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78Score
r/algotrading
SaaS subscription
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Skipped-Trade Edge Journal

Create a trade journaling platform that records both executed and skipped setups so traders can evaluate whether filters improve edge or merely reduce activity. This solves a blind spot that normal broker histories and journals do not cover.

Steigend +200%4 Kanäle30-Tage-Erwähnungstrend: latest 2, peak 2, 30-day series
Auf Reddit ansehen
Entdeckt 21. Juli 2026

Warum das wichtig ist

You think your filter is improving your strategy because the trades you took look better. The problem is you never measured what would have happened if you had taken the opportunities you skipped. That means you cannot tell whether your rules add real value, cut out losers, or simply make you trade less. Standard journaling tools mostly start at the moment an order exists, which leaves a major gap in the research loop. For traders who mix discretion with rules, this missing dataset quietly prevents learning and causes false confidence in filter logic.

  • · Entwickelt für Rule-based discretionary traders and light-automation users who generate many candidate setups and want to quantify whether their filters and overrides help..
  • · Wahrscheinlichste Monetarisierung: SaaS subscription.

Der Schmerz · Narrativ

You think your filter is improving your strategy because the trades you took look better. The problem is you never measured what would have happened if you had taken the opportunities you skipped. That means you cannot tell whether your rules add real value, cut out losers, or simply make you trade less. Standard journaling tools mostly start at the moment an order exists, which leaves a major gap in the research loop. For traders who mix discretion with rules, this missing dataset quietly prevents learning and causes false confidence in filter logic.

Score-Details

Schmerzintensität8/10
Zahlungsbereitschaft6/10
Umsetzbarkeit6/10
Nachhaltigkeit8/10

Marktsignal

30-Tage-ErwähnungstrendSpitze: 2
Sparkline: latest 2, peak 2, 30-day series
Abgedeckte Kanäle
algotradingsaasChatGPTfront_page

Markteinführung

Genauer Zielnutzer

Active discretionary or semi-automated traders who evaluate 10 or more candidate setups per week and already keep some form of trading journal.

Geschätzte Nutzeranzahl

~100K-300K globally

Primärer Akquisekanal

r/<community> organic

Preisanker

$29/month

Erster Meilenstein

50 weekly active users logging both taken and skipped setups for 4 consecutive weeks

MVP-Umfang · 1–2 Wochen

Woche 1
  • Design a setup schema for candidate trade, filter state, and horizon outcome
  • Build manual and CSV-based setup logging flow
  • Create dashboard for taken versus skipped trade outcome comparison
  • Add expectancy and win-rate breakdown by filter or reason code
  • Publish a simple onboarding guide for spreadsheet users
Woche 2
  • Add browser-based form for rapid intraday setup capture
  • Implement reminder system to finalize horizon outcomes automatically
  • Build rule tags for common filters like volatility, trend, and liquidity
  • Add import from one broker export and one charting alert source
  • Interview first 10 active users to refine workflow friction
MVP-Funktionen: Capture engine for all detected setups, not only placed orders · Side-by-side analysis of taken versus skipped outcomes · Filter attribution dashboard showing impact on expectancy and frequency · Missed-trade reminders and review workflow

Differenzierung

Bestehende Lösungen
Treeova
Unser Ansatz
There is an unmet need for beginner-to-intermediate algo trading software that combines realistic backtesting, skipped-trade analysis, production monitoring, and non-programmer usability in one workflow.

Warum dies scheitern könnte

Selbstwiderlegung — das wichtigste Vertrauenssignal

  1. 1If setup capture feels like extra admin work, users will not log enough data for the product to prove value.
  2. 2Many traders lack a systematic signal-generation step, reducing fit for the product.
  3. 3The insight may be valuable but too niche to support a large standalone business without adjacent journaling features.

Evidenzzusammenfassung

Wie KI diese Erkenntnis synthetisiert hat — keine wörtlichen Zitate

The clearest unique insight in the discussion was that traders rarely measure the opportunities they reject, leaving them unable to judge whether filters create edge. Another comment reinforced the consistency problem by noting that partial automation reduced missed trades. Together, these signals support a product focused on the untracked area between signal and execution.

1 1 Beitrag analysiert4 4 KanäleAI · KI-synthetisiert · keine wörtliche Wiedergabe

Aktionsplan

Validiere diese Gelegenheit, bevor du Code schreibst

Empfohlener nächster Schritt

Bauen

Starke Nachfragesignale erkannt. Echter Schmerz und Zahlungsbereitschaft vorhanden — fang an, ein MVP zu bauen.

Landing Page Textpaket

Druckfertige Texte basierend auf echten Reddit-Kommentaren — direkt einfügen

Überschrift

Skipped-Trade Edge Journal

Unterüberschrift

Create a trade journaling platform that records both executed and skipped setups so traders can evaluate whether filters improve edge or merely reduce activity. This solves a blind spot that normal broker histories and journals do not cover.

Für Wen

Für Rule-based discretionary traders and light-automation users who generate many candidate setups and want to quantify whether their filters and overrides help.

Funktionsliste

✓ Capture engine for all detected setups, not only placed orders ✓ Side-by-side analysis of taken versus skipped outcomes ✓ Filter attribution dashboard showing impact on expectancy and frequency ✓ Missed-trade reminders and review workflow

Wo Validieren

Teile deine Landing Page in r/r/algotrading — genau dort wurden diese Schmerzpunkte entdeckt.

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Häufig gestellte Fragen

Wer spürt diesen Schmerz?
Rule-based discretionary traders and light-automation users who generate many candidate setups and want to quantify whether their filters and overrides help.
Ist das eine echte Chance?
Diese Chance erreicht 78/100 bei der zusammengesetzten Metrik von Pain Spotter (Schmerzintensität, Zahlungsbereitschaft, technische Machbarkeit und Nachhaltigkeit). Validieren Sie weiter, bevor Sie Entwicklungszeit investieren.
Wie sollte ich das validieren?
Führen Sie 5 Customer-Discovery-Gespräche mit der Zielgruppe, veröffentlichen Sie eine Landingpage mit Warteliste und prüfen Sie den verlinkten Quellbeitrag auf aktuelle Aktivitäten, bevor Sie mit der Entwicklung beginnen.