The course focuses as much as possible on hands-on examples of real problems involved in quantitative trading. We will start with setting up developing environment and getting historic price data. After that we will backtest a couple of typical trading strategies. A final part of the course focuses on automated trading through Interactive Brokers API. Theoretical part (math & computer science) will be kept to a minimum and only treated where needed.
We will start by setting up a Python environment and get a basic feel of the language. Then we will jump right in and use case studies to get accustomed to working with data aalysis and strategy development.
Parts 1,2 &3
The material has been restructured to a more book-like form, with its own index and is now available as a single-file download .
You can download a part of the material for free, available here . No strings attached.
Table of contents: ------------------------------------------- Introduction Preface Installation Development tools IPython Source code editors Jupyter notebook Overview of python basics Working with modules Visualizing data Visualization with matplotlib Plotting with Pandas Bokeh plots TWP plotting class Simulating leveraged etfs How about 3x leverage? Day of week seasonality of SPY Get the data Working with dates and times Analyse weekday seasonality Working with csv files Reading csv Writing csv files Building a stock price database Download historic data Multi-index Save data to file Load data from file Performance metrics Sharpe ratio Drawdown Profit ratio Backtesting with TWP backtesting module Test on real price data Walk-forward moving averages strategy Moving averages crossover strategy Divide dataset Develop strategy Make a parameter scan Conclusion Permanent portfolio Get price data Simulate portfolio Conclusion XLP strategy Rewrite strategy to a single function Make a scan of ALL parameters Conclusion Improvements Pairs trading examples Get list of XLE components Get the price data Visualise dataset Build spread and visualise data Create a trading strategy Conclusion VXX strategy Strategy thesis Get the data Get data from CBOE Research relationship VIX-VIX3M More precise simulation Leveraged ETF backtest YTD Create pairs Nearest neighbors strategy Strategy thesis Prepare data Create trader class Conclusion External references
The final thing you need for building an automated trading system is a connection to a broker. In this part we will focus on using Interactive Brokers API for receiving real-time data and submitting orders.
- Connecting to Interactive Brokers with ibpy
- Downloading historic intraday data
- Getting real time stock data
- Placing orders