| 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 Tracking Error 0 Treynor Ratio 0 Total Fees $0.00 |
from sklearn import linear_model
import numpy as np
import pandas as pd
from scipy import stats
from math import floor
from datetime import timedelta
class PairsTradingAlgo(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2018,1,1)
self.SetEndDate(2020,1,20)
self.SetCash(10000)
self.numdays = 250 # set length of the training period
tickers = ["AMD","ADBE"]
self.symbols = []
self.threshold = 1.
for i in tickers :
self.symbols.append(self.AddSecurity(SecurityType.Equity , i , Resolution.Daily).Symbol)
for i in self.symbols:
i.hist_window = RollingWindow[Tradebar](self.numdays)
def OnData(self, data):
if not (data.ContainsKey("AMD") and data.ContainsKey("ADBE")): return
for symbol in self.symbol():
symbol.hist_window.Add(data[symbol])
price_x = pd.Series([float(i.Close) for i in self.symbols[0].hist_window],
index = [i.Time for i in self.symbols[0].hist_window])
price_y = pd.Series([float(i.Close) for i in self.symbols[1].hist_window],
index = [i.Time for i in self.symbols[1].hist_window])
if len (price_x) < 250: return
spread = self.regr(np.log(price_x), np.log(price_y))
#try later the rolling version and see if you can get a better results
mean = np.mean(spread)
std = np.std(spread)
# >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
# >>> s.rolling(3).std()
ratio = floor(self.Portfolio[self.symbols[1]].Price / self.Portfolio[self.symbols[0]].Price)
# quantity = float(self.CalculateOrderQuantity(self.symbols[0] , 0.4)
# upper threshold i.e. exit and entry signals
if spread [-1] > mean + self.threshold * std:
if not self.Portfolio[self.symbols[0]].Quantity > 0 and not self.Portfolio[self.symbols[0]].Quantity < 0:
self.Sell(self.symbols[1], 100)
self.Buy(self.symbols[0], ratio * 100)
elif spread [-1] < mean - self.threshold * std:
if not self.Portfolio[self.symbols[0]].Quantity < 0 and not self.Portfolio[self.symbols[0]].Quantity > 0:
self.Sell(self.symbols[0], 100)
self.Buy(self.symbols[1], ratio * std)
else:
self.Liquidate()
# Your New Python Fileclass fading_the_gap(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2018, 7, 19) # Set Start Date
self.SetCash(100000) # Set Strategy Cash
# self.AddEquity("TSLA", Resolution.Minute)
# Create an event to run every day 0-minute after the close