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Options Backtest Reality Checker
Build a SaaS tool that audits options backtests for realistic execution outcomes. The product would rerun strategies under configurable spread, delay, and fill assumptions so traders can see whether an edge survives outside optimistic replay conditions.
Why this matters
You have a strategy that looks strong in a notebook, but the moment you ask whether the fills are realistic, confidence collapses. If you trade short-duration options, a few cents of extra friction, a delayed entry, or a wider spread can completely change the result. Today you either build custom simulations yourself or rely on rough assumptions that are easy to challenge. What you need is not another performance chart. You need a credibility layer that tells you whether your strategy survives conditions closer to live execution, and where the edge breaks down.
- · Built for Independent options traders and small systematic trading teams running intraday or same-day expiry strategies who currently rely on custom scripts and raw historical data..
- · Most likely monetization: SaaS subscription.
The Pain · Narrative
You have a strategy that looks strong in a notebook, but the moment you ask whether the fills are realistic, confidence collapses. If you trade short-duration options, a few cents of extra friction, a delayed entry, or a wider spread can completely change the result. Today you either build custom simulations yourself or rely on rough assumptions that are easy to challenge. What you need is not another performance chart. You need a credibility layer that tells you whether your strategy survives conditions closer to live execution, and where the edge breaks down.
Score Breakdown
Market Signal
Go-to-Market
Retail and semi-pro options traders already running Python-based backtests for intraday contracts and actively buying historical data.
~20K-50K active globally
Twitter dev community
$99/month
20 paying users who upload at least one strategy file and rerun three or more realism scenarios within 30 days
MVP Scope · 1–2 weeks
- Define a CSV schema for trade logs with timestamps, option symbol, side, entry, exit, and quantity
- Build a FastAPI upload endpoint and store parsed runs in PostgreSQL
- Implement a simple scenario engine for fixed extra spread and delay assumptions
- Create a first-pass dashboard showing baseline vs stressed P&L and max drawdown
- Recruit 10 target users and collect 5 sample backtest files for validation
- Add bid-ask fill presets for optimistic, mid, and conservative execution assumptions
- Build a break-even friction calculator that identifies the edge survival threshold
- Add visual equity curve overlays and per-trade attribution of friction impact
- Integrate Stripe for subscriptions and gated scenario limits
- Run live onboarding sessions with 5 users and iterate on confusing assumptions
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1The strongest users may prefer fully custom local tooling and distrust any black-box execution model.
- 2Without high-quality quote data, the simulator may feel too approximate for serious traders.
- 3The niche may be too small unless the product expands from options into broader systematic trading validation.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
Execution realism dominated the discussion. Roughly a dozen comments focused on spread, slippage, delayed entry, bid-ask execution, or suspiciously smooth drawdowns. Several users shared manual stress tests showing that a small increase in friction sharply reduced profitability, which strongly validates demand for a tool that quantifies edge sensitivity under more realistic assumptions.
Action Plan
Validate this opportunity before writing code
Recommended Next Step
Build
Strong demand signals detected. Real pain, real willingness to pay — start building an MVP.
Landing Page Copy Kit
Ready-to-paste copy based on real Reddit community language — no editing required
Headline
Options Backtest Reality Checker
Sub-headline
Build a SaaS tool that audits options backtests for realistic execution outcomes. The product would rerun strategies under configurable spread, delay, and fill assumptions so traders can see whether an edge survives outside optimistic replay conditions.
Who It's For
For Independent options traders and small systematic trading teams running intraday or same-day expiry strategies who currently rely on custom scripts and raw historical data.
Feature List
✓ Upload strategy trades or connect Python backtest outputs ✓ Scenario engine for slippage, spread, entry delay, and partial-fill assumptions ✓ Bid-ask based fill simulator with conservative and aggressive presets ✓ Survival report showing break-even friction thresholds ✓ Visual comparison of optimistic vs realistic equity curves
Where to Validate
Share your landing page in r/r/algotrading — that's exactly where these pain points were discovered.
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