harmonic-pattern-engine
Harmonic pattern detection — Gartley, Butterfly, Bat, Crab, Cypher, Shark with Fibonacci ratio validation. Use for "harmonic pattern", "Gartley", "Butterfly pattern", "Bat pattern", "Crab pattern", "Cypher", "XABCD", "harmonic trading", "Scott Carney", or any harmonic analysis. Works with fibonacci-strategy-engine and chart-pattern-scanner.
Informations de source
- Dépôt
- mahmoud20138/Tradecraft
- Dernière activité de la source
- 23 avril 2026 à 08:40
- Langue détectée de SKILL.md
- anglais
- Étoiles
- 15
- Forks
- 4
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Le prompt qui vérifie d'abord la source est sélectionné par défaut. Vous pouvez passer à une commande directe ou télécharger une copie locale.
Vérifiez les fichiers source
Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.
Affichage de SKILL.md
SKILL.md
Instructions source · Aperçu en lecture seule- name
- harmonic-pattern-engine
- description
- Harmonic pattern detection — Gartley, Butterfly, Bat, Crab, Cypher, Shark with Fibonacci ratio validation. Use for "harmonic pattern", "Gartley", "Butterfly pattern", "Bat pattern", "Crab pattern", "Cypher", "XABCD", "harmonic trading", "Scott Carney", or any harmonic analysis. Works with fibonacci-strategy-engine and chart-pattern-scanner.
- kind
- engine
- category
- trading/analysis
- status
- active
- tags
- ["analysis","engine","fibonacci","harmonic","harmonics","pattern","trading"]
- related_skills
- ["elliott-wave-engine","fibonacci-harmonic-wave"]
# Harmonic Pattern Engine
```python
import pandas as pd, numpy as np
from scipy.signal import argrelextrema
HARMONIC_RATIOS = {
"gartley": {"XB": (0.618, 0.618), "AC": (0.382, 0.886), "BD": (1.272, 1.618), "XD": (0.786, 0.786)},
"butterfly": {"XB": (0.786, 0.786), "AC": (0.382, 0.886), "BD": (1.618, 2.618), "XD": (1.272, 1.618)},
"bat": {"XB": (0.382, 0.500), "AC": (0.382, 0.886), "BD": (1.618, 2.618), "XD": (0.886, 0.886)},
"crab": {"XB": (0.382, 0.618), "AC": (0.382, 0.886), "BD": (2.240, 3.618), "XD": (1.618, 1.618)},
"cypher": {"XB": (0.382, 0.618), "AC": (1.130, 1.414), "BD": (1.272, 2.000), "XD": (0.786, 0.786)},
}
class HarmonicEngine:
@staticmethod
def detect_xabcd(df: pd.DataFrame, tolerance: float = 0.05) -> list[dict]:
"""Detect XABCD harmonic patterns from swing points."""
highs = argrelextrema(df["high"].values, np.greater, order=5)[0]
lows = argrelextrema(df["low"].values, np.less, order=5)[0]
swings = []
for i in highs: swings.append({"idx": i, "price": df["high"].iloc[i], "type": "H"})
for i in lows: swings.append({"idx": i, "price": df["low"].iloc[i], "type": "L"})
swings.sort(key=lambda s: s["idx"])
patterns = []
for i in range(len(swings) - 4):
X, A, B, C, D = [swings[j]["price"] for j in range(i, i + 5)]
XA = abs(A - X)
if XA == 0: continue
AB = abs(B - A)
BC = abs(C - B)
CD = abs(D - C)
XB_ratio = AB / XA
AC_ratio = BC / AB if AB > 0 else 0
BD_ratio = CD / BC if BC > 0 else 0
XD_ratio = abs(D - X) / XA
for name, ratios in HARMONIC_RATIOS.items():
xb_min, xb_max = ratios["XB"][0] - tolerance, ratios["XB"][1] + tolerance
xd_min, xd_max = ratios["XD"][0] - tolerance, ratios["XD"][1] + tolerance
if xb_min <= XB_ratio <= xb_max and xd_min <= XD_ratio <= xd_max:
bullish = D < X if swings[i]["type"] == "L" else D > X
patterns.append({
"pattern": name,
"bullish": bullish,
"X": round(X, 5), "A": round(A, 5), "B": round(B, 5), "C": round(C, 5), "D": round(D, 5),
"XB": round(XB_ratio, 3), "XD": round(XD_ratio, 3),
"prz": round(D, 5),
"signal": f"{'BUY' if bullish else 'SELL'} at PRZ {round(D, 5)}",
"stop": round(X, 5),
"tp1": round(D + (A - D) * 0.382, 5) if bullish else round(D - (D - A) * 0.382, 5),
"tp2": round(D + (A - D) * 0.618, 5) if bullish else round(D - (D - A) * 0.618, 5),
})
return patterns
```
Voir sur GitHub