Pinned Repositories
A-Deep-Learning-Based-Illegal-Insider-Trading-Detection-and-Prediction-Technique-in-Stock-Market
We used LSTM Neural Network to predict stock market volatility and then used discrete signal processing to find anomalous time spans that might be due to an illegal insider trading. Technology used: Keras, Tensorflow, Matlab, Python.
cppvnpy
cvxportfolio
Portfolio optimization and simulation in Python
DevilYuan
DevilYuan可视化股票量化系统，支持选股，历史数据自动下载，策略回测及参数优化，实盘交易和常用的统计功能
Machine_Learning
and data mining
PGPortfolio
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).
poboquant
quant strategy backtesting from pobo financial
pytdx
Python tdx数据接口
QUANTAXIS
QUANTAXIS 从数据爬取-清洗存储-分析回测-可视化-交易复盘的本地一站式解决方案
shinny-futures-android
一个开源的 android 平台期货行情交易终端
xueshufeng's Repositories
xueshufeng/A-Deep-Learning-Based-Illegal-Insider-Trading-Detection-and-Prediction-Technique-in-Stock-Market
We used LSTM Neural Network to predict stock market volatility and then used discrete signal processing to find anomalous time spans that might be due to an illegal insider trading. Technology used: Keras, Tensorflow, Matlab, Python.
xueshufeng/cppvnpy
xueshufeng/cvxportfolio
Portfolio optimization and simulation in Python
xueshufeng/DevilYuan
DevilYuan可视化股票量化系统，支持选股，历史数据自动下载，策略回测及参数优化，实盘交易和常用的统计功能
xueshufeng/Machine_Learning
and data mining
xueshufeng/PGPortfolio
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).
xueshufeng/poboquant
quant strategy backtesting from pobo financial
xueshufeng/pytdx
Python tdx数据接口
xueshufeng/QUANTAXIS
QUANTAXIS 从数据爬取-清洗存储-分析回测-可视化-交易复盘的本地一站式解决方案
xueshufeng/shinny-futures-android
一个开源的 android 平台期货行情交易终端
xueshufeng/shinny-futures-h5
一个开源的 HTML5 期货行情交易终端
xueshufeng/stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
xueshufeng/strategies
quantitative trading with Javascript, Python, C++, Blockly, MyLanguage(麦语言)
xueshufeng/thunder-trader
A industrial high-performance High Frequency Trading System by C++11, support CTP, Femas and so on. 基于C++11开发的量化交易平台，可实现CTP、飞马等平台的高频交易策略。
xueshufeng/vnpy_future
xueshufeng/volatility-trading
A complete set of volatility estimators based on Euan Sinclair's Volatility Trading