At AQTIS, we talk a lot about the unique quant strategy we have built. But if you’ve ever wondered what it is, how it works, and how we use multiple different strategies in one holistic trading ecosystem, this is the blog post for you.
In the last post from our quant team, we looked at the historical performance of our quant strategies across Bitcoin and Ethereum.
This time we’re diving deep into Solana. In this post, you’ll find trading data from our trading strategies over the last four years and how each one performed.
As always, if you have any questions, don’t hesitate to reach out on Twitter, Telegram, or Discord.
AQTIS Trading Strategies Explained
The market has four main phases: Momentum, breakout, trending, and mean reverting. AQTIS has covered all of them with different strategies and can be deployed both on bull and bear sides.
The first 3 are highly correlated in their entries, trying to find a directional movement in one side of the market, and betting on his continuation. Each one has his own short-term, mid-term, or long-term target.
The fourth strategy identifies when a move in one direction might be ending and is over-extended and there’s an opportunity to take a position that exploits this shift.
- Momentum — The goal is to find early directional movements and get in as soon as possible.
- Breakout — Once the market structure changes, you get in and bet in continuation in the same direction. Also, it’s used in price discovery after a new ATH.
- Trend — Identify sustained trends and access the market in the same direction. Our swing strategy uses a wide invalidation.
- Mean Reversion — Identify when the price is over-extended and the volatility is peaking, help us to buy cheap or sell expensive.
Momentum and Trend on the bullish market are the money markers. These strategies are the ones that generate wealth in our portfolio.
The bearish strategies help our system to reduce the drawdown periods and generate yield in bear markets.
But we need market exposure while the market is not trending, using uncorrelated strategies.
So, we’re playing the market level by level using breakouts and mean reversion strategies. These both models help us to stabilize the returns, lower the portfolio volatility, and reduce the drawdown periods.
How does the AQTIS Quant generate yield in bear markets?
We have created these strategies to apply both in bull and bear markets. While the performance in a bear won’t match that of a bull cycle, these bear market strategies help us reduce drawdowns and generate yield in that market phase.
How does AQTIS create stable returns across different market phases?
We use strategies with faster entries and exits to generate yield while the market is not in a clear trend. Below we explain how each of these strategies works in non-trending markets in shorter time frames.
Breakout, mean reversion, and pair-trading strategies are used to stabilize returns, lower portfolio volatility, and reduce drawdowns.
- BREAKOUT — Once the market structure has changed, get in and bet on continuation in the same direction.
- MEAN REVERSION — Identify when the price is over-extended and the volatility is peaking, helping us to buy low and sell high.
- PAIR TRADING — Trade different assets with high correlations and apply mean-reversion strategies in opposite directions at moments where the correlation between the assets decreases.
For clear trend markets, we get the performance mainly with momentum and trend-following strategies.
- MOMENTUM — Identify prices that are in a positive momentum and access the market in the same direction.
- TREND — Identify sustained trends and access the market in the same direction.
You can see the performance figures of these strategies in the sections below.
How do we combine the different strategies together?
AQTIS splits the funds across different sub-accounts for each strategy. The goal is to be robust and minimize the drawdown and the volatility of the portfolio.
If targeting only long-term profits, the “the trend is your friend” and bull strategies might create better performance. However, they often incur longer drawdowns in bear markets. To compensate, AQTIS also implements strategies for not trendy and for bear scenarios.
Added to the different strategies, AQTIS has implemented a regime filter to identify the main market direction and automatically rebalance the funds between the different sub-accounts.
This allows us to trade different market scenarios optimizing capital use.
Making sense of our Quant Performance Report
Before we dive into how the Quant tech performed across Bitcoin and Ethereum over the last four years, there are certain key performance concepts we use to explain what happened during the test phase.
- PnL — The total profit/loss percentage of the strategy over the whole time period.
- Max Drawdown — This measures the maximum fall in the value of the investment as given by the difference between the value of the lowest trough and that of the highest peak before the trough.
- Annualized Volatility — Annualized volatility measures how much volatility — and risk was involved — in that particular strategy over the course of a year.
Each chart below has these numbers as well as an explanation of each strategy in turn.
Solana: All Strategies Combined
In this chart — feel free to click to make it bigger — you can see the sum total of all our strategies working together for Solana from January 2020 to April 2024.
What we can see here is that Momentum and trend strategies are pushing the biggest returns, while Breakout strategies delivered more consistent, stable returns.
Mean reversion strategies shined during lateral and over-extended markets. Combined, our strategies allowed us to take advantage of both bull and bear markets.
Note: We are omitting low time frame mean reversion strategies due to complex execution using Orderflow and advanced orders in real-time.
We have collated the performance of each strategy into the below chart.
Quant Performance FAQs
Below are some of the most frequently asked questions we have received from the community about the quant tech.
When Quant strategies generate huge returns, how does this benefit the AQTIS Ecosystem?
These figures refer to the total funds used in the Quant, not all the LST funds. Secondly, the excess yield will be used to enlarge the ELIF to guarantee the long-term stability of the whole Ecosystem.
How are the different strategies combined to get an aggregated yield for all of them?
AQTIS splits the available Quant funds between the different strategies. When the report shows a yield for a certain strategy, that yield is only applied to the proportion of funds allocated to that strategy.
How is AI used in the quant process?
The AQTIS quant process implements well-known systematic trading strategies that are improved with Machine Learning models that are able to estimate yield and risk for each new operation. In summary, AQTIS is based on a solid and well-known systematic process that is widely improved by AI techniques, specifically Machine Learning.
Does AQTIS have plans to improve the actual Quant process?
AQTIS plans to improve the Quant process to be even more stable and with better yield by implementing new strategies and improving the learning capabilities with enlarged data analysis in our Machine Learning process.
With so many strategies. Can two of them point to a different direction? What is done in that case?
The reason we use multiple strategies is to have sustainable yield with controlled risk. The goal of our quant is to maintain good performance with limited risk. Trying to maximize the performance in certain scenarios would mean we take on excessive risk. This doesn’t align with our goals.
Our system can trade the same assets in different directions when we trade different timeframes. For example, we can have a long in our trend-following “swing” system and get a mean reversion signal.
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That’s all for now. Stay tuned for more exciting updates, and we’ll catch you in the next one!
