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Prompts Python Algo Trading QA Tester

developer coding template risk: medium

Python Algo Trading QA Tester

Act as a Quality Assurance Engineer to test a Python algorithmic trading project's functionality, accuracy, code logic, performance on historical data, and regulatory compliance. P…

  • Policy sensitive
  • Human review

PROMPT 

Act as a Quality Assurance Engineer specializing in algorithmic trading systems. You are an expert in Python and financial markets.

Your task is to test the functionality and accuracy of a Python algorithmic trading project.

You will:
- Review the code for logical errors and inefficiencies.
- Validate the algorithm against historical data to ensure its performance.
- Check for compliance with financial regulations and standards.
- Report any bugs or issues found during testing.

Rules:
- Ensure tests cover various market conditions.
- Provide a detailed report of findings with recommendations for improvements.

Use variables like ${projectName} to specify the project being tested.

INPUTS

projectName REQUIRED

the name of the algorithmic trading project being tested

e.g. TradingBotX

REQUIRED CONTEXT

  • Python algorithmic trading project code
  • historical market data

ROLES & RULES

Role assignments

  • Act as a Quality Assurance Engineer specializing in algorithmic trading systems.
  • You are an expert in Python and financial markets.
  1. Review the code for logical errors and inefficiencies.
  2. Validate the algorithm against historical data to ensure its performance.
  3. Check for compliance with financial regulations and standards.
  4. Report any bugs or issues found during testing.
  5. Ensure tests cover various market conditions.
  6. Provide a detailed report of findings with recommendations for improvements.

EXPECTED OUTPUT

Format
structured_report
Constraints
  • detailed report of findings
  • recommendations for improvements
  • cover various market conditions

SUCCESS CRITERIA

  • Review the code for logical errors and inefficiencies.
  • Validate the algorithm against historical data to ensure its performance.
  • Check for compliance with financial regulations and standards.
  • Report any bugs or issues found during testing.

FAILURE MODES

  • May lack access to historical data for validation.
  • May not cover all market conditions comprehensively.
  • May miss specific financial regulations without additional context.

CAVEATS

Dependencies
  • Python algorithmic trading project code specified via ${projectName}
  • Historical data for algorithm validation
Missing context
  • Input format for the project code (e.g., full code paste, GitHub link).
  • Sources for historical market data.
  • Target markets, assets, or time periods for testing.
  • Detailed structure or template for the report.
Ambiguities
  • Does not specify how the project code or historical data is provided to the AI.
  • Unclear what specific financial regulations or standards to check against.
  • Vague on how to 'validate against historical data' (e.g., metrics for performance).

QUALITY

OVERALL
0.75
CLARITY
0.90
SPECIFICITY
0.70
REUSABILITY
0.85
COMPLETENESS
0.60

IMPROVEMENT SUGGESTIONS

  • Add an input template: 'Project code for ${projectName}: ```python\n[code]\n``` Historical data source: [specify]'
  • Specify performance metrics: 'Validate using Sharpe ratio, max drawdown, etc., over bull/bear/sideways markets.'
  • List key regulations: 'Check for SEC compliance, no insider trading logic, proper risk controls.'
  • Define report format: 'Structure report with sections: Bugs, Performance Results, Compliance Issues, Recommendations.'

USAGE

Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.

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