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.