Datasets:

ClarusC64
/

cascade-multi-ai-finance-credit-liquidity-confidence-crunch-v0.1

scenario_id string	ai_trading_penetration float64	model_error_propagation_rate float64	market_liquidity_thinning float64	credit_spread_widening float64	margin_call_velocity float64	asset_price_volatility float64	capital_flight_pressure float64	bank_funding_stress float64	regulatory_response_lag_days int64	central_bank_intervention_intensity float64	confidence_decay_index float64	buffer_capital_adequacy float64	buffer_liquidity_facilities float64	cascade_severity_score float64	label_cascade_event int64
FIN-001	0.24	0.18	0.22	0.2	0.15	0.28	0.19	0.21	18	0.32	0.16	0.72	0.7	0.36	0
FIN-002	0.68	0.61	0.72	0.7	0.64	0.78	0.73	0.76	54	0.82	0.66	0.34	0.35	0.92	1
FIN-003	0.41	0.36	0.44	0.42	0.31	0.48	0.39	0.43	33	0.52	0.38	0.58	0.6	0.56	0
FIN-004	0.82	0.78	0.86	0.84	0.79	0.9	0.85	0.88	66	0.9	0.76	0.26	0.28	0.96	1
FIN-005	0.55	0.49	0.58	0.56	0.46	0.61	0.52	0.57	45	0.63	0.54	0.49	0.51	0.71	0
FIN-006	0.18	0.14	0.2	0.18	0.12	0.26	0.15	0.17	16	0.3	0.12	0.76	0.74	0.3	0
FIN-007	0.91	0.88	0.93	0.91	0.87	0.95	0.9	0.92	74	0.94	0.84	0.21	0.23	0.99	1
FIN-008	0.36	0.32	0.4	0.38	0.28	0.46	0.33	0.37	29	0.56	0.34	0.62	0.64	0.6	0
FIN-009	0.74	0.69	0.8	0.78	0.71	0.88	0.79	0.82	61	0.87	0.7	0.29	0.31	0.94	1

What this repo does

This dataset tests whether a model can detect a financial cascade where AI-driven trading and modeling errors propagate into liquidity stress, credit widening, and confidence collapse.

You provide structured signals describing:

AI trading penetration and error propagation
liquidity thinning and credit spread widening
margin pressure and volatility
regulatory lag and central bank response
confidence decay and buffer strength

The model predicts whether the interaction crosses into a cascade event.

Core cascade

Four interacting systems:

AI-driven markets

ai_trading_penetration
model_error_propagation_rate

Liquidity and credit

market_liquidity_thinning
credit_spread_widening

Financial stress propagation

margin_call_velocity
bank_funding_stress

Confidence and policy

confidence_decay_index
regulatory_response_lag_days

Prediction target

Target column:

label_cascade_event

Meaning:

0 = financial stress remains containable
1 = liquidity and confidence collapse propagate across markets and institutions

Row structure

Each row is a scenario snapshot.

Key columns:

ai_trading_penetration
model_error_propagation_rate
market_liquidity_thinning
credit_spread_widening
margin_call_velocity
asset_price_volatility
capital_flight_pressure
bank_funding_stress
regulatory_response_lag_days
central_bank_intervention_intensity
confidence_decay_index
buffer_capital_adequacy
buffer_liquidity_facilities
cascade_severity_score

Files

data/train.csv
10-line labeled sample
data/tester.csv
10-line labeled sample
scorer.py
Binary metrics and confusion matrix

Evaluation

Run:

python scorer.py --gold data/tester.csv --pred your_predictions.csv

Outputs:

accuracy
precision
recall
f1
confusion matrix

License

MIT

This dataset identifies a measurable coupling pattern associated with systemic instability. The sample demonstrates the geometry. Production-scale data determines operational exposure.

What Production Deployment Enables • 50K–1M row datasets calibrated to real operational patterns • Pair, triadic, and quad coupling analysis • Real-time coherence monitoring • Early warning before cascade events • Collapse surface and recovery window modeling • Integration and implementation support Small samples reveal structure. Scale reveals consequence.

Enterprise & Research Collaboration Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains. For dataset expansion, custom coherence scorers, or deployment architecture: team@clarusinvariant.com

Instability is detectable. Governance determines whether it propagates.

Downloads last month: 9

Number of rows:

Total file size:

9.88 kB