| import numpy as np
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| from sklearn.linear_model import LogisticRegression
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| from sklearn.preprocessing import StandardScaler
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|
|
| class TradingModel:
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| def __init__(self):
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| self.scaler = StandardScaler()
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| self.model = LogisticRegression(random_state=42, max_iter=1000)
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|
|
| def train(self, X_train, y_train):
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|
|
| n_samples, window_size, n_features = X_train.shape
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| X_flat = X_train.reshape(n_samples, window_size * n_features)
|
|
|
|
|
| X_scaled = self.scaler.fit_transform(X_flat)
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|
|
|
|
| self.model.fit(X_scaled, y_train)
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|
|
| def predict(self, X):
|
|
|
| n_samples, window_size, n_features = X.shape
|
| X_flat = X.reshape(n_samples, window_size * n_features)
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| X_scaled = self.scaler.transform(X_flat)
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|
|
|
|
| return self.model.predict_proba(X_scaled)[:, 1]
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|
|
| def train_and_get_prediction(X_train, y_train, X_latest):
|
| """
|
| Utility function to quickly train on historical data and predict next movement.
|
| """
|
| tm = TradingModel()
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| tm.train(X_train, y_train)
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|
|
| pred = tm.predict(X_latest)
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| return pred[0] |
