surupendu / futures_trading Goto Github PK

This repository has the RL framework for performing futures trading in stock market. The trading environment currently supports trading in near month contracts. Discrete action space is supported for Q learning, SARSA and DQN. Continous action space is supported for PPO. Continuous observation space is supported for DQN and PPO. Discrete obeservation space is supported for Q learning and SARSA.

1. deep_rl_trading: This folder consists of the PPO and DQN based RL models.
2. rl_trading: This folder consists of the Q learning and SARSA based RL models.

1. Price data: https://www.kaggle.com/datasets/nishanthsalian/indian-stock-index-1minute-data-2008-2020
2. News data: https://economictimes.indiatimes.com/archive.cms?from=mdr

1. Total Profit
2. Return (%)
3. Maximum Drawdown
4. Volatility
5. Sharpe Ratio
6. Sortino Ratio

1. stable-baselines3==1.7.0
2. TA-Lib==0.4.25
3. rich==13.3.1
4. torch==1.13.0

1. Futures trading: https://zerodha.com/varsity/module/futures-trading/
2. Technical Indicator: https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/overview
3. FinBERT: https://arxiv.org/abs/1908.10063
4. PPO: https://spinningup.openai.com/en/latest/algorithms/ppo.html
5. DQN: https://arxiv.org/abs/1312.5602
6. Q Learning: https://blog.floydhub.com/an-introduction-to-q-learning-reinforcement-learning/
7. SARSA: https://builtin.com/machine-learning/sarsa