Common Trading Mistakes
For most traders, strategy development is simply a means to an end---a necessary path toward profitability. However, the process of building a trading system is often more challenging than its eventual execution. While live trading can be exciting, traders are prone to taking shortcuts during system development, leading to potential errors. Although these shortcuts may simplify the process in the short term, they often undermine performance when the system is applied in real-time. Fortunately, many of the mistakes made during strategy development are common and correctable once identified. That said, addressing these issues can be difficult. Without shortcuts, the path from strategy creation to execution becomes more complex. Here, we will explore three common mistakes traders make and explain their potential negative impacts, how to identify them, and ways to fix them. Avoiding these mistakes will help you build a more robust trading system.
Mistake 1: Overcomplicating Your Strategy
In trading, it is easy to encounter overly complex strategies. Discretionary traders may fall into “analysis paralysis,” where charts are cluttered with multiple technical indicators, trendlines, and support/resistance zones. Algorithmic traders, on the other hand, may develop systems with thousands of lines of code, adjusting dozens or even hundreds of variables. Both approaches are marked by unnecessary complexity, leading many inexperienced traders to assume that more indicators and rules will yield better results. This, however, is a misconception.
A strategy that performs well on historical data does not guarantee success in live trading. Continuously adding indicators or rules to fine-tune a strategy might create a false sense of security. The reality is, simpler systems often outperform complex ones. For discretionary traders, clean charts with just one or two indicators, combined with a deep understanding of price action, tend to be far more effective than overly cluttered setups. Similarly, in algorithmic trading, a straightforward entry rule often performs better than one requiring five to ten conditions.
Mistake 2: Ignoring Market Friction
When comparing different trading systems, you’ll often notice that many omit commissions and slippage from their calculations. Similarly, traders developing their own systems frequently underestimate or entirely overlook these costs. The reasoning is often that “commissions vary by broker” or “my system only uses limit orders,” but in reality, excluding these factors makes the system appear more profitable than it truly is. Once trading costs are accounted for, profitable systems are much harder to find.
Consider a system trading E-Mini S&P 500 futures (CME
), designed to capture small market movements through frequent intraday trades. Without factoring in commissions and slippage, this system might generate 20 trades per day, each averaging $15 in profit, for a total of $300 daily. However, after accounting for $5 per round-turn commission and one basis point of slippage, the $300 daily profit turns into a $50 loss.
This highlights a critical issue: systems that ignore trading costs can lead traders to overtrade. For instance, a system that executes 20 trades a day may appear better than one that trades just once daily. Yet, after factoring in commissions and slippage, the opposite may be true, and the lower-frequency system might be the only one generating actual profit. Therefore, incorporating realistic cost estimates early in system development is essential.
Mistake 3: Testing on All Historical Data
Another common mistake traders make is testing their strategies on all available historical data. Many inexperienced traders optimize their strategies using the full dataset, believing this ensures the strategy adapts to recent market conditions. If initial tests fail, they add more rules or filters, often overcomplicating the system. Eventually, they might develop a seemingly successful strategy, but when applied to live trading, it often falls short.
A more effective, though challenging, approach is to validate the system using out-of-sample data. For example, a trader might use the past 10 years of data to develop a strategy, while setting aside the most recent year for testing. Once the system is built, it can be tested on the unseen data. If it performs well, the system is likely ready for live trading. Another method, known as moving-window testing, uses multiple out-of-sample periods, providing a more robust validation.
However, it’s important to note that once out-of-sample testing is conducted, any further tests will no longer use “true” out-of-sample data, as it has already been incorporated into the development process. Despite this limitation, out-of-sample testing remains a superior approach compared to optimizing on all available data.
No Shortcuts in System Development
Designing a viable trading strategy is a complex and challenging process. Many traders never achieve this because they take shortcuts or make overly simplistic errors during system development. Adding extra rules or ignoring the impact of commissions and slippage may make it easier to create a seemingly profitable system, but these strategies often fall apart in real trading environments. Additionally, optimizing systems on all available data can result in strategies that only succeed within the specific time frame tested.
The key takeaway from these common mistakes is that if a method simplifies system design or drastically improves backtest results, it may be a warning sign of underlying issues. A well-designed trading system is difficult to achieve, but avoiding shortcuts and common pitfalls is crucial to long-term success.