Overall Statistics |

Total Trades 6 Average Win 28.12% Average Loss -10.46% Compounding Annual Return 7.503% Drawdown 19.800% Expectancy 1.458 Net Profit 45.389% Sharpe Ratio 0.62 Loss Rate 33% Win Rate 67% Profit-Loss Ratio 2.69 Alpha 0.068 Beta -0.029 Annual Standard Deviation 0.105 Annual Variance 0.011 Information Ratio -0.202 Tracking Error 0.158 Treynor Ratio -2.236 Total Fees $17.55 |

# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals. # Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import clr clr.AddReference("System") clr.AddReference("QuantConnect.Algorithm") clr.AddReference("QuantConnect.Common") from System import * from QuantConnect import * from QuantConnect.Algorithm import * import numpy as np from sklearn.linear_model import LinearRegression class ScikitLearnLinearRegressionAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2013, 10, 7) # Set Start Date self.SetEndDate(2018, 12, 8) # Set End Date self.lookback = 60 # number of previous days for training self.SetCash(100000) # Set Strategy Cash spy = self.AddEquity("SPY", Resolution.Minute) self.symbols = [ spy.Symbol ] # In the future, we can include more symbols to the list in this way self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 28), self.Regression) self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 30), self.Trade) def Regression(self): # Daily historical data is used to train the machine learning model history = self.History(self.symbols, self.lookback, Resolution.Daily) # price dictionary: key: symbol; value: historical price self.prices = {} # slope dictionary: key: symbol; value: slope self.slopes = {} for symbol in self.symbols: if not history.empty: # get historical open price self.prices[symbol] = list(history.loc[symbol.Value]['open']) # A is the design matrix A = range(self.lookback + 1) for symbol in self.symbols: if symbol in self.prices: # response Y = self.prices[symbol] # features X = np.column_stack([np.ones(len(A)), A]) # data preparation length = min(len(X), len(Y)) X = X[-length:] Y = Y[-length:] A = A[-length:] # fit the linear regression reg = LinearRegression().fit(X, Y) # run linear regression y = ax + b b = reg.intercept_ a = reg.coef_[1] # store slopes for symbols self.slopes[symbol] = a/b def Trade(self): # if there is no open price if not self.prices: return thod_buy = 0.001 # threshold of slope to buy thod_liquidate = -0.001 # threshold of slope to liquidate for holding in self.Portfolio.Values: slope = self.slopes[holding.Symbol] # liquidate when slope smaller than thod_liquidate if holding.Invested and slope < thod_liquidate: self.Liquidate(holding.Symbol) for symbol in self.symbols: # buy when slope larger than thod_buy if self.slopes[symbol] > thod_buy: self.SetHoldings(symbol, 1 / len(self.symbols))