Fibonacci Harmonic Wave

Fibonacci, Harmonic Patterns & Elliott Wave Engine

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Section 1: Fibonacci Analysis

Core Fibonacci Levels Reference

RETRACEMENT LEVELS:
  23.6% → Minor support/resistance (weak)
  38.2% → Moderate pullback level
  50.0% → Psychological midpoint (widely watched, not true Fibonacci)
  61.8% → "Golden Ratio" — MOST IMPORTANT level
  78.6% → Deep retracement (= √0.618)
  88.6% → Very deep (= √0.786); used in harmonic patterns

EXTENSION LEVELS (profit targets):
  127.2% = 1st extension (= √1.272)
  138.2%
  161.8% = Most common major target
  200.0% = Double the prior move
  261.8% = Strong extension target

Entry Strategy:
  Conservative: Wait for price to react at level + candle confirmation
  Aggressive: Enter directly at level with tight stop

Stop Loss: Just beyond next Fibonacci level (e.g., short at 61.8%, stop above 78.6%)

Extensions — How to Draw:
  Uptrend: From swing low (A) to swing high (B) to retracement low (C)
  Target = C + (A to B distance × extension %)

Fibonacci Time Zones

After swing high or low, count forward:
Bars 1, 2, 3, 5, 8, 13, 21, 34, 55, 89...
→ Significant reactions likely at these time intervals

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Fibonacci Strategy Engine (Code)

import pandas as pd, numpy as np
from scipy.signal import argrelextrema

FIB_LEVELS = [0, 0.236, 0.382, 0.5, 0.618, 0.786, 1.0]
FIB_EXTENSIONS = [1.0, 1.272, 1.414, 1.618, 2.0, 2.618]

class FibonacciEngine:

    @staticmethod
    def retracement(swing_high: float, swing_low: float, direction: str = "up") -> dict:
        diff = swing_high - swing_low
        levels = {}
        for fib in FIB_LEVELS:
            if direction == "up":
                levels[f"{fib:.3f}"] = round(swing_high - fib * diff, 5)
            else:
                levels[f"{fib:.3f}"] = round(swing_low + fib * diff, 5)
        return {
            "direction": direction, "swing_high": swing_high, "swing_low": swing_low,
            "levels": levels,
            "golden_zone": f"{levels['0.618']} — {levels['0.786']}",
            "strategy": "Buy at 0.618-0.786 in uptrend, sell at 0.618-0.786 in downtrend",
        }

    @staticmethod
    def extension(point_a: float, point_b: float, point_c: float) -> dict:
        diff = abs(point_b - point_a)
        direction = 1 if point_b > point_a else -1
        levels = {}
        for ext in FIB_EXTENSIONS:
            levels[f"{ext:.3f}"] = round(point_c + direction * diff * ext, 5)
        return {"extensions": levels, "primary_target": levels["1.618"]}

    @staticmethod
    def auto_fib(df: pd.DataFrame, order: int = 10) -> dict:
        """Automatically detect last major swing and compute fibs."""
        highs = argrelextrema(df["high"].values, np.greater, order=order)[0]
        lows = argrelextrema(df["low"].values, np.less, order=order)[0]
        if len(highs) == 0 or len(lows) == 0:
            return {"error": "No swings found"}
        last_high = df["high"].iloc[highs[-1]]
        last_low = df["low"].iloc[lows[-1]]
        direction = "up" if lows[-1]  list:
        """Find confluence zones where multiple fib levels cluster together."""
        all_levels = []
        for fib_set in fibs_list:
            for level_name, price in fib_set.get("levels", {}).items():
                all_levels.append(price)
        all_levels.sort()
        clusters = []
        i = 0
        while i = 2:
                clusters.append({
                    "zone_center": round(np.mean(cluster), 5),
                    "zone_width": round(max(cluster) - min(cluster), 5),
                    "n_fibs_confluent": len(cluster),
                    "strength": "STRONG" if len(cluster) >= 3 else "MODERATE",
                })
            i += 1
        return sorted(clusters, key=lambda c: c["n_fibs_confluent"], reverse=True)

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Section 2: Harmonic Patterns

Harmonic Pattern Reference Table

| Pattern | XB Ratio | AC Ratio | BD Ratio | XD Ratio (PRZ) | Reliability | |---------|----------|----------|----------|-----------------|-------------| | Gartley | 0.618 | 0.382–0.886 | 1.272–1.618 | 0.786 | 65–70% | | Butterfly | 0.786 | 0.382–0.886 | 1.618–2.618 | 1.272–1.618 | 70–75% | | Bat | 0.382–0.500 | 0.382–0.886 | 1.618–2.618 | 0.886 | 72–78% | | Crab | 0.382–0.618 | 0.382–0.886 | 2.240–3.618 | 1.618 | 65–70% | | Cypher | 0.382–0.618 | 1.130–1.414 | 1.272–2.000 | 0.786 | 70–75% | | Shark | any | 1.130 ext | 0.886 | 0.886 of 0X | 60–65% |

ABCD Pattern

AB = CD (time and price symmetry)
BC: 61.8% or 78.6% of AB
CD: 127.2% or 161.8% of BC

Bullish ABCD: Buy at D | Bearish ABCD: Sell at D
Stop: Beyond D by structure | Target: B level (38.2% and 61.8% of AD)

Gartley

XA: Initial move | AB: 61.8% retrace of XA
BC: 38.2–88.6% retrace of AB | CD: 78.6% retrace of XA (PRZ)
Bullish: Buy at D (78.6% of XA) | Stop: Below X
Target 1: 61.8% of CD | Target 2: 127.2% of CD

Butterfly

AB: 78.6% retrace of XA
CD: 127.2% OR 161.8% extension of XA (beyond X — D extends past X)
PRZ: 127.2–161.8% of XA | Reliability: 70–75%

Bat

AB: 38.2–50% retrace of XA (key differentiator from Gartley)
CD: 88.6% retrace of XA (PRZ) | Reliability: 72–78% | Tighter stops

Crab

AB: 38.2–61.8% retrace of XA
CD: 161.8% extension of XA (deepest extension) | PRZ: 161.8% of XA
Reliability: 65–70% | Use tight stops

Cypher

AB: 38.2–61.8% retrace of XA
BC: 127.2–141.4% extension of XA
CD: 78.6% retrace of XC (PRZ) | Reliability: 70–75%

Harmonic Pattern Trading Rules

Entry: At PRZ (Potential Reversal Zone)
Confirmation: Wait for reversal candle at PRZ
Stop: Beyond the extreme of pattern (D or X)
Target 1: 38.2% retrace of CD
Target 2: 61.8% retrace of CD
Target 3: Full retracement to AB or beyond
Risk: 1–1.5% per harmonic trade | Best timeframes: 1H, 4H, Daily

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Harmonic Pattern Engine (Code)

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:
        """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
            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  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

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Section 3: Elliott Wave Theory

Elliott Wave Rules (MUST be satisfied)

5-wave Impulse:
  Rule 1: Wave 2 NEVER retraces more than 100% of Wave 1
  Rule 2: Wave 3 is NEVER the shortest impulse wave
  Rule 3: Wave 4 NEVER overlaps into Wave 1 price territory
           (Exception: Diagonal triangles in Wave 1 or 5)

Elliott Wave Guidelines

Wave 1: Often muted; not widely recognized
Wave 2: Typically 50–61.8% retracement of Wave 1
Wave 3: Longest and strongest; 161.8% of Wave 1 common
Wave 4: Typically 38.2% retracement of Wave 3
Wave 5: Often equals Wave 1 in length; or 61.8% of W1+W3

Corrective Waves (A-B-C):
  Zigzag: Sharp correction (5-3-5)
  Flat: Sideways correction (3-3-5)
  Triangle: Converging correction (3-3-3-3-3)

Fibonacci Relationships in Elliott

| Wave | Typical Fibonacci Relationship | |------|-------------------------------| | Wave 2 | 50%, 61.8% retrace of Wave 1 | | Wave 3 | 161.8%, 261.8% of Wave 1 | | Wave 4 | 38.2% retrace of Wave 3 | | Wave 5 | 61.8% or 100% of Wave 1 | | Wave A | 100% of Wave 5 often | | Wave B | 50–78.6% retrace of Wave A | | Wave C | 100%, 161.8% of Wave A |

Wave Degrees

Grand Supercycle → Supercycle → Cycle → Primary → Intermediate
→ Minor → Minute → Minuette → Sub-Minuette

Each degree contains 5-wave impulse + 3-wave correction nested fractally.
Trade in direction of at least 2 higher-degree waves for highest probability.

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Elliott Wave Engine (Code)

import pandas as pd, numpy as np
from scipy.signal import argrelextrema

class ElliottWaveEngine:

    @staticmethod
    def find_waves(df: pd.DataFrame, order: int = 10) -> dict:
        highs_idx = argrelextrema(df["high"].values, np.greater, order=order)[0]
        lows_idx = argrelextrema(df["low"].values, np.less, order=order)[0]
        swings = []
        for i in highs_idx:
            swings.append({"idx": int(i), "price": df["high"].iloc[i], "type": "high", "time": df.index[i]})
        for i in lows_idx:
            swings.append({"idx": int(i), "price": df["low"].iloc[i], "type": "low", "time": df.index[i]})
        swings.sort(key=lambda s: s["idx"])
        waves = ElliottWaveEngine._classify_impulse(swings)
        return {
            "swings_found": len(swings), "waves": waves,
            "current_wave": waves[-1] if waves else None,
            "note": "Elliott Waves are subjective. Use as confluence, not primary signal.",
        }

    @staticmethod
    def _classify_impulse(swings: list) -> list:
        waves = []
        if len(swings)  s[0]["price"]
            if is_bullish:
                w2_above_w1_start = s[1]["price"] > s[0]["price"]
                waves.append({
                    "type": "impulse_bullish",
                    "wave_1": {"start": round(s[0]["price"], 5), "end": round(s[1]["price"], 5)},
                    "wave_2": {"start": round(s[1]["price"], 5), "end": round(s[2]["price"], 5) if len(s) > 2 else 0},
                    "w2_valid": w2_above_w1_start, "position": i,
                })
        return waves if waves else [{"wave": "no_clear_impulse"}]

    @staticmethod
    def fibonacci_targets(wave_1_start: float, wave_1_end: float, wave_2_end: float) -> dict:
        w1_range = abs(wave_1_end - wave_1_start)
        direction = 1 if wave_1_end > wave_1_start else -1
        return {
            "wave_3_targets": {
                "1.000": round(wave_2_end + direction * w1_range * 1.0, 5),
                "1.618": round(wave_2_end + direction * w1_range * 1.618, 5),
                "2.618": round(wave_2_end + direction * w1_range * 2.618, 5),
            },
            "invalidation": round(wave_1_start, 5),
        }

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Section 4: Combined Workflow

How to Use All Three Together

Step 1 — FIBONACCI: Run auto_fib() to identify key retracement/extension levels
Step 2 — HARMONICS: Run detect_xabcd() to find XABCD patterns forming at fib levels
Step 3 — ELLIOTT: Run find_waves() to confirm wave position and project next wave target
Step 4 — CONFLUENCE: Use cluster_zones() to find where all three methods agree

High-probability setup:
  → Price at Fibonacci golden zone (61.8–78.6%)
  → Harmonic PRZ at same level
  → Elliott Wave 2 or Wave 4 retracement landing there
  → Confirmed by reversal candle

Confluence Scoring

| Signal | Score | |--------|-------| | Fibonacci 61.8% level | +1 | | Fibonacci 78.6% level | +1 | | Fib cluster (3+ levels converging) | +2 | | Harmonic PRZ match | +2 | | Elliott Wave retrace landing zone | +1 | | Multiple pattern type agreement | +2 | | Reversal candle at zone | +1 | | Total ≥ 6 → High-confidence entry | |

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: mahmoud20138
• Source: mahmoud20138/Tradecraft
• License: MIT

Install and usage instructions live in the source repository linked above.