## How to Improve your Trading Strategy using R Multiples | Van Tharp

Measuring in R Multiples just makes sense. Let’s look at a couple of alternatives to investigate this bold statement further.

### Pips

Let’s say you enter 1 lot on EURUSD, and you make 100 pips. Is that good or bad? How do you know? If you risked, say 500 pips, it’s maybe not so good. Remember, pips are only a unit of measure. So they don’t really tell us much about the trade, only that it moved 100 pips in profit.

## Percentage

What about if you entered 1 lot on GBPUSD and it made 2%. Again, profit is nice, but at what risk did you make that 2%. If the SL was 10% and you have a 50% win rate, it won’t be long before the account is empty and you’re left scratching your head.

These basic concepts are missing an essential piece of information, risk. When looking at any trade or trading strategy, you need to consider the risk. Only looking at the returns side of the equation is not enough.

## This is where R Multiples comes in.

If you use R Multiples, you can gain further insight into a trades success with the risk you took to get there.

Using the two examples above. A 100 pip trade using 1 lot on EURUSD with a 500 pip SL is only 0.2R. In comparison, that same 100 pip trade is 1R using a 100 pip SL. The return is the same in pips but very different in R Multiples.

Another consideration is that in the pips example, the risk would equate to \$5,000 (\$10 per lot * 500 pips). In contrast, the risk with a 100 pip SL is \$1,000.

If the trade made 2% with an SL set at 10% in the percentage example, the corresponding R is again 0.2R. If you set the SL at 2%, the corresponding R is 1R. Using R Multiples, you can accurately gauge the risk to achieve a potential return.

The risk of these two percentage example trades would be dependant on your account size, but either way, the risk is five times greater using the 10% SL than the 2% SL. But why is this important?

## And why R Multiples?

Using R Multiples, you can set the lot size of your trade and level the risk across all trades. This allows you to compare two different strategies. It is fair to assume that a strategy that allows more risk could expect to have increased returns. This increased risk doesn’t mean that the more risky strategy is the better one to choose.

The take-home point of this post is that you need to find a way of levelling the playing field when comparing two very different trading strategies. Van Tharp’s R Multiples combined with Van Tharp’s Expectancy achieves this.

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:
https://www.darwinex.com/legal/risk-disclaimer

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.

## 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:
https://www.darwinex.com/legal/risk-disclaimer

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.