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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/SetEquityDataNormalizationModeOnAddEquity.py
+25 -25
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@@ -15,39 +15,39 @@ from AlgorithmImports import *
### <summary>
### This regression algorithm has examples of how to add an equity indicating the <see cref="DataNormalizationMode"/>
### directly with the <see cref="QCAlgorithm.AddEquity"/> method instead of using the <see cref="Equity.SetDataNormalizationMode"/> method.
### directly with the <see cref="QCAlgorithm.add_equity"/> method instead of using the <see cref="Equity.SET_DATA_NORMALIZATION_MODE"/> method.
### </summary>
class SetEquityDataNormalizationModeOnAddEquity(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2013, 10, 7)
self.SetEndDate(2013, 10, 7)
def initialize(self):
self.set_start_date(2013, 10, 7)
self.set_end_date(2013, 10, 7)
spyNormalizationMode = DataNormalizationMode.Raw
ibmNormalizationMode = DataNormalizationMode.Adjusted
aigNormalizationMode = DataNormalizationMode.TotalReturn
spy_normalization_mode = DataNormalizationMode.RAW
ibm_normalization_mode = DataNormalizationMode.ADJUSTED
aig_normalization_mode = DataNormalizationMode.TOTAL_RETURN
self._priceRanges = {}
self._price_ranges = {}
spyEquity = self.AddEquity("SPY", Resolution.Minute, dataNormalizationMode=spyNormalizationMode)
self.CheckEquityDataNormalizationMode(spyEquity, spyNormalizationMode)
self._priceRanges[spyEquity] = (167.28, 168.37)
spy_equity = self.add_equity("SPY", Resolution.MINUTE, data_normalization_mode=spy_normalization_mode)
self.check_equity_data_normalization_mode(spy_equity, spy_normalization_mode)
self._price_ranges[spy_equity] = (167.28, 168.37)
ibmEquity = self.AddEquity("IBM", Resolution.Minute, dataNormalizationMode=ibmNormalizationMode)
self.CheckEquityDataNormalizationMode(ibmEquity, ibmNormalizationMode)
self._priceRanges[ibmEquity] = (135.864131052, 136.819606508)
ibm_equity = self.add_equity("IBM", Resolution.MINUTE, data_normalization_mode=ibm_normalization_mode)
self.check_equity_data_normalization_mode(ibm_equity, ibm_normalization_mode)
self._price_ranges[ibm_equity] = (135.864131052, 136.819606508)
aigEquity = self.AddEquity("AIG", Resolution.Minute, dataNormalizationMode=aigNormalizationMode)
self.CheckEquityDataNormalizationMode(aigEquity, aigNormalizationMode)
self._priceRanges[aigEquity] = (48.73, 49.10)
aig_equity = self.add_equity("AIG", Resolution.MINUTE, data_normalization_mode=aig_normalization_mode)
self.check_equity_data_normalization_mode(aig_equity, aig_normalization_mode)
self._price_ranges[aig_equity] = (48.73, 49.10)
def OnData(self, slice):
for equity, (minExpectedPrice, maxExpectedPrice) in self._priceRanges.items():
if equity.HasData and (equity.Price < minExpectedPrice or equity.Price > maxExpectedPrice):
raise Exception(f"{equity.Symbol}: Price {equity.Price} is out of expected range [{minExpectedPrice}, {maxExpectedPrice}]")
def on_data(self, slice):
for equity, (min_expected_price, max_expected_price) in self._price_ranges.items():
if equity.has_data and (equity.price < min_expected_price or equity.price > max_expected_price):
raise Exception(f"{equity.symbol}: Price {equity.price} is out of expected range [{min_expected_price}, {max_expected_price}]")
def CheckEquityDataNormalizationMode(self, equity, expectedNormalizationMode):
subscriptions = [x for x in self.SubscriptionManager.Subscriptions if x.Symbol == equity.Symbol]
if any([x.DataNormalizationMode != expectedNormalizationMode for x in subscriptions]):
raise Exception(f"Expected {equity.Symbol} to have data normalization mode {expectedNormalizationMode} but was {subscriptions[0].DataNormalizationMode}")
def check_equity_data_normalization_mode(self, equity, expected_normalization_mode):
subscriptions = [x for x in self.subscription_manager.subscriptions if x.symbol == equity.symbol]
if any([x.data_normalization_mode != expected_normalization_mode for x in subscriptions]):
raise Exception(f"Expected {equity.symbol} to have data normalization mode {expected_normalization_mode} but was {subscriptions[0].data_normalization_mode}")