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pep8 conversion of python algo #8 (#7946)

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* pep8 conversion

* Minor tweaks

---------

Co-authored-by: Martin Molinero <martin.molinero1@gmail.com>
This commit is contained in:
Louis Szeto
2024-04-19 03:08:34 +08:00
committed by GitHub
parent e8362c12a3
commit 7e5b8d6243
26 changed files with 806 additions and 804 deletions
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Algorithm.Python/HistoryWithDifferentDataNormalizationModeRegressionAlgorithm.py
+28 -27
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@@ -17,40 +17,41 @@ from AlgorithmImports import *
### Regression algorithm illustrating how to request history data for different data normalization modes.
### </summary>
class HistoryWithDifferentDataMappingModeRegressionAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2013, 10, 7)
self.SetEndDate(2014, 1, 1)
self.aaplEquitySymbol = self.AddEquity("AAPL", Resolution.Daily).Symbol
self.esFutureSymbol = self.AddFuture(Futures.Indices.SP500EMini, Resolution.Daily).Symbol
def OnEndOfAlgorithm(self):
equityDataNormalizationModes = [
DataNormalizationMode.Raw,
DataNormalizationMode.Adjusted,
DataNormalizationMode.SplitAdjusted
def initialize(self):
self.set_start_date(2013, 10, 7)
self.set_end_date(2014, 1, 1)
self.aapl_equity_symbol = self.add_equity("AAPL", Resolution.DAILY).symbol
self.es_future_symbol = self.add_future(Futures.Indices.SP_500_E_MINI, Resolution.DAILY).symbol
def on_end_of_algorithm(self):
equity_data_normalization_modes = [
DataNormalizationMode.RAW,
DataNormalizationMode.ADJUSTED,
DataNormalizationMode.SPLIT_ADJUSTED
]
self.CheckHistoryResultsForDataNormalizationModes(self.aaplEquitySymbol, self.StartDate, self.EndDate, Resolution.Daily,
equityDataNormalizationModes)
self.check_history_results_for_data_normalization_modes(self.aapl_equity_symbol, self.start_date, self.end_date, Resolution.DAILY,
equity_data_normalization_modes)
futureDataNormalizationModes = [
DataNormalizationMode.Raw,
DataNormalizationMode.BackwardsRatio,
DataNormalizationMode.BackwardsPanamaCanal,
DataNormalizationMode.ForwardPanamaCanal
future_data_normalization_modes = [
DataNormalizationMode.RAW,
DataNormalizationMode.BACKWARDS_RATIO,
DataNormalizationMode.BACKWARDS_PANAMA_CANAL,
DataNormalizationMode.FORWARD_PANAMA_CANAL
]
self.CheckHistoryResultsForDataNormalizationModes(self.esFutureSymbol, self.StartDate, self.EndDate, Resolution.Daily,
futureDataNormalizationModes)
self.check_history_results_for_data_normalization_modes(self.es_future_symbol, self.start_date, self.end_date, Resolution.DAILY,
future_data_normalization_modes)
def CheckHistoryResultsForDataNormalizationModes(self, symbol, start, end, resolution, dataNormalizationModes):
historyResults = [self.History([symbol], start, end, resolution, dataNormalizationMode=x) for x in dataNormalizationModes]
historyResults = [x.droplevel(0, axis=0) for x in historyResults] if len(historyResults[0].index.levels) == 3 else historyResults
historyResults = [x.loc[symbol].close for x in historyResults]
def check_history_results_for_data_normalization_modes(self, symbol, start, end, resolution, data_normalization_modes):
history_results = [self.history([symbol], start, end, resolution, data_normalization_mode=x) for x in data_normalization_modes]
history_results = [x.droplevel(0, axis=0) for x in history_results] if len(history_results[0].index.levels) == 3 else history_results
history_results = [x.loc[symbol].close for x in history_results]
if any(x.size == 0 or x.size != historyResults[0].size for x in historyResults):
if any(x.size == 0 or x.size != history_results[0].size for x in history_results):
raise Exception(f"History results for {symbol} have different number of bars")
# Check that, for each history result, close prices at each time are different for these securities (AAPL and ES)
for j in range(historyResults[0].size):
closePrices = set(historyResults[i][j] for i in range(len(historyResults)))
if len(closePrices) != len(dataNormalizationModes):
for j in range(history_results[0].size):
close_prices = set(history_results[i][j] for i in range(len(history_results)))
if len(close_prices) != len(data_normalization_modes):
raise Exception(f"History results for {symbol} have different close prices at the same time")