AI Supply Chain — Lab Intelligence

1 · In one sentence

Shows the recent move, a 30-day outlook and the relatively most extended or compressed segments.

2 · Why it exists

Read the AI economy as a chain rather than a single stock or industry.

3 · What it produces

Market pulse plus hottest/coldest cards with z-scores and interpretation caveats.

How to read the latest output: A high z-score signals relative extension, not a certain reversal; the forecast cone represents uncertainty.

4 · How it works

From sources to output.

Workflow pipeline diagram — Pipeline diagram: from public sources through checks and computation to the published cards, with human oversight.

1Public series

2Baskets

3Index

4Outlook

5Z-scores

63 cards

5 · Data used

Data used
Source	Variable	Frequency	Limitation
Public market series	11 AI-chain baskets	Weekly	Listed proxies; basket composition may change

6 · Models and rules

Models and rules

Composite index, five-day returns, 30-day outlook, basket z-scores and bootstrap uncertainty.

7 · Controls

Controls

Freshness gate, numeric fingerprint, exact and visual hashes, stale-card removal and independent bilingual publication.

8 · Results and metrics

Results and metrics

Model-versus-naïve comparison, outlook error and basket stability over time.

9 · Limits

Limits

Financial proxies do not directly measure industrial orders, physical capacity or real demand.

Possible applications of the pattern

Thematic baskets and normalisation

Possible applications: value-chain, supplier, technology or asset analysis through coherent baskets and relative comparisons. Proxies must be documented and reviewed over time.

Transferable components

versioned universe and baskets
z-scores and rolling returns
outlook and bootstrap
network and diagnostics

Research questions

basket robustness
survivorship bias
proxies vs physical data
network stability

Application map Shared technical details

Workflow technical dossier

IMPLEMENTEDDescribes behaviour present in the workflow.EXTENSIONIndicates a possible check or evolution, not an operating feature.

Implementation detail and assessment criteria.

This section connects the visible output to data-engineering, modelling, validation and delivery choices present in the repository.

Architecture and data

Universe organised into AI supply-chain baskets, macro data and analyst targets.
Separate histories for equity curves, positions, trades, forecasts, networks, diagnostics and parameters.
Analysis, simulation, reporting and publication pipeline with centralised risk configuration.

Features and methods

SMA20/50/200 trends, RSI, momentum, volatility, analyst upside and network signals.
Two-state Gaussian HMM for regime; NetworkX for relationships and centrality; 0–100 multi-factor score.
Half-Kelly sizing with caps, regime multiplier, ATR and trailing stops in the simulation layer.

Validation and failure modes

Backtests, attribution, trade diagnostics and 30-day forecast history.
Separation between research and ordinary runs; history synchronisation only after valid execution.
Freshness and duplicate controls for bilingual social cards.

Runtime and delivery

pandas, NumPy, SciPy, hmmlearn, NetworkX, yfinance and pandas-datareader.
Jinja2, lxml, WeasyPrint/ReportLab, matplotlib and GitHub Actions.

Compare all workflows Research and development

10 · Operating timeline

Operating timeline

Status: Active · experimental
Frequency: Weekly
Page updated: July 2026

11 · Technical detail

Method and assumptions