orderpulse 0.2.17

High-performance exchange feed parser and orderflow analytics engine with Rust and Python bindings

fastreader

🚀 High-performance binary market data parser and order book engine for Python, powered by Rust + PyO3.

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Overview

fastreader is a high-performance Python library built with Rust and PyO3 for parsing exchange binary feed files and constructing order books efficiently.

The library is designed for:

• Quantitative trading systems
• HFT research
• Tick-level backtesting
• Exchange feed analysis
• Order flow analytics
• Order book reconstruction
• Ultra-fast binary parsing

Unlike traditional Python parsers, fastreader executes the heavy parsing logic in Rust, delivering significantly better speed and lower memory overhead.

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Features

✅ Ultra-fast binary message parsing

✅ Rust-powered backend for maximum performance

✅ Python-friendly API

✅ Parse order and trade messages

✅ Token-based filtering

✅ Cursor-based streaming access

✅ Batch message processing

✅ Top-of-book order book reconstruction

✅ Efficient memory handling

✅ Built for large exchange data files

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Installation

Install from PyPI

pip install orderpulse

Upgrade to Latest Version

pip install -U orderpulse

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Quick Start

Import Library

from fastreader import ReadMsgFromBinary, OrderBookBuilder

Load Binary Feed File

reader = ReadMsgFromBinary("market_data.bin")

Get Summary

reader.summary()

Output:

Total Messages: 1520000
Total Orders: 1300000
Total Trades: 220000

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Core Classes

1. ReadMsgFromBinary

Main parser class used to read exchange binary files.

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Constructor

ReadMsgFromBinary(path, token=None)

Parameters

Parameter	Type	Description
path	str	Binary file path
token	int (optional)	Filter messages by token

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Example: Read Full File

from fastreader import ReadMsgFromBinary

reader = ReadMsgFromBinary("data.bin")

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Example: Token Filtered Reading

reader = ReadMsgFromBinary(
    "data.bin",
    token=26000
)

Only messages belonging to token 26000 will be loaded.

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Available Methods

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total_messages()

Returns total parsed messages.

count = reader.total_messages()
print(count)

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total_orders()

Returns total order messages.

orders = reader.total_orders()
print(orders)

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total_trades()

Returns total trade messages.

trades = reader.total_trades()
print(trades)

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summary()

Prints complete message summary.

reader.summary()

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reset_cursor()

Resets internal message cursor.

Useful when iterating incrementally.

reader.reset_cursor()

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Message Extraction APIs

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get_all_messages()

Returns all parsed messages.

messages = reader.get_all_messages()

With Limit

messages = reader.get_all_messages(limit=10)

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get_order_messages()

Returns only order messages.

orders = reader.get_order_messages()

With Limit

orders = reader.get_order_messages(limit=5)

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get_trade_messages()

Returns only trade messages.

trades = reader.get_trade_messages()

With Limit

trades = reader.get_trade_messages(limit=5)

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Incremental Reading

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get_next_msg()

Reads one message at a time.

Ideal for streaming-style processing.

while True:
    msg = reader.get_next_msg()

    if msg == "END":
        break

    print(msg)

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MessageBatch

MessageBatch is a lightweight container used for efficient message selection and downstream processing.

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Selection APIs

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select_all_messages()

Returns all messages as a MessageBatch.

batch = reader.select_all_messages()

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select_order_messages()

Returns only order messages.

order_batch = reader.select_order_messages()

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select_trade_messages()

Returns only trade messages.

trade_batch = reader.select_trade_messages()

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select_next_messages(limit)

Returns next N messages using internal cursor.

batch = reader.select_next_messages(100)

Useful for chunk-wise processing of very large files.

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MessageBatch Methods

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len()

Returns number of messages.

print(batch.len())

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is_empty()

Checks whether batch is empty.

print(batch.is_empty())

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to_list()

Converts batch into Python list.

data = batch.to_list()

With Limit

data = batch.to_list(limit=10)

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OrderBookBuilder

The OrderBookBuilder reconstructs top-of-book snapshots using parsed order and trade messages.

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Create Builder

from fastreader import OrderBookBuilder

ob = OrderBookBuilder()

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create_orderbook_all_messages()

Builds order book using all messages from reader.

rows = ob.create_orderbook_all_messages(reader)

print(rows)

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create_orderbook()

Build order book from a MessageBatch.

batch = reader.select_order_messages()

rows = ob.create_orderbook(batch)

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Example: Complete Workflow

from fastreader import ReadMsgFromBinary
from fastreader import OrderBookBuilder

# Load file
reader = ReadMsgFromBinary(
    "market_data.bin",
    token=26000
)

# Print summary
reader.summary()

# Get first 10 messages
msgs = reader.get_all_messages(limit=10)

for msg in msgs:
    print(msg)

# Select order messages
batch = reader.select_order_messages()

# Build order book
ob = OrderBookBuilder()

rows = ob.create_orderbook(batch)

print("Generated Rows:", rows)

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Message Format

Messages are returned as formatted strings.

Example:

Order Message: SeqNo12345, msg_len64, Msg_Type'O', Exch_ts123456789, local_ts123456790, order_id11111, Token26000, order_Type'B', Price25000, Quantity100, missed0

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Message Fields

Field	Description
SeqNo	Stream sequence number
msg_len	Packet length
Msg_Type	Exchange message type
Exch_ts	Exchange timestamp
local_ts	Local receive timestamp
order_id	Order identifier
order_id_buy	Buy order ID
order_id_sell	Sell order ID
Token	Instrument token
order_Type	Order type
Price	Order/trade price
Quantity	Quantity
missed	Gap indicator

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Performance

fastreader is optimized for:

• Large binary files
• Low-latency parsing
• Efficient memory usage
• High throughput message processing

Because parsing logic is implemented in Rust, performance is significantly faster than pure Python implementations.

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Use Cases

• Tick data analysis
• HFT research
• Exchange feed decoding
• Backtesting engines
• Order flow analytics
• Market microstructure research
• Real-time strategy simulation
• Order book reconstruction

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Why Rust + PyO3?

This library combines:

Rust

• Memory safety
• High speed
• Zero-cost abstractions
• Parallel-friendly architecture

Python

• Easy integration
• Quant ecosystem support
• Data science workflows
• Rapid research iteration

Result:

🔥 Production-grade performance with Python simplicity.

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Recommended Workflow

Binary Feed File
        ↓
ReadMsgFromBinary
        ↓
MessageBatch Selection
        ↓
OrderBookBuilder
        ↓
Analytics / Alpha / Backtesting

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Future Roadmap

Planned improvements:

• Direct NumPy export
• Pandas DataFrame support
• Async streaming parser
• Multi-threaded parsing
• Depth-wise order book snapshots
• CSV export
• Arrow / Parquet integration
• Real-time websocket ingestion

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Contributing

Contributions are welcome.

You can contribute by:

• Reporting bugs
• Improving performance
• Adding exchange decoders
• Extending order book logic
• Writing benchmarks
• Improving documentation

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License

MIT License

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Author

Built for quantitative trading and ultra-fast market data processing.

If this library helps your research or trading workflow, consider starring the repository.

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Support

If you encounter issues:

1. Open a GitHub issue
2. Share sample binary data
3. Include stack trace/output
4. Mention library version

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⚡ fastreader — Rust Speed. Python Simplicity.