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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.
為什麼這很重要
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.
- · 專為 Semi-professional options traders and options alert service operators who need credible, fill-realistic performance metrics for their strategies and subscriber-facing reports 打造。
- · 最可能的變現方式：SaaS subscription with tiered pricing based on alert volume and number of strategies backtested。
痛點敘事
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.
得分構成
市場信號
Go-to-Market 啟動方案
Independent options alert service operators with 50-500 paying subscribers who need credible fill-realistic performance to retain and grow their subscriber base
Several thousand alert service operators and tens of thousands of serious retail options backtesters globally
Organic presence in options trading communities and targeted outreach to alert service operators
$49/month for individual backtesters, $149/month for alert service operators with public grading
15 paying users within 30 days of launch, with at least 3 alert service operators using the public grading badge
MVP 方案 · 1-2 週
- 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
- 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
差異化
為什麼這件事可能失敗
自我反駁——最重要的信任度信號
- 1Premium options data feed costs ($500-2000/month) may make pricing prohibitive for individual traders, squeezing margins before reaching scale
- 2Established backtesting platforms like Thinkorswim, TradeStation, or OptionAlpha could add realistic-fill features relatively quickly, neutralizing the differentiation
- 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
證據綜述
AI 如何合成此洞察——無原話引用
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.
行動計畫
在寫程式之前，先驗證這個商機
建議下一步
直接做
需求訊號強烈。痛點真實、付費意願明確——啟動 MVP 開發。
落地頁文案包
基於真實 Reddit 評論整理的即用文案，可直接貼到落地頁
主標題
Realistic Fill Backtesting Engine for Options
副標題
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.
目標使用者
適合：Semi-professional options traders and options alert service operators who need credible, fill-realistic performance metrics for their strategies and subscriber-facing reports
功能列表
✓ 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
去哪裡驗證
把落地頁連結發布到 r/r/options——這裡就是這些痛點被發現的地方。
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