Awesome

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

Models
Agents
Realtime Agent
Data Explorations
Simulations
Tensorflow-js
Misc
Results

Models

Deep-learning models

LSTM
LSTM Bidirectional
LSTM 2-Path
GRU
GRU Bidirectional
GRU 2-Path
Vanilla
Vanilla Bidirectional
Vanilla 2-Path
LSTM Seq2seq
LSTM Bidirectional Seq2seq
LSTM Seq2seq VAE
GRU Seq2seq
GRU Bidirectional Seq2seq
GRU Seq2seq VAE
Attention-is-all-you-Need
CNN-Seq2seq
Dilated-CNN-Seq2seq

Bonus

How to use one of the model to forecast t + N, how-to-forecast.ipynb
Consensus, how to use sentiment data to forecast t + N, sentiment-consensus.ipynb

Stacking models

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

Agents

Turtle-trading agent
Moving-average agent
Signal rolling agent
Policy-gradient agent
Q-learning agent
Evolution-strategy agent
Double Q-learning agent
Recurrent Q-learning agent
Double Recurrent Q-learning agent
Duel Q-learning agent
Double Duel Q-learning agent
Duel Recurrent Q-learning agent
Double Duel Recurrent Q-learning agent
Actor-critic agent
Actor-critic Duel agent
Actor-critic Recurrent agent
Actor-critic Duel Recurrent agent
Curiosity Q-learning agent
Recurrent Curiosity Q-learning agent
Duel Curiosity Q-learning agent
Neuro-evolution agent
Neuro-evolution with Novelty search agent
ABCD strategy agent

Data Explorations

stock market study on TESLA stock, tesla-study.ipynb
Outliers study using K-means, SVM, and Gaussian on TESLA stock, outliers.ipynb
Overbought-Oversold study on TESLA stock, overbought-oversold.ipynb
Which stock you need to buy? which-stock.ipynb

Simulations

Simple Monte Carlo, monte-carlo-drift.ipynb
Dynamic volatility Monte Carlo, monte-carlo-dynamic-volatility.ipynb
Drift Monte Carlo, monte-carlo-drift.ipynb
Multivariate Drift Monte Carlo BTC/USDT with Bitcurate sentiment, multivariate-drift-monte-carlo.ipynb
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

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

Results

Results Agent

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

Turtle-trading agent, turtle-agent.ipynb

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

Signal rolling agent, signal-rolling-agent.ipynb

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Neuro-evolution agent, neuro-evolution.ipynb

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

ABCD strategy agent, abcd-strategy.ipynb

Results signal prediction

I will cut the dataset to train and test datasets,

Train dataset derived from starting timestamp until last 30 days
Test dataset derived from last 30 days until end of the dataset

So we will let the model do forecasting based on last 30 days, 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.

LSTM, accuracy 95.693%, time taken for 1 epoch 01:09

LSTM Bidirectional, accuracy 93.8%, time taken for 1 epoch 01:40

LSTM 2-Path, accuracy 94.63%, time taken for 1 epoch 01:39

GRU, accuracy 94.63%, time taken for 1 epoch 02:10

GRU Bidirectional, accuracy 92.5673%, time taken for 1 epoch 01:40

GRU 2-Path, accuracy 93.2117%, time taken for 1 epoch 01:39

Vanilla, accuracy 91.4686%, time taken for 1 epoch 00:52

Vanilla Bidirectional, accuracy 88.9927%, time taken for 1 epoch 01:06

Vanilla 2-Path, accuracy 91.5406%, time taken for 1 epoch 01:08

LSTM Seq2seq, accuracy 94.9817%, time taken for 1 epoch 01:36

LSTM Bidirectional Seq2seq, accuracy 94.517%, time taken for 1 epoch 02:30

LSTM Seq2seq VAE, accuracy 95.4190%, time taken for 1 epoch 01:48

GRU Seq2seq, accuracy 90.8854%, time taken for 1 epoch 01:34

GRU Bidirectional Seq2seq, accuracy 67.9915%, time taken for 1 epoch 02:30

GRU Seq2seq VAE, accuracy 89.1321%, time taken for 1 epoch 01:48

Attention-is-all-you-Need, accuracy 94.2482%, time taken for 1 epoch 01:41

CNN-Seq2seq, accuracy 90.74%, time taken for 1 epoch 00:43

Dilated-CNN-Seq2seq, accuracy 95.86%, time taken for 1 epoch 00:14