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Scripts

These are mostly python scripts used for data analysis, both in the command line and by the UI component:

  • setup_db.py - Initialize the database and load basic data
  • get_stocks.py - Get stock historical data
  • get_regressions.py - Calculate regressions for stocks versus factors
  • bmg_series.py - Create your own BMG series of high versus low climate risk stock returns
  • correlate.py - Calculate correlation of your BMG series versus other factors and create orthognalized series
  • bmg_anlayze.py - Analyze your BMG series's effectiveness in number of climate risk stocks identified by sector

Each of them has a --help option which explains what the parameters are.

Examples

Then use get_stocks.py to get stock information for stocks table and return history for the stock_data table:

  • python scripts/get_stocks.py -f some_ticker_file.csv for using a csv source file
  • python scripts/get_stocks.py -t ALB to load a single stock with ticker ALB
  • python scripts/get_stocks.py -s ALB shows whether there is data stored for the ticker ALB

If your stock ticker is a composite stock, it will calculate the historical returns using the weights in the stock_components table.

By default the script will get the Monthly stock values, to get Daily values use --frequency=DAILY, eg:

  • python scripts/get_stocks.py -f some_ticker_file.csv --frequency=DAILY for using a csv source file
  • python scripts/get_stocks.py -t ALB --frequency=DAILY to load a single stock with ticker ALB
  • python scripts/get_stocks.py -s ALB --frequency=DAILY shows whether there is data stored for the ticker ALB

To run the regression, use get_regressions.py to save the output in the stock_stats table:

  • python scripts/get_regressions.py for running all the stocks in the database
  • python scripts/get_regressions.py -f some_ticker_file.csv for using a csv source file
  • python scripts/get_regressions.py -t ALB to run and store the regression for a given stock

Likewise for using Daily values, eg:

  • python scripts/get_regressions.py -t ALB --frequency=DAILY to run and store the regression for a given stock

It will use the stock returns in the database by default, or if none are found, get them first. It has some optional parameters:

  • -s YYYY-MM-DD to specify an optional start date, by default it will start at the earliest common date from the stocks and risk factors
  • -e YYYY-MM-DD to specify an optional end date, by default it will end at the latest common date from the stocks and risk factors
  • -i N for the regression interval in months (defaults to 60 months).
  • -n FACTOR_NAME to specify the BMG factor to use. If not specified, DEFAULT will be used.
  • -h to see all parameters available

To calculate a BMG series and store it in the database:

python scripts/bmg_series.py -n XOP-SMOG -g SMOG -b XOP

where

  • -n <series name> is the name of your bmg series
  • -b is the ticker of your Brown stock
  • -g is the ticker of your Green stock

Likewise for using Daily values, eg:

python scripts/bmg_series.py -n XOP-SMOG -g SMOG -b XOP --frequency=DAILY

Command Line Scripts

These have been deprecated but are still available and can be used to run regressions in the command line without the database:

python scripts/factor_regression.py

The inputs are:

  • Stock return data: Use the stock_data.csv or enter a ticker
  • Carbon data: The BMG return history. By default use carbon_risk_factor.csv.
  • Fama-French factors: Use either ff_factors.csv, which are the Fama/French Developed 3 Factors and Developed Momentum Factor (Mom), or ff_factors_north_american.csv, which are the Fama/French North American 3 Factors and the North American Momentum Factor (Mom) series, from the Dartmouth Ken French Data Library The original CARIMA project used the data from ff_factors.csv

The output will be a print output of the statsmodel object, the statsmodel coefficient summary, including the coefficient & p-Values (to replicate that of the CARIMA paper)

stock_price_function.py adjusts this so it returns an object (which is used later)

factor_regression.py loads in the stock prices, the carbon risk factor and the Fama-French factors. The names of these CSVs are asked for. If stock data would be liked to be downloaded, then it will use stock_price_function.py to do so

  • Ensure that you have the relevant modules installed
  • Have stock_price_script.py in the same folder as factor_regression.py
  • Have your factor CSVs saved
  • Run factor_regression.py and follow the prompts and enter the names of the CSVs as asked