Global Factor Data

Python
R

Install packages (uv/pip/conda all fine):

uv add pandas sqlalchemy psycopg2-binary keyring

Store WRDS password securely (replace WRDS_USERNAME and WRDS_PASSWORD):

import keyring
keyring.set_password("wrds", "WRDS_USERNAME", "WRDS_PASSWORD")

Install packages:

install.packages(c("DBI", "RPostgres", "keyring"))

Store WRDS password securely (replace WRDS_USERNAME):

keyring::key_set(service = "wrds", username = "WRDS_USERNAME")

Python
R

import keyring
import pandas as pd
from sqlalchemy import create_engine, text

# --- Credentials from OS keychain (keyring) ---
creds = keyring.get_credential("wrds", None)
if creds is None:
    raise RuntimeError(
        """No WRDS credentials stored.
        Run: keyring.set_password('wrds', 'WRDS_USERNAME', 'WRDS_PASSWORD')."""
    )
wrds_un, wrds_pw = creds.username, creds.password

# --- WRDS Postgres connection (SSL required) ---
engine = create_engine(
    f"postgresql+psycopg2://{wrds_un}:{wrds_pw}"
    "@wrds-pgdata.wharton.upenn.edu:9737/wrds?sslmode=require"
)

# --- Helper: simple fetch ---
def wrds_fetch(sql: str) -> pd.DataFrame:
    with engine.connect() as conn:
        return pd.read_sql_query(text(sql), conn)

# --- Download tables ---
ctff_features  = wrds_fetch("SELECT * FROM contrib_global_factor.ctff_features;")
ctff_chars     = wrds_fetch("SELECT * FROM contrib_global_factor.ctff_chars;")
ctff_daily_ret = wrds_fetch("SELECT * FROM contrib_global_factor.ctff_daily_ret;")

# --- Save locally ---
# For example, we use (requires pyarrow package):
#    ctff_features.to_parquet("data/raw/ctff_features.parquet", index=False)
#    ctff_chars.to_parquet("data/raw/ctff_chars.parquet", index=False)
#    ctff_daily_ret.to_parquet("data/raw/ctff_daily_ret.parquet", index=False)

Note

Memory issues? If memory is tight for, e.g., ctff_chars, fetch in chunks:

with engine.connect() as conn:
    parts = pd.read_sql_query(
        text("SELECT * FROM contrib_global_factor.ctff_chars;"),
        conn,
        chunksize=500_000,
    )
    ctff_chars = pd.concat(parts, ignore_index=True)

# --- Libraries ---
library(keyring)
library(DBI)
library(RPostgres)

# --- Credentials from OS keychain (keyring) ---
if (nrow(key_list("wrds"))==0) {
  stop(
    "No WRDS credentials stored.\n",
    "Run: keyring::key_set(service = 'wrds', username = 'WRDS_USERNAME')."
  )
}
wrds_un <- key_list(service = "wrds")$username[1]
wrds_pw <- key_get("wrds", wrds_un)

# --- Connect to WRDS (SSL required) ---
con <- dbConnect(
  RPostgres::Postgres(),
  host    = "wrds-pgdata.wharton.upenn.edu",
  port    = 9737,
  dbname  = "wrds",
  sslmode = "require",
  user    = wrds_un,
  password = wrds_pw
)
on.exit(dbDisconnect(con), add = TRUE)

# --- Helper: simple fetch ---
wrds_fetch <- function(sql) DBI::dbGetQuery(con, sql)

# --- Download tables ---
ctff_features  <- wrds_fetch("SELECT * FROM contrib_global_factor.ctff_features;")
ctff_chars     <- wrds_fetch("SELECT * FROM contrib_global_factor.ctff_chars;")
ctff_daily_ret <- wrds_fetch("SELECT * FROM contrib_global_factor.ctff_daily_ret;")

# --- Save locally ---
# For example, we use (requires arrow package):
#   arrow::write_parquet(ctff_features, "data/raw/ctff_features.parquet")
#   arrow::write_parquet(ctff_chars, "data/raw/ctff_chars.parquet")
#   arrow::write_parquet(ctff_daily_ret, "data/raw/ctff_daily_ret.parquet")

Getting CTF Data from WRDS

Overview

Prerequisites

General

Tool-specific setup

Data download