def __init__(self):
# Create some assets:
assetsList = [Asset('WS30', 'traditional', 'historical'), # Index US
Asset('XAUUSD', 'traditional', 'historical'), # Commodity
Asset('GDAXIm', 'traditional', 'historical'), # Index EUR
Asset('EURUSD', 'traditional', 'historical'), # Major
Asset('GBPJPY', 'traditional', 'historical') # Minor
]
# Initialize the ResearchStudy class:
super().__init__('MarkovAutoRegressiveModels', assetsList)
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
# Initialize the ResearchStudy class:
super().__init__('HiddenMarkovModel', assetsList)
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')
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'])
def __init__(self):
# Create some assets:
assetsList = [
Asset('WS30', 'traditional', 'historical'), # Index US
Asset('XAUUSD', 'traditional', 'historical'), # Commodity
Asset('GDAXIm', 'traditional', 'historical'), # Index EUR
Asset('EURUSD', 'traditional', 'historical'), # Major
Asset('GBPJPY', 'traditional', 'historical')
] # Minor
# Initialize the ResearchStudy class:
super().__init__('HiddenMarkovModel', assetsList)
# Make a random seed to reproduce results:
np.random.seed(33)
def __init__(self):
# Create some assets:
assetsList = [Asset('WS30', 'traditional', 'historical'), # Index US
Asset('XAUUSD', 'traditional', 'historical'), # Commodity
Asset('GDAXIm', 'traditional', 'historical'), # Index EUR
Asset('EURUSD', 'traditional', 'historical'), # Major
Asset('GBPJPY', 'traditional', 'historical')] # Minor
# Initialize the ResearchStudy class:
super().__init__('KalmanFilterCloseModel', assetsList)
# Make a random seed to reproduce results:
np.random.seed(33)
# Print to see if working:
logger.warning(self.PORTFOLIO._portfolioDict['WS30'])
### First, we append the previous level to the sys.path var:
import sys, os
### We append the repository path to the sys.path so that we can import packages easily.
sys.path.append(os.path.expandvars('${HOME}/Desktop/quant-research-env/'))
# Import the class:
from RegimeAnalysisContentSeries.Python_Classes.AssetClass import Asset
from RegimeAnalysisContentSeries.Python_Classes.ResearchStudyClass import ResearchStudy
import os, pprint
# 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')
# Apply the reseach study we want:
R_STUDY._generateDARWINTickBars(threshold=5000)
from RegimeAnalysisContentSeries.Python_Classes.ResearchStudyClass import ResearchStudy
from RegimeAnalysisContentSeries.Python_Classes.AssetClass import Asset
import os
# Create some path variables > Point them to the specific folder:
homeStr = os.path.expanduser("~")
plotsSaveDirectory = os.path.expandvars(
f'{homeStr}/Desktop/quant-research-env/RegimeAnalysisContentSeries/Plots/Plots_Others'
)
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)