for each row of pandas DataFrame (ie. for each day in back testing interval)
[ ] compute daily percent changes for all stocks in portfolio (ie. all stock bought yesterday; portfolio market value = sum of all percent changes * allocation)
[ ] compute total portfolio weight (starts 100% on day 1 of back testing interval; portfolio book value is 100%)
[ ] determine whether a each stock is buy or not
[ ] compute allocation for each 'buy' stock (version 1 assume uniform: portfolio market value / number of buy stocks)
[ ] log portfolio holdings (stocks bought and proportion of portfolio allocated to each)
grahamcrowell
commented
7 years ago
for each row of pandas DataFrame (ie. for each day in back testing interval)