/ Machine Learning

Gathers machine learning and deep learning models for Stock forecasting

Gathers machine learning and deep learning models for Stock forecasting

Stock-Prediction-Models

Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations.

Contents

Models

Stacking models

  1. Deep Feed-forward Auto-Encoder Neural Network to reduce dimension + Deep Recurrent Neural Network + ARIMA + Extreme Boosting Gradient Regressor
  2. Adaboost + Bagging + Extra Trees + Gradient Boosting + Random Forest + XGB

Deep-learning models

  1. LSTM Recurrent Neural Network
  2. Encoder-Decoder Feed-forward + LSTM Recurrent Neural Network
  3. LSTM Bidirectional Neural Network
  4. 2-Path LSTM Recurrent Neural Network
  5. GRU Recurrent Neural Network
  6. Encoder-Decoder Feed-forward + GRU Recurrent Neural Network
  7. GRU Bidirectional Neural Network
  8. 2-Path GRU Recurrent Neural Network
  9. Vanilla Recurrent Neural Network
  10. Encoder-Decoder Feed-forward + Vanilla Recurrent Neural Network
  11. Vanilla Bidirectional Neural Network
  12. 2-Path Vanilla Recurrent Neural Network
  13. LSTM Sequence-to-Sequence Recurrent Neural Network
  14. LSTM with Attention Recurrent Neural Network
  15. LSTM Sequence-to-Sequence with Attention Recurrent Neural Network
  16. LSTM Sequence-to-Sequence Bidirectional Recurrent Neural Network
  17. LSTM Sequence-to-Sequence with Attention Bidirectional Recurrent Neural Network
  18. LSTM with Attention Scaled-Dot Recurrent Neural Network
  19. LSTM with Dilated Recurrent Neural Network
  20. Only Attention Neural Network
  21. Multihead Attention Neural Network
  22. LSTM with Bahdanau Attention
  23. LSTM with Luong Attention
  24. LSTM with Bahdanau + Luong Attention
  25. DNC Recurrent Neural Network
  26. Residual LSTM Recurrent Neural Network
  27. Byte-net
  28. Attention is all you need
  29. Fairseq

Agents

  1. Turtle-trading agent
  2. Moving-average agent
  3. Signal rolling agent
  4. Policy-gradient agent
  5. Q-learning agent
  6. Evolution-strategy agent
  7. Double Q-learning agent
  8. Recurrent Q-learning agent
  9. Double Recurrent Q-learning agent
  10. Duel Q-learning agent
  11. Double Duel Q-learning agent
  12. Duel Recurrent Q-learning agent
  13. Double Duel Recurrent Q-learning agent
  14. Actor-critic agent
  15. Actor-critic Duel agent
  16. Actor-critic Recurrent agent
  17. Actor-critic Duel Recurrent agent
  18. Curiosity Q-learning agent
  19. Recurrent Curiosity Q-learning agent
  20. Duel Curiosity Q-learning agent
  21. Neuro-evolution agent
  22. Neuro-evolution with Novelty search agent
  23. ABCD strategy agent

Data Explorations

  1. stock market study on TESLA stock, tesla-study.ipynb
  2. fashion trending prediction with cross-validation, fashion-forecasting.ipynb
  3. Bitcoin analysis with LSTM prediction, bitcoin-analysis-lstm.ipynb
  4. Outliers study using K-means, SVM, and Gaussian on TESLA stock outliers.ipynb
  5. Kijang Emas Bank Negara, kijang-emas-bank-negara.ipynb

Simulations

  1. Stock market simulation using Monte Carlo, stock-forecasting-monte-carlo.ipynb
  2. Stock market simulation using Monte Carlo Markov Chain Metropolis-Hasting, mcmc-stock-market.ipynb

Tensorflow-js

I code LSTM Recurrent Neural Network and Simple signal rolling agent inside Tensorflow JS, you can try it here, huseinhouse.com/stock-forecasting-js

Results

Results Agent

This agent only able to buy or sell 1 unit per transaction.

  1. Turtle-trading agent, turtle-agent.ipynb
  1. Moving-average agent, moving-average-agent.ipynb
  1. Signal rolling agent, signal-rolling-agent.ipynb
  1. Policy-gradient agent, policy-gradient-agent.ipynb
  1. Q-learning agent, q-learning-agent.ipynb
  1. Evolution-strategy agent, evolution-strategy-agent.ipynb
  1. Double Q-learning agent, double-q-learning-agent.ipynb
  1. Recurrent Q-learning agent, recurrent-q-learning-agent.ipynb
  1. Double Recurrent Q-learning agent, double-recurrent-q-learning-agent.ipynb
  1. Duel Q-learning agent, duel-q-learning-agent.ipynb
  1. Double Duel Q-learning agent, double-duel-q-learning-agent.ipynb
  1. Duel Recurrent Q-learning agent, duel-recurrent-q-learning-agent.ipynb
  1. Double Duel Recurrent Q-learning agent, double-duel-recurrent-q-learning-agent.ipynb
  1. Actor-critic agent, actor-critic-agent.ipynb
  1. Actor-critic Duel agent, actor-critic-duel-agent.ipynb
  1. Actor-critic Recurrent agent, actor-critic-recurrent-agent.ipynb
  1. Actor-critic Duel Recurrent agent, actor-critic-duel-recurrent-agent.ipynb
  1. Curiosity Q-learning agent, curiosity-q-learning-agent.ipynb
  1. Recurrent Curiosity Q-learning agent, recurrent-curiosity-q-learning.ipynb
  1. Duel Curiosity Q-learning agent, duel-curiosity-q-learning-agent.ipynb
  1. Neuro-evolution agent, neuro-evolution.ipynb
  1. Neuro-evolution with Novelty search agent, neuro-evolution-novelty-search.ipynb
  1. ABCD strategy agent, abcd-strategy.ipynb

Results free agent

This agent able to buy or sell N-units per transaction.

evolution strategy agent evolution-strategy-agent.ipynb

total gained 11037.529911, total investment 110.375299 %

evolution strategy with bayesian agent evolution-strategy-bayesian-agent.ipynb

total gained 13295.469683, total investment 132.954697 %

Results signal prediction

LSTM Recurrent Neural Network

LSTM Bidirectional Neural Network

2-Path LSTM Recurrent Neural Network

Deep Feed-forward Auto-Encoder Neural Network to reduce dimension + Deep Recurrent Neural Network + ARIMA + Extreme Boosting Gradient Regressor

LSTM Sequence-to-Sequence Recurrent Neural Network

LSTM Sequence-to-Sequence with Attention Recurrent Neural Network

LSTM Sequence-to-Sequence with Attention Bidirectional Recurrent Neural Network

Encoder-Decoder Feed-forward + LSTM Recurrent Neural Network

Adaboost + Bagging + Extra Trees + Gradient Boosting + Random Forest + XGB

GitHub