mlfinlab - MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools

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MlFinlab is a python package which helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools. This repo is public facing and exists for the sole purpose of providing users with an easy way to raise bugs, feature requests, and other issues.

finance machine-learning research trading investing portfolio-optimization quantitative-finance algorithmic-trading portfolio-management financial-machine-learning

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

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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.

trading technical-indicators neural-network random-forest stock currency algorithmic-trading-library machine-learning algorithmic-trading

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

•    Jupyter

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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.

deep-reinforcement-learning openai-gym sharpe-ratio ddpg stock-trading ppo a2c-algorithm ensemble-strategy stock-trading-strategy automated-stock-trading

business-machine-learning - A curated list of practical business machine learning (BML) and business data science (BDS) applications for Accounting, Customer, Employee, Legal, Management and Operations (by @firmai)

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Animated Investment Management Research at Sov.ai — Sponsoring open source AI, Machine learning, and Data Science initiatives. A curated list of applied business machine learning (BML) and business data science (BDS) examples and libraries. The code in this repository is in Python (primarily using jupyter notebooks) unless otherwise stated. The catalogue is inspired by awesome-machine-learning.

machine-learning jupyter example jupyter-notebook datascience practical-machine-learning business-machine-learning

industry-machine-learning - A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)

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Animated Investment Management Research at Sov.ai — Sponsoring open source AI, Machine learning, and Data Science initiatives. Have a look at the newly started FirmAI Medium publication where we have experts of AI in business, write about their topics of interest.

data-science machine-learning example jupyter-notebook datascience practical-machine-learning firmai

awesome-google-colab - Google Colaboratory Notebooks and Repositories (by @firmai)

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A curated list of repositories with fully functional click-and-run colab notebooks with data, code and description. The code in these repositories are in Python unless otherwise stated. To learn more about they whys and hows of Colab see this post. For a few tips and tricks see this post.

data-science machine-learning tutorial jupyter-notebook coursera notebooks google-colab google-colab-notebook

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

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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-learning monte-carlo trading-bot lstm stock-market stock-price-prediction seq2seq learning-agents stock-price-forecasting evolution-strategies lstm-sequence stock-prediction-models deep-learning-stock strategy-agent monte-carlo-markov-chain

Zipline - A Pythonic Algorithmic Trading Library

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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.

algorithmic-trading trading machine-learning stock-analysis

catalyst - An Algorithmic Trading Library for Crypto-Assets in Python

•    Python

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Catalyst is an algorithmic trading library for crypto-assets written in Python. It allows trading strategies to be easily expressed and backtested against historical data (with daily and minute resolution), providing analytics and insights regarding a particular strategy's performance. Catalyst also supports live-trading of crypto-assets starting with four exchanges (Binance, Bitfinex, Bittrex, and Poloniex) with more being added over time. Catalyst empowers users to share and curate data and build profitable, data-driven investment strategies. Please visit catalystcrypto.io to learn more about Catalyst. Catalyst builds on top of the well-established Zipline project. We did our best to minimize structural changes to the general API to maximize compatibility with existing trading algorithms, developer knowledge, and tutorials. Join us on the Catalyst Forum for questions around Catalyst, algorithmic trading and technical support. We also have a Discord group with the #catalyst_dev and #catalyst_setup dedicated channels.

cryptocurrency trading algorithmic-trading cryptocurrencies

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

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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.

machine-learning support-vector-machines portfolio-simulation backtesting-trading-strategies stock-market

python-business-analytics - Python solutions to solve practical business problems.

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Animated Investment Management Research at Sov.ai — Sponsoring open source AI, Machine learning, and Data Science initiatives. A series looking at implementing python solutions to solve practical business problems. Share your own projects on this subreddit, r/datascienceproject. Every week we will look at hand picked businenss solutions. See the following google drive for all the code and github for all the data. If you follow the LinkedIn page, you would be able to see the lastest developments.

business-intelligence business-analytics practical-machine-learning applied-machine-learning business-machine-learning python-business-analytics python-for-business business-data-science

stock-analysis-engine - Backtest 1000s of minute-by-minute trading algorithms for training AI with automated pricing data from: IEX, Tradier and FinViz

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Build and tune investment algorithms for use with artificial intelligence (deep neural networks) with a distributed stack for running backtests using live pricing data on publicly traded companies with automated datafeeds from: IEX Cloud, Tradier and FinViz (includes: pricing, options, news, dividends, daily, intraday, screeners, statistics, financials, earnings, and more). This will pull Redis and Minio docker images.

docker kubernetes redis deep-neural-networks options deep-learning jupyter tensorflow helm s3 keras minio iex helm-charts stocks algorithmic-trading deep-learning-tutorial tradier backtesting iexcloud iextrading

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

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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 deep-reinforcement-learning openai-gym fintech stock-trading multi-agent-learning stock-markets pythorch tensorflow2 drl-trading-agents drl-algorithms finrl-library drl-framework trading-tasks

pandapy - PandaPy has the speed of NumPy and the usability of Pandas 10x to 50x faster (by @firmai)

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Animated investment research at Sov.ai, sponsoring open source initiatives. PandaPy software, similar to the original Pandas project, is developed to improve the usability of python for finance. Structured datatypes are designed to be able to mimic ‘structs’ in the C language, and share a similar memory layout. PandaPy currently houses more than 30 functions. Structured NumPy are meant for interfacing with C code and for low-level manipulation of structured buffers, for example for interpreting binary blobs. For these purposes they support specialized features such as subarrays, nested datatypes, and unions, and allow control over the memory layout of the structure.

finance data-science machine-learning numpy pandas data-structures arrays structured-data algorithmic-trading

PyAlgoTrade - Python Algorithmic Trading Library

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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.

trading stock algorithmic-trading library trading-strategies

useR-machine-learning-tutorial - useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016

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Instructions for how to install the necessary software for this tutorial is available here. Data for the tutorial can be downloaded by running ./data/get-data.sh (requires wget). Certain algorithms don't scale well when there are millions of features. For example, decision trees require computing some sort of metric (to determine the splits) on all the feature values (or some fraction of the values as in Random Forest and Stochastic GBM). Therefore, computation time is linear in the number of features. Other algorithms, such as GLM, scale much better to high-dimensional (n << p) and wide data with appropriate regularization (e.g. Lasso, Elastic Net, Ridge).

machine-learning deep-learning random-forest gradient-boosting-machine tutorial data-science ensemble-learning r

rl-portfolio-management - Attempting to replicate "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" https://arxiv

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Attempting to replicate "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" by Jiang et. al. 2017 [1]. This paper trains an agent to choose a good portfolio of cryptocurrencies. It's reported that it can give 4-fold returns in 50 days and the paper seems to do all the right things so I wanted to see if I could acheive the same results.

portfolio-management deep-reinforcement-learning deeprl cryptocurrency openai-gym-environments openai-gym

computer-vision-in-action - 《计算机视觉实战演练：算法与应用》中文电子书、源码、读者交流社区（持续更新中

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《计算机视觉实战演练：算法与应用》中文电子书、源码、读者交流社区（持续更新中 ...） 📘 在线电子书 https://charmve.github.io/computer-vision-in-action/ 👇项目主页

machine-learning tutorial books computer-vision deep-learning neural-network notebook jupyter-notebook handbook pytorch transformer ipynb deep-learning-tutorial computer-vision-algorithms colab-notebook in-action charmve

algotrading - Algorithmic trading framework for cryptocurrencies.

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Algotrading Framework is a repository with tools to build and run working trading bots, backtest strategies, assist on trading, define simple stop losses and trailing stop losses, etc. This framework work with data directly from Crypto exchanges API, from a DB or CSV files. Can be used for data-driven and event-driven systems. Made exclusively for crypto markets for now and written in Python.

bot framework crypto trading realtime trading-bot trading-api cryptocurrency algotrading trading-algorithms cryptocurrencies hft hft-trading algorithmic-trading trading-simulator backtesting-trading-strategies backtest high-frequency-trading cryptocurrency-exchanges crypto-algotrading

amazon-sagemaker-examples - Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker

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Example Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models.

training aws data-science machine-learning reinforcement-learning deep-learning examples jupyter-notebook inference sagemaker mlops