Overall Statistics Total Trades 13 Average Win 0.67% Average Loss -0.37% Compounding Annual Return 11.538% Drawdown 3.800% Expectancy 0.882 Net Profit 2.351% Sharpe Ratio 1.098 Loss Rate 33% Win Rate 67% Profit-Loss Ratio 1.82 Alpha 0.06 Beta 0.791 Annual Standard Deviation 0.104 Annual Variance 0.011 Information Ratio 0.769 Tracking Error 0.06 Treynor Ratio 0.144 Total Fees \$32.72
```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):
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.AfterMarketOpen('SPY', 1),
Action(self.one_minute_after_open_market))

self.Schedule.On(self.DateRules.EveryDay(),
self.TimeRules.BeforeMarketClose('SPY', 1),
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.Log('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.Log('GOING SHORT')

def before_close_market(self):
"""
At the end of the day, if there is a short position, close it.
"""
if self.Portfolio["SPY"].IsShort:
self.Liquidate("SPY")
self.Log('LIQUIDATE SHORT End of Day')

# Add second bar to window rolling window
def OnData(self, data):
if data["SPY"] is None:
return
return
# self.Log("haha")
factor = d.Decimal(1.01)

currBar = self.window[0].Close

# Every second, check the price and if it's higher than the price the stock was bought for times 1.01, close the position.
if self.Portfolio["SPY"].Invested and self.Portfolio["SPY"].AveragePrice * factor < currBar:
self.Liquidate("SPY")
self.Log('LIQUIDATE AT THRESHOLD REACHED.')

def OnEndOfDay(self):
self.Plot("Portfolio", "MarginRemaining", self.Portfolio.MarginRemaining)

def OnEndOfAlgorithm(self):
self.Liquidate()
self.Log('LIQUIDATE AT End Of Algorithm.')                        ```