huseinzol05/Stock-Prediction-Models resource

Health v2 · maintenance only

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Adoption not part of the score

9480 stars · 3046 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of Jupyter Notebook implementations of machine learning and deep learning models for stock price forecasting, including 30+ forecasting models (LSTM, GRU, Seq2seq, Transformers) and 23 trading agents (Q-learning, actor-critic, evolution strategies). It also includes trading simulations and data exploration studies.

Use cases

• predict stock prices with LSTM or seq2seq models
• learn reinforcement learning trading agents
• backtest trading strategies in simulation
• compare deep learning models for time series forecasting
• study Q-learning and actor-critic for trading
• forecast t+N future stock values
• learn Monte Carlo simulation for stock markets

When to choose

• you want reference implementations of many forecasting and trading-agent models in one place
• you are learning ML/DL applied to financial time series
• you want notebook-style code to experiment with and modify

When to avoid

• you need production-ready, maintained trading software
• you expect guaranteed profitable trading signals
• you need a supported library with stable APIs and package releases

Facets

learning-resource · maturity maintenance

machine-learning deep-learning reinforcement-learning data-science simulation fintech machine-learning deep-learning data-science time-series python stock-forecasting trading-bots lstm seq2seq reinforcement-learning-agents monte-carlo jupyter-notebooks time-series-forecasting

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markdown · JSON · MCP: product_card(name="huseinzol05/Stock-Prediction-Models")