File size: 1,488 Bytes
20dcc05 |
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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
# NSE LSTM Model Usage Example
import tensorflow as tf
import pickle
import numpy as np
import pandas as pd
def load_model():
"""Load the trained NSE LSTM model and scaler"""
model = tf.keras.models.load_model("nse_lstm_model.keras")
with open("nse_lstm_scaler.pkl", "rb") as f:
scaler = pickle.load(f)
return model, scaler
def prepare_features(data):
"""Prepare features for prediction"""
# This is a simplified example - you'll need to implement
# the same feature engineering used during training
features = []
for i in range(len(data) - 4): # 5-day window
window = data[i:i+5]
# Calculate your 25 features here
# For now, using dummy data
feature_vector = np.random.randn(25)
features.append(feature_vector)
return np.array(features).reshape(-1, 5, 25)
def predict_stock_price(symbol_data):
"""Predict next day's stock price"""
model, scaler = load_model()
# Prepare features
features = prepare_features(symbol_data)
# Make prediction
prediction = model.predict(features)
return prediction
# Example usage
if __name__ == "__main__":
# Load your stock data here
# data = pd.read_csv("your_stock_data.csv")
# For demonstration, using random data
dummy_data = np.random.randn(100, 5) # 100 days, 5 features
prediction = predict_stock_price(dummy_data)
print(f"Predicted price change: {prediction[0][0]}")
|