DaM
f85c522f22
- Implemented portfolio engine with risk-based allocation (50/50) - Added equity-based metrics for system-level evaluation - Validated portfolio against standalone strategies - Reduced max drawdown and volatility at system level - Quantitative decision closed before paper trading phase
65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
# src/strategies/breakout.py
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import pandas as pd
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from src.strategies.base import Strategy
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from src.core.strategy import Signal
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class DonchianBreakout(Strategy):
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"""
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Estrategia de ruptura de canales Donchian
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Señales:
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- BUY: El precio rompe el máximo de los últimos N periodos
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- SELL: El precio rompe el mínimo de los últimos N periodos
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- HOLD: En cualquier otro caso
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Parámetros:
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lookback: Ventana de cálculo del canal
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Valores por defecto:
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lookback = 20
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≈ 1 día en timeframe 1h
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Parámetro clásico del sistema Turtle
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Notas:
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- Es una estrategia de momentum puro
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- No intenta comprar barato, compra fortaleza
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- Filtra ruido al exigir ruptura real
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"""
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def __init__(self, lookback: int = 20):
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params = {
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"lookback": lookback
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}
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super().__init__(name="DonchianBreakout", params=params)
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self.lookback = lookback
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# ------------------------------------------------------------------
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def init_indicators(self, data: pd.DataFrame) -> pd.DataFrame:
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data["donchian_high"] = data["high"].rolling(self.lookback).max()
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data["donchian_low"] = data["low"].rolling(self.lookback).min()
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return data
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def generate_signal(self, idx: int) -> Signal:
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if idx < self.lookback:
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return Signal.HOLD
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high = self.data["high"]
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low = self.data["low"]
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close = self.data["close"]
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max_high = high.iloc[idx - self.lookback : idx].max()
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min_low = low.iloc[idx - self.lookback : idx].min()
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price = close.iloc[idx]
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if price > max_high:
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return Signal.BUY
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elif price < min_low:
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return Signal.SELL
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return Signal.HOLD
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