What does a real call return?
In 30 synthetic trades, the engine counted 15 round trips and a recycled-volume ratio of 50%, returning a_examiner (to examine). That is a pattern, not proof of manipulation.
Recorded on 2 November 2026 through the public MCP endpoint, service release 0.17.1. Inputs and full responses, response SHA-256 d1080e759fa6…. Verdict values are the literal values the tool returns. Run the same inputs yourself to compare; elapsed times vary, and a new release may change the figures.
Checked against
- Designed repeating-volume and organic-flow fixtures, input sorting and overflow rejection.
Recorded on release 0.16.1. These checks cover the listed cases, not every input. Evidence cards.
How it works
The engine screens time, price and volume records for recycling-like patterns under a chosen window and price tolerance. It reports the strength of this pattern without claiming to identify account ownership.
Inputs and units
Supply a bounded array of timestamp, price and volume records, plus a time window and percentage price band. The transport accepts request bodies up to 1 MiB.
What the result contains
Recycled-volume ratio, round-trip counts, price span and timing summaries describe the observed pattern and explain why it may merit further examination.
Connect and inspect the inputs
Initialize an MCP connection, call tools/list and inspect the schema for screen_wash_trading. The live lab provides a complete example with editable inputs.
Call this engine in one command
Node.js 20+ and npm. This downloads a small remote MCP client; it does not install the engine locally.
npx --yes --package https://scorecompute.com/downloads/scorecompute-call-0.14.2.tgz scorecompute-call screen_wash_trading --arguments '{"trades":[{"ts_s":1000,"price":100,"volume":10},{"ts_s":1010,"price":100.04,"volume":10},{"ts_s":1020,"price":100,"volume":10},{"ts_s":1030,"price":100.04,"volume":10},{"ts_s":1040,"price":100,"volume":10},{"ts_s":1050,"price":100.04,"volume":10},{"ts_s":1060,"price":100,"volume":10},{"ts_s":1070,"price":100.04,"volume":10},{"ts_s":1080,"price":100,"volume":10},{"ts_s":1090,"price":100.04,"volume":10},{"ts_s":1100,"price":100,"volume":10},{"ts_s":1110,"price":100.04,"volume":10},{"ts_s":1120,"price":100,"volume":10},{"ts_s":1130,"price":100.04,"volume":10},{"ts_s":1140,"price":100,"volume":10},{"ts_s":1150,"price":100.04,"volume":10},{"ts_s":1160,"price":100,"volume":10},{"ts_s":1170,"price":100.04,"volume":10},{"ts_s":1180,"price":100,"volume":10},{"ts_s":1190,"price":100.04,"volume":10},{"ts_s":1200,"price":100,"volume":10},{"ts_s":1210,"price":100.04,"volume":10},{"ts_s":1220,"price":100,"volume":10},{"ts_s":1230,"price":100.04,"volume":10},{"ts_s":1240,"price":100,"volume":10},{"ts_s":1250,"price":100.04,"volume":10},{"ts_s":1260,"price":100,"volume":10},{"ts_s":1270,"price":100.04,"volume":10},{"ts_s":1280,"price":100,"volume":10},{"ts_s":1290,"price":100.04,"volume":10}],"window_s":3600,"price_band_pct":0.25}'Client download, PowerShell input and verification →
Cite this engine
Engine identified by source hash, source SHA-256 e6c4cdff6923…. Evidence recorded on release 0.16.1; the public service currently runs 0.17.1. Replace the access date when you cite it.
ScoreCompute. Trade-pattern consistency screening (screen_wash_trading), identified by source hash, source SHA-256 e6c4cdff6923c90a7f8f022f1f60552f393262651715d75086ba2485eeb2aae3. Evidence release 0.16.1, service release 0.17.1. https://scorecompute.com/engines/screen-wash-trading/ (accessed YYYY-MM-DD).
All engine citations · Evidence card
Execution and availability
ScoreCompute exposes this tool through MCP Streamable HTTP. Rust computation runs on a connected worker; the public website and MCP gateway run separately. Among these 22 scientific tools, CUDA is implemented for simulate_pi; the other scientific engines run on CPU. The separate contributor network runs two compiled public workloads: a π estimate and fixed synthetic schedule probes. Requests are bounded and concurrent work may be refused when capacity is occupied.
Record inputs, assumptions and returned provenance when sharing a result. The public observatory displays software client names and tool activity, without publishing calculation arguments or results.