mirror of
https://github.com/PyP-Quant/quant-trading-strategy-templates.git
synced 2026-08-17 20:48:06 +00:00
93 lines
3.8 KiB
Python
93 lines
3.8 KiB
Python
import numpy as np
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import pandas as pd
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from lightgbm import LGBMClassifier
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from sklearn.preprocessing import StandardScaler
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def _prep(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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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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d = close.diff()
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g = d.clip(lower=0).ewm(alpha=1 / n, adjust=False).mean()
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l = (-d.clip(upper=0)).ewm(alpha=1 / n, adjust=False).mean()
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return 100 - 100 / (1 + g / (l + 1e-9))
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def _features(df):
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c = df["close"]
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v = df["volume"]
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f = pd.DataFrame(index=df.index)
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for n in [1, 2, 4, 8, 16, 32, 64]:
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f[f"ret{n}"] = c.pct_change(n)
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f["ema_8_21"] = (c.ewm(span=8, adjust=False).mean() - c.ewm(span=21, adjust=False).mean()) / c
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f["ema_21_55"] = (c.ewm(span=21, adjust=False).mean() - c.ewm(span=55, adjust=False).mean()) / c
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f["ema_55_144"] = (c.ewm(span=55, adjust=False).mean() - c.ewm(span=144, adjust=False).mean()) / c
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f["rsi14"] = (_rsi(c, 14) - 50) / 50
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f["rsi5"] = (_rsi(c, 5) - 50) / 50
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f["volatility_32"] = c.pct_change().rolling(32).std()
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f["volatility_96"] = c.pct_change().rolling(96).std()
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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 train(data, config):
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p = config.get("parameters", {})
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df = _prep(data)
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x = _features(df)
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horizon = int(p.get("horizon", 6))
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threshold = float(p.get("threshold", 0.0015))
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fwd = df["close"].pct_change(horizon).shift(-horizon)
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y = pd.Series(1, index=df.index)
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y[fwd > threshold] = 2
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y[fwd < -threshold] = 0
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y = y.reindex(x.index).fillna(1).astype(int)
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scaler = StandardScaler()
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x_scaled = scaler.fit_transform(x.values.astype(np.float32))
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clf = LGBMClassifier(
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n_estimators=int(p.get("n_estimators", 350)),
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learning_rate=float(p.get("learning_rate", 0.035)),
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num_leaves=int(p.get("num_leaves", 31)),
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max_depth=int(p.get("max_depth", -1)),
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subsample=float(p.get("subsample", 0.85)),
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colsample_bytree=float(p.get("colsample_bytree", 0.85)),
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random_state=42,
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verbose=-1,
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)
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clf.fit(x_scaled, y.values)
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pred = clf.predict(x_scaled)
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return {"model": clf, "scaler": scaler, "features": list(x.columns)}, {
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"training_bars": int(len(x)),
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"feature_count": int(x.shape[1]),
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"class_dist": {"SELL": int((y == 0).sum()), "HOLD": int((y == 1).sum()), "BUY": int((y == 2).sum())},
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"buy_signals": int((pred == 2).sum()),
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"sell_signals": int((pred == 0).sum()),
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"hold_signals": int((pred == 1).sum()),
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}
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def predict(model, market_data, config):
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p = config.get("parameters", {})
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candles = market_data.get("candles", [])
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if len(candles) < int(p.get("lookback", 180)):
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return {"signal": "HOLD", "confidence": 0.0, "metadata": {"reason": "not_enough_candles"}}
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df = _prep(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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x = model["scaler"].transform(row[model["features"]].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(p.get("min_confidence", 0.48)):
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signal = "HOLD"
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return {"signal": signal, "confidence": round(conf, 4), "metadata": {"p_sell": round(float(prob[0]), 4), "p_hold": round(float(prob[1]), 4), "p_buy": round(float(prob[2]), 4), "model": "lightgbm-fx-multifeature"}}
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