High-Frequency Trading

Introduction

High-frequency trading (HFT) demands a platform capable of handling massive data volumes, executing trades at lightning speed, and responding to market changes in real-time. Kdb+'s in-memory architecture, vectorized operations, and low latency make it an ideal choice for HFT systems. This chapter delves into the core components of an HFT system using kdb+.

Data Ingestion and Processing

Efficiently capturing and processing market data is crucial for HFT.

Code snippet

// Define a table schema for market data
trade:([]sym:symbol;time:`times$;price:float;size:int;exchange:symbol)

// Function to handle incoming market data
handle_market_data:{[data]
  // Parse incoming data
  parsed_data:parse_data[data]
  
  // Insert data into kdb+ table
  trade insert parsed_data
  
  // Trigger calculations and strategies
  calculate_indicators[parsed_data]
  execute_strategy[parsed_data]
}

Market Data Enrichment

Enriching market data with additional information enhances trading decisions.

Code snippet

// Join with reference data
ref_data:([]sym:symbol;industry:`tech`finance`tech`)
joined_data:join trade ref_data by sym

// Calculate technical indicators
trade[`ma20]:mov(price, 20)
trade[`rsi]:rsi(price, 14)

Order Management System (OMS)

An OMS handles order placement, modification, and cancellation.

Code snippet

// Define an order table
order:([]order_id:int;sym:symbol;side:`buy`sell;price:float;size:int;status:`new`open`filled`cancelled)

// Function to place an order
place_order:{[sym, side, price, size]
  // Generate order ID
  order_id:next order_id
  
  // Insert order into order table
  order insert ([]order_id;sym;side;price;size;status:`new)
  
  // Send order to exchange
  send_order[order_id]
}

Risk Management

Real-time risk monitoring is essential for HFT.

Code snippet

// Calculate portfolio value
portfolio_value:sum trade[`price] * trade[`size] where sym in portfolio

// Calculate VaR
portfolio_var:quantile[0.05] portfolio_value

Execution Engine

The execution engine determines the optimal execution strategy.

Code snippet

// Simple VWAP strategy
vwap_strategy:{[data]
  target_vwap:avg price from data
  current_vwap:avg price from data[til count data - 1]
  
  if[current_vwap < target_vwap; `buy; `sell]
}

Low Latency Architecture

Optimize system performance for minimal latency.

• Use in-memory databases.

• Employ efficient data structures.

• Minimize network hops.

• Leverage hardware acceleration.

Backtesting and Optimization

Backtesting evaluates trading strategies on historical data.

Code snippet

// Backtest a simple moving average crossover strategy
backtest:{[data]
  // Calculate moving averages
  data[`ma_short]:mov(price, 10)
  data[`ma_long]:mov(price, 20)
  
  // Generate trading signals
  data[`signal]:(ma_short > ma_long) - (ma_short < ma_long)
  
  // Calculate returns
  ...
}

Conclusion

Building a robust HFT system requires careful consideration of data ingestion, order management, risk management, execution, and performance optimization. Kdb+'s capabilities make it a powerful tool for addressing these challenges.

Note: This chapter provides a high-level overview of HFT components. Real-world HFT systems are far more complex and involve additional considerations such as market microstructure analysis, algorithmic trading, and compliance.