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

Table of contents

• Models
• Agents
• Realtime Agent
• Data Explorations
• Simulations
• Tensorflow-js
• Misc
• Results
  • Results Agent
  • Results signal prediction
  • Results analysis
  • Results simulation

Contents

Models

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
30. Dilation CNN + LSTM RNN

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

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. Outliers study using K-means, SVM, and Gaussian on TESLA stock, outliers.ipynb
3. Overbought-Oversold study on TESLA stock, overbought-oversold.ipynb
4. Which stock you need to buy? which-stock.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
3. Portfolio optimization, portfolio-optimization.ipynb, inspired from https://pythonforfinance.net/2017/01/21/investment-portfolio-optimisation-with-python/

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, you can download any historical CSV and upload dynamically.

Misc

1. fashion trending prediction with cross-validation, fashion-forecasting.ipynb
2. Bitcoin analysis with LSTM prediction, bitcoin-analysis-lstm.ipynb
3. Kijang Emas Bank Negara, kijang-emas-bank-negara.ipynb

Results

Results Agent

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

1. Turtle-trading agent, turtle-agent.ipynb

2. Moving-average agent, moving-average-agent.ipynb

3. Signal rolling agent, signal-rolling-agent.ipynb

4. Policy-gradient agent, policy-gradient-agent.ipynb

5. Q-learning agent, q-learning-agent.ipynb

6. Evolution-strategy agent, evolution-strategy-agent.ipynb

7. Double Q-learning agent, double-q-learning-agent.ipynb

8. Recurrent Q-learning agent, recurrent-q-learning-agent.ipynb

9. Double Recurrent Q-learning agent, double-recurrent-q-learning-agent.ipynb

10. Duel Q-learning agent, duel-q-learning-agent.ipynb

11. Double Duel Q-learning agent, double-duel-q-learning-agent.ipynb

12. Duel Recurrent Q-learning agent, duel-recurrent-q-learning-agent.ipynb

13. Double Duel Recurrent Q-learning agent, double-duel-recurrent-q-learning-agent.ipynb

14. Actor-critic agent, actor-critic-agent.ipynb

15. Actor-critic Duel agent, actor-critic-duel-agent.ipynb

16. Actor-critic Recurrent agent, actor-critic-recurrent-agent.ipynb

17. Actor-critic Duel Recurrent agent, actor-critic-duel-recurrent-agent.ipynb

18. Curiosity Q-learning agent, curiosity-q-learning-agent.ipynb

19. Recurrent Curiosity Q-learning agent, recurrent-curiosity-q-learning.ipynb

20. Duel Curiosity Q-learning agent, duel-curiosity-q-learning-agent.ipynb

21. Neuro-evolution agent, neuro-evolution.ipynb

22. Neuro-evolution with Novelty search agent, neuro-evolution-novelty-search.ipynb

23. 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

I will cut the dataset to train and test datasets,

1. Train dataset derived from starting timestamp until last 15 days
2. Test dataset derived from last 15 days until end of the dataset

So we will let the model do forecasting based on last 15 hours, and we will going to repeat the experiment for 10 times. You can increase it locally if you want, and tuning parameters will help you by a lot.

1. LSTM Recurrent Neural Network, 95.693%

Results analysis

1. Outliers study using K-means, SVM, and Gaussian on TESLA stock

2. Overbought-Oversold study on TESLA stock

3. Which stock you need to buy?

Results simulation

1. Stock market simulation using Monte Carlo

2. Stock market simulation using Monte Carlo Markov Chain Metropolis-Hasting

3. Portfolio optimization