| Overall Statistics |
|
Total Trades 36 Average Win 0.29% Average Loss -0.41% Compounding Annual Return -26.125% Drawdown 2.500% Expectancy -0.341 Net Profit -2.512% Sharpe Ratio -4.298 Loss Rate 61% Win Rate 39% Profit-Loss Ratio 0.70 Alpha -0.262 Beta -0.072 Annual Standard Deviation 0.067 Annual Variance 0.005 Information Ratio -5.211 Tracking Error 0.127 Treynor Ratio 3.999 Total Fees $143.39 |
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(2013, 05, 1) # Set Start Date
self.SetEndDate(2013, 06, 01)
self.SetCash(100000) # Set Strategy Cash
self.symbolData = dict()
for ticker in ["SPY", "FB", "TWTR"]:
symbol = self.AddEquity(ticker, Resolution.Second).Symbol
consolidator_daily = TradeBarConsolidator(timedelta(1))
consolidator_daily.DataConsolidated += self.OnDailyData
self.SubscriptionManager.AddConsolidator(symbol, consolidator_daily)
consolidator_minute = TradeBarConsolidator(60)
consolidator_minute.DataConsolidated += self.OnMinuteData
self.SubscriptionManager.AddConsolidator(symbol, consolidator_minute)
self.symbolData[symbol] = SymbolData()
self.Schedule.On(self.DateRules.EveryDay(),
self.TimeRules.AfterMarketOpen('SPY', 2),
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):
self.symbolData[bar.Symbol].daily_rw.Add(bar)
def OnMinuteData(self, sender, bar):
self.symbolData[bar.Symbol].minute_rw.Add(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.
"""
for symbol in self.Securities.Keys:
window = self.symbolData[symbol].window
daily = self.symbolData[symbol].daily_rw
minute = self.symbolData[symbol].minute_rw
if not (window.IsReady and daily.IsReady and minute.IsReady): continue
last_close = window[0].Close
yesterday_daily_close = daily[1].Close
first_minute_close = minute[1].Close
first_minute_open = minute[1].Open
gap = last_close - yesterday_daily_close
first_minute_bar = first_minute_close - first_minute_open
if not self.Portfolio[symbol].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(symbol, 1.0/3.0)
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(symbol, -1.0/3.0)
self.Log('GOING SHORT')
def before_close_market(self):
"""
At the end of the day, if there is a short position, close it.
"""
self.Liquidate()
self.Log('LIQUIDATE SHORT End of Day')
def OnData(self, data):
for symbol in data.Keys:
if data[symbol] is None: continue
# Create local variable to readability
window = self.symbolData[symbol].window
# Update the window. If not ready, continue
window.Add(data[symbol])
if not window.IsReady: continue
if self.Portfolio[symbol].Invested:
# 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[symbol].AveragePrice * d.Decimal(1.01) < window[0].Close:
self.Liquidate(symbol)
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.')
class SymbolData(object):
def __init__(self):
self.daily_rw = RollingWindow[TradeBar](2)
self.minute_rw = RollingWindow[TradeBar](2)
self.window = RollingWindow[TradeBar](2)