quant-trading-strategy-templates/templates/xauusd-atr-breakout/strategy.py

import numpy as np
import pandas as pd


def _df(candles):
    df = pd.DataFrame(candles, columns=["open", "high", "low", "close", "volume"])
    for col in df.columns:
        df[col] = pd.to_numeric(df[col], errors="coerce")
    return df.dropna().reset_index(drop=True)


def _atr(df, n):
    prev = df["close"].shift()
    tr = pd.concat([(df["high"] - df["low"]), (df["high"] - prev).abs(), (df["low"] - prev).abs()], axis=1).max(axis=1)
    return tr.ewm(span=n, adjust=False).mean()


def train(data, config):
    params = config.get("parameters", {})
    return {
        "params": {
            "lookback": int(params.get("lookback", 64)),
            "atr_window": int(params.get("atr_window", 14)),
            "breakout_window": int(params.get("breakout_window", 12)),
            "atr_mult": float(params.get("atr_mult", 0.05)),
        },
        "name": "xauusd_atr_breakout",
    }, {"training_bars": int(len(data)), "model": "modal_python_rule_baseline"}


def predict(model, market_data, config):
    params = {**model.get("params", {}), **config.get("parameters", {})}
    lookback = int(params.get("lookback", 64))
    atr_window = int(params.get("atr_window", 14))
    breakout_window = int(params.get("breakout_window", 12))
    atr_mult = float(params.get("atr_mult", 0.05))
    candles = market_data.get("candles", [])
    if len(candles) < lookback:
        return {"signal": "HOLD", "confidence": 0.0, "metadata": {"reason": "not_enough_candles"}}

    df = _df(candles[-lookback:])
    atr = float(_atr(df, atr_window).iloc[-1])
    close = float(df["close"].iloc[-1])
    high = float(df["high"].iloc[-breakout_window:-1].max())
    low = float(df["low"].iloc[-breakout_window:-1].min())
    upper = high + atr * atr_mult
    lower = low - atr * atr_mult

    if close > upper:
        edge = (close - upper) / max(atr, 1e-9)
        return {"signal": "UP", "confidence": round(min(0.55 + edge, 0.92), 4), "metadata": {"breakout": "high", "atr": atr, "upper": upper}}
    if close < lower:
        edge = (lower - close) / max(atr, 1e-9)
        return {"signal": "DOWN", "confidence": round(min(0.55 + edge, 0.92), 4), "metadata": {"breakout": "low", "atr": atr, "lower": lower}}

    return {"signal": "HOLD", "confidence": 0.2, "metadata": {"high": high, "low": low, "atr": atr, "upper": upper, "lower": lower}}