A strong on-chain tool on your investing toolbox

Before we start, we asked Chat GPT to elucidate the DCA+ white paper just like the reader was 5 and that is what it returned.

ELI5:

Investing is like playing a game along with your money. Sometimes you’ll be able to win and get more cash, but sometimes you’ll be able to lose and have less money. One investing game people wish to play is named cryptocurrency. It’s like digital money that you may use to purchase things, but it could actually be really tricky to know when to purchase and sell since the value goes up and down loads.

There’s a special technique to play this game called Dollar-Cost Averaging (DCA), which implies you invest somewhat little bit of money occasionally, regardless of what’s happening in the sport. This helps you not lose an excessive amount of money when the sport will not be going well.

But, there’s an issue with DCA — you would possibly not win as much money as you might. So, some really smart people made a recent technique to play the sport called DCA Plus. DCA Plus uses a special computer program that appears at how the sport goes and helps you make higher decisions about when to purchase and the way much to purchase. This implies you’ll be able to win more cash while still being secure and never losing an excessive amount of money.

It’s like having a helper who watches the sport and tells you what to achieve this you’ll be able to win more. And, it’s very easy to make use of, so that you don’t should worry about doing anything complicated. With DCA Plus, you’ll be able to play the sport and have more fun while also winning more cash!

Introduction

The dollar cost averaging strategy is all about reducing your risk of losing capital. We intuitively feel — perhaps more-so in crypto than every other market — that deploying all our capital in a single lump sum is incredibly dangerous. If the market goes down 10% in a day, so does our net price. If it goes down 90%, it’s a really painful experience.

Investors will probably want to reduce their risk, but there’s a value for doing so. That cost is missing out on potential gains. Just as crypto can tank 90%, so can also it pump 100%… maybe even 100x! And when it does, for those who’ve only got somewhat skin in the sport, then your mate who aped in all his funds a month ago will likely be laughing.

To create a buying strategy that increases potential gains relative to plain DCA without sacrificing the favourable low-risk profile that standard DCA offers. Same risk, higher gains.

The journey to our final DCA+ solution starts with an overview of how standard DCA reduces risk. It does so by increasing the sample size of buy prices such that the boldness interval across the difference between your average buy-price and the present price is reduced. In other words, the more times you purchase, the lower the likelihood that each single certainly one of those buys will likely be way above the longer term price.

That is, after all, an indirect way of managing risk. Could we go straight for the jugular and as an alternative assess risk more directly and reply to it accordingly? During research and development, we followed this line of questioning and a recent (so far as we all know), previously unexplored type of temporal asset allocation emerged: we term it ‘risk averaging’.

It really works like this: you are taking some data and process it in some technique to establish a risk rating. You compare this risk rating to the long-term average risk, after which use this ratio to change your ‘usual’ buy-amount. More formally, the dollar value (‘buy-amount’ from here onwards) of your purchase is calculated in accordance with:

Where R is the chance rating, which is calculated as an exponentially weighted moving average over a variable variety of days, X, set in accordance with the user’s desired strategy duration. The r value on any given day might be calculated as follows:

B-dot is the USD value of a purchase order that might have been made in accordance with a Standard DCA strategy:

And at last, r-hat is the long-term mean risk, which is decided empirically relatively than a long-term average of algorithmically generated scores.

The upper-and lower-bounds of R is moderated by the accuracy of our risk assessments (read on for more about this), however it ranges between 0 and 1, meaning that buy-amount is infinite to the upside and at all times greater than 0. This is very important because our research shows that skipping buys, even when markets look dangerous, runs an enormous risk of missing out on a continued bull-run and further gains.

So, to summarise, in accordance with our Risk Averaging formula, if risk (R) is lower than expected (r-hat), you purchase more. If it’s higher than expected, you purchase less.

Theoretically, ‘risk averaging’ could tackle as many various forms as there are definitions of risk (there’s lots!), and we’re definitely excited by the prospect that we’re opening up recent avenues for others to explore fruitful alternatives to the classic DCA strategy.

Nonetheless, with the range of possible definitions of risk, there’ll come a wide range of performances when applying the strategy. And that is where the majority of our research has been directed. It’s also where our secret sauce lies. We will’t spill all our secrets but suffice to say that statistical tools and machine learning techniques have gotten increasingly powerful and there may be a growing body of literature demonstrating their effectiveness in identifying patterns in asset markets and forecasting certain trends. And thru DCA+, we’re putting our state-of-the-art proprietary risk assessment criteria in your hands.

So, for those who wrap our proprietary risk assessment algorithm into the Risk Averaging strategy, you get DCA Plus: the machine-learning-driven, set-and-forget strategy for all market conditions. The query is, how does it perform?

We’ll let the numbers do the talking.

Throughout the duration of our backtesting window (on ‘unseen’ data), the DCA Plus strategy was consistently capable of accumulate more tokens than its Standard DCA counterpart. You’ll be able to see that the DCA Plus strategy performs particularly well in the course of the run up of a bull market, for the reason that algorithm has suggested higher buy-amounts in comparison with Standard DCA when perceived risk was low. For strategies starting within the window 2020–2021, DCA Plus provided, on average, a whopping 50% higher returns than Standard DCA. At its best, DCA Plus outperformed Standard DCA by over 100%!

Similarly, DCA Plus is capable of outperform when leading into the bear market, since risk is appropriately perceived as ‘high’ by the algorithm and subsequently less capital is deployed at the highest. The figure below compares the 2 approaches for trials starting within the six month window between August 2021 and February 2022.

And risk? As you’ll be able to see within the figure above, the worst loss achieved by standard DCA was nearly 60%. The worst loss by DCA Plus? 56.4%. It’s arguably less dangerous!

Mission completed! But how confident can we be in these back-tests? Machine learning algorithms are exceptional at pattern recognition, often outperforming humans. Every assessment, nevertheless, should be taken with a grain of salt — the algorithm cannot predict large-scale hidden fraud or SEC regulatory intervention. Does this mean we should always discard our predictions? No! As an alternative, we assess size of the grain of salt — our confidence — and use it as a key metric when moderating buy-amount. We’ve been conservative here: confidence in our algorithmic assessments was defined using a 50:50 test/train data split. Which means we decided how accurate our risk assessment is by testing it on about 3 years of ‘unseen’ data. Although prediction accuracy could be higher within the trained algorithm that has ‘seen’ the whole dataset, we’re taking the secure road.

So how will DCA Plus work? Under the hood, we’ll be scanning a wide range of market-related metrics on a each day basis. Every day, the fresh set of values is input into the trained model and an assessment of risk is generated. It will initially be held off-chain though we’re investigating avenues for training models on-chain. As mentioned earlier, the chance rating will then be in comparison with a long-term mean and this final rating will likely be used to moderate / alter regular buy-amount.

And what’s going to you should do? Not much. Simply input how much you’d like to speculate and the timeframe over which you’d like to speculate and the algorithm and smart contract will do the remaining. After all, since buy-amount each interval relies on market conditions, the duration of your actual DCA might be longer or shorter.

In terms of selecting a timeframe over which to speculate, we restrict the minimum duration to 30 days. It is because, consistent with our goal — to help long run wealth generation — we’ve tailored our model to perform well across a spread of market conditions. The sort of timeframes over which a market switches from bear markets to bull markets are measured in weeks to months, and that is subsequently the sort of time horizon on which our model has been fine-tuned to supply its most accurate assessments of risk. Our goal was that users is not going to should change their strategy when the market turns. DCA+ is designed to be a really set-and-forget strategy for the entire market cycle. It takes the place of your friend who keeps you updated when the main things are happening. It’s your eyes and ears available on the market all year long, without having to make use of your individual eyes and ears. It does what you’d do, without you having to do it… and perhaps even with barely higher pattern recognition skills 😉

After all, we all know that many individuals decide to DCA because they often don’t have lump sums able to deploy. For these users, we’ve top up options for our DCA+ strategies. You’ll simply determine whether you’d wish to extend your strategy through time (keep your ‘regular buy amount’ constant but make the strategy go longer) or increase your regular buy-amount (and keep your expected finish time constant).

We’re incredibly excited to place DCA Plus in your hands. But we’re not done here. With more data comes more accurate assessments of risk and thus higher performance. We’ll proceed to update and train our model commonly, and ultimately we hope to maneuver this on-chain.

Moreover, we’re within the research phase of understanding the results of varied transformation functions acting on our risk parameter ‘R’, such that selling could occur at high-risk times. More will likely be announced when the time is true.

Be sure you follow our progress on CALC’s twitter!

And visit the Calculated Finance dAPP: https://app.calculated.fi/