What makes Options Trading Backtester notable?

1.7K downloads. Trust evidence available

How should Options Trading Backtester be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Options Trading Backtester

Automated options trading backtester tool. Built by Shubh's autonomous Money Machine — self-improving based on live market data. Skill: Options Trading Backtester Owner: ssidharhubble Summary: Automated options trading backtester tool. Built by Shubh's autonomous Money Machine — self-improving based on live market data. Tags: latest:1.0.18 Version history: v1.0.18 | 2026-06-22T14:02:55.396Z | auto - Updated version number in SKILL.md from 1.0.17 to 1.0.18. - No other functional or content changes were made. v1.0.17 | 2026-06-22T13:54:20.923Z |

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.7k

Updated

Oct 10, 2026

Version

1.0.18

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.7K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.7K downloadsadoption · observed Oct 10, 2026
Latest release: 1.0.18release · observed Jun 22, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s1751xfhp40q8vjjqd22y5vaw584gpke:options-trading-backtester

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-ssidharhubble-options-trading-backtester/snapshot"

Documentation

CLAWHUB

89,583 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: options-trading-backtester
version: 1.0.18
description: |
  Build and run options strategy backtests in Python. Supports Iron Condor, Strangle,
  Calendar Spread, Vertical Credit Spread. Tests against historical data with realistic
  slippage, commission ($0.65/contract), and IV crush modeling. Outputs Sharpe ratio,
  win rate, max drawdown, expectancy, and equity curve. Use when user asks to backtest
  an options strategy, test a config, or analyze trade history.
compatibility: Python 3.10+, pandas, numpy, scipy, matplotlib. Optional: yfinance (free data).
metadata:
  author: ssyopro.zo.computer
  category: finance
  display-name: Options Trading Backtester
  tags: options, backtesting, trading-strategy, python, quant-finance, iron-condor, strangle
---

# Options Trading Backtester

Event-driven backtester for options strategies. Tests against synthetic or real historical data.

## Strategy Types

| Strategy | Description | Best For |
|---|---|---|
| Iron Condor | Sell OTM put spread + OTM call spread | Neutral markets, high IV |
| Strangle | Sell OTM put + OTM call, same expiration | Low-cost setup, volatile markets |
| Calendar Spread | Buy long-dated, sell short-dated same strike | Time decay, mean reversion |
| Vertical Credit Spread | Bull put or Bear call spread | Directional trades with defined risk |

## Backtest Engine

```python
#!/usr/bin/env python3
"""Options Trading Backtester v1.0."""
import json, argparse, numpy as np
from typing import List, Dict

COMMISSION = 0.65  # $/contract
SLIPPAGE = 0.02    # $/share

def simulate_iron_condor(price_at_entry: float, iv: float, days_to_exp: int, 
                         short_delta: float = 0.20, width: float = 5.0) -> Dict:
    """Simulate Iron Condor P&L."""
    put_short_strike = price_at_entry * (1 - short_delta)
    put_long_strike  = put_short_strike - width
    call_short_strike = price_at_entry * (1 + short_delta)
    call_long_strike  = call_short_strike + width
    
    # Simplified premium model (uses IV and moneyness)
    def premium(strike, is_put):
        dist = abs(price_at_entry - strike) / price_at_entry
        base = iv * price_at_entry * 0.3
        return base * np.exp(-dist * 3) * (0.85 if is_put else 0.75)
    
    short_put_credit  = premium(put_short_strike, True)
    long_put_debit    = premium(put_long_strike, True)
    short_call_credit = premium(call_short_strike, False)
    long_call_debit   = premium(call_long_strike, False)
    
    net_credit = (short_put_credit + short_call_credit) - (long_put_debit + long_call_debit)
    
    # Expiration P&L (simplified)
    expiries = np.random.normal(0, price_at_entry * 0.02, 100)
    outcomes = []
    for final_price in expiries:
        put_pnl  = (short_put_credit - long_put_debit) * 100 if final_price < put_long_strike else \
                   (short_put_credit - long_put_debit) * 100 if final_price < put_short_strike else \
                   -(width * 100)
        call_pnl = (short_call_credit - long_

_meta.json

{
  "ownerId": "kn723dy43mmbqpy0cp62hwptm984gga5",
  "slug": "options-trading-backtester",
  "version": "1.0.18",
  "publishedAt": 1782136975396
}

skill-card.md

## Description:

Builds Python options strategy backtesting simulations for iron condors, strangles, calendar spreads, and vertical credit spreads, with outputs such as win rate, Sharpe ratio, drawdown, expectancy, and equity curve.

This skill is ready for commercial/non-commercial use.

## Publisher:

[ssidharhubble](https://clawhub.ai/user/ssidharhubble)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and analysts use this skill to draft and run Python-based options strategy simulations, test parameters, and summarize synthetic backtest-style metrics. Outputs should be reviewed before being used for trading decisions.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Users may mistake simplified or synthetic options backtest outputs for realistic historical trading evidence or investment advice.

Mitigation: Treat outputs as synthetic simulations unless real data ingestion, strategy logic, risk filters, and financial disclaimers are independently reviewed and implemented.

## Reference(s):


## Skill Output:

**Output Type(s):** [text, markdown, code, shell commands, configuration]

**Output Format:** [Markdown with Python, bash, and JSON snippets]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May produce synthetic options backtest metrics and optional JSON result files when an output path is configured.]

## Skill Version(s):

1.0.18 (source: frontmatter and server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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