Trading signals are easy to demo and hard to ship responsibly in production.
If you're building signals into an investing product, you're not just building indicators. You're building decision support under uncertainty: explainability, trust, alerting, performance transparency, and guardrails.
This is the PRD template I use to keep teams aligned, risk-aware, and credible with users.
TL;DR
- Define the product as signals + context + risk controls + transparency
- Choose a clear persona first (mid-frequency retail, not "everyone")
- Treat accuracy as a measured system, not a claim
- Ship with guardrails: risk profiles, throttled alerts, and performance history
1) Problem statement (write this like a constraint)
Users who want to trade more actively often lack:
- timely, consistent signals inside the product
- a clear reason why the signal exists (context)
- a disciplined way to manage risk (stop-loss / take-profit guidance)
- proof that the tool is trustworthy (history, track record, transparency)
Product constraint: if the tool increases noise or feels manipulative, users will churn (and support will spike).
2) Goals and non-goals
Goals
- Provide actionable signals that users can filter and understand
- Enable users to set alerts without being spammed
- Improve engagement and retention through "repeatable value"
- Build trust via clear performance reporting (including misses)
Non-goals (for MVP)
- Full strategy backtesting / scripting engine
- Institutional-grade execution tooling
- "Perfect accuracy" marketing claims
3) Personas (pick one primary)
Primary persona: Retail trader who uses technical cues but wants the product to do the heavy lifting.
Secondary personas:
- Passive investor graduating to trading (needs education + guardrails)
- Younger investor comfortable with algorithmic insights (mobile-first UX)
For each persona, define:
- what "a good decision" looks like
- what risk they will tolerate
- what they consider spam vs value
4) Core capabilities (MVP scope)
A) Technical indicator signals
Start with a small set of indicators users already recognize:
- MACD, RSI, moving averages, Bollinger Bands
Define for each signal:
- trigger logic (inputs, thresholds, timeframes)
- confidence/strength model (even if simple)
- explanation copy ("why this triggered")
B) Customizable alerts
Alerts are the product's distribution engine. They must be controllable.
Minimum requirements:
- pick instruments (watchlist + search)
- choose triggers (indicator thresholds)
- choose frequency (real-time, once/day, weekly digest)
- choose channel (push, email, in-app)
C) Risk management suggestions
Signals without risk controls create user harm.
Add:
- suggested stop-loss / take-profit ranges
- risk profiles: conservative / moderate / aggressive
- default position sizing guidance (even if basic)
D) Performance dashboard (trust layer)
Transparency is a feature. Show:
- signal history (wins, losses, time-to-target)
- ROI distribution and drawdown ranges (where possible)
- "what changed" notes when the model updates
5) Dependencies and architecture (make it explicit)
Treat these as product requirements, not "engineering details":
- Market data: real-time or delayed feed, with clear SLAs
- Signal engine: a deterministic rules layer first; evolve later
- Alert infrastructure: scheduling, rate-limits, retries, user preferences
- Visualization: charting that supports "reasoning" not decoration
- Compliance/security: audit logging for key actions, consent management, retention policies
6) UX patterns that reduce regret
Signals create action bias. Your UX should slow users down just enough.
Add patterns like:
- short explanation cards: "what triggered" + "what to watch next"
- "I understand" education tooltips for indicators
- explicit "risk profile" toggle (not hidden in settings)
- alert previews: "you will receive X notifications per week"
7) Metrics (KPIs that match the product)
Define success as behavior, not vanity.
Examples:
- engagement: % users creating alerts within 7 days
- retention: 30/60-day retention for users who used signals vs not
- trust: support tickets per 1,000 users related to signals/alerts
- conversion (if premium): upgrade rate among active signal users
- quality: % signals that reach defined outcomes (and distribution of misses)
8) Rollout plan (phased)
- Discovery (1-2 weeks): validate data source, confirm persona, draft explainability copy
- MVP build (3-8 weeks): signal engine + alerts + basic performance dashboard
- Beta (2 weeks): limited cohort with tight feedback loops
- Public launch: launch messaging that emphasizes transparency and guardrails
9) Risks and mitigations
- Noise fatigue: default to fewer alerts; let users opt into more
- Misinterpretation: include education and "not financial advice" disclaimers where appropriate
- Trust failure: show misses and uncertainty; do not hide under marketing copy
- Model drift: version signals and annotate changes in the dashboard
10) MVP acceptance checklist
- [ ] Signals have deterministic definitions and clear copy
- [ ] Users can configure alerts and control frequency
- [ ] Risk profile settings are visible and explainable
- [ ] Performance history exists (including misses)
- [ ] Alert system has throttling + preference center
- [ ] Analytics is instrumented for engagement + trust metrics
Quick takeaways
- Ship signals as decision support, not as “accuracy claims.”
- Trust comes from transparency: explainability + performance history + guardrails.
- Alerting is part of the product; without throttling and preferences, it becomes spam.
If you are working on a similar product problem and want a practical second opinion, reach out via the Contact section.