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Phase 1 complete: S3-S6 strategies, S4 variant analysis, learnings doc
- 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
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
dce54845c2
commit
edbe359d1b
@@ -4,12 +4,14 @@ Strategy 3: Key Level Momentum Breakout.
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Entry TF: H1. Key levels identified from swing point clusters.
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Entry conditions (LONG):
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- H1 candle closes above a key level (horizontal S/R with 2+ touches)
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- H1 candle closes above a key level (horizontal S/R with 3+ touches)
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- Volume spike: current volume > 1.5x 20-bar average
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- Strong close: candle body > 50% of range (conviction candle)
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- MACD histogram same sign as direction
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- ADX > 15
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- Candle body > 30% of range (conviction candle)
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- ADX > 20 (trending market)
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- Session: London + NY overlap (08:00-16:00 UTC)
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SL: Back inside key level + 1x ATR buffer
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SL: Back inside key level — level_price -/+ 0.5x ATR
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TP1: 1.5x ATR, TP2: 2.5x ATR, TP3: 4x ATR
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"""
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from typing import Optional
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@@ -24,6 +26,7 @@ class S3_KeyLevel_Breakout(BaseStrategy):
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name = "S3_Key_Level_Breakout"
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def __init__(self):
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super().__init__()
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self._cached_levels = None
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self._cache_idx = -1
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@@ -33,44 +36,51 @@ class S3_KeyLevel_Breakout(BaseStrategy):
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if idx < 100:
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return None
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# Session filter: London/NY only (08:00-17:00 UTC)
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# Session filter: London + NY overlap (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 >= 17:
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if hour < 8 or hour >= 16:
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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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# ADX filter: require trending market
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adx_val = current.get("adx_14", 0)
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if adx_val < 20:
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return None
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# Strong close: candle body > 50% of range
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close = current["close"]
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body = abs(close - current["open"])
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full_range = current["high"] - current["low"]
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if full_range <= 0 or body / full_range < 0.50:
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return None
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# Volume spike: current volume > 1.5x 20-bar average
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vol = current.get("volume", 0)
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if vol > 0 and idx >= 20:
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vol_avg = data["volume"].iloc[idx - 20:idx].mean()
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if vol_avg > 0 and vol < 1.5 * vol_avg:
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return None
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prev_close = data.iloc[idx - 1]["close"]
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# MACD
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macd_h = current.get("macd_hist", 0)
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# Recalculate key levels every 20 bars using larger lookback
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if self._cached_levels is None or idx - self._cache_idx >= 20:
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start = max(0, idx - 1000)
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window = data.iloc[start:idx] # exclude current bar
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self._cached_levels = identify_key_levels(
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window, lookback=5, tolerance_atr_mult=0.75, min_touches=2
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window, lookback=5, tolerance_atr_mult=0.75, min_touches=3
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)
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self._cache_idx = idx
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if not self._cached_levels:
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return None
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close = current["close"]
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prev_close = data.iloc[idx - 1]["close"]
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# Candle body filter
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body = abs(close - current["open"])
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full_range = current["high"] - current["low"]
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if full_range <= 0 or body / full_range < 0.3:
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return None
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# MACD
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macd_h = current.get("macd_hist", 0)
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# ADX
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adx_val = current.get("adx_14", 0)
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if adx_val < 15:
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return None
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for level_price, touch_count in self._cached_levels:
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tolerance = 0.3 * atr_val
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@@ -85,9 +95,10 @@ class S3_KeyLevel_Breakout(BaseStrategy):
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if ema_50 and ema_200 and ema_50 <= ema_200:
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continue
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confluence = self._calc_confluence(current, data, idx, "LONG", touch_count)
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confluence = self._calc_confluence(current, data, idx,
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"LONG", touch_count, vol)
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sl = level_price - 0.3 * atr_val # Tight SL just inside key level
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sl = level_price - 0.5 * atr_val
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tp1 = close + 1.5 * atr_val
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tp2 = close + 2.5 * atr_val
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tp3 = close + 4.0 * atr_val
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@@ -96,6 +107,7 @@ class S3_KeyLevel_Breakout(BaseStrategy):
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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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"entry_pattern": "key_level_break",
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"tp_splits": (0.40, 0.40, 0.20),
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"trail_atr_mult": 2.0,
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"max_bars": 150,
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@@ -112,9 +124,10 @@ class S3_KeyLevel_Breakout(BaseStrategy):
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if ema_50 and ema_200 and ema_50 >= ema_200:
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continue
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confluence = self._calc_confluence(current, data, idx, "SHORT", touch_count)
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confluence = self._calc_confluence(current, data, idx,
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"SHORT", touch_count, vol)
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sl = level_price + 0.3 * atr_val # Tight SL just inside key level
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sl = level_price + 0.5 * atr_val
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tp1 = close - 1.5 * atr_val
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tp2 = close - 2.5 * atr_val
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tp3 = close - 4.0 * atr_val
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@@ -123,6 +136,7 @@ class S3_KeyLevel_Breakout(BaseStrategy):
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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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"entry_pattern": "key_level_break",
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"tp_splits": (0.40, 0.40, 0.20),
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"trail_atr_mult": 2.0,
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"max_bars": 150,
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@@ -130,7 +144,7 @@ class S3_KeyLevel_Breakout(BaseStrategy):
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return None
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def _calc_confluence(self, current, data, idx, direction, touch_count):
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def _calc_confluence(self, current, data, idx, direction, touch_count, vol):
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confluence = 1 # breakout confirmed
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# More touches = stronger level
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@@ -139,18 +153,16 @@ class S3_KeyLevel_Breakout(BaseStrategy):
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if touch_count >= 5:
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confluence += 1
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# Volume spike strength (>2x avg = extra point)
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if vol > 0 and idx >= 20:
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vol_avg = data["volume"].iloc[idx - 20:idx].mean()
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if vol_avg > 0 and vol > 2.0 * vol_avg:
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confluence += 1
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rsi = current.get("rsi_14", 50)
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if direction == "LONG" and 50 < rsi < 75:
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confluence += 1
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elif direction == "SHORT" and 25 < rsi < 50:
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confluence += 1
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ema_50 = current.get("ema_50", 0)
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ema_200 = current.get("ema_200", 0)
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if ema_50 and ema_200:
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if direction == "LONG" and ema_50 > ema_200:
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confluence += 1
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elif direction == "SHORT" and ema_50 < ema_200:
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confluence += 1
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return min(confluence, 5)
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