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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.
| Date | Stars |
|---|---|
| 2026-07-24 | 1408 |
| 2026-07-25 | 1408 |
| 2026-07-28 | 1408 |
| 2026-07-30 | 1408 |
| 2026-07-31 | 1408 |
| 2026-08-06 | 1408 |
Today
— stars today
This week
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— stars this month
Momentum
0.0
growth rate 0.00%/day
[](/LICENSE)
[](https://www.tensorflow.org/)
[]()
[](https://hub.docker.com/r/ceruleanwang/personae/)
# Personae - RL & SL Methods and Envs For Quantitative Trading
Personae is a repo that implements papers proposed methods in Deep Reinforcement Learning & Supervised Learning and applies them to Financial Market.
Now Personae includes 4 RL & 3 SL implements and a simulate Financial Market supporting Stock and Future. (Short Sale is still implementing)
More RL & SL methods are updating!
# WARNING
This repo is being reconstructing,
It will start from 2018-08-24 to ~2018-09-01~ a timestamp that I successfully found a job.
# Attentions
- The features as inputs are naive.
- Day frequency is clearly not enough.
- It's recommended that you could replace the features here to your own.
# Contents
+ [Deep Deterministic Policy Gradient (DDPG)](algorithm/RL/DDPG.py)
Implement of DDPG with TensorFlow.
> arXiv:1509.02971: [Continuous control with deep reinforcement learning](https://arxiv.org/abs/1509.02971)
+ [Double DQN](algorithm/RL/DoubleDQN.py)
Implement of Double-DQN with TensorFlow.
> arXiv:1509.06461: [Deep Reinforcement Learning with Double Q-learning](https://arxiv.org/abs/1509.06461)
+ [Dueling-DQN](algorithm/RL/DuelingDQN.py)
Implement of Dueling-DQN with TensorFlow.
> arXiv:1511.06581: [Dueling Network Architectures for Deep Reinforcement Learning](https://arxiv.org/abs/1511.06581)
+ [Policy Gradient](algorithm/RL/PolicyGradient.py)
Implement of Policy Gradient with TensorFlow.
> NIPS. Vol. 99. 1999: [Policy gradient methods for reinforcement learning with function approximation](https://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf)
+ [DA-RNN (DualAttnRNN)](algorithm/SL/DualAttnRNN.py)
Implement of arXiv:1704.02971, DA-RNN with TensorFlow.
> arXiv:1704.02971: [A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction](https://arxiv.org/abs/1704.02971)
+ [TreNet (HNN)](algorithm/SL/TreNet.py)
Implement of TreNet with TensorFlow.
> IJCAI 2017. [Hybrid Neural Networks for Learning the Trend in Time Series](https://www.ijcai.org/proceedings/2017/0316.pdf)
+ [Naive-LSTM (LSTM)](algorithm/SL/NaiveLSTM.py)
Implement of simple LSTM based model with TensorFlow.
> arXiv:1506.02078: [Visualizing and Understanding Recurrent Networks](https://arxiv.org/abs/1506.02078)
# Environment
A basic simulate environment of Financial Market is implemented.
- [Market](base/env/market.py)
Implement of Market, Trader, Positions as a gym env (gym is not required), which can give a env for regression or sequence data generating for RL or SL model.
For now, Market support Stock Data and Future Data.
Also, more functions are updating.
# Experiments
+ [Deep Deterministic Policy Gradient (DDPG)](algorithm/SL/DualAttnRNN.py)
+ [Double-DQN](algorithm/RL/DoubleDQN.py)
+ [Dueling-DQN](algorithm/RL/DuelingDQN.py)
+ [Policy Gradient (PG)](algorithm/RL/PolicyGradient.py)
Train a Agent to trade in stock market, using stock data set from 2012-01-01 to 2018-01-01 where 70% are training data, 30% are testing data.
<p align='center'>
<img src='static/images/Profits.jpg'>
<em>Total Profits and Baseline Profits. (Test Set)</em>
</p>
+ [DA-RNN (DualAttnRNN)](algorithm/SL/DualAttnRNN.py)
+ [Naive-LSTM (LSTM)](algorithm/SL/NaiveLSTM.py)
+ [TreNet (HNN)](algorithm/SL/TreNet.py)
Train a Predictor to predict stock prices, using stock data set from 2008-01-01 to 2018-01-01, where 70% are training data, 30% are testing data.
<p align='center'>
<img src='static/images/Prices.jpg'>
<Excerpt of 7,758 characters
Read on GitHub
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:74c4bb3bf60fff2a, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning
matched fp:74c4bb3bf60fff2a, topic:trading, readme:financial, desc:quantitative