System Quality Number (SQN): A Deep Dive for Crypto Traders

System Quality Number (SQN): A Deep Dive for Crypto Traders

Definition

Imagine you're building a house. You wouldn't just look at how much the materials cost; you'd also consider the quality of the materials and how well the house is built. The System Quality Number (SQN) does something similar for trading systems. It's a metric that tells you how good a trading system is by looking at its profitability, consistency, and how well it manages risk. It’s a measure of the overall quality of a trading system, providing insights beyond simple profit expectations.

Key Takeaway

SQN is a critical metric for evaluating and comparing trading systems, measuring their profitability, consistency, and risk-adjusted performance.

Mechanics

The SQN is calculated using a specific formula, incorporating several key elements:

SQN = √(Number of Trades) * (Expectancy / Standard Deviation of R-Multiple)

Let’s break down each component:

• Number of Trades: This is simply the total number of trades the system has taken over a specific period. A larger sample size (more trades) generally leads to a more reliable SQN.
• Expectancy: This represents the average profit or loss per trade, expressed as a ratio of the risk taken (R-Multiple). It's calculated by multiplying the win rate by the average profit of winning trades and subtracting the loss rate multiplied by the average loss of losing trades. It tells you, on average, how much you expect to make (or lose) per trade. For example, if your expectancy is 0.2, you expect to make 0.2 units of risk for every trade.
• Standard Deviation of R-Multiple: This measures the volatility or dispersion of the R-Multiple values around the expectancy. It indicates how consistent the system's results are. A lower standard deviation suggests more consistent results, while a higher standard deviation indicates greater variability in profits and losses.
• R-Multiple: The R-multiple is the profit or loss of a trade expressed as a multiple of the initial risk. For example, if you risk $100 on a trade and make $200, the R-Multiple is +2R. If you lose $50, the R-Multiple is -0.5R. The R-Multiple standardizes the results across all trades, regardless of the initial risk amount. This is a critical component for evaluating the performance of a trading system.

The formula essentially weighs the expected profitability (expectancy) against the risk and consistency (standard deviation), while also considering the number of trades (sample size). This provides a comprehensive view of the trading system’s quality.

Trading Relevance

SQN is highly relevant for crypto trading because it helps you answer critical questions:

• System Evaluation: Is your trading strategy actually good? SQN gives you a number to measure and compare different strategies. A higher SQN generally indicates a more robust and consistent trading strategy.
• Strategy Comparison: Which of your trading strategies is the best? By calculating the SQN for each strategy, you can objectively compare their performance and choose the most promising one.
• Risk Management: A high SQN system is often more suitable for aggressive position sizing strategies, as it indicates a system with a history of consistent and profitable performance. Conversely, a low SQN system may require more conservative position sizing to manage risk effectively.
• Trend Quality: SQN can also be applied to assess the quality of trends in the market. A high SQN in a particular trend suggests the trend is strong and potentially more reliable for trading.

Here's a general interpretation of SQN values:

• < 1.5: Probably Very Hard To Trade.
• 1.51 - 2.0: Average System (Should Be At Least 1.7).
• 2.01 to 3.00: Good System.
• 3.01 to 5.00: Excellent System.
• 5.01 to 7.00: Super System (Few Exist).
• 7.01 Or Higher: Holy Grail System.

Risks

While SQN is a valuable tool, it's essential to be aware of its limitations:

• Sample Size Dependency: SQN is more reliable with a large sample size of trades. A small number of trades can lead to misleading results.
• Historical Data Bias: SQN is based on past performance. Past performance is not indicative of future results. Market conditions can change, and a system with a high SQN in the past may not perform as well in the future.
• Over-Optimization: Do not optimize a trading system solely for a high SQN. Over-optimization can lead to a system that performs well on historical data but fails in live trading.
• Does Not Guarantee Profit: A high SQN does not guarantee profits. It only indicates the quality of the system. You still need to manage your risk and execute trades discipline.
• Requires Accurate Data: The accuracy of the SQN depends on the quality of the data used in its calculation. Incorrect data will lead to incorrect SQN values.

History/Examples

The SQN was developed by Dr. Van Tharp, a renowned trading coach and author of Trading Systems and Methods and The Definitive Guide to Position Sizing. Tharp's research highlighted the importance of measuring not just profitability but also the consistency and risk profile of a trading system. The SQN provides a more comprehensive view of the system’s overall quality.

Let’s imagine a hypothetical scenario:

• System A: Has a win rate of 40%, an average win of 2R, an average loss of -1R, and a total of 500 trades. After calculations, this system has an SQN of 2.5.
• System B: Has a win rate of 60%, an average win of 1.5R, an average loss of -1R, and a total of 500 trades. After calculations, this system has an SQN of 1.8.

In this example, System A, despite having a lower win rate, has a higher SQN because of its better risk-reward ratio and consistency. This demonstrates how SQN considers not just the win rate, but also the profitability and risk-adjusted performance of a system.

SQN provides a more comprehensive view of the system’s overall quality.