Ceta Research Backtests

Reproducible quantitative strategy backtests. Three ways to run: local (DuckDB), cloud Code Execution, or cloud Projects.

Strategies

Strategy	Backtest	Screen	Description
QARP	qarp/backtest.py	qarp/screen.py	Quality at a Reasonable Price. 7-factor signal combining Piotroski F-Score, ROE, D/E, P/E, current ratio, income quality, and market cap. Semi-annual rebalance, 2000-2025.
Piotroski	piotroski/backtest.py	piotroski/screen.py	Piotroski F-Score applied to value stocks. Bottom 20% by P/B, 9-signal scoring, annual April rebalance. Three portfolio tracks: Score 8-9, Score 0-2, All Value. 1985-2025.
Low P/E	low-pe/backtest.py	low-pe/screen.py	Classic value investing. P/E < 15, ROE > 10%, D/E < 1.0, top 30 by lowest P/E. Quarterly rebalance, 2000-2025.

Quick start

# 1. Clone
git clone https://github.com/ceta-research/backtests.git
cd backtests

# 2. Install dependencies
pip install -r requirements.txt

# 3. Set your API key
export CR_API_KEY="your_key_here"

# 4. Run locally (fetches data, caches in DuckDB, runs on your machine)
python3 qarp/backtest.py
python3 low-pe/screen.py
python3 piotroski/backtest.py --verbose

# 5. Or run on cloud compute (no local dependencies needed)
python3 qarp/screen.py --cloud --preset us
python3 qarp/backtest.py --cloud --preset us

Three ways to run

1. Local mode (default)

Scripts fetch data via the Ceta Research SQL API, cache in a local DuckDB instance, and run the backtest on your machine. Requires duckdb and requests.

export CR_API_KEY="your_key_here"
python3 qarp/backtest.py

2. Cloud: Code Execution API

Submit self-contained scripts to run on managed cloud compute. Good for screens and quick analyses.

python3 qarp/screen.py --cloud --preset us

Or use the client directly:

from cr_client import CetaResearch

cr = CetaResearch()
result = cr.execute_code("print('hello from the cloud')")
print(result["stdout"])

3. Cloud: Projects API

Upload multi-file projects and run on cloud compute. Good for full backtests with shared modules.

python3 qarp/backtest.py --cloud --preset us

Or use the Projects API directly:

from cr_client import CetaResearch

cr = CetaResearch()
project = cr.create_project("my-analysis", dependencies=["duckdb", "requests"])
cr.upsert_file(project["id"], "main.py", "print('hello')")
result = cr.run_project(project["id"])
print(result["stdout"])

See examples/ for complete working examples.

API surface

The cr_client.py client provides three API groups:

API	Methods	Use case
Data Explorer	query(), query_saved()	SQL queries against financial data warehouse
Code Execution	execute_code(), execute_from_repo(), get_execution_status(), get_execution_files(), cancel_execution(), get_execution_limits()	Run single scripts on cloud compute
Projects	create_project(), upsert_file(), run_project(), list_projects(), get_run(), import_project_from_git()	Multi-file project management and execution

Data sources

Ceta Research (default)

Set CR_API_KEY and run. Scripts use the Ceta Research SQL API out of the box. Backward-compatible with TS_API_KEY.

The client supports two formats:

• JSON (default): For quick queries, returns list[dict]. Easy iteration.
• Parquet (bulk): For backtests, returns raw bytes. 10-100x smaller payloads, loads directly into DuckDB.

Swapping providers

Each backtest has a fetch_data_via_api() function that loads data into DuckDB tables. Replace that function to use a different source.

CSV / Parquet (bring your own data)

import duckdb
con = duckdb.connect(":memory:")
con.execute("CREATE TABLE prices_cache AS SELECT * FROM read_csv('my_prices.csv')")
# ... populate remaining tables, then pass `con` to run_backtest()

Common CLI flags

All backtests share these flags (via cli_utils.py):

# Exchange selection
python3 qarp/backtest.py --exchange BSE,NSE          # Specific exchanges
python3 qarp/backtest.py --preset india               # Exchange preset
python3 qarp/backtest.py --global                     # All exchanges

# Parameters
python3 qarp/backtest.py --frequency quarterly         # Rebalancing frequency
python3 qarp/backtest.py --risk-free-rate 0.0          # Risk-free rate (default: 2%)
python3 low-pe/backtest.py --no-costs                  # Disable transaction costs

# Cloud execution
python3 qarp/backtest.py --cloud                       # Run on cloud compute

# Output
python3 qarp/backtest.py --output results.json --verbose

Available presets: us, india, china, japan, korea, uk, germany, france, australia, canada, brazil, hongkong, taiwan, thailand, singapore, and more. See cli_utils.py for the full list.

How it works

1. Fetch: Scripts query the Ceta Research SQL API for historical financial data
2. Cache: Data is loaded into a local in-memory DuckDB database
3. Backtest: Screening and return calculations run entirely on your machine (or cloud with --cloud)
4. Results: Summary statistics printed to console, optionally saved to JSON

See METHODOLOGY.md for complete backtesting methodology, metrics definitions, and exchange coverage.

Project structure

backtests/
  cr_client.py            # Ceta Research API client (data + code exec + projects)
  ts_client.py            # Backward-compat shim (re-exports CetaResearch as TradingStudio)
  cloud_runner.py         # Cloud execution helper (Projects API wrapper)
  data_utils.py           # Data loading, price lookups, rebalance dates
  metrics.py              # Shared metrics computation (Sharpe, Sortino, Calmar, etc.)
  costs.py                # Transaction cost models (size-tiered, flat)
  cli_utils.py            # Shared CLI args, exchange presets
  requirements.txt        # requests, duckdb
  METHODOLOGY.md          # Complete backtesting methodology
  qarp/
    backtest.py           # Full QARP backtest (2000-2025)
    screen.py             # Current QARP screen
    generate_charts.py    # Regenerate charts from results
    README.md             # Strategy details
    results/              # Pre-computed results
    charts/               # Generated charts
  piotroski/
    backtest.py           # Full Piotroski backtest (1985-2025)
    screen.py             # Current Piotroski screen
    README.md             # Strategy details
  low-pe/
    backtest.py           # Full Low P/E backtest (2000-2025)
    screen.py             # Current Low P/E screen
    generate_charts.py    # Regenerate charts from results
    README.md             # Strategy details
    results/              # Pre-computed results
    charts/               # Generated charts
  examples/
    code_execution_example.py  # Code Execution API example
    projects_example.py        # Projects API example

Metrics

All backtests compute a comprehensive metrics suite via the shared metrics.py module:

Risk-adjusted: Sharpe Ratio, Sortino Ratio, Calmar Ratio Risk: Max Drawdown, VaR 95%, CVaR 95%, Annualized Volatility Relative: Information Ratio, Tracking Error, Up/Down Capture, Alpha, Beta Return: CAGR, Total Return, Win Rate

See METHODOLOGY.md for all 17 Tier 1 + Tier 2 metrics with formulas.

License

MIT