| Overall Statistics |
|
Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 0 Tracking Error 0 Treynor Ratio 0 Total Fees $0.00 |
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 k in self.symbolData:
if not (self.symbolData[k].window.IsReady and self.symbolData[k].daily_rw.IsReady and self.symbolData[k].minute_rw.IsReady): return
last_close = self.symbolData[k].window[0].Close
yesterday_daily_close = self.symbolData[k].daily_rw[1].Close
first_minute_close = self.symbolData[k].minute_rw[1].Close
first_minute_open = self.symbolData[k].minute_rw[1].Open
gap = last_close - yesterday_daily_close
first_minute_bar = first_minute_close - first_minute_open
if not self.Portfolio[k].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(k, 0.333)
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(k, -0.333)
self.Log('GOING SHORT')
def before_close_market(self):
"""
At the end of the day, if there is a short position, close it.
"""
for k in self.symbolData:
if self.Portfolio[k].Invested:
self.Liquidate(k)
self.Log('LIQUIDATE SHORT End of Day')
def OnData(self, data):
if data["SPY"] is None:
return
for k in self.symbolData:
if not self.symbolData[k].window.IsReady:
return
if self.Portfolio[k].Invested:
factor = d.Decimal(1.01)
currBar = self.symbolData[stock].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[k].AveragePrice * factor < currBar:
self.Liquidate(k)
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)