Stock Prediction Models — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Stock Prediction Models. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Apr 16, 2023 • Sentiment updated: Apr 18, 2026

GitHub Statistics

Community Sentiment

Community Buzz: Unrelated, but it's surprisingly hard to get backtesting right. I found a bug in a popular stock forecasting model on github where future knowledge subtly leaks into the training data, said a user on HackerNews. Another user on GitHub mentioned 'Adaptive Regime-Aware Stock Price Prediction Using Autoencoder-Gated Dual Node Transformers with Reinforcement Learning Control'

Pros & Cons

What People Love

accurate predictions, useful for trading

Common Complaints

bugs in the model, data leakage

Biggest Positive: Accurate predictions

Biggest Negative: Bug in forecast

Why Stock Prediction Models Stands Out

This repository is valuable because it provides a wide range of deep learning models and agent architectures for stock forecasting and trading, which can be used to build a robust and accurate forecasting system. The repository's use of evolution strategies and recurrent Q-learning agents provides a unique approach to stock forecasting that is different from other repositories. Additionally, the repository's inclusion of simulations and portfolio optimization tools makes it a comprehensive resource for building a stock forecasting and trading system.

Built With

Build a stock forecasting model that predicts prices with 80% accuracy — This repository provides 30 deep learning models for stock forecasting, including LSTM and GRU, which can be used to build a robust forecasting system, Build a trading bot that automates stock trades based on real-time market data — The repository includes 23 agent models, such as the turtle-trading agent and moving-average agent, which can be used to build a trading bot, Build a stock market simulator that models real-world market behavior — The repository includes simulations such as the Monte Carlo drift and dynamic volatility Monte Carlo, which can be used to build a realistic stock market simulator, Build a portfolio optimization tool that recommends the best stock portfolio for a given risk tolerance — The repository includes a portfolio optimization simulation that can be used to build a portfolio optimization tool, Build a stock price forecasting web application that provides real-time forecasts — The repository includes a Tensorflow JS implementation of the LSTM recurrent neural network, which can be used to build a web application

Getting Started

  1. Install the required libraries by running `pip install -r requirements.txt`
  2. Clone the repository and navigate to the `deep-learning` directory
  3. Run `python lstm.ipynb` to train an LSTM model on the stock data
  4. Configure the agent parameters in the `agent` directory and run `python turtle-agent.ipynb` to test the turtle-trading agent
  5. Try running `python monte-carlo-drift.ipynb` to verify that the Monte Carlo drift simulation works

About

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

Category & Tags

Category: trading

Tags: deep-learning, deep-learning-stock, evolution-strategies, learning-agents, lstm, lstm-sequence, monte-carlo, monte-carlo-markov-chain, seq2seq, stock-market, stock-prediction-models, stock-price-forecasting, stock-price-prediction, strategy-agent, trading-bot

Market Context

Competitive market with multiple models available