If your whale alerts feel noisy, the fix isn’t another heuristic—it’s calibration. This playbook shows how to choose alert thresholds using precision/recall, stratified backtests (by liquidity, market category, and regime), and out-of-sample validation so you don’t tune to yesterday’s tape.
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Visit Research SeriesMost alert systems fail in the same way:
They confuse activity with signal.
If you alert on every large trade, you will get:
The alternative is not magic. It’s calibration.
This post explains how to choose alert thresholds in a way that:
Internal links:
An alert is not “correct.” An alert is useful.
Pick a concrete label that matches how users trade. Examples:
Do not mix labels. Pick one per calibration run.
Accuracy is useless here because “good alerts” are rare. Use:
The precision–recall curve is the right tool because it explicitly shows the tradeoff between precision and recall across thresholds.
Source: scikit-learn documentation on precision–recall curves and operating points. https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html
Treat each threshold choice as an operating point.
Every threshold is a policy choice:
Most teams tune thresholds based on vibes.
Instead, tune them based on the cost of errors:
If your product promise is “only the top signals,” accept lower recall and optimize precision. If your promise is “never miss big moves,” accept more noise and optimize recall.
The fastest way to overfit is to calibrate one global threshold.
Markets aren’t one distribution.
Stratify by at least:
Liquidity changes everything (spread, impact, fill reliability). A $10k trade means different things in:
Crypto minute markets are not election markets. Sports markets are not geopolitical markets.
News-driven months behave differently from quiet months.
Implementation tip:
Build a backtest table where each row is an alert candidate and add columns:
Then calibrate per bucket, or at least validate per bucket.
Here’s the loop that works:
Start with a permissive candidate set:
For each candidate alert:
Choose a parameterization:
Then grid-sweep and compute precision/recall for each combo.
Pick thresholds per segment (liquidity × category). Prefer stable points that:
Use a time split:
Do not randomly shuffle; time order matters.
If a threshold set requires 12 special cases, it will die in production.
Prefer a simpler policy that’s slightly worse in-sample but stable.
Re-tuning daily is how you chase noise.
Set a cadence:
Keep an “alert postmortem” log:
If you want a baseline that is usually sane:
Polymarket documents that some market types have taker fees enabled and describes how fees vary with price (peaking near 50%).
Source: Polymarket “Fees” documentation. https://docs.polymarket.com/trading/fees
Our job isn’t to alert on “large trades.” It’s to alert on trades that are:
If you want to calibrate your own alerts, use:
If you want the calibrated stream (quiet, high-signal), upgrade:
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