Module 2: Market Research · 20 min

Building a Personal Watchlist of Tracked Markets

// sabak

Turn this lesson into one checked practice output

By the end, you should be able to explain the core idea behind “Building a Personal Watchlist of Tracked Markets” 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 20-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.

A research watchlist is a bounded dataset of contracts selected by a written rule. It is not a list of tips. Its purpose is to preserve comparable observations, identify upcoming verification work, and prevent attention from jumping only to dramatic or favorable markets.

Define Eligibility

Choose a neutral universe: one category, date window, public-data availability, unambiguous resolution source, minimum observation frequency, and ethical exclusions. Exclude markets involving personal harm, nonpublic information, manipulation opportunity, unclear terms, or content you should not amplify.

Record the selection rule before choosing markets. This reduces cherry-picking and survivorship bias. Keep excluded items and reasons.

Design the Schema

Store:

  • event/market stable identifiers and canonical URL;
  • question, outcomes, category, status, close/resolution dates;
  • resolution source/rules and ambiguity note;
  • observed bid, ask, midpoint, last trade, spread, depth, volume fields as defined;
  • retrieval UTC time, API endpoint/schema version;
  • source-desk state and next verification time;
  • paper forecast, rationale source IDs, and immutable forecast timestamp;
  • resolution/outcome and scoring fields after closure.

Do not overwrite observations. Append time-series rows. Separate raw API data from normalized analytics.

Choose Responsible Refresh Rules

Use official read-only APIs and current rate-limit guidance. Cache responses, apply backoff, identify failures, and avoid aggressive polling. Refresh frequency should match the research question; a daily calibration study does not need millisecond data.

Detect schema/status changes and missing data. A failed request is not a zero price. Mark stale rows and prevent dashboards from displaying them as current.

Add Decision Hygiene

Before viewing later outcomes, write a forecast/range and the reasons that could change it. Include a no-view/no-update period if constant price watching harms judgment. Do not send “urgent opportunity” alerts.

Use the watchlist to ask: which contracts are ambiguous, which sources need checking, how calibrated were frozen forecasts, and where did reasoning fail? Do not rank markets by hypothetical profit alone.

Worked Example

A researcher selects every eligible public economic-policy market closing in one quarter, not just three interesting ones. Two are excluded for ambiguous resolution sources. Daily snapshots preserve spread and staleness. At resolution, every included market is scored—even embarrassing forecasts.

This dataset can evaluate calibration. A hand-picked list of successful calls cannot.

Failure Cases

  • Selecting only volatile or social-media-popular markets.
  • Overwriting the latest price without history.
  • Treating missing data as zero.
  • Polling more frequently than needed or allowed.
  • Ignoring resolved/invalid/cancelled status.
  • Changing paper forecasts after seeing outcomes.
  • Using watchlist alerts to encourage real-money urgency.

🇵🇰 Pakistan Angle

Use UTC as the stored timestamp and display PKT separately. Network interruptions should mark data stale, not trigger rapid retry storms. If a source is blocked or unavailable, respect the restriction and remove it from the study rather than circumventing access.

The watchlist must stay read-only. Do not store wallet keys, account IDs, KYC documents, or deposits. Check PVARA/SECP and platform rules on the access date if research scope changes.

Hands-On Exercise

Create a ten-market watchlist with a prewritten eligibility rule and at least three exclusions. Collect seven daily read-only snapshots, preserve raw/normalized data, mark failures/staleness, and freeze one paper forecast per market. Write a monitoring and retirement policy.

Completion Rubric

  • Complete: selection is neutral, schema/version/time are explicit, history is append-only, refresh is responsible, and forecasts are frozen.
  • Needs revision: the table works but bias, staleness, or resolution handling is weak.
  • Not complete: it functions as a tips list, circumvents controls, or stores trading credentials.

Sources

Key takeaway: A watchlist is a preselected, append-only research dataset for calibration and source work—not a stream of opportunities.

Self-check

Before you mark Lesson 2.3 complete

  • Can I explain “Building a Personal Watchlist of Tracked Markets” 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?