gap-trading-strategy
Gap trading — opening gaps, gap fill probability, gap-and-go, fade the gap. Use for "gap trading", "opening gap", "gap fill", "gap and go", "fade the gap", "Sunday gap", "weekend gap", "gap statistics", "gap probability", or any gap-based trading. Works with session-profiler.
소스 정보
- 저장소
- mahmoud20138/Tradecraft
- 최근 소스 활동
- 2026년 4월 23일 08:40
- 감지된 SKILL.md 언어
- 영어
- 스타
- 15
- 포크
- 4
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SKILL.md
소스 지침 · 읽기 전용 미리보기- name
- gap-trading-strategy
- description
- Gap trading — opening gaps, gap fill probability, gap-and-go, fade the gap. Use for "gap trading", "opening gap", "gap fill", "gap and go", "fade the gap", "Sunday gap", "weekend gap", "gap statistics", "gap probability", or any gap-based trading. Works with session-profiler.
- kind
- strategy
- category
- trading/strategies
- status
- active
- tags
- ["gap","strategies","strategy","trading"]
- related_skills
- ["jdub-price-action-strategy","session-scalping","asian-session-scalper","grid-trading-engine","session-profiler"]
# Gap Trading Strategy
```python
import pandas as pd, numpy as np
class GapTradingStrategy:
@staticmethod
def detect_gaps(df: pd.DataFrame, min_gap_atr: float = 0.5) -> list[dict]:
atr = (df["high"] - df["low"]).rolling(14).mean()
gaps = []
for i in range(1, len(df)):
gap = df.iloc[i]["open"] - df.iloc[i-1]["close"]
if abs(gap) > min_gap_atr * atr.iloc[i]:
filled = False
if gap > 0: # Gap up
filled = (df.iloc[i:min(i+20, len(df))]["low"].min() <= df.iloc[i-1]["close"])
else: # Gap down
filled = (df.iloc[i:min(i+20, len(df))]["high"].max() >= df.iloc[i-1]["close"])
gaps.append({
"time": df.index[i], "gap_pips": round(gap * 10000, 1),
"direction": "up" if gap > 0 else "down",
"gap_atr": round(abs(gap) / atr.iloc[i], 2),
"filled_within_20_bars": filled,
})
return gaps
@staticmethod
def gap_fill_statistics(df: pd.DataFrame) -> dict:
gaps = GapTradingStrategy.detect_gaps(df)
if not gaps: return {"n_gaps": 0}
fill_rate = sum(1 for g in gaps if g["filled_within_20_bars"]) / len(gaps)
up_gaps = [g for g in gaps if g["direction"] == "up"]
down_gaps = [g for g in gaps if g["direction"] == "down"]
return {
"n_gaps": len(gaps),
"fill_rate_pct": round(fill_rate * 100, 1),
"up_gap_fill_rate": round(sum(1 for g in up_gaps if g["filled_within_20_bars"]) / max(len(up_gaps), 1) * 100, 1),
"down_gap_fill_rate": round(sum(1 for g in down_gaps if g["filled_within_20_bars"]) / max(len(down_gaps), 1) * 100, 1),
"avg_gap_size_pips": round(np.mean([abs(g["gap_pips"]) for g in gaps]), 1),
"strategy": "FADE THE GAP" if fill_rate > 0.65 else "GAP AND GO" if fill_rate < 0.40 else "MIXED — use confirmation",
"note": f"Gaps fill {fill_rate*100:.0f}% of the time within 20 bars on this pair",
}
@staticmethod
def sunday_gap_trade(friday_close: float, sunday_open: float, atr: float) -> dict:
gap = sunday_open - friday_close
return {
"strategy": "sunday_gap_fade",
"gap_pips": round(gap * 10000, 1),
"direction": "SELL (fade gap up)" if gap > 0 else "BUY (fade gap down)",
"entry": round(sunday_open, 5),
"target": round(friday_close, 5),
"stop": round(sunday_open + (gap * 0.5 if gap > 0 else gap * 0.5), 5),
"note": "Sunday gaps fill ~70% of the time. Use small size due to wide spreads.",
}
```
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