OptionChainAnalytics

OptionChainAnalytics provides point-in-time option-chain containers, feed normalisation, chain reconstruction, queries, and visualisation in Python for quantitative research.

It is the data-container layer: provider retrieval, pricing models, portfolio backtests, and proprietary datasets remain separate. Pricing and implied-volatility inversion are delegated to vanilla-option-pricers; generic time-series and plotting utilities come from qis.

Install

OptionChainAnalytics requires Python 3.10 or newer. CI covers Python 3.10 through 3.14.

Install the published package:

pip install option-chain-analytics

For development from a clone:

pip install -e .

Provider-specific integrations are optional:

For example, pip install "option-chain-analytics[cboe]" installs the CBOE file dependency without installing unrelated network providers.

First success: no data or credentials

The authoritative offline example constructs a deterministic Black-Scholes-Merton option panel, reconstructs a historical chain, queries its front-expiry ATM strike and volatility, and selects a weekly roll maturity:

python examples/first_success.py

Expected evidence:

ticker=SYNTH
observation_times=2
contracts_at_first_time=30
expiries=['12Jan2024', '19Jan2024', '16Feb2024']
first_expiry_atm=100.00, vol=0.2057
weekly_roll_expiries=['12Jan2024']

See examples/first_success.py for the executable source. The documentation includes that file directly, so the tutorial cannot drift into a second implementation.

Data model

• OptionsDataDFs holds an option-observation panel (chain_ts) plus an aligned spot-price frame.
• SlicesChain reconstructs all available expiries at one exact observation time.
• ExpirySlice provides call/put, ATM, delta-strike, volatility, open-interest, and execution-price queries for one expiry.
• SliceColumn defines the common option-feed schema, including source time, contract, forward, discount factor, strike, expiry, quote, implied volatility, Greeks, volume, and open interest.

Observation and expiry timestamps are timezone-aware. Exact lookup is the reconstruction default; scheduled studies can explicitly select the latest previous observation, but never a later one. Volatilities are decimals (0.20 means 20%), time to maturity is in years, and each adapter must preserve and document its price/multiplier convention.

Empirical feeds

Local adapters cover Deribit/Tardis crypto histories and SPX/VIX CBOE fitted-chain files. These datasets are not distributed. Set OCA_DATA_PATH to an ignored local data root; generated output uses OCA_OUTPUT_PATH. CBOE files can be mapped with:

from option_chain_analytics import OptionsDataDFs
from option_chain_analytics.ts_loaders import load_local_cboe_options_data

options_data = OptionsDataDFs(
    **load_local_cboe_options_data(
        ticker='SPX',
        start='2023-01-03',
        end='2023-01-03',
    )
)

The CBOE mapper always infers bid/ask implied volatilities from the source bid/ask prices using the contemporaneous forward, discount factor, and time to maturity. This keeps every CBOE-backed OptionsDataDFs instance on the same complete schema.

For repeated empirical studies, build one normalized Parquet cache per underlying after installing the cboe extra:

from option_chain_analytics.ts_loaders import build_local_cboe_options_cache

build_local_cboe_options_cache(ticker='SPX')
build_local_cboe_options_cache(ticker='VIX')

This creates ignored cboe_options/spx_options_oca.parquet and cboe_options/vix_options_oca.parquet files. The normal loader uses a valid cache automatically and still accepts start/end filters. OCA embeds its cache schema and source-file fingerprint in each Parquet file and rejects stale caches. Use overwrite=True to rebuild deliberately.

CBOE data supplies implied forwards but no independent spot series. Pass spot_data, or use is_use_front_forward_as_spot=True only for visualisation; a forward proxy is not a valid spot return series for backtesting.

The Bloomberg BVOL-to-synthetic-option mapping remains a TODO: it must define maturity rolling and price generation before BVOL surfaces can be represented as option panels.

Documentation and development

Start with the documentation site, then read the schema contract, point-in-time reconstruction, and data-source guide.

pytest -q
ruff check src tests examples tools docs/conf.py
sphinx-build -W -b html docs docs/_build/html
python -m build

The installable package lives under src/option_chain_analytics/; repository-only scripts live in examples/. Local datasets, agent reports, and generated outputs live in ignored data/, agents/, and outputs/ directories.

Research and licensing boundary

OCA can provide a public, auditable input layer for empirical studies and replication. Strategy logic and the QF-paper backtests remain in SigmaStrats, and a public example is not expected to reproduce results computed from a private production dataset exactly.

The software is released under the MIT License. Dataset licences and access terms are separate from the software licence. Citation metadata is provided in CITATION.cff, and contribution guidance is provided in CONTRIBUTING.md.