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data_processor

README.md

FinRL Live trading

Step 1: Query data from WRDS

1.1 Get all historical S&P 500 components

1.2 Get fundamental data from WRDS

  • Go to WRDS-Fundamentals Quarterly, start from 1996/01 to the most current date and do the query
  • Input: sp500_tickers.txt
  • output: raw csv file contains all fundamental data for the tickers in sp500_tickers, about 200MB

1.3 Get daily price from WRDS

  • Go to WRDS-Security Daily, start from 1996/01/01 to the most current date and do the query
  • Input: sp500_tickers.txt
  • output: raw csv file contains all daily price for the tickers in sp500_tickers, about 1GB

Step 2: Preprocess fundamental data

1.1 Use Trade date instead of quarterly report date

  • We also extend the trade date by two months lag beyond the standard quarter end date in case some companies have a non-standard quarter end date, e.g. Apple released its earnings report on 2010/07/20 for the second quarter of year 2010. Thus for the quarter between 04/01 and 06/30, our trade date is adjusted to 09/01 (same method for other three quarters).

1.2 Get next quarter's return

  • Our goal is to predict S&P 500 forward quarter log-return; At a given time T of the financial horizon, the 1-quarter forward log-returns of a certain stock price S are defined as:
image

1.3 Calculate Financial Ratios (Welcome to add more)

  • Profitability ratios: PE (Price–to-Earnings Ratio), PS (Price-to-Sales Ratio), PB (Price-to-Book Ratio), OPM (Operating Margin), NPM (Net Profit Margin), ROA (Return On Assets), ROE (Return on Equity), EPS (Earnings Per Share), BPS (Book Per Share), DPS (Dividend Per Share)
  • Liquidity ratios: Current ratio, Quick ratio, Cash ratio
  • Efficiency ratios: Inventory turnover ratio, Receivables turnover ratio, Payable turnover ratio,
  • Leverage ratios: Debt ratio, Debt to Equity ratio

1.4 Split the financial ratios by sector

  • In order to build a sector-neutral portfolio, we split the dataset by the Global Industry Classification Standard (GICS) sectors (total 11 sectors).
  • 10-Energy, 15-Materials, 20-Industrials, 25-Consumer Discretionary, 30-Consumer Staples, 35-Health Care, 40-Financials, 45-Information Technology, 50-Communication Services, 55-Utilities, 60-Real Estate
  • We handle missing data separately by sector: if one factor has more than 5% missing data, we delete this factor; if a certain stock generates the most missing data, we delete this stock.
  • Output: final_ratios.csv and ratios by sector

Disclaimer: Nothing herein is financial advice, and NOT a recommendation to trade real money. Please use common sense and always first consult a professional before trading or investing.