# 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.

from AlgorithmImports import *
from sklearn.linear_model import LinearRegression

class ScikitLearnLinearRegressionAlgorithm(QCAlgorithm):

 def initialize(self):
 self.set_start_date(2013, 10, 7) # Set Start Date
 self.set_end_date(2013, 10, 8) # Set End Date

 self.lookback = 30 # number of previous days for training

 self.set_cash(100000) # Set Strategy Cash
 spy = self.add_equity("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.date_rules.every_day("SPY"), self.time_rules.after_market_open("SPY", 28), self.regression)
 self.schedule.on(self.date_rules.every_day("SPY"), self.time_rules.after_market_open("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.set_holdings(symbol, 1 / len(self.symbols))