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https://github.com/BrentNeale1/fx-quant.git
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Add S10 VWAP Mean Reversion and S11 ADX Trend Pullback strategies, drop both from portfolio
Implemented two M15 intraday strategies to diversify the portfolio: - S10: VWAP mean reversion in ranging markets (ADX<30, RSI(9) extremes) - S11: ADX trend pullback to 20 EMA in strong trends (ADX>30, rising) Added rsi_9 and atr_10 to the indicator pipeline for both strategies. Backtested on IS (2021-2022) and OOS (2023): both strategies produced insufficient trade counts on M15 and failed generalization. S10 best result was EUR_GBP at Gen 0.65 (WARN). S11 collapsed to 0% WR OOS across all param sweep combos. Both dropped from active portfolio — 3-strategy core (S7_Tight, S9_Filtered, S3) remains unchanged. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Brent Neale
@@ -95,32 +95,128 @@
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"verdict": "PASS"
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}
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},
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"_portfolio": {
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"S10_EUR_USD": {
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"pair": "EUR_USD",
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"timeframe": "M15",
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"is_metrics": {
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"trades": 149,
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"wr": 57.7,
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"pf": 1.29,
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"sharpe": 1.68,
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"pnl_pips": 865.8,
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"max_dd_pips": -597.5,
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"expectancy": 5.81
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"trades": 13,
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"wr": 53.8,
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"pf": 1.67,
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"sharpe": 3.17,
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"pnl_pips": 20.1,
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"max_dd_pips": -13.1,
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"expectancy": 1.55
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},
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"oos_metrics": {
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"trades": 56,
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"wr": 64.3,
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"pf": 1.55,
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"sharpe": 3.03,
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"pnl_pips": 553.3,
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"max_dd_pips": -182.9,
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"expectancy": 9.88
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"trades": 6,
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"wr": 33.3,
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"pf": 1.19,
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"sharpe": 0.97,
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"pnl_pips": 1.5,
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"max_dd_pips": -7.9,
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"expectancy": 0.25
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},
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"generalization": {
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"composite": 1.455,
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"composite": 0.45,
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"detail": {
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"wr": 1.114,
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"pf": 1.202,
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"expectancy": 1.701,
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"sharpe": 1.804
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"wr": 0.619,
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"pf": 0.713,
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"expectancy": 0.161,
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"sharpe": 0.306
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},
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"verdict": "FAIL"
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}
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},
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"S10_USD_JPY": {
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"pair": "USD_JPY",
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"timeframe": "M15",
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"is_metrics": {
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"trades": 20,
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"wr": 50.0,
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"pf": 2.08,
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"sharpe": 4.18,
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"pnl_pips": 51.5,
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"max_dd_pips": -19.3,
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"expectancy": 2.58
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},
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"oos_metrics": {
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"trades": 9,
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"wr": 33.3,
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"pf": 0.76,
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"sharpe": -1.48,
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"pnl_pips": -10.1,
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"max_dd_pips": -9.8,
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"expectancy": -1.13
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},
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"generalization": {
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"composite": 0.258,
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"detail": {
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"wr": 0.666,
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"pf": 0.365,
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"expectancy": 0.0,
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"sharpe": 0.0
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},
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"verdict": "FAIL"
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}
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},
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"S10_EUR_GBP": {
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"pair": "EUR_GBP",
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"timeframe": "M15",
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"is_metrics": {
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"trades": 15,
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"wr": 33.3,
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"pf": 0.98,
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"sharpe": -0.14,
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"pnl_pips": -0.8,
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"max_dd_pips": -29.2,
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"expectancy": -0.06
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},
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"oos_metrics": {
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"trades": 7,
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"wr": 42.9,
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"pf": 1.28,
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"sharpe": 1.61,
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"pnl_pips": 3.5,
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"max_dd_pips": -9.4,
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"expectancy": 0.49
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},
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"generalization": {
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"composite": 0.649,
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"detail": {
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"wr": 1.288,
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"pf": 1.306,
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"expectancy": 0,
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"sharpe": 0
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},
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"verdict": "WARN"
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}
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},
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"_portfolio": {
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"is_metrics": {
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"trades": 197,
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"wr": 54.8,
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"pf": 1.3,
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"sharpe": 1.58,
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"pnl_pips": 936.6,
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"max_dd_pips": -597.5,
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"expectancy": 4.75
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},
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"oos_metrics": {
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"trades": 78,
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"wr": 56.4,
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"pf": 1.51,
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"sharpe": 2.52,
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"pnl_pips": 548.2,
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"max_dd_pips": -208.7,
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"expectancy": 7.03
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},
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"generalization": {
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"composite": 1.316,
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"detail": {
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"wr": 1.029,
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"pf": 1.162,
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"expectancy": 1.48,
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"sharpe": 1.595
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},
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"verdict": "PASS"
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}
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+951
-1143
File diff suppressed because it is too large
Load Diff
@@ -386,9 +386,11 @@ def compute_all_indicators(df: pd.DataFrame) -> pd.DataFrame:
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# RSI
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df["rsi_14"] = rsi(df["close"], 14)
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df["rsi_9"] = rsi(df["close"], 9)
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# ATR
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df["atr_14"] = atr(df, 14)
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df["atr_10"] = atr(df, 10)
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# MACD
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df["macd"], df["macd_signal"], df["macd_hist"] = macd(df["close"])
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@@ -76,6 +76,8 @@ SWEEP_CONFIGS = [
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"KEY_LEVEL_TOLERANCE": [0.5, 0.75],
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},
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},
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# S10 dropped: insufficient trade counts on M15, no pair passes generalization
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# S11 dropped: OOS collapsed across all param combos (0% WR, Gen=0.00)
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]
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@@ -48,6 +48,8 @@ CONFIGS = [
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"factory": lambda: S9_London_Session(pair="GBP_AUD", filtered=True)},
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{"name": "S3", "pair": "GBP_JPY", "tf": "H1", "htf_tf": "H1",
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"factory": lambda: S3_KeyLevel_Breakout()},
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# S10 dropped: insufficient trade counts on M15, no pair passes generalization
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# S11 dropped: OOS collapsed across all param combos (0% WR, Gen=0.00)
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]
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@@ -0,0 +1,140 @@
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"""
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Strategy S10: VWAP Mean Reversion (M15).
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Concept: Fade extreme deviations from session VWAP in ranging markets.
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Price stretched beyond 2 SD from VWAP with RSI(9) at extremes signals
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mean reversion back to VWAP.
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Entry conditions (ALL must be true):
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1. Session filter: 08:00-16:00 UTC
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2. Ranging regime: ADX(14) < 25
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3. Price >= 2 SD from session VWAP (z-score)
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4. RSI(9) at extreme: <25 for longs, >75 for shorts
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5. Volume declining (fading momentum)
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Exit:
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- SL: Tighter of VWAP 3-SD band and 1.5x ATR(10)
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- TP: Session VWAP (100% exit at mean)
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- Time stop: 8 bars (2 hours on M15)
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Target pairs: EUR_USD, USD_JPY, EUR_GBP (range-bound majors)
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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 S10_VWAP_MeanReversion(BaseStrategy):
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strategy_id = 10
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name = "S10_VWAP_MeanReversion"
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# Tunable parameters
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VWAP_SD_ENTRY = 2.0 # Min SD from VWAP to enter
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VWAP_SD_SL = 3.0 # SL at this SD band
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SL_ATR_MULT = 1.5 # Alternative SL: 1.5x ATR(10)
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MAX_ADX = 30 # Ranging regime gate
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RSI9_LONG_THRESH = 25 # RSI(9) < 25 for longs
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RSI9_SHORT_THRESH = 75 # RSI(9) > 75 for shorts
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MAX_BARS = 8 # Time stop: 8 bars = 2 hours on M15
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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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# Warmup
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if idx < 50:
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return None
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# Session filter: 08:00-16:00 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 >= 16:
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return None
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# Regime gate: ranging market only
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adx_val = current.get("adx_14", 50)
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if np.isnan(adx_val) or adx_val >= self.MAX_ADX:
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return None
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# VWAP data required
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vwap = current.get("session_vwap", np.nan)
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vwap_std = current.get("vwap_std", np.nan)
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if np.isnan(vwap) or np.isnan(vwap_std) or vwap_std <= 0:
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return None
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price = current["close"]
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atr_10 = current.get("atr_10", 0)
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if atr_10 <= 0 or np.isnan(atr_10):
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return None
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rsi9 = current.get("rsi_9", 50)
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if np.isnan(rsi9):
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return None
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# Z-score: how many SDs from VWAP
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z_score = (price - vwap) / vwap_std
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# Volume declining check (fading momentum)
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vol = current.get("volume", 0)
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vol_window = data["volume"].iloc[max(0, idx - 3):idx]
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if len(vol_window) == 0:
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return None
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vol_avg = vol_window.mean()
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if vol_avg <= 0 or vol >= vol_avg:
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return None # Volume not declining
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# HTF trend for confluence (soft — not a gate)
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htf_bullish = False
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htf_bearish = False
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if htf_row is not None:
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htf_close = htf_row.get("close", np.nan)
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htf_ema200 = htf_row.get("ema_200", np.nan)
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if not np.isnan(htf_close) and not np.isnan(htf_ema200):
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htf_bullish = htf_close > htf_ema200
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htf_bearish = htf_close < htf_ema200
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direction = None
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# LONG: price well below VWAP, RSI oversold
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if z_score <= -self.VWAP_SD_ENTRY and rsi9 < self.RSI9_LONG_THRESH:
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direction = "LONG"
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# SHORT: price well above VWAP, RSI overbought
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elif z_score >= self.VWAP_SD_ENTRY and rsi9 > self.RSI9_SHORT_THRESH:
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direction = "SHORT"
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if direction is None:
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return None
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# Confluence scoring
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confluence = 2 # Base: VWAP deviation + RSI extreme
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if abs(z_score) >= 2.5:
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confluence += 1 # Deeper deviation
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if (direction == "LONG" and htf_bullish) or \
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(direction == "SHORT" and htf_bearish):
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confluence += 1 # HTF aligns with mean reversion direction
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if vol_avg > 0 and vol < 0.7 * vol_avg:
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confluence += 1 # Strong fade signal
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# Exit levels
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if direction == "LONG":
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sl_vwap = vwap - self.VWAP_SD_SL * vwap_std
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sl_atr = price - self.SL_ATR_MULT * atr_10
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sl = max(sl_vwap, sl_atr) # Tighter (closer to price) for longs
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tp = vwap # Mean reversion target
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else:
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sl_vwap = vwap + self.VWAP_SD_SL * vwap_std
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sl_atr = price + self.SL_ATR_MULT * atr_10
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sl = min(sl_vwap, sl_atr) # Tighter (closer to price) for shorts
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tp = vwap
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return {
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"direction": direction,
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"sl": sl,
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"tp1": tp,
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"tp2": tp,
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"tp3": tp,
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"confluence": confluence,
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"entry_pattern": f"vwap_mean_reversion_{direction.lower()}",
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"tp_splits": (1.0, 0.0, 0.0),
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"trail_atr_mult": 0,
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"max_bars": self.MAX_BARS,
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}
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@@ -0,0 +1,180 @@
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"""
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Strategy S11: ADX Trend Pullback to 20 EMA (M15).
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Concept: In strong trending environments (ADX>30, rising), enter on pullbacks
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to the 20 EMA when RSI(9) has reset to a neutral zone, confirmed by a
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candle closing in the trend direction.
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Entry conditions (ALL must be true):
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1. Session filter: 08:00-16:00 UTC
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2. Strong trend: ADX(14) > 30 and rising
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3. Trend direction: EMA(20) > EMA(50) for uptrend (opposite for downtrend)
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4. HTF alignment: close above/below EMA(200)
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5. Pullback to EMA(20): price within 0.3x ATR(14)
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6. Candle confirmation: close in trend direction
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7. RSI(9) reset: 40-55 for longs, 45-60 for shorts
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8. Volume declining during pullback
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Exit:
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- SL: Tighter of EMA(50) and 1.5x ATR(10)
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- TP1: 1.0x risk (50%), TP2: 1.8x risk (50%)
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- Trail: 1.0x ATR after TP1
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- Time stop: 40 bars (~10 hours on M15)
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Target pairs: USD_JPY, GBP_USD, GBP_JPY
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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 S11_ADX_TrendPullback(BaseStrategy):
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strategy_id = 11
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name = "S11_ADX_TrendPullback"
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# Tunable parameters
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MIN_ADX = 30 # Strong trend gate
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EMA_PROXIMITY_ATR = 0.3 # Pullback within 0.3x ATR of EMA(20)
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SL_ATR_MULT = 1.5 # SL: 1.5x ATR(10) or beyond EMA(50)
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TP_RR_MULT = 1.8 # TP2: 1.8x risk distance
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RSI9_LONG_LOW = 40 # RSI(9) reset zone for longs
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RSI9_LONG_HIGH = 55
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RSI9_SHORT_LOW = 45 # RSI(9) reset zone for shorts
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RSI9_SHORT_HIGH = 60
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MAX_BARS = 40 # ~10 hours on M15
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TRAIL_ATR_MULT = 1.0 # Trail after 1x risk profit
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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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# Warmup
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if idx < 50:
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return None
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# Session filter: 08:00-16:00 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 >= 16:
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return None
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# ADX gate: strong and rising
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adx_val = current.get("adx_14", 0)
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if np.isnan(adx_val) or adx_val <= self.MIN_ADX:
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return None
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prev_adx = data["adx_14"].iloc[idx - 1] if idx > 0 else 0
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if np.isnan(prev_adx) or adx_val <= prev_adx:
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return None # ADX must be rising
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# EMA values
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ema_20 = current.get("ema_20", np.nan)
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ema_50 = current.get("ema_50", np.nan)
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if np.isnan(ema_20) or np.isnan(ema_50):
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return None
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# Trend direction from EMA alignment
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uptrend = ema_20 > ema_50
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downtrend = ema_20 < ema_50
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if not uptrend and not downtrend:
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return None
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# HTF alignment required
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if htf_row is None:
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return None
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htf_close = htf_row.get("close", np.nan)
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htf_ema200 = htf_row.get("ema_200", np.nan)
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if np.isnan(htf_close) or np.isnan(htf_ema200):
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return None
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if uptrend and htf_close <= htf_ema200:
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return None
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if downtrend and htf_close >= htf_ema200:
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return None
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price = current["close"]
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open_price = current["open"]
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atr_14 = current.get("atr_14", 0)
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atr_10 = current.get("atr_10", 0)
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if atr_14 <= 0 or np.isnan(atr_14) or atr_10 <= 0 or np.isnan(atr_10):
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return None
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# Pullback proximity: price near EMA(20)
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if abs(price - ema_20) > self.EMA_PROXIMITY_ATR * atr_14:
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return None
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# RSI(9) reset check
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rsi9 = current.get("rsi_9", 50)
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if np.isnan(rsi9):
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return None
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# Volume declining during pullback
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vol = current.get("volume", 0)
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vol_window = data["volume"].iloc[max(0, idx - 3):idx]
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if len(vol_window) == 0:
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return None
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vol_avg = vol_window.mean()
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if vol_avg <= 0 or vol >= vol_avg:
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return None
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||||
|
||||
direction = None
|
||||
|
||||
if uptrend:
|
||||
# Candle closes bullish
|
||||
if price <= open_price:
|
||||
return None
|
||||
# RSI(9) in reset zone for longs
|
||||
if self.RSI9_LONG_LOW <= rsi9 <= self.RSI9_LONG_HIGH:
|
||||
direction = "LONG"
|
||||
elif downtrend:
|
||||
# Candle closes bearish
|
||||
if price >= open_price:
|
||||
return None
|
||||
# RSI(9) in reset zone for shorts
|
||||
if self.RSI9_SHORT_LOW <= rsi9 <= self.RSI9_SHORT_HIGH:
|
||||
direction = "SHORT"
|
||||
|
||||
if direction is None:
|
||||
return None
|
||||
|
||||
# Confluence scoring
|
||||
confluence = 3 # Base: ADX strong + pullback to EMA + RSI reset
|
||||
# HTF alignment already confirmed above
|
||||
confluence += 1
|
||||
if adx_val > 40:
|
||||
confluence += 1 # Very strong trend
|
||||
# Volume spike on confirmation candle (relative to longer window)
|
||||
vol_avg_20 = data["volume"].iloc[max(0, idx - 20):idx].mean()
|
||||
if vol_avg_20 > 0 and vol > 1.5 * vol_avg_20:
|
||||
confluence += 1
|
||||
|
||||
# Exit levels
|
||||
if direction == "LONG":
|
||||
sl_ema = ema_50
|
||||
sl_atr = price - self.SL_ATR_MULT * atr_10
|
||||
sl = max(sl_ema, sl_atr) # Tighter: whichever is closer to price
|
||||
risk = price - sl
|
||||
if risk <= 0:
|
||||
return None
|
||||
tp1 = price + 1.0 * risk # Breakeven target
|
||||
tp2 = price + self.TP_RR_MULT * risk # Full target
|
||||
else:
|
||||
sl_ema = ema_50
|
||||
sl_atr = price + self.SL_ATR_MULT * atr_10
|
||||
sl = min(sl_ema, sl_atr) # Tighter: whichever is closer to price
|
||||
risk = sl - price
|
||||
if risk <= 0:
|
||||
return None
|
||||
tp1 = price - 1.0 * risk
|
||||
tp2 = price - self.TP_RR_MULT * risk
|
||||
|
||||
return {
|
||||
"direction": direction,
|
||||
"sl": sl,
|
||||
"tp1": tp1,
|
||||
"tp2": tp2,
|
||||
"tp3": tp2,
|
||||
"confluence": confluence,
|
||||
"entry_pattern": f"adx_trend_pullback_{direction.lower()}",
|
||||
"tp_splits": (0.50, 0.50, 0.0),
|
||||
"trail_atr_mult": self.TRAIL_ATR_MULT,
|
||||
"max_bars": self.MAX_BARS,
|
||||
}
|
||||
Reference in New Issue
Block a user