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Backtest-to-Live Data Reconciliation SaaS
Build a debugging platform that compares historical training data against live or broker feeds bar by bar and pinpoints why a trading model fails outside backtests. The product would surface mismatches in volume, session boundaries, roll dates, and adjustments before users blame the model or spend on unnecessary vendor changes.
為什麼這很重要
You spend months building a strategy that looks promising on historical futures data, then it falls apart the moment you test it in a paper or live environment. The issue is not obvious because price may look roughly similar while volume, session cutoffs, or rollover handling quietly drift enough to break your features. Existing broker dashboards and raw CSV checks make this painfully manual, and premium data vendors do not necessarily explain where the mismatch lives. What you need is a tool that shows exactly which bars differ, how the differences propagate into indicators, and whether your edge was real or came from a dataset artifact.
- · 專為 Independent systematic traders, small quant teams, and ML-based futures traders who research with one dataset and execute through a broker or separate live feed. 打造。
- · 最可能的變現方式：SaaS subscription。
痛點敘事
You spend months building a strategy that looks promising on historical futures data, then it falls apart the moment you test it in a paper or live environment. The issue is not obvious because price may look roughly similar while volume, session cutoffs, or rollover handling quietly drift enough to break your features. Existing broker dashboards and raw CSV checks make this painfully manual, and premium data vendors do not necessarily explain where the mismatch lives. What you need is a tool that shows exactly which bars differ, how the differences propagate into indicators, and whether your edge was real or came from a dataset artifact.
得分構成
市場信號
Go-to-Market 啟動方案
Solo and two-to-five person quant trading teams running futures or intraday strategies with separate research and execution data sources.
~20K-50K active globally
SEO long-tail
$79/month
10 paying users who upload two feeds and run at least three reconciliation jobs each within 30 days
MVP 方案 · 1-2 週
- Build CSV upload and schema mapping for OHLCV bars from two sources
- Implement timestamp alignment and diff logic for price and volume fields
- Create a basic web UI showing mismatched bars in a sortable table
- Add summary diagnostics for session boundary and missing-bar anomalies
- Prepare sample futures datasets and three reproducible mismatch test cases
- Add feature-level comparison for common indicators and model inputs
- Implement continuous contract roll-date comparison and alerts
- Ship a report export that summarizes likely root causes
- Integrate one broker API and one external data API for direct ingestion
- Launch a landing page with a self-serve trial and feedback capture
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
- 1The market may be too narrow because many users debug feed mismatches only once, reducing long-term retention.
- 2Serious quants may distrust a third-party diagnostics tool and prefer internal scripts they can inspect fully.
- 3Data licensing or broker API inconsistencies may prevent reliable automated ingestion across the providers users care about most.
證據綜述
AI 如何合成此洞察——無原話引用
The discussion strongly centered on discrepancies between backtest data and broker or live bars. Roughly half the comments pointed to aggregation, volume, roll dates, and session boundaries as likely causes of model failure. Multiple participants described manual reconciliation workflows and warned that apparent alpha often disappears once feeds are matched properly. That combination indicates a sharp, expensive debugging problem with immediate value.
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
Backtest-to-Live Data Reconciliation SaaS
副標題
Build a debugging platform that compares historical training data against live or broker feeds bar by bar and pinpoints why a trading model fails outside backtests. The product would surface mismatches in volume, session boundaries, roll dates, and adjustments before users blame the model or spend on unnecessary vendor changes.
目標使用者
適合：Independent systematic traders, small quant teams, and ML-based futures traders who research with one dataset and execute through a broker or separate live feed.
功能列表
✓ Bar-by-bar historical versus live feed diff engine ✓ Automated detection of volume, timestamp, roll, and adjustment mismatches ✓ Feature parity checks that show downstream signal impact
去哪裡驗證
把落地頁連結發布到 r/r/algotrading——這裡就是這些痛點被發現的地方。
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