fix(web): 自动刷新保留 K 线视窗；威科夫与图表增量更新
自动刷新改用 tail update 与 scrollToPosition 恢复视窗，避免 setData 后跳到最右；拆分 chart_tv 模块并扩展 analyze/recent API。同步威科夫分析、pipeline 增量构建及相关策略与配置。 Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
jackyu66git
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# Spring Baseline V1 — FROZEN SNAPSHOT
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勿改本目录文件。可运行副本在：
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- `strategies/Wyckoff_BTC_V1_BASELINE.py`
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- `config/Wyckoff_BTC_V1_BASELINE.json`
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## Evidence (cost-adjusted)
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| Window | Profit | n | DD | Net PF |
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|--------|--------|---|-----|--------|
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| Train | +1.66% | 12 | 3.6% | 1.17 |
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| Validate | +9.99% | 6 | 1.8% | 6.20 |
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| Test | +0.85% | 2 | 0.7% | 2.18 |
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| Full | +12.74% | 20 | 3.6% | 2.02 |
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| fee+slip 5bps | +6.78% | 20 | — | **1.45** |
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Status: **PASS + Limited Evidence** (N=20)
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Next: Phase3 → N≥50（延历史 / 多品种），不改规则。
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{
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"$schema": "https://schema.freqtrade.io/schema.json",
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"max_open_trades": 1,
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"stake_currency": "USDT",
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"stake_amount": "unlimited",
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"tradable_balance_ratio": 0.99,
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"fiat_display_currency": "USD",
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"dry_run": true,
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"db_url": "sqlite:///tradesv3.wyckoff_btc_v1_baseline.sqlite",
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"dry_run_wallet": 10000,
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"cancel_open_orders_on_exit": true,
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"trading_mode": "futures",
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"margin_mode": "isolated",
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"can_short": true,
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"timeframe": "1h",
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"process_only_new_candles": true,
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"unfilledtimeout": {
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"entry": 60,
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"exit": 60,
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"exit_timeout_count": 5,
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"unit": "minutes"
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},
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"entry_pricing": {
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"price_side": "same",
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"use_order_book": true,
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"order_book_top": 1,
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"price_last_balance": 0.0,
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"check_depth_of_market": {
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"enabled": false,
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"bids_to_ask_delta": 1
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}
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},
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"exit_pricing": {
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"price_side": "same",
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"use_order_book": true,
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"order_book_top": 1
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},
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"exchange": {
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"name": "binance",
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"key": "",
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"secret": "",
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"ccxt_config": {
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"proxies": {
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"http": "http://127.0.0.1:7897",
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"https": "http://127.0.0.1:7897"
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}
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},
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"ccxt_async_config": {
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"aiohttp_proxy": "http://127.0.0.1:7897"
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},
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"pair_whitelist": [
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"BTC/USDT:USDT"
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],
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"pair_blacklist": [
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"BNB/.*"
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]
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},
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"pairlists": [
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{
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"method": "StaticPairList"
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}
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],
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"telegram": {
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"enabled": false,
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"token": "",
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"chat_id": ""
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},
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"api_server": {
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"enabled": false,
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"listen_ip_address": "127.0.0.1",
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"listen_port": 8823,
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"verbosity": "error",
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"enable_openapi": false,
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"jwt_secret_key": "wyckoff-v1-baseline-change-me",
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"ws_token": "wyckoff-v1-baseline-ws-change-me",
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"CORS_origins": [],
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"username": "freqtrader",
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"password": "FreqTrade007"
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},
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"bot_name": "wyckoff_btc_v1_baseline",
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"initial_state": "running",
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"force_entry_enable": false,
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"internals": {
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"process_throttle_secs": 5
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}
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}
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# --- Do not remove these libs ---
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"""
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Wyckoff BTC V1.0 BASELINE — FROZEN
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Status: BASELINE FROZEN
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Evidence: PASS (+ Limited Evidence, N=20)
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Cost Adjusted: PASS (net PF 1.45 @ fee+slip 5bps)
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Risk: small sample — 目标积累 N>=50 再谈规模
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Branch A: Spring Reversal
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8h bias + 4h structure + 1h Spring/UTAD
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Range disabled（regime_mode=trend）
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ATR + 结构止损
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setup_type: SPRING / UTAD
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证据: user_data/Chan/scripts/wyckoff_v1_baseline_phase2.json
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LPS 是独立 Setup 研究，禁止并入本文件调参。
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"""
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from freqtrade.strategy import (
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IStrategy, IntParameter, DecimalParameter, CategoricalParameter,
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merge_informative_pair, stoploss_from_open, stoploss_from_absolute,
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)
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from freqtrade.persistence import Trade
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import talib.abstract as ta
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from pandas import DataFrame
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import pandas as pd
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import numpy as np
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from datetime import datetime
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from typing import Optional
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import logging
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logger = logging.getLogger(__name__)
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# freqtrade backtesting -c ./user_data/Chan/config/Wyckoff_BTC_V1_BASELINE.json \
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# --strategy Wyckoff_BTC_V1_BASELINE --strategy-path ./user_data/Chan/strategies --timerange=20230101-
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class Wyckoff_BTC_V1_BASELINE(IStrategy):
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"""冻结基线：Spring 反转。禁止继续调参；对比实验请用独立分支。"""
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INTERFACE_VERSION = 3
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STRATEGY_VERSION = "V1.0_BASELINE"
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SETUP_FAMILY = "SPRING"
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timeframe = "1h"
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structure_timeframe = "4h"
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bias_timeframe: Optional[str] = "8h"
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use_bias_filter = True
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# trend = bull|bear only（Range disabled — 理论一致性约束，非调参）
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regime_mode: str = "trend"
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can_short = True
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process_only_new_candles = True
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startup_candle_count = 220
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minimal_roi = {
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"0": 0.10,
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"1440": 0.05,
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"4320": 0.025,
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"10080": 0,
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}
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stoploss = -0.10
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use_custom_stoploss = True
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trailing_stop = True
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trailing_stop_positive = 0.02
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trailing_stop_positive_offset = 0.04
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trailing_only_offset_is_reached = True
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use_exit_signal = True
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exit_profit_only = False
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# ---- 冻结默认值（optimize=False）----
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range_lookback = IntParameter(12, 48, default=24, space="buy", optimize=False)
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spring_pierce_pct = DecimalParameter(0.001, 0.012, default=0.004, decimals=3, space="buy", optimize=False)
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vol_spike_mult = DecimalParameter(1.1, 2.5, default=1.8, decimals=1, space="buy", optimize=False)
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adx_min = IntParameter(10, 28, default=14, space="buy", optimize=False)
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tr_pos_long_max = DecimalParameter(0.35, 0.55, default=0.45, decimals=2, space="buy", optimize=False)
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tr_pos_short_min = DecimalParameter(0.45, 0.65, default=0.55, decimals=2, space="buy", optimize=False)
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atr_sl_mult = DecimalParameter(1.2, 3.5, default=1.5, decimals=1, space="sell", optimize=False)
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atr_sl_min = DecimalParameter(0.012, 0.04, default=0.018, decimals=3, space="sell", optimize=False)
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atr_sl_max = DecimalParameter(0.05, 0.12, default=0.08, decimals=2, space="sell", optimize=False)
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time_stop_hours = IntParameter(48, 240, default=120, space="sell", optimize=False)
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# Branch A：仅 Spring / UTAD
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use_spring_sig = CategoricalParameter([True, False], default=True, space="buy", optimize=False)
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use_utad_sig = CategoricalParameter([True, False], default=True, space="buy", optimize=False)
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use_sos_sig = CategoricalParameter([True, False], default=False, space="buy", optimize=False)
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use_sow_sig = CategoricalParameter([True, False], default=False, space="buy", optimize=False)
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lev = 1.0
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def informative_pairs(self):
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pairs = self.dp.current_whitelist() if self.dp else []
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tfs = {self.structure_timeframe}
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if self.bias_timeframe and self.use_bias_filter:
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tfs.add(self.bias_timeframe)
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return [(pair, tf) for pair in pairs for tf in tfs]
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def _add_wyckoff_structure(self, df: DataFrame) -> DataFrame:
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lb = int(self.range_lookback.value)
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df["atr"] = ta.ATR(df, timeperiod=14)
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df["ema50"] = ta.EMA(df, timeperiod=50)
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df["ema200"] = ta.EMA(df, timeperiod=200)
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df["adx"] = ta.ADX(df, timeperiod=14)
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df["rsi"] = ta.RSI(df, timeperiod=14)
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df["volume_ma"] = ta.SMA(df, timeperiod=20, price="volume")
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df["tr_high"] = df["high"].rolling(lb).max()
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df["tr_low"] = df["low"].rolling(lb).min()
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df["tr_mid"] = (df["tr_high"] + df["tr_low"]) / 2.0
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df["tr_width"] = (df["tr_high"] - df["tr_low"]) / df["tr_mid"].replace(0, np.nan)
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df["tr_width_ma"] = df["tr_width"].rolling(lb).mean()
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rng = (df["tr_high"] - df["tr_low"]).replace(0, np.nan)
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df["tr_pos"] = (df["close"] - df["tr_low"]) / rng
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df["in_range"] = (df["tr_width"] < df["tr_width_ma"] * 1.35) & (df["adx"] < 28)
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df["ema50_slope"] = df["ema50"] - df["ema50"].shift(8)
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df["prior_down"] = df["ema50_slope"].shift(lb) < 0
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df["prior_up"] = df["ema50_slope"].shift(lb) > 0
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down_bar = df["close"] < df["open"]
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up_bar = df["close"] > df["open"]
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vol_down = np.where(down_bar, df["volume"], np.nan)
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vol_up = np.where(up_bar, df["volume"], np.nan)
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df["vol_down_ma"] = pd.Series(vol_down, index=df.index).rolling(10, min_periods=3).mean()
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df["vol_up_ma"] = pd.Series(vol_up, index=df.index).rolling(10, min_periods=3).mean()
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df["effort_absorb"] = (
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df["vol_down_ma"].notna()
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& df["vol_up_ma"].notna()
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& (df["vol_up_ma"] > df["vol_down_ma"] * 1.05)
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)
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df["accum_ctx"] = (
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df["in_range"]
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& (df["prior_down"] | (df["close"] < df["ema50"]))
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& (df["tr_pos"] < float(self.tr_pos_long_max.value))
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)
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df["distrib_ctx"] = (
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df["in_range"]
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& (df["prior_up"] | (df["close"] > df["ema50"]))
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& (df["tr_pos"] > float(self.tr_pos_short_min.value))
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)
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df["bull_bias"] = (df["close"] > df["ema200"]) & (df["ema50"] > df["ema200"])
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df["bear_bias"] = (df["close"] < df["ema200"]) & (df["ema50"] < df["ema200"])
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df["vol_spike"] = df["volume"] > df["volume_ma"] * float(self.vol_spike_mult.value)
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return df
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def _merge_tf(self, dataframe: DataFrame, pair: str, tf: str) -> DataFrame:
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inf = self.dp.get_pair_dataframe(pair=pair, timeframe=tf)
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inf = self._add_wyckoff_structure(inf)
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keep = [
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"date", "atr", "ema50", "ema200", "adx", "rsi",
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"tr_high", "tr_low", "tr_mid", "tr_width", "tr_pos",
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"in_range", "accum_ctx", "distrib_ctx",
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"vol_spike", "effort_absorb", "prior_down", "prior_up",
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"bull_bias", "bear_bias",
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]
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inf = inf[[c for c in keep if c in inf.columns]].copy()
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return merge_informative_pair(dataframe, inf, self.timeframe, tf, ffill=True)
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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pair = metadata["pair"]
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stf = self.structure_timeframe
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dataframe = self._merge_tf(dataframe, pair, stf)
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btf = self.bias_timeframe
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if btf and self.use_bias_filter and btf != stf:
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dataframe = self._merge_tf(dataframe, pair, btf)
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ss = f"_{stf}"
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dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
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dataframe["ema21"] = ta.EMA(dataframe, timeperiod=21)
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dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50)
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dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
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dataframe["volume_ma"] = ta.SMA(dataframe, timeperiod=20, price="volume")
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dataframe["vol_ok"] = dataframe["volume"] > dataframe["volume_ma"] * float(self.vol_spike_mult.value)
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tr_high = dataframe[f"tr_high{ss}"]
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tr_low = dataframe[f"tr_low{ss}"]
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pierce = float(self.spring_pierce_pct.value)
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accum_soft = (
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dataframe[f"accum_ctx{ss}"].fillna(False).astype(bool)
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| (
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dataframe[f"in_range{ss}"].fillna(False).astype(bool)
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& dataframe[f"prior_down{ss}"].fillna(False).astype(bool)
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& (dataframe[f"tr_pos{ss}"] < float(self.tr_pos_long_max.value))
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)
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)
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distrib_soft = (
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dataframe[f"distrib_ctx{ss}"].fillna(False).astype(bool)
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| (
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dataframe[f"in_range{ss}"].fillna(False).astype(bool)
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& dataframe[f"prior_up{ss}"].fillna(False).astype(bool)
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& (dataframe[f"tr_pos{ss}"] > float(self.tr_pos_short_min.value))
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)
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)
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if btf and self.use_bias_filter:
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bs = f"_{btf}" if btf != stf else ss
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if f"bear_bias{bs}" in dataframe.columns:
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dataframe["bias_long_ok"] = ~dataframe[f"bear_bias{bs}"].fillna(False).astype(bool)
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dataframe["bias_short_ok"] = ~dataframe[f"bull_bias{bs}"].fillna(False).astype(bool)
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else:
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dataframe["bias_long_ok"] = True
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dataframe["bias_short_ok"] = True
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else:
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dataframe["bias_long_ok"] = True
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dataframe["bias_short_ok"] = True
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vol_mild = dataframe["volume"] > dataframe["volume_ma"] * max(1.1, float(self.vol_spike_mult.value) * 0.85)
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dataframe["spring"] = (
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tr_low.notna()
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& (dataframe["low"] < tr_low * (1.0 - pierce))
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& (dataframe["close"] > tr_low)
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& (dataframe["close"] > dataframe["open"])
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& accum_soft
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& vol_mild
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& (dataframe["rsi"] < 58)
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& dataframe["bias_long_ok"]
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)
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dataframe["utad"] = (
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tr_high.notna()
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& (dataframe["high"] > tr_high * (1.0 + pierce))
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& (dataframe["close"] < tr_high)
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& (dataframe["close"] < dataframe["open"])
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& distrib_soft
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& vol_mild
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& (dataframe["rsi"] > 42)
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& dataframe["bias_short_ok"]
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)
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# 基线不进 SOS/SOW；保留列供 exit 参考
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dataframe["sos"] = False
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dataframe["sow"] = False
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for col in ["spring", "utad", "sos", "sow", "vol_ok", "bias_long_ok", "bias_short_ok"]:
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dataframe[col] = dataframe[col].fillna(False).astype(bool)
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dataframe["setup_type"] = ""
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dataframe.loc[dataframe["spring"], "setup_type"] = "SPRING_LONG"
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dataframe.loc[dataframe["utad"], "setup_type"] = "UTAD_SHORT"
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe["enter_long"] = 0
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dataframe["enter_short"] = 0
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dataframe["enter_tag"] = ""
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vol_ok = dataframe["volume"] > 0
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# 分开标签：禁止把 SPRING / UTAD 混成同一统计桶
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if bool(self.use_spring_sig.value):
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cond = vol_ok & dataframe["spring"]
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dataframe.loc[cond, ["enter_long", "enter_tag"]] = (1, "SPRING_LONG")
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if bool(self.use_utad_sig.value):
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cond = vol_ok & dataframe["utad"]
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dataframe.loc[cond, ["enter_short", "enter_tag"]] = (1, "UTAD_SHORT")
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self._apply_regime_filter(dataframe)
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return dataframe
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def _apply_regime_filter(self, dataframe: DataFrame) -> None:
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rm = getattr(self, "regime_mode", "all")
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if rm == "all" or not self.bias_timeframe:
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return
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bs = f"_{self.bias_timeframe}"
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bc, ec = f"bull_bias{bs}", f"bear_bias{bs}"
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if bc not in dataframe.columns or ec not in dataframe.columns:
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return
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bull = dataframe[bc].fillna(False).astype(bool)
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bear = dataframe[ec].fillna(False).astype(bool)
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both = bull & bear
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bull, bear = bull & ~both, bear & ~both
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range_m = (~bull) & (~bear)
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if rm == "bull":
|
||||
mask = ~bull
|
||||
elif rm == "bear":
|
||||
mask = ~bear
|
||||
elif rm == "range":
|
||||
mask = ~range_m
|
||||
elif rm == "trend":
|
||||
mask = range_m # Range disabled
|
||||
else:
|
||||
return
|
||||
dataframe.loc[mask, ["enter_long", "enter_short"]] = (0, 0)
|
||||
dataframe.loc[mask, "enter_tag"] = ""
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe["exit_long"] = 0
|
||||
dataframe["exit_short"] = 0
|
||||
dataframe["exit_tag"] = ""
|
||||
ss = f"_{self.structure_timeframe}"
|
||||
|
||||
exit_long = dataframe["utad"] | (
|
||||
dataframe[f"distrib_ctx{ss}"].fillna(False).astype(bool)
|
||||
& (dataframe["close"] < dataframe["ema21"])
|
||||
& (dataframe["rsi"] < 45)
|
||||
)
|
||||
exit_short = dataframe["spring"] | (
|
||||
dataframe[f"accum_ctx{ss}"].fillna(False).astype(bool)
|
||||
& (dataframe["close"] > dataframe["ema21"])
|
||||
& (dataframe["rsi"] > 55)
|
||||
)
|
||||
dataframe.loc[exit_long, ["exit_long", "exit_tag"]] = (1, "wyckoff_phase_flip")
|
||||
dataframe.loc[exit_short, ["exit_short", "exit_tag"]] = (1, "wyckoff_phase_flip")
|
||||
return dataframe
|
||||
|
||||
def custom_stoploss(
|
||||
self, pair: str, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, after_fill: bool, **kwargs,
|
||||
) -> Optional[float]:
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
if dataframe.empty:
|
||||
return None
|
||||
last = dataframe.iloc[-1]
|
||||
atr = float(last["atr"]) if pd.notna(last["atr"]) else 0.0
|
||||
if atr <= 0 or trade.open_rate <= 0:
|
||||
return None
|
||||
|
||||
atr_dist = float(self.atr_sl_mult.value) * atr
|
||||
tag = trade.enter_tag or ""
|
||||
buffer = atr * 0.15
|
||||
|
||||
if after_fill and trade.get_custom_data("struct_stop") is None:
|
||||
if trade.is_short:
|
||||
trade.set_custom_data("struct_stop", float(last["high"]) + buffer)
|
||||
else:
|
||||
trade.set_custom_data("struct_stop", float(last["low"]) - buffer)
|
||||
|
||||
struct = trade.get_custom_data("struct_stop")
|
||||
if trade.is_short:
|
||||
atr_stop = trade.open_rate + atr_dist
|
||||
stop_price = min(atr_stop, float(struct)) if struct is not None else atr_stop
|
||||
else:
|
||||
atr_stop = trade.open_rate - atr_dist
|
||||
stop_price = max(atr_stop, float(struct)) if struct is not None else atr_stop
|
||||
|
||||
raw = abs(trade.open_rate - stop_price) / trade.open_rate
|
||||
raw = min(max(raw, float(self.atr_sl_min.value)), float(self.atr_sl_max.value))
|
||||
if struct is not None and tag in (
|
||||
"SPRING_LONG", "UTAD_SHORT", "SPRING", "UTAD", "wyckoff_spring", "wyckoff_utad",
|
||||
):
|
||||
sl = stoploss_from_absolute(
|
||||
stop_price, current_rate, is_short=trade.is_short, leverage=trade.leverage
|
||||
)
|
||||
return sl if sl and sl > 0 else None
|
||||
return stoploss_from_open(
|
||||
-raw, current_profit, is_short=trade.is_short, leverage=trade.leverage
|
||||
) or None
|
||||
|
||||
def custom_exit(
|
||||
self, pair: str, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, **kwargs,
|
||||
) -> Optional[str]:
|
||||
hours = (current_time - trade.open_date_utc).total_seconds() / 3600
|
||||
if hours > float(self.time_stop_hours.value) and current_profit < 0:
|
||||
return "wyckoff_time_stop"
|
||||
if hours > float(self.time_stop_hours.value) * 2:
|
||||
return "wyckoff_time_stop_max"
|
||||
return None
|
||||
|
||||
def leverage(
|
||||
self, pair: str, current_time: datetime, current_rate: float,
|
||||
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str],
|
||||
side: str, **kwargs,
|
||||
) -> float:
|
||||
return min(self.lev, max_leverage)
|
||||
@@ -0,0 +1,460 @@
|
||||
{
|
||||
"branches": {
|
||||
"Spring_V1": {
|
||||
"wfo": {
|
||||
"train": {
|
||||
"timerange": "20230101-20250101",
|
||||
"profit_pct": 1.6587295176,
|
||||
"trades": 12,
|
||||
"dd_pct": 3.644907735100005,
|
||||
"pf": 1.1700179329477578,
|
||||
"winrate": 25.0,
|
||||
"final": 10165.87295176,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"validate": {
|
||||
"timerange": "20250101-20260101",
|
||||
"profit_pct": 9.990534148400002,
|
||||
"trades": 6,
|
||||
"dd_pct": 1.797834787912851,
|
||||
"pf": 6.201791679101682,
|
||||
"winrate": 66.66666666666666,
|
||||
"final": 10999.05341484,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"test": {
|
||||
"timerange": "20260101-",
|
||||
"profit_pct": 0.8458820224000001,
|
||||
"trades": 2,
|
||||
"dd_pct": 0.7197049309999966,
|
||||
"pf": 2.175317808681236,
|
||||
"winrate": 50.0,
|
||||
"final": 10084.58820224,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"full": {
|
||||
"timerange": "20230101-",
|
||||
"profit_pct": 12.7374753063,
|
||||
"trades": 20,
|
||||
"dd_pct": 3.644907735100005,
|
||||
"pf": 2.0183507402435503,
|
||||
"winrate": 40.0,
|
||||
"final": 11273.74753063,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
}
|
||||
},
|
||||
"regimes": {
|
||||
"trend": {
|
||||
"profit_pct": 12.7374753063,
|
||||
"trades": 20,
|
||||
"dd_pct": 3.644907735100005,
|
||||
"pf": 2.0183507402435503,
|
||||
"winrate": 40.0,
|
||||
"final": 11273.74753063,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"bull": {
|
||||
"profit_pct": 8.166882314399999,
|
||||
"trades": 12,
|
||||
"dd_pct": 3.4837023928902555,
|
||||
"pf": 1.9398544482027922,
|
||||
"winrate": 33.33333333333333,
|
||||
"final": 10816.688231439999,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "bull"
|
||||
},
|
||||
"bear": {
|
||||
"profit_pct": 4.2503064875000005,
|
||||
"trades": 8,
|
||||
"dd_pct": 3.173714645599994,
|
||||
"pf": 2.084295240772406,
|
||||
"winrate": 50.0,
|
||||
"final": 10425.03064875,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "bear"
|
||||
},
|
||||
"range": {
|
||||
"profit_pct": -4.3352539371,
|
||||
"trades": 6,
|
||||
"dd_pct": 4.404162180500007,
|
||||
"pf": 0.15746188404490422,
|
||||
"winrate": 16.666666666666664,
|
||||
"final": 9566.47460629,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "range"
|
||||
},
|
||||
"all": {
|
||||
"profit_pct": 7.831216539699999,
|
||||
"trades": 26,
|
||||
"dd_pct": 7.883451762900004,
|
||||
"pf": 1.454582067425369,
|
||||
"winrate": 34.61538461538461,
|
||||
"final": 10783.12165397,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "all"
|
||||
}
|
||||
},
|
||||
"cost_stress": {
|
||||
"fee_5bps": {
|
||||
"profit_pct": 12.7374753063,
|
||||
"trades": 20,
|
||||
"dd_pct": 3.644907735100005,
|
||||
"pf": 2.0183507402435503,
|
||||
"winrate": 40.0,
|
||||
"final": 11273.74753063,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"fee_5bps+slip_5bps": {
|
||||
"profit_pct": 6.782772099999998,
|
||||
"trades": 20,
|
||||
"dd_pct": 7.851805397900007,
|
||||
"pf": 1.4511324473780693,
|
||||
"winrate": 35.0,
|
||||
"final": 10678.27721,
|
||||
"fee_used": 0.001,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"fee_10bps+slip_10bps": {
|
||||
"profit_pct": 3.182909279400001,
|
||||
"trades": 20,
|
||||
"dd_pct": 9.126146157700004,
|
||||
"pf": 1.1834520309921508,
|
||||
"winrate": 35.0,
|
||||
"final": 10318.29092794,
|
||||
"fee_used": 0.002,
|
||||
"regime_loaded": "trend"
|
||||
}
|
||||
},
|
||||
"target": {
|
||||
"pf": 1.3,
|
||||
"dd": 10.0,
|
||||
"note": "Spring: PF>1.3 DD<10%"
|
||||
},
|
||||
"verdict": {
|
||||
"full_pf": 2.0183507402435503,
|
||||
"full_dd": 3.644907735100005,
|
||||
"trades_per_year": 5.555555555555555,
|
||||
"net_mid_pf": 1.4511324473780693,
|
||||
"target_pf_ok": true,
|
||||
"target_dd_ok": true
|
||||
}
|
||||
},
|
||||
"LPS_V1": {
|
||||
"wfo": {
|
||||
"train": {
|
||||
"timerange": "20230101-20250101",
|
||||
"profit_pct": -1.2518571096,
|
||||
"trades": 1,
|
||||
"dd_pct": 1.251857109600005,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 9874.81428904,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"validate": {
|
||||
"timerange": "20250101-20260101",
|
||||
"profit_pct": -0.24613064569999998,
|
||||
"trades": 1,
|
||||
"dd_pct": 0.24613064569999552,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 9975.38693543,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"test": {
|
||||
"timerange": "20260101-",
|
||||
"profit_pct": 0.0,
|
||||
"trades": 0,
|
||||
"dd_pct": 0.0,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 10000.0,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"full": {
|
||||
"timerange": "20230101-",
|
||||
"profit_pct": -1.4952529704,
|
||||
"trades": 2,
|
||||
"dd_pct": 1.4952529703999973,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 9850.47470296,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
}
|
||||
},
|
||||
"regimes": {
|
||||
"trend": {
|
||||
"profit_pct": -1.4952529704,
|
||||
"trades": 2,
|
||||
"dd_pct": 1.4952529703999973,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 9850.47470296,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"bull": {
|
||||
"profit_pct": -1.4952529704,
|
||||
"trades": 2,
|
||||
"dd_pct": 1.4952529703999973,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 9850.47470296,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "bull"
|
||||
},
|
||||
"bear": {
|
||||
"profit_pct": 0.0,
|
||||
"trades": 0,
|
||||
"dd_pct": 0.0,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 10000.0,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "bear"
|
||||
},
|
||||
"range": {
|
||||
"profit_pct": 0.0,
|
||||
"trades": 0,
|
||||
"dd_pct": 0.0,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 10000.0,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "range"
|
||||
},
|
||||
"all": {
|
||||
"profit_pct": -1.4952529704,
|
||||
"trades": 2,
|
||||
"dd_pct": 1.4952529703999973,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 9850.47470296,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "all"
|
||||
}
|
||||
},
|
||||
"cost_stress": {
|
||||
"fee_5bps": {
|
||||
"profit_pct": -1.4952529704,
|
||||
"trades": 2,
|
||||
"dd_pct": 1.4952529703999973,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 9850.47470296,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"fee_5bps+slip_5bps": {
|
||||
"profit_pct": -1.6902037039,
|
||||
"trades": 2,
|
||||
"dd_pct": 1.6902037039000062,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 9830.97962961,
|
||||
"fee_used": 0.001,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"fee_10bps+slip_10bps": {
|
||||
"profit_pct": -2.0801051709,
|
||||
"trades": 2,
|
||||
"dd_pct": 2.080105170900006,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 9791.98948291,
|
||||
"fee_used": 0.002,
|
||||
"regime_loaded": "trend"
|
||||
}
|
||||
},
|
||||
"target": {
|
||||
"pf": 1.2,
|
||||
"dd": 15.0,
|
||||
"note": "LPS: PF>1.2, 次数增加"
|
||||
},
|
||||
"version": "LPS_V1.1",
|
||||
"verdict": {
|
||||
"full_pf": 0.0,
|
||||
"full_dd": 1.4952529703999973,
|
||||
"trades_per_year": 0.5555555555555556,
|
||||
"net_mid_pf": 0.0,
|
||||
"target_pf_ok": false,
|
||||
"target_dd_ok": true
|
||||
}
|
||||
},
|
||||
"LPS_V2": {
|
||||
"version": "LPS_V2",
|
||||
"wfo": {
|
||||
"train": {
|
||||
"timerange": "20230101-20250101",
|
||||
"profit_pct": -3.5049591933000004,
|
||||
"trades": 3,
|
||||
"dd_pct": 3.504959193300001,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 9649.50408067,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"validate": {
|
||||
"timerange": "20250101-20260101",
|
||||
"profit_pct": -1.6143743830000001,
|
||||
"trades": 2,
|
||||
"dd_pct": 1.614374382999995,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 9838.5625617,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"test": {
|
||||
"timerange": "20260101-",
|
||||
"profit_pct": 1.2174468187999996,
|
||||
"trades": 2,
|
||||
"dd_pct": 1.4924489317000007,
|
||||
"pf": 1.81573767312309,
|
||||
"winrate": 50.0,
|
||||
"final": 10121.74468188,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"full": {
|
||||
"timerange": "20230101-",
|
||||
"profit_pct": -3.9055710949000004,
|
||||
"trades": 7,
|
||||
"dd_pct": 6.479119889400008,
|
||||
"pf": 0.39720654015221873,
|
||||
"winrate": 14.285714285714285,
|
||||
"final": 9609.44289051,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
}
|
||||
},
|
||||
"regimes": {
|
||||
"trend": {
|
||||
"profit_pct": -3.9055710949000004,
|
||||
"trades": 7,
|
||||
"dd_pct": 6.479119889400008,
|
||||
"pf": 0.39720654015221873,
|
||||
"winrate": 14.285714285714285,
|
||||
"final": 9609.44289051,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"bull": {
|
||||
"profit_pct": -5.0599199851,
|
||||
"trades": 5,
|
||||
"dd_pct": 5.059919985100005,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 9494.00800149,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "bull"
|
||||
},
|
||||
"bear": {
|
||||
"profit_pct": 1.2174468187999996,
|
||||
"trades": 2,
|
||||
"dd_pct": 1.4924489317000007,
|
||||
"pf": 1.81573767312309,
|
||||
"winrate": 50.0,
|
||||
"final": 10121.74468188,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "bear"
|
||||
},
|
||||
"range": {
|
||||
"profit_pct": 0.0,
|
||||
"trades": 0,
|
||||
"dd_pct": 0.0,
|
||||
"pf": 0.0,
|
||||
"winrate": 0.0,
|
||||
"final": 10000.0,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "range"
|
||||
},
|
||||
"all": {
|
||||
"profit_pct": -3.9055710949000004,
|
||||
"trades": 7,
|
||||
"dd_pct": 6.479119889400008,
|
||||
"pf": 0.39720654015221873,
|
||||
"winrate": 14.285714285714285,
|
||||
"final": 9609.44289051,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "all"
|
||||
}
|
||||
},
|
||||
"cost_stress": {
|
||||
"fee_5bps": {
|
||||
"profit_pct": -3.9055710949000004,
|
||||
"trades": 7,
|
||||
"dd_pct": 6.479119889400008,
|
||||
"pf": 0.39720654015221873,
|
||||
"winrate": 14.285714285714285,
|
||||
"final": 9609.44289051,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"fee_5bps+slip_5bps": {
|
||||
"profit_pct": -4.5549168852,
|
||||
"trades": 7,
|
||||
"dd_pct": 7.020877735199993,
|
||||
"pf": 0.3512325585213674,
|
||||
"winrate": 14.285714285714285,
|
||||
"final": 9544.50831148,
|
||||
"fee_used": 0.001,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"fee_10bps+slip_10bps": {
|
||||
"profit_pct": -5.371762636800001,
|
||||
"trades": 7,
|
||||
"dd_pct": 8.1297357765,
|
||||
"pf": 0.33924511392759654,
|
||||
"winrate": 14.285714285714285,
|
||||
"final": 9462.82373632,
|
||||
"fee_used": 0.002,
|
||||
"regime_loaded": "trend"
|
||||
}
|
||||
},
|
||||
"target": {
|
||||
"pf": 1.2,
|
||||
"dd": 15.0,
|
||||
"note": "LPS V2: 4h SOS→1h LPS; PF>1.2; ~5-15/yr"
|
||||
},
|
||||
"verdict": {
|
||||
"full_pf": 0.39720654015221873,
|
||||
"full_dd": 6.479119889400008,
|
||||
"trades_per_year": 1.9444444444444444,
|
||||
"net_mid_pf": 0.3512325585213674,
|
||||
"target_pf_ok": false,
|
||||
"target_dd_ok": true,
|
||||
"freq_ok": false,
|
||||
"regime_logic_ok": true,
|
||||
"status": "FAIL",
|
||||
"hypothesis": "4h native SOS → 1h LPS"
|
||||
}
|
||||
}
|
||||
},
|
||||
"portfolio_note": {
|
||||
"spring_tpy": 5.555555555555555,
|
||||
"lps_tpy": 0.5555555555555556,
|
||||
"sum_tpy_approx": 6.111111111111111,
|
||||
"combined_target_tpy": "15-25",
|
||||
"lps_status": "FAIL",
|
||||
"spring_status": "PASS"
|
||||
},
|
||||
"system_status": {
|
||||
"spring": "BASELINE FROZEN / PASS + Limited Evidence",
|
||||
"lps": "FAIL",
|
||||
"spring_tpy": 5.555555555555555,
|
||||
"lps_tpy": 1.9444444444444444,
|
||||
"next": "若 LPS PASS → 组合层；否则 Spring-only"
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,141 @@
|
||||
{
|
||||
"note": "V1 BASELINE frozen; Range disabled; Spring/UTAD only; net cost included",
|
||||
"wfo": {
|
||||
"train": {
|
||||
"timerange": "20230101-20250101",
|
||||
"profit_pct": 1.6587295176,
|
||||
"trades": 12,
|
||||
"dd_pct": 3.644907735100005,
|
||||
"pf": 1.1700179329477578,
|
||||
"winrate": 25.0,
|
||||
"final": 10165.87295176,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"validate": {
|
||||
"timerange": "20250101-20260101",
|
||||
"profit_pct": 9.990534148400002,
|
||||
"trades": 6,
|
||||
"dd_pct": 1.797834787912851,
|
||||
"pf": 6.201791679101682,
|
||||
"winrate": 66.66666666666666,
|
||||
"final": 10999.05341484,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"test": {
|
||||
"timerange": "20260101-",
|
||||
"profit_pct": 0.8458820224000001,
|
||||
"trades": 2,
|
||||
"dd_pct": 0.7197049309999966,
|
||||
"pf": 2.175317808681236,
|
||||
"winrate": 50.0,
|
||||
"final": 10084.58820224,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"full": {
|
||||
"timerange": "20230101-",
|
||||
"profit_pct": 12.7374753063,
|
||||
"trades": 20,
|
||||
"dd_pct": 3.644907735100005,
|
||||
"pf": 2.0183507402435503,
|
||||
"winrate": 40.0,
|
||||
"final": 11273.74753063,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
}
|
||||
},
|
||||
"regimes": {
|
||||
"trend": {
|
||||
"profit_pct": 12.7374753063,
|
||||
"trades": 20,
|
||||
"dd_pct": 3.644907735100005,
|
||||
"pf": 2.0183507402435503,
|
||||
"winrate": 40.0,
|
||||
"final": 11273.74753063,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"bull": {
|
||||
"profit_pct": 8.166882314399999,
|
||||
"trades": 12,
|
||||
"dd_pct": 3.4837023928902555,
|
||||
"pf": 1.9398544482027922,
|
||||
"winrate": 33.33333333333333,
|
||||
"final": 10816.688231439999,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "bull"
|
||||
},
|
||||
"bear": {
|
||||
"profit_pct": 4.2503064875000005,
|
||||
"trades": 8,
|
||||
"dd_pct": 3.173714645599994,
|
||||
"pf": 2.084295240772406,
|
||||
"winrate": 50.0,
|
||||
"final": 10425.03064875,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "bear"
|
||||
},
|
||||
"range": {
|
||||
"profit_pct": -4.3352539371,
|
||||
"trades": 6,
|
||||
"dd_pct": 4.404162180500007,
|
||||
"pf": 0.15746188404490422,
|
||||
"winrate": 16.666666666666664,
|
||||
"final": 9566.47460629,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "range"
|
||||
},
|
||||
"all": {
|
||||
"profit_pct": 7.831216539699999,
|
||||
"trades": 26,
|
||||
"dd_pct": 7.883451762900004,
|
||||
"pf": 1.454582067425369,
|
||||
"winrate": 34.61538461538461,
|
||||
"final": 10783.12165397,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "all"
|
||||
}
|
||||
},
|
||||
"cost_stress": {
|
||||
"fee_5bps": {
|
||||
"profit_pct": 12.7374753063,
|
||||
"trades": 20,
|
||||
"dd_pct": 3.644907735100005,
|
||||
"pf": 2.0183507402435503,
|
||||
"winrate": 40.0,
|
||||
"final": 11273.74753063,
|
||||
"fee_used": 0.0005,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"fee_5bps+slip_5bps": {
|
||||
"profit_pct": 6.782772099999998,
|
||||
"trades": 20,
|
||||
"dd_pct": 7.851805397900007,
|
||||
"pf": 1.4511324473780693,
|
||||
"winrate": 35.0,
|
||||
"final": 10678.27721,
|
||||
"fee_used": 0.001,
|
||||
"regime_loaded": "trend"
|
||||
},
|
||||
"fee_10bps+slip_10bps": {
|
||||
"profit_pct": 3.182909279400001,
|
||||
"trades": 20,
|
||||
"dd_pct": 9.126146157700004,
|
||||
"pf": 1.1834520309921508,
|
||||
"winrate": 35.0,
|
||||
"final": 10318.29092794,
|
||||
"fee_used": 0.002,
|
||||
"regime_loaded": "trend"
|
||||
}
|
||||
},
|
||||
"verdict": {
|
||||
"full_pf": 2.0183507402435503,
|
||||
"full_dd": 3.644907735100005,
|
||||
"trades_per_year": 5.555555555555555,
|
||||
"net_mid_pf": 1.4511324473780693,
|
||||
"target_pf_ok": true,
|
||||
"target_dd_ok": true
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user