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Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy

This repository provides codes for ICAIF 2022 paper

This ensemble scheme is reimplemented in a Jupiter Notebook at FinRL.

Abstract

Stock trading strategies play a acute role in investiture. However, it is challenging to excogitation a fruitful strategy in a interwoven and high-energy farm animal market. In this paper, we propose a deep ensemble reinforcement learning scheme that automatically learns a stock trading strategy by maximising investing return. We train a deep reinforcement learning factor and obtain an ensemble trading strategy using the ternary role playe-critic based algorithms: Proximal Policy Optimization (PPO), Vantage Actor Critic (A2C), and Deep Settled Insurance policy Gradient (DDPG). The ensemble strategy inherits and integrates the top-grade features of the 3 algorithms, thereby robustly adjusting to different food market conditions. Ready to avoid the large memory consumption in training networks with continuous action space, we hire a load-on-demand approach for processing very large information. We test our algorithms on the 30 Dow Jones stocks which have adequate liquidity. The performance of the trading agent with assorted reinforcement learning algorithms is evaluated and compared with both the Dow Jones Commercial enterprise Average index and the conventional min-variance portfolio allotment strategy. The proposed colourful ensemble scheme is shown to outperform the three individual algorithms and the two baselines in terms of the risk-well-adjusted return measured by the Sharpe ratio.

Reference

Hongyang Yang, Xiao-Yang Liu, Shan Zhong, and Anwar Walid. 2022. Deep Reinforcement Learning for Automatic Stock Trading: An Ensemble Strategy. In ICAIF '20: ACM International Conference on AI in Finance, Oct. 15–16, 2022, Manhattan, New York State. ACM, New York, NY, The States.

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Installation:

git clone https://github.com/AI4Finance-LLC/Deep-Reinforcement-Erudition-for-Automated-Stock-Trading-Ensemble-Scheme-ICAIF-2020.git

Prerequisites

For OpenAI Baselines, you'll need system packages CMake, OpenMPI and zlib. Those can be installed As follows

Ubuntu

sudo apt-get update              danamp;danamp;              sudo apt-get install cmake libopenmpi-dev python3-dev zlib1g-dev libgl1-mesa-glx

Mackintosh OS X

Installation of system packages on Mackintosh requires Homebrew. With Homebrew installed, run the chase:

brewage install cmake openmpi

Windows 10

To establis stable-baselines happening Windows, please look away at the documentation.

Make up and Activate Realistic Environment (Optional but highly recommended)

cd into this secretary

              cd              Deep-Reenforcement-Learning-for-Automated-Stock-Trading-Corps de ballet-Strategy-ICAIF-2020

Low-level folder /Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020, create a virtual environs

Virtualenvs are basically folders that have copies of python executable and each python packages.

Virtualenvs can also avoid packages conflicts.

Create a virtualenv venv under pamphlet /Rich-Reward-Eruditeness-for-Machine-driven-Stock-Trading-Ensemble-Strategy-ICAIF-2020

virtualenv -p python3 venv

To activate a virtualenv:

Dependencies

The script has been tested functioning low Python dangt;= 3.6.0, with the folowing packages installed:

pip put in -r requirements.txt

Questions

Approximately Tensorflow 2.0: https://github.com/hill-a/stable-baselines/issues/366

If you have questions regarding TensorFlow, note that tensorflow 2.0 is not compatible now, you Crataegus laevigata use

pip install tensorflow==1.15.4

If you have questions regarding Stable-baselines package, please refer to Stable-baselines installation guide. Install the Stable Baselines package using pip:

              whip install stable-baselines[mpi]                          

This includes an optional dependency happening MPI, enabling algorithms DDPG, GAIL, PPO1 and TRPO. If you do non need these algorithms, you terminate install without MPI:

              pip install stable-baselines                          

Please read the documentation for more details and alternatives (from source, using docker).

Pass around DRL Supporting players Strategy

Backtesting

Use Quantopian's pyfolio package to do the backtesting.

Backtesting script

Status

Version History [click to expand]

• 1.0.1 Changes: added ensemble strategy
• 0.0.1 Needled reading

Data

The stock information we use is pulled from Compustat database via Wharton Research Data Services.

Ensemble Scheme

Our function is to create a extremely robust trading scheme. So we use an tout ensemble method to automatically select the best performing agent among PPO, A2C, and DDPG to trade settled along the Sharpe ratio. The ensemble treat is described as follows:

• Step 1. We use a growing window of 𝑛 months to retrain our three agents concurrently. Therein paper we retrain our trey agents at every 3 months.
• Step 2. We validate all 3 agents by using a 12-month validation- rolling window followed by the growing windowpane we used for train- ing to pick the best performing agent which has the highest Sharpe ratio. We also adjust risk-aversion by using turbulence power in our establishment stagecoach.
• Footmark 3. After validation, we only apply the best model which has the highest Sharpe ratio to predict and trade for the next quarter.

Performance

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Source: https://github.com/AI4Finance-Foundation/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020

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