删除不要的东西

datasvc/Dockerfile

@@ -1,26 +0,0 @@
-FROM python:3.11-slim
-
-ENV PYTHONUNBUFFERED=1 \
-    PIP_NO_CACHE_DIR=1
-
-WORKDIR /app
-
-COPY requirements.txt /app/requirements.txt
-RUN pip install -r /app/requirements.txt
-
-COPY app /app/app
-COPY pairs.json /app/pairs.json
-
-ENV DATA_DIR=/data \
-    EXCHANGE=binance \
-    TIMEFRAMES=1m,1h,1d,1w,1M \
-    START_FROM=2025-01-01 \
-    POLL_FACTOR=0.5
-
-VOLUME ["/data"]
-
-EXPOSE 9000
-
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "9000"]
-
-

datasvc/README.md

@@ -1,145 +0,0 @@
-# Local Data Service（REST + WebSocket）
-
-本服务基于 FastAPI + ccxt，自动拉取交易所行情、写入本地 Parquet，同时提供 REST 和 WebSocket 数据访问。  
-自带时间周期聚合能力：只需抓取 `1m / 1h / 1d / 1w / 1M` 等基础周期，即可自动生成 `2m/3m/.../30m`、`2h/3h/.../16h` 等衍生周期。
-
----
-
-## 1. 环境准备
-
-### 1.1 依赖
-- Python ≥ 3.10（本地运行方式需要）
-- `pip install -r requirements.txt`（包含 `fastapi`, `uvicorn`, `ccxt`, `pandas`, `pyarrow`, `technical` 等）
-- 或者直接使用仓库内的 `docker-compose.yml`
-
-### 1.2 关键环境变量
-| 变量 | 说明 | 默认 |
-| --- | --- | --- |
-| `DATA_DIR` | 本地 Parquet 存储目录 | `/data` |
-| `EXCHANGE` | 交易所标识（目前支持 binance） | `binance` |
-| `TIMEFRAMES` | 基础抓取周期，逗号分隔 | `1m,1h,1d,1w,1M` |
-| `START_FROM` | 首次启动回补的起始 UTC 时间（ISO 字符串或毫秒时间戳） | `2022-01-01` |
-| `POLL_FACTOR` | 拉取间隔因子，实际间隔 = 周期毫秒 × factor | `0.5` |
-| `REST_MAX_CONCURRENCY` | REST 历史拉取并发数 | `4` |
-| `VERIFY_MAX_CONCURRENCY` | 校验请求并发数 | `2` |
-| `WS_ENABLED` | 是否启用 Binance WebSocket 增量（`true`/`false`） | `false` |
-| `REST_POLL_INTERVAL` | 实时轮询 REST 的间隔秒数 | `5` |
-| `REST_POLL_WINDOW` | 实时轮询时拉取的最新 K 线数量 | `10` |
-| `BACKOFF_BASE / BACKOFF_MAX` | 异常重试的指数退避参数 | `2.0 / 30.0` |
-
-> 衍生周期列表由程序自动推导，无需手动写入 `TIMEFRAMES`。
-
----
-
-## 2. 启动与关闭
-
-### 2.1 Docker 方式
-```bash
-cd user_data/Chan/datasvc
-docker compose up -d          # 启动
-docker compose logs -f        # 查看日志
-docker compose down           # 关闭
-```
-
-### 2.2 本地运行（无 Docker）
-```bash
-export DATA_DIR=./data
-export TIMEFRAMES="1m,1h,1d"
-
-cd /Users/jack/Project/freqtrade
-uvicorn user_data.Chan.datasvc.app.main:app --reload
-```
-
-关闭时 Ctrl+C 即可，服务会自动取消后台抓取任务并释放资源。
-
----
-
-## 3. 数据存储与聚合
-
-### 3.1 基础周期
-只会为 `TIMEFRAMES` 声明的基础周期创建抓取任务（例如 `1m / 1h / 1d`）。
-
-### 3.2 衍生周期
-启动后自动维护以下聚合：
-
-| 基础周期 | 自动生成 |
-| --- | --- |
-| `1m` | `2m, 3m, 4m, 5m, 10m, 15m, 20m, 25m, 30m` |
-| `1h` | `2h, 3h, 4h, 6h, 8h, 12h, 16h` |
-| `1d` | `2d, 3d, 4d, 5d, 6d` |
-| `1w` | `2w` |
-| `1M` | `2M, 3M, 6M` |
-
-聚合过程通过 `technical.util.resample_to_interval` 完成，写入同一 Parquet 数据目录。  
-所有周期都可以被 REST/WS 访问。
-
-### 3.3 数据目录
-```
-{DATA_DIR}/{timeframe}/{symbol}.parquet
-```
-
----
-
-## 4. 接口调用
-
-### 4.1 健康检查
-```
-GET /health
-```
-返回运行状态、基础/衍生周期列表、各抓取任务的最新进度与错误计数，便于监控。
-
-### 4.2 REST API
-```
-GET /api/candles?symbol=BTC/USDT:USDT&tf=2h&start=1700000000000&end=1700003600000
-```
-参数说明：
-- `symbol`：交易对（必须在 `SYMBOLS` 列表中）
-- `tf`：时间周期（支持基础或衍生）
-- `start` / `end`：毫秒时间戳，可选
-
-返回示例：
-```json
-[
-  {"timestamp": 1700000000000, "open": 36000.0, "high": 36120.0, "low": 35980.0, "close": 36050.0, "volume": 125.4},
-  ...
-]
-```
-
-### 4.3 WebSocket
-```
-ws://localhost:8000/ws?symbol=ETH/USDT:USDT&tf=15m&since=1700000000000
-```
-- 首次连接：收到 `snapshot` 消息（快照数组）
-- 后续增量：收到 `upsert` 消息（最新几根K线），以及周期性 `ping`
-
-消息示例：
-```json
-{"topic":"candles.ETH/USDT:USDT.15m","type":"snapshot","data":[{"t":1700000000000,"o":2000.0,"h":2005.0,"l":1995.0,"c":2002.5,"v":312.7}, ...]}
-{"topic":"candles.ETH/USDT:USDT.15m","type":"upsert","data":{"t":1700000900000,"o":2002.5,"h":2006.0,"l":2000.0,"c":2004.0,"v":120.8}}
-```
-
----
-
-## 5. 停机与维护
-
-- **正常关闭**：`docker compose down` 或 Ctrl+C。服务会等待所有抓取任务结束并关闭 `ccxt` 客户端。
-- **异常恢复**：若网络异常，服务会自动指数退避重试；可通过 `/health` 的 `consecutive_errors` 与 `last_error` 排查。
-- **数据清理**：直接删除 `DATA_DIR` 下对应的 Parquet 文件即可，下次启动会重新回补。
-
----
-
-## 6. 常见问题
-
-1. **缺少 `technical` 模块**  
-   聚合周期会跳过，并在日志中提示；先执行 `pip install technical` 再重启。
-
-2. **接收不到某个周期的数据**  
-   确认该周期在 `TIMEFRAMES` 或自动聚合列表中；若是衍生周期，需要确保对应基础周期已在运行。
-
-3. **如何新增交易对/周期**  
-   - 交易对：编辑 `pairs.json`，每行一个字符串，保存后重启服务。  
-   - 周期：修改 `TIMEFRAMES` 环境变量（Docker 或本地启动命令）后重启。
-
----
-
-欢迎结合自身策略或可视化前端直接消费本地数据服务。若要集成到其他项目，可直接引用 `/api/candles` 的 JSON 响应或订阅 `/ws` 的实时推送。

datasvc/app/storage.py

@@ -1,172 +0,0 @@
-import logging
-import os
-import shutil
-import threading
-from datetime import datetime
-from typing import List, Optional
-
-import pandas as pd
-import pyarrow.dataset as ds
-
-logger = logging.getLogger("datasvc")
-
-
-_lock = threading.Lock()
-
-
-def ensure_storage(base_dir: str):
-    os.makedirs(base_dir, exist_ok=True)
-
-
-def _path(base_dir: str, symbol: str, timeframe: str) -> str:
-    safe_symbol = symbol.replace("/", "_").replace(":", "_")
-    d = os.path.join(base_dir, timeframe)
-    os.makedirs(d, exist_ok=True)
-    return os.path.join(d, f"{safe_symbol}.parquet")
-
-
-def read_candles(base_dir: str, symbol: str, timeframe: str, start: Optional[int], end: Optional[int]) -> pd.DataFrame:
-    p = _path(base_dir, symbol, timeframe)
-    if not os.path.exists(p):
-        return pd.DataFrame(columns=["timestamp", "open", "high", "low", "close", "volume"])  # empty
-    try:
-        df = pd.read_parquet(p)
-    except Exception as exc:
-        with _lock:
-            backup = _backup_corrupted_file(p)
-        extra = f"，已备份至 {backup}" if backup else ""
-        logger.warning(
-            "读取缓存失败，将视为空数据 [%s %s]%s：%s",
-            symbol,
-            timeframe,
-            extra,
-            exc,
-        )
-        return pd.DataFrame(columns=["timestamp", "open", "high", "low", "close", "volume"])
-    if start is not None:
-        df = df[df["timestamp"] >= int(start)]
-    if end is not None:
-        df = df[df["timestamp"] <= int(end)]
-    df = df.sort_values("timestamp")
-    return df
-
-
-def upsert_candles(base_dir: str, symbol: str, timeframe: str, candles: List[List[float]]):
-    p = _path(base_dir, symbol, timeframe)
-    new_df = pd.DataFrame(candles, columns=["timestamp", "open", "high", "low", "close", "volume"])
-    with _lock:
-        if os.path.exists(p):
-            try:
-                old = pd.read_parquet(p)
-            except Exception as exc:
-                backup = _backup_corrupted_file(p)
-                extra = f"，已备份至 {backup}" if backup else ""
-                logger.warning(
-                    "读取缓存失败，准备重建文件 [%s %s]%s：%s",
-                    symbol,
-                    timeframe,
-                    extra,
-                    exc,
-                )
-                old = pd.DataFrame(columns=["timestamp", "open", "high", "low", "close", "volume"])
-            merged = pd.concat([old, new_df], ignore_index=True)
-            merged = merged.drop_duplicates(subset=["timestamp"], keep="last").sort_values("timestamp")
-        else:
-            merged = new_df.sort_values("timestamp")
-        temp_path = f"{p}.tmp"
-        try:
-            merged.to_parquet(temp_path, index=False)
-            os.replace(temp_path, p)
-        finally:
-            if os.path.exists(temp_path):
-                try:
-                    os.remove(temp_path)
-                except OSError:
-                    pass
-
-
-def write_candles_snapshot(base_dir: str, symbol: str, timeframe: str, df: pd.DataFrame):
-    columns = ["timestamp", "open", "high", "low", "close", "volume"]
-    if df.empty:
-        safe_df = pd.DataFrame(columns=columns)
-    else:
-        safe_df = df[columns].copy()
-        safe_df = safe_df.drop_duplicates(subset=["timestamp"], keep="last").sort_values("timestamp").reset_index(drop=True)
-    p = _path(base_dir, symbol, timeframe)
-    with _lock:
-        temp_path = f"{p}.tmp"
-        try:
-            safe_df.to_parquet(temp_path, index=False)
-            os.replace(temp_path, p)
-        finally:
-            if os.path.exists(temp_path):
-                try:
-                    os.remove(temp_path)
-                except OSError:
-                    pass
-
-
-def candle_path(base_dir: str, symbol: str, timeframe: str) -> str:
-    return _path(base_dir, symbol, timeframe)
-
-
-def get_last_timestamp(base_dir: str, symbol: str, timeframe: str) -> Optional[int]:
-    p = _path(base_dir, symbol, timeframe)
-    if not os.path.exists(p):
-        return None
-    try:
-        df = pd.read_parquet(p)
-    except Exception as exc:
-        with _lock:
-            backup = _backup_corrupted_file(p)
-        extra = f"，已备份至 {backup}" if backup else ""
-        logger.warning(
-            "获取最后时间戳失败 [%s %s]%s：%s",
-            symbol,
-            timeframe,
-            extra,
-            exc,
-        )
-        return None
-    if df.empty:
-        return None
-    return int(df["timestamp"].iloc[-1])
-
-
-def read_candle_exact(base_dir: str, symbol: str, timeframe: str, timestamp: int) -> pd.DataFrame:
-    p = _path(base_dir, symbol, timeframe)
-    if not os.path.exists(p):
-        return pd.DataFrame(columns=["timestamp", "open", "high", "low", "close", "volume"])
-    try:
-        dataset = ds.dataset(p, format="parquet")
-        table = dataset.to_table(filter=ds.field("timestamp") == int(timestamp))
-    except Exception as exc:
-        with _lock:
-            backup = _backup_corrupted_file(p)
-        extra = f"，已备份至 {backup}" if backup else ""
-        logger.warning(
-            "读取指定时间 K 线失败 [%s %s]%s：%s",
-            symbol,
-            timeframe,
-            extra,
-            exc,
-        )
-        return pd.DataFrame(columns=["timestamp", "open", "high", "low", "close", "volume"])
-    if table.num_rows == 0:
-        return pd.DataFrame(columns=["timestamp", "open", "high", "low", "close", "volume"])
-    return table.to_pandas()
-
-
-def _backup_corrupted_file(path: str) -> Optional[str]:
-    try:
-        if not os.path.exists(path):
-            return None
-        timestamp = datetime.utcnow().strftime("%Y%m%d%H%M%S")
-        backup_path = f"{path}.corrupted.{timestamp}"
-        shutil.move(path, backup_path)
-        return backup_path
-    except Exception as exc:
-        logger.warning("备份损坏文件失败 (%s)：%s", path, exc)
-        return None
-
-

datasvc/app/worker.py

@@ -1,27 +0,0 @@
-import asyncio
-import signal
-from threading import Event
-
-from .main import logger, run_engine
-
-
-async def _async_main():
-    stop_event = Event()
-    loop = asyncio.get_running_loop()
-    for sig in (signal.SIGINT, signal.SIGTERM):
-        try:
-            loop.add_signal_handler(sig, stop_event.set)
-        except NotImplementedError:
-            # 信号处理在某些平台（如 Windows）不可用，忽略即可
-            pass
-    await run_engine(stop_event)
-
-
-def main():
-    logger.info("worker 进程启动")
-    asyncio.run(_async_main())
-
-
-if __name__ == "__main__":
-    main()
-

datasvc/docker-compose.yml

@@ -1,19 +0,0 @@
-services:
-  datasvc:
-    build: .
-    container_name: datasvc
-    restart: unless-stopped
-    environment:
-      - EXCHANGE=binance
-      - TIMEFRAMES=1m,1h,1d,1w,1M
-      - START_FROM=2025-01-01
-      - POLL_FACTOR=0.5
-      - DATA_DIR=/data
-      - TZ=Asia/Shanghai
-      - WS_ENABLED=false
-      - DATASVC_ENGINE=1
-    ports:
-      - "9000:9000"
-    volumes:
-      - ./data:/data
-

datasvc/pairs.json

@@ -1,5 +0,0 @@
-[
-  "BTC/USDT:USDT",
-  "ETH/USDT:USDT",
-  "SOL/USDT:USDT"
-]

datasvc/requirements.txt

@@ -1,9 +0,0 @@
-fastapi==0.111.0
-uvicorn[standard]==0.29.0
-ccxt==4.4.27
-pandas==2.2.2
-pyarrow==16.1.0
-orjson==3.10.3
-technical==1.5.0
-websockets==12.0
-