Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Design a simplified market simulator capable of generating realistic order book data. Crucially, define at least three distinct algorithmic manipulation scenarios (e.g., spoofing, layering, quote stuffing) that can be injected into the market data. Detail the parameters for each manipulation type and how they would affect the order book.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Detect Algorithmic Market Manipulation with Llama 3.2 and LangChain
This challenge focuses on building an AI-powered system to detect and analyze sophisticated algorithmic market manipulation, leveraging insights from game theory. Participants will simulate a financial market where adversarial algorithms attempt to influence prices. The core task is to develop a detection system capable of identifying patterns indicative of manipulative practices (e.g., spoofing, layering, quote stuffing) by analyzing market data streams. The solution will use a high-performing large language model (like Llama 3.2, or an equivalent accessible via OpenAI API) integrated through LangChain to provide real-time threat intelligence, explain detected anomalies, and suggest defensive strategies. The system should go beyond simple rule-based detection, employing advanced analytical techniques and potentially game-theoretic models to understand and predict adversarial behavior.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.