open-climate-investing
Application and data for analyzing and structuring portfolios for climate investing.
https://github.com/opentaps/open-climate-investing
Category: Sustainable Development
Sub Category: Sustainable Investment
Keywords
climate-change climate-data esg factor-analysis fama-french finance hacktoberfest hacktoberfest2021 modern-portfolio-analysis modern-portfolio-theory stock stock-market
Keywords from Contributors
volttron blockchain-network blockchain-technology climate dlt
Last synced: about 13 hours ago
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Repository metadata
Application and data for analyzing and structuring portfolios for climate investing.
- Host: GitHub
- URL: https://github.com/opentaps/open-climate-investing
- Owner: opentaps
- License: agpl-3.0
- Created: 2021-08-09T18:53:57.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-10-20T23:35:21.000Z (over 2 years ago)
- Last Synced: 2025-04-17T22:43:44.198Z (9 days ago)
- Topics: climate-change, climate-data, esg, factor-analysis, fama-french, finance, hacktoberfest, hacktoberfest2021, modern-portfolio-analysis, modern-portfolio-theory, stock, stock-market
- Language: JavaScript
- Homepage: https://climate-investing-book.opensourcestrategies.com/v/main/book
- Size: 39.6 MB
- Stars: 49
- Watchers: 4
- Forks: 14
- Open Issues: 9
- Releases: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
README.md
open-climate-investing
This project's mission is to make climate investing actionable. It includes both open source software and a free book to help you identify relative value trades, optimize portfolios, and structure benchmarks for climate aligned investing.
The Software
The software is a multi-factor equity returns model which adds a climate factor, or Brown Minus Green, to the popular Fama French and Carhart models. See the short video
This additional Brown Minus Green (BMG) return factor could be used for a variety of climate investing applications, including:
- Calculate the market-implied carbon risk of a stock, investment portfolio, mutual fund, or bond based on historical returns
- Determine the market reaction to the climate policies of a company
- Optimize a portfolio to minimize carbon risk subject to other parameters, such as index tracking or growth-value-sector investment strategies.
Setting It Up
Install the required python modules (use pip3
instead of pip
according to your python installation):
pip install -r requirements.txt
Initialize the Database using:
python3 scripts/setup_db.py -R -d
Trying It Out
Let's get the historical stock prices and returns of the MSCI World Index and its constituent sectors:
python scripts/get_stocks.py -f data/msci_etf_sector_mapping.csv
python scripts/get_stocks.py -f data/msci_constituent_details.csv
Now let's calculate the risk factor loadings for these stocks using 60 months of monthly data at a time:
python scripts/get_regressions.py -d -f data/msci_etf_sector_mapping.csv -s 2010-01-01 -e 2021-01-31 --frequency MONTHLY -n DEFAULT -i 60 -b
python scripts/get_regressions.py -d -f data/msci_constituent_details.csv -s 2010-01-01 -e 2021-01-31 --frequency MONTHLY -n DEFAULT -i 60 -b
Next, let's create a daily version of the BMG climate risk series based on the difference between the stocks XOP (brown) and SMOG (green):
python scripts/bmg_series.py -n XOP-SMOG -b XOP -g SMOG -s 2018-01-01 -e 2022-02-01 --frequency DAILY
Finally, let's calculate the risk factor loadings for stocks using 2 years of daily data. This will take a long time:
python3 scripts/get_regressions.py -d -f data/msci_etf_sector_mapping.csv -s 2018-01-01 -e 2021-01-31 --frequency DAILY -i 730 -n XOP-SMOG -b
python3 scripts/get_regressions.py -d -f data/msci_constituent_details.csv -s 2018-01-01 -e 2021-01-31 --frequency DAILY -i 703 -n XOP-SMOG -b
Viewing the Results
Follow directions from the dashboard README page to look at your results.
The Book
The included free book on climate investing explains both climate investing concepts and how to use this project. You can also read it online at gitbook.
Project Files
- scripts/ contains the python scripts used to run the models.
- ui/ contains the dashboard.
- data/ contains the data files for the models and a list of their sources.
- R/ contains R scripts which were used to develop the models.
- book/ is the included book on climate investing.
References
- Constructing and Validating Climate Risk Factors from ESG Data: an Empirical Comparison
- Carbon Risk Management (CARIMA) Manual
- A Practitioner's Guide to Factor Models
- The Barra US Equity Model (USE4)
- Network for Greening the Financial System, Case Studies of Environmental Risk Analysis Methodologies
Get Updates
Sign up for our email newsletter to get updates on this book and the Open Climate Investing project.
Disclaimer
This content is published for informational purposes only and not investment advice or inducement or advertising to purchase or sell any security. See full disclaimer and license.
Owner metadata
- Name: opentaps
- Login: opentaps
- Email:
- Kind: organization
- Description:
- Website:
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/79276703?v=4
- Repositories: 4
- Last ynced at: 2023-03-05T03:28:09.010Z
- Profile URL: https://github.com/opentaps
GitHub Events
Total
- Watch event: 6
Last Year
- Watch event: 6
Committers metadata
Last synced: 4 days ago
Total Commits: 573
Total Committers: 7
Avg Commits per committer: 81.857
Development Distribution Score (DDS): 0.471
Commits in past year: 0
Committers in past year: 0
Avg Commits per committer in past year: 0.0
Development Distribution Score (DDS) in past year: 0.0
Name | Commits | |
---|---|---|
Si Chen | s****n@o****m | 303 |
Jeremy Wickersheimer | j****s@g****m | 132 |
Matthew Bowler | m****s@g****m | 78 |
Si Chen | 1****4 | 46 |
Ignacio Mangini | i****3@g****m | 8 |
Konstantin Rybalko | k****o@g****m | 5 |
hai-yr | w****r@u****u | 1 |
Committer domains:
Issue and Pull Request metadata
Last synced: 1 day ago
Total issues: 30
Total pull requests: 1
Average time to close issues: about 1 month
Average time to close pull requests: about 3 hours
Total issue authors: 3
Total pull request authors: 1
Average comments per issue: 1.67
Average comments per pull request: 0.0
Merged pull request: 1
Bot issues: 0
Bot pull requests: 0
Past year issues: 0
Past year pull requests: 0
Past year average time to close issues: N/A
Past year average time to close pull requests: N/A
Past year issue authors: 0
Past year pull request authors: 0
Past year average comments per issue: 0
Past year average comments per pull request: 0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- sichen1234 (26)
- mattbowler (3)
- clintonTE (1)
Top Pull Request Authors
- hai-yr (1)
Top Issue Labels
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Score: 6.0063531596017325