We're still working on getting historical data queried without first tracking data for a year - once that's solved you won't have to worry about your second question. As for an immediate solution, the easiest solution is simply accumulate a data panel day-over-day until it has enough data for you to do your calculations on.
The algorithm was created before schedule_function was released but you could just as easily replace it with schedule_function with an optional offset.
As for the shorts, it wasn't an error as much as that there were A LOT of stocks with scores less than 2. Go figure. The best way to offset this is simply limit the number of shorts that you find, like I do in this code:
def rebalance(context, data, scores):
This method takes in the scores found by get_piotroski_scores and orders our portfolio accordingly
#: Find which stocks we need to long and which ones we need to short
num_long = [stock for stock in scores if scores[stock] >= 7]
num_short = [stock for stock in scores if scores[stock] <= 2][:len(num_long)]
#: Stocks to long
for stock in num_long:
if stock in data:
log.info("Going long on stock %s with score %s" % (stock.symbol, scores[stock]))
#: Stocks to short
for stock in num_short:
if stock in data:
log.info("Going short on stock %s with score %s" % (stock.symbol, scores[stock]))
#: Exit any positions we might have
for stock in context.portfolio.positions:
if stock in data and stock not in num_long and stock not in num_short:
log.info("Exiting our positions on %s" % (stock.symbol))
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