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User Journey Map

Source: Notion | Last edited: 2026-01-05 | ID: 2df2d2dc-3ef...


Help retail traders build, understand, refine, and validate trading strategies with natural language + AI-assisted research, moving them from “idea” → “strategy” → “validated outcome” without requiring coding skills.


  • Curious about strategy creation
  • Overwhelmed by existing tools (TradingView, bots, spreadsheets, etc.)
  • Doesn’t know how to transition from idea to actionable strategy
  • Social media ads (Twitter, YouTube, TikTok)
  • Community posts (Reddit, Discord)
  • Blog posts explaining “Build strategies with natural language”
  • Influencer demos
  • Learn if there’s a simpler way to build and evaluate strategies
  • Get a sense that AI can help reduce friction
  • Clicks a link
  • Visits landing page
  • Reads introductory copy
  • Slight curiosity
  • Mild skepticism
  • Clear, benefit-oriented messaging: “Say what you want. Get a strategy that you can understand.”

  • On landing page
  • Has a rough strategy idea (e.g., “trend following BTC”), but no code
  • Wants to see if this tool actually delivers
  • Landing page input field
  • Quick examples / preset prompts
  • Get something real out of one natural language prompt
  • Enters strategy idea
  • Clicks “Generate”
  • Expectation: Maybe this will work
  • Anxiety: Will this give me a black box?
  • Fast feedback is critical: deliver the first result in <60s
  • Show human-readable description + visual strategy structure

  • Strategy has been generated
  • Results page loaded
  • Strategy Blueprint (Data → Feature → Signal → Risk)
  • Key Metrics (Return, Drawdown, Win Rate, Sharpe)
  • Visual charts
  • Understand what was generated
  • Check if it matches their idea
  • Reads summary
  • Looks at chart
  • Asks themselves: Does this reflect my idea?
  • Early satisfaction if summary matches prompt
  • Curiosity + power when seeing structure
  • Might be overwhelmed by charts if not explained well
  • Put summary first, charts second
  • Label with plain language: “This strategy enters when momentum > 0 and exits on 5% loss.”

Aha #1 — “My idea got turned into something real I can inspect”


  • Has a baseline strategy
  • Wants it to “feel better” (risk/return balance)
  • Parameter controls
  • Side-by-side comparison (Before/After)
  • AI suggestion snippets (“Consider adding RSI filter”)
  • Improve without random tweaking
  • See cause → effect clearly
  • Adjust parameters
  • Select improvement paths (More aggressive / More robust / Simpler)
  • Observe updated backtest
  • “This is actually meaningful!”
  • Absence of randomness reduces frustration
  • Focus on intent-driven refinement not raw sliders Examples:

    • “Reduce drawdown”
    • “Improve trend capture”
    • “Reduce trading frequency” Aha #2 — “Small changes have predictable effects”

Aha #3 — “I’m choosing risk/return location, not guessing parameters”


  • Has a refined strategy
  • Unsure if it fits them personally
  • Fit Profile card
  • Behavioral match recommendations
  • Know if this strategy suits their preferences
  • Set the right expectations
  • Reads match info (“Not suitable if you can’t stand 3 straight losses”)
  • May adjust preferences
  • Relieved or validated
  • Show user-centric insights, not just stats “Your tolerance aligns with this type of behavior”

Aha #4 — “This strategy fits me, not just a random metric”


  • Wants confidence before deploying real funds
  • Paper trading
  • Real-world vs backtest comparisons
  • Observe strategy behavior in live conditions
  • Understand execution differences
  • Runs paper trading
  • Checks deviations
  • Confidence grows with consistency
  • Frustration if data isn’t explained
  • Provide deviation explanation (“This deviation is likely due to slippage + latency”)

Aha #5 — “This isn’t just backtest math — I can see it behave in the real world”


  • Ready to export or deploy
  • Considering paid upgrade (Pro features)
  • Export code/Webhook/Signal
  • Connect exchange
  • Upgrade prompts
  • Bring strategy to real trading stack
  • Decide if they want ongoing guidance
  • Chooses export method
  • Upgrades plan
  • Connects to execution
  • “I made something that actually does the thing I imagine.”
  • Highlight responsibility + risk transparency
  • Provide deployment confidence messages

  • Has executed one or more strategies
  • Uses platform repeatedly
  • Strategy management dashboard
  • Notifications & alerts
  • Performance reports
  • Community / Sharing
  • Maintain, evolve strategies
  • Compare performance
  • Learn from insights
  • Saves strategies
  • Runs new experiments
  • Shares or explores templates
  • Increasing mastery
  • Sense of ownership
  • Library of strategy templates
  • Community contributions
  • Automated reporting

Summary — Key Aha Moments Aligned with Journey

Section titled “Summary — Key Aha Moments Aligned with Journey”

User Emotional Arc (One Sentence Each Stage)

Section titled “User Emotional Arc (One Sentence Each Stage)”
  • Awareness: Is this possible?
  • First Result: Wow — that’s my idea realized!
  • Optimization: I can actually shape this!
  • Fit Feedback: This fits how I trade.
  • Validation: This feels real.
  • Deployment: This is mine.
  • Engagement: I’m improving over time.


  • Don’t lead with graphs — lead with meaning. Show explanation first.
  • Reinforce user agency — the user should feel in control of results.
  • Avoid black-box illusions — transparency builds trust and stickiness.
  • Balance “fast success” with “long-term growth” — early wins fuel retention.