Algo Trading - Why Simple Algos Are Better

Summary

Kevin Davey, a Champion chair, emphasizes the superiority of simple algorithms in trading due to their reliability and performance over complicated ones that often fail in live scenarios. He shares insights from his Strategy Factory Club, where strategies are tested for six months, and highlights the importance of avoiding high slippage markets and scalping strategies. Davey suggests focusing on simple strategies with a few rules and variables for better results, supported by statistical data from over 1500 submitted strategies.

Takeaways

• 😌 Simple algorithms are often the best because they are less likely to fall apart when put into live trading compared to complex ones.
• 📉 Over-optimization in algorithmic trading can lead to a 'curve fitting' effect, which may not hold up in real-world trading scenarios.
• 🤔 Even experienced traders can have strategies that look great in backtesting but fail in live trading, often due to unnecessary complexity.
• 🚀 Kevin Davey's Strategy Factory Club offers a unique way to gain access to a variety of proven trading strategies by submitting and analyzing student submissions.
• 📊 Davey has analyzed over 1500 strategies and found that simplicity is key to successful algorithmic trading.
• 💡 The 'easy way' in algorithmic trading involves avoiding high slippage markets, not overcomplicating strategies, and focusing on longer-term trading bars.
• 🛑 High slippage markets, such as Lumber, Orange Juice, and Palladium, are difficult for algorithmic trading due to the costs involved in order execution.
• 📈 Low slippage markets, like certain stock indices and currencies, are easier for algorithmic strategies and tend to yield better results.
• ⏳ Scalping strategies that use very short time frames (e.g., 1-minute bars) are generally less profitable and harder to develop successfully.
• 📊 Longer-term strategies, such as those using daily bars, are easier to develop and more likely to be profitable, according to Davey's analysis.
• 🔍 The 'sweet spot' for algorithmic strategies lies in the middle ground—simple strategies with a few rules and variables, but not overly simplistic or complex.

Q & A

Why does Kevin Davey believe simple algorithms are the best for trading?

Kevin Davey believes simple algorithms are the best because they are less prone to fall apart when going live, unlike more complicated ones that may look good in back tests but fail in real-time trading.

What is the common issue with algorithms that have too many parameters and optimizations?

Algorithms with too many parameters and optimizations often result in overly smooth backtest curves, which can lead to strategies that perform poorly in live trading, sometimes losing money or barely breaking even.

What does Kevin refer to as the 'hard way' in algorithmic trading?

The 'hard way' in algorithmic trading, according to Kevin, involves trying to reinvent the wheel by making overly complicated strategies and not taking the easier, more straightforward approach.

What is the main advantage of using simple strategies according to the speaker?

The main advantage of using simple strategies is that they tend to perform better in live trading, avoiding the pitfalls of over-optimization and complexity that can lead to failure.

How does Kevin Davey gather data to support his views on algorithmic trading strategies?

Kevin gathers data from his Strategy Factory Club, where students submit strategies that he analyzes in real-time for six months. If they pass performance criteria, the students receive a variety of proven strategies in return.

What does Kevin suggest is the 'sweet spot' for strategy complexity in terms of performance?

The 'sweet spot' for strategy complexity lies in the middle, with simple strategies that have a few rules and variables but avoid excessive optimization.

Why are high slippage markets difficult for algorithmic trading?

High slippage markets are difficult because an algorithm must first overcome the slippage to have a chance at profitability, which is challenging and can lead to higher costs and lower success rates.

What are some examples of markets with high slippage mentioned in the script?

Examples of markets with high slippage mentioned are Lumber, Orange Juice, milk, Palladium, and some stock indices with higher slippage.

What type of strategies does Kevin advise against for algorithmic trading?

Kevin advises against developing scalping strategies and very complicated strategies, as well as strategies for markets with high slippage, due to their difficulty and potential for lower profitability.

What does Kevin suggest as an alternative to high slippage and scalping strategies?

Kevin suggests focusing on markets with low slippage and longer-term bars, such as daily bars, and using simple strategies for easier and more profitable algorithmic trading.

How does Kevin define 'simple strategies' in the context of algorithmic trading?

In the context of algorithmic trading, 'simple strategies' are those with a few rules and variables, avoiding excessive parameters and optimization, which makes them more robust and easier to manage.