Pinned Repositories
ai_quant_trade
股票AI操盘手：从学习、模拟到实盘，一站式平台。包含股票知识、策略实例、因子挖掘、传统策略、机器学习、深度学习、强化学习、图网络、高频交易、C++部署和聚宽实例代码等，可以方便学习、模拟及实盘交易
alphas
alpha101, alpha191, alphalens, backtrader, 量化研究
finhack
一个简单的A股量化框架
gpt4-pdf-chatbot-langchain
GPT4 & LangChain Chatbot for large PDF docs
iching
Iching is a deep meta reinforcement learning quantitative trading platform.
Momentum-Ranker
Get a ranking of the top momentum stocks of the S&P 500
multi-factor-gm-wind-joinquant
基于掘金+万得+聚宽的多因子策略开发框架
QuantsPlaybook
量化研究-券商金工研报复现
stock-analysis
Simple to use interfaces for basic technical analysis of stocks.
stock-data-analysis-and-prediction
根据北向和主力资金的行为分析和预测后市股票的涨跌
kaviyang's Repositories
kaviyang/ai_quant_trade
股票AI操盘手：从学习、模拟到实盘，一站式平台。包含股票知识、策略实例、因子挖掘、传统策略、机器学习、深度学习、强化学习、图网络、高频交易、C++部署和聚宽实例代码等，可以方便学习、模拟及实盘交易
kaviyang/alphas
alpha101, alpha191, alphalens, backtrader, 量化研究
kaviyang/finhack
一个简单的A股量化框架
kaviyang/gpt4-pdf-chatbot-langchain
GPT4 & LangChain Chatbot for large PDF docs
kaviyang/iching
Iching is a deep meta reinforcement learning quantitative trading platform.
kaviyang/Momentum-Ranker
Get a ranking of the top momentum stocks of the S&P 500
kaviyang/multi-factor-gm-wind-joinquant
基于掘金+万得+聚宽的多因子策略开发框架
kaviyang/QuantsPlaybook
量化研究-券商金工研报复现
kaviyang/stock-analysis
Simple to use interfaces for basic technical analysis of stocks.
kaviyang/stock-data-analysis-and-prediction
根据北向和主力资金的行为分析和预测后市股票的涨跌
kaviyang/Stock-Prediction-and-Quantitative-Analysis-Based-on-Machine-Learning-Method
This project studies the intrinsic relationship between the stocks’ multiple factors and the investment value of the stocks listed in China Securities Index 800 Index through the machine method. The investment system pipeline has been implemented including data acquirement, data preprocessing, model tuning and selection based on the XGBoost boosted tree model.
kaviyang/Stock_Analysis_For_Quant
Different Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau
kaviyang/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.
kaviyang/streamlit_alphalens
基于streamlit的因子分析app
kaviyang/surpriver
Find big moving stocks before they move using machine learning and anomaly detection
kaviyang/TimeSeries2DBarChartImageCNN
Conversion of the time series values to 2-D stock bar chart images and prediction using CNN (using Keras-Tensorflow)