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JazzDaBot

MQL5 Skripte Debugging eines Roboters/Indikators Optimierung von Strategien Python

Spezifikation

""" Wick Reversal Strategy — core logic ===================================== Rules (as specified): - Timeframe: 15-minute candles - Entry: candle wick >= WICK_MULTIPLIER x body -> signal candle - long lower wick -> enter LONG at next candle's open - long upper wick -> enter SHORT at next candle's open - Exit: an opposite-direction wick candle forms while in a position - Stop-loss: just beyond the signal candle's extreme (high/low) - Position sizing: risk RISK_PCT of account equity per trade """ from dataclasses import dataclass from enum import Enum import pandas as pd class Signal(Enum): NONE = "none" LONG_WICK = "long_wick" # long lower wick -> bullish signal SHORT_WICK = "short_wick" # long upper wick -> bearish signal @dataclass class StrategyParams: wick_multiplier: float = 2.0 # wick must be >= this x body size risk_pct: float = 0.05 # fraction of equity risked per trade stop_buffer: float = 0.0 # extra buffer beyond wick extreme (in price units) def classify_candle(open_: float, high: float, low: float, close: float, params: StrategyParams) -> Signal: """ Classify a single candle as a wick signal or not. body = |close - open| upper_wick = high - max(open, close) lower_wick = min(open, close) - low """ body = abs(close - open_) upper_wick = high - max(open_, close) lower_wick = min(open_, close) - low # Guard against a zero/near-zero body (doji) inflating the ratio infinitely. # Use a tiny epsilon floor so a real body of 0 doesn't produce a false signal # from noise, but still allows genuine doji-with-wick setups to qualify. epsilon = 1e-9 effective_body = max(body, epsilon) is_lower_signal = lower_wick >= params.wick_multiplier * effective_body and lower_wick > 0 is_upper_signal = upper_wick >= params.wick_multiplier * effective_body and upper_wick > 0 # If both sides qualify (rare, e.g. near-zero body with wicks both sides), # take the larger wick as the dominant signal. if is_lower_signal and is_upper_signal: return Signal.LONG_WICK if lower_wick >= upper_wick else Signal.SHORT_WICK if is_lower_signal: return Signal.LONG_WICK if is_upper_signal: return Signal.SHORT_WICK return Signal.NONE def position_size(equity: float, entry_price: float, stop_price: float, params: StrategyParams) -> int: """ Shares = (equity * risk_pct) / |entry - stop| Returns whole shares (floored). Returns 0 if stop distance is invalid. """ risk_amount = equity * params.risk_pct stop_distance = abs(entry_price - stop_price) if stop_distance <= 0: return 0 shares = int(risk_amount // stop_distance) return max(shares, 0) def annotate_signals(df: pd.DataFrame, params: StrategyParams) -> pd.DataFrame: """ Adds a 'signal' column to an OHLC dataframe. Expects columns: open, high, low, close """ df = df.copy() df["signal"] = df.apply( lambda row: classify_candle(row["open"], row["high"], row["low"], row["close"], params).value, axis=1, ) return df

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Projektdetails

Budget
30+ USD