Over here I was kind of detracting away from the topic of how great Quantopian's accomplishments are, with a backtest that looks pretty extraordinary, so here's a place we can discuss it a bit if anyone's interested.
The screenshot again
Then, this Trade Info collects a lot of information, although it is from a later run with stops changed from tight to even tighter as an experiment.
KP asked whether its for real or maybe revealing a bug. There's at least one thing off and it could be in my drawdown code actually, mine says .8. This is every minute.
if pf.returns > b['pf_hi']: # For drawdown b['pf_hi'] = pf.returns b['pf_lo'] = b['pf_hi'] # Prep to look for new low elif pf.returns and pf.returns < b['pf_lo']: b['pf_lo'] = pf.returns if b['pf_hi'] and b['pf_lo']: b['dd_max'] = max(b['dd_max'], 100 * (b['pf_hi'] - b['pf_lo']))
You can see in the trade info that there isn't a ton being invested in any stock. It's mainly that the winners far outweighed losers. Maximum lost on any stock was 15, yeah, dollars. PnL is the sort column. It invested in just over 1000 stocks, buying and selling. Commissions are default and were high, 5496. Since there are conditions on entering positions, they don't always go through, so only half of the initial capital actually ever wound up invested and max leverage was jawdropping low. Long-term and from every start date I've tried, it does great. That's not my experience with code that can qualify for the contest (this can't), in contest code, I could always find a start date where my algo would collapse, darn. So I guess the question is, can some of this be applied to an algorithm for the contest? I've tried some things so far, with very different results. By the way I should point out I have a collaborator on this. I'll add a zero to the input next.
Always a reason for optimism:
" ... the [phrase] that heralds new discoveries, is not 'Eureka!', but 'That's funny ...' " --Isaac Asimov