Avoiding Bear Markets

StatsEdgeTrading

Line Your Own Pockets: Position Sizing and Strategy Performance

Welcome back to another episode of Line Your Own Pockets! In today’s discussion, we tackle a great question from our Twitter follower, Day Trading Zoo, who asked about managing position sizes when strategies are performing well or poorly. This sparked such an interesting conversation between Dave and me that we had to turn on the cameras mid-chat.

Let’s dive into it.

The Core Question

Here’s what Day Trading Zoo wanted to know:

“When a strategy is doing well, should I size up? And when it’s doing poorly, should I size down? How do I approach this systematically?”

This question opens up a broader discussion on adapting strategies to market conditions and optimizing position sizing. Here’s how we broke it down.

Two Perspectives: Day Trading vs. Swing Trading

Dave and I have slightly different approaches, which makes for a good debate.

• Dave’s Perspective (Day Trading):

As an intraday trader, he avoids market-wide filters. His focus is on finding strategies that are robust enough to stand on their own.

• Why? Market filters like the S&P 500’s behavior often feel arbitrary intraday, where price moves are driven by unique events like gaps or news.

• Instead of adjusting based on the market, he emphasizes designing systems with strong signals that don’t rely on overall market conditions.

• Mike’s Perspective (Swing Trading):

For longer-term swing trading, I’m more open to using regime filters.

• Example: A simple rule like “don’t trade long positions when the S&P 500 is below its 200-day moving average” can significantly improve some trend-following strategies.

• Why? Over longer time frames, market conditions (bullish vs. bearish regimes) impact strategy performance more predictably.

Challenges with Adjusting Position Sizing

While it might seem intuitive to adjust position sizes dynamically, we outlined some potential pitfalls:

1. Correlation Isn’t Always Reliable:

• It’s tempting to correlate strategy performance with overall market behavior. However, such correlations often break down when tested rigorously.

• Example: A gap strategy might seem to perform better when the market gaps up, but the data often shows no consistent pattern.

2. Simplicity is Key:

• Adding complexity like “only trade when X indicator is above Y value” can overfit your system to past data.

• If you can’t explain the logic to a skeptical trader (or even a 5-year-old), it’s likely too convoluted.

3. Emotional Impact:

• Turning a strategy on and off based on arbitrary rules can lead to second-guessing, emotional reactions, and missed opportunities.

• For systematic traders, a consistent approach is often less stressful and more effective.

Exploring Alternatives

We discussed some alternative ways to manage strategy performance dynamically:

1. Equity Curve-Based Rules:

• Some traders use their strategy’s equity curve to decide whether to trade.

• Example: Apply a moving average to your equity curve—trade when the curve is above the average, and stop trading when it’s below.

• Our Take: This approach often feels arbitrary and overly reactive. Your equity curve doesn’t impact the strength of your signals.

2. Switching Between Systems:

• A better approach might be rotating between complementary systems.

• Example: Turn off trend-following strategies during a bear market and activate mean-reversion systems instead.

• This requires backtesting to ensure you’re not leaving profits on the table during “off” periods.

3. Position Sizing Adjustments:

• Instead of turning systems off, consider scaling position sizes based on performance metrics.

• Example: Reduce size for underperforming strategies but don’t eliminate them entirely.

Building Robust Systems

One of the key takeaways from this discussion was the importance of robust system design:

• For Day Traders: Signals should be strong and stand alone without reliance on external market factors.

• For Swing Traders: A regime filter or market condition check can be valuable, but only if it’s simple, logical, and rigorously tested.

• For All Traders: Always have a plan for when your strategy underperforms. Holding “reserve rules” for optimization can prevent emotional overreactions during drawdowns.

Key Quotes

Here are some gems from today’s conversation:

• Dave: “If a system needs a regime filter to work, I feel like I haven’t done my job in designing a strong signal.”

• Mike: “Systematic trading doesn’t remove emotions—it just moves them to different decisions, like whether to tweak or stick with a strategy.”

• On Simplicity: “You should be able to explain your trading logic to a 5-year-old or a skeptical trader. If you can’t, rethink it.”

Practical Takeaways

1. Test Everything: Whether it’s a regime filter or position sizing adjustment, run extensive backtests to confirm your logic.

2. Keep It Simple: Avoid adding unnecessary complexity. Focus on strong signals and coherent logic.

3. Plan for Drawdowns: Prepare rules in advance for when your strategy struggles, so you’re not forced to make emotional decisions later.

4. Complementary Systems: Consider developing multiple systems to suit different market conditions.

Shoutout and Thanks

Big thanks to Day Trading Zoo for the thought-provoking question. It’s feedback like this that helps us bring valuable discussions to the podcast. If you have a question or topic you’d like us to cover, reach out on Twitter or drop us a comment!