A collection of notebooks I used in my Medium articles.
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Updated
Jul 24, 2022 - Jupyter Notebook
A collection of notebooks I used in my Medium articles.
cnvar.cn及个人微信公众号【QuantInvest】里面提及的编程代码, 对股票各种研究和折腾分析A股市场的各种现象和投资机会,涉及编程、股票模型、分析研究、杂谈等,代码是python,以jupyter notebook展示。
Predicting opening price, closing price - all in Jupyter Notebooks
Neuro evolution agent to buy and sell stocks atumatically
A collection of notebooks and different prediction models that can predict the stock prices. Also a comparison of how all these models performed.
This notebook provides some skills to perform financial analysis on economical data.
Detecting critical transitions in financial networks with topological data analysis.
Analyze historical market data using Jupyter Notebooks
A 3 part series of Jupyter notebooks to help one find alpha in the stock market with AI
Algo Trading - Notebooks: Start to End of Algo Trading Development. Data:Stocks up till 2019 with all daily close data.
A set of Jupyther notebooks for processing Market Indicators from the Web for predicting market movements. Julia, Python, TuriCreate DNN
A system for visualizing interesting stocks. Has powerful comparison capabilities and works seamlessly with your jupyter notebook. Written in QT with matplotlib.
Presentation and notebook for the lightning talk A Quick Intro to Hidden Markov Models Applied to Stock Volatility presented in R/Finance 2017.
Portfolio accounting and analysis using Emacs Org-mode and Jupyter Notebooks.
A Jupyter Notebook to analyze revenue per seat mile of US commercial airlines
This Jupyter Notebook can be used for continuous monitoring of the status of a trading portfolio in Dhaka Stock Exchange.
Predict tomorrow's S&P 500 index price using historical data. The data analysis has been preformed mainly using Jupyter Notebook and Python Machine Learning library of sklearn -RandomForestClassifier
A machine learning project for classifying stocks into their respective sectors using historical stock price data. Implemented in a Jupyter Notebook, the project includes data fetching, preprocessing, feature extraction, model training, and prediction.
In this notebook, I investigate how the various sectors performed on the stock market in 2022 by creating a market cap-weighted index of each sector (according to the GCIS), and plotting its returns over the year.
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