# goldspanlabs/optopsy

A nimble options research and backtesting library for Python

Repository: https://github.com/goldspanlabs/optopsy
Canonical: https://ross.abutalabs.com/products/optopsy
Homepage: https://goldspanlabs.github.io/optopsy/
Language: Python
License: AGPL-3.0
License Family: copyleft
Topics: options-spreads, options-strategies, trade-options, options, trading, options-trading, options-framework, option-pricing, option-chain, algorithmic, algorithmic-trading, algorithmic-trading-engine, algorithmic-trading-library, backtesting-trading-strategies, backtest, backtesting-frameworks, backtesting, option-strategies, dataframe
Last push: 2026-06-30T17:46:49+00:00

## Health v2 (maintenance only)
Score: 86/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 90, release rhythm 72, longevity 100
- inputs: {"age_days": 3273, "days_push": 64, "days_rel": 184, "gap_med": 1.5, "n_releases_24m": 5}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1462, forks 223 (observed 2026-08-28T04:04:47.630986+00:00)

## What it is
Optopsy is a Python backtesting and statistics library for options strategies, offering 38 built-in strategies, per-leg delta targeting, trade and portfolio simulation, risk metrics, and slippage/commission modeling. It works natively with pandas DataFrames and any options data source, and includes a data CLI and plugin system.

## Use cases
- backtest iron condors on SPX with profit targets and stop losses
- compare 45-DTE options strategies vs holding to expiration
- compute Sharpe, Sortino, VaR and other risk metrics for option spreads
- filter strategy entries with technical indicators like RSI and MACD
- simulate multi-strategy options portfolios with capital tracking
- download and cache historical options chain data
- model commissions and slippage in options backtests

## When to choose
- you need to research and backtest options spread strategies in Python
- you want pandas-native results that fit an existing data workflow
- you need per-leg delta targeting, early exits, and realistic fill modeling
- you want quick statistics on options strategies without building a spreadsheet

## When to avoid
- you need live order execution or broker integration rather than research/backtesting
- you trade equities, futures, or crypto rather than options
- you require a GUI-first trading platform
- you need a license more permissive than AGPL-3.0

## Facets
- artifact type: library
- maturity: active
- function: trading, simulation, data-science, cli, plugin-system
- domain: fintech, data-science, developer-tools
- platform: python, cli
- tags: options-trading, backtesting, options-strategies, pandas, risk-metrics, slippage-modeling, algorithmic-trading, trading

## Member repositories
- goldspanlabs/optopsy (main) score 86

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:47.630986+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:35:17.360718+00:00, confidence not recorded.
  - readme: https://github.com/goldspanlabs/optopsy (fetched 2026-08-28T04:04:47.630986+00:00, sha 30a0b070d512)
  - homepage: https://goldspanlabs.github.io/optopsy/ (fetched 2026-08-29T11:43:47.768736+00:00, sha 6ec21a3351ad)
  - registry_pypi: https://pypi.org/pypi/optopsy/json (fetched 2026-08-29T11:43:47.771333+00:00, sha ea832a6c30de)
- Data as of 2026-08-30T08:39:29.467469+00:00.