Python Asset Examples

Example #1

File: MarkovARModel3.py Project: vaiblast/quant-research-env

    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)

Example #2

    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)

Example #3

    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')

Example #4

    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')

Example #5

File: TSDecompositionTradAssets.py Project: faroresearch/quant-research-env

    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'])

Example #6

    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)

Example #7

File: KalmanFilterClose.py Project: vaiblast/quant-research-env

    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'])

Example #8

### 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)

Example #9

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)