Backtesting dilemmas: pyalgotrade review

Ok, moving on to the next contestant: PyAlgoTrade

First impression: actively developed, pretty good documentation, more than enough feautures ( TA indicators, optimizers etc) . Looks good, so I went on with the installation which also went smoothly.

The tutorial seems to be a little bit out of date, as the first command yahoofinance.get_daily_csv() throws an error about unknown function. No worries, the documentation is up to date and I find that the missing function is now renamed to yahoofinance.download_daily_bars(symbol,year,csvFile). The problem is that this function only downloads data for one year instead of everything from that year to current date. So pretty useless.
After I downloaded the data myself and saved it to csv, I needed to adjust the column names because apparently pyalgotrade expects Date,Adj Close,Close,High,Low,Open,Volume to be in the header. That is all minor trouble.

Following through to performance testing on an SMA strategy that is provided in the tutorial. My dataset consists of 5370 days of SPY:

%timeit myStrategy.run()
1 loops, best of 3: 1.2 s per loop

That is actually pretty good for an event-based framework.

But then I tried searching documentation for functionality needed to backtest spreads and multiple asset portfolios and just could not find any. Then I tried to find a way to feed pandas DataFrame as an input to a strategy and it happens to be not possible, which is again a big disappointment. I did not state it as a requirement in the previous post, but now I come to realisation that pandas support is a must for any framework that works with time series data. Pandas was a reason for me to switch from Matlab to Python and I never want to go back.

Conclusion pyalgotrade does not meet my requrement for flexibility. It looks like it was designed with classic TA in mind and single instrument trading. I don’t see it as a good tool for backtesting strategies that involve multiple assets, hedging etc.