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pep8 conversion of python algos (#7948)

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* pep8 conversion of python algos

* adding 10 more pep8 converted algos

* PEP8 updates/fixes

---------

Co-authored-by: Jhonathan Abreu <jdabreu25@gmail.com>
This commit is contained in:
Ashutosh
2024-04-19 19:36:48 +05:30
committed by GitHub
parent 77591f90c7
commit 3c30e255fe
21 changed files with 490 additions and 491 deletions
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Algorithm.Python/ScikitLearnLinearRegressionAlgorithm.py
+38 -38
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@@ -1,10 +1,10 @@
# 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");
#
# 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.
@@ -16,77 +16,77 @@ from sklearn.linear_model import LinearRegression
class ScikitLearnLinearRegressionAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2013, 10, 7) # Set Start Date
self.SetEndDate(2013, 10, 8) # Set End Date
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.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):
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)
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'])
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):
def trade(self):
# if there is no open price
if not self.prices:
return
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]
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)
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))
self.set_holdings(symbol, 1 / len(self.symbols))