msci.sdk 1.9.0

Provides easy interface for users to integrate MSCI APIs, content and data into their own choice of platform.

MSCI SDK Introduction

The MSCI Python SDK provides easy-to-use Python libraries that enable users to easily access and use MSCI APIs, content, and data into their own choice of platform.

The current version of the MSCI Python SDK provides access to the MSCI Optimization Service API, which is offered as part of MSCI Quantitative Investment Solutions (QIS). You can use the API to:

• upload user portfolios and tax lots in JSON format

• create tax aware optimization strategies

• retrieve statistics to evaluate performance of the strategy

• customize portfolios with various optimization methodologies

For more information about the MSCI Optimization Service API, see https://support.msci.com/msci-optimization-service.

Documentation

For more details, see https://developer.msci.com/code-libraries/msci-python-sdk

Prerequisites

• Python >= 3.8

• Access to MSCI Optimization Service API

For assistance in requesting access or confirming your access, contact your MSCI representative.

Installation

pip install msci.sdk

Example

Tax Optimization of portfolio using msci.sdk library. It demonstrates connection to the API, tax lot upload, rebalance, trade list generation, and review of results.

To download below example as notebook, kindly refer https://developer.msci.com/code-libraries/qis-tax-optimization-with-msci-sdk-python-notebooks

from msci.sdk.calc.portfolio import mos
import pandas as pd

# 1. Connect to the MOS API

session = mos.MOSSession()
session.ping()

# 2. Load a portfolio using tax lots
sample_data = pd.DataFrame([{"openTradeDate": "2016-12-30", "ISIN": "US02079K3059", "quantity": 1000, "openCostBasis": 792.45, "Asset Name": "ALPHABET INC"},
                            {"openTradeDate": "2016-12-30", "ISIN": "US0231351067", "quantity": 450, "openCostBasis": 749.87, "Asset Name": "AMAZON.COM INC"},
                            {"openTradeDate": "2016-12-30", "ISIN": "US30303M1027", "quantity": 900, "openCostBasis": 115.05, "Asset Name": "FACEBOOK INC"}])

portfolio = session.upload_taxlot_portfolio(
    portfolio_id='MyTaxLotPortfolio', as_of_date='2021-12-31', asset_id='ISIN', taxlot_df=sample_data)

# 3. Run the rebalance using the preferred strategy
mytemplate = mos.TaxAdvantagedModelTrackingTemplate(analysis_date='2022-01-03',portfolio=portfolio)

# 4. Executing the MSCI Optimizer session
job = session.execute(profile=mytemplate)

# Wait for the results to come back
job.wait()

# 5. Show the portfolio level results
job.get_valuations()

# Fetching the optimizer result
opt_result = job.optimizer_result()

# Portfolio Summary
opt_result.get_portfolio_summary_detail()

# Display portfolio and trade suggestions
job.rebalanced_portfolio_on('2022-01-03')
job.trade_suggestions_on('2022-01-03')

Support

For questions about MSCI Python SDK or other MSCI resources mentioned here, please contact a salesperson at MSCI: https://www.msci.com/contact-us#/contact-sales, or use any of our support channels for existing MSCI clients.

Copyright

Copyright 2023 MSCI Inc

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.