This is a series of studies of Jupyter Notebook for Finance.

1. How to get cumulative return for your asset and portfolio in Python.ipynb
2. How to calculate historical volatility and sharpe ratio in Python.ipynb
3. How to get a distribution of returns and draw a probability plot for the distribution in Python.ipynb
4. Mastering DataFrame - how to aggregate OHLCV data in a different time period.ipynb
5. How to compute price correlation for financial data in Python.ipynb
6. How to build Sentiment Analysis with NLTK and Sciki-learn in Python.ipynb
7. How to draw a candle stick chart with DataFrame in Python (mplfinance, plotly and bokeh).ipynb
8. How to draw 4 most common trend indicators in matplotlib in Python.ipynb
9. How to draw a trend line with DataFrame in Python.ipynb
10. How to draw support and resistence lines with DataFrame in Python.ipynb
11. Coefficient variable for crypto assets.ipynb
12. 3 ways to do test of normality with Scipy library in Python.ipynb
13. What are standarization and normalization? Test with iris data set in Scikit-learn.ipynb
14. Scikit-learn LinearRegression vs Numpy Polyfit.ipynb
15. 3 ways to do dimensional reduction techniques in Scikit-learn.ipynb

This code is licensed under the MIT License.