OnlineResamplers.jl Documentation

Welcome to the comprehensive documentation for OnlineResamplers.jl - a high-performance Julia package for real-time resampling of financial market data.

Documentation Overview

📚 Getting Started

• Tutorial - Step-by-step guide from basic concepts to advanced usage
  • Installation and setup
  • Core concepts (MarketDataPoint, TimeWindow, OHLC)
  • Basic resampling strategies
  • Real-time processing patterns
  • Performance optimization tips

📖 Comprehensive Reference

• User Guide - Complete guide with detailed examples and best practices

  • Installation options
  • Core concepts and data structures
  • Basic and advanced usage patterns
  • Real-world examples (CSV processing, multi-timeframe analysis)
  • Performance optimization strategies
  • OnlineStats integration
  • Troubleshooting and debugging

• API Reference - Detailed technical documentation

  • Complete function signatures and parameters
  • Type specifications and compatibility
  • Return value documentation
  • Performance characteristics
  • Usage examples for each function

• Edge Cases & Limitations - Important behaviors and gotchas

  • Out-of-order data handling
  • Empty windows and single data points
  • Type mismatch issues
  • Memory considerations
  • Best practices for edge cases

💡 Examples and Patterns

• Advanced Examples - Complex real-world scenarios
  • usage_example.jl - Basic usage patterns
  • advanced_examples.jl - Complex scenarios including:
    • Multi-timeframe analysis
    • High-precision calculations
    • Real-time streaming
    • Parallel processing
    • Performance demonstrations
  • out_of_order_data.jl - Handling non-chronological data

🧪 Tests

• Test Suite - Comprehensive test coverage
  • test_resampler.jl - Core functionality tests

Quick Navigation

By Use Case

By Experience Level

By Topic

Core Concepts

• MarketDataPoint Structure
• Time Windows
• OHLC Data Format
• OnlineStatsBase Integration

Resampling Methods

• OHLC Resampling - Candlestick aggregation
• Mean Price Resampling - Average price calculation
• Volume Sum Resampling - Volume aggregation

Advanced Features

• Chronological Validation - Built-in out-of-order data detection
• Custom Numeric Types
• Parallel Processing
• Multi-timeframe Analysis
• Performance Optimization

Integration

• OnlineStats Ecosystem
• CSV Data Processing
• Real-time Data Streams

Package Architecture

OnlineResamplers.jl is built on a clean, extensible architecture:

OnlineResamplers.jl
├── Core Types
│   ├── MarketDataPoint{T,P,V}      # Input data structure
│   ├── OHLC{P}                     # Price aggregation result
│   └── TimeWindow{T}               # Time interval definition
├── Abstract Types
│   └── AbstractResampler{T,P,V}    # Base for all resamplers
├── Concrete Resamplers
│   ├── OHLCResampler{T,P,V}        # OHLC price aggregation
│   ├── MeanResampler{T,P,V}        # Mean price aggregation
│   ├── SumResampler{T,P,V}         # Sum aggregation (volumes)
│   └── MarketResampler{T,P,V}      # Composite resampler
└── OnlineStatsBase Integration
    ├── fit!(resampler, data)       # Process data
    ├── value(resampler)            # Get results
    ├── nobs(resampler)             # Count observations
    └── merge!(r1, r2)              # Combine resamplers

Type System

The package uses a comprehensive parametric type system:

• T: Timestamp type (DateTime, NanoDate, ZonedDateTime, etc.)
• P: Price type (Float64, FixedDecimal, Rational, etc.)
• V: Volume type (Float64, FixedDecimal, Int64, etc.)

This design enables:

• Type Safety: Compile-time type checking prevents runtime errors
• Performance: Type-stable operations for maximum speed
• Flexibility: Support for any numeric type with appropriate operations
• Precision: Use exact arithmetic types for financial calculations

Performance Characteristics

OnlineResamplers.jl is designed for high-performance applications:

• Memory: O(1) constant memory usage regardless of data volume
• Speed: ~50 nanoseconds per operation on modern hardware
• Throughput: >2 million operations per second
• Allocations: Zero allocations in steady-state processing
• Scalability: Supports parallel processing with merge operations

Contributing

We welcome contributions! Areas where help is especially appreciated:

• New Resampling Methods: Implement additional aggregation strategies
• Performance Improvements: Optimize hot code paths
• Documentation: Improve examples and explanations
• Testing: Add test cases for edge conditions
• Integration: Examples with other Julia packages

See the source code and test files for implementation examples.

Support and Community

• Issues: Report bugs and request features on GitHub Issues
• Discussions: Ask questions on GitHub Discussions
• Documentation: Contribute improvements to help others learn

This documentation covers OnlineResamplers.jl v0.1.0 and later. For earlier versions, please refer to the appropriate git tags.