Using Python to Aggregate and Score Signals Automatically

Automate deterministic arithmetic on an approved fixture, not data collection or trading. Python should validate rows, group common origins, compute a transparent paper score, and preserve warnings. It must refuse malformed or future-dated evidence rather than silently coercing it.

Define the Fixture

Create signals.json using synthetic or approved public data:

[
  {"claim_id":"c1","origin_id":"o1","weight":0.7,"direction":1,"approved":true},
  {"claim_id":"c2","origin_id":"o2","weight":0.3,"direction":-1,"approved":true}
]

direction is an educational encoding: 1 supports, -1 contradicts, 0 neutral. A weight is not a probability. In production-quality research, dimensions and provenance should remain separate; this compact fixture exists to teach validation and grouping.

Write a Safe Aggregator

import json
from pathlib import Path

rows = json.loads(Path("signals.json").read_text(encoding="utf-8"))
required = {"claim_id", "origin_id", "weight", "direction", "approved"}

origins = {}
for row in rows:
    if set(row) != required:
        raise ValueError(f"unexpected schema for {row.get('claim_id', 'unknown')}")
    if row["approved"] is not True:
        continue
    if row["direction"] not in {-1, 0, 1}:
        raise ValueError("direction must be -1, 0, or 1")
    if not 0 <= row["weight"] <= 1:
        raise ValueError("weight outside 0..1")
    if row["origin_id"] in origins:
        raise ValueError("duplicate origin requires manual dependency rule")
    origins[row["origin_id"]] = row

denominator = sum(row["weight"] for row in origins.values())
score = None if denominator == 0 else sum(
    row["weight"] * row["direction"] for row in origins.values()
) / denominator

print(json.dumps({"approved_origins": len(origins), "paper_score": score}, indent=2))

The program rejects duplicate origins instead of double counting. It does not convert paper_score into odds, submit an order, or make a recommendation.

Add Reproducibility

Pin Python version/dependencies, hash the fixture, record cutoff UTC time, store configuration, and create tests for empty input, invalid weights, duplicate origins, unknown keys, and non-approved claims. Write output to a new versioned file rather than overwriting evidence.

For Brier score after resolution, use frozen probabilities that were created by a separate documented mapping/calibration step—not the raw directional score. Confirm outcomes and exclude/label unresolved or invalid contracts according to a predeclared rule.

Worked Example

Two articles share origin_id=o1. A naive script counts both and pushes the score positive. The safe program rejects duplication, forcing a reviewer to decide dependency. After grouping, the result is near neutral and marked sensitive to one source.

The automation improves consistency by refusing ambiguity, not by predicting better automatically.

Failure Cases

• Fetching live data and calculating on it without preserving raw input.
• Hard-coding API secrets or wallet keys.
• Treating missing, null, or failed fetch as zero.
• Allowing unknown schema fields silently.
• Double counting common origins.
• Mapping a score to probability without calibration.
• Adding an order-placement function “for later.”

🇵🇰 Pakistan Angle

Store timestamps in UTC and render PKT separately. Use Unicode UTF-8 and tests for Urdu text/identifiers, but keep numeric fields locale-independent. Never place CNIC, phone, account, wallet, or private messages in fixtures.

Learners can run this entirely offline. No exchange login, funding, VPN, or paid data is required.

Hands-On Exercise

Implement the script, add at least eight fixture rows and five automated tests, then introduce a duplicate, invalid weight, missing key, and empty dataset. Produce a versioned output with input hash and a plain-language limitation note.

Completion Rubric

• Complete: schema, dependencies, failure states, tests, provenance, and offline-only boundaries are enforced.
• Needs revision: math works but malformed/missing data or reproducibility is weak.
• Not complete: the script includes credentials, live order placement, or uncalibrated recommendations.

Sources

• Python json documentation
• Python pathlib documentation
• scikit-learn Brier score loss
• Polymarket official market-data guidance

Key takeaway: Automate validation and reproducible scoring on approved evidence; refuse ambiguity and keep execution completely outside the system.