Optimization: How to test strategy on different datafeeds

[jf]

Dear community,

I managed to test different parameters of a strategy (eg. different moving-average window lengths) on a fixed datafeed. However, I failed so far to test a fixed set of parameters on different datafeeds.

For example (simplified), I would like to test a simple moving average crossover strategy with a window length of 7 days for three different stocks such as Apple, Google, Facebook, etc and see where it performs best.

I created a list with the different tickers, and then use

asset = random.sample(tickerlist,1)

Then for import something like:

data = bt.feeds.PandasData(dataname=asset)

This way I get a ticker out of my list. I then set all the optimization parameters that could affect data pre-loading to false:

cerebro = bt.Cerebro(stdstats=False, optreturn=False, runonce=False, preload=False, optdatas=False, maxcpus=1)

However, it seems that the "random.sample" is not re-executed at each run, as it always re-loads the same datafeed.

Any idea on how I could solve this would be highly appreciated!

Thanks,
Jascha

[backtrader]

Some selected code lines don't really help. But most probably you believe that you can run multiple times, which isn't true. You need to create and run a new cerebro instance for each scenario you want to tes.