TAOQuant

Quant Subnet Analytics & Risk Matrix for Bittensor

Deterministic MathOpen SourceNo AI
Isometric TAOQuant Web3 analytics dashboard with risk gauges and performance charts

Bittensor analytics

TAOQuant

Deterministic subnet intelligence, risk scoring, and APY stability analysis in one auditable workspace.

IRA Model Parameters

Adjust the weights of the Adjusted Risk Index equation. Weights always normalize to 100%.

40%
35%
25%
Total100%

Subnet Analytics

Data source: Local mock dataset (no requests spent)

SN1Text Prompting
14.2%0.180.229%
83
SN8Time-Series Prediction
9.40%0.120.297%
83
SN21FileTAO Storage
12.1%0.250.3314%
75
SN2Machine Translation
11.7%0.310.4118%
69
SN5Image Generation
18.9%0.440.3821%
64
SN27Compute Subnet
16.6%0.390.4826%
61
SN4Multi Modality
22.5%0.620.5534%
47
SN18Cortex.t
27.8%0.780.6746%
34

Model Math & Audit

The IRA is a fully transparent, reproducible weighted score. No black boxes.

Formula
IRA = 100 × (
  w1 · stability +
  w2 · decentralization +
  w3 · efficiency
)

// each pillar is clamped to [0, 1] — never negative
stability        = max(0, 1 − CV)
decentralization = clamp(1 − HHI)
efficiency       = max(0, 1 − churn / 100)

// churn is a percentage (0..100); scale clamped to [0,1]
// constraint
w1 + w2 + w3 = 1.0

Statistical pillars

APY Stability

Rewards consistent yield. Derived from 1 − normalized Coefficient of Variation.

HHI Decentralization

Rewards distributed staking. Derived from 1 − Herfindahl-Hirschman Index.

Efficiency / Churn

Rewards network health. Derived from 1 − normalized miner churn rate.

Every score above is computed client-side from the values in the table. Change a weight and re-audit instantly.

Public Goods Funding

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