spb/sp-wallet

1"""Volatility-targeted trend: trend-following position, scaled down when realised volatility is high.23Universe-generic (version 3): the symbols are whatever the agent trades; same rule and parameters as version 1.4"""56import numpy as np78META = {9    "name": "vol_target_trend",10    "version": "3",11    "universe": ["*"],12    "interval": "1h",13    "lookback": 1000,14    "params": {"fast": 72, "slow": 480, "vol_window": 168, "target_vol": 0.4},15}1617SECONDS_PER_YEAR = 365.0 * 86400.0181920def target_weights(data, params):21    fast, slow = int(params["fast"]), int(params["slow"])22    vol_window, target_vol = int(params["vol_window"]), float(params["target_vol"])23    n = len(data)24    weights = {}25    for symbol, df in data.items():26        close = df["close"].to_numpy()27        if len(close) < max(fast, slow, vol_window + 1) or fast >= slow or vol_window < 2:28            weights[symbol] = 0.029            continue30        if not close[-fast:].mean() > close[-slow:].mean():31            weights[symbol] = 0.032            continue33        bar_seconds = (df.index[-1] - df.index[-2]).total_seconds()34        log_returns = np.diff(np.log(close[-(vol_window + 1):]))35        realised = log_returns.std(ddof=1) * np.sqrt(SECONDS_PER_YEAR / bar_seconds)36        scale = min(1.0, target_vol / realised) if realised > 0 else 0.037        weights[symbol] = scale / n38    return weights39