borisbanushev/stockpredictionai resource
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later. observed · 2026-08-28
Health v2 · maintenance only
49/100
- Activity 37
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2793
- days_rel: n/a
- days_push: 379
- n_releases_24m: 0
Adoption not part of the score
5593 stars · 1884 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
An educational Jupyter notebook demonstrating a complete pipeline for predicting stock price movements using a GAN with an LSTM generator and CNN discriminator, plus BERT sentiment analysis, Fourier transforms, autoencoders, and ARIMA. It is a tutorial/learning resource rather than production software.
Use cases
- learn how to predict stock prices with deep learning
- understand GANs applied to time series forecasting
- see how BERT sentiment analysis feeds into trading models
- study hyperparameter tuning of GANs with Bayesian optimization and reinforcement learning
- example of combining technical indicators with NLP for market prediction
When to choose
- you want a step-by-step educational walkthrough of AI-based stock prediction
- you want to learn how GANs, LSTMs, and BERT can be combined in one pipeline
- you are studying feature engineering for financial time series
When to avoid
- you need a production-ready trading system or API
- you want maintained, licensed, reusable software
- you expect guaranteed accurate stock predictions
Facets
learning-resource · maturity maintenance
machine-learning deep-learning data-science nlp trading machine-learning deep-learning fintech data-science tutorials python gan lstm stock-prediction notebook bert reinforcement-learning time-series mxnet gpu
1 source
- readme: https://github.com/borisbanushev/stockpredictionai · fetched 2026-08-28 · 82523beae1e8
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| borisbanushev/stockpredictionai | main | 49 |
For agents
markdown · JSON · MCP: product_card(name="borisbanushev/stockpredictionai")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem