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
|
import numpy as np from datetime import datetime class williams(QCAlgorithm): def Initialize(self): self.SetStartDate(2017,12,1) #Set Start Date self.SetEndDate(2017,12,14) #Set End Date self.SetCash(100000) #Set Strategy Cash self.AddEquity("SPY", Resolution.Minute) self.spy = self.AddEquity("SPY", Resolution.Minute).Symbol # at market close self.Schedule.On(self.DateRules.EveryDay(self.spy), \ self.TimeRules.BeforeMarketClose(self.spy, -5), \ Action(self.EveryDayAtMarketClose)) def OnData(self,data): pass def EveryDayAtMarketClose(self): lookback = 14 hist = self.History(self.spy, lookback, Resolution.Daily) highest_high = max(hist['high'][-lookback:]) lowest_low = min(hist['low'][-lookback:]) close = float(self.Securities[self.spy].Close) self.will = (highest_high - close) / (highest_high - lowest_low) * - 100 self.Log('will {}'.format(self.will)) self.Plot('Trade Plot', 'Williams%R:', self.will)