strategy-tuning

name	strategy-tuning
description	Guidelines and historical learnings for tuning individual trading strategies. Use when modifying any strategy file in backtest/strategies/ to understand what has been tried, what worked, and what didn't.

Strategy Tuning

General Rules

1. ATR-based parameters over fixed values:

   • SL: 1.0 × ATR as default, widen to 1.5 × ATR if too many stop-outs
   • TP: 2.0 × ATR as minimum, 3.0 × ATR for trending conditions
   • Buffer/entry filters: 0.3 - 0.5 × ATR
   • Use self._atr_buf(df, i-1, multiplier) from BaseStrategy

2. R:R ratio: Target minimum 1:2 (risk:reward). 1:3 for strategies with lower win rates. Never go below 1:1.5.

3. Lookback periods: Shorter lookbacks capture more trades but increase noise. Start at 10-20 and adjust based on signal quality.

4. Zero-trade strategies: If a strategy makes 0 trades, relax entry conditions or reduce lookback. Check that the indicator values are being computed correctly.

Historical Tuning Log

Run 1 (Initial, no changes)

• 20 strategies, all with various original parameters
• Portfolio: net_profit = -$50,525, WR = 62.4%, PF = 0.46, max DD = -51.1%
• Only ORB_Hybrid and ICT_Killzone_Macro were profitable

Run 2 (First batch — easier parameter tweaks)

• SR_Breakout_Retest: lookback 40→5, buffer 0.5→1.0×ATR, relaxed retest condition
• ADX_TrendStrength: ADX threshold 25→20
• Donchian_Breakout: period 20→10, EMA 50→20
• Liquidity Sweep FVG: fixed threshold→ATR-based 0.5×
• ORB_Session: orb_duration 30→15 min
• Result: still ~$50K loss, minimal change

Run 3 (Second batch — ATR-based SL/TP for ALL strategies)

• Added ATR-based SL/TP to: AVWAP_Confluence, PDH_PDL_BreakReversal, Asian_Range_Fakeout, VWAP_MultiBand_Regime, Volatility_Squeeze, PriceAction_SR, NY_Session_Reversal, Smart_Swing_Bias, Liquidity_Sweep_Breakout, SuperTrend_EMA, TrendPullback, Bollinger_MR
• Results: Smart_Swing_Bias and PriceAction_SR got worse; Bollinger_MR stopped trading
• Net: -$50,055 (+$470 improvement) — essentially no change

Run 4 (Third batch — fix strategies that got worse)

• Smart_Swing_Bias: SL widened to 1.5×ATR, TP to 3.0×ATR
• PriceAction_SR: SL to 1.0×ATR, buffer to 0.5×ATR
• Bollinger_MR: SL to 1.5×ATR, TP to 3.0×ATR
• Liquidity Sweep FVG: ATR-based SL/TP (1:2 R:R)
• SR_Breakout_Retest: lookback 5→10, buffer 1.0→0.3×ATR
• Donchian_Breakout: period 10→5, EMA 20→10, ATR-based SL/TP, start_idx 50→20
• Result: -$50,701, WR 62.5%, PF 0.46 — identical to Run 1

Key Insight

After 4 runs and 20+ strategy modifications, the portfolio metrics barely changed: net_profit hovered between -$50K and -$50.7K (±1%). This means:

Strategy parameter tuning has reached diminishing returns.

The core problem is NOT individual strategy parameters but portfolio-level:

1. All strategies lose during Q1-Q2 2022 (Fed hiking cycle, Ukraine war)
2. The portfolio dies by May 2022 and never recovers
3. Correlation between losing strategies amplifies the drawdown

Next Steps (Not Yet Attempted)

1. Portfolio-level circuit breaker: Stop trading when DD exceeds 30%, resume only when conditions improve
2. Dynamic position sizing: Scale down as DD increases, scale up when equity grows
3. Market regime filter: Skip trading during high-volatility regimes (2022-style)
4. Strategy selection: Drop the worst 3-5 strategies, increase allocation to the best 2
5. Walk-forward optimization: Test parameters on rolling windows instead of full history