|
|
||||||
"1M": 60,
|
|
||||||
}
|
|
||||||
# Default D/W/M stack (kept for compat); combos may request more TFs from provider.
|
|
||||||
TF_PROVIDER = ("1h", "4h", "8h", "1d", "1w")
|
|
||||||
TF_LIST = ("1d", "1w", "1M")
|
|
||||||
LOCAL_ONLY_TFS = frozenset({"1M"})
|
|
||||||
|
|
||||||
|
|
||||||
def ensure_dirs() -> None:
|
|
||||||
DATA_DIR.mkdir(parents=True, exist_ok=True)
|
|
||||||
|
|
||||||
|
|
||||||
def _symbol_key(symbol: str) -> str:
|
|
||||||
return symbol.replace("/", "_").replace(":", "_")
|
|
||||||
|
|
||||||
|
|
||||||
def _bars_conn() -> sqlite3.Connection:
|
|
||||||
ensure_dirs()
|
|
||||||
conn = sqlite3.connect(str(BARS_DB), timeout=60)
|
|
||||||
conn.execute(
|
|
||||||
"""
|
|
||||||
CREATE TABLE IF NOT EXISTS bars (
|
|
||||||
symbol TEXT NOT NULL,
|
|
||||||
tf TEXT NOT NULL,
|
|
||||||
ts INTEGER NOT NULL,
|
|
||||||
open REAL, high REAL, low REAL, close REAL, volume REAL,
|
|
||||||
PRIMARY KEY (symbol, tf, ts)
|
|
||||||
)
|
|
||||||
"""
|
|
||||||
)
|
|
||||||
conn.execute("CREATE INDEX IF NOT EXISTS idx_bars_sym_tf ON bars(symbol, tf)")
|
|
||||||
return conn
|
|
||||||
|
|
||||||
|
|
||||||
def fetch_candles(
|
|
||||||
symbol: str,
|
|
||||||
tf: str,
|
|
||||||
*,
|
|
||||||
limit: int | None = None,
|
|
||||||
start_ms: int | None = None,
|
|
||||||
end_ms: int | None = None,
|
|
||||||
timeout: float = 15.0,
|
|
||||||
) -> list[dict]:
|
|
||||||
params: dict = {"symbol": symbol, "tf": tf}
|
|
||||||
if limit is not None:
|
|
||||||
params["limit"] = int(limit)
|
|
||||||
if start_ms is not None:
|
|
||||||
params["start"] = int(start_ms)
|
|
||||||
if end_ms is not None:
|
|
||||||
params["end"] = int(end_ms)
|
|
||||||
resp = requests.get(f"{DATA_SERVICE_URL}/api/candles", params=params, timeout=timeout)
|
|
||||||
resp.raise_for_status()
|
|
||||||
data = resp.json()
|
|
||||||
if not isinstance(data, list):
|
|
||||||
return []
|
|
||||||
out = []
|
|
||||||
for row in data:
|
|
||||||
try:
|
|
||||||
ts = int(float(row["timestamp"]))
|
|
||||||
out.append(
|
|
||||||
{
|
|
||||||
"ts": ts,
|
|
||||||
"open": float(row["open"]),
|
|
||||||
"high": float(row["high"]),
|
|
||||||
"low": float(row["low"]),
|
|
||||||
"close": float(row["close"]),
|
|
||||||
"volume": float(row.get("volume") or 0),
|
|
||||||
}
|
|
||||||
)
|
|
||||||
except (KeyError, TypeError, ValueError):
|
|
||||||
continue
|
|
||||||
out.sort(key=lambda r: r["ts"])
|
|
||||||
return out
|
|
||||||
|
|
||||||
|
|
||||||
def upsert_bars(symbol: str, tf: str, rows: list[dict]) -> int:
|
|
||||||
if not rows:
|
|
||||||
return 0
|
|
||||||
conn = _bars_conn()
|
|
||||||
try:
|
|
||||||
conn.executemany(
|
|
||||||
"""
|
|
||||||
INSERT INTO bars(symbol, tf, ts, open, high, low, close, volume)
|
|
||||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?)
|
|
||||||
ON CONFLICT(symbol, tf, ts) DO UPDATE SET
|
|
||||||
open=excluded.open, high=excluded.high, low=excluded.low,
|
|
||||||
close=excluded.close, volume=excluded.volume
|
|
||||||
""",
|
|
||||||
[
|
|
||||||
(symbol, tf, r["ts"], r["open"], r["high"], r["low"], r["close"], r["volume"])
|
|
||||||
for r in rows
|
|
||||||
],
|
|
||||||
)
|
|
||||||
conn.commit()
|
|
||||||
return len(rows)
|
|
||||||
finally:
|
|
||||||
conn.close()
|
|
||||||
|
|
||||||
|
|
||||||
def is_intraday_tf(tf: str) -> bool:
|
|
||||||
"""True for minute/hour TFs that need clock time on charts."""
|
|
||||||
t = (tf or "").strip()
|
|
||||||
return t.endswith("m") or t.endswith("h")
|
|
||||||
|
|
||||||
|
|
||||||
def load_bars_with_ts(
|
|
||||||
symbol: str, tf: str, lookback: int | None = None
|
|
||||||
) -> list[dict]:
|
|
||||||
"""Return OHLCV rows with UTC ms ts (for chart labels).
|
|
||||||
|
|
||||||
``datetime`` is wall-clock in Asia/Shanghai (UTC+8) for display.
|
|
||||||
"""
|
|
||||||
from zoneinfo import ZoneInfo
|
|
||||||
|
|
||||||
tz_cn = ZoneInfo("Asia/Shanghai")
|
|
||||||
if lookback is None:
|
|
||||||
try:
|
|
||||||
from crypto_wyckoff.combos import lookback_for
|
|
||||||
|
|
||||||
lookback = lookback_for(tf)
|
|
||||||
except Exception:
|
|
||||||
lookback = LOOKBACK.get(tf, 100)
|
|
||||||
lookback = lookback or LOOKBACK.get(tf, 100)
|
|
||||||
conn = _bars_conn()
|
|
||||||
try:
|
|
||||||
cur = conn.execute(
|
|
||||||
"""
|
|
||||||
SELECT ts, open, high, low, close, volume FROM bars
|
|
||||||
WHERE symbol=? AND tf=?
|
|
||||||
ORDER BY ts DESC LIMIT ?
|
|
||||||
""",
|
|
||||||
(symbol, tf, lookback),
|
|
||||||
)
|
|
||||||
rows = list(reversed(cur.fetchall()))
|
|
||||||
finally:
|
|
||||||
conn.close()
|
|
||||||
out = []
|
|
||||||
for ts, o, h, l, c, v in rows:
|
|
||||||
dt_utc = datetime.fromtimestamp(ts / 1000.0, tz=timezone.utc)
|
|
||||||
dt_cn = dt_utc.astimezone(tz_cn)
|
|
||||||
out.append(
|
|
||||||
{
|
|
||||||
"ts": int(ts),
|
|
||||||
"datetime": dt_cn.strftime("%Y-%m-%dT%H:%M:%S+08:00"),
|
|
||||||
"date": dt_cn.strftime("%Y-%m-%d"),
|
|
||||||
"open": o,
|
|
||||||
"high": h,
|
|
||||||
"low": l,
|
|
||||||
"close": c,
|
|
||||||
"volume": v,
|
|
||||||
}
|
|
||||||
)
|
|
||||||
return out
|
|
||||||
|
|
||||||
|
|
||||||
def load_frame(symbol: str, tf: str, lookback: int | None = None) -> OHLCVFrame | None:
|
|
||||||
rows = load_bars_with_ts(symbol, tf, lookback)
|
|
||||||
if not rows:
|
|
||||||
return None
|
|
||||||
return OHLCVFrame(
|
|
||||||
ts_code=symbol,
|
|
||||||
timeframe=tf,
|
|
||||||
trade_dates=[
|
|
||||||
datetime.fromtimestamp(r["ts"] / 1000.0, tz=timezone.utc).date() for r in rows
|
|
||||||
],
|
|
||||||
open=[r["open"] for r in rows],
|
|
||||||
high=[r["high"] for r in rows],
|
|
||||||
low=[r["low"] for r in rows],
|
|
||||||
close=[r["close"] for r in rows],
|
|
||||||
volume=[r["volume"] for r in rows],
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
def bar_count(symbol: str, tf: str) -> int:
|
|
||||||
conn = _bars_conn()
|
|
||||||
try:
|
|
||||||
cur = conn.execute(
|
|
||||||
"SELECT COUNT(*) FROM bars WHERE symbol=? AND tf=?", (symbol, tf)
|
|
||||||
)
|
|
||||||
return int(cur.fetchone()[0])
|
|
||||||
finally:
|
|
||||||
conn.close()
|
|
||||||
|
|
||||||
|
|
||||||
def rebuild_monthly_from_daily(symbol: str) -> int:
|
|
||||||
"""Aggregate UTC calendar-month OHLCV from local daily bars (provider has no 1M)."""
|
|
||||||
conn = _bars_conn()
|
|
||||||
try:
|
|
||||||
cur = conn.execute(
|
|
||||||
"""
|
|
||||||
SELECT ts, open, high, low, close, volume FROM bars
|
|
||||||
WHERE symbol=? AND tf='1d' ORDER BY ts ASC
|
|
||||||
""",
|
|
||||||
(symbol,),
|
|
||||||
)
|
|
||||||
daily = cur.fetchall()
|
|
||||||
finally:
|
|
||||||
conn.close()
|
|
||||||
if not daily:
|
|
||||||
return 0
|
|
||||||
|
|
||||||
months: dict[tuple[int, int], dict] = {}
|
|
||||||
for ts, o, h, l, c, v in daily:
|
|
||||||
dt = datetime.fromtimestamp(ts / 1000.0, tz=timezone.utc)
|
|
||||||
key = (dt.year, dt.month)
|
|
||||||
# month bar open timestamp = first day 00:00 UTC
|
|
||||||
month_ts = int(datetime(dt.year, dt.month, 1, tzinfo=timezone.utc).timestamp() * 1000)
|
|
||||||
if key not in months:
|
|
||||||
months[key] = {
|
|
||||||
"ts": month_ts,
|
|
||||||
"open": o,
|
|
||||||
"high": h,
|
|
||||||
"low": l,
|
|
||||||
"close": c,
|
|
||||||
"volume": v or 0.0,
|
|
||||||
}
|
|
||||||
else:
|
|
||||||
m = months[key]
|
|
||||||
m["high"] = max(m["high"], h)
|
|
||||||
m["low"] = min(m["low"], l)
|
|
||||||
m["close"] = c
|
|
||||||
m["volume"] = (m["volume"] or 0) + (v or 0)
|
|
||||||
|
|
||||||
rows = sorted(months.values(), key=lambda r: r["ts"])
|
|
||||||
# drop stale months then upsert
|
|
||||||
conn = _bars_conn()
|
|
||||||
try:
|
|
||||||
conn.execute("DELETE FROM bars WHERE symbol=? AND tf='1M'", (symbol,))
|
|
||||||
conn.commit()
|
|
||||||
finally:
|
|
||||||
conn.close()
|
|
||||||
return upsert_bars(symbol, "1M", rows)
|
|
||||||
|
|
||||||
|
|
||||||
def backfill_symbol(symbol: str, tfs: Iterable[str] = TF_LIST) -> dict:
|
|
||||||
"""Pull history for requested TFs; monthly derived from daily when needed."""
|
|
||||||
wanted = list(dict.fromkeys(tfs))
|
|
||||||
stats: dict = {}
|
|
||||||
need_monthly = "1M" in wanted
|
|
||||||
if need_monthly and "1d" not in wanted:
|
|
||||||
wanted = ["1d", *wanted]
|
|
||||||
|
|
||||||
for tf in wanted:
|
|
||||||
if tf in LOCAL_ONLY_TFS:
|
|
||||||
continue
|
|
||||||
need = LOOKBACK.get(tf, 100)
|
|
||||||
if tf == "1d" and need_monthly:
|
|
||||||
need = max(need, LOOKBACK["1M"] * 31)
|
|
||||||
try:
|
|
||||||
rows = fetch_candles(symbol, tf, limit=need)
|
|
||||||
n = upsert_bars(symbol, tf, rows)
|
|
||||||
stats[tf] = n
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning("backfill %s %s failed: %s", symbol, tf, e)
|
|
||||||
stats[tf] = 0
|
|
||||||
time.sleep(0.05)
|
|
||||||
|
|
||||||
if need_monthly:
|
|
||||||
try:
|
|
||||||
stats["1M"] = rebuild_monthly_from_daily(symbol)
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning("monthly rebuild %s failed: %s", symbol, e)
|
|
||||||
stats["1M"] = 0
|
|
||||||
return stats
|
|
||||||
|
|
||||||
|
|
||||||
def tip_update_symbol(symbol: str, tfs: Iterable[str] = TF_LIST) -> bool:
|
|
||||||
"""Update forming tip bars (limit=3). Returns True if any bar changed."""
|
|
||||||
wanted = list(dict.fromkeys(tfs))
|
|
||||||
changed = False
|
|
||||||
for tf in wanted:
|
|
||||||
if tf in LOCAL_ONLY_TFS:
|
|
||||||
continue
|
|
||||||
try:
|
|
||||||
rows = fetch_candles(symbol, tf, limit=3)
|
|
||||||
if not rows:
|
|
||||||
continue
|
|
||||||
before = _tip_fingerprint(symbol, tf)
|
|
||||||
upsert_bars(symbol, tf, rows)
|
|
||||||
after = _tip_fingerprint(symbol, tf)
|
|
||||||
if before != after:
|
|
||||||
changed = True
|
|
||||||
except Exception as e:
|
|
||||||
logger.debug("tip %s %s: %s", symbol, tf, e)
|
|
||||||
time.sleep(0.02)
|
|
||||||
if "1M" in wanted:
|
|
||||||
before_m = _tip_fingerprint(symbol, "1M")
|
|
||||||
try:
|
|
||||||
rebuild_monthly_from_daily(symbol)
|
|
||||||
except Exception as e:
|
|
||||||
logger.debug("monthly tip %s: %s", symbol, e)
|
|
||||||
after_m = _tip_fingerprint(symbol, "1M")
|
|
||||||
if before_m != after_m:
|
|
||||||
changed = True
|
|
||||||
return changed
|
|
||||||
|
|
||||||
|
|
||||||
def _tip_fingerprint(symbol: str, tf: str) -> tuple | None:
|
|
||||||
conn = _bars_conn()
|
|
||||||
try:
|
|
||||||
cur = conn.execute(
|
|
||||||
"""
|
|
||||||
SELECT ts, open, high, low, close, volume FROM bars
|
|
||||||
WHERE symbol=? AND tf=? ORDER BY ts DESC LIMIT 1
|
|
||||||
""",
|
|
||||||
(symbol, tf),
|
|
||||||
)
|
|
||||||
row = cur.fetchone()
|
|
||||||
return tuple(row) if row else None
|
|
||||||
finally:
|
|
||||||
conn.close()
|
|
||||||
|
|
||||||
|
|
||||||
def fetch_symbols_from_provider() -> list[str]:
|
|
||||||
try:
|
|
||||||
resp = requests.get(f"{DATA_SERVICE_URL}/health", timeout=8)
|
|
||||||
resp.raise_for_status()
|
|
||||||
payload = resp.json()
|
|
||||||
symbols = payload.get("symbols") or payload.get("symbol_list") or []
|
|
||||||
return [s for s in symbols if isinstance(s, str)]
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning("health symbols failed: %s", e)
|
|
||||||
return []
|
|
||||||
@@ -1,78 +0,0 @@
|
|||||||
"""Phase Engine — Phase A–E via Rule Registry."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from crypto_wyckoff.domain_models import EngineResult, WyckoffPhase
|
|
||||||
from crypto_wyckoff.rules.registry import rule_registry
|
|
||||||
|
|
||||||
|
|
||||||
class PhaseEngine:
|
|
||||||
name = "Phase"
|
|
||||||
version = "1.0.0"
|
|
||||||
|
|
||||||
def run(self, cycle: EngineResult, feature: EngineResult, timeframe: str) -> EngineResult:
|
|
||||||
if feature.payload.get("insufficient") or cycle.payload.get("cycle") == "Unknown":
|
|
||||||
return EngineResult(
|
|
||||||
name=self.name,
|
|
||||||
version=self.version,
|
|
||||||
confidence=20.0,
|
|
||||||
score=30.0,
|
|
||||||
reasons=["数据/周期不足，Phase=None"],
|
|
||||||
warnings=["insufficient_features"],
|
|
||||||
payload={
|
|
||||||
"phase": WyckoffPhase.NONE.value,
|
|
||||||
"timeframe": timeframe,
|
|
||||||
"cycle": cycle.payload.get("cycle"),
|
|
||||||
"structure_score": 30.0,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
|
|
||||||
context = {
|
|
||||||
"features": feature.payload,
|
|
||||||
"cycle": cycle.payload,
|
|
||||||
"timeframe": timeframe,
|
|
||||||
}
|
|
||||||
hits = []
|
|
||||||
for rule in rule_registry.by_category("phase", timeframe):
|
|
||||||
hit = rule.evaluate(context)
|
|
||||||
if hit and hit.phase:
|
|
||||||
hits.append(hit)
|
|
||||||
|
|
||||||
if not hits:
|
|
||||||
return EngineResult(
|
|
||||||
name=self.name,
|
|
||||||
version=self.version,
|
|
||||||
confidence=40.0,
|
|
||||||
score=cycle.score * 0.5,
|
|
||||||
reasons=["未识别明确 Phase"],
|
|
||||||
payload={
|
|
||||||
"phase": WyckoffPhase.NONE.value,
|
|
||||||
"timeframe": timeframe,
|
|
||||||
"cycle": cycle.payload.get("cycle"),
|
|
||||||
"structure_score": cycle.score * 0.5,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
|
|
||||||
best = max(hits, key=lambda h: h.confidence)
|
|
||||||
structure_score = best.score
|
|
||||||
# Phase D/E stronger structure
|
|
||||||
if best.phase in (WyckoffPhase.D.value, WyckoffPhase.E.value):
|
|
||||||
structure_score = max(structure_score, 80.0)
|
|
||||||
elif best.phase == WyckoffPhase.C.value:
|
|
||||||
structure_score = max(structure_score, 72.0)
|
|
||||||
|
|
||||||
return EngineResult(
|
|
||||||
name=self.name,
|
|
||||||
version=self.version,
|
|
||||||
confidence=best.confidence,
|
|
||||||
score=structure_score,
|
|
||||||
reasons=best.reasons,
|
|
||||||
metrics=best.metrics,
|
|
||||||
payload={
|
|
||||||
"phase": best.phase,
|
|
||||||
"timeframe": timeframe,
|
|
||||||
"cycle": cycle.payload.get("cycle"),
|
|
||||||
"rule_id": best.rule_id,
|
|
||||||
"structure_score": structure_score,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
@@ -1,181 +0,0 @@
|
|||||||
"""Scan pipeline: load local frames → engines → store (per TF combo)."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import json
|
|
||||||
import logging
|
|
||||||
from datetime import date, datetime, timezone
|
|
||||||
|
|
||||||
from crypto_wyckoff.combos import ROLE_HIGH, ROLE_LOW, ROLE_MID, get_combo, lookback_for
|
|
||||||
from crypto_wyckoff.cycle import CycleEngine
|
|
||||||
from crypto_wyckoff.decision import DecisionEngine
|
|
||||||
from crypto_wyckoff.domain_models import WyckoffScanRow
|
|
||||||
from crypto_wyckoff.event import EventEngine
|
|
||||||
from crypto_wyckoff.features import FeatureEngine
|
|
||||||
from crypto_wyckoff.io import load_frame
|
|
||||||
from crypto_wyckoff.phase import PhaseEngine
|
|
||||||
from crypto_wyckoff.plan import PlanEngine
|
|
||||||
from crypto_wyckoff.signal import SignalEngine
|
|
||||||
from crypto_wyckoff.store import upsert_row
|
|
||||||
from crypto_wyckoff.symbols_cn import display_name_cn
|
|
||||||
from crypto_wyckoff.version import WYCKOFF_ENGINE_VERSION
|
|
||||||
|
|
||||||
logger = logging.getLogger(__name__)
|
|
||||||
|
|
||||||
|
|
||||||
def analyze_symbol(
|
|
||||||
low_frame,
|
|
||||||
mid_frame,
|
|
||||||
high_frame,
|
|
||||||
*,
|
|
||||||
feature_eng: FeatureEngine,
|
|
||||||
cycle_eng: CycleEngine,
|
|
||||||
phase_eng: PhaseEngine,
|
|
||||||
event_eng: EventEngine,
|
|
||||||
signal_eng: SignalEngine,
|
|
||||||
decision_eng: DecisionEngine,
|
|
||||||
plan_eng: PlanEngine,
|
|
||||||
) -> dict:
|
|
||||||
"""Run engines with D/W/M *role* aliases so existing rules match.
|
|
||||||
|
|
||||||
Frames may be any TF combo (e.g. 1h/4h/8h); rules still see 1d/1w/1M roles.
|
|
||||||
"""
|
|
||||||
f_d = feature_eng.run(low_frame, ROLE_LOW)
|
|
||||||
f_w = feature_eng.run(mid_frame, ROLE_MID)
|
|
||||||
f_m = feature_eng.run(high_frame, ROLE_HIGH)
|
|
||||||
|
|
||||||
c_m = cycle_eng.run(f_m, ROLE_HIGH)
|
|
||||||
c_w = cycle_eng.run(f_w, ROLE_MID)
|
|
||||||
|
|
||||||
p_w = phase_eng.run(c_w, f_w, ROLE_MID)
|
|
||||||
p_d = phase_eng.run(c_w, f_d, ROLE_LOW)
|
|
||||||
|
|
||||||
e_w = event_eng.run(c_w, p_w, f_w, ROLE_MID)
|
|
||||||
e_d = event_eng.run(c_w, p_d, f_d, ROLE_LOW)
|
|
||||||
|
|
||||||
s_d = signal_eng.run(e_d, p_d)
|
|
||||||
decision = decision_eng.run(c_m, c_w, p_w, e_w, e_d, s_d)
|
|
||||||
plan = plan_eng.run(f_d, decision)
|
|
||||||
|
|
||||||
return {
|
|
||||||
"f_d": f_d, "f_w": f_w, "f_m": f_m,
|
|
||||||
"c_m": c_m, "c_w": c_w, "p_w": p_w,
|
|
||||||
"e_w": e_w, "e_d": e_d, "s_d": s_d,
|
|
||||||
"decision": decision, "plan": plan,
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def _to_row(
|
|
||||||
trade_date: date,
|
|
||||||
symbol: str,
|
|
||||||
result: dict,
|
|
||||||
*,
|
|
||||||
combo_id: str,
|
|
||||||
combo_label: str,
|
|
||||||
) -> WyckoffScanRow:
|
|
||||||
d = result["decision"]
|
|
||||||
p = result["plan"]
|
|
||||||
c_m, c_w, p_w = result["c_m"], result["c_w"], result["p_w"]
|
|
||||||
e_w, e_d, s_d = result["e_w"], result["e_d"], result["s_d"]
|
|
||||||
f_d, f_w, f_m = result["f_d"], result["f_w"], result["f_m"]
|
|
||||||
|
|
||||||
snapshot = {
|
|
||||||
"combo_id": combo_id,
|
|
||||||
"combo_label": combo_label,
|
|
||||||
"daily": {k: f_d.payload.get(k) for k in (
|
|
||||||
"ma20", "ma60", "ma120", "atr", "adx", "volume_ratio",
|
|
||||||
"range_high", "range_low", "swing_high", "swing_low", "close",
|
|
||||||
)},
|
|
||||||
"weekly": {k: f_w.payload.get(k) for k in ("ma20", "ma60", "adx", "close")},
|
|
||||||
"monthly": {k: f_m.payload.get(k) for k in ("ma20", "ma60", "adx", "close")},
|
|
||||||
}
|
|
||||||
markers = []
|
|
||||||
for key, typ in (("entry", "entry"), ("stop", "stop"), ("target1", "target1"), ("target2", "target2")):
|
|
||||||
if p.payload.get(key) is not None:
|
|
||||||
markers.append({"type": typ, "price": p.payload[key]})
|
|
||||||
|
|
||||||
return WyckoffScanRow(
|
|
||||||
trade_date=trade_date,
|
|
||||||
ts_code=symbol,
|
|
||||||
name=display_name_cn(symbol),
|
|
||||||
industry="crypto",
|
|
||||||
engine_version=WYCKOFF_ENGINE_VERSION,
|
|
||||||
m_cycle=c_m.payload.get("cycle", "Unknown"),
|
|
||||||
cycle_confidence=c_m.confidence,
|
|
||||||
trend_score=float(d.payload.get("trend_score", c_m.score)),
|
|
||||||
w_cycle=c_w.payload.get("cycle", "Unknown"),
|
|
||||||
w_phase=p_w.payload.get("phase", "None"),
|
|
||||||
w_current_event=e_w.payload.get("current_event", "None"),
|
|
||||||
w_recent_events_json=json.dumps(
|
|
||||||
e_w.payload.get("active_events") or e_w.payload.get("recent_events") or [],
|
|
||||||
ensure_ascii=False,
|
|
||||||
),
|
|
||||||
phase_confidence=p_w.confidence,
|
|
||||||
structure_score=float(d.payload.get("structure_score", p_w.score)),
|
|
||||||
d_current_event=e_d.payload.get("current_event", "None"),
|
|
||||||
d_recent_events_json=json.dumps(
|
|
||||||
e_d.payload.get("active_events") or e_d.payload.get("recent_events") or [],
|
|
||||||
ensure_ascii=False,
|
|
||||||
),
|
|
||||||
event_confidence=e_d.confidence,
|
|
||||||
entry_score=float(d.payload.get("entry_score", e_d.score)),
|
|
||||||
entry=p.payload.get("entry"),
|
|
||||||
stop=p.payload.get("stop"),
|
|
||||||
target1=p.payload.get("target1"),
|
|
||||||
target2=p.payload.get("target2"),
|
|
||||||
rr=p.payload.get("rr"),
|
|
||||||
alignment=float(d.payload.get("alignment", 0)),
|
|
||||||
stars=int(d.payload.get("stars", 1)),
|
|
||||||
decision_signal=d.payload.get("decision_signal", "Watch"),
|
|
||||||
signal_confidence=s_d.confidence,
|
|
||||||
overall_confidence=float(d.payload.get("overall_confidence", d.confidence)),
|
|
||||||
overall_score=float(d.payload.get("overall_score", d.score)),
|
|
||||||
risk=d.payload.get("risk", "Medium"),
|
|
||||||
reasons_json=json.dumps(d.reasons + d.warnings, ensure_ascii=False),
|
|
||||||
feature_snapshot_json=json.dumps(snapshot, ensure_ascii=False),
|
|
||||||
markers_json=json.dumps(markers, ensure_ascii=False),
|
|
||||||
scanned_at=datetime.now(timezone.utc),
|
|
||||||
combo_id=combo_id,
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
_ENGINES = None
|
|
||||||
|
|
||||||
|
|
||||||
def _engines():
|
|
||||||
global _ENGINES
|
|
||||||
if _ENGINES is None:
|
|
||||||
_ENGINES = {
|
|
||||||
"feature_eng": FeatureEngine(),
|
|
||||||
"cycle_eng": CycleEngine(),
|
|
||||||
"phase_eng": PhaseEngine(),
|
|
||||||
"event_eng": EventEngine(),
|
|
||||||
"signal_eng": SignalEngine(),
|
|
||||||
"decision_eng": DecisionEngine(),
|
|
||||||
"plan_eng": PlanEngine(),
|
|
||||||
}
|
|
||||||
return _ENGINES
|
|
||||||
|
|
||||||
|
|
||||||
def analyze_and_store(
|
|
||||||
symbol: str,
|
|
||||||
trade_date: date | None = None,
|
|
||||||
*,
|
|
||||||
combo_id: str | None = None,
|
|
||||||
) -> WyckoffScanRow | None:
|
|
||||||
eng = _engines()
|
|
||||||
combo = get_combo(combo_id)
|
|
||||||
low_tf, mid_tf, high_tf = combo["low"], combo["mid"], combo["high"]
|
|
||||||
|
|
||||||
low = load_frame(symbol, low_tf, lookback_for(low_tf))
|
|
||||||
mid = load_frame(symbol, mid_tf, lookback_for(mid_tf))
|
|
||||||
high = load_frame(symbol, high_tf, lookback_for(high_tf))
|
|
||||||
if low is None or len(low) < 40:
|
|
||||||
return None
|
|
||||||
result = analyze_symbol(low, mid, high, **eng)
|
|
||||||
td = trade_date or (
|
|
||||||
low.trade_dates[-1] if low.trade_dates else datetime.now(timezone.utc).date()
|
|
||||||
)
|
|
||||||
row = _to_row(td, symbol, result, combo_id=combo["id"], combo_label=combo["label"])
|
|
||||||
upsert_row(row)
|
|
||||||
return row
|
|
||||||
@@ -1,78 +0,0 @@
|
|||||||
"""Plan Engine — Entry / Stop / Target / RR only when Decision is tradable."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from crypto_wyckoff.domain_models import DecisionSignal, EngineResult
|
|
||||||
|
|
||||||
|
|
||||||
_TRADABLE = {
|
|
||||||
DecisionSignal.STRONG_BUY.value,
|
|
||||||
DecisionSignal.BUY.value,
|
|
||||||
DecisionSignal.SELL.value,
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
class PlanEngine:
|
|
||||||
name = "Plan"
|
|
||||||
version = "1.0.0"
|
|
||||||
|
|
||||||
def run(self, daily_feature: EngineResult, decision: EngineResult) -> EngineResult:
|
|
||||||
f = daily_feature.payload
|
|
||||||
close = float(f.get("close") or 0)
|
|
||||||
atr = float(f.get("atr") or 0) or close * 0.02
|
|
||||||
swing_low = float(f.get("swing_low") or close - 2 * atr)
|
|
||||||
swing_high = float(f.get("swing_high") or close + 2 * atr)
|
|
||||||
range_high = float(f.get("range_high") or swing_high)
|
|
||||||
signal = decision.payload.get("decision_signal", DecisionSignal.WATCH.value)
|
|
||||||
|
|
||||||
entry = stop = t1 = t2 = rr = None
|
|
||||||
reasons: list[str] = []
|
|
||||||
|
|
||||||
if signal not in _TRADABLE or close <= 0:
|
|
||||||
reasons.append(f"无交易计划（信号={signal}）")
|
|
||||||
return EngineResult(
|
|
||||||
name=self.name,
|
|
||||||
version=self.version,
|
|
||||||
confidence=decision.confidence,
|
|
||||||
score=decision.score,
|
|
||||||
reasons=reasons,
|
|
||||||
payload={
|
|
||||||
"entry": None,
|
|
||||||
"stop": None,
|
|
||||||
"target1": None,
|
|
||||||
"target2": None,
|
|
||||||
"rr": None,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
|
|
||||||
if signal in (DecisionSignal.STRONG_BUY.value, DecisionSignal.BUY.value):
|
|
||||||
entry = round(close, 4)
|
|
||||||
stop = round(min(swing_low, close - 1.5 * atr), 4)
|
|
||||||
risk = max(entry - stop, 1e-6)
|
|
||||||
t1 = round(entry + 2.0 * risk, 4)
|
|
||||||
t2 = round(max(range_high, entry + 3.0 * risk), 4)
|
|
||||||
rr = round((t1 - entry) / risk, 2)
|
|
||||||
reasons.append(f"入场={entry} 止损={stop} 目标一={t1} 盈亏比={rr}")
|
|
||||||
else: # Sell
|
|
||||||
entry = round(close, 4)
|
|
||||||
stop = round(max(swing_high, close + 1.5 * atr), 4)
|
|
||||||
risk = max(stop - entry, 1e-6)
|
|
||||||
t1 = round(entry - 2.0 * risk, 4)
|
|
||||||
t2 = round(entry - 3.0 * risk, 4)
|
|
||||||
rr = round((entry - t1) / risk, 2)
|
|
||||||
reasons.append(f"做空计划 入场={entry} 止损={stop} 目标一={t1}")
|
|
||||||
|
|
||||||
return EngineResult(
|
|
||||||
name=self.name,
|
|
||||||
version=self.version,
|
|
||||||
confidence=decision.confidence,
|
|
||||||
score=decision.score,
|
|
||||||
reasons=reasons,
|
|
||||||
payload={
|
|
||||||
"entry": entry,
|
|
||||||
"stop": stop,
|
|
||||||
"target1": t1,
|
|
||||||
"target2": t2,
|
|
||||||
"rr": rr,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
@@ -1,3 +0,0 @@
|
|||||||
from crypto_wyckoff.rules.registry import rule_registry
|
|
||||||
|
|
||||||
__all__ = ["rule_registry"]
|
|
||||||
@@ -1,33 +0,0 @@
|
|||||||
"""Rule protocol for Wyckoff Rule Registry."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from abc import ABC, abstractmethod
|
|
||||||
from dataclasses import dataclass, field
|
|
||||||
from typing import Any
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class RuleHit:
|
|
||||||
"""A single rule match."""
|
|
||||||
|
|
||||||
rule_id: str
|
|
||||||
event: str | None = None
|
|
||||||
phase: str | None = None
|
|
||||||
cycle: str | None = None
|
|
||||||
confidence: float = 0.0
|
|
||||||
score: float = 0.0
|
|
||||||
reasons: list[str] = field(default_factory=list)
|
|
||||||
metrics: dict[str, Any] = field(default_factory=dict)
|
|
||||||
|
|
||||||
|
|
||||||
class WyckoffRule(ABC):
|
|
||||||
"""Pluggable rule. Engines iterate registry; never hardcode rule lists."""
|
|
||||||
|
|
||||||
rule_id: str
|
|
||||||
category: str # cycle | phase | event
|
|
||||||
timeframes: tuple[str, ...] = ("1d", "1w", "1M")
|
|
||||||
|
|
||||||
@abstractmethod
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
"""Return RuleHit if matched, else None. Pure — no I/O."""
|
|
||||||
@@ -1,126 +0,0 @@
|
|||||||
"""Cycle classification rules (monthly / weekly)."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from typing import Any
|
|
||||||
|
|
||||||
from crypto_wyckoff.domain_models import WyckoffCycle
|
|
||||||
from crypto_wyckoff.rules.base import RuleHit, WyckoffRule
|
|
||||||
|
|
||||||
|
|
||||||
def _f(ctx: dict[str, Any], key: str, default: float = 0.0) -> float:
|
|
||||||
v = ctx.get("features", {}).get(key, default)
|
|
||||||
try:
|
|
||||||
return float(v) if v is not None else default
|
|
||||||
except (TypeError, ValueError):
|
|
||||||
return default
|
|
||||||
|
|
||||||
|
|
||||||
class MarkupCycleRule(WyckoffRule):
|
|
||||||
rule_id = "cycle_markup"
|
|
||||||
category = "cycle"
|
|
||||||
timeframes = ("1M", "1w")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
close = _f(context, "close")
|
|
||||||
ma20 = _f(context, "ma20")
|
|
||||||
ma60 = _f(context, "ma60")
|
|
||||||
ma120 = _f(context, "ma120")
|
|
||||||
adx = _f(context, "adx")
|
|
||||||
slope = _f(context, "ma60_slope")
|
|
||||||
if close > ma20 > ma60 and (ma60 >= ma120 or slope > 0) and adx >= 18:
|
|
||||||
conf = min(95.0, 55 + adx + (10 if close > ma120 else 0))
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
cycle=WyckoffCycle.MARKUP.value,
|
|
||||||
confidence=conf,
|
|
||||||
score=conf,
|
|
||||||
reasons=["价格位于均线多头排列", f"ADX={adx:.1f}"],
|
|
||||||
metrics={"adx": adx, "slope": slope},
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class MarkdownCycleRule(WyckoffRule):
|
|
||||||
rule_id = "cycle_markdown"
|
|
||||||
category = "cycle"
|
|
||||||
timeframes = ("1M", "1w")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
close = _f(context, "close")
|
|
||||||
ma20 = _f(context, "ma20")
|
|
||||||
ma60 = _f(context, "ma60")
|
|
||||||
ma120 = _f(context, "ma120")
|
|
||||||
adx = _f(context, "adx")
|
|
||||||
slope = _f(context, "ma60_slope")
|
|
||||||
if close < ma20 < ma60 and (ma60 <= ma120 or slope < 0) and adx >= 18:
|
|
||||||
conf = min(95.0, 55 + adx + (10 if close < ma120 else 0))
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
cycle=WyckoffCycle.MARKDOWN.value,
|
|
||||||
confidence=conf,
|
|
||||||
score=conf,
|
|
||||||
reasons=["价格位于均线空头排列", f"ADX={adx:.1f}"],
|
|
||||||
metrics={"adx": adx},
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class AccumulationCycleRule(WyckoffRule):
|
|
||||||
rule_id = "cycle_accumulation"
|
|
||||||
category = "cycle"
|
|
||||||
timeframes = ("1M", "1w")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
adx = _f(context, "adx")
|
|
||||||
range_pct = _f(context, "range_pct_60")
|
|
||||||
close = _f(context, "close")
|
|
||||||
ma120 = _f(context, "ma120")
|
|
||||||
vol_trend = _f(context, "volume_trend")
|
|
||||||
# Range-bound after decline: strictly at/below MA120 (mutually exclusive vs Distribution)
|
|
||||||
if adx < 22 and range_pct < 0.28 and close <= ma120:
|
|
||||||
conf = 60 + (10 if vol_trend > 0 else 0) + (10 if close < ma120 else 0)
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
cycle=WyckoffCycle.ACCUMULATION.value,
|
|
||||||
confidence=min(90.0, conf),
|
|
||||||
score=min(90.0, conf),
|
|
||||||
reasons=["低趋势强度区间震荡", "疑似吸筹区间"],
|
|
||||||
metrics={"adx": adx, "range_pct_60": range_pct},
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class DistributionCycleRule(WyckoffRule):
|
|
||||||
rule_id = "cycle_distribution"
|
|
||||||
category = "cycle"
|
|
||||||
timeframes = ("1M", "1w")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
adx = _f(context, "adx")
|
|
||||||
range_pct = _f(context, "range_pct_60")
|
|
||||||
close = _f(context, "close")
|
|
||||||
ma120 = _f(context, "ma120")
|
|
||||||
vol_trend = _f(context, "volume_trend")
|
|
||||||
# Range-bound near highs: strictly above MA120 (mutually exclusive vs Accumulation)
|
|
||||||
if adx < 22 and range_pct < 0.28 and close > ma120:
|
|
||||||
conf = 60 + (10 if vol_trend < 0 else 0) + (10 if close > ma120 else 0)
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
cycle=WyckoffCycle.DISTRIBUTION.value,
|
|
||||||
confidence=min(90.0, conf),
|
|
||||||
score=min(90.0, conf),
|
|
||||||
reasons=["高位低趋势震荡", "疑似派发区间"],
|
|
||||||
metrics={"adx": adx, "range_pct_60": range_pct},
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
def build_rules() -> list[WyckoffRule]:
|
|
||||||
# Order: trend cycles first (more decisive), then range cycles
|
|
||||||
return [
|
|
||||||
MarkupCycleRule(),
|
|
||||||
MarkdownCycleRule(),
|
|
||||||
AccumulationCycleRule(),
|
|
||||||
DistributionCycleRule(),
|
|
||||||
]
|
|
||||||
@@ -1,254 +0,0 @@
|
|||||||
"""Event rules: Spring/SOS/LPS/UTAD/SC/AR/ST/..."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from typing import Any
|
|
||||||
|
|
||||||
from crypto_wyckoff.domain_models import WyckoffCycle, WyckoffEvent, WyckoffPhase
|
|
||||||
from crypto_wyckoff.rules.base import RuleHit, WyckoffRule
|
|
||||||
|
|
||||||
|
|
||||||
def _f(ctx: dict[str, Any], key: str, default: float = 0.0) -> float:
|
|
||||||
v = ctx.get("features", {}).get(key, default)
|
|
||||||
try:
|
|
||||||
return float(v) if v is not None else default
|
|
||||||
except (TypeError, ValueError):
|
|
||||||
return default
|
|
||||||
|
|
||||||
|
|
||||||
def _cycle(ctx: dict[str, Any]) -> str:
|
|
||||||
return (ctx.get("cycle") or {}).get("cycle") or ""
|
|
||||||
|
|
||||||
|
|
||||||
def _phase(ctx: dict[str, Any]) -> str:
|
|
||||||
return (ctx.get("phase") or {}).get("phase") or ""
|
|
||||||
|
|
||||||
|
|
||||||
class SpringRule(WyckoffRule):
|
|
||||||
rule_id = "event_spring"
|
|
||||||
category = "event"
|
|
||||||
timeframes = ("1d",)
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
cycle = _cycle(context)
|
|
||||||
if cycle not in (WyckoffCycle.ACCUMULATION.value, WyckoffCycle.RE_ACCUMULATION.value,
|
|
||||||
WyckoffCycle.MARKUP.value):
|
|
||||||
# Allow spring only in accumulative contexts; Decision will filter MTF
|
|
||||||
if cycle == WyckoffCycle.DISTRIBUTION.value:
|
|
||||||
pass # still detect for facts but lower confidence
|
|
||||||
pierce = _f(context, "pierce_below_range")
|
|
||||||
reclaim = _f(context, "reclaim_speed")
|
|
||||||
vol_ratio = _f(context, "volume_ratio")
|
|
||||||
close_in_range = _f(context, "close_back_in_range")
|
|
||||||
if pierce >= 0.002 and close_in_range >= 0.5 and reclaim >= 0.3:
|
|
||||||
strength = min(98.0, 50 + pierce * 2000 + reclaim * 20 + (15 if vol_ratio < 1.2 else 5))
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
event=WyckoffEvent.SPRING.value,
|
|
||||||
confidence=strength,
|
|
||||||
score=strength,
|
|
||||||
reasons=[
|
|
||||||
f"跌破区间后收回 (pierce={pierce:.3%})",
|
|
||||||
f"回收速度={reclaim:.2f}",
|
|
||||||
f"量比={vol_ratio:.2f}",
|
|
||||||
],
|
|
||||||
metrics={"pierce": pierce, "reclaim": reclaim, "volume_ratio": vol_ratio},
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class TestRule(WyckoffRule):
|
|
||||||
rule_id = "event_test"
|
|
||||||
category = "event"
|
|
||||||
timeframes = ("1d", "1w")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
pos = _f(context, "range_position")
|
|
||||||
vol_ratio = _f(context, "volume_ratio")
|
|
||||||
near_low = pos < 0.2
|
|
||||||
if near_low and vol_ratio < 0.85:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
event=WyckoffEvent.TEST.value,
|
|
||||||
confidence=68.0,
|
|
||||||
score=65.0,
|
|
||||||
reasons=["低位缩量回测"],
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class SOSRule(WyckoffRule):
|
|
||||||
rule_id = "event_sos"
|
|
||||||
category = "event"
|
|
||||||
timeframes = ("1d", "1w")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
breakout = _f(context, "breakout_above_range")
|
|
||||||
vol_ratio = _f(context, "volume_ratio")
|
|
||||||
close = _f(context, "close")
|
|
||||||
ma20 = _f(context, "ma20")
|
|
||||||
if breakout >= 0.0 and vol_ratio >= 1.2 and close > ma20:
|
|
||||||
conf = min(95.0, 70 + vol_ratio * 8)
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
event=WyckoffEvent.SOS.value,
|
|
||||||
confidence=conf,
|
|
||||||
score=conf,
|
|
||||||
reasons=["放量突破区间上沿 (SOS)"],
|
|
||||||
metrics={"vol_ratio": vol_ratio},
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class LPSRule(WyckoffRule):
|
|
||||||
rule_id = "event_lps"
|
|
||||||
category = "event"
|
|
||||||
timeframes = ("1d", "1w")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
# Pullback hold above broken range / MA20 after prior strength
|
|
||||||
pullback = _f(context, "pullback_hold")
|
|
||||||
vol_ratio = _f(context, "volume_ratio")
|
|
||||||
above_ma = _f(context, "close") > _f(context, "ma20")
|
|
||||||
if pullback >= 0.5 and above_ma and vol_ratio <= 1.1:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
event=WyckoffEvent.LPS.value,
|
|
||||||
confidence=74.0,
|
|
||||||
score=76.0,
|
|
||||||
reasons=["突破后缩量回踩支撑 (LPS)"],
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class SCRule(WyckoffRule):
|
|
||||||
rule_id = "event_sc"
|
|
||||||
category = "event"
|
|
||||||
timeframes = ("1w", "1d")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
vol_ratio = _f(context, "volume_ratio")
|
|
||||||
bar_range = _f(context, "bar_range_atr")
|
|
||||||
pos = _f(context, "range_position")
|
|
||||||
if vol_ratio >= 1.8 and bar_range >= 1.5 and pos < 0.35:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
event=WyckoffEvent.SC.value,
|
|
||||||
confidence=72.0,
|
|
||||||
score=70.0,
|
|
||||||
reasons=["低位放量宽幅，疑似 Selling Climax"],
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class ARRule(WyckoffRule):
|
|
||||||
rule_id = "event_ar"
|
|
||||||
category = "event"
|
|
||||||
timeframes = ("1w", "1d")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
# Automatic rally: bounce from lows
|
|
||||||
bounce = _f(context, "bounce_from_low")
|
|
||||||
if bounce >= 0.04:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
event=WyckoffEvent.AR.value,
|
|
||||||
confidence=65.0,
|
|
||||||
score=62.0,
|
|
||||||
reasons=["低点后自动反弹 (AR)"],
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class STRule(WyckoffRule):
|
|
||||||
rule_id = "event_st"
|
|
||||||
category = "event"
|
|
||||||
timeframes = ("1w", "1d")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
pos = _f(context, "range_position")
|
|
||||||
vol_ratio = _f(context, "volume_ratio")
|
|
||||||
if 0.15 < pos < 0.45 and vol_ratio < 1.0:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
event=WyckoffEvent.ST.value,
|
|
||||||
confidence=60.0,
|
|
||||||
score=58.0,
|
|
||||||
reasons=["次级测试 (ST)"],
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class UTADRule(WyckoffRule):
|
|
||||||
rule_id = "event_utad"
|
|
||||||
category = "event"
|
|
||||||
timeframes = ("1w", "1d")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
cycle = _cycle(context)
|
|
||||||
pierce_up = _f(context, "pierce_above_range")
|
|
||||||
fail = _f(context, "fail_back_into_range")
|
|
||||||
if cycle in (WyckoffCycle.DISTRIBUTION.value, WyckoffCycle.RE_DISTRIBUTION.value,
|
|
||||||
WyckoffCycle.MARKUP.value):
|
|
||||||
if pierce_up >= 0.002 and fail >= 0.5:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
event=WyckoffEvent.UTAD.value,
|
|
||||||
confidence=76.0,
|
|
||||||
score=74.0,
|
|
||||||
reasons=["冲高失败回到区间 (UTAD)"],
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class JumpRule(WyckoffRule):
|
|
||||||
rule_id = "event_jump"
|
|
||||||
category = "event"
|
|
||||||
timeframes = ("1d",)
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
gap = _f(context, "gap_up_pct")
|
|
||||||
vol_ratio = _f(context, "volume_ratio")
|
|
||||||
if gap >= 0.03 and vol_ratio >= 1.3:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
event=WyckoffEvent.JUMP.value,
|
|
||||||
confidence=70.0,
|
|
||||||
score=72.0,
|
|
||||||
reasons=["放量向上跳跃 (Jump)"],
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class BackupRule(WyckoffRule):
|
|
||||||
rule_id = "event_backup"
|
|
||||||
category = "event"
|
|
||||||
timeframes = ("1d",)
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
pullback = _f(context, "pullback_hold")
|
|
||||||
after_jump = _f(context, "after_strength")
|
|
||||||
if after_jump >= 0.5 and pullback >= 0.5:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
event=WyckoffEvent.BACKUP.value,
|
|
||||||
confidence=68.0,
|
|
||||||
score=70.0,
|
|
||||||
reasons=["跳跃后回踩 (Backup)"],
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
def build_rules() -> list[WyckoffRule]:
|
|
||||||
return [
|
|
||||||
SpringRule(),
|
|
||||||
UTADRule(),
|
|
||||||
SOSRule(),
|
|
||||||
LPSRule(),
|
|
||||||
SCRule(),
|
|
||||||
JumpRule(),
|
|
||||||
BackupRule(),
|
|
||||||
TestRule(),
|
|
||||||
ARRule(),
|
|
||||||
STRule(),
|
|
||||||
]
|
|
||||||
@@ -1,163 +0,0 @@
|
|||||||
"""Phase A–E rules (primarily weekly)."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from typing import Any
|
|
||||||
|
|
||||||
from crypto_wyckoff.domain_models import WyckoffCycle, WyckoffPhase
|
|
||||||
from crypto_wyckoff.rules.base import RuleHit, WyckoffRule
|
|
||||||
|
|
||||||
|
|
||||||
def _f(ctx: dict[str, Any], key: str, default: float = 0.0) -> float:
|
|
||||||
v = ctx.get("features", {}).get(key, default)
|
|
||||||
try:
|
|
||||||
return float(v) if v is not None else default
|
|
||||||
except (TypeError, ValueError):
|
|
||||||
return default
|
|
||||||
|
|
||||||
|
|
||||||
def _cycle(ctx: dict[str, Any]) -> str:
|
|
||||||
return (ctx.get("cycle") or {}).get("cycle") or WyckoffCycle.UNKNOWN.value
|
|
||||||
|
|
||||||
|
|
||||||
class PhaseARule(WyckoffRule):
|
|
||||||
rule_id = "phase_a"
|
|
||||||
category = "phase"
|
|
||||||
timeframes = ("1w", "1d")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
cycle = _cycle(context)
|
|
||||||
if cycle not in (WyckoffCycle.ACCUMULATION.value, WyckoffCycle.DISTRIBUTION.value,
|
|
||||||
WyckoffCycle.RE_ACCUMULATION.value, WyckoffCycle.RE_DISTRIBUTION.value):
|
|
||||||
return None
|
|
||||||
# Stopping action: high vol + large range recently, still range-bound
|
|
||||||
vol_ratio = _f(context, "volume_ratio")
|
|
||||||
range_last = _f(context, "bar_range_atr")
|
|
||||||
if vol_ratio >= 1.4 and range_last >= 1.2:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
phase=WyckoffPhase.A.value,
|
|
||||||
confidence=70.0,
|
|
||||||
score=65.0,
|
|
||||||
reasons=["放量宽幅波动，疑似 Phase A 停止行为"],
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class PhaseBRule(WyckoffRule):
|
|
||||||
rule_id = "phase_b"
|
|
||||||
category = "phase"
|
|
||||||
timeframes = ("1w", "1d")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
cycle = _cycle(context)
|
|
||||||
if cycle not in (WyckoffCycle.ACCUMULATION.value, WyckoffCycle.DISTRIBUTION.value):
|
|
||||||
return None
|
|
||||||
adx = _f(context, "adx")
|
|
||||||
range_pct = _f(context, "range_pct_60")
|
|
||||||
pos = _f(context, "range_position") # 0=low 1=high of range
|
|
||||||
if adx < 20 and 0.25 < pos < 0.75 and range_pct < 0.30:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
phase=WyckoffPhase.B.value,
|
|
||||||
confidence=72.0,
|
|
||||||
score=68.0,
|
|
||||||
reasons=["区间中部震荡，疑似 Phase B 建仓/派发"],
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class PhaseCRule(WyckoffRule):
|
|
||||||
rule_id = "phase_c"
|
|
||||||
category = "phase"
|
|
||||||
timeframes = ("1w", "1d")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
cycle = _cycle(context)
|
|
||||||
pos = _f(context, "range_position")
|
|
||||||
spring_like = _f(context, "spring_score_hint")
|
|
||||||
utad_like = _f(context, "utad_score_hint")
|
|
||||||
if cycle in (WyckoffCycle.ACCUMULATION.value, WyckoffCycle.RE_ACCUMULATION.value):
|
|
||||||
if pos < 0.25 or spring_like >= 50:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
phase=WyckoffPhase.C.value,
|
|
||||||
confidence=75.0 + min(15.0, spring_like * 0.15),
|
|
||||||
score=78.0,
|
|
||||||
reasons=["区间低位测试，疑似 Phase C (Spring/Test)"],
|
|
||||||
)
|
|
||||||
if cycle in (WyckoffCycle.DISTRIBUTION.value, WyckoffCycle.RE_DISTRIBUTION.value):
|
|
||||||
if pos > 0.75 or utad_like >= 50:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
phase=WyckoffPhase.C.value,
|
|
||||||
confidence=75.0,
|
|
||||||
score=78.0,
|
|
||||||
reasons=["区间高位测试，疑似 Phase C (UTAD)"],
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class PhaseDRule(WyckoffRule):
|
|
||||||
rule_id = "phase_d"
|
|
||||||
category = "phase"
|
|
||||||
timeframes = ("1w", "1d")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
cycle = _cycle(context)
|
|
||||||
close = _f(context, "close")
|
|
||||||
ma20 = _f(context, "ma20")
|
|
||||||
range_high = _f(context, "range_high")
|
|
||||||
range_low = _f(context, "range_low")
|
|
||||||
vol_ratio = _f(context, "volume_ratio")
|
|
||||||
if cycle in (WyckoffCycle.ACCUMULATION.value, WyckoffCycle.RE_ACCUMULATION.value):
|
|
||||||
if close > ma20 and range_high > 0 and close >= range_high * 0.98 and vol_ratio >= 1.1:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
phase=WyckoffPhase.D.value,
|
|
||||||
confidence=80.0,
|
|
||||||
score=82.0,
|
|
||||||
reasons=["突破区间上沿放量，疑似 Phase D SOS"],
|
|
||||||
)
|
|
||||||
if cycle in (WyckoffCycle.DISTRIBUTION.value, WyckoffCycle.RE_DISTRIBUTION.value):
|
|
||||||
if close < ma20 and range_low > 0 and close <= range_low * 1.02:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
phase=WyckoffPhase.D.value,
|
|
||||||
confidence=80.0,
|
|
||||||
score=82.0,
|
|
||||||
reasons=["跌破区间下沿，疑似 Phase D SOW"],
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
class PhaseERule(WyckoffRule):
|
|
||||||
rule_id = "phase_e"
|
|
||||||
category = "phase"
|
|
||||||
timeframes = ("1w", "1d")
|
|
||||||
|
|
||||||
def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
|
|
||||||
cycle = _cycle(context)
|
|
||||||
# Markup/Markdown already imply trend continuation (Phase E of prior structure)
|
|
||||||
if cycle == WyckoffCycle.MARKUP.value:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
phase=WyckoffPhase.E.value,
|
|
||||||
confidence=78.0,
|
|
||||||
score=80.0,
|
|
||||||
reasons=["趋势上行，对应 Phase E Markup"],
|
|
||||||
)
|
|
||||||
if cycle == WyckoffCycle.MARKDOWN.value:
|
|
||||||
return RuleHit(
|
|
||||||
rule_id=self.rule_id,
|
|
||||||
phase=WyckoffPhase.E.value,
|
|
||||||
confidence=78.0,
|
|
||||||
score=80.0,
|
|
||||||
reasons=["趋势下行，对应 Phase E Markdown"],
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
def build_rules() -> list[WyckoffRule]:
|
|
||||||
# More specific phases first
|
|
||||||
return [PhaseDRule(), PhaseCRule(), PhaseARule(), PhaseBRule(), PhaseERule()]
|
|
||||||
@@ -1,39 +0,0 @@
|
|||||||
"""Rule Registry — register Wyckoff rules without modifying engines."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from crypto_wyckoff.rules.base import WyckoffRule
|
|
||||||
|
|
||||||
|
|
||||||
class RuleRegistry:
|
|
||||||
def __init__(self) -> None:
|
|
||||||
self._rules: dict[str, WyckoffRule] = {}
|
|
||||||
|
|
||||||
def register(self, rule: WyckoffRule) -> None:
|
|
||||||
self._rules[rule.rule_id] = rule
|
|
||||||
|
|
||||||
def get(self, rule_id: str) -> WyckoffRule | None:
|
|
||||||
return self._rules.get(rule_id)
|
|
||||||
|
|
||||||
def by_category(self, category: str, timeframe: str | None = None) -> list[WyckoffRule]:
|
|
||||||
out = [r for r in self._rules.values() if r.category == category]
|
|
||||||
if timeframe:
|
|
||||||
out = [r for r in out if timeframe in r.timeframes]
|
|
||||||
return out
|
|
||||||
|
|
||||||
def all(self) -> list[WyckoffRule]:
|
|
||||||
return list(self._rules.values())
|
|
||||||
|
|
||||||
|
|
||||||
rule_registry = RuleRegistry()
|
|
||||||
|
|
||||||
|
|
||||||
def _register_defaults() -> None:
|
|
||||||
from crypto_wyckoff.rules import cycle_rules, event_rules, phase_rules
|
|
||||||
|
|
||||||
for mod in (cycle_rules, phase_rules, event_rules):
|
|
||||||
for rule in mod.build_rules():
|
|
||||||
rule_registry.register(rule)
|
|
||||||
|
|
||||||
|
|
||||||
_register_defaults()
|
|
||||||
@@ -1,127 +0,0 @@
|
|||||||
"""Background tip + scan scheduler for crypto wyckoff (all enabled combos)."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import logging
|
|
||||||
import threading
|
|
||||||
from datetime import datetime, timezone
|
|
||||||
|
|
||||||
from crypto_wyckoff.combos import all_tfs_for_combos, list_combos
|
|
||||||
from crypto_wyckoff.io import (
|
|
||||||
backfill_symbol,
|
|
||||||
bar_count,
|
|
||||||
fetch_symbols_from_provider,
|
|
||||||
tip_update_symbol,
|
|
||||||
)
|
|
||||||
from crypto_wyckoff.pipeline import analyze_and_store
|
|
||||||
|
|
||||||
logger = logging.getLogger(__name__)
|
|
||||||
|
|
||||||
_thread: threading.Thread | None = None
|
|
||||||
_stop = threading.Event()
|
|
||||||
_status: dict = {
|
|
||||||
"running": False,
|
|
||||||
"last_tick_at": None,
|
|
||||||
"last_error": None,
|
|
||||||
"symbols_total": 0,
|
|
||||||
"symbols_scanned": 0,
|
|
||||||
"tick_interval_sec": 60,
|
|
||||||
"backfill_done": False,
|
|
||||||
}
|
|
||||||
_status_lock = threading.Lock()
|
|
||||||
|
|
||||||
|
|
||||||
def _set(**kwargs):
|
|
||||||
with _status_lock:
|
|
||||||
_status.update(kwargs)
|
|
||||||
|
|
||||||
|
|
||||||
def get_status() -> dict:
|
|
||||||
with _status_lock:
|
|
||||||
return dict(_status)
|
|
||||||
|
|
||||||
|
|
||||||
def run_tick(max_symbols: int | None = None, force_rescan: bool = False) -> dict:
|
|
||||||
"""One cycle: refresh symbols, tip-update, analyze each combo."""
|
|
||||||
symbols = fetch_symbols_from_provider()
|
|
||||||
if max_symbols:
|
|
||||||
symbols = symbols[:max_symbols]
|
|
||||||
combos = list_combos()
|
|
||||||
tfs = all_tfs_for_combos(combos)
|
|
||||||
_set(symbols_total=len(symbols), running=True, last_error=None)
|
|
||||||
scanned = 0
|
|
||||||
errors = 0
|
|
||||||
changed_n = 0
|
|
||||||
|
|
||||||
for i, sym in enumerate(symbols):
|
|
||||||
try:
|
|
||||||
# Prefer low-TF of first combo for "enough history" gate
|
|
||||||
low0 = combos[0]["low"] if combos else "1d"
|
|
||||||
if bar_count(sym, low0) < 40:
|
|
||||||
backfill_symbol(sym, tfs)
|
|
||||||
tip_changed = tip_update_symbol(sym, tfs)
|
|
||||||
if tip_changed:
|
|
||||||
changed_n += 1
|
|
||||||
if force_rescan or tip_changed:
|
|
||||||
for combo in combos:
|
|
||||||
row = analyze_and_store(sym, combo_id=combo["id"])
|
|
||||||
if row:
|
|
||||||
scanned += 1
|
|
||||||
except Exception as e:
|
|
||||||
errors += 1
|
|
||||||
if errors <= 5:
|
|
||||||
logger.warning("tick %s: %s", sym, e)
|
|
||||||
_set(last_error=str(e))
|
|
||||||
if (i + 1) % 25 == 0:
|
|
||||||
_set(symbols_scanned=scanned)
|
|
||||||
logger.info("wyckoff tick progress %s/%s scanned=%s", i + 1, len(symbols), scanned)
|
|
||||||
|
|
||||||
_set(
|
|
||||||
running=False,
|
|
||||||
symbols_scanned=scanned,
|
|
||||||
last_tick_at=datetime.now(timezone.utc).isoformat(),
|
|
||||||
backfill_done=True,
|
|
||||||
)
|
|
||||||
return {
|
|
||||||
"symbols": len(symbols),
|
|
||||||
"scanned": scanned,
|
|
||||||
"changed_tips": changed_n,
|
|
||||||
"errors": errors,
|
|
||||||
"combos": [c["id"] for c in combos],
|
|
||||||
"tfs": tfs,
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def _loop(interval: int, max_symbols: int | None):
|
|
||||||
try:
|
|
||||||
run_tick(max_symbols=max_symbols, force_rescan=True)
|
|
||||||
except Exception as e:
|
|
||||||
logger.exception("initial tick failed: %s", e)
|
|
||||||
_set(last_error=str(e), running=False)
|
|
||||||
while not _stop.wait(interval):
|
|
||||||
try:
|
|
||||||
# Tip-driven: only force full rescan when tips change is handled inside
|
|
||||||
run_tick(max_symbols=max_symbols, force_rescan=False)
|
|
||||||
except Exception as e:
|
|
||||||
logger.exception("tick failed: %s", e)
|
|
||||||
_set(last_error=str(e), running=False)
|
|
||||||
|
|
||||||
|
|
||||||
def start_scheduler(interval_sec: int = 60, max_symbols: int | None = None) -> None:
|
|
||||||
global _thread
|
|
||||||
if _thread and _thread.is_alive():
|
|
||||||
return
|
|
||||||
_stop.clear()
|
|
||||||
_set(tick_interval_sec=interval_sec)
|
|
||||||
_thread = threading.Thread(
|
|
||||||
target=_loop,
|
|
||||||
args=(interval_sec, max_symbols),
|
|
||||||
name="crypto-wyckoff-scheduler",
|
|
||||||
daemon=True,
|
|
||||||
)
|
|
||||||
_thread.start()
|
|
||||||
logger.info("crypto wyckoff scheduler started interval=%ss", interval_sec)
|
|
||||||
|
|
||||||
|
|
||||||
def stop_scheduler() -> None:
|
|
||||||
_stop.set()
|
|
||||||
@@ -1,35 +0,0 @@
|
|||||||
"""Signal Engine — timeframe-local status labels only (not tradability)."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from crypto_wyckoff.domain_models import EngineResult, WyckoffEvent
|
|
||||||
|
|
||||||
|
|
||||||
class SignalEngine:
|
|
||||||
"""Maps local Event/Phase into a status label. Decision decides tradability."""
|
|
||||||
|
|
||||||
name = "Signal"
|
|
||||||
version = "1.0.0"
|
|
||||||
|
|
||||||
def run(self, event: EngineResult, phase: EngineResult | None = None) -> EngineResult:
|
|
||||||
current = event.payload.get("current_event", WyckoffEvent.NONE.value)
|
|
||||||
conf = event.confidence
|
|
||||||
label = current # status label mirrors event for V1
|
|
||||||
reasons = [f"本地事件标签: {label}"]
|
|
||||||
if phase and phase.payload.get("phase"):
|
|
||||||
reasons.append(f"本地阶段: {phase.payload.get('phase')}")
|
|
||||||
|
|
||||||
return EngineResult(
|
|
||||||
name=self.name,
|
|
||||||
version=self.version,
|
|
||||||
confidence=conf,
|
|
||||||
score=event.score,
|
|
||||||
reasons=reasons,
|
|
||||||
payload={
|
|
||||||
"signal_label": label,
|
|
||||||
"current_event": current,
|
|
||||||
"phase": (phase.payload.get("phase") if phase else None),
|
|
||||||
"active_events": event.payload.get("active_events")
|
|
||||||
or event.payload.get("recent_events", []),
|
|
||||||
},
|
|
||||||
)
|
|
||||||
@@ -1,236 +0,0 @@
|
|||||||
"""SQLite persistence for crypto wyckoff scan rows (per combo)."""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import sqlite3
|
|
||||||
from datetime import datetime
|
|
||||||
from typing import Any
|
|
||||||
|
|
||||||
from crypto_wyckoff.domain_models import WyckoffScanRow
|
|
||||||
from crypto_wyckoff.io import SCAN_DB, ensure_dirs
|
|
||||||
|
|
||||||
_COLS = [
|
|
||||||
"trade_date", "combo_id", "ts_code", "name", "industry", "engine_version",
|
|
||||||
"m_cycle", "cycle_confidence", "trend_score",
|
|
||||||
"w_cycle", "w_phase", "w_current_event", "w_recent_events_json",
|
|
||||||
"phase_confidence", "structure_score",
|
|
||||||
"d_current_event", "d_recent_events_json", "event_confidence", "entry_score",
|
|
||||||
"entry", "stop", "target1", "target2", "rr",
|
|
||||||
"alignment", "stars", "decision_signal", "signal_confidence",
|
|
||||||
"overall_confidence", "overall_score", "risk", "reasons_json",
|
|
||||||
"feature_snapshot_json", "markers_json", "scanned_at",
|
|
||||||
]
|
|
||||||
|
|
||||||
_CREATE_SQL = """
|
|
||||||
CREATE TABLE IF NOT EXISTS wyckoff_scan (
|
|
||||||
trade_date TEXT NOT NULL,
|
|
||||||
combo_id TEXT NOT NULL DEFAULT 'd_w_m',
|
|
||||||
ts_code TEXT NOT NULL,
|
|
||||||
name TEXT DEFAULT '',
|
|
||||||
industry TEXT DEFAULT '',
|
|
||||||
engine_version TEXT,
|
|
||||||
m_cycle TEXT, cycle_confidence REAL, trend_score REAL,
|
|
||||||
w_cycle TEXT, w_phase TEXT, w_current_event TEXT, w_recent_events_json TEXT,
|
|
||||||
phase_confidence REAL, structure_score REAL,
|
|
||||||
d_current_event TEXT, d_recent_events_json TEXT, event_confidence REAL, entry_score REAL,
|
|
||||||
entry REAL, stop REAL, target1 REAL, target2 REAL, rr REAL,
|
|
||||||
alignment REAL, stars INTEGER, decision_signal TEXT, signal_confidence REAL,
|
|
||||||
overall_confidence REAL, overall_score REAL, risk TEXT, reasons_json TEXT,
|
|
||||||
feature_snapshot_json TEXT, markers_json TEXT, scanned_at TEXT,
|
|
||||||
PRIMARY KEY (trade_date, combo_id, ts_code)
|
|
||||||
)
|
|
||||||
"""
|
|
||||||
|
|
||||||
|
|
||||||
def _migrate(c: sqlite3.Connection) -> None:
|
|
||||||
cur = c.execute(
|
|
||||||
"SELECT name FROM sqlite_master WHERE type='table' AND name='wyckoff_scan'"
|
|
||||||
)
|
|
||||||
if not cur.fetchone():
|
|
||||||
c.execute(_CREATE_SQL)
|
|
||||||
c.execute(
|
|
||||||
"CREATE INDEX IF NOT EXISTS idx_cw_score "
|
|
||||||
"ON wyckoff_scan(trade_date, combo_id, overall_score DESC)"
|
|
||||||
)
|
|
||||||
return
|
|
||||||
|
|
||||||
cols = {r[1] for r in c.execute("PRAGMA table_info(wyckoff_scan)")}
|
|
||||||
if "combo_id" in cols:
|
|
||||||
c.execute(
|
|
||||||
"CREATE INDEX IF NOT EXISTS idx_cw_score "
|
|
||||||
"ON wyckoff_scan(trade_date, combo_id, overall_score DESC)"
|
|
||||||
)
|
|
||||||
return
|
|
||||||
|
|
||||||
# Legacy PK (trade_date, ts_code) → add combo_id via table rebuild
|
|
||||||
c.execute("ALTER TABLE wyckoff_scan RENAME TO wyckoff_scan_old")
|
|
||||||
c.execute(_CREATE_SQL)
|
|
||||||
old_cols = [r[1] for r in c.execute("PRAGMA table_info(wyckoff_scan_old)")]
|
|
||||||
shared = [col for col in _COLS if col != "combo_id" and col in old_cols]
|
|
||||||
col_sql = ",".join(shared)
|
|
||||||
c.execute(
|
|
||||||
f"""
|
|
||||||
INSERT INTO wyckoff_scan (combo_id, {col_sql})
|
|
||||||
SELECT 'd_w_m', {col_sql} FROM wyckoff_scan_old
|
|
||||||
"""
|
|
||||||
)
|
|
||||||
c.execute("DROP TABLE wyckoff_scan_old")
|
|
||||||
c.execute(
|
|
||||||
"CREATE INDEX IF NOT EXISTS idx_cw_score "
|
|
||||||
"ON wyckoff_scan(trade_date, combo_id, overall_score DESC)"
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
def _conn() -> sqlite3.Connection:
|
|
||||||
ensure_dirs()
|
|
||||||
c = sqlite3.connect(str(SCAN_DB), timeout=60)
|
|
||||||
c.row_factory = sqlite3.Row
|
|
||||||
_migrate(c)
|
|
||||||
c.commit()
|
|
||||||
return c
|
|
||||||
|
|
||||||
|
|
||||||
def upsert_row(row: WyckoffScanRow) -> None:
|
|
||||||
combo_id = getattr(row, "combo_id", None) or "d_w_m"
|
|
||||||
vals = (
|
|
||||||
row.trade_date.isoformat() if hasattr(row.trade_date, "isoformat") else str(row.trade_date),
|
|
||||||
combo_id,
|
|
||||||
row.ts_code, row.name, row.industry, row.engine_version,
|
|
||||||
row.m_cycle, row.cycle_confidence, row.trend_score,
|
|
||||||
row.w_cycle, row.w_phase, row.w_current_event, row.w_recent_events_json,
|
|
||||||
row.phase_confidence, row.structure_score,
|
|
||||||
row.d_current_event, row.d_recent_events_json, row.event_confidence, row.entry_score,
|
|
||||||
row.entry, row.stop, row.target1, row.target2, row.rr,
|
|
||||||
row.alignment, row.stars, row.decision_signal, row.signal_confidence,
|
|
||||||
row.overall_confidence, row.overall_score, row.risk, row.reasons_json,
|
|
||||||
row.feature_snapshot_json, row.markers_json,
|
|
||||||
row.scanned_at.isoformat() if isinstance(row.scanned_at, datetime) else str(row.scanned_at),
|
|
||||||
)
|
|
||||||
c = _conn()
|
|
||||||
try:
|
|
||||||
placeholders = ",".join("?" * len(_COLS))
|
|
||||||
col_sql = ",".join(_COLS)
|
|
||||||
updates = ",".join(
|
|
||||||
f"{col}=excluded.{col}"
|
|
||||||
for col in _COLS
|
|
||||||
if col not in ("trade_date", "combo_id", "ts_code")
|
|
||||||
)
|
|
||||||
c.execute(
|
|
||||||
f"""
|
|
||||||
INSERT INTO wyckoff_scan ({col_sql}) VALUES ({placeholders})
|
|
||||||
ON CONFLICT(trade_date, combo_id, ts_code) DO UPDATE SET {updates}
|
|
||||||
""",
|
|
||||||
vals,
|
|
||||||
)
|
|
||||||
c.commit()
|
|
||||||
finally:
|
|
||||||
c.close()
|
|
||||||
|
|
||||||
|
|
||||||
def latest_trade_date(combo_id: str | None = None) -> str | None:
|
|
||||||
c = _conn()
|
|
||||||
try:
|
|
||||||
if combo_id:
|
|
||||||
cur = c.execute(
|
|
||||||
"SELECT MAX(trade_date) FROM wyckoff_scan WHERE combo_id=?",
|
|
||||||
(combo_id,),
|
|
||||||
)
|
|
||||||
else:
|
|
||||||
cur = c.execute("SELECT MAX(trade_date) FROM wyckoff_scan")
|
|
||||||
row = cur.fetchone()
|
|
||||||
return row[0] if row and row[0] else None
|
|
||||||
finally:
|
|
||||||
c.close()
|
|
||||||
|
|
||||||
|
|
||||||
def count_for_date(trade_date: str | None = None, combo_id: str | None = None) -> int:
|
|
||||||
td = trade_date or latest_trade_date(combo_id)
|
|
||||||
if not td:
|
|
||||||
return 0
|
|
||||||
c = _conn()
|
|
||||||
try:
|
|
||||||
if combo_id:
|
|
||||||
cur = c.execute(
|
|
||||||
"SELECT COUNT(*) FROM wyckoff_scan WHERE trade_date=? AND combo_id=?",
|
|
||||||
(td, combo_id),
|
|
||||||
)
|
|
||||||
else:
|
|
||||||
cur = c.execute("SELECT COUNT(*) FROM wyckoff_scan WHERE trade_date=?", (td,))
|
|
||||||
return int(cur.fetchone()[0])
|
|
||||||
finally:
|
|
||||||
c.close()
|
|
||||||
|
|
||||||
|
|
||||||
def query_scan(
|
|
||||||
*,
|
|
||||||
trade_date: str | None = None,
|
|
||||||
combo_id: str | None = None,
|
|
||||||
m_cycle: str | None = None,
|
|
||||||
w_phase: str | None = None,
|
|
||||||
d_event: str | None = None,
|
|
||||||
decision_signal: str | None = None,
|
|
||||||
min_overall_score: float | None = None,
|
|
||||||
min_alignment: float | None = None,
|
|
||||||
sort: str = "overall_score",
|
|
||||||
limit: int = 100,
|
|
||||||
offset: int = 0,
|
|
||||||
) -> list[dict[str, Any]]:
|
|
||||||
cid = combo_id or "d_w_m"
|
|
||||||
td = trade_date or latest_trade_date(cid)
|
|
||||||
if not td:
|
|
||||||
return []
|
|
||||||
sort_col = sort if sort in {
|
|
||||||
"overall_score", "alignment", "entry_score", "trend_score", "structure_score", "stars"
|
|
||||||
} else "overall_score"
|
|
||||||
clauses = ["trade_date=?", "combo_id=?"]
|
|
||||||
args: list[Any] = [td, cid]
|
|
||||||
if m_cycle:
|
|
||||||
clauses.append("m_cycle=?")
|
|
||||||
args.append(m_cycle)
|
|
||||||
if w_phase:
|
|
||||||
clauses.append("w_phase=?")
|
|
||||||
args.append(w_phase)
|
|
||||||
if d_event:
|
|
||||||
clauses.append("d_current_event=?")
|
|
||||||
args.append(d_event)
|
|
||||||
if decision_signal:
|
|
||||||
clauses.append("decision_signal=?")
|
|
||||||
args.append(decision_signal)
|
|
||||||
if min_overall_score is not None:
|
|
||||||
clauses.append("overall_score>=?")
|
|
||||||
args.append(min_overall_score)
|
|
||||||
if min_alignment is not None:
|
|
||||||
clauses.append("alignment>=?")
|
|
||||||
args.append(min_alignment)
|
|
||||||
where = " AND ".join(clauses)
|
|
||||||
args.extend([limit, offset])
|
|
||||||
c = _conn()
|
|
||||||
try:
|
|
||||||
cur = c.execute(
|
|
||||||
f"SELECT * FROM wyckoff_scan WHERE {where} ORDER BY {sort_col} DESC LIMIT ? OFFSET ?",
|
|
||||||
args,
|
|
||||||
)
|
|
||||||
return [dict(r) for r in cur.fetchall()]
|
|
||||||
finally:
|
|
||||||
c.close()
|
|
||||||
|
|
||||||
|
|
||||||
def get_symbol(
|
|
||||||
ts_code: str,
|
|
||||||
trade_date: str | None = None,
|
|
||||||
combo_id: str | None = None,
|
|
||||||
) -> dict[str, Any] | None:
|
|
||||||
cid = combo_id or "d_w_m"
|
|
||||||
td = trade_date or latest_trade_date(cid)
|
|
||||||
if not td:
|
|
||||||
return None
|
|
||||||
c = _conn()
|
|
||||||
try:
|
|
||||||
cur = c.execute(
|
|
||||||
"SELECT * FROM wyckoff_scan WHERE trade_date=? AND combo_id=? AND ts_code=?",
|
|
||||||
(td, cid, ts_code),
|
|
||||||
)
|
|
||||||
row = cur.fetchone()
|
|
||||||
return dict(row) if row else None
|
|
||||||
finally:
|
|
||||||
c.close()
|
|
||||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user