Position sizing is an important topic but many investors don’t give it much thought.
In this article I will look at a method that uses the RSI indicator to size positions. This means we can buy more when the price is low and buy less when the price is high.
Simple RSI-2 Trading Strategy
Before we look at the impact of dynamic position sizing with RSI we first need a trading strategy that we can use to compare our results.
For this, we will use a basic end of day mean reversion strategy based on the RSI-2 indicator.
Very simply, we are going to go long SPY when the RSI-2 indicator is less than 25 and we are going to sell when the RSI-2 is more than 50.
The Relative Strength Index (RSI) is a momentum oscillator that calculates the speed and change of a security’s movements.
The following results and equity curve show how the strategy has performed between 1/2000 – 1/2017:
Net Profit: $55,466.51
Max Drawdown: -23.82%
Win Rate: 68.09%
Risk Adjusted Return: 23.39%
# Trades: 445
Buy/Hold CAR: 4.51%
Buy/Hold MDD: -55%
As you can see, this simplistic system has done fairly well on SPY over the last 17 years and has beaten buy and hold.
However, the focus of this article is not the strategy performance but the impact of different position sizing.
RSI Position Sizing
To understand this, I will run the same test but this time the position size will be determined by the previous bar’s RSI-2 value. The formula we will use is as follows:
Percent of Equity = 50 – RSI(2)
In Amibroker, this can be written as follows:
pctPosSize = 50 - Ref(RSI(2),-1);
This formula means that our position size will change in response to how oversold the stock is. When RSI-2 is low we will buy more shares and when RSI-2 is high we will buy fewer shares.
As an example, if RSI-2 closes at a value of 10, we will use 40% of our equity (50 – 10) on the next trade. Likewise, if RSI-2 closes at 22, we will use 28%.
Since our buy rule requires RSI-2 to be under 25, and because RSI is a bounded oscillator, the highest our position size can go is 50% and the lowest is 25%.
The following results and equity curve show the performance using this approach:
Net Profit: $14,292.24
Max Drawdown: -6.94%
Win Rate: 68.09%
Risk Adjusted Return: 26.99%
As you can see, using RSI-2 for position sizing has reduced the annualised return and the overall net profit.
But did you notice that using RSI to set the position size has improved the risk-adjusted return?
Our drawdown has shrunk to -6.94% so we now have a risk adjusted return of 26.99% (as opposed to 23.29%) and a CAR/MDD of 0.39 (as opposed to 0.30).
In short, using RSI-2 for dynamic position sizing has given us a better return for each dollar invested. This means our money is working a little harder.
It also means that we have unused cash that we can use for another strategy.
I think this method works quite well in this example because using RSI for position sizing is in line with the strategy itself.
If you were to use this approach with a different strategy, such as a breakout system, then I’m not sure the results would be as good because the method doesn’t make sense.
Truthfully, I am not 100% sold on this method yet but I thought I’d share it as it could be powerful given the right conditions. It also opens up the opportunity to use other indicators in a similar way.
There are many different oscillators and indicators that could be experimented with. For example:
- Stochastic %D
The purpose of this article was not to provide a complete trading strategy but to show a different approach to sizing positions.
We have shown that the RSI-2 is a flexible indicator that can also be used to size trades.
Using the RSI in this way means we buy more when the stock is more oversold and buy less when the stock is less oversold.
I have found that this technique can work better than using a percentage of equity, fixed amount or ATR sizing.
I think it’s worth trying this out in your own time and seeing what you can find.
All simulations assume transaction costs of $0.01 per share and all trades are placed on the next open after the trading signal with no stop losses included.
Thank You For Reading
Joe Marwood is an independent trader and the founder of Decoding Markets. He worked as a professional futures trader and has a passion for investing and building mechanical trading strategies. If you are interested in more quantitative trading strategies, investing ideas and tutorials make sure to check out our program Marwood Research.
This post expresses the opinions of the writer and is for information, entertainment purposes only. Joe Marwood is not a registered financial advisor or certified analyst. The reader agrees to assume all risk resulting from the application of any of the information provided. Past performance is not a reliable indicator of future returns and financial trading is full of risk. Please read the Full disclaimer.