ironcondor (Python bindings)

Python bindings for ironcondor, a high-performance options-strategy backtester with order-book-level fill simulation, built on optionstratlib.

Status: v0.5.0, published. The backtest API (BacktestConfig, run, Bundle) is wired over the same Rust engine, and the typed exception hierarchy landed with #40: IronCondorError is the base, with ConfigError, DataError, ExecutionError, StrategyError, BundleError, and EngineError beneath it (ConfigError is also a ValueError; DataError and BundleError are also OSError), and no panic crosses the FFI boundary. The distribution is on PyPI as ironcondor: pip install ironcondor. The 0.5.0 upload carries the macOS arm64 cp310-abi3 wheel plus the sdist; on Linux and macOS x86_64 pip builds from the sdist for now, which needs a Rust toolchain.

import ironcondor as ic

cfg = (
    ic.BacktestConfig(seed=42, capital_cents=1_000_000)   # $10,000, integer cents
      .data_parquet("chains/spx_2025.parquet")
      .strategy_iron_condor(
          underlying="SPX",
          underlying_price_cents=500_000,
          short_call_strike_cents=510_000, long_call_strike_cents=520_000,
          short_put_strike_cents=490_000,  long_put_strike_cents=480_000,
          expiration_ns=1_752_883_200_000_000_000,
          quantity=1,
          premium_short_call_cents=2_000, premium_short_put_cents=1_800,
          premium_long_call_cents=800,    premium_long_put_cents=700,
      )
      .exit_profit_percent(0.5)            # optionstratlib ExitPolicy
      .execution_naive(slippage_cents=5)   # or .execution_realistic()
      .fees(per_contract_cents=65, per_order_cents=100)
)

bundle  = ic.run(cfg)              # releases the GIL; runs the Rust engine AND
                                   # atomically writes <output_dir>/<run_id>/
print(bundle.path)                 # the on-disk bundle directory run() published
equity  = bundle.equity_curve()    # -> pandas.DataFrame  (needs the [pandas] extra)
metrics = bundle.metrics()         # -> dict (from manifest.json)

reopened = ic.load_bundle(bundle.path)   # validated through the hardened reader

Money crosses the boundary as integer cents (*_cents); the only floats are the analytic exception (implied volatility, the two rates, ExitPolicy percentages). Only what the engine wires end to end is exposed — a Parquet source and the iron condor strategy; a CSV source and the short strangle are not yet reachable through the binding.

DataFrame accessors (optional pandas extra)

bundle.fills(), .equity_curve(), .positions(), .greeks_attribution() return pandas DataFrames read lazily from the on-disk Parquet. pandas (and its pyarrow engine) is an optional soft dependency:

pip install ironcondor[pandas]

run(), load_bundle, metrics(), and bundle.path work without it — and because the bundle is plain Parquet + JSON, polars / pyarrow can read bundle.path directly instead.

Building locally

Wheels are built with maturin using the abi3 stable ABI, so one cp310-abi3 wheel per platform serves Python 3.10+. The published wheel is a self-contained backtester — python (bindings) plus orderbook (realistic execution) plus simulator (the synthetic-chain feed):

# from this directory (python/)
maturin build --release --features "python orderbook simulator"
# or, for an editable install into the current interpreter:
maturin develop --features "python orderbook simulator"

The crate manifest lives at the repository root; pyproject.toml points maturin at it via manifest-path = "../Cargo.toml", and its [tool.maturin] features already lists the same set (plus pyo3/extension-module), so a bare maturin build --release produces the same wheel.

Releases and PyPI status

ironcondor is published on PyPI; the name is registered and publishing stays a deliberate, owner-approved action (docs/06 §8, CLAUDE.md).

  • On every pipeline, CI (.github/workflows/ci.yml, job python-wheels) builds and pytest-runs a cp310-abi3 wheel on Linux + macOS.
  • On a v* tag, the release workflow (.github/workflows/release.yml) builds release-grade wheels (Linux manylinux, macOS x86_64, macOS arm64) plus an sdist, then publishes to PyPI via trusted publishing (OIDC, no token), behind a manually-approved GitHub pypi environment. Windows wheels remain a deferred wishlist item.
  • Known gap at 0.5.0. The index carries only the macOS arm64 wheel and the sdist: the wheel matrix failed on the v0.5.0 tag run and the fix (bd7313d) landed after it, so the Linux and macOS x86_64 wheels still owe a green release run.

Project links

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Avatar for joaquinbejar from gravatar.com joaquinbejar

Credits

Author: Joaquin Bejar

GitHub Statistics

Data verified by PyPI on Sep 4, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.10

Provides Extra

pandas

Tags

finance options trading backtesting quant

Classifiers

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