def __init__(self):
# Create some assets:
assetsList = [
Asset('PLF_4.1', 'darwin', 'historical'),
Asset('LVS_4.20', 'darwin', 'historical'),
Asset('SYO_4.24', 'darwin', 'historical')
]
self.R_STUDY = ResearchStudy(assetsList,
formOrRead='read_darwin',
formerOrNew='former')
self.R_STUDY._generateResampledAndFilteredSeries(resampleRule='1D')
def __init__(self):
# Create some assets:
assetsList = [
Asset('WS30', 'traditional', 'historical'), # Index US
Asset('XAUUSD', 'traditional', 'historical'), # CryptoCurrency
Asset('GDAXIm', 'traditional', 'historical'), # Index EUR
Asset('EURUSD', 'traditional', 'historical'), # Major
Asset('GBPJPY', 'traditional', 'historical')
] # Minor
# Create the RSTUDY object:
self.R_STUDY = ResearchStudy(assetsList, formOrRead='read_features')
class TSDecomposition(BaseModel):
def __init__(self):
# Create some assets:
assetsList = [
Asset('PLF_4.1', 'darwin', 'historical'),
Asset('LVS_4.20', 'darwin', 'historical'),
Asset('SYO_4.24', 'darwin', 'historical')
]
self.R_STUDY = ResearchStudy(assetsList,
formOrRead='read_darwin',
formerOrNew='former')
self.R_STUDY._generateResampledAndFilteredSeries(resampleRule='1D')
def executeSeasonalDecompose(self, saveDirectory):
# Loop the portfolio dict:
for eachAssetName, eachAssetDataFrame in self.R_STUDY.PORTFOLIO._portfolioDict.items(
):
# Make the decompose:
TS_DECOMPOSED = seasonal_decompose(eachAssetDataFrame['close'],
model='aditive',
period=20)
#TS_DECOMPOSED = seasonal_decompose(eachAssetDataFrame['Returns'], model='multiplicative', period=20)
print(
f'[executeSeasonalDecompose] - Will plot decomposed series for asset {eachAssetName}'
)
# Plot and show it:
TS_DECOMPOSED.plot()
# In PNG:
plt.savefig(saveDirectory + f'/TSDec_DARWIN_{eachAssetName}.png')
plt.show()
def __init__(self):
# Create some assets:
assetsList = [
Asset('WS30', 'traditional', 'historical'), # Index US
Asset('XAUUSD', 'traditional', 'historical'), # CryptoCurrency
Asset('GDAXIm', 'traditional', 'historical'), # Index EUR
Asset('EURUSD', 'traditional', 'historical'), # Major
Asset('GBPJPY', 'traditional', 'historical')
] # Minor
# Create the RSTUDY object:
self.R_STUDY = ResearchStudy(assetsList, formOrRead='read_features')
# Print to see if working:
print(self.R_STUDY.PORTFOLIO._portfolioDict['WS30'])
assetsList = [
Asset('PLF_4.1', 'darwin', 'historical'),
Asset('LVS_4.20', 'darwin', 'historical'),
Asset('SYO_4.24', 'darwin', 'historical')
]
# Create some path variables > Point them to the specific folder:
homeStr = os.path.expanduser("~")
plotsSaveDirectory = os.path.expandvars(
f'{homeStr}/Desktop/quant-research-env/DARWINStrategyContentSeries/Data')
dataframesSaveDirectory = os.path.expandvars(
f'{homeStr}/Desktop/quant-research-env/DARWINStrategyContentSeries/Data')
#R_STUDY = ResearchStudy(assetsList, formOrRead='form_darwin', saveTheData=True)
R_STUDY = ResearchStudy(assetsList,
formOrRead='read_darwin',
formerOrNew='former')
# Apply the reseach study we want:
R_STUDY._generateDARWINTickBars(threshold=5000)
#R_STUDY._generateDARWINDollarBars(threshold=1000000000)
# Plot candles and indicators:
R_STUDY._plotCandleAndIndicatorsOtherBars(plotsSaveDirectory, showIt=True)
# Generate the plots:
R_STUDY._plotReturnsOtherBars(plotsSaveDirectory, showIt=True)
R_STUDY._plotDistributionOtherBars(plotsSaveDirectory, showIt=True)
#R_STUDY._plotQQPlotOtherBars(plotsSaveDirectory, showIt=True)
# Save the generated dataframes:
assetsList = [
Asset('PLF_4.1', 'darwin', 'historical'),
Asset('LVS_4.20', 'darwin', 'historical'),
Asset('SYO_4.24', 'darwin', 'historical')
]
# Create some path variables > Point them to the specific folder:
homeStr = os.path.expanduser("~")
plotsSaveDirectory = os.path.expandvars(
f'{homeStr}/Desktop/quant-research-env/DARWINStrategyContentSeries/Data')
dataframesSaveDirectory = os.path.expandvars(
f'{homeStr}/Desktop/quant-research-env/DARWINStrategyContentSeries/Data')
#R_STUDY = ResearchStudy(assetsList, formOrRead='form_darwin', saveTheData=True)
R_STUDY = ResearchStudy(assetsList,
formOrRead='read_darwin',
formerOrNew='former')
# Print it:
pprint.pprint(R_STUDY.PORTFOLIO._portfolioDict)
###################### DARWIN SPECIFICS ######################
# Apply the reseach study we want:
R_STUDY._generateResampledAndFilteredSeries(resampleRule='1D')
#R_STUDY._generateResampledAndFilteredSeries(resampleRule='4H')
#R_STUDY._generateResampledAndFilteredSeries(resampleRule='30min')
###################### DARWIN SPECIFICS ######################
# Apply the reseach study we want:
#R_STUDY._generateRawReturns()
R_STUDY._generateLogReturns()
dataframesSaveDirectory = os.path.expandvars(
f'{homeStr}/Desktop/quant-research-env/RegimeAnalysisContentSeries/Data/Data_Others'
)
# Create some assets:
assetsList = [
Asset('WS30', 'traditional', 'historical'), # Index US
Asset('XAUUSD', 'traditional', 'historical'), # CryptoCurrency
Asset('GDAXIm', 'traditional', 'historical'), # Index EUR
Asset('EURUSD', 'traditional', 'historical'), # Major
Asset('GBPJPY', 'traditional', 'historical')
] # Minor
# Read it from file:
# NOTE: If the data is not in the /Data directory>
# we will need to change the path in the AssetClass _readBidAndAskHistoricalData method.
R_STUDY = ResearchStudy(assetsList,
formOrRead='read',
dateHourString='2020-02-04_23')
# Apply the reseach study we want:
R_STUDY._generateTickBars(endDate='2020-02-04_23', threshold=1522)
#R_STUDY._generateDollarBars(endDate='2020-02-04_23', threshold=7000000000)
# Generate the plots:
#R_STUDY._plotReturnsOtherBars(plotsSaveDirectory, showIt=True)
R_STUDY._plotDistributionOtherBars(plotsSaveDirectory, showIt=True)
R_STUDY._plotQQPlotOtherBars(plotsSaveDirectory, showIt=True)
# Save the generated dataframes:
#R_STUDY._saveGeneratedDataFramesOtherBars(dataframesSaveDirectory)
)
# Create some assets:
assetsList = [
Asset('WS30', 'traditional', 'historical'), # Index US
Asset('XAUUSD', 'traditional', 'historical'), # CryptoCurrency
Asset('GDAXIm', 'traditional', 'historical'), # Index EUR
Asset('EURUSD', 'traditional', 'historical'), # Major
Asset('GBPJPY', 'traditional', 'historical')
] # Minor
# Create the research study object:
# Get the tick data or some time aggregated representation:
#R_STUDY = ResearchStudy(assetsList, formOrRead='form', sampleFormat='tick')
#R_STUDY = ResearchStudy(assetsList, formOrRead='form', sampleFormat='5T')
# Or read it from file:
# NOTE: If the data is not in the /Data directory>
# we will need to change the path in the AssetClass _readBidAndAskHistoricalData method.
R_STUDY = ResearchStudy(assetsList,
formOrRead='read',
dateHourString='2020-02-04_23')
# Let's see the data:
pprint.pprint(R_STUDY.PORTFOLIO._portfolioDict['WS30'])
# Perform some calculations on the data:
R_STUDY._generateRawReturns()
# Save the generated dataframes:
R_STUDY._saveGeneratedDataFrames(dataframesSaveDirectory)
)
# Create some assets:
assetsList = [
Asset('WS30', 'traditional', 'historical'), # Index US
Asset('XAUUSD', 'traditional', 'historical'), # CryptoCurrency
Asset('GDAXIm', 'traditional', 'historical'), # Index EUR
Asset('EURUSD', 'traditional', 'historical'), # Major
Asset('GBPJPY', 'traditional', 'historical')
] # Minor
# Read it from file:
# NOTE: If the data is not in the /Data directory>
# we will need to change the path in the AssetClass _readBidAndAskHistoricalData method.
R_STUDY = ResearchStudy(assetsList,
formOrRead='read',
dateHourString='2020-02-04_23')
# Print the whole dict, some asset or the shape:
#pprint.pprint.warning(R_STUDY.PORTFOLIO._portfolioDict)
#pprint.pprint.warning(R_STUDY.PORTFOLIO._portfolioDict['WS30'])
#pprint.pprint.warning(R_STUDY.PORTFOLIO._portfolioDict['WS30'].shape)
# Apply the reseach study we want:
R_STUDY._generateRawReturns()
#R_STUDY._generateLogReturns()
#R_STUDY._generateRollingMean()
#R_STUDY._generateRollingMean(rollingWindow=40)
# Generate the plots:
#R_STUDY._plotReturns(plotsSaveDirectory, showIt=True)
# Create some assets:
assetsList = [
Asset('PLF_4.1', 'darwin', 'historical'),
Asset('LVS_4.20', 'darwin', 'historical'),
Asset('SYO_4.24', 'darwin', 'historical')
]
# Create some path variables > Point them to the specific folder:
homeStr = os.path.expanduser("~")
plotsSaveDirectory = os.path.expandvars(
f'{homeStr}/Desktop/quant-research-env/DARWINStrategyContentSeries/Data')
dataframesSaveDirectory = os.path.expandvars(
f'{homeStr}/Desktop/quant-research-env/DARWINStrategyContentSeries/Data')
#R_STUDY = ResearchStudy(assetsList, formOrRead='form_darwin', saveTheData=True)
R_STUDY = ResearchStudy(assetsList,
formOrRead='read_darwin',
formerOrNew='former')
# Print it:
pprint.pprint(R_STUDY.PORTFOLIO._portfolioDict)
# Apply the reseach study we want:
R_STUDY._generateResampledAndFilteredSeries(resampleRule='4H')
# Print it:
pprint.pprint(R_STUDY.PORTFOLIO._portfolioDict)
# Save the generated dataframes:
R_STUDY._saveDarwinGeneratedDataFrames(dataframesSaveDirectory)