Overall Statistics Total Trades 15 Average Win 0.82% Average Loss -0.56% Compounding Annual Return 12.635% Drawdown 4.100% Expectancy 0.639 Net Profit 2.564% Sharpe Ratio 1.163 Loss Rate 33% Win Rate 67% Profit-Loss Ratio 1.46 Alpha 0.071 Beta 0.775 Annual Standard Deviation 0.106 Annual Variance 0.011 Information Ratio 0.829 Tracking Error 0.068 Treynor Ratio 0.16 Total Fees \$42.89
```from QuantConnect.Data.Market import TradeBar
from datetime import timedelta
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
import decimal as d

class MyAlgorithm(QCAlgorithm):
def Initialize(self):
'''Initialise  the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

self.SetStartDate(2015, 05, 1)  # Set Start Date
self.SetEndDate(2015, 07, 19)
self.SetCash(100000)  # Set Strategy Cash

consolidator_daily.DataConsolidated += self.OnDailyData

consolidator_minute.DataConsolidated += self.OnMinuteData

self.Schedule.On(self.DateRules.EveryDay(),
self.TimeRules.At(9, 31),
Action(self.one_minute_after_open_market))

self.Schedule.On(self.DateRules.EveryDay(),
self.TimeRules.At(15, 59, 56),
Action(self.before_close_market))

# Add daily bar to daily rolling window
def OnDailyData(self, sender, bar):

def OnMinuteData(self, sender, bar):

def one_minute_after_open_market(self):
"""
At 9:31 check if there has been a gap at the market open from the previous day.
If so and the stock is gapping up and the first minute bar is negative, create a short selling signal.
If the stock is gapping down and the first minute bar is positive, create a buying signal.
"""
last_close = self.window[0].Close
yesterday_daily_close = self.daily_rw[1].Close
first_minute_close = self.minute_rw[1].Close
first_minute_open = self.minute_rw[1].Open

gap = last_close - yesterday_daily_close
first_minute_bar = first_minute_close - first_minute_open

if not self.Portfolio["SPY"].Invested:
# If the stock is gapping down and the first minute bar is positive, create a buying signal.
if gap < 0 and first_minute_bar > 0:
self.SetHoldings("SPY", 1)
self.Debug('GOING LONG')
# If the stock is gapping up and the first minute bar is negative, create a short selling signal
elif gap > 0 and first_minute_bar < 0:
self.SetHoldings("SPY", -1)
self.Debug('GOING SHORT')

def before_close_market(self):
if self.Portfolio["SPY"].IsShort:
self.Liquidate("SPY")
self.Debug('LIQUIDATE SHORT End of Day')

# Add second bar to window rolling window
def OnData(self, data):
if data["SPY"] is None:
return