Getting Started
Installation
You can install it via pip on the terminal by typing:
pip install mstarpy
You can also install it via git on the terminal bu using :
pip install git+https://github.com/Mael-J/mstarpy.git@master
First commands
Look for funds with search_funds
You can look for funds by using the method search_funds. In the following example, we will look for 40 funds in the US market with the term “technology” in their name. We want to get the name, the ID and the 12 months return. We transform the result in a pandas DataFrame to make it more clear.
import mstarpy
import pandas as pd
response = mstarpy.search_funds(term="technology", field=["Name", "fundShareClassId", "GBRReturnM12"], country="us", pageSize=40, currency ="USD")
df = pd.DataFrame(response)
print(df.head())
Name fundShareClassId GBRReturnM12
0 Baron Technology Instituitional F00001CUJ3 -21.64
1 Baron Technology R6 F00001CUJ1 -21.88
2 Baron Technology Retail F00001CUJ2 -21.91
3 Black Oak Emerging Technology FOUSA00LIX -8.33
4 BlackRock Technology Opportunities K F000014AX6 -21.09
Look for fields with search_field
You can find the field you need for the search_funds and search_stock methods using search_field. In the following example, we get all fields.
from mstarpy import search_field
response = search_field(pattern='')
print(response)
['AdministratorCompanyId', 'AlphaM36', 'AnalystRatingScale', 'AverageCreditQualityCode', 'AverageMarketCapital', 'BetaM36', 'BondStyleBox', 'brandingCompanyId', 'categoryId', 'CategoryName', 'ClosePrice', 'currency', 'DebtEquityRatio', 'distribution', 'DividendYield', 'EBTMarginYear1', 'EffectiveDuration', 'EPSGrowth3YYear1', 'equityStyle', 'EquityStyleBox', 'exchangeCode', 'ExchangeId', 'ExpertiseAdvanced', 'ExpertiseBasic', 'ExpertiseInformed', 'FeeLevel', 'fundShareClassId', 'fundSize', 'fundStyle', 'FundTNAV', 'GBRReturnD1', 'GBRReturnM0', 'GBRReturnM1', 'GBRReturnM12', 'GBRReturnM120', 'GBRReturnM3', 'GBRReturnM36', 'GBRReturnM6', 'GBRReturnM60', 'GBRReturnW1', 'geoRegion', 'globalAssetClassId', 'globalCategoryId', 'iMASectorId', 'IndustryName', 'InitialPurchase', 'instrumentName', 'investment', 'investmentExpertise', 'investmentObjective', 'investmentType', 'investorType', 'InvestorTypeEligibleCounterparty', 'InvestorTypeProfessional', 'InvestorTypeRetail', 'LargestSector', 'LegalName', 'managementStyle', 'ManagerTenure', 'MarketCap', 'MarketCountryName', 'MaxDeferredLoad', 'MaxFrontEndLoad', 'MaximumExitCostAcquired', 'MorningstarRiskM255', 'Name', 'NetMargin', 'ongoingCharge', 'OngoingCostActual', 'PEGRatio', 'PERatio', 'PerformanceFeeActual', 'PriceCurrency', 'QuantitativeRating', 'R2M36', 'ReturnD1', 'ReturnM0', 'ReturnM1', 'ReturnM12', 'ReturnM120', 'ReturnM3', 'ReturnM36', 'ReturnM6', 'ReturnM60', 'ReturnProfileGrowth', 'ReturnProfileHedging', 'ReturnProfileIncome', 'ReturnProfileOther', 'ReturnProfilePreservation', 'ReturnW1', 'RevenueGrowth3Y', 'riskSrri', 'ROATTM', 'ROETTM', 'ROEYear1', 'ROICYear1', 'SecId', 'SectorName', 'shareClassType', 'SharpeM36', 'StandardDeviationM36', 'starRating', 'StarRatingM255', 'SustainabilityRank', 'sustainabilityRating', 'TenforeId', 'Ticker', 'totalReturn', 'totalReturnTimeFrame', 'TrackRecordExtension', 'TransactionFeeActual', 'umbrellaCompanyId', 'Universe', 'Yield_M12', 'yieldPercent']
Analysis of funds
Once, you know what fund you want to analyse, you can load it with the class Funds and then access all the methods to get data.
import mstarpy
fund = mstarpy.Funds(term="FOUSA00LIX", country="us")
You can access to his property name.
print(fund.name)
'Black Oak Emerging Technology Fund'
You can show the equity holdings of the fund.
df_equity_holdings = fund.holdings(holdingType="equity")
print(df_equity_holdings[["securityName", "weighting", "susEsgRiskScore"]].head())
securityName weighting susEsgRiskScore
0 Apple Inc 5.03336 16.6849
1 KLA Corp 4.90005 16.6870
2 Kulicke & Soffa Industries Inc 4.23065 17.2155
3 SolarEdge Technologies Inc 4.13637 24.6126
4 Ambarella Inc 4.10950 33.1408
You can find the historical Nav and total return of the fund.
import datetime
import pandas as pd
start_date = datetime.datetime(2023,1,1)
end_date = datetime.datetime(2023,3,2)
#get historical data
history = fund.nav(start_date=start_date,end_date=end_date, frequency="daily")
#convert it in pandas DataFrame
df_history = pd.DataFrame(history)
print(df_history.head())
nav totalReturn date
0 6.28 10.21504 2022-12-30
1 6.23 10.13371 2023-01-03
2 6.31 10.26383 2023-01-04
3 6.18 10.05238 2023-01-05
4 6.37 10.36143 2023-01-06
Look for stock with search_stock
You can look for stocks by using the method search_stock. In the following example, we will look for 20 stocks on the Paris Stock Exchange with the term “AB” in their name. We want to get the name, the ID and the Sector. We transform the result in a pandas DataFrame to make it more clear.
import mstarpy
import pandas as pd
response = mstarpy.search_stock(term="AB",field=["Name", "fundShareClassId", "SectorName"], exchange='PARIS',pageSize=20)
df = pd.DataFrame(response)
print(df.head())
Name fundShareClassId SectorName
0 AB Science 0P0000NQNE Healthcare
1 ABC arbitrage SA 0P00009W9I Financial Services
2 Abeo SA 0P00018PIU Consumer Cyclical
3 Abionyx Pharma Ordinary Shares 0P00015JGM Healthcare
4 Abivax SA 0P00016673 Healthcare
Tips : You can get different exchange by looking at the variable EXCHANGE in mstarpy.utils. ‘WORLDWIDE_EQUITY’ allows you to search in all exchanges.
from mstarpy.utils import EXCHANGE
print(list(EXCHANGE))
['NYSE', 'NASDAQ', 'LSE', 'AMSTERDAM', 'ATHENS', 'BOLSA_DE_VALORES', 'BOMBAY', 'BORSA_ITALIANA', 'BRUSSELS', 'COPENHAGEN', 'HELSINKI', 'HONG-KONG', 'ICELAND', 'INDIA', 'IPSX', 'IRELAND', 'ISTANBUL', 'LISBON', 'LUXEMBOURG', 'OSLO_BORS', 'PARIS', 'RIGA', 'SHANGAI', 'SHENZHEN', 'SINGAPORE', 'STOCKHOLM', 'SWISS', 'TAIWAN', 'TALLIN', 'THAILAND', 'TOKYO', 'VILNIUS', 'WARSAW', 'WIENER_BOERSE', 'WORLDWIDE_EQUITY']
Analysis of stocks
Once, you know what stock you want to analyse, you can load it with the class Stock and then access all the methods to get data.
import mstarpy
stock = stock = mstarpy.Stock(term="0P00018PIU", exchange="PARIS")
You can access to his property name.
print(stock.name)
'Abeo SA'
You can find the historical price and volume of the stock.
import datetime
import pandas as pd
start_date = datetime.datetime(2023,1,1)
end_date = datetime.datetime(2023,3,2)
#get historical data
history = stock.historical(start_date=start_date,end_date=end_date, frequency="daily")
#convert it in pandas DataFrame
df_history = pd.DataFrame(history)
print(df_history.head())
open high low close volume previousClose date
0 18.60 18.60 18.55 18.55 194 18.55 2022-12-30
1 18.70 18.70 18.70 18.70 9 18.55 2023-01-02
2 18.65 18.70 18.55 18.60 275 18.70 2023-01-03
3 18.65 18.65 18.50 18.60 994 18.60 2023-01-04
4 18.65 18.95 18.50 18.60 999 18.60 2023-01-05
You can show the financial statements such as the balance sheet.
bs = stock.balanceSheet(period='annual', reportType='original')
More commands
You can find all the methods of the classes Funds and Stocks in the part Indices and tables of this documentation.