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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.
Why this matters
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.
- · Built for 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..
- · Most likely monetization: SaaS subscription.
The Pain · Narrative
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.
Score Breakdown
Market Signal
Go-to-Market
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 Scope · 1–2 weeks
- 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
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 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.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
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.
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
Backtest-to-Live Data Reconciliation SaaS
Sub-headline
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.
Who It's For
For 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.
Feature List
✓ 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
Where to Validate
Share your landing page in r/r/algotrading — that's exactly where these pain points were discovered.
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