DV-PMI · Execution edge
Execution Edge Monitor
Updated 2026-10-12
As of 2026-10-12, the algorithmic-minus-retail Prelec alpha gap stands at -0.014, with a 52-week rolling z-score of -0.48. A positive gap indicates algorithmic wallets weight tails more heavily than active retail — the behavioral signature of execution-quality divergence. Algorithmic Prelec alpha: 1.001. Active retail: 1.015.
A positive gap indicates algorithmic wallets weight tails more heavily than active retail. The bold line is a 13-week moving average.
Current per-type alphas
| Wallet class | Prelec alpha |
|---|---|
| Algorithmic | 1.001 |
| Active Retail | 1.015 |
| Sophisticated | 1.446 |
| Casual | 0.967 |
| One-Shot | 0.973 |
52-week rolling statistics on the gap
| Statistic | Value |
|---|---|
| Mean | 0.030 |
| SD | 0.092 |
| Min | -0.180 |
| Max | 0.321 |
Methodology
For each wallet class (algorithmic, active retail, sophisticated, casual, one-shot), we fit a one-parameter Prelec weighting function to that class’s weekly calibration curve. The resulting alpha captures how strongly the class distorts probabilities away from the identity. The Execution Edge gap is alpha(algorithmic) minus alpha(active retail). In Paper 1, algorithmic wallets capture −4.25 bps effective spread on their trades while active retail pays +11.68 bps; the alpha gap is the behavioral signature of that divergence and tracks it week-by-week.
Data
execution_history.csv (wide, 192 weeks from 2023-01-16) · execution_by_type_history.csv (long) · execution_latest.json
Cite this index
@misc{dellavedova2026execution,
title = {Execution Edge Monitor},
author = {Della Vedova, Joshua},
year = {2026},
url = {https://jdellavedova.com/execution}
}