trad4r's Stars
phmatray/TaLibStandard
TaLib in C# for .NET Standard
jrothschild33/learn_backtrader
BackTrader中文教程笔记（by：量化投资与机器学习），系统性介绍Bactrader的特性、策略构建、数据结构、回测交易等，彻底掌握量化神器的使用方法。章节：介绍篇、数据篇、指标篇、交易篇、策略篇、可视化篇……（持续更新中）
jrothschild33/Black-Litterman-Model
使用Python复现Black-Litterman模型。Black-Litterman模型创造性地采用贝叶斯方法将投资者对预期收益的主观看法与资产的市场均衡收益相结合，有效地解决了Markowitz均值-方差模型中投资者难以准确估计各个投资品种预期收益率、以及其权重对预期收益率的极度敏感性这两大问题。本项目使用美国市场2009年-2019年十年间的10只股票数据进行回测，证明了合理观点对资产组合收益率具有显著的正面影响效果。
sabirjana/blog
Code and data for my blogs
as4456/Potential_Output
Implementation of a finance-neutral output gap as explained in "Rethinking Potential Output" paper by Borio et al (2015)
niklaswalter/Binary_Classifier_Stock_Movements
A simple deep learning approach is present to predict the direction of high-frequency stock price changes.
jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data
zhouhaoyi/Informer2020
The GitHub repository for the paper "Informer" accepted by AAAI 2021.
pengxungui/LSTM_stock
基于LSTM的股票数据分析，数据来源于Tushare
bshaw2019/Ensembled-LSTM-Stock-Prediction
Ensembling LSTM and Binary Classification to drive a stock market prediction algorithm for any specified ticker - with tensorboard dash
yohann84L/plot_metric
Python package to simplify plotting of metric like ROC curve, confusion matrix etc..
hichenway/stock_predict_with_LSTM
Predict stock with LSTM supporting pytorch, keras and tensorflow
dgleaso/Stock-Binary-Classification-LSTM
Uses an LSTM to predict the next days stock movement based on sequence of previous days
Yangami/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。
cxdsz/stock-price-prediction-algorithms
使用随机森林、bp神经网络、LSTM神经网络、GRU对股票收盘价进行回归预测。Random forest, BP neural network, LSTM neural network and GRU are used to predict the closing price.
JINGEWU/Stock-Market-Trend-Analysis-Using-HMM-LSTM
Stock Market Trend Analysis Using Hidden Markov Model and Long Short Term Memory
borisbanushev/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.
lilianweng/stock-rnn
Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.
moyuweiqing/A-stock-prediction-algorithm-based-on-machine-learning
（陆续更新）重新整理过的基于机器学习的股票价格预测算法，里面包含了基本的回测系统以及各种不同的机器学习算法的股票价格预测，包含：LSTM算法、Prophet算法、AutoARIMA、朴素贝叶斯、SVM、随机森林等
goodboyv/Sklearn_Mochine_leanring
利用sklearn实现机器学习算法：线性回归、逻辑回归、决策树、随机森林、SVM等
ericwayman/SparseIndex
Code for the sparse index tracking problem.
dpey/index_replication
Case study
Chenxq0709/IndexReplication
This project utilized PCA and AE to identify the most communal features of the stocks composing the NASDAQ-Composite index, to replicate such an index.
peanutshawny/lstm-stock-predictor
Using past price data and sentiment analysis from news and other documents to predict the S&P500 index using a LSTM RNN. Idea replicated from https://arxiv.org/abs/1912.07700 and https://arxiv.org/abs/1010.3003.
memalette/IndexReplicator
Reinforcement learning Index Replicator
MF803Group/index_replication
YaaQ/CGBRP
Xutaott/DNS-Model
Dynamic Nelson Siegel Model
WencaiZheng/fix-income-quant-trading
some interest rate models such as Vasicek and dynamic Nelson-Siegel model
werleycordeiro/Dynamic_Nelson_Siegel_Svensson_Kalman_Filter
Python Package: Fitting and Forecasting the yield curve