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78score
r/algotrading
SaaS subscription
Build

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.

Rising +200%4 channels30-day mention trend: latest 2, peak 2, 30-day series
View on Reddit
Discovered Jul 21, 2026

Why this matters

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.

  • · Built for Rule-based discretionary traders and light-automation users who generate many candidate setups and want to quantify whether their filters and overrides help..
  • · Most likely monetization: SaaS subscription.

The Pain · Narrative

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 Breakdown

Pain Intensity8/10
Willingness to Pay6/10
Ease of Build6/10
Sustainability8/10

Market Signal

30-day mention trendPeak: 2
Sparkline: latest 2, peak 2, 30-day series
Channels covered
algotradingsaasChatGPTfront_page

Go-to-Market

Exact target user

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

Estimated user count

~100K-300K globally

Primary acquisition channel

r/<community> organic

Price anchor

$29/month

First milestone

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

MVP Scope · 1–2 weeks

Week 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
Week 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 Features: 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

Differentiation

Existing solutions
Treeova
Our angle
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.

Why This Might Fail

Self-rebuttal — the most important trust signal

  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.

Evidence Summary

How AI synthesized this insight — no verbatim quotes

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 post analyzed4 4 channelsAI · AI synthesized · no verbatim

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

Skipped-Trade Edge Journal

Sub-headline

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.

Who It's For

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

Feature List

✓ 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

Where to Validate

Share your landing page in r/r/algotrading — that's exactly where these pain points were discovered.

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Report & PRDBUSINESS

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Frequently asked questions

Who feels this pain?
Rule-based discretionary traders and light-automation users who generate many candidate setups and want to quantify whether their filters and overrides help.
Is this a real opportunity?
This opportunity scores 78/100 on Pain Spotter's composite metric (pain intensity, willingness to pay, technical feasibility and sustainability). Validate further before committing engineering time.
How should I validate it?
Run 5 customer-discovery conversations with the target audience, post a landing page with a waitlist, and check the linked source post for recent activity before building.