import pickle
import pandas as pd
from tensorflow.keras.models import load_model
import numpy as np

# Load model and scaler
model = load_model("AAPL_lstm.keras")
with open("AAPL_scaler.pkl", "rb") as f:
    scaler = pickle.load(f)

# Prepare your DataFrame with columns: close, returns, ma5, ma20, volatility, rsi, macd
# df = your_dataframe  (at least 60 rows)

features = ["close", "returns", "ma5", "ma20", "volatility", "rsi", "macd"]
data = df[features].values
scaled = scaler.transform(data)
seq = scaled[-60:].reshape(1, 60, len(features))

pred_scaled = model.predict(seq)[0]
dummy = np.zeros((5, len(features)))
dummy[:, 0] = pred_scaled
predicted_prices = scaler.inverse_transform(dummy)[:, 0]
print("5-day forecast:", predicted_prices)

Disclaimer

Architecture: Stacked LSTM (128 → 64 → 32 units) with BatchNorm + Dropout
Framework: TensorFlow / Keras
Input: 60-day sliding window of 7 features
Output: 5-day price forecast
Features: Close price, Returns, MA5, MA20, Volatility, RSI(14), MACD

This model is for educational and research purposes only. Not financial advice. Past performance does not guarantee future results.

Karthikeya1233444/trademind-lstm

TradeMind LSTM Stock Predictor

Model Details

Trained Symbols

Usage

Disclaimer

Karthikeya1233444/
trademind-lstm