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996quant

35岁程序员退路之量化投资学习笔记

advanced-deep-trading

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

alpha-scientist

alphasickle

多因子指数增强策略/多因子全流程实现

calgobot

Projet de création d'une librarie pour cAlgo en Csharp et des robots modulaires multi indicateurs.

czsc

缠中说禅技术分析工具；缠论

deep-learning-machine-learning-stock

Stock for Deep Learning and Machine Learning

drawiofiles

eemd-lstm-do-prediction

EEMD(集合经验模态分解)、LSTM(长短时记忆网络)、time series prediction(时间序列预测)、DO（dissolved oxygen,溶解氧）、Deep Learning

financeanalysis

Along with FinanceCenter project, magic begin from here

golemq

PowerToys based QUANTAXIS

hs300

沪深300指数纯因子组合构建

learning_to_rank

利用lightgbm做(learning to rank)排序学习，包括数据处理、模型训练、模型决策可视化、模型可解释性以及预测等。Use LightGBM to learn ranking, including data processing, model training, model decision visualization, model interpretability and prediction, etc.

machine-learning-for-beginners

Start writing machine learning code in just 10 hours

machine-learning-with-python

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

ml-nlp

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

note

learning notes

qapubsub

QA PUB/SUB

qastrategy

策略基类/ 支持QIFI协议

qstock

qstock由“Python金融量化”公众号开发，试图打造成个人量化投研分析包，目前包括数据获取（data）、可视化(plot)、选股(stock)和量化回测（策略backtest）模块。 qstock将为用户提供简洁的数据接口和规整化后的金融市场数据。可视化模块为用户提供基于web的交互图形的简单接口； 选股模块提供了同花顺的选股数据和自定义选股，包括RPS、MM趋势、财务指标、资金流模型等； 回测模块为大家提供向量化（基于pandas）和基于事件驱动的基本框架和模型。 关注“Python金融量化“微信公众号，获取更多应用信息。

quantaxis

QUANTAXIS 支持任务调度 分布式部署的 股票/期货/期权 数据/回测/模拟/交易/可视化/多账户 纯本地量化解决方案

quantaxis_strategy

QUANTAXIS 策略文档中心

quantsplaybook

量化研究-券商金工研报复现

sequoia

A股自动选股程序，实现了海龟交易法则、缠中说禅牛市买点，以及其他若干种技术形态

stock-analysis

Identify patterns in Stock Market for Day Trading

stock-pattern-recorginition

stock-prediction-portfolio-optimization

A Streamlit based application to predict future Stock Price and pipeline to let anyone train their own multiple Machine Learning models on multiple stocks to generate Buy/Sell signals. This is a WIP and I will keep on adding new ideas to this in future.

stock-price-specific-lstm

This is a basic LSTM application on stock (securities) market. The project uses Keras+ Tensorflow.

stock.indicators

Stock indicator technical analysis library package for .NET. Send in price quote history and get back the desired technical indicators. Nothing more. It can be used in any market analysis software using standard OHLCV price quotes for equities, commodities, forex, cryptocurrencies, and others. We had private trading algorithms, machine learning, and charting systems in mind when originally creating this community library. Current indicators include: Accumulation Distribution Line (ADL), Aroon, Arnaud Legoux Moving Average (ALMA), Average Directional Index (ADX), Average True Range (ATR), Beta Coefficient, Bollinger Bands, Chaikin Money Flow (CMF), Chaikin Oscillator, Chandelier Exit, ConnorsRSI, Commodity Channel Index (CCI), Correlation Coefficient, Donchian Channel, Double EMA, Exponential Moving Average, Fractal, Heikin-Ashi, Hull Moving Average, Ichimoku Cloud, Kaufman's Adaptive Moving Average, Keltner Channel, Linear Regression and Slope, MESA Adaptive Moving Average (MAMA), Money Flow Index (MFI), Momentum Oscillator, Moving Average Convergence/Divergence (MACD), On-balance Volume (OBV), Parabolic SAR, Price Momentum Oscillator (PMO), Price [Comparative] Relative Strength, Rate of Change (ROC), Relative Strength Index (RSI), R-Squared, Simple Moving Average, Standard Deviation, Stochastic Oscillator, Stochastic RSI, SuperTrend, Triple Exponential Moving Average (TEMA), Trix, Ulcer Index, Ultimate Oscillator, Linear Weighted Moving Average (WMA / LWMA), William %R, Zig Zag

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.