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Challenge

Dynamic Crude Pricing Differential Optimizer with OpenAI Agents SDK

Crude pricing differentials relative to Oman/Dubai and Dated Brent benchmarks fluctuate daily. Local and foreign firms bidding under Iraq's interim export framework require automated dynamic pricing adjusters. Build an autonomous market optimizer agent using OpenAI Agents SDK. The agent monitors benchmark futures curves, calculates freight rate adjustments, and computes optimal Official Selling Price (OSP) differentials to maximize state revenue while ensuring cargo clearance.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop an autonomous crude pricing differential optimizer using OpenAI Agents SDK tool execution.

Delivery guide

How work is evaluated

Evaluation

Evaluates OSP differential calculations against market netback targets.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

netback_match

Verify netback math matches target within margin.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

pricing_precision

Precision score of pricing differential relative to optimal curve. • target: 0.99 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Implement OpenAI Agents SDK tool functions for futures curve interpolation.

  • Calculate Netback margins based on freight rates and refining margins.

  • Automate dynamic OSP differential recommendations for monthly trading cycles.

Resources and assets

Reference links and supporting material

Dataset notes

Historical regional crude benchmark prices, freight rates, and SOMO OSP differentials.

Challenge setup

Evaluates OSP differential calculations against market netback targets.

Challenge input

JSON with benchmark_price_usd, freight_cost_usd, target_netback_usd

OpenAI Agents SDK

Lightweight python framework for function calling and tool execution.

Evaluated output

JSON with recommended_osp_differential, calculated_netback_usd

Evaluation checks
  • Verify netback math matches target within margin.
  • Precision score of pricing differential relative to optimal curve. • target: 0.99 • range: 0-1
Configured evaluation evidence
  • Benchmark: Fin-Trade-Bench
  • Pricing Precision target: 0.99
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • OpenAI · Required
  • Voiceflow · Optional
Policy Serving
  • Vercel AI Gateway · Optional
Orchestration
  • Vercel AI Gateway · Optional

Evaluation contract

  • netback_match · Weight 1
  • pricing_precision · Weight 1

Metrics

  • pricing_precision · Target 0.99

Benchmarks

  • Fin-Trade-Bench
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with benchmark_price_usd, freight_cost_usd, target_netback_usd

    Output: JSON with recommended_osp_differential, calculated_netback_usd

    Execution harness: Python

Evaluation tools

  • OpenAI Agents SDK · Agent Core

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Dynamic Crude Pricing Differential Optimizer with OpenAI Agents SDK