When Should Your System Trade? 8 Tools to Help You Decide
StrategyQuantPlugins
Determine when your strategy should trade using eight filters that identify performance cycles in your backtest results. Reduces trades and drawdowns while improving risk-adjusted returns by trading only during favorable conditions.
What-If analysis in StrategyQuant is like having a time machine for your trading strategies. You run a backtest, then ask “What if I had been smarter about when to trade?” These filters analyze your completed trades and show you what would have happened if you’d only traded during favorable conditions.
The core insight is brilliant in its simplicity: strategies go through hot streaks and cold streaks, just like everything else in life. During hot streaks, when your strategy is in sync with market conditions, you want to be aggressive and take every signal. During cold streaks, when your strategy is fighting current market conditions, you want to step back and wait for better times.
These eight filters represent different ways to identify and respond to these performance cycles. Some look at momentum (are we making money consistently?), others look at volatility (are we making money smoothly?), and still others look at more sophisticated concepts like acceleration (are we making money at an increasing rate?) or statistical boundaries around performance.
What makes this approach so powerful is that it’s completely objective. There’s no emotion, no second-guessing, no “this time is different” thinking. The filters simply analyze the mathematical properties of your equity curve and make binary decisions: trade or don’t trade.
Select What IF – Strategy Performance Management: 8 Tools to Control When Your System Should Trade
What IF – Strategy Performance Management: 8 Tools to Control When Your System Should Trade
And here’s the kicker – these filters only use information that was available before each trade. No future peeking, no hindsight bias, no cheating. They just make smarter decisions about when your strategy should be active versus when it should be dormant.
The result? You typically end up taking fewer trades but with dramatically better risk-adjusted returns. Your drawdowns shrink, your win rates improve, and your overall performance becomes much more consistent and tradeable.
You know me as clonex. I’m Ivan Hudec — I built the free blocks you’re using here, and I write about the research behind them at algotrading.space. Need something built? Work with me.
