Correlation Portfolio Diversification

The Van Tharp SQN and other performance comparison metrics

The Van Tharp SQN is a trading metric designed to ascertain the quality of a trading strategy. Now, there are many ways to do this. You can use

  • Total returns
  • Profit Factor
  • D-Score
  • Risk-reward ratio
  • Sharpe ratio
  • Win ratio & Avg win/loss
  • Max drawdown

The list could go on, but you get the idea, there’s a lot. So what makes The Van Tharp SQN any different or even better than any on the above list. This post looks at the following four metrics and dukes them out against each other.

  1. Profit Factor
  2. Sharpe Ratio
  3. The Van Tharp SQN
  4. D-Score

1. Profit Factor (PF) = (gross profits/gross loss) 

A friendly and straightforward calculation that provides a gauge of the overall performance of a trading strategy. Above 2 is excellent. Below 1 is bad. Between 1 and 2 the trader is somewhere between breaking even and making a small profit.

PF doesn’t factor in the length of the trading history. Suppose you had closed two trades, a win of $50 and a loss of $25, the PF = 2. Two trades are hardly a good indication of the robustness of the trading strategy. 

2. Sharpe Ratio (SR) = (excess return/StdDev of returns)

This metric, created by American Economist William Sharpe in 1966, was designed to measure the risk-adjusted performance of a trading strategy.

Whilst it takes into account the Std Dev (Standard Deviation is the observed deviation from the sample’s mean) of returns. It makes some assumptions in its calculation which can be problematic. You can find more info on this here.

Therefore, the Sharpe Ratio is more involved in its calculations but still doesn’t comprehensively address the problems in its design.

3. SQN = (Expectancy/StdDev(R-Multiples) * sqrt number of trades (capped at 100))

The Van Tharp SQN looks to solve some of the problems with the PF and SR metrics above. Using a time input, The Van Tharp SQN looks to prioritise trading strategies with a more extended and, thus, more reliable trading history. 

By also using the StdDev of R-Multiples, it also aims to prioritise consistency in returns. The idea behind this is that a trading strategy with better consistency in its returns will be more reliable. 

4. D-Score = As this is a proprietary tool created by Darwinex for use on the Darwinex platform, the exact formula is not available. 

The D-Score looks to rank the quality of the returns of a DARWIN over the last five years. It looks at factors such as positive returns over the medium term, the momentum of returns and some of Darwinex’s other Investible Attributes. 

It is easily the most comprehensive performance metric here. The downside is that it is only available for use on the Darwinex platform, but given the breadth of assets you can trade on the Darwinex platform, it really adds value. The upside is that Darwinex has done all the hard work to create trading metrics that allow you to make informed decisions.

Back to The Van Tharp SQN

Given that the primary use of The Van Tharp SQN is to measure the quality of a trading strategy and thus allow an accurate comparison of different strategies.

Only the D-Score beats it. But as the D-Score isn’t available for use outside of Darwinex, the SQN offers a healthy alternative. 

Unlike PF and SR the SQN considers the size of the data set as a determining factor of the quality of the result. Another thing that makes the SQN interesting is that Van Tharp amends the formula to account for vastly different size data sets. 

If he can do it, what’s to stop you from doing the same. Much of trading is trial and error. You could explore a tiered system. Whereby you set different thresholds for different quantities of data.

You can even use the Van Tharp SQN to consider the benefits of varying trading parameters during your optimisation process. 

Why not have a play with the formula, and let us know how you get on @Darwinexchange 

Brought to you by Darwinex: UK FCA Regulated Broker, Asset Manager & Trader Exchange where Traders can legally attract Investor Capital and charge Performance Fees.

Risk disclosure:

Content Disclaimer: The contents of this video (and all other videos by the presenter) are for educational purposes only, and are not to be construed as financial and/or investment advice.

portfolio diversification strategies

Free Backtesting and Optimization Education and Tutorials Video Series

Does Free Backtesting Education really exist? Is it any good? Do I need to spend hours sifting through uninsightful videos to find what to focus on?

In short, no. Today’s video gets straight to the point and highlights the most insightful content in the Backtesting and optimization playlist.

Like the previous video, which focused on algo trading for a living, today’s focus is on

Backtesting and optimization.

Episodes 1 & 2. This mini-series made up of 8 videos shines a light on how to extract the best parameters from your backtest to implement in your live trading. This is no easy task, hence the length and detail of information in this mini-series.

Episodes 3 – 6 consist of 11 videos and covers the critical topic of over-fitting. When looking at your backtest results, it’s important not to cherry-pick the one with the greatest return. Over-fitting your backtest to historical data is dangerous, as this part of the series covers this in great detail.

Episode 7 delves into the world of Walk Forward Optimisation. Walk Forward Optimization is a way of optimizing parameter selection without overfitting. This is thoroughly recommended viewing.

Episodes 8 & 9 expand on lessons learnt in episode 7 and build on this by getting to the heart of the optimization process. It does this by looking at, amongst other things, compelling optimization profiles. This is important because it highlights how to deal with unpredictable results and outliers.

Episode 10 covers performance metrics and how to use these metrics to increase your backtests robustness further and, as such, improve your live results.

Then, episode 11 analyses price data models used in backtesting processes. This episode helps select the most appropriate settings to use in Backtesting. These should closely resemble that of live trading.

If you were to use wildly different price data, your backtest would not be a fair representation of what you can expect to see in live trading.

The remaining 13 videos help tie all the above together. By the end of this video series, you will have a deep and powerful insight into some best practice techniques for handling price data, selecting parameters and optimizing your strategy ready for live trading.

Monitoring your live strategy

Additionally, you can monitor the performance of your trading strategy by using some of the Darwinex Investible attributes and performance metrics.

If you look at the Open Strategy (Os) & close Strategy (Cs) Investible attributes, these rate the performance of your strategies opening/closing trades compared to if it opened/closed earlier or later.

You could use this information to see if the market has changed and if the edge you researched previously still holds up, or if it has started to decay.

The Darwinex platform has a wealth of tools to help the trader and investor alike. Feel free to have a play, and if you need further clarification on anything on the Darwinex platform, don’t hesitate to get in touch.

Brought to you by Darwinex: UK FCA Regulated Broker, Asset Manager & Trader Exchange where Traders can legally attract Investor Capital and charge Performance Fees.

Risk disclosure:

Content Disclaimer: The contents of this video (and all other videos by the presenter) are for educational purposes only, and are not to be construed as financial and/or investment advice.