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r/algotrading
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Regime Detection Analytics for Scalpers

Build a SaaS that classifies intraday market regimes and shows how each regime affects a trader's expectancy, win rate, and drawdown. The key value is not predicting the market perfectly, but helping traders stop using blunt filters that remove both bad trades and the best breakouts.

En hausse +486%5 canauxTendance des mentions sur 30 jours: latest 2, peak 4, 30-day series
Voir sur Reddit
Découvert 15 juil. 2026

Pourquoi c'est important

You already know that some days your setup works and other days it gets chopped apart, but your current tools mostly show total results. When you try a simple filter, it often blocks the exact breakout you wanted to catch, so you are left guessing whether the filter reduced noise or just removed opportunity. You need a way to label market conditions consistently, replay how your strategy behaved in each regime, and see whether chop is causing a manageable drag or quietly destroying your edge. Generic chart indicators are not enough because the real question is strategy performance under changing conditions, not just what the price chart looked like.

  • · Conçu pour Independent retail scalpers and part-time systematic traders in equities, futures, and crypto who already backtest or journal trades but lack regime-specific analytics..
  • · Monétisation la plus probable : SaaS subscription.

La douleur · Récit

You already know that some days your setup works and other days it gets chopped apart, but your current tools mostly show total results. When you try a simple filter, it often blocks the exact breakout you wanted to catch, so you are left guessing whether the filter reduced noise or just removed opportunity. You need a way to label market conditions consistently, replay how your strategy behaved in each regime, and see whether chop is causing a manageable drag or quietly destroying your edge. Generic chart indicators are not enough because the real question is strategy performance under changing conditions, not just what the price chart looked like.

Détail du score

Intensité du problème9/10
Volonté de payer7/10
Facilité de réalisation6/10
Durabilité8/10

Signal du marché

Tendance des mentions sur 30 joursPic : 4
Sparkline: latest 2, peak 4, 30-day series
Canaux couverts
algotradingfront_pageproductivitystartupsChatGPT

Mise sur le marché

Utilisateur cible exact

Retail scalpers who already export trade logs and actively tweak entry filters for intraday equity or crypto strategies.

Nombre d'utilisateurs estimé

~50K-150K serious active users globally

Canal d'acquisition principal

SEO long-tail

Ancre de prix

$49/month

Premier jalon

20 paying users who connect trade logs and review at least 100 trades by regime within 30 days

Périmètre MVP · 1–2 semaines

Semaine 1
  • Define 3 initial regime models: efficiency ratio, ATR compression, and directional persistence
  • Build CSV trade-log importer for common broker export formats
  • Create a basic backend that maps each trade to a regime label at entry time
  • Design a simple dashboard for PnL, win rate, and drawdown by regime
  • Set up landing page with waitlist and one example report
Semaine 2
  • Add filter simulator to compare all-trades versus regime-filtered trades
  • Implement missed-move report showing skipped winners after filtering
  • Support one live data source for daily regime labeling
  • Add user-configurable thresholds and saved presets
  • Run onboarding calls or surveys with first 10 testers and refine labels
Fonctions MVP: Automated regime classification using multiple definitions of chop, trend, and transition · PnL attribution dashboard by regime, timeframe, and instrument · Trade filter simulator showing impact on expectancy and missed-opportunity cost

Différenciation

Solutions existantes
Self-built scripts and spreadsheetsGeneric charting platforms
Notre angle
There is an unmet need for trader-facing software that turns regime detection from a vague concept into measurable, actionable analytics tied directly to entries, exits, and expectancy.

Pourquoi cela pourrait échouer

Auto-contre-argument — le signal de confiance le plus important

  1. 1The strongest objection is that regime definitions may be too subjective, causing traders to distrust labels and fall back to their own discretionary views.
  2. 2If the tool cannot show a clear improvement in expectancy quickly, users may treat it as interesting research rather than a recurring must-have product.
  3. 3Cheap charting tools and community indicators may satisfy enough of the market unless the product proves direct strategy-level impact.

Résumé des preuves

Comment l'IA a synthétisé cet aperçu — pas de citations textuelles

Several participants focused on the difficulty of identifying chop without excluding strong directional moves. Multiple comments emphasized that simple filters are insufficient and that the real task is defining regimes and measuring how a strategy performs inside each one. There was repeated concern that drawdowns come from range-bound conditions, which supports a product centered on regime attribution rather than generic indicators.

1 1 publication analysée5 5 canauxAI · Synthétisé par IA · pas de citations

Plan d'Action

Validez cette opportunité avant d'écrire du code

Prochaine Étape Recommandée

Construire

Signaux de demande forts. Vraie douleur et volonté de payer détectées — commencez à construire un MVP.

Kit de Textes pour Landing Page

Textes prêts à coller, basés sur le langage réel de la communauté Reddit

Titre Principal

Regime Detection Analytics for Scalpers

Sous-titre

Build a SaaS that classifies intraday market regimes and shows how each regime affects a trader's expectancy, win rate, and drawdown. The key value is not predicting the market perfectly, but helping traders stop using blunt filters that remove both bad trades and the best breakouts.

Pour Qui

Pour Independent retail scalpers and part-time systematic traders in equities, futures, and crypto who already backtest or journal trades but lack regime-specific analytics.

Liste des Fonctionnalités

✓ Automated regime classification using multiple definitions of chop, trend, and transition ✓ PnL attribution dashboard by regime, timeframe, and instrument ✓ Trade filter simulator showing impact on expectancy and missed-opportunity cost

Où Valider

Partagez votre landing page sur r/r/algotrading — c'est exactement là que ces points de douleur ont été découverts.

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Questions fréquentes

Qui rencontre ce problème ?
Independent retail scalpers and part-time systematic traders in equities, futures, and crypto who already backtest or journal trades but lack regime-specific analytics.
Est-ce une réelle opportunité ?
Cette opportunité obtient un score de 82/100 selon la métrique composite de Pain Spotter (intensité du problème, propension à payer, faisabilité technique et viabilité). Validez-la davantage avant d'y consacrer du temps de développement.
Comment dois-je la valider ?
Menez 5 entretiens de découverte client avec le public cible, publiez une landing page avec une liste d'attente, et vérifiez l'activité récente sur le post source lié avant de commencer le développement.