2. Financial Machine Learning using Python – A Bootcamp!
Key topics to be covered:
- Python essentials – A crash course on python to set the foundation
- Numpy for high speed numerical processing
- Pandas for efficient data analysis
- Pandas and matplotlib for data visualization
- Data ingestion using pandas-datareader & Quandl
- Time Series Analysis (TSA)
- Stock Returns Analysis;Cumulative Daily Returns
- Volatility and Securities Risk
- Exponentially Weighted Moving Average (EWMA)
- Statsmodels – A python module for estimation of different statistical models, statistical tests and statistical data exploration.
- Error-Trend-Seasonality (ETS) – A forecast model
- Auto-regressive Integrated Moving Averages (ARIMA) – A Time Series Forecasting model; Auto-correlation & Partial auto-correlation data plotting
- Sharpe Ratio – A tool for investors to understand the return of an investment compared to its risk; Portfolio Allocation Optimization
- Efficient Frontier and MarkowitzOptimization
- Types of Funds, Order Books, Short Selling
- Capital Asset Pricing Model; Stock Splits and Dividends
- Efficient Market Hypothesis
- Algorithmic Trading with Quantopian (Quantopian provides free education, data, and tools so that anyone can pursue quantitative finance)
- Futures Trading and much more…….!!!
All of the topics will be coved with hands on training using Python and jupyter note book. Real datasets will be used wherever needed.
Objective and prerequisites
Along with theory lectures and key financial & trading concepts will be provided. The course will provide comprehensive training using state of the art python libraries and real datasets. Some familiarity with the financial terms, basic knowledge of python andsome understating of mathematics/statistics is recommended, however it is not required for the course. Along with theory lectures, the course includes detailed code notebooks for every lecture and practice exercises on real data for each topic you cover. The goal is “Learn by Doing“!