Displaying 1 to 10 from 10 results

Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020 - Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy

  •    Jupyter

This ensemble strategy is reimplemented in a Jupiter Notebook at FinRL. Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose a deep ensemble reinforcement learning scheme that automatically learns a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using the three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market conditions. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand approach for processing very large data. We test our algorithms on the 30 Dow Jones stocks which have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble scheme is shown to outperform the three individual algorithms and the two baselines in terms of the risk-adjusted return measured by the Sharpe ratio.

FinRL - A Deep Reinforcement Learning Library for Automated Trading in Quantitative Finance

  •    Jupyter

FinRL is an open source library that provides practitioners a unified framework for pipeline strategy development. In reinforcement learning (or Deep RL), an agent learns by continuously interacting with an environment, in a trial-and-error manner, making sequential decisions under uncertainty and achieving a balance between exploration and exploitation. The open source community AI4Finance (to efficiently automate trading) provides educational resources about deep reinforcement learning (DRL) in quantitative finance. To contribute? Please check the end of this page.

finance-go - :bar_chart: Financial markets data library implemented in go.

  •    Go

This go package aims to provide a go application with access to current and historical financial markets data in streamlined, well-formatted structures. A neatly formatted detailed list of implementation instructions and examples will be available on the piquette website.

alpaca-backtrader-api - Alpaca Trading API integrated with backtrader

  •    Python

alpaca-backtrader-api is a python library for the Alpaca trade API within backtrader framework. It allows rapid trading algo development easily, with support for the both REST and streaming interfaces. For details of each API behavior, please see the online API document. Note this module supports only python version 3.5 and above, due to the underlying library alpaca-trade-api.

insomnia-workspace - An Insomnia Workspace for Alpaca API


If you're unfamiliar, Alpaca is a technology platform for financial services including a paper trading API as well as a live commission free stock trading API (brokerage services are offered offered through Alpaca Securities LLC). While building applications using Alpaca API, it's often helpful to be able to manually test API requests to see what's going on. It can help you debug your own code and also learn the API in a more hands on way.

alpha-vantage-cli - Command line tool and API for retrieving stock market data from Alpha Vantage

  •    TypeScript

This is a command line tool and small Node.js API for retreiving data from Alpha Vantage. You can use this from the command line to download stock data from Alpha Vantage to a CSV file.

Deep-Reinforcement-Learning-for-Stock-Trading-DDPG-Algorithm-NIPS-2018 - Practical Deep Reinforcement Learning Approach for Stock Trading

  •    Python

The master branch supports Tensorflow from version 1.4 to 1.14. For Tensorflow 2.0 support, please use tf2 branch. Refer to TensorFlow installation guide for more details.

Machine-Learning-for-Stock-Recommendation-IEEE-2018 - A Practical Machine Learning Approach for Dynamic Stock Recommendation

  •    Jupyter

Stock recommendation is vital to investment companies and investors. However, no single stock selection strategy will always win while analysts may not have enough time to check all S&P 500 stocks (the Standard & Poor’s 500). In this paper, we propose a practical scheme that recommends stocks from S&P 500 using machine learning. Our basic idea is to buy and hold the top 20% stocks dynamically. First, we select representative stock indicators with good explanatory power. Secondly, we take five frequently used machine learning methods, including linear regression, ridge regression, stepwise regression, random forest and generalized boosted regression, to model stock indicators and quarterly log-return in a rolling window. Thirdly, we choose the model with the lowest Mean Square Error in each period to rank stocks. Finally, we test the selected stocks by conducting portfolio allocation methods such as equally weighted, mean- variance, and minimum-variance. Our empirical results show that the proposed scheme outperforms the long-only strategy on the S&P 500 index in terms of Sharpe ratio and cumulative returns.

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