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This insight was synthesized by AI from public community discussions. We do not display original user posts or comments verbatim—all content has been rewritten and aggregated. Verify before acting on it.

78score
r/options
SaaS subscription with tiered pricing based on alert volume and number of strategies backtested
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Realistic Fill Backtesting Engine for Options

A SaaS backtesting and alert validation platform that defaults to realistic bid/ask fill assumptions instead of mid-price, allowing options traders and alert service operators to measure true strategy performance. Includes historical spread analysis by strike and moneyness, real-time spread monitoring, and auto-filtering of untradeable setups.

Rising +105%4 channels30-day mention trend: latest 23, peak 33, 30-day series
View on Reddit
Discovered Oct 9, 2026

Why this matters

You run an options alert service or actively backtest strategies, and your numbers look fantastic on paper — 90% win rates, +20% average returns. But when you actually trade your own signals, buying at the ask and selling at the bid, the spread devours your edge and you are left at breakeven. The far-OTM plays that looked brilliant in backtesting are the worst culprits: market makers widen those spreads to absurd levels during moves, making them untradeable despite correct directional calls. You have started manually logging spreads and filtering to near-the-money strikes, but you have no automated way to validate whether your alerts are actually tradeable at realistic fills before publishing them to subscribers.

  • · Built for Semi-professional options traders and options alert service operators who need credible, fill-realistic performance metrics for their strategies and subscriber-facing reports.
  • · Most likely monetization: SaaS subscription with tiered pricing based on alert volume and number of strategies backtested.

The Pain · Narrative

You run an options alert service or actively backtest strategies, and your numbers look fantastic on paper — 90% win rates, +20% average returns. But when you actually trade your own signals, buying at the ask and selling at the bid, the spread devours your edge and you are left at breakeven. The far-OTM plays that looked brilliant in backtesting are the worst culprits: market makers widen those spreads to absurd levels during moves, making them untradeable despite correct directional calls. You have started manually logging spreads and filtering to near-the-money strikes, but you have no automated way to validate whether your alerts are actually tradeable at realistic fills before publishing them to subscribers.

Score Breakdown

Pain Intensity9/10
Willingness to Pay7/10
Ease of Build5/10
Sustainability7/10

Market Signal

30-day mention trendPeak: 33
Sparkline: latest 23, peak 33, 30-day series
Channels covered
optionsDaytradingalgotradingValueInvesting

Go-to-Market

Exact target user

Independent options alert service operators with 50-500 paying subscribers who need credible fill-realistic performance to retain and grow their subscriber base

Estimated user count

Several thousand alert service operators and tens of thousands of serious retail options backtesters globally

Primary acquisition channel

Organic presence in options trading communities and targeted outreach to alert service operators

Price anchor

$49/month for individual backtesters, $149/month for alert service operators with public grading

First milestone

15 paying users within 30 days of launch, with at least 3 alert service operators using the public grading badge

MVP Scope · 1–2 weeks

Week 1
  • Set up Polygon.io or ORATS options data integration for historical bid/ask quotes
  • Build core backtesting engine that evaluates strategies at buy-at-ask/sell-at-bid instead of mid-price
  • Create spread analysis module that calculates spread-to-edge ratio for each historical trade
  • Design simple web dashboard showing mid-price vs. realistic-fill performance comparison
  • Build API endpoint to ingest strategy parameters and return fill-realistic backtest results
Week 2
  • Add historical spread database with filtering by underlying, strike, DTE, and moneyness
  • Implement auto-filtering that flags and removes untradeable far-OTM setups from backtest results
  • Create alert grading report that operators can share with subscribers showing realistic fill metrics
  • Build comparison view showing strategy performance across strike zones (far-OTM vs. near-ATM vs. ITM)
  • Add real-time spread monitor that checks current spread conditions before a signal is published
MVP Features: Backtesting engine using buy-at-ask/sell-at-bid fill assumptions by default · Historical spread database searchable by underlying, strike, DTE, and market condition · Spread-to-edge ratio scoring that flags strategies as tradeable or untradeable · Alert grading dashboard that publishes realistic fill performance alongside mid-price metrics · Real-time spread monitor that warns when current spreads make a signal untradeable

Differentiation

Existing solutions
Thinkorswim (Schwab)FidelityTraditional backtesting platforms (unspecified)
Our angle
No brokerage-agnostic tool exists that combines realistic bid/ask fill backtesting, real-time spread monitoring, and cross-platform walk-limit order automation for options traders

Why This Might Fail

Self-rebuttal — the most important trust signal

  1. 1Premium options data feed costs ($500-2000/month) may make pricing prohibitive for individual traders, squeezing margins before reaching scale
  2. 2Established backtesting platforms like Thinkorswim, TradeStation, or OptionAlpha could add realistic-fill features relatively quickly, neutralizing the differentiation
  3. 3Options data licensing restrictions may prevent redistributing spread analytics to subscribers, limiting the alert-service-operator use case that has the highest willingness to pay

Evidence Summary

How AI synthesized this insight — no verbatim quotes

Approximately 8 commenters described the core problem: strategies and alerts that look excellent at mid-price become breakeven or negative at realistic bid/ask fills. Multiple users confirmed they manually track spreads alongside signals and filter to near-the-money strikes to preserve tradeable win rates. Several noted that far-OTM contracts are the worst offenders, with market makers widening spreads dramatically during moves. The discussion revealed that no automated solution exists for validating alerts at realistic fills — users are doing this manually with spreadsheets and ad-hoc logging.

1 1 post analyzed4 4 channelsAI · AI synthesized · no verbatim

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

Realistic Fill Backtesting Engine for Options

Sub-headline

A SaaS backtesting and alert validation platform that defaults to realistic bid/ask fill assumptions instead of mid-price, allowing options traders and alert service operators to measure true strategy performance. Includes historical spread analysis by strike and moneyness, real-time spread monitoring, and auto-filtering of untradeable setups.

Who It's For

For Semi-professional options traders and options alert service operators who need credible, fill-realistic performance metrics for their strategies and subscriber-facing reports

Feature List

✓ Backtesting engine using buy-at-ask/sell-at-bid fill assumptions by default ✓ Historical spread database searchable by underlying, strike, DTE, and market condition ✓ Spread-to-edge ratio scoring that flags strategies as tradeable or untradeable ✓ Alert grading dashboard that publishes realistic fill performance alongside mid-price metrics ✓ Real-time spread monitor that warns when current spreads make a signal untradeable

Where to Validate

Share your landing page in r/r/options — that's exactly where these pain points were discovered.

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Report & PRDBUSINESS

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Frequently asked questions

Who feels this pain?
Semi-professional options traders and options alert service operators who need credible, fill-realistic performance metrics for their strategies and subscriber-facing reports
Is this a real opportunity?
This opportunity scores 78/100 on Pain Spotter's composite metric (pain intensity, willingness to pay, technical feasibility and sustainability). Validate further before committing engineering time.
How should I validate it?
Run 5 customer-discovery conversations with the target audience, post a landing page with a waitlist, and check the linked source post for recent activity before building.