lilianweng/stock-rnn
Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.
This project helps quantitative analysts and traders explore how historical stock price data can be used to forecast future market movements. You input historical price data for individual stocks or market indices like the S&P 500, and it generates predictions for future price trends. This is useful for researchers in quantitative finance looking to test neural network models for market forecasting.
1,973 stars. No commits in the last 6 months.
Use this if you are a quantitative researcher or data scientist interested in applying recurrent neural networks, specifically LSTMs, to model and predict stock prices using historical market data.
Not ideal if you need a production-ready, highly accurate stock prediction system, as this project prioritizes demonstrating the model's structure over predictive performance.
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1,973
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678
Language
Python
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Last pushed
Jul 28, 2022
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