Hi Community! Looking for some guidance on how to best code my strategy. Unfortunately, i haven't been able to piece everything together in order to share my backtest (errors) but I've copied/pasted what i have so far. Can anyone help me implement this type of system into the new Optimize format?
Strategy:
1. Get a list of QTradable stocks.
2. Find the gap up or down by percentage from the gap from the day before yesterday's close to today's open (minimum gap up/down = 5% or -5%).
3. Sort the stocks in order by gap up or down percentage
4. For the top N gap up stocks (largest gainers): go long at current day open and sell at the current close of the trading day
5. For the bottom N stocks (largest losers): go short at current day open and cover at the close of the trading day
class ValueDaybeforeYesterday(CustomFactor):
window_length = 2
def compute(self, today, assets, out, values):
out[:] = values[0]
class ChangeAverage(CustomFactor):
def compute(self, today, assets, out, values):
mean = pd.DataFrame(values).pct_change().mean()
out[:] = mean.values
class ChangeAverageLog(CustomFactor):
def compute(self, today, assets, out, values):
df = pd.DataFrame(values)
mean = (df.shift(1) / df).mean().apply(np.log)
out[:] = mean.values
class calc_gap(context, data):
def compute(self,today,assets,out,values):
df = pd.DataFrame(values)
open_price = data.current(sids,'price').rename("open")
df = pd.concat([context.output, open_price], axis = 1)
df['gap'] = df['open'] / df['close_day_before_yeseterday'] - 1
out[:] = df[(df['gap'] > 0.05) ]
"*************************************Make_Pipeline*************************************" def make_pipeline():
pipe = Pipeline()
base_universe = QTradableStocksUS()
dollar_volume = AverageDollarVolume(window_length=30)
high_dollar_volume = dollar_volume.percentile_between(95, 100)
close_day_before_yeseterday = ValueDaybeforeYesterday(inputs = [USEquityPricing.close])
volume_day_before_yeseterday = ValueDaybeforeYesterday(inputs = [USEquityPricing.volume])
pipe.add(close_day_before_yeseterday, "close_day_before_yeseterday")
return_change_mean = ChangeAverage(inputs = [USEquityPricing.close], window_length = 5)
volume_change_mean = ChangeAverage(inputs = [USEquityPricing.volume], window_length = 5)
open_price = data.current(context.security_list,price,1,"1m")
longs = open_price > close_day_before_yeseterday
shorts = open_price < close_day_before_yeseterday
my_screen = base_universe & high_dollar_volume
pipe.set_screen(my_screen)
return pipe 
