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Phase 1 complete: S3-S6 strategies, S4 variant analysis, learnings doc

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- S3 Key Level Breakout: best performer (52-53% WR, PF ~1.0 on JPY crosses)
- S4 EMA Ribbon: tested 7 variants (D/E/F/F-v2/G/G-Minimal), exhausted
  - Only EUR_AUD S4-F marginally profitable (PF 1.06)
  - Detailed filter funnel analysis revealed contradictory filter stacking
- S5 Momentum Exhaustion: extended to 5 pairs, PF 0.43-0.77
- S6 EMA Bounce: 59-60% WR but PF 0.83-0.84, needs SL/TP restructuring
- Added STRATEGY_LEARNINGS.md with design principles and next steps
- Added M5 data downloader for 3-timeframe strategies
- Updated README with full strategy scorecard

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Brent Neale
2026-02-18 20:42:16 +10:00
parent dce54845c2
commit edbe359d1b
88 changed files with 12570 additions and 2963 deletions
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src/backtester/engine.py
+30 -11
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@@ -68,6 +68,7 @@ class Position:
confluence_score: int = 0
signal_features: dict = field(default_factory=dict)
realized_pnl: float = 0.0 # Tracks PnL from partial closes
no_breakeven: bool = False # When True, don't move SL to breakeven after TP1
@dataclass
@@ -99,6 +100,7 @@ class TradeRecord:
candle_body_ratio: float = 0.0
hour_of_day: int = 0
day_of_week: int = 0
entry_pattern: str = ""
exit_price: float = 0.0
exit_reason: str = ""
exit_time: pd.Timestamp = None
@@ -168,12 +170,12 @@ class Backtester:
return False
def _calculate_position_size(self, sl_distance: float,
confluence_score: int) -> float:
"""Fixed 1% risk position sizing."""
confluence_score: int,
risk_pct: float = 0.01) -> float:
"""Risk-based position sizing (default 1%, strategies can override)."""
if sl_distance <= 0:
return 0.0
risk_pct = 0.01 # Flat 1% risk for consistency
risk_amount = self.equity * risk_pct
# Position size = risk_amount / SL distance in price
position_size = risk_amount / sl_distance
@@ -238,14 +240,19 @@ class Backtester:
close_size = pos.initial_size * pos.tp_splits[0]
self._partial_close(pos, pos.tp1_price, close_size, "TP1", candle)
pos.tp1_hit = True
# Move SL to breakeven after TP1
pos.trailing_sl = pos.entry_price
if not pos.no_breakeven:
# Move SL to breakeven after TP1
pos.trailing_sl = pos.entry_price
# Check TP2
if not pos.tp2_hit and pos.tp1_hit and high >= pos.tp2_price:
close_size = pos.initial_size * pos.tp_splits[1]
self._partial_close(pos, pos.tp2_price, close_size, "TP2", candle)
if close_size > 0:
self._partial_close(pos, pos.tp2_price, close_size, "TP2", candle)
pos.tp2_hit = True
# If no breakeven was set, start trailing from original SL
if pos.trailing_sl is None:
pos.trailing_sl = pos.sl_price
# Check TP3
if not pos.tp3_hit and pos.tp2_hit and high >= pos.tp3_price:
@@ -271,13 +278,17 @@ class Backtester:
close_size = pos.initial_size * pos.tp_splits[0]
self._partial_close(pos, pos.tp1_price, close_size, "TP1", candle)
pos.tp1_hit = True
pos.trailing_sl = pos.entry_price
if not pos.no_breakeven:
pos.trailing_sl = pos.entry_price
# Check TP2
if not pos.tp2_hit and pos.tp1_hit and low <= pos.tp2_price:
close_size = pos.initial_size * pos.tp_splits[1]
self._partial_close(pos, pos.tp2_price, close_size, "TP2", candle)
if close_size > 0:
self._partial_close(pos, pos.tp2_price, close_size, "TP2", candle)
pos.tp2_hit = True
if pos.trailing_sl is None:
pos.trailing_sl = pos.sl_price
# Check TP3
if not pos.tp3_hit and pos.tp2_hit and low <= pos.tp3_price:
@@ -391,6 +402,7 @@ class Backtester:
candle_body_ratio=features.get("candle_body_ratio", 0),
hour_of_day=features.get("hour_of_day", 0),
day_of_week=features.get("day_of_week", 0),
entry_pattern=features.get("entry_pattern", ""),
exit_price=exit_price,
exit_reason=exit_detail,
exit_time=exit_time,
@@ -433,6 +445,9 @@ class Backtester:
def run(self) -> dict:
"""Run the backtest. Returns performance report dict."""
# Give the strategy access to full HTF data (strategy filters by timestamp)
self.strategy.htf_data = self.htf_data
# Need at least 200 bars for indicators to warm up
warmup = 200
@@ -479,12 +494,14 @@ class Backtester:
tp_splits = signal.get("tp_splits", (0.40, 0.40, 0.20))
trail_mult = signal.get("trail_atr_mult", 1.5)
max_bars = signal.get("max_bars", 200)
no_breakeven = signal.get("no_breakeven", False)
risk_pct = signal.get("risk_pct", 0.01)
# Minimum 1.5:1 RR check (TP1 vs SL distance)
# Minimum RR check (TP1 vs SL distance)
entry = candle["close"]
sl_dist = abs(entry - sl)
tp1_dist = abs(tp1 - entry)
if sl_dist == 0 or tp1_dist / sl_dist < 1.5:
if sl_dist == 0 or tp1_dist / sl_dist < 0.5:
continue
# Apply spread and slippage to entry
@@ -498,12 +515,13 @@ class Backtester:
continue
# Position sizing
size = self._calculate_position_size(sl_dist_adj, confluence)
size = self._calculate_position_size(sl_dist_adj, confluence, risk_pct)
if size <= 0:
continue
# Build features for logging
features = self._build_signal_features(candle, i)
features["entry_pattern"] = signal.get("entry_pattern", "")
# Open position
pos = Position(
@@ -522,6 +540,7 @@ class Backtester:
strategy_id=self.strategy.strategy_id,
confluence_score=confluence,
signal_features=features,
no_breakeven=no_breakeven,
)
self.open_positions.append(pos)