Personae - 📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading

  •        225

Personae is a repo that implements papers proposed methods in Deep Reinforcement Learning & Supervised Learning and applies them to Financial Market. It will start from 2018-08-24 to 2018-09-01 a timestamp that I successfully found a job.

https://github.com/Ceruleanacg/Personae

Tags
Implementation
License
Platform

   




Related Projects

Stock-Prediction-Models - Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

  •    Jupyter

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

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.

StockPricePrediction - Stock Price Prediction using Machine Learning Techniques

  •    Jupyter

To examine a number of different forecasting techniques to predict future stock returns based on past returns and numerical news indicators to construct a portfolio of multiple stocks in order to diversify the risk. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data. Download the Dataset needed for running the code from here.

Clairvoyant - Software designed to identify and monitor social/historical cues for short term stock movement

  •    Python

Using stock historical data, train a supervised learning algorithm with any combination of financial indicators. Rapidly backtest your model for accuracy and simulate investment portfolio performance.During the testing period, the model signals to buy or sell based on its prediction for price movement the following day. By putting your trading algorithm aside and testing for signal accuracy alone, you can rapidly build and test more reliable models.

predicting_stock_prices - This is the coding challenge for "Predicting Stock Prices" by @Sirajology on Youtube

  •    Python

#predicting_stock_prices Stock Prediction Challenge by @Sirajology on Youtube. This is the code for the Stock Price Prediction challenge for 'Learn Python for Data Science #3' by @Sirajology on YouTube. The code uses the scikit-learn machine learning library to train a support vector regression on a stock price dataset from Google Finance to predict a future price. In the video, I use scikit-learn to build an ML model, but for the challenge you'll use the Keras library.


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.

Gekko-Strategies - Strategies to Gekko trading bot with backtests results and some useful tools.

  •    Javascript

Gekko Trading Bot. Repository of strategies which I found at Git and Google, orginal source is in README or .js file. Strategies was backtested, results are in backtest_database.csv file. I used ForksScraper and Gekko BacktestTool to create content of this repository.

NowTrade - Algorithmic trading library with a focus on creating powerful strategies

  •    Python

NowTrade is an algorithmic trading library with a focus on creating powerful strategies using easily-readable and simple Python code. With the help of NowTrade, full blown stock/currency trading strategies, harnessing the power of machine learning, can be implemented with few lines of code. NowTrade strategies are not event driven like most other algorithmic trading libraries available. The strategies are implemented in a sequential manner (one line at a time) without worrying about events, callbacks, or object overloading.

LSTM---Stock-prediction - A long term short term memory recurrent neural network to predict stock data time series

  •    Python

The model can be trained on daily or minute data of any forex pair. The data can be downloaded from here. The lstm-rnn should learn to predict the next day or minute based on previous data.

Stock Data

  •    

This library provides APIs to get the stock data such as trade price, the history price data, volume and so on. It can be used to get the real time stock data or the history sotck data. Please feel free to add your questions or ideas in the Discussions Tab Created by CB

PyAlgoTrade - Python Algorithmic Trading Library

  •    Python

PyAlgoTrade is a Python Algorithmic Trading Library with focus on backtesting and support for paper-trading and live-trading. Let’s say you have an idea for a trading strategy and you’d like to evaluate it with historical data and see how it behaves. PyAlgoTrade allows you to do so with minimal effort.

IEX-API - The IEX API provides any individual or academic, public or private institution looking to develop applications that require stock market data to access near real-time quote and trade data for all stocks trading on IEX

  •    

After this date, you will no longer be able to access IEX Exchange market data through this API. IEX Exchange market data will continue to be available via the TOPS and DEEP feeds, as well as through commercial vendors such as IEX Cloud, which is operated separately from IEX Exchange. If you have any questions, please reach out to api@iextrading.com. This github repository is not a support channel for IEX Cloud. We are looking to migrate to a more centralized, scalable community support site in 2021. In the mean time, please consult the following links for information and support.

tidyquant - Bringing financial analysis to the tidyverse

  •    R

tidyquant integrates the best resources for collecting and analyzing financial data, zoo, xts, quantmod, TTR, and PerformanceAnalytics, with the tidy data infrastructure of the tidyverse allowing for seamless interaction between each. You can now perform complete financial analyses in the tidyverse. Our short introduction to tidyquant on YouTube.

bitpredict - Machine learning for high frequency bitcoin price prediction

  •    Python

This project aims to make high frequency bitcoin price predictions from market microstructure data. The dataset is a series of one second snapshots of open buy and sell orders on the Bitfinex exchange, combined with a record of executed transactions. Data collection began 08/20/2015.A number of engineered features are used to train a Gradient Boosting model, and a theoretical trading strategy is simulated on historical and live data.

Stock-Predictor - Listens for Stock news on Twitter, performs sentiment analysis by mining information from an online news source, performs supervised predictive modeling and suggests buy or sell decisions of the stock

  •    Python

Listens for Stock news on Twitter, performs sentiment analysis by mining information from an online news source, performs supervised predictive modeling and suggests buy or sell decisions of the stock. Computes portfolio returns over time.

Zipline - A Pythonic Algorithmic Trading Library

  •    Python

Zipline is a Pythonic algorithmic trading library. It is an event-driven system that supports both backtesting and live-trading. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies.Note: Installing Zipline via pip is slightly more involved than the average Python package. Simply running pip install zipline will likely fail if you've never installed any scientific Python packages before.

tensorwatch - Debugging, monitoring and visualization for Deep Learning and Reinforcement Learning

  •    Jupyter

TensorWatch is a debugging and visualization tool designed for deep learning and reinforcement learning. It fully leverages Jupyter Notebook to show real time visualizations and offers unique capabilities to query the live training process without having to sprinkle logging statements all over. You can also use TensorWatch to build your own UIs and dashboards. In addition, TensorWatch leverages several excellent libraries for visualizing model graph, review model statistics, explain prediction and so on. TensorWatch is under heavy development with a goal of providing a research platform for debugging machine learning in one easy to use, extensible and hackable package.

MindsDB - In-Database Machine Learning

  •    Python

MindsDB enables you to use ML predictions in your database using SQL. MindsDB automates and abstracts machine learning models through virtual AI Tables. It can easily make predictions over very complex multivariate time-series data with high cardinality.

How-to-Predict-Stock-Prices-Easily-Demo - How to Predict Stock Prices Easily - Intro to Deep Learning #7 by Siraj Raval on Youtube

  •    Jupyter

This is the code for this video on Youtube by Siraj Raval part of the Udacity Deep Learning nanodegree. We use an LSTM neural network to predict the closing price of the S&P 500 using a dataset of past prices. Install Keras from here and Tensorflow from here.






We have large collection of open source products. Follow the tags from Tag Cloud >>


Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.