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lusic2018's Projects
introneuralnetworks
Introducing neural networks to predict stock prices
knn-for-pattern-recognize-
KNN形态识别 股票形态识别（如W双底）用图像识别的方法准确率高但速度慢（因要画图），用K-近邻方法以数值型数据计算快准确率基本符合要求(查准率70%左右)，可用于对决策时间有要求的交易。 工作完成情况： 1、W双底识别模型查准确率约70% 2、模型文件上载到聚宽后可在回测中调用。
lob
Benchmark Dataset of Limit Order Book in China Markets
lstm
基于LSTM神经网络的时间序列预测
lstm-for-price-prediction
算法根据单个板块或单只股票的历史数据判断板块指数或个股次日收盘价信息，得到相应的调仓对策。可回归（预测具体价格）可分类（预测涨跌）。 长短期记忆模型(LSTM)是循环神经网络(RNN)的一种，每个输入样本都是一个序列（如某板块20天的四价一量）用这个序列预测结果。它认为某些指标长期的趋势对预测值有影响，有些无影响，让神经元控制短期记忆和长期记忆，克服了实践中时间越长影响参数越小的问题。
lstm-future-prediction
LSTM铁矿石价格预测
lstm-multi
多因子lstm预测
lstm-stock-prediction
LSTM和BP神经网络实现对股票开盘价的预测。
lstm_quantitative_analysis
利用LSTM进行股指期货的预测与交易
lstm_realtmpredict_rhyc
基于卡口实时过车数据进行交通流量的实时预测分析，使用LSTM循环神经网络模型进行融合预测，准确率达到90%以上。
lstm_stock
Learing the process of LSTM, and use keras achieve stock prediction using LSTM。LSTM步骤解释，然后使用keras实现用LSTM预测股票走势
lstm_stock-1
采用lstm方法预测股票的下一秒的价格
lstm_stock_price_prediction
LSTM神经网络预测沪深300指数及其涨跌
machine-learning
:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归
machine-learning-for-trading
Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.
machine_learning_prediction_system
机器学习预测系统汇总：包括贝叶斯网络、马尔科夫模型、线性回归、岭回归、多项式回归、决策树回归、深度神经网络预测
many-search-algorithm-optimize-bp-neural-network
利用各种算法优化BP
markethub
CTP期货数据收集与中转程序
multilayerperceptron
Python program to implement the multilayer perceptron neural network.
multiprocessctp
基于多进程的期货程序化交易系统
net_for_cryptocurrency
采用神经网络来预测加密货币涨跌，同时采用贝叶斯优化方法调整超参数
optimalportfolio
An open source library for portfolio optimisation
order_imbalance_hft
非平衡订单流高频交易模型
pandoratrader
CTP 高频量化交易平台 C++ Trade Platform for quant developer
pivot_point_breakout
枢轴点突破模型
predicting-stock-trends-by-lstm
基于LSTM预测沪深300指数的趋势，用深度学习来处理普通机器学习项目的预测
pylimitbook
Python implementation of fast limit-order book.
pystrategies
Deep learning framework for HFT algorithmic trading strategy development
quant-strategies-hft
source : http://coin.wne.uw.edu.pl/pwojcik/hfd_en.html
ring-log
Ring-Log是一个高效简洁的C++异步日志， 其特点是效率高（每秒支持至少125万+日志写入）、易拓展，尤其适用于频繁写日志的场景
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