To implement a harmonic trading strategy for intraday trading across multiple timeframes and
instruments, follow this structured approach:
Core Strategy Components
1. Pattern Detection: Identify harmonic patterns (Gartley, Butterfly, Bat, Crab) using
Fibonacci ratios.
2. Timeframe Analysis: Scan 15M, 30M, 1H, and 4H charts for convergence.
3. Instrument Coverage: Apply to EURUSD, Gold, BTC, and GBP crosses.
4. Risk Management: Define stop-loss/take-profit based on pattern structure.
Step-by-Step Implementation
1. Harmonic Pattern Identification
Use Fibonacci ratios to validate patterns. Key ratios:
Gartley: AB = 61.8% of XA, BC = 61.8–78.6% of AB, CD = 127.2–161.8% of BC.
Butterfly: AB = 78.6% of XA, BC = 38.2–88.6% of AB, CD = 161.8–261.8% of BC.
Bat: AB = 38.2–50% of XA, BC = 38.2–88.6% of AB, CD = 161.8–261.8% of BC.
Pseudocode for Gartley Detection:
python
def detect_gartley(highs, lows, tolerance=0.05):
extrema = find_local_extrema(highs, lows) # Get swing highs/lows
for i in range(len(extrema)-4):
if valid_sequence(extrema[i:i+5]): # Check low-high-low-high-low order
X, A, B, C, D = extract_points(extrema[i:i+5])
# Calculate Fibonacci ratios
XA = A['price'] - X['price']
AB = A['price'] - B['price']
BC = C['price'] - B['price']
CD = C['price'] - D['price']
ab_xa = AB / XA
bc_ab = BC / AB
cd_bc = CD / BC
# Validate ratios within tolerance
if (is_near(ab_xa, 0.618, tolerance) or is_near(ab_xa, 0.382, tolerance)) and \
(is_near(bc_ab, 0.618, tolerance) or is_near(bc_ab, 0.786, tolerance)) and \
(is_near(cd_bc, 1.272, tolerance) or is_near(cd_bc, 1.618, tolerance)):
return {'pattern': 'Gartley', 'type': 'bullish', 'points': {X, A, B, C, D}}
return None
2. Multi-Timeframe Analysis
Scan all specified timeframes and aggregate signals:
python
timeframes = ['15m', '30m', '1h', '4h']
instruments = ['EURUSD', 'XAUUSD', 'BTCUSD', 'GBPUSD']
signals = {}
for tf in timeframes:
for symbol in instruments:
data = fetch_data(symbol, tf) # Get OHLC data
pattern = detect_gartley(data['high'], data['low'])
if pattern:
signals[symbol][tf] = pattern
3. Trade Execution Rules
Entry: At point D of a valid pattern.
Stop-Loss: Below D (bullish) or above D (bearish).
Take-Profit: 1.272–1.618 extensions of XA.
Confirmation: Wait for candlestick reversal (e.g., hammer, engulfing) at D.
Example Entry Logic:
if pattern['type'] == 'bullish':
entry_price = pattern['points']['D']['price']
stop_loss = entry_price - (0.5 * (pattern['points']['A']['price'] - pattern['points']['X']['price']))
take_profit = entry_price + (1.618 * (pattern['points']['A']['price'] - pattern['points']['X']['price']))
execute_trade('BUY', entry_price, stop_loss, take_profit)
4. Risk Management
Position Sizing: Risk ≤1% of capital per trade.
Leverage: Use moderate leverage (max 10:1 for crypto, 20:1 for forex).
Correlation Filter: Avoid clustering trades in correlated assets (e.g., EURUSD and GBPUSD).
Key Considerations
1. False Signals: Use additional filters (RSI divergence, volume spikes).
2. Backtesting: Validate strategy on historical data (2018–2023).
3. Automation: Implement via Python (Pandas, TA-Lib) or trading platforms (TradingView,
MT5).
4. Market Conditions: Prioritize patterns forming during trends (not ranging markets).
Tools & Libraries
Data: MetaTrader 5 (MT5), TradingView, Alpha Vantage.
Code: Python (Pandas, NumPy, TA-Lib), Pine Script (TradingView).
Execution: Brokers with API access (e.g., OANDA, Interactive Brokers).
By systematically applying harmonic patterns across timeframes and instruments, you can
identify high-probability reversals while managing risk effectively.
