mstarpy 0.0.0

Mutual funds data extraction from MorningStar with Python

Mutual funds data extraction from MorningStar with Python

mstarpy is a Python package to get mutual funds data from MorningStar.

Installation

To get this package working you will need to install it via pip (with a Python 3.10 version or higher) on the terminal by typing:

$ pip install mstarpy

Usage

Carbon footprint

Understand the carbon footprint of the funds.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.carbonMetrics())

{'carbonPortfolioCoveragePct': '97.70', 'carbonRiskScore': '3.24', 'carbonRiskScoreCategoryAverage': '4.42', 'carbonRiskScoreCategoryHigh': '20.60', 'carbonRiskScoreCategoryLow': '0.11', 'carbonRiskScoreCategoryAverageDate': '2022-06-30T05:00:00.000', 'carbonRiskScoreCategoryRankPct': '44', 'carbonRiskScoreDate': '2022-06-30T05:00:00.000', 'categoryDate': '2022-06-30T05:00:00.000', 'categoryName': 'Sector Equity Technology', 'fossilFuelInvolvementPctCategoryAverage': '0.40', 'fossilFuelInvolvementPct': '0.00', 'fossilFuelInvolvementPctCategoryHigh': '16.12', 'fossilFuelInvolvementPctCategoryLow': '0.00', 'isLowCarbon': 'true'} 

ESG commitment

Discover the ESG commotment of the funds.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.esgData())

{'userType': 'Free', 'esgData': {'sociallyResponsibleFund': None, 'ethicalIssueStrategyFocus': None, 'portfolioDate': '2022-06-30T00:00:00.000', 'portfolioDateSustainabilityRating': '2022-06-30T05:00:00.000', 'fundESGScore': None, 'percentAUMCoveredESG': 100.0, 'fundSustainabilityScore': 18.24, 'percentAUMCoveredControversy': 100.0, 'categoryRankDate': '2022-06-30T05:00:00.000', 'sustainabilityFundQuintile': 4, 'sustainabilityPercentCategoryRank': 22.0, 'sustainabilityMandate': 'No', 'secId': 'F00000PXI1', 'performanceId': '0P0000YSYV', 'tradingSymbol': None, 'iSIN': None, 'fundId': 'FS0000A5KD', 'masterPortfolioId': '753260', 'categoryId': 'EUCA000542', 'name': 'BNP Paribas Disrpt Tech Cl C', 'controversyDeduction': None, 'categoryName': 'Sector Equity Technology', 'globalCategoryName': 'Technology Sector Equity', 'fundHistoryAvgSustainabilityScore': 18.55163, 'historicalSustainabilityScoreGlobalCategoryAverage': 20.73745, 'currentSustainabilityScoreGlobalCategoryAverage': 20.53731, 'numberofFundsAnalyzedinCategorySustainability': 1051, 'HistoryAvgSustainabilityPercentCategoryRank': None, 'sustainabilityRatingCorporateContributionPercent': 100.0, 'sustainabilityRatingSovereignContributionPercent': 0.0, 'portfolioSovereignsustainabilityscore': None, 'historicalSovereignSustainabilityScore': None, 'historicalSovereignSustainabilityCategoryAverage': 16.12813, 'sovereignSustainabilityRatingPercentOfEligiblePortfolioCovered': None}, 'esgScoreCalculation': {'basedPercentAUM': 100.0, 'portfolioESGScore': None, 'portfolioESGScoreCategory': None, 'controversyScore': None, 'controversyScoreCategory': None, 'sustainabilityScore': 18.24, 'sustainabilityScoreCategory': 20.53731, 'environmentalScore': 2.76, 'environmentalScoreCategory': None, 'socialScore': 7.86, 'socialScoreCategory': None, 'governanceScore': 6.68, 'governanceScoreCategory': None, 'portfolioDate': '2022-06-30T05:00:00.000', 'portfolioSustainabilityScore': 18.24, 'portfolioEnvironmentalRiskScore': 2.76, 'portfolioSocialRiskScore': 7.86, 'portfolioGovernanceRiskScore': 6.68, 'portfolioUnallocatedEsgRiskScore': 0.94, 'percentAUMCoveredControversy': 100.0, 'esgFundQuintile': None, 'esgPercentCategoryRank': None, 'controversyFundQuintile': None, 'controversyPercentCategoryRank': None, 'ePercentCategoryRank': None, 'sPercentCategoryRank': None, 'gPercentCategoryRank': None, 'categoryRankDate': 
'2022-06-30T05:00:00.000', 'historicalSustainabilityScoreGlobalCategoryAverage': 20.73745, 'currentSustainabilityScoreGlobalCategoryAverage': 20.53731, 'HistoryAvgSustainabilityPercentCategoryRank': 22.0, 'numberofFundsAnalyzedinCategorySustainability': 1051}, 'esgHoldingsAnalyst': '_PO_', 'esgScoreDistribution': '_PO_', 'esgLevelDistribution': '_PO_', 'sustainabilityIntentionality': {'investmentId': 'FS0000A5KD', 'eSGIncorporation': False, 'eSGEngagement': True, 'genderDiversity': False, 'lowCarbonFossilFuelFree': False, 'communityDevelopment': False, 'environmental': False, 'otherImpactThemes': False, 'renewableEnergy': False, 'waterFocused': False, 'generalEnvironmentalSector': False, 'sustainableInvestmentOverall': False, 'eSGFundOverall': False, 'impactFundOverall': False, 'environmentalSectorOverall': False, 'createdOn': '2019-02-28 04:01:00.0', 'lastUpdateDate': '2022-08-23 04:01:00.0', 'usesNormsBasedScreening': None, 'excludesAbortionStemCells': None, 'excludesAdultEntertainment': None, 'excludesAlcohol': None, 'excludesAnimalTesting': None, 'excludesControversialWeapons': None, 'excludesFurSpecialtyLeather': None, 'excludesGambling': None, 'excludesGMOs': None, 'excludesMilitaryContracting': None, 'excludesNuclear': None, 'excludesPalmOil': None, 'excludesPesticides': None, 'excludesSmallArms': None, 'excludesThermalCoal': None, 'excludesTobacco': None, 'excludesOther': None, 'employsExclusionsOverall': None}}

Credit quality

Monitor the credit quality of the funds.

from mstarpy import MS

ms = MS("credit")
print(ms.creditQuality())

{'fundName': 'AB Financial Credit I2 GBP H', 'categoryName': 'Other Bond', 'indexName': None, 'fund': {'creditQualityDate': '2022-06-30T05:00:00.000', 'creditQualityAAA': '3.42000', 'creditQualityAA': '1.13000', 'creditQualityA': '5.14000', 'creditQualityBBB': '41.80000', 'creditQualityBB': '32.55000', 'creditQualityB': '6.38000', 'creditQualityBelowB': '0.00000', 'creditQualityNotRated': '9.58000'}, 'category': {'creditQualityDate': '2022-06-30T05:00:00.000', 'creditQualityAAA': '14.53919', 'creditQualityAA': '4.60652', 'creditQualityA': '11.72232', 'creditQualityBBB': '23.47180', 'creditQualityBB': '15.10036', 'creditQualityB': '14.18666', 'creditQualityBelowB': '1.99186', 'creditQualityNotRated': '14.37841'}, 'index': {'creditQualityDate': None, 'creditQualityAAA': None, 'creditQualityAA': None, 'creditQualityA': None, 'creditQualityBBB': None, 'creditQualityBB': None, 'creditQualityB': None, 'creditQualityBelowB': None, 'creditQualityNotRated': None}}

Fixed income style

Get all information about funds invested in fixed income.

from mstarpy import MS

ms = MS("credit")
print(ms.fixedIncomeStyle())

{'isCan': False, 'portfolioDate': '2022-06-30T05:00:00.000', 'assetType': 'FIXEDINCOME', 'fixedIncStyleBox': 5, 'fund': {'secId': 'F000010J32', 'secName': 'AB 
Financial Credit I2 GBP H', 'portfolioDate': '2022-06-30T05:00:00.000', 'avgEffectiveDuration': 3.64, 'modifiedDuration': None, 'avgEffectiveMaturity': None, 'avgCreditQualityName': 'BBB-', 'surveyedAverageSurveyedCreditRating': 'BBB-', 'calculatedAverageCreditRating': None, 'avgCreditQualityDate': '2022-06-30T05:00:00.000', 'avgCoupon': 5.74412, 'avgPrice': 95.84698, 'yieldToMaturity': None}, 'categoryAverage': {'secId': 'EUCA000771', 'secName': 'Other Bond', 'portfolioDate': '2022-07-31T05:00:00.000', 'avgEffectiveDuration': 3.58052, 'modifiedDuration': 3.9772, 'avgEffectiveMaturity': 7.15901, 'avgCreditQualityName': None, 'surveyedAverageSurveyedCreditRating': None, 'calculatedAverageCreditRating': None, 'avgCreditQualityDate': '2022-06-30T05:00:00.000', 'avgCoupon': 3.44482, 'avgPrice': 96.53862, 'yieldToMaturity': 4.08176}}

Sector

Sector breakdown of funds investments.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.sector())

{'FIXEDINCOME': {'fundPortfolio': {'portfolioDate': '2022-06-30T05:00:00.000', 'government': 0.0, 'municipal': 0.0, 'corporate': 0.0, 'securitized': 0.0, 'cashAndEquivalents': 100.0, 'derivative': 0.0}, 'categoryPortfolio': {'portfolioDate': '2022-07-31T05:00:00.000', 'government': 3.21361, 'municipal': 0.0, 'corporate': 1.51201, 'securitized': 0.00731, 'cashAndEquivalents': 75.71447, 'derivative': 19.5526}, 'indexPortfolio': {'portfolioDate': None, 'government': None, 'municipal': None, 'corporate': None, 'securitized': None, 'cashAndEquivalents': None, 'derivative': None}, 'categoryName': 'Sector Equity Technology', 'indexName': 'Morningstar Gbl Tech TME GR USD', 'fundName': 'BNP Paribas Disrpt Tech Cl C', 'assetType': 'FIXEDINCOME'}, 'EQUITY': {'fundPortfolio': {'portfolioDate': '2022-06-30T05:00:00.000', 'basicMaterials': 0.0, 'consumerCyclical': 9.58233, 'financialServices': 7.34383, 'realEstate': 4.07224, 'communicationServices': 5.0627, 'energy': 0.0, 'industrials': 3.37488, 'technology': 66.49262, 'consumerDefensive': 0.0, 'healthcare': 4.0714, 'utilities': 0.0}, 'categoryPortfolio': {'portfolioDate': '2022-07-31T05:00:00.000', 'basicMaterials': 0.44454, 'consumerCyclical': 7.59378, 'financialServices': 3.78231, 'realEstate': 0.74965, 'communicationServices': 11.50859, 'energy': 0.1766, 'industrials': 5.14909, 'technology': 67.39924, 'consumerDefensive': 0.14448, 'healthcare': 3.01767, 'utilities': 0.03404}, 'indexPortfolio': {'portfolioDate': '2022-07-31T05:00:00.000', 'basicMaterials': 0.0, 'consumerCyclical': 0.0, 'financialServices': 0.01996, 'realEstate': 0.0, 'communicationServices': 0.0, 'energy': 0.0, 'industrials': 0.0, 'technology': 99.98003, 'consumerDefensive': 0.0, 'healthcare': 0.0, 'utilities': 0.0}, 'categoryName': 'Sector Equity Technology', 'indexName': 'Morningstar Gbl Tech TME GR USD', 'fundName': 'BNP Paribas Disrpt Tech Cl C', 'assetType': 'EQUITY'}, 'assetType': 'EQUITY'}

Financial metrics

Compare funds by their financial metrics.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.financialMetrics())

{'userType': 'Free', 'fund': {'masterPortfolioId': '753260', 'portfolioDate': '2022-06-30T05:00:00.000', 'wideMoatPercentage': '_PO_', 'narrowMoatPercentage': 
'_PO_', 'noMoatPercentage': '_PO_', 'financialHealthGradeType': '_PO_', 'profitabilityGradeType': '_PO_', 'growthGradeType': '_PO_', 'roic': '_PO_', 'cashReturn': '_PO_', 'freeCashFlowYield': '_PO_', 'debtToCapital': '_PO_', 'securityName': 'BNP Paribas Disrpt Tech Cl C'}, 'category': {'masterPortfolioId': '204272', 
'portfolioDate': '2022-07-31T05:00:00.000', 'wideMoatPercentage': '_PO_', 'narrowMoatPercentage': '_PO_', 'noMoatPercentage': '_PO_', 'financialHealthGradeType': '_PO_', 'profitabilityGradeType': '_PO_', 'growthGradeType': '_PO_', 'roic': '_PO_', 'cashReturn': '_PO_', 'freeCashFlowYield': '_PO_', 'debtToCapital': '_PO_', 'securityName': 'Sector Equity Technology'}, 'index': {'masterPortfolioId': '2595009', 'portfolioDate': '2022-07-31T05:00:00.000', 'wideMoatPercentage': '_PO_', 'narrowMoatPercentage': '_PO_', 'noMoatPercentage': '_PO_', 'financialHealthGradeType': '_PO_', 'profitabilityGradeType': '_PO_', 'growthGradeType': '_PO_', 'roic': '_PO_', 'cashReturn': '_PO_', 'freeCashFlowYield': '_PO_', 'debtToCapital': '_PO_', 'securityName': 'Morningstar Gbl Tech TME GR USD'}}

Market capitalization

Market capitalization breakdown of funds investments.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.marketCapitalization())

{'portfolioDate': '2022-06-30T05:00:00.000', 'assetType': 'EQUITY', 'currencyId': 'EUR', 'fund': {'portfolioDate': '2022-06-30T05:00:00.000', 'name': 'BNP Paribas Disrpt Tech Cl C', 'avgMarketCap': 78931.51451, 'giant': 38.47836, 'large': 28.35261, 'medium': 22.68117, 'small': 9.28113, 'micro': 0.0}, 'category': {'portfolioDate': '2022-07-31T05:00:00.000', 'name': 'Sector Equity Technology', 'avgMarketCap': 129289.22524, 'giant': 53.67279, 'large': 12.12215, 'medium': 20.91581, 'small': 7.07545, 'micro': 0.66737}, 'index': {'portfolioDate': '2022-07-31T05:00:00.000', 'name': 'Morningstar Gbl Tech TME GR USD', 'avgMarketCap': 284903.03483, 'giant': 63.17788, 'large': 26.90885, 'medium': 9.80996, 'small': 0.1033, 'micro': 0.0}}

Stock style

Get all information about funds invested in stocks.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.stockStyle())

{'portfolioDate': '2022-06-30T05:00:00.000', 'assetType': 'EQUITY', 'fund': {'prospectiveEarningsYield': 22.33371, 'prospectiveBookValueYield': 3.35888, 'prospectiveRevenueYield': 3.5807, 'prospectiveCashFlowYield': 14.15026, 'prospectiveDividendYield': 0.59518, 'forecasted5YearEarningsGrowth': 15.10913, 'forecastedEarningsGrowth': 23.57385, 'forecastedBookValueGrowth': 11.92521, 'forecastedRevenueGrowth': 11.12691, 'forecastedCashFlowGrowth': 21.08331, 'portfolioDate': '2022-06-30T05:00:00.000', 'name': 'BNP Paribas Disrpt Tech Cl C', 'secId': 'F00000PXI1', 'currencyId': 'EUR'}, 'categoryAverage': {'prospectiveEarningsYield': 20.36308, 'prospectiveBookValueYield': 4.25587, 'prospectiveRevenueYield': 3.03068, 'prospectiveCashFlowYield': 12.23865, 'prospectiveDividendYield': 1.03853, 'forecasted5YearEarningsGrowth': 13.58992, 'forecastedEarningsGrowth': 27.43487, 'forecastedBookValueGrowth': 12.13658, 'forecastedRevenueGrowth': 11.7264, 'forecastedCashFlowGrowth': 20.65426, 'portfolioDate': '2022-07-31T05:00:00.000', 'name': 'Sector Equity Technology', 'secId': 'EUCA000542', 'currencyId': ''}, 'indexAverage': {'prospectiveEarningsYield': 19.96891, 'prospectiveBookValueYield': 4.4252, 'prospectiveRevenueYield': 3.21928, 'prospectiveCashFlowYield': 11.98533, 'prospectiveDividendYield': 1.27762, 'forecasted5YearEarningsGrowth': 11.53482, 'forecastedEarningsGrowth': 24.48762, 'forecastedBookValueGrowth': 12.51472, 'forecastedRevenueGrowth': 11.00318, 'forecastedCashFlowGrowth': 20.83471, 'portfolioDate': '2022-07-31T05:00:00.000', 'name': 'Morningstar Gbl Tech TME GR USD', 'secId': 'F000016WQ2', 'currencyId': ''}}

Factor profile

See funds throught their factor profile.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.factorProfile())

{'name': 'BNP Paribas Disrpt Tech Cl C', 'categoryId': 'EUCA000542', 'categoryName': 'Sector Equity Technology', 'indexId': 'F000016WQ2', 'indexName': 'Morningstar Gbl Tech TME GR USD', 'indexEffectiveDate': '2022-06-30', 'categoryEffectiveDate': '2022-05-31', 'ticker': None, 'id': '0P0000YSYV', 'effectiveDate': '2022-05-31', 'factors': {'style': {'categoryAvg': 9.118533, 'indexAvg': 13.27911, 'percentile': 7.446323, 'historicRange': [{'year': '1', 'min': 5.979849, 'max': 
14.989949}, {'year': '3', 'min': 4.54678, 'max': 14.989949}, {'year': '5', 'min': 3.521247, 'max': 14.989949}]}, 'yield': {'categoryAvg': 79.385994, 'indexAvg': 64.95552, 'percentile': 87.375185, 'historicRange': [{'year': '1', 'min': 79.268357, 'max': 88.344966}, {'year': '3', 'min': 77.984547, 'max': 91.62376}, {'year': '5', 'min': 73.649627, 'max': 93.899166}]}, 'quality': {'categoryAvg': 8.92221, 'indexAvg': 5.441309, 'percentile': 15.569475, 'historicRange': [{'year': '1', 'min': 13.722369, 'max': 21.177328}, {'year': '3', 'min': 12.153747, 'max': 29.024136}, {'year': '5', 'min': 6.169225, 'max': 29.024136}]}, 'momentum': {'categoryAvg': 57.03058, 'indexAvg': 57.131156, 'percentile': 58.656596, 'historicRange': [{'year': '1', 'min': 22.760606, 'max': 78.978493}, {'year': '3', 'min': 11.541477, 'max': 78.978493}, {'year': '5', 'min': 5.150902, 'max': 78.978493}]}, 'volatility': {'categoryAvg': 14.994364, 'indexAvg': 45.490358, 'percentile': 15.46092, 'historicRange': [{'year': '1', 'min': 14.291359, 'max': 39.62354}, {'year': '3', 'min': 14.291359, 'max': 67.397608}, {'year': '5', 'min': 14.291359, 'max': 87.19611}]}, 'liquidity': {'categoryAvg': 25.641999, 'indexAvg': 54.618222, 'percentile': 19.521341, 'historicRange': [{'year': '1', 'min': 16.255203, 'max': 37.783154}, {'year': '3', 'min': 16.255203, 'max': 38.50419}, {'year': '5', 'min': 12.777605, 'max': 72.742365}]}, 'size': {'categoryAvg': 77.30959, 'indexAvg': 94.028959, 'percentile': 53.32816, 'historicRange': [{'year': '1', 'min': 52.109345, 'max': 61.723463}, {'year': '3', 'min': 50.432021, 'max': 64.86263}, {'year': '5', 'min': 50.432021, 'max': 96.006679}]}}}

Ownership zone

Ownership zone of the funds.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.ownershipZone())

{'portfolioDate': '2022-06-30T05:00:00.000', 'fund': {'portfolioDate': '2022-06-30T05:00:00.000', 'scaledSizeScore': 266.974, 'scaledStyleScore': 269.356, 'sizeVariance': 117.428, 'styleVariance': 86.836, 'rho': 0.323, 'secId': 'F00000PXI1', 'name': 'BNP Paribas Disrpt Tech Cl C', 'objectZone75Percentile': 2.349}, 'benchmark': {'portfolioDate': '2022-07-31T05:00:00.000', 'scaledSizeScore': 352.941, 'scaledStyleScore': 245.922, 'sizeVariance': 83.159, 'styleVariance': 104.01, 'rho': 0.153, 'secId': 'F000016WQ2', 'name': 'Morningstar Gbl Tech TME GR USD', 'objectZone75Percentile': 3.444}, 'category': {'portfolioDate': '2022-07-31T05:00:00.000', 'scaledSizeScore': 315.811, 'scaledStyleScore': 250.8, 'sizeVariance': 115.564, 'styleVariance': 95.037, 'rho': 0.133, 'secId': 'EUCA000542', 'name': 'Sector Equity Technology', 'objectZone75Percentile': 2.372}}

Asset allocation

Asset allocation breakdown.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.assetAllocation())

{'assetType': 'EQUITY', 'portfolioDate': '2022-06-30T05:00:00.000', 'portfolioDateCategory': '2022-07-31T05:00:00.000', 'portfolioDateIndex': '2022-07-31T05:00:00.000', 'portfolioDateGlobal': '2022-06-30T05:00:00.000', 'portfolioDateCategoryGlobal': '2022-07-31T05:00:00.000', 'portfolioDateIndexGlobal': '2022-07-31T05:00:00.000', 'fundName': 'BNP Paribas Disrpt Tech Cl C', 'categoryName': 'Sector Equity Technology', 'indexName': 'Morningstar Gbl Tech TME GR USD', 'allocationMap': {'AssetAllocCash': {'netAllocation': '1.20673', 'shortAllocation': None, 'longAllocation': '1.20673', 'longAllocationIndex': '0.00000', 'longAllocationCategory': '5.08025', 'targetAllocation': None}, 'AssetAllocNotClassified': {'netAllocation': '0.0', 'shortAllocation': None, 'longAllocation': '0.0', 'longAllocationIndex': '0.0', 'longAllocationCategory': '0.058410000000000004', 'targetAllocation': None}, 'AssetAllocNonUSEquity': {'netAllocation': '12.71682', 'shortAllocation': None, 'longAllocation': '12.71682', 'longAllocationIndex': '21.99999', 'longAllocationCategory': '31.07350', 'targetAllocation': None}, 'AssetAllocOther': {'netAllocation': '0.00000', 'shortAllocation': None, 'longAllocation': '0.00000', 'longAllocationIndex': '0.00000', 'longAllocationCategory': '2.11402', 'targetAllocation': None}, 'AssetAllocUSEquity': {'netAllocation': '86.07645', 'shortAllocation': None, 'longAllocation': '86.07645', 'longAllocationIndex': '78.00000', 'longAllocationCategory': '63.95990', 'targetAllocation': None}, 'AssetAllocBond': {'netAllocation': '0.00000', 'shortAllocation': None, 'longAllocation': '0.00000', 'longAllocationIndex': '0.00000', 'longAllocationCategory': '0.19399', 'targetAllocation': None}}, 'countryCode': 'LUX', 'securityType': 
None, 'dualViewData': {'performanceId': '0P0000YSYV', 'marketValueStockLong': '98.79327', 'marketValueStockShort': None, 'marketValueStockNet': '98.79327', 'marketValueBondLong': None, 'marketValueBondShort': None, 'marketValueBondNet': None, 'marketValueCashLong': '1.20673', 'marketValueCashShort': '0', 'marketValueCashNet': '1.20673', 'marketValueDerivativeLong': None, 'marketValueDerivativeShort': None, 'marketValueDerivativeNet': None, 'marketValueFundLong': None, 'marketValueFundShort': None, 'marketValueFundNet': None, 'marketValueOtherLong': None, 'marketValueOtherShort': None, 'marketValueOtherNet': None, 'economicExposureCurrencyLong': '1.20673', 'economicExposureCurrencyShort': '0', 'economicExposureCurrencyNet': '1.20673', 'economicExposureFixedIncomeLong': None, 'economicExposureFixedIncomeShort': None, 'economicExposureFixedIncomeNet': None, 'economicExposureEquityLong': '98.79327', 'economicExposureEquityShort': None, 'economicExposureEquityNet': '98.79327', 'economicExposureOtherLong': None, 'economicExposureOtherShort': None, 'economicExposureOtherNet': None, 'marketValueAsOf': '2022-06-30T05:00:00.000', 'economicExposureAsOf': '2022-06-30T05:00:00.000', 'dualViewAsOf': '2022-06-30T05:00:00.000', 'marketValueTotal': {'longVal': 100.0, 'shortVal': 0.0, 'netVal': 100.0}, 'economicExposureTotal': {'longVal': 100.0, 'shortVal': 0.0, 'netVal': 100.0}}, 'targetDate': None, 'hasRegionalAssetAlloc': 
False, 'globalAllocationMap': {'assetAllocPreferred': {'netAllocation': '0.00000', 'shortAllocation': None, 'longAllocation': '0.00000', 'longAllocationIndex': '0.00000', 'longAllocationCategory': '0.04954', 'targetAllocation': None}, 'assetAllocOther': {'netAllocation': '0.00000', 'shortAllocation': None, 'longAllocation': '0.00000', 'longAllocationIndex': '0.00000', 'longAllocationCategory': '2.11402', 'targetAllocation': None}, 'assetAllocEquity': {'netAllocation': '98.79327', 'shortAllocation': None, 'longAllocation': '98.79327', 'longAllocationIndex': '99.99999', 'longAllocationCategory': '95.03340', 'targetAllocation': None}, 'assetAllocCash': {'netAllocation': '1.20673', 'shortAllocation': None, 'longAllocation': '1.20673', 'longAllocationIndex': '0.00000', 'longAllocationCategory': '5.08025', 'targetAllocation': None}, 'assetAllocConvertible': {'netAllocation': '0.00000', 'shortAllocation': None, 'longAllocation': '0.00000', 'longAllocationIndex': '0.00000', 'longAllocationCategory': '0.00887', 'targetAllocation': None}, 'assetAllocFixedIncome': {'netAllocation': '0.00000', 'shortAllocation': None, 'longAllocation': '0.00000', 'longAllocationIndex': '0.00000', 'longAllocationCategory': '0.19399', 'targetAllocation': None}}}

Holdings

Full transparency with holdings of the funds.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.holdings())

                                     securityName       secId performanceId holdingTypeId  ...  qualRating  quantRating  bestRatingType  securityType
0                                  Microsoft Corp  0P000003MH    0P000003MH             E  ...           4            3            Qual            ST
1                                       Apple Inc  0P000000GY    0P000000GY             E  ...           2            3            Qual            ST
2                            Alphabet Inc Class A  0P000002HD    0P000002HD             E  ...           4            4            Qual            ST
3                                Visa Inc Class A  0P0000CPCP    0P0000CPCP             E  ...           3            3            Qual            ST
4                      Advanced Micro Devices Inc  0P0000006A    0P0000006A             E  ...           4            5            Qual            ST
5                                 First Solar Inc  0P00006TF8    0P00006TF8             E  ...           2            3            Qual            ST
6                                    Entegris Inc  0P000001Z2    0P000001Z2             E  ...           3            3            Quan            ST
7   Taiwan Semiconductor Manufacturing Co Ltd ADR  0P000005AR    0P000005AR             E  ...           5            4            Qual            ST

objective Investment

objective of the funds.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.objectiveInvestment())

Increase the value of its assets over the medium term by investing primarily in innovative technology companies.

Benchmark

benchmark of the funds.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.benchmark())

MSCI World NR EUR

Category

category of the funds.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.category())

MSCI World/Information Tech NR USD

Funds annual performance

Annual performance of the funds.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.fundsAnnualPerformance())

{'funds_annual_performance_2015': '13.62', 'funds_annual_performance_2016': '28.15', 'funds_annual_performance_2017': '23.64', 'funds_annual_performance_2018': '9.13', 'funds_annual_performance_2019': '32.00', 'funds_annual_performance_2020': '42.78', 'funds_annual_performance_2021': '25.35', 'funds_annual_performance_current': '-14.50'}

Index annual performance

Annual performance of the index.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.indexAnnualPerformance())

{'index_annual_performance_2015': '2.79', 'index_annual_performance_2016': '-4.80', 'index_annual_performance_2017': '-2.63', 'index_annual_performance_2018': 
'5.67', 'index_annual_performance_2019': '-9.86', 'index_annual_performance_2020': '3.44', 'index_annual_performance_2021': '-5.70', 'index_annual_performance_current': '-2.97'}

Index annual performance

Annual performance of the category.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.categoryAnnualPerformance())

{'category_annual_performance_2015': '5.05', 'category_annual_performance_2016': '0.23', 'category_annual_performance_2017': '-1.66', 'category_annual_performance_2018': '12.49', 'category_annual_performance_2019': '1.89', 'category_annual_performance_2020': '-0.93', 'category_annual_performance_2021': '10.38', 'category_annual_performance_current': '5.33'}

Funds annual rank

Annual rank of the funds.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.fundsAnnualRank())

{'rank_annual_performance_2015': '20', 'rank_annual_performance_2016': '46', 'rank_annual_performance_2017': '60', 'rank_annual_performance_2018': '6', 'rank_annual_performance_2019': '45', 'rank_annual_performance_2020': '40', 'rank_annual_performance_2021': '24', 'rank_annual_performance_current': '23'}

Funds cumulative performance

cumulative performance of the funds.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.fundsCumulativePerformance())

{'cumulative_performance_date': '22/08/2022', 'funds_cumulative_performance_1 Day': '-2.56', 'funds_cumulative_performance_1 Week': '-2.51', 'funds_cumulative_performance_1 Month': '7.20', 'funds_cumulative_performance_3 Months': '14.21', 'funds_cumulative_performance_6 Months': '4.85', 'funds_cumulative_performance_YTD': '-11.65', 'funds_cumulative_performance_1 Year': '-2.66', 'funds_cumulative_performance_3 Years Annualised': '17.85', 'funds_cumulative_performance_5 Years Annualised': '19.16', 'funds_cumulative_performance_10 Years Annualised*': '18.79'}

Index cumulative performance

cumulative performance of the index.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.indexCumulativePerformance())

{'cumulative_performance_date': '22/08/2022', 'index_cumulative_performance_1 Day': '0.07', 'index_cumulative_performance_1 Week': '0.57', 'index_cumulative_performance_1 Month': '0.80', 'index_cumulative_performance_3 Months': '-0.43', 'index_cumulative_performance_6 Months': '-2.41', 'index_cumulative_performance_YTD': '-2.68', 'index_cumulative_performance_1 Year': '-2.93', 'index_cumulative_performance_3 Years Annualised': '-3.22', 'index_cumulative_performance_5 Years Annualised': '-1.70', 'index_cumulative_performance_10 Years Annualised*': '-2.10'}

Category cumulative performance

cumulative performance of the category.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.categoryCumulativePerformance())

{'cumulative_performance_date': '22/08/2022', 'category_cumulative_performance_1 Day': '-0.66', 'category_cumulative_performance_1 Week': '-0.31', 'category_cumulative_performance_1 Month': '4.12', 'category_cumulative_performance_3 Months': '8.19', 'category_cumulative_performance_6 Months': '9.46', 'category_cumulative_performance_YTD': '6.88', 'category_cumulative_performance_1 Year': '9.95', 'category_cumulative_performance_3 Years Annualised': '3.63', 'category_cumulative_performance_5 Years Annualised': '4.72', 'category_cumulative_performance_10 Years Annualised*': '2.19'}

Funds quarterly performance

quarterly performance of the funds.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.fundsQuarterlyPerformance())

{'quarterly_performance_date': '22/08/2022', 'performance_2022_quarter_1': '-9.47', 'performance_2022_quarter_2': '-16.62', 'performance_2022_quarter_3': '-', 
'performance_2022_quarter_4': '-', 'performance_2021_quarter_1': '-1.54', 'performance_2021_quarter_2': '9.73', 'performance_2021_quarter_3': '5.78', 'performance_2021_quarter_4': '9.69', 'performance_2020_quarter_1': '-6.43', 'performance_2020_quarter_2': '28.63', 'performance_2020_quarter_3': '8.18', 'performance_2020_quarter_4': '9.65', 'performance_2019_quarter_1': '16.87', 'performance_2019_quarter_2': '7.77', 'performance_2019_quarter_3': '1.73', 'performance_2019_quarter_4': '3.01', 'performance_2018_quarter_1': '4.02', 'performance_2018_quarter_2': '9.84', 'performance_2018_quarter_3': '9.95', 'performance_2018_quarter_4': '-13.13', 'performance_2017_quarter_1': '8.92', 'performance_2017_quarter_2': '0.53', 'performance_2017_quarter_3': '4.67', 'performance_2017_quarter_4': '7.88'}

Funds contact

information about the funds and asset manager.

from mstarpy import MS

ms = MS("disruptive technology")
print(ms.contact())

{'Name of Company': 'BNP Paribas Asset Management Luxembourg', 'Phone': '[phone]', 'Website': 'www.bnpparibas-am.com', 'Address': '10 rue Edward Steichen', '\xa0': 'Luxembourg', 'Domicile': 'Luxembourg', 'Legal Structure': 'SICAV', 'UCITS': 'Yes', 'Fund Manager': '1995', 'Manager Start Date': '13/09/2016', 'Career Start Year': '1994', 'Education': 'Pamela\xa0Hegarty', 'Biography': '13/09/2016'}

Funds fees

fees of the funds.

from mstarpy import MS

ms = MS("F00000270E", country="fr")  
print(ms.fees())

{'Frais de souscription max': '3,50%', 'Frais de rachat max.': 'n/a', 'Frais de conversion': '-', 'Frais de gestion annuels maximum': '1,93%', 'Frais courants': '1,94%'}

Data points

Get any information of the funds.

from mstarpy import MS

ms = MS("disruptive technology") 
print(ms.dataPoint(['largestSector', 'SharpeM36', 'ongoingCharge']))

[{'largestSector': 'SB_Technology', 'SharpeM36': 0.95, 'ongoingCharge': 1.98}]

Contribute

You can download the open-source project on GitHub mstarpy and add your touch to make the package better.

Disclaimer

mstarpy works as an API of MorningStar. It allows to get data from the site in an easy way. mstarpy is not affiliated with MorningStar and is completly independant.