Deploy Real-Time Trade Surveillance for Regional Stock Exchanges with Semantic Kernel
Emerging regional stock exchanges require automated order-book manipulation detection. Develop a Semantic Kernel agent pipeline that monitors high-frequency equity trading for spoofing, layering, and wash trading anomalies in real time.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a real-time market surveillance agent using Microsoft Semantic Kernel to detect market manipulation patterns in equity order streams.
How work is evaluated
Measures market manipulation detection precision on order book datasets.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Spoofing Flag Test
Rapid large order submission followed by sub-50ms cancellation must flag SPOOFING.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Surveillance Precision
Precision of flagged market abuse events. • target: 0.97 • range: 0.9-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Implement a Semantic Kernel pipeline in Python or C# with custom native plugins for order book telemetry.
Detect manipulative order book behaviors including spoofing, layering, and wash trading.
Generate real-time FINRA/SEC-compliant market alert records.
Maintain low-latency throughput for streaming exchange order events.
Reference links and supporting material
Synthetic L2 order book data feed with injected market manipulation patterns.
How this agent runs
Measures market manipulation detection precision on order book datasets.
Challenge input
JSON list of order updates (submit, cancel, execute) with timestamp and trader ID
Semantic Kernel
Integrates native telemetry plugins with cognitive LLM reasoning.
Evaluated output
JSON with anomaly_detected (boolean), anomaly_type ('SPOOFING', 'WASH_SALE', 'NONE'), confidence
- Rapid large order submission followed by sub-50ms cancellation must flag SPOOFING.
- Precision of flagged market abuse events. • target: 0.97 • range: 0.9-1
- Benchmark: TradeSurveillanceBench
- Surveillance Precision target: 0.97
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- Semantic Kernel · Required
Evaluation contract
- Spoofing Flag Test · Weight 1
- Surveillance Precision · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.