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47 lines
1.8 KiB
Python
47 lines
1.8 KiB
Python
import numpy as np
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import pandas as pd
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SYMBOL = "BTCUSDT"
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MODEL_NAME = "btcusdt-mean-reversion-5m"
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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 _zscore(close, window):
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mean = close.rolling(window).mean()
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std = close.rolling(window).std()
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return (close - mean) / (std + 1e-9)
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def train(data, config):
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p = config.get("parameters", {})
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return {"window": int(p.get("window", 48)), "entry_z": float(p.get("entry_z", 1.4)), "exit_z": float(p.get("exit_z", 0.3)), "symbol": SYMBOL}, {"model_family": "rule_based_mean_reversion", "training_bars": int(len(data))}
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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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lookback = int(p.get("lookback", 100))
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if len(candles) < lookback:
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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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window = int(model.get("window", p.get("window", 48)))
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z = float(_zscore(df["close"], window).iloc[-1])
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entry_z = float(model.get("entry_z", p.get("entry_z", 1.4)))
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signal = "HOLD"
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if z <= -entry_z:
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signal = "UP"
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elif z >= entry_z:
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signal = "DOWN"
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confidence = min(abs(z) / max(entry_z, 1e-9), 1.0)
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return {"signal": signal, "confidence": round(float(confidence), 4), "metadata": {"zscore": round(z, 4), "window": window, "model": MODEL_NAME, "symbol": SYMBOL}}
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