Examples Catalog

Every example below is a file in this repository that you can execute. The point of this page is that you should not have to trust a snippet: each entry names the file, the command that runs it, and what it demonstrates.

All of them need the Python bindings built first — see Python Bindings Setup. The Rust example needs only cargo.

Examples that ship in the repo

File	Run it with	Demonstrates
`crates/openquant/examples/research_notebook_smoke.rs`	`cargo run -p openquant --example research_notebook_smoke`	The minimum Rust path: CUSUM event sampling → max-Sharpe allocation → VaR/ES/CDaR. ~30 lines, asserts its own invariants.
`notebooks/python/scripts/smoke_all.py`	`just notebook-smoke`	Executes all eight research notebooks headlessly. The broadest single check that the Python surface works.
`experiments/run_pipeline.py`	`just exp-run`	A config-driven pipeline run (`experiments/configs/futures_oil_baseline.toml`) that writes artifacts to `experiments/artifacts`.
`python/benchmarks/benchmark_pipeline.py`	`just py-bench`	Times the mid-frequency pipeline over 30 iterations at 2048 bars.
`python/benchmarks/benchmark_data_processing.py`	`just py-bench-data`	Ingestion and bar-building throughput at 200k rows × 4 symbols. Use this if you want a real memory profile before feeding it production data.
`python/tests/`	`just py-test`	Nine test modules that double as usage examples — `test_pipeline_api.py` and `test_bindings_contract.py` are the two worth reading first.

The eight notebooks under notebooks/python/ are numbered in reading order, from 01_event_labeling_and_pipeline.ipynb to 08_algo_wheel_experiments.ipynb, and 06_afml_real_data_end_to_end.ipynb is the one that runs on real data rather than synthetic.

Worked example: cleaning a messy OHLCV file

The repository ships a deliberately awful CSV at python/tests/fixtures/ohlcv_us_equities.csv — non-canonical column names, two symbols interleaved, rows out of chronological order, and a duplicated 2024-01-02 bar for AAPL:

Date,Ticker,Open,High,Low,Close,Volume,Adj Close
2024-01-03,AAPL,186.10,187.00,185.50,186.30,5000,186.10
2024-01-01,AAPL,184.00,185.00,183.50,184.80,8000,184.70
2024-01-02,AAPL,185.00,186.20,184.70,185.90,7000,185.80
2024-01-02,AAPL,185.05,186.30,184.60,186.00,7100,185.90
2024-01-01,MSFT,370.10,372.00,369.90,371.50,6000,371.30
2024-01-03,MSFT,372.20,373.10,371.40,372.00,5500,371.90

load_ohlcv canonicalises the header (Date → ts, Ticker → symbol, Adj Close → adj_close), sorts by symbol and timestamp, and drops the duplicate — keeping the last occurrence by default, which is the convention you want when a vendor restates a bar:

from openquant import data

frame, report = data.load_ohlcv(
    "python/tests/fixtures/ohlcv_us_equities.csv",
    return_report=True,
)
print(frame)
print(report)

Ask for the report whenever the data is not yours. Silent deduplication is how a survivorship or restatement bug gets into a backtest without anyone noticing.

Worked example: the whole research loop

The end-to-end loops are long enough to deserve their own pages, with the stages explained rather than just listed:

Rust Core Workflow — one program: event sampling → triple-barrier labels → bet sizing → purged CV → risk and allocation, with its printed output.
Python Core Workflow — the same ground in Python, ending in a promotion decision.
Notebook Research Workflow — the notebook-driven version.

Where to start

If you want to…	Go here
See OpenQuant produce a number, once	Quickstart
Understand the Rust API	Rust Core Workflow
Understand the Python API	Python Core Workflow
Look up one module	Modules by AFML chapter
Build and test the repo	Local Build Setup