Files
foxhunt/trading_engine
jgrusewski 89d98f8c5a 🧪 Waves 100-102: Test Coverage Initiative + Compilation Fixes
WAVE 100: Test Coverage Expansion (8/10 agents, 308 tests added)
├─ Agent 4: Execution error path tests (trading_service)
├─ Agent 5: ML training pipeline timeout analysis
├─ Agent 6: Audit persistence comprehensive tests
├─ Agent 7: ML pipeline coverage tests + rate limiting
├─ Agent 8: Algorithm comprehensive tests (adaptive-strategy)
├─ Agent 9: Coverage measurement analysis
└─ Result: 308 new tests across 8 components

WAVE 101: Compilation Error Fixes (14 errors → 0)
├─ Fixed backtesting_comprehensive.rs (6 compilation errors)
│  ├─ Added `use rust_decimal::MathematicalOps;` import
│  ├─ Removed 3 invalid `?` operators from void methods
│  └─ Fixed 4 i64 type casting issues for ChronoDuration::days()
├─ performance_tracking_comprehensive.rs: Already fixed (38/38 tests pass)
└─ algorithm_comprehensive.rs: Already fixed (38/40 tests pass)

WAVE 102: Runtime Test Failure Analysis (10 failures documented)
├─ Issue #1: Benchmark comparison stub (backtesting/metrics.rs:657-669)
│  └─ Always returns None, needs beta/alpha/tracking error implementation
├─ Issue #2: Daily returns calculation edge cases (3 tests affected)
│  └─ Returns empty Vec for < 2 snapshots, triggers "No daily returns calculated"
├─ Issue #3: Timestamp offsets in replay tests (1 hour, 60 day differences)
│  └─ Possible timezone/DST issue or Utc::now() non-determinism
├─ Issue #4: Monthly performance calculation (< 11 months generated)
└─ Issue #5: Max drawdown peak-to-trough assertion

TEST RESULTS:
├─ Compilation:  100% (all 3 Wave 100 test files compile)
├─ Test Pass Rate: 108/118 tests (91.5%)
│  ├─ algorithm_comprehensive: 38/40 (95%)
│  ├─ backtesting_comprehensive: 32/40 (80%)
│  └─ performance_tracking: 38/38 (100%)
└─ Coverage Impact: Estimated +5-10 points toward 95% target

FILES CHANGED:
├─ New Tests: 11 files (algorithm, backtesting, performance tracking, etc.)
├─ Fixed: backtesting_comprehensive.rs (6 compilation errors resolved)
├─ Documentation: 8 new agent reports (Wave 100-101)
└─ Analysis: wave102_test_failures_analysis.txt

TIMELINE:
├─ Wave 100: 308 tests added (90% completion, 2 agents hit timeout)
├─ Wave 101: All compilation errors resolved (100% success)
├─ Wave 102: Root cause analysis complete (10 failures documented)
└─ Next: Wave 103 to fix 10 runtime test failures (5-10 hours estimated)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-04 16:05:34 +02:00
..

Trading Engine Crate

Overview

The trading_engine crate provides the high-performance core infrastructure essential for High-Frequency Trading (HFT) operations. It focuses on ultra-low latency execution, precise timing, and efficient order management to handle demanding market conditions.

Features

  • Extreme Performance Optimization: Utilizes RDTSC for precise timing, CPU affinity for dedicated core execution, and SIMD instructions for vectorized data processing.
  • Robust Order Management: Manages the lifecycle of orders, from placement to execution and cancellation, ensuring accuracy and low-latency updates.
  • Flexible Execution Engine: Implements a highly optimized engine capable of processing trading strategies and executing orders across various venues.
  • Multi-Broker Connectivity: Seamlessly integrates with multiple brokers, including Interactive Brokers and ICMarkets, via specialized adapters.
  • Event-Sourced Architecture: Employs event sourcing for deterministic state reconstruction, coupled with comprehensive metrics and persistent storage.
  • Concurrent Lock-Free Data Structures: Leverages advanced lock-free data structures to minimize contention and maximize throughput in multi-threaded environments.

Architecture

The trading_engine is structured around several key components:

  • Execution Core: The central logic for strategy evaluation and trade decision-making.
  • Order Manager: Handles all order-related operations, maintaining order state and communicating with broker adapters.
  • Broker Adapters: Abstract interfaces and concrete implementations for connecting to specific trading venues (e.g., IbAdapter, IcMarketsAdapter).
  • Performance Utilities: Modules for RDTSC access, CPU core pinning, and SIMD instruction sets.
  • Event Store: A mechanism for recording all significant events, enabling replay and auditability.
  • Metrics System: Collects and reports performance and operational statistics.
  • Persistence Layer: Stores critical state and event data for recovery and analysis.
  • Concurrency Primitives: Custom lock-free queues, rings, and other data structures.

Usage

To initialize the trading engine and place a simple order:

use trading_engine::{
    engine::TradingEngine,
    order::{Order, OrderSide, OrderType},
    broker::BrokerType,
};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut engine = TradingEngine::new();
    engine.connect_broker(BrokerType::InteractiveBrokers).await?;

    let order = Order {
        symbol: "ESZ23".to_string(),
        side: OrderSide::Buy,
        order_type: OrderType::Limit,
        quantity: 1,
        price: Some(4500.0),
        // ... other order details
    };

    let order_id = engine.place_order(order).await?;
    println!("Placed order with ID: {}", order_id);

    Ok(())
}

Testing

To run the tests for the trading_engine crate:

cargo test --package trading_engine

Documentation

Comprehensive API documentation is available at docs.rs/trading_engine.