Transcription
Hey everyone, welcome back! One of the coaches of Unger Academy here, and today we're going to be talking about a different type of position sizing called Volatility Position Sizing.
Alright, so as mentioned in the intro, we’re going to be talking about Volatility Position Sizing.
The most common thing a trader does, also because it is the most natural thing to do when starting to develop trading systems, is to use a fixed size, such as one contract for the S&P 500 future, one contract for the DAX future, or a fixed amount of $10,000 for stocks. This is because it's the most spontaneous and perhaps most immediate thing to do.
However, there's another approach, which is what we're going to discuss today. Before going into the details of how to code it, let's just take a quick look at the volatility of an underlying asset..
Let's take Bitcoin for example and add to the chart the Average True Range, which is an indicator used to measure market volatility, in this case over a 14-day time frame.
What does this indicator tell us? It tells us that on average Bitcoin over the last 14 days, at this stage of the market, has fluctuated by $2,800. This means that in this market phase if I had bought 1 Bitcoin, I would have seen my account swing of $2,800 on average.
In contrast, today we're experiencing a much quieter market phase. And indeed we see that the average swing is $1,600. Let's go back a little bit more into the past. We can see that there have been even more volatile phases. In this case, we have Bitcoin showing a daily swing that is close to $4,000.
This is the first aspect to think about because in case I invested a fixed amount of money, let's say $10,000, if I still wanted to buy $10,000 worth of Bitcoin, I would find myself, in a market phase like this, with larger fluctuations in my account. Why? Because the intraday swings are greater so my account could swing a lot more with the same amount of capital invested than it does during this market phase where Bitcoin is much calmer.
This means that the risk I'm going to take with my investment is different. Not only that. Let's suppose that I want to apply a stop loss to my strategy, which is right to do.
The stop loss that maybe is adequate for a low volatile market phase, could be inadequate in a phase where the market moves a lot and Bitcoin shows really large intraday swings.
So, I'd run the risk of having a stop loss that is correctly calibrated for a low volatility phase, while it would be too tight for a high volatility phase.
Well, this kind of problem is solved by the concept of Volatility Position Sizing. What does this kind of approach to the calculation of the trading size imply for us?
Well, it tells us that the more volatile the market is, the less I'll invest. Vice versa, when the market isn't very volatile, I can afford to invest a greater amount of money, because the market at that moment shows a lower risk.
In general, this is also a common-sense rule. Think about buying an e-Mini S&P 500 future during Covid and think about doing the same thing during a low volatility phase. I mean the two risks are not comparable.
The same reasoning also applies to the stop loss. In a phase of high volatility, I'll need a wider stop loss because the market swings are greater, there's more market noise and a stop loss that is too tight would make me prematurely exit the trade.
So, let's try to apply a Volatility Position Sizing formula to a very simple trading system that works on cryptos. Why cryptos? Because thanks to their granularity, cryptos are well suited to money management strategies.
What does our system do? It buys the next day, as we're working on daily bars, at today's close plus a true range, so a daily swing to simplify. And it will close all trades following a monetary stop loss or at the end of the day.
Let's go look at the results of this simple system. Let's go back to our chart. We'll check that the Volatility Position Sizing is inactive, so we'll set it to zero. We'll put a stop loss let's say at 5%. Maybe it is a bit wide, let's see the results.
So, here's the result of the system. It's still a good result. The profit factor, which is the synthetic indicator of the profitability of the system, gives us a value of 1.76, which is certainly a good value.
Let's try now to apply a Volatility Position Sizing strategy, which means that we'll buy many variable contracts inversely proportional to volatility. So, when volatility is high, we'll buy less, when volatility is low, we'll buy more.
This allows us to do two things: one, to risk less in the highly volatile phases of the market and second, to have a stop loss that is more or less always adjusted to the market volatility.
Let's look at the result of our trading system. The profit factor rises to 2.08. Equity is nice, too. So my impression is that by using the variable size, the system tends to perform better. And this isn't only on Bitcoin but also on other cryptos on which I've tested this trading system.
Let's now take a look at this simple Volatility Position Sizing formula. What does it do? It calculates my trade size based on the monetary risk I want to take daily, say $500, and divides it by the Average True Range of the underlying asset multiplied by its Big Point Value.
Let's make an example. Let's say Bitcoin moves $2,000 per day on average and I want to risk $2,000. I'll buy one Bitcoin. Instead, Bitcoin, at this stage of the market, is moving $4,000 per day. So, I'll buy half a Bitcoin.
At this point, I'd suggest applying it to other cryptocurrencies. Let's try to apply it to ADA and look at the optimization report. I've optimized the stop loss and the monetary risk subject to my size.
Let's order by profit factor which, as anticipated, is the synthetic indicator of the earning capacity of my system. We can see that the highest profit factors all have the volatility position sizing equal to 1, which is activated.
So, we'll go from 2.19 down to the lowest value of 1.80. Then, once the position sizing is removed, the profit factor drops from 1.62 to 1.38.
Let's now look at the same report for another crypto. Let's take Ethereum for example. Let's take a look at the optimization report. Let's order the results by profit factor, from the highest to the lowest.
Here, you can see the highest ones all have a Volatility Position Sizing value equal to 1, which means the Volatility Position Sizing is active. We go from 2.50 to 2.24 and these are still good values.
Finally, let's perform the same test also on the BNB. Let's look at the optimization report that I've already prepared. Here it is. Let's order by profit factor. The highest is 2.55 and the Volatility Position Sizing is active. So, we go from 2.55 to 2.36. While without the position sizing we go from 2.30 to 2.06.
So, the test is fairly uniform for all cryptos. There seems to be an improvement by using, instead of a fixed monetary size, a size that is calculated based on the volatility of the instrument.
It's a concept that I also use in my live trading, so my advice is to try and test these different types of formulas and you’ll see that there's a wide variety of them. We've only seen one example in this video. Because there are indeed benefits in working in this way in the cryptocurrency market, which is one of the few markets that allows for enough granularity to fully apply the Volatility Position Sizing and money management formulas.
And with that, this video is over!
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