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100-days-of-ml-code

100-Days-Of-ML-Code中文版

448project

High Frequency Trading

active-strategy-framework

A flexible framework for active LP strategy simulations that use the full Uniswap v3 swap history

ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

algotrading-example

algorithm trading backtest and optimization examples

alpha

alpha-1

因子构建、单因子测试

alpha-strategy-in-chinese-stock-market-2016.10

Basic Structure of Alpha Strategy

am

atlas

Machine learning workflow for use with the Gryphon Framework based on Tensorflow

awesome-chinese-nlp

A curated list of resources for Chinese NLP 中文自然语言处理相关资料

awesome-deep-reinforcement-learning-in-finance

🔬 A collection for those AI (RL / DL / SL / Evoluation / Genetic Algorithm) used in financial market. otherwise, we add Technology Analysis / Alpha Research / Arbitrage and other useful strategies tools & docs in quantitative finance market.

awesome-quant

**的Quant相关资源索引

awesome-quant-machine-learning-trading

Quant/Algorithm trading resources with an emphasis on Machine Learning

barra-model

An internship project: Implement Barra model to take risk or style factor attribution based on multi-factor model.

barra-multiple-factor-risk-model

Barra-Multiple-factor-risk-model

bond_backtest

clairvoyant

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

contrarian

Contrarian & Small Market Capital Quantitative Trading Strategy Research on Chinese A-Shares.

cta_strategy

GF Work Strategy

deep_trader

This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.

evolutionary-trading-strategies

This code illustrates the use of genetic programming to evolve financial trading strategies for a single equity stock. Individuals (strategies) are considered as functions of historical price data, outputting a position allocation. Strategy fitness evaluation is computed by simulating the strategy over historical financial data. Because financial investment requires a fundamental tradeoff between risk and return, strategies are evaluated on multi-objective fitness functions depending on profit and maximum drawdown of the strategy and ranging from very risk-prone to very risk-averse. The population of individual strategies is evolved using tournament selection, single-point crossover, and random mutation as evolutionary operators. Strategies with the best fitness at any stage in the evolutionary process are recorded in a ‘hall-of-fame’. At the end of the evolutionary process, strategies in the ‘hall-of-fame’ are evaluated over a set of test data and selected based on a train-test criterion which penalizes strategies that do not generalize well.

example-hftish

Example Order Book Imbalance Algorithm

factorset

factorset: 提供**A股市场因子集合，包含各类常用及特异因子计算方法，持续更新中。提供轻量级因子计算框架，高可扩展。持续更新中。

gryphon

Powerful, proven, and extensible framework for building trading strategies at any frequency, with a focus on crypto currencies. Battle-tested with billions traded.

hs300

沪深300指数纯因子组合构建

jqfactor_analyzer

聚宽单因子分析工具

keras-neuro-evolution-trading-bot-skeleton

A skeleton for creating a neuro-evolution reinforcement learning trading bot with a Keras neural network.

learning_notebook

利用python进行财务分析

lihang_book_algorithm

致力于将李航博士《统计学习方法》一书中所有算法实现一遍