Project case study

Libertrade LOOP

An operating system for discretionary trading that connects preparation, risk, execution and review, turning fragmented decisions into a repeatable workflow.

LOOP connects readiness, planning, risk, execution, and review so yesterday's lesson can shape the next session.

System
Trading process & journal system
Role
Solo product designer & builder — strategy, UX/UI, full-stack delivery
Status
Live
Built with
Next.js · Supabase · Chart.js · ChatGPT · Vercel

01 Opening snapshot

One daily loop for preparation, execution, and reflection.

Libertrade LOOP is a process-first trading journal for discretionary futures traders. It connects readiness, planning, risk, execution, and review so lessons can influence the next decision—not simply describe the last one.

Libertrade LOOP pre-market readiness check-in
Readiness before risk
Screen 02 Session plan Replace with final product screenshot
Decisions before pressure
Screen 03 Close the loop Replace with final product screenshot
Process before outcome
Who it serves
Discretionary futures traders operating under time, risk, and emotional pressure.
Core problem
The plan, the trade, and the lesson live in separate places.
Current signal
Early product build. Validation evidence and baseline measurements will be added here.

02 The trading problem

The problem wasn’t missing analytics. It was a broken feedback loop.

Traders prepare, execute, and reflect in different tools—or skip parts entirely. Risk decisions then move into the most pressurised moment, while journals explain P&L after the fact instead of reinforcing a better process before the next session.

01

Prepare

Bias and levels in notes, charts, or memory.

02

Execute

Risk choices made live while attention is divided.

03

Record

Broker data captures outcomes, not decision quality.

04

Reflect

Lessons are written down but rarely carried forward.

Chosen opportunity

Bring readiness, planning, execution, and reflection into one low-friction daily cycle.
  • Keep the trader’s state visible before capital is at risk.
  • Commit risk decisions before the market creates urgency.
  • Review adherence separately from whether the session made money.

03 — Product thesis

Better performance does not come from recording more data alone. It comes from reinforcing a better decision process every trading day.

  1. 01Process before outcome
  2. 02Decisions before pressure
  3. 03Readiness before risk
  4. 04Reflect while the session is fresh
  5. 05Carry one lesson into the next session
  6. 06Useful structure without excessive friction

04 The LOOP model

A closed process, not another collection of dashboards.

Each stage prepares the next. Supporting features extend this cycle; they do not compete with it.

  1. 01

    Check-in

    State, mindset, context

  2. 02

    Session plan

    Bias, levels, setups, risk

  3. 03

    Trade

    Plan and risk rails in view

  4. 04

    Close the loop

    Import, adherence, reflection

  5. 05

    Review patterns

    History, analytics, weekly review

  6. 06

    Next session

    Carry one lesson forward

05 Three product deep dives

The decisions that make the daily loop useful.

Each chapter will document the problem, insight, design decision, iteration, final experience, and evidence.

Deep dive 01

Knowing your state before risking capital

Problem

Readiness affects decisions but is usually considered informally.

Insight

The check-in must be useful without becoming a long questionnaire.

Decision

Capture only state inputs that can change the session plan or warning state.

Evidence

Testing notes and readiness-to-plan conversion will be documented here.

Deep dive 01 Check-in iterations Final screen + earlier iteration
Placeholder — readiness and warning states

Deep dive 02

Removing decisions from the moment

Problem

Bias, limits, and invalidation become negotiable under pressure.

Insight

Risk has to be committed while the trader can still think deliberately.

Decision

Keep the plan and risk rails visible without pretending the product controls behaviour.

Evidence

Plan completion and adherence signals will be documented here.

Deep dive 02 Plan and risk rails Final screen + interaction detail
Placeholder — bias, levels, setups, and limits

Deep dive 03

Reviewing process rather than only P&L

Problem

A green day can hide broken rules; a red day can still contain good decisions.

Insight

The useful review compares planned behaviour with actual behaviour.

Decision

Close each session while context is fresh and carry one lesson forward.

Evidence

Close-loop completion and weekly review usage will be documented here.

Deep dive 03 Post-market review Final screen + analytics connection
Placeholder — adherence, reflection, and lesson

06 Supporting product system

A broader product organised around the daily loop.

The supporting system reveals patterns over time without turning every capability into a separate story.

01

Home dashboard

Today’s state, plan, and open loop

02

History & calendar

Sessions placed in time and context

03

Trade analytics

Patterns across setups, timing, and risk

04

Process adherence

Plan quality separated from P&L

05

Weekly reviews

A deliberate adjustment for the next week

06

Prop-firm economics

Rules, drawdown, and account constraints

07

LOOP Intelligence

Assisted synthesis across the workflow

08

Process settings

Personal rules without rebuilding the system

Architecture placeholder Workflow components · data model · privacy · reliability Replace with information architecture or technical system diagram

07 Validation and outcomes

Evidence at the right level of maturity.

No invented metrics. This framework separates what has been observed from what the next validation cycle must measure.

01

Usability

Can traders understand and complete the workflow?

  • Onboarding completion
  • Check-in to plan conversion
  • Points of confusion or abandonment
02

Behaviour

Do traders return and consistently close the loop?

  • Completed close-loop reviews
  • Weekly review usage
  • Return frequency
03

Product value

Does LOOP improve pattern recognition and risk decisions?

  • Risk-plan adherence
  • Useful patterns identified
  • Qualitative trader feedback
Current evidence placeholder

Add usability findings, product behaviour, trader feedback, and changes made after testing.

08 — Reflection and next chapter

The most important design challenge was not helping traders document the past. It was making yesterday’s lesson useful before the next trade.

What changedThe product moved from an analytics-first idea toward a process-first operating loop.

What remainsValidate which moments deserve structure and where structure becomes friction.

Next questionCan LOOP reinforce better risk decisions without becoming another task to maintain?