mirror of
https://github.com/BrentNeale1/fx-quant.git
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Brent NealeandClaude Opus 4.6
dce54845c2
- Built event-driven backtesting engine with spread/slippage modeling, 3-TP partial closes, trailing stops, and rich trade logging (20+ features) - Implemented 5 strategy signal generators (MA Breakout, VWAP Reversal, Key Level Breakout, EMA Ribbon Scalp, Momentum Exhaustion) - Full indicator library (EMA, SMA, RSI, ATR, MACD, ADX, Stochastic, Session VWAP bands, swing points, key levels, RSI divergence) - Data pipeline: Dukascopy download, validation, 70/30 train/test split - Baseline results: all 5 strategies generate 200+ trades on training data (Jan 2021 - Aug 2023), best profit factors 0.82-0.96 on select pairs - Trade logs and reports saved for Phase 3 ML feature engineering Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
141 lines
4.6 KiB
Python
141 lines
4.6 KiB
Python
"""
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Strategy 2: Session VWAP Reversal.
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Entry TF: M15, No HTF filter needed.
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Entry conditions (LONG):
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- Price crosses below VWAP -2 sigma band
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- RSI < 30 (oversold confirmation)
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- Session filter: London/NY hours only (08:00-17:00 UTC)
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- Minimum band width: vwap_std > 0.3 * ATR
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Entry conditions (SHORT):
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- Price crosses above VWAP +2 sigma band
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- RSI > 70 (overbought confirmation)
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TP1: Return to VWAP, TP2: Opposite 0.5 sigma band
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SL: Beyond session high/low OR 1x ATR (whichever tighter)
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"""
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from typing import Optional
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import numpy as np
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import pandas as pd
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from .base import BaseStrategy
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class S2_VWAP_Reversal(BaseStrategy):
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strategy_id = 2
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name = "S2_Session_VWAP_Reversal"
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def check_signal(self, data: pd.DataFrame, idx: int,
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current: pd.Series,
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htf_row: Optional[pd.Series] = None) -> Optional[dict]:
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if idx < 30:
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return None
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# Session filter: only London + NY (08:00-17:00 UTC)
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hour = current.name.hour if hasattr(current.name, 'hour') else 0
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if hour < 8 or hour >= 17:
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return None
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vwap = current.get("session_vwap", None)
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upper_2 = current.get("vwap_upper_2", None)
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lower_2 = current.get("vwap_lower_2", None)
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if vwap is None or upper_2 is None or lower_2 is None:
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return None
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if any(np.isnan(v) for v in [vwap, upper_2, lower_2]):
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return None
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# Skip if bands are too tight (early in session, no deviation yet)
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vwap_std = current.get("vwap_std", 0)
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if vwap_std is None or np.isnan(vwap_std) or vwap_std <= 0:
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return None
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atr_val = current.get("atr_14", 0)
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if atr_val <= 0 or np.isnan(atr_val):
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return None
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# Minimum band width: std must be meaningful relative to ATR
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if vwap_std < 0.3 * atr_val:
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return None
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rsi = current.get("rsi_14", 50)
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if np.isnan(rsi):
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return None
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close = current["close"]
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prev = data.iloc[idx - 1]
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# Session high/low for SL
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start = max(0, idx - 80)
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session_high = data["high"].iloc[start:idx + 1].max()
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session_low = data["low"].iloc[start:idx + 1].min()
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# LONG: price below -2 sigma + RSI < 30 + price just crossed below
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if close < lower_2 and rsi < 30:
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prev_lower_2 = prev.get("vwap_lower_2", None)
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if prev_lower_2 is not None and not np.isnan(prev_lower_2):
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if prev["close"] >= prev_lower_2:
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return self._build_long(close, vwap, atr_val, vwap_std,
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session_low, current)
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# SHORT: price above +2 sigma + RSI > 70 + price just crossed above
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if close > upper_2 and rsi > 70:
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prev_upper_2 = prev.get("vwap_upper_2", None)
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if prev_upper_2 is not None and not np.isnan(prev_upper_2):
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if prev["close"] <= prev_upper_2:
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return self._build_short(close, vwap, atr_val, vwap_std,
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session_high, current)
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return None
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def _build_long(self, close, vwap, atr_val, vwap_std, session_low, current):
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sl_session = session_low - 0.5 * atr_val
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sl_atr = close - 1.5 * atr_val # Wider SL for mean reversion
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sl = max(sl_session, sl_atr)
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tp1 = vwap
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tp2 = vwap + 0.5 * vwap_std
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tp3 = vwap + 1.5 * vwap_std
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confluence = 2
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if current.get("adx_14", 30) < 25:
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confluence += 1
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macd_h = current.get("macd_hist", 0)
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if macd_h > 0:
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confluence += 1
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return {
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"direction": "LONG",
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"sl": sl, "tp1": tp1, "tp2": tp2, "tp3": tp3,
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"confluence": confluence,
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"tp_splits": (0.50, 0.35, 0.15),
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"trail_atr_mult": 1.0,
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"max_bars": 80,
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}
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def _build_short(self, close, vwap, atr_val, vwap_std, session_high, current):
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sl_session = session_high + 0.5 * atr_val
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sl_atr = close + 1.5 * atr_val # Wider SL for mean reversion
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sl = min(sl_session, sl_atr)
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tp1 = vwap
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tp2 = vwap - 0.5 * vwap_std
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tp3 = vwap - 1.5 * vwap_std
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confluence = 2
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if current.get("adx_14", 30) < 25:
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confluence += 1
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macd_h = current.get("macd_hist", 0)
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if macd_h < 0:
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confluence += 1
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return {
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"direction": "SHORT",
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"sl": sl, "tp1": tp1, "tp2": tp2, "tp3": tp3,
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"confluence": confluence,
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"tp_splits": (0.50, 0.35, 0.15),
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"trail_atr_mult": 1.0,
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"max_bars": 80,
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}
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