File size: 1,300 Bytes
03d7347 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
class TradingModel:
def __init__(self):
self.scaler = StandardScaler()
self.model = LogisticRegression(random_state=42, max_iter=1000)
def train(self, X_train, y_train):
# Flatten 3D sequences into 2D for scikit-learn
n_samples, window_size, n_features = X_train.shape
X_flat = X_train.reshape(n_samples, window_size * n_features)
# Scale features
X_scaled = self.scaler.fit_transform(X_flat)
# Train model
self.model.fit(X_scaled, y_train)
def predict(self, X):
# Flatten and scale
n_samples, window_size, n_features = X.shape
X_flat = X.reshape(n_samples, window_size * n_features)
X_scaled = self.scaler.transform(X_flat)
# Return probability of positive class
return self.model.predict_proba(X_scaled)[:, 1]
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()
tm.train(X_train, y_train)
pred = tm.predict(X_latest)
return pred[0] |