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https://github.com/PyP-Quant/quant-trading-strategy-templates.git
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73 lines
3.3 KiB
Python
73 lines
3.3 KiB
Python
import numpy as np
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import pandas as pd
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.preprocessing import StandardScaler
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from sklearn.pipeline import Pipeline
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SYMBOL = "BRENTUSD"
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MODEL_NAME = "brentoil-trend-rf-1h"
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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 _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, 4, 8, 16, 32, 64]:
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f[f"ret{n}"] = c.pct_change(n)
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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["volatility_fast"] = c.pct_change().rolling(16).std()
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f["volatility_slow"] = c.pct_change().rolling(64).std()
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f["volatility_ratio"] = f["volatility_fast"] / (f["volatility_slow"] + 1e-9)
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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", 0.003))
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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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model = Pipeline([
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("scaler", StandardScaler()),
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("clf", RandomForestClassifier(n_estimators=350, max_depth=8, min_samples_leaf=8, class_weight="balanced_subsample", random_state=42, n_jobs=-1)),
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])
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model.fit(x.values.astype(np.float32), y.values)
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pred = model.predict(x.values.astype(np.float32))
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return {"model": model, "features": list(x.columns), "symbol": SYMBOL}, {"training_bars": int(len(x)), "feature_count": int(x.shape[1]), "buy_signals": int((pred == 2).sum()), "sell_signals": int((pred == 0).sum()), "hold_signals": int((pred == 1).sum())}
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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", 140)):
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return {"signal": "HOLD", "confidence": 0.0, "metadata": {"reason": "not_enough_candles", "model": MODEL_NAME}}
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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", "model": MODEL_NAME}}
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prob = model["model"].predict_proba(row[model["features"]].values.astype(np.float32))[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", 0.5)):
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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": MODEL_NAME, "symbol": SYMBOL}}
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