Stock Price Forecasting Model 📈
Model Description
This model predicts future stock prices based on historical time-series data using LSTM (Long Short-Term Memory) networks. It is trained on publicly available stock price datasets and aims to assist in forecasting stock market trends.
Framework: TensorFlow / PyTorch
Model Type: LSTM-based Recurrent Neural Network
Task: Time-Series Forecasting
Input: Historical stock prices (Open, High, Low, Close, Volume)
Output: Next-day (or multi-step) stock price predictions
Intended Use
For educational and research purposes in financial forecasting.
Not intended for actual financial investment advice 🚫💸.
How to Use
Install dependencies
pip install torch numpy pandas matplotlib
Example Usage
import torch
import numpy as np
import pandas as pd
from model import StockPricePredictor # <- adjust to your class name
Load the model
model = StockPricePredictor()
model.load_state_dict(torch.load("stock_price_model.pt", map_location="cpu"))
model.eval()
Example input: last 60 days of stock prices (normalized)
example_input = torch.randn(1, 60, 5) # batch_size=1, sequence_length=60, features=5
with torch.no_grad():
prediction = model(example_input)
print("Predicted Next-Day Price:", prediction.item())
Training Details
Training Data: Historical stock datasets (e.g., Yahoo Finance, Kaggle datasets).
Epochs: 50
Optimizer: Adam
Loss Function: MSE (Mean Squared Error)
Evaluation
Metrics used: RMSE, MAE
Performance varies by dataset and stock ticker.
