Algorithmic Trading Strategies

Algorithmic Trading Overview

• Algorithmic trading involves the use of computer programs to execute orders automatically.
• This approach is also known as automated trading, black-box trading, or algo-trading.
• It can be applied to any asset class.

Benefits of Algorithmic Trading

• Reduced transaction costs
• Optimal price acquisition
• Minimized human error
• Opportunity for backtesting strategies
• Faster trade execution
• Higher trading frequency
• Facilitation of complex strategies

Types of Algorithmic Trading Strategies

• Trend Following
• Mean Reversion
• Execution
• Market Making
• Arbitrage

Trend Following Strategies

• These strategies aim to identify assets trending in a specific direction and trade accordingly
• Moving Averages Crossover: Buy when short-term MA exceeds long-term MA, sell when short-term MA is below long-term MA.
• Channels: Buy when the price breaks above a defined channel, sell when it breaks below.

Mean Reversion Strategies

• These strategies focus on identifying assets that have strayed from their average and trading to capitalize on the return to the mean.
• Pairs Trading: Identify correlated assets and trade when the spread between them deviates from the mean.
• Statistical Arbitrage: Similar to pairs trading, but it involves more assets and employs statistical models to detect mispricings.

Execution Algorithms

• These algorithms break down large orders into smaller ones to be executed over time
• VWAP (Volume Weighted Average Price): Executes orders to align with the day's VWAP, trading more with high volume and less with low volume.
• TWAP (Time Weighted Average Price): Executes orders uniformly over a set period.
• Implementation Shortfall: Aims to minimize the difference between the actual and expected prices.

Market Making

• Market makers provide liquidity by placing buy and sell orders.
• They profit from the spread between the bid and ask prices.

Arbitrage

• Capitalizes on price discrepancies between different markets.
• Offers the potential for risk-free profit.

Backtesting Strategies

• Backtesting involves testing a trading strategy on historical data.
• It helps identify potential issues but does not guarantee future success.

Things to Consider During Backtesting

• Overfitting the model to the data
• Data mining bias
• Transaction costs
• Slippage
• Execution quality
• Data quality
• Survivorship bias

Resources for Algorithmic Trading

• Books:
  • Algorithmic Trading: Winning Strategies and Their Rationale by Emmanual Derman
  • Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernest Chan
  • Python for Finance: Analyze Big Financial Data by Yves Hilpisch
• Websites:
  • Quantopian
  • QuantConnect
  • Investopedia

Summary

• Algorithmic trading can enhance trading performance, but it requires understanding the associated risks.
• Backtesting is essential, yet it should not be seen as a guarantee of future results.