xybare

xybare

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Backtrader-MQL5-API

Python Backtrader - Metaquotes MQL5 - API

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MQL5-JSON-API

Metaquotes MQL5 - JSON - API

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py4fi2nd

Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch.

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stable-baselines3

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

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Advanced-Deep-Trading

Mostly experiments based on "Advances in financial machine learning" book

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DarwinexLabs

Datasets, tools and more from Darwinex Labs - Prop Investing Arm & Quant Team @ Darwinex

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ElegantRL

Lightweight and scalable deep reinforcement learning using PyTorch. 🔥

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FinRL

Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance. NeurIPS 2020 & ICAIF 2021. 🔥

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gym

A toolkit for developing and comparing reinforcement learning algorithms.

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gym-anytrading

The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)

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Hands-On-Machine-Learning-for-Algorithmic-Trading

Hands-On Machine Learning for Algorithmic Trading, published by Packt

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Introduction-to-Time-Series-forecasting-Python

Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.

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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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Mql5-Python-Integration

I created this library because the development of an Expert Advisor in MQL5 can be complicated,while, in python, the same task flows better.

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NeoFinRL

Tens of market environments for quant finance.

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pairstrade-fyp-2019

Trying to replicate the results of Pair Trading

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PythonDataScienceHandbook

Python Data Science Handbook: full text in Jupyter Notebooks

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shap

A game theoretic approach to explain the output of any machine learning model.

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stable-baselines

A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

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Stock-Price-Prediction-Model

Stock Price prediction using news data. The datasets used consists news and stock price data from 2008 to 2016. The polarity(Subjectivity, Objectivity, Positive, Negative, Neutral) data is gathered from the news data and further used to predict stock prices. Achieved an accuracy of 94% using XGBoost.

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Stock-Trading-Environment

A custom OpenAI gym environment for simulating stock trades on historical price data.

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strategy

掘金策略集锦

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ThinkBayes

Code repository for Think Bayes.

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ThinkBayes2

Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.

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ThinkStats2

Text and supporting code for Think Stats, 2nd Edition

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TradingGym

Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.

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v2ray-agent

神秘力量,自行体会

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vnpy

基于Python的开源量化交易平台开发框架

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VNPY2_BILLY

基于VNPY 2版本的个人代码

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