ohlcv-to-orderbook 0.1.7

Convert OHLCV data to order book data and vice versa

OHLCV to Orderbook Converter

A Python library for converting OHLCV (Open, High, Low, Close, Volume) data to synthetic Level 1 orderbook representation and vice versa, designed for financial data processing and validation.

Overview

This project provides a bidirectional conversion pipeline between OHLCV bars and synthetic orderbook snapshots. The conversion maintains data integrity and allows for round-trip validation, making it useful for:

• Financial data preprocessing
• Trading algorithm testing
• Market data validation
• Quantitative research

Features

• Bidirectional Conversion: Convert OHLCV data to orderbook snapshots and back
• Synthetic Orderbook Generation: Creates realistic Level 1 orderbook data from OHLCV bars
• Parquet I/O Support: Efficient reading and writing of financial data using Apache Parquet
• Data Validation: Built-in validation for data integrity and consistency
• Type Safety: Full type annotations and mypy compatibility
• Configurable Parameters: Customizable spread generation and price path estimation
• Comprehensive Testing: Full test coverage with round-trip validation

Installation

From PyPI (Recommended)

pip install ohlcv-to-orderbook

From PyPI with development dependencies

pip install "ohlcv-to-orderbook[dev]"

Prerequisites

• Python 3.8 or higher
• pip or uv package manager

Install from source

git clone https://github.com/luca/ohlcv-to-orderbook.git
cd ohlcv-to-orderbook
pip install -e .

Development installation

git clone https://github.com/luca/ohlcv-to-orderbook.git
cd ohlcv-to-orderbook
pip install -e ".[dev]"

Quick Start

Basic Usage

from ohlcv_to_orderbook import OrderbookGenerator, OHLCVGenerator
import pandas as pd

# Create sample OHLCV data
ohlcv_data = pd.DataFrame({
    'timestamp': [1640995200, 1640995260, 1640995320],
    'open': [50000.0, 50100.0, 50050.0],
    'high': [50200.0, 50150.0, 50100.0],
    'low': [49900.0, 50000.0, 49950.0],
    'close': [50100.0, 50050.0, 50080.0],
    'volume': [1.5, 2.3, 1.8]
})

# Convert OHLCV to orderbook
orderbook_gen = OrderbookGenerator()
orderbook_data = orderbook_gen.generate_orderbook(ohlcv_data)

# Convert back to OHLCV
ohlcv_gen = OHLCVGenerator()
reconstructed_ohlcv = ohlcv_gen.generate_ohlcv(orderbook_data)

print("Original OHLCV:", ohlcv_data)
print("Reconstructed OHLCV:", reconstructed_ohlcv)

Working with Parquet Files

from ohlcv_to_orderbook import OrderbookGenerator
from ohlcv_to_orderbook.io_handlers import ParquetHandler

# Initialize components
generator = OrderbookGenerator()
io_handler = ParquetHandler()

# Read OHLCV data from Parquet
ohlcv_data = io_handler.read_ohlcv("input_data.parquet")

# Convert to orderbook
orderbook_data = generator.generate_orderbook(ohlcv_data)

# Save orderbook data
io_handler.write_orderbook(orderbook_data, "orderbook_output.parquet")

Generating Synthetic Test Data

from ohlcv_to_orderbook import generate_test_data

# Generate synthetic data for testing
ohlcv_data, orderbook_data = generate_test_data(
    n_bars=100,
    symbol="BTCUSD",
    start_price=50000.0
)

Command Line Interface (CLI)

The package provides a powerful command-line interface for batch processing and automation.

Installation

After installing the package, the ohlcv-converter command will be available:

# Install the package
pip install -e .

# The CLI command is now available
ohlcv-converter --help

CLI Usage

Convert OHLCV to Orderbook

# Basic conversion
ohlcv-converter ohlcv-to-orderbook input_ohlcv.parquet output_orderbook.parquet

# With custom parameters
ohlcv-converter ohlcv-to-orderbook \
    --spread 0.002 \
    --points 6 \
    --size-factor 0.4 \
    --price-decimals 8 \
    --volume-decimals 6 \
    --validate \
    --verbose \
    input_ohlcv.parquet output_orderbook.parquet

Convert Orderbook to OHLCV

# Basic conversion
ohlcv-converter orderbook-to-ohlcv input_orderbook.parquet output_ohlcv.parquet

# With validation and verbose output
ohlcv-converter orderbook-to-ohlcv \
    --validate \
    --verbose \
    input_orderbook.parquet output_ohlcv.parquet

CLI Options

OHLCV to Orderbook Options:

• --spread, -s: Spread percentage (default: 0.001 = 0.1%)
• --points, -p: Number of orderbook points per OHLCV bar (default: 4)
• --size-factor, -f: Volume distribution factor (default: 0.3)
• --price-decimals: Decimal places for prices (default: 8)
• --volume-decimals: Decimal places for volumes (default: 8)
• --validate: Validate the conversion result
• --verbose, -v: Enable verbose output

Orderbook to OHLCV Options:

• --validate: Validate the conversion result
• --verbose, -v: Enable verbose output

CLI Examples

# Convert with high precision and validation
ohlcv-converter ohlcv-to-orderbook \
    --spread 0.0005 \
    --points 8 \
    --validate \
    --verbose \
    btc_ohlcv_1m.parquet btc_orderbook_l1.parquet

# Round-trip conversion test
ohlcv-converter ohlcv-to-orderbook original.parquet temp_orderbook.parquet
ohlcv-converter orderbook-to-ohlcv temp_orderbook.parquet reconstructed.parquet

# Process multiple timeframes
for file in data/ohlcv_*.parquet; do
    output="orderbook_$(basename "$file")"
    ohlcv-converter ohlcv-to-orderbook --validate "$file" "$output"
done

Configuration

The library supports various configuration options:

from ohlcv_to_orderbook.config import OrderbookConfig

config = OrderbookConfig(
    min_spread_bps=1.0,      # Minimum spread in basis points
    max_spread_bps=10.0,     # Maximum spread in basis points
    volume_distribution='uniform',  # Volume distribution method
    price_precision=2,       # Decimal places for prices
    volume_precision=8       # Decimal places for volumes
)

generator = OrderbookGenerator(config=config)

Data Format

OHLCV Data Format

The expected OHLCV data format is a pandas DataFrame with the following columns:

• timestamp: Unix timestamp (int)
• open: Opening price (float)
• high: Highest price (float)
• low: Lowest price (float)
• close: Closing price (float)
• volume: Volume traded (float)

Orderbook Data Format

The generated orderbook data is a pandas DataFrame with:

• timestamp: Unix timestamp (int)
• bid_price: Best bid price (float)
• bid_size: Best bid volume (float)
• ask_price: Best ask price (float)
• ask_size: Best ask volume (float)

Algorithm Details

OHLCV to Orderbook Conversion

The conversion algorithm estimates the price path within each OHLCV bar:

1. Path Determination: Decides the price sequence based on the relationship between Open, High, and Low:

   • If Open is closer to High: Open → High → Low → Close
   • If Open is closer to Low: Open → Low → High → Close

2. Spread Generation: Creates realistic bid-ask spreads based on:

   • Market volatility (derived from High-Low range)
   • Volume patterns
   • Configurable spread parameters

3. Volume Distribution: Distributes the total volume across generated orderbook snapshots

Orderbook to OHLCV Conversion

The reverse conversion aggregates orderbook snapshots:

1. Price Aggregation: Calculates OHLCV values from bid/ask prices over time intervals
2. Volume Summation: Sums volumes across all snapshots in the time period
3. Timestamp Alignment: Groups snapshots by time intervals

Testing

Run the complete test suite:

# Run all tests
pytest

# Run with coverage
python run_tests_with_coverage.py

# Run specific test files
pytest tests/test_conversions.py
pytest tests/test_pipeline.py

Round-trip Validation

The library includes comprehensive round-trip tests to ensure data integrity:

# Example of round-trip validation
original_ohlcv = generate_test_data(num_bars=50)
orderbook_data = orderbook_gen.generate_orderbook(original_ohlcv)
reconstructed_ohlcv = ohlcv_gen.generate_ohlcv(orderbook_data)

# Validate reconstruction accuracy
assert_ohlcv_similarity(original_ohlcv, reconstructed_ohlcv, tolerance=0.01)

Development

Setting up Development Environment

git clone <repository-url>
cd ohlcv-to-orderbook
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e ".[dev]"

Code Quality

The project uses several tools for code quality:

# Type checking
mypy ohlcv_to_orderbook/

# Run tests with coverage
pytest --cov=ohlcv_to_orderbook tests/

# Format code (if using black)
black ohlcv_to_orderbook/ tests/

Project Structure

ohlcv-to-orderbook/
├── ohlcv_to_orderbook/          # Main package
│   ├── __init__.py              # Package initialization
│   ├── config.py                # Configuration classes
│   ├── data_types.py            # Type definitions
│   ├── exceptions.py            # Custom exceptions
│   ├── io_handlers.py           # Parquet I/O operations
│   ├── ohlcv_to_orderbook.py    # OHLCV → Orderbook conversion
│   ├── orderbook_to_ohlcv.py    # Orderbook → OHLCV conversion
│   └── synthetic_data.py        # Test data generation
├── tests/                       # Test files
│   ├── test_conversions.py      # Conversion tests
│   └── test_pipeline.py         # End-to-end pipeline tests
├── pyproject.toml              # Project configuration
├── mypy.ini                    # Type checking configuration
└── README.md                   # This file

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for new functionality
5. Ensure all tests pass (pytest)
6. Run type checking (mypy ohlcv_to_orderbook/)
7. Commit your changes (git commit -m 'Add amazing feature')
8. Push to the branch (git push origin feature/amazing-feature)
9. Open a Pull Request

License

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

Acknowledgments

• Built with pandas for data manipulation
• Uses PyArrow for efficient Parquet I/O
• Type safety provided by mypy
• Testing framework: pytest

Support

For questions, issues, or contributions, please open an issue on the project repository.