AmirhosseinHonardoust/Stock-LSTM-Forecasting

Predict stock prices using LSTM networks in PyTorch. This project covers data preprocessing, sliding window creation, model training with early stopping, and evaluation with RMSE/MAE/MAPE. Includes visualizations of training loss, predicted vs actual prices, and short-horizon forecasts.

24
/ 100
Experimental

Predicts future stock prices using historical OHLCV data. It takes a CSV file of past stock movements or fetches data from Yahoo Finance, then outputs predicted stock prices along with visualizations showing performance and short-term forecasts. This is useful for financial analysts, traders, or quantitative researchers looking to explore deep learning for market analysis.

No commits in the last 6 months.

Use this if you want to build and test a deep learning model for forecasting stock prices using historical data.

Not ideal if you need an out-of-the-box trading bot or a tool that incorporates real-time news and sentiment analysis.

stock-forecasting quantitative-finance algorithmic-trading time-series-analysis market-prediction
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 7 / 25
Maturity 15 / 25
Community 0 / 25

How are scores calculated?

Stars

40

Forks

Language

Python

License

MIT

Last pushed

Sep 13, 2025

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/AmirhosseinHonardoust/Stock-LSTM-Forecasting"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.