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https://github.com/PyP-Quant/quant-trading-strategy-templates.git
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121 lines
4.4 KiB
Python
121 lines
4.4 KiB
Python
import numpy as np
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import pandas as pd
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from sklearn.preprocessing import StandardScaler
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from xgboost import XGBClassifier
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def _normalise(data):
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df = data.copy()
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df.columns = [str(c).strip().lower() for c in df.columns]
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aliases = {"o": "open", "h": "high", "l": "low", "c": "close", "v": "volume"}
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df.rename(columns=aliases, inplace=True)
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if "volume" not in df.columns:
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df["volume"] = 1.0
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for col in ["open", "high", "low", "close", "volume"]:
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df[col] = pd.to_numeric(df[col], errors="coerce")
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return df.dropna(subset=["open", "high", "low", "close"]).reset_index(drop=True)
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def _rsi(close, n=14):
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delta = close.diff()
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gain = delta.clip(lower=0).ewm(alpha=1 / n, adjust=False).mean()
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loss = (-delta.clip(upper=0)).ewm(alpha=1 / n, adjust=False).mean()
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return 100 - 100 / (1 + gain / (loss + 1e-9))
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def _features(df):
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c = df["close"]
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h = df["high"]
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l = df["low"]
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o = df["open"]
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v = df["volume"]
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rng = (h - l).replace(0, np.nan)
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f = pd.DataFrame(index=df.index)
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for n in [1, 3, 6, 12, 24]:
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f[f"ret{n}"] = c.pct_change(n)
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f["range_pct"] = rng / c
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f["body_pct"] = (c - o) / rng
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f["close_pos"] = (c - l) / (rng + 1e-9)
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f["volatility_24"] = c.pct_change().rolling(24).std()
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f["volatility_72"] = c.pct_change().rolling(72).std()
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f["ema_12_48"] = (c.ewm(span=12, adjust=False).mean() - c.ewm(span=48, adjust=False).mean()) / c
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f["ema_24_96"] = (c.ewm(span=24, adjust=False).mean() - c.ewm(span=96, adjust=False).mean()) / c
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f["rsi14"] = (_rsi(c, 14) - 50) / 50
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f["volume_z"] = (v - v.rolling(48).mean()) / (v.rolling(48).std() + 1e-9)
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return f.replace([np.inf, -np.inf], np.nan).dropna()
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def _labels(close, index, horizon, threshold):
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fwd = close.pct_change(horizon).shift(-horizon)
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y = pd.Series(1, index=close.index)
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y[fwd > threshold] = 2
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y[fwd < -threshold] = 0
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return y.reindex(index).fillna(1).astype(int)
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def train(data, config):
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params = config.get("parameters", {})
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df = _normalise(data)
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feat = _features(df)
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horizon = int(params.get("horizon", 4))
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threshold = float(params.get("threshold", 0.0012))
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y = _labels(df["close"], feat.index, horizon, threshold)
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scaler = StandardScaler()
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x = scaler.fit_transform(feat.values.astype(np.float32))
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clf = XGBClassifier(
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n_estimators=int(params.get("n_estimators", 250)),
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max_depth=int(params.get("max_depth", 4)),
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learning_rate=float(params.get("learning_rate", 0.04)),
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subsample=float(params.get("subsample", 0.8)),
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colsample_bytree=float(params.get("colsample_bytree", 0.85)),
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objective="multi:softprob",
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num_class=3,
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eval_metric="mlogloss",
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tree_method="hist",
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random_state=42,
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)
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clf.fit(x, y.values)
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preds = clf.predict(x)
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metrics = {
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"training_bars": int(len(feat)),
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"feature_count": int(feat.shape[1]),
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"class_dist": {
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"SELL": int((y == 0).sum()),
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"HOLD": int((y == 1).sum()),
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"BUY": int((y == 2).sum()),
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},
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"buy_signals": int((preds == 2).sum()),
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"sell_signals": int((preds == 0).sum()),
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"hold_signals": int((preds == 1).sum()),
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}
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return {"model": clf, "scaler": scaler, "features": list(feat.columns)}, metrics
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def predict(model, market_data, config):
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params = config.get("parameters", {})
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candles = market_data.get("candles", [])
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if len(candles) < int(params.get("lookback", 140)):
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return {"signal": "HOLD", "confidence": 0.0, "metadata": {"reason": "not_enough_candles"}}
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df = _normalise(pd.DataFrame(candles, columns=["open", "high", "low", "close", "volume"]))
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row = _features(df).tail(1)
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if row.empty:
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return {"signal": "HOLD", "confidence": 0.0, "metadata": {"reason": "no_features"}}
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row = row[model["features"]]
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x = model["scaler"].transform(row.values.astype(np.float32))
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prob = model["model"].predict_proba(x)[0]
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klass = int(np.argmax(prob))
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conf = float(np.max(prob))
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signal = {0: "DOWN", 1: "HOLD", 2: "UP"}[klass]
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if conf < float(params.get("min_confidence", 0.48)):
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signal = "HOLD"
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return {
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"signal": signal,
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"confidence": round(conf, 4),
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"metadata": {
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"p_sell": round(float(prob[0]), 4),
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"p_hold": round(float(prob[1]), 4),
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"p_buy": round(float(prob[2]), 4),
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"model": "eurusd-xgboost-1h",
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},
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}
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