tradata-core 0.2.3

Professional core framework for trading applications

Tradata Core

Professional core framework designed for trading and market data applications.

Features

• 🚀 FastAPI Middleware - Automatic request logging with trace IDs
• 📊 Structured JSON Logging - Consistent, searchable log format
• 🔗 Request Correlation - Track requests across microservices
• ⚡ High Performance - Async-first design with minimal overhead
• 🎯 Trading-Specific - Specialized logging for financial applications
• 🔧 Easy Configuration - Environment variables and programmatic setup
• 🎭 Context Management - Automatic operation and step tracking
• 🏷️ Decorators - One-liner logging for API endpoints

Quick Start

Installation

This is a private package accessible only to your GitHub organization.

Option 1: Automated Setup (Recommended)

# Download and run the setup script
curl -O https://raw.githubusercontent.com/your-org/tradata-core-py/main/setup_private_install.sh
chmod +x setup_private_install.sh
./setup_private_install.sh YOUR_GITHUB_TOKEN_HERE

Option 2: Manual Installation

# Install from private GitHub Packages
pip install tradata-core-py

# Or install directly from private repository
pip install git+https://[e-mail]/your-org/tradata-core-py.git

See Private Package Setup Guide for detailed instructions.

Basic Usage

from tradata_core import get_logger

logger = get_logger("my-service")
await logger.info("Service started", "Service", "Startup")

FastAPI Integration

from fastapi import FastAPI
from tradata_core import LoggingMiddleware

app = FastAPI()
app.add_middleware(LoggingMiddleware)

# Automatic request logging with trace IDs!

Why Tradata Logger?

For Trading Applications

• Operation Tracking: Log business operations (e.g., "QuoteRetrieval", "TradeExecution")
• Step Monitoring: Track specific steps within operations (e.g., "Validation", "API_Call")
• Performance Metrics: Built-in timing and performance logging
• Cache Operations: Specialized logging for cache hits/misses
• Client Tracking: Monitor external API usage and performance

For Microservices

• Trace ID Propagation: Follow requests across service boundaries
• Request Correlation: Link related operations with unique identifiers
• Structured Logging: Consistent JSON format for log aggregation
• Middleware Integration: Framework-specific middleware for automatic logging

For Development Teams

• Clean APIs: Decorators and context managers for minimal code
• Comprehensive Coverage: Automatic logging of requests, errors, and performance
• Flexible Configuration: Environment-based and programmatic configuration
• Extensible Design: Easy to add custom formatters and handlers

Core Concepts

Operation and Step Tracking

Every log entry includes:

• Operation: High-level business operation (e.g., "QuoteRetrieval")
• Step: Specific step within the operation (e.g., "API_Call", "Validation")

await logger.info("Quote retrieved", "QuoteRetrieval", "API_Call", symbol="AAPL")

Context Variables

Automatic tracking of:

• Trace ID: Unique identifier for request chains
• Request ID: Unique identifier for individual requests
• Client: External service being used

Structured Logging

All logs include structured data for easy searching and analysis:

{
  "timestamp": "2024-01-15T10:30:00Z",
  "level": "INFO",
  "message": "Quote retrieved successfully",
  "operation": "QuoteRetrieval",
  "step": "API_Call",
  "trace_id": "trace_12345",
  "request_id": "req_67890",
  "client": "alpaca_api",
  "symbol": "AAPL",
  "duration_ms": 150.5
}

Usage Examples

Basic Logging

from tradata_core import get_logger

logger = get_logger("quotes-service")

# Basic logging
await logger.info("Processing started", "DataProcessing", "Validation")
await logger.error("API call failed", "ExternalAPI", "Call", error="Connection timeout")

# Specialized logging
await logger.log_cache_operation("Cache_Get", True, "quote:AAPL", 5.0)
await logger.log_client_operation("API_Call", "alpaca_api", "get_quote", 150.0)

API Endpoint Logging

from tradata_core.utils.decorators import log_endpoint

@app.get("/quotes/{symbol}")
@log_endpoint("/quotes/{symbol}", "quote_retrieval", "quotes_service")
async def get_quote(symbol: str):
    # Automatic logging of request, processing, and response
    return {"symbol": symbol, "price": 150.00}

Context Management

from tradata_core.utils.context import logging_context

async with logging_context("data_processing", "batch_validation") as ctx:
    ctx.add_context(batch_size=1000, dataset="market_data")
    
    for item in data:
        # Process item
        pass
    
    # Automatic completion logging with duration

FastAPI Middleware

from tradata_core import LoggingMiddleware, PerformanceLoggingMiddleware

app = FastAPI()

# Basic logging middleware
app.add_middleware(LoggingMiddleware)

# Performance-focused middleware
app.add_middleware(PerformanceLoggingMiddleware)

Configuration

Environment Variables

LOG_LEVEL=INFO
LOG_FORMAT=json
LOG_INCLUDE_TRACE_ID=true
LOG_PERFORMANCE=true
LOG_CACHE_OPERATIONS=true

Programmatic Configuration

from tradata_core.config import LogConfig, HandlerConfig

config = LogConfig(
    level="DEBUG",
    format="console",
    handlers=[
        HandlerConfig(type="console", level="DEBUG"),
        HandlerConfig(type="file", filename="app.log", level="INFO")
    ]
)

Installation

From PyPI

pip install tradata-logger

From Source

git clone https://github.com/tradata/tradata-logger-py.git
cd tradata-logger-py
pip install -e .

Development Dependencies

pip install -e ".[dev]"

Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=tradata_core

# Run specific test file
pytest tests/test_logger.py

Examples

See the examples/ directory for complete working examples:

• basic_usage.py - Basic logger usage
• fastapi_example.py - FastAPI integration
• flask_example.py - Flask integration (placeholder)

Documentation

Comprehensive documentation is available in the docs/ directory:

• API Reference
• Examples
• Configuration Guide

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Run the test suite
6. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

• GitHub Issues: Report bugs or request features
• Documentation: Comprehensive guides and examples
• Examples: Working code examples

Roadmap

• Flask middleware implementation
• Additional log formatters
• Cloud logging integrations
• Performance dashboard
• Log aggregation tools
• More specialized logging methods

Acknowledgments

• Built for the trading and financial technology community
• Inspired by modern logging practices and observability needs
• Designed with microservices and distributed systems in mind

---

Tradata Logger - Professional logging for professional applications.