Hello world in quantitative trading

Since I started on this field the focus has been to be able to build a set of

I found this training course with the slides posted, where Michael Halls-Moore explains the basis of the backtesting with Python and Pandas.

Things to remind

  • Taxonomy of trading strategies: forecasting, mean reversion, momentum and high frequency trading.
  • Review of the backtesting pitfalls: the same ones that mentioned by Ernie Chang.
  • Things to do after you perform the moving average crossover strategy:
    • Multi-symbol portfolio.
    • Risk management (the most important topic).
    • True event-driven backtesting, realistic handling of transaction costs, fees…

Moving Average Crossover Strategy

this is defined as the “Hello World” of quantitative trading. It’s included a specific article,

Exponentially weighted moving average (EWMA)

EWMA is a type of infinite impulse response filter that applies weighting factors which decrease exponentially. The weighting for each older datum decreases exponentially, never reaching zero.

Code written in Python 2

The proposed code is in python 2, but here you can find a guy that adapted it to python3. It uses Quandl library for obtaining financial data easily.



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