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 Probabilistic 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 0.124 Tracking Error 0.149 Treynor Ratio 0 Total Fees $0.00 |
from collections import deque from datetime import datetime, timedelta from numpy import sum, std from QuantConnect.Data.Market import QuoteBar class PriceOverreactionsForex(QCAlgorithm): def Initialize(self): self.SetStartDate(2017, 12, 11) # Set Start Date self.SetEndDate(2018, 12, 31) # Set End Date self.SetCash(100000) # Set Strategy Cash self.syls = ["USDCAD","AUDUSD","EURJPY", "EURUSD", "USDCAD"] # Define Symbols for Trading self.days = 20 # Paramater to calculate reference self.open = {} self.averageDay = {} self.intraDay = {} for symbol in self.syls: self.AddForex(symbol, Resolution.Hour, Market.Oanda) self.averageDay[symbol] = AverageDay(symbol, self.days) self.RegisterIndicator(symbol, self.averageDay[symbol], Resolution.Daily) self.intraDay[symbol] = 0.0 self.open[symbol] = 0.0 self.SetWarmup(self.days) def OnData(self, data): if self.IsWarmingUp: return # for symbol in self.syls: # if self.open[symbol] == 0.0: # self.open[symbol] = data[symbol].Open # self.intraDay[symbol] = self.UpdateIntraday(symbol, data[symbol].Close) # def UpdateIntraday(self, symbol, lastClose): # r = 0.0 # r = ((lastClose / self.open[symbol])- 1 ) * 100 # return r class AverageDay(PythonIndicator): def __init__(self, name, period): self.Name = name self.Time = datetime.min self.Value = 0 self.queue = deque(maxlen=period) self.Positive = 0 self.Negative = 0 def __repr__(self): return "{0} -> IsReady: {1}. Time: {2}. Value: {3}".format(self.Name, self.IsReady, self.Time, self.Value) # Update method is mandatory def Update(self, input): r = ((input.Close / input.Open) - 1 ) * 100 self.queue.appendleft(r) count = len(self.queue) self.Time = input.EndTime average = sum(self.queue) / count deviation = std(self.queue) self.Positive = average + (2 * deviation) self.Negative = average - (2 * deviation) return count == self.queue.maxlen