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🔍 Audit Questionnaire Responses

  1. Programming Language(s) C# / .NET 8 (primary language)

  2. Description in 4–6 lines (objective, architecture, status) Objective: Multi-component automated trading system for Binance platform, integrating artificial intelligence for market prediction and automated decision-making in cryptocurrency trading.

Architecture: Modular solution with 6 .NET 8 projects: main trading engine, unit tests, machine learning module (ML.NET), backtesting simulation, data collection service, and technical calculations library. Clean architecture using dependency injection with abstract interfaces.

Status: Mature project with 107 commits, fully functional automated test suite (78 tests passing), CI/CD integration via GitHub Actions with Codecov, and experimental ML features. All tests are now passing successfully after recent fixes.

  1. Project age and workload (solo/team) Age: ~2 years (first commit: December 25, 2023) Volume: 107 total commits across development lifecycle Team: Solo project (developed by bogardt) Activity: Active development with recent commits on ML bot and test fixes
  2. Tests (unit/integration?) and repository structure (PRs, CI) Testing:

Unit tests: 78 tests using MSTest and Moq (mocking framework) Integration tests: Service layer integration testing Code coverage: Automated coverage reporting with Codecov ✅ Current status: All 78 tests passing (100% success rate) Test categories: API validation, trading logic, technical indicators, service configuration Repository structure:

CI/CD: GitHub Actions pipeline (.NET Coverage and GitHub Pages) Branching: main and dev branches with Pull Request workflow Quality gates: Build status badges and code coverage monitoring Monitoring: Codecov integration for coverage tracking and reporting 5. AI tools usage (not used/low/moderate) Level: MODERATE

AI Integration:

Microsoft ML.NET framework (version 3.0.1) Machine Learning for price prediction using FastTree regression algorithm Predictive analytics based on historical market data (OHLCV + technical indicators) Automated buy/sell decisions driven by AI predictions Complete ML pipeline: data collection, training, evaluation, real-time prediction Feature engineering: Technical indicators (RSI, SMA, volatility) combined with ML predictions AI Components:

BinanceBotML: Primary AI module with prediction engine AnalyzerML: ML model training and prediction service TradingBot: AI-driven automated trading decisions BinanceBotML.Feeder: Automated data collection for model training MarketData & MarketPrediction: ML data models and prediction outputs 📊 Technical Summary for Audit Maturity: Stable project with solid architecture and 2-year development history Quality: Full CI/CD pipeline, 78 passing tests, automated monitoring Innovation: Advanced ML integration for financial trading with real-time prediction Maintenance: Active development with recent bug fixes and feature additions Reliability: 100% test success rate, robust error handling, comprehensive mocking Scalability: Modular architecture allowing independent component development