eventstudytools — Python API wrapper

Python client for the eventstudytools.com research apps: run abnormal-return event studies (and the other research apps) straight from Python. No third-party dependencies — standard library only, Python 3.8+.

Installation

pip install git+https://github.com/EventStudyTools/python-wrapper.git

API key

Request your free API key at eventstudytools.com/api-key. Pass it as EventStudyAPI("...") or set the EST_API_KEY environment variable.

Quickstart: abnormal-return event study (ARC)

from eventstudytools import EventStudyAPI, ARCInput

api = EventStudyAPI("YOUR_API_KEY")

results = api.run(
    ARCInput(benchmark_model="mm", return_type="log"),
    files={
        "request_file": "01_RequestFile.csv",
        "firm_data": "02_FirmData.csv",
        "market_data": "03_MarketData.csv",
    },
    dest_dir="results",
)

for rf in results:
    print(rf.name, "->", rf.local_path)

A complete runnable example with sample data (20 US firms, S&P 500 benchmark) lives in examples/.

Step-by-step API

run() wraps the five protocol steps; you can also drive them yourself:

api = EventStudyAPI("YOUR_API_KEY")
api.authenticate()                 # create task, obtain task token
api.configure(ARCInput())          # send analysis configuration
api.upload("request_file", "01_RequestFile.csv")
api.upload("firm_data", "02_FirmData.csv")
api.upload("market_data", "03_MarketData.csv")
api.commit()                       # server-side input validation
api.process()                      # launch calculation
api.download_results("results")    # poll + download

Input files

Semicolon-separated CSV files without header rows, dates as dd.mm.yyyy.

Request file (one row per event):

Column	Content
1	Event ID (unique integer)
2	Firm ID (must match firm data)
3	Market/index ID (must match market data)
4	Event date (dd.mm.yyyy)
5	Grouping variable (for grouped AAR/CAAR results)
6	Event window start (e.g. -10)
7	Event window end (e.g. 10)
8	Estimation window end (e.g. -11)
9	Estimation window length (e.g. 120)

Firm data: Firm ID; Date; Closing price — Market data: Market ID; Date; Index value.

Parameters (ARC)

Parameter	Values	Default
benchmark_model	mm, mm-sw, mam, cpmam, ff3fm, ffm4fm, ff5fm, garch, egarch, capm	mm
return_type	log, simple	log
non_trading_days	later, earlier, keep, skip	later
result_file_type	csv, xls, xlsx, ods	csv
test_statistics	see eventstudytools.ARC_TEST_STATISTICS	six workhorse statistics

Other research apps

• AVCInput — abnormal volume calculator
• AVyCInput — abnormal volatility calculator
• CATAInput — content analysis (news analytics); uploads text_data + keywords_data
• EDIInput — event date identification; uploads text_data

Result files (ARC)

ar_results.csv, car_results.csv, aar_results.csv, caar_results.csv, analysis_report.csv, results_table_export.txt.

License

MIT