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Module 5: Risk and Responsible Research · 30 min

Capstone: Build a Signal-to-Paper-Decision Workflow

// sabak

Turn this lesson into one checked practice output

By the end, you should be able to explain the core idea behind “Capstone: Build a Signal-to-Paper-Decision Workflow” in your own words, apply it to one small real or sample task, and identify what still needs human review.

  1. 1

    Learn

    Read the 30-minute lesson without copying an output blindly.

  2. 2

    Try

    Use a small, non-sensitive example that you can inspect line by line.

  3. 3

    Review

    Check facts, fit, and risk; save one improvement note for next time.

Build a read-only forecasting research system that ingests public evidence, preserves source lineage, makes frozen paper forecasts, and evaluates calibration. It must contain no wallet, funding, authentication, private keys, order endpoints, real-money sizing, or automation that can trade.

1. Define Scope and Ethics

Choose one non-harmful category, fixed date range, prewritten market eligibility rule, and excluded topics. Record terms/rate-limit review, data minimization, retention, reviewer, and stop conditions. Check current platform geographic rules plus PVARA/SECP scope; do not infer legal permission.

Use only public read-only data and official/authorized sources. Do not collect personal/nonpublic information or circumvent access.

2. Build the Watchlist and Raw Store

Select at least 20 eligible markets without future-outcome cherry-picking. Save stable IDs, questions, resolution rules/sources, status, and timestamps. Append read-only price/order-book observations with bid, ask, midpoint, last, spread, and depth definitions.

Preserve raw payloads, hashes, endpoint/schema version, errors, and staleness. Failed requests remain failures, not zero values.

3. Create the Evidence Desk

Translate every contract into verification questions. Build source packets with primary-source hierarchy, event/publication/retrieval times, versions, independence/lineage, contradiction, and correction history.

If using an LLM, constrain it to packet IDs, require NOT IN PACK, validate schema, and manually verify every decisive claim. Record model/prompt version and errors.

4. Aggregate Transparently

Use deterministic code on approved rows. Reject duplicate origins, malformed schema, post-cutoff evidence, and unresolved source status. Keep score dimensions visible. Any mapping to paper probability needs a documented method and calibration evidence.

Freeze each paper forecast/range and rationale before later data. Abstain when no-action gates fail.

5. Simulate Without Execution

Use fictional points with no currency mapping, cluster caps, paper bid/ask/depth fill assumptions, append-only entry/update/exit rules, and hard error/wellbeing stops. The application must have no order dependency, credential field, or “connect wallet” interface.

Run an automated scan for terms such as private key, seed phrase, place order, deposit, wallet connector, and authenticated trading endpoint; review any match and remove execution capability.

6. Evaluate Honestly

For the full preselected universe, report coverage, abstentions, unresolved/invalid contracts, data failures, calibration/Brier score for valid frozen probabilities, price-comparison method, paper-fill limitations, worst errors, source mistakes, rule violations, and method versions.

Do not call fictional point changes profit, ROI, income, or backtested returns.

7. Threat and Failure Test

Test copied-source duplication, fake breaking report, corrected official document, timezone cutoff, API outage/rate limit, stale snapshot, ambiguous resolution, schema change, model hallucination, prompt injection inside a source, correlated-event cluster, and attempt to add credentials. The safe result is often abstain/fail closed.

🇵🇰 Pakistan Angle

Add a dated regulator/source desk for PVARA, SECP, and any responsible body for the chosen events. Store UTC and display PKT. Human-review Urdu/English legal/status terms. Do not provide virtual-asset services, signals, managed accounts, or investment promotions.

The capstone can be presented as a forecasting/data-engineering portfolio piece: source verification, API hygiene, Python validation, calibration, and risk controls. It is not evidence of earning ability.

Capstone Deliverables

Submit:

  1. scope/ethics/terms sheet and 20-market universe;
  2. raw/normalized append-only schema with failures;
  3. five versioned evidence packets and lineage graphs;
  4. validated offline Python aggregation with tests;
  5. paper forecast ledger with abstentions and point-only caps;
  6. calibration/process review with full denominator;
  7. threat/failure test report;
  8. no-execution scan and signed boundary statement;
  9. public portfolio README with limitations.

Completion Rubric

  • Complete: scope is ethical/read-only, evidence is primary/versioned, forecasts are frozen, automation is validated, full results are honest, and execution is technically absent.
  • Needs revision: research works but source lineage, failure handling, calibration, or no-execution proof is incomplete.
  • Not complete: credentials, wallets, orders, real-money sizing, circumvention, advice, or profit claims appear.

Sources

Key takeaway: The finished project proves source verification, forecasting calibration, and safe data engineering while making real execution impossible.

Self-check

Before you mark Lesson 5.3 complete

  • Can I explain “Capstone: Build a Signal-to-Paper-Decision Workflow” without reading the lesson back word for word?
  • Did I complete the lesson’s practice step on a real or clearly labelled sample task?
  • Did I check the result for invented facts, private data, unsafe actions, and mismatch with the brief?
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