Daniel Calugar Presents Optimization Techniques for Enhancing Algorithmic Trading Performance
Algorithmic trading has already become a major driver in the investment world, and it’s only expected to become more prevalent in the years ahead. Today, some estimates say that anywhere from 60 to 73 percent of U.S. equity trading is executed via algorithmic trading.
There’s no denying the popularity of algorithmic trading and the benefits that it can provide to investors who are able to harness its power.
The algo trading market places a great deal of emphasis on the research and analysis that helps investors land on the specific strategies they want to employ. They then use this information to focus on the production, development, and testing of the algorithms themselves.
One aspect of algorithmic trading that can sometimes be overlooked is that traders can — and need to — consistently fine-tune their algorithmic trading strategies in order to achieve better performance.
Below, Dan Calugar will dive deeper into this topic and provide practical tips to enhance trading outcomes and minimize risks.
How and Why to Monitor Your Trading Algorithms
The market is constantly changing. That’s one of the reasons algorithmic trading is so beneficial: It’s able to conduct millions of analyses instantaneously and then execute trades at just the right time. In other words, algo trading is able to do what no person could ever do manually.
The fact that the market is constantly changing, though, also means that you need to constantly update your algorithms to reflect the market, your approach to the market and/or your risk tolerance. What was sound logic last year, for instance, may not be sound logic this year.
As such, Daniel Calugar says that if you want to be consistently successful at algorithmic trading, you need to be constantly monitoring the algorithms themselves and assessing them for success.
The algorithms are essentially the processes you take to achieve a goal, which in trading is to make a profit. Like any other process you use to achieve any goal, if you want to be successful, you need to be able to track and measure various indicators and metrics to see how they’re performing.
There are plenty of strategy-specific metrics that you’ll want to track. Generally speaking, you should be tracking benchmarks for indicators such as overall performance, liquidity, volatility, fill rate, execution speed, and order size.
A good idea is to compare the algorithms you have created to historical data, current market conditions, and other alternative strategies you might take.
The great part about trading algorithms is that you can monitor and track them in real time. This means that you can immediately look back and see how things went and then make changes as quickly as possible to correct a wrong.
You’ll be able to monitor performance as the market moves so that you can identify any anomalies, outliers, glitches, or errors that are occurring. This flexibility will help you minimize losses due to errant thinking or poor code, for example.
All good algo trading platforms should be set up with extensive dashboards that include reports, logs, and alerts that all happen in real-time, according to Dan Calugar. This will give you the information you need to make the necessary changes to power a successful strategy.
The Importance of Backtesting
At the heart of every algorithmic trading platform is extensive and in-depth backtesting. The process of backtesting is often mentioned as part of the beginning stages of algo trading before the strategies and algorithms are actually put into action in the real market.
While backtesting is a key component of the pre-market stages of algo trading, it should not be forgotten once the strategy has been launched. Instead, backtesting needs to be utilized constantly as a way to figure out if anything needs to be changed, implemented, or scrapped altogether — and why.
Backtesting is how you identify potential flaws in your algorithm so that you can make the necessary changes to ensure a successful operation. It involves pitting your algorithm against historical data to see how the parameters might play out in the real world under different market conditions.
One of the challenges of backtesting in the pre-market stages is that you don’t always know the market conditions to test against. In other words, when you initially backtest your algorithms, there may be market conditions coming on the horizon that would be impossible for you to predict at that moment in time.
This means that when the market changes and those unpredictable conditions present themselves, you may not have backtested your algorithm against them. This, again, is why all algo traders need to consistently integrate backtesting into their process.
By testing your algorithm against a wide swath of potential market conditions, you can reduce the potential for catastrophe right from the get-go. But backtesting can also be used with a narrower focus — against a particular market condition that’s currently presenting itself.
Suppose you don’t like what you’re seeing in terms of the results of your trading strategy. In that case, Daniel Calugar says you should immediately start backtesting your strategies to figure out what’s not working, why, and whether you need to adjust or stay the course.
Optimization Techniques for Enhancing Algorithmic Trading Performance
There are many different ways that you can enhance your algorithmic trading performance. Below, Dan Calugar will describe some of the most common optimization techniques to help you succeed in algo trading.
- Parameter Optimization
Parameter optimization is perhaps the most basic yet important technique for enhancing algorithmic trading performance.
Every algorithm will have parameters that serve as the instructions that tell them what to do and when. Three main goals of setting parameters in algo trading are to ensure that a particular thing happens, prevent a particular thing from happening, and limit the extent to which that thing happens.
Parameters will ensure that the instructions you give the algorithms stay within certain defined confines based on what the data tells the computer.
They can be set for everything from determining the percentage of securities you want to own to deciding when you want to buy or sell a specific individual stock, a group of stocks within a certain sector, or an entire asset class, and much, much more.
Many successful algo traders also typically utilize parameters such as drawdown, net profit -drawdown, spread, slippage, commissions, walk-forward efficiency, average trade, strategy correlation, and curve fitting, among others.
It’s extremely important to properly define your algorithms’ parameters and ensure they align with your overall trading strategy. Then, through backtesting, you can ensure that they are living up to your expectations.
Even the smallest adjustments in an algorithm’s parameters can cause significant changes in the output performance. This is why Daniel Calugar says you always need to make sure your parameters are optimized to fit the current state of the market.
In addition to the current market conditions, you also need to consider what data you’re using and what the objective of the algorithm is. There are many different techniques that you can use to adjust the parameters, including:
- Grid Search: Also referred to as brute force optimization, this involves evaluating how every point on a multi-dimensional grid functions and then going with the best result. In other words, you’ll literally be trying each combination of a set of values for every parameter. It’s a basic optimization strategy, but it works.
- Random Search: For this method, you’ll select random combinations of parameters and use them to train a specific model. Then you’ll use the best combinations of parameters based on the results. In this way, it’s similar to Grid Search. One of the key differences, though, is that you won’t specify all the potential values for all parameters. You’ll just sample some values from parts of each parameter.
- Bayesian Optimization: This is much more sophisticated and in-depth, but the logic behind it is very straightforward. This type of modeling updates prior beliefs to reflect new information learned to produce a new and updated belief. In other words, this method will take the logic behind the original parameters, include an analysis of the current market conditions and how the parameters performed, and then help to optimize those parameters into new ones.
Remember, it’s essential to always optimize the parameters of your algorithms if you want to be successful in this type of trading. With markets changing constantly, it’s a necessary part of the game.
- Portfolio Optimization
There are a few potential pitfalls to algorithmic trading. For example, one part of your strategy could outperform the others. It’s also possible that only a few aspects of your strategy may be applicable if the market conditions defined in your parameters are only present for those specific parameters.
In both of those examples, your portfolio can become “out of whack” from where you’d like it to be. Essentially, you could find yourself with a portfolio that doesn’t match your risk tolerance and/or isn’t balanced with the right formula of assets.
This is where portfolio optimization comes into play. It helps to prevent these scenarios from occurring in the first place by investing in certain asset portfolios that allow traders to maximize their returns while diversifying their risks.
At the same time, portfolio optimization can be used to correct an unbalanced portfolio created by algo trading strategies.
In general, portfolio optimization needs to balance risk while taking into consideration asset allocation, volatility, and liquidity. It achieves this by utilizing statistical models to identify your portfolio’s optimal asset allocation.
There are three main techniques for portfolio optimization:
- Monte Carlo Simulation: With this approach, you’ll generate a bunch of random scenarios so that they will simulate returns for your portfolio. This technique will help you evaluate your portfolio’s performance against multiple market conditions, allowing you to identify your optimal strategy for asset allocation.
- Mean-Variance: This technique focuses on maximizing expected return and minimizing volatility at the same time. It’s generally considered the most classic approach to portfolio optimization. The assumption of this technique is that all returns will be distributed normally and that all investors are averse to risk.
- Risk Parity: This technique flips Mean-Variance on its head. It focuses on allocating the weights of a portfolio based not on expected returns but on risk. Each asset in the portfolio will have its risk balanced so that no single asset ends up dominating the overall risk of the portfolio as a whole.
To properly implement portfolio optimization techniques, you’ll need to use historical price data for assets to estimate both the covariance and returns of the assets. You’ll then use the estimates to construct the weights of the portfolio that are optimal so that you can maximize its performance metrics.
- Execution Optimization
Optimizing your algorithms’ execution is obviously an extremely important aspect of algo trading. Execution can refer to many different things, but one of the main focuses of optimization in this realm refers to the speed at which your algorithms perform.
Those investors who are trading a huge amount of money in algo trading will always be looking to improve the speed at which their algorithms operate and execute. After all, a key reason that algorithmic trading is so beneficial is that it can operate at lightning-fast speeds. With so much of the success of algo trading counting on timing trades perfectly, it’s essential that their system is performing up to optimal standards.
Even the slightest of delays in a trade with algo trading can ultimately result in losses that are quite significant. This can either come in the form of missed opportunities or actual money lost.
But, even if you’re a beginner to algorithmic trading, it’s a good idea to optimize your execution. Increasing speed is important to reduce slippage — the difference between what the expected trade price is versus the trade’s actual price upon execution — but it’s not the only factor to take into consideration.
Dan Calugar says that an algorithm that performs at top speed is only good if it’s actually executing the correct trades. In other words, speed doesn’t matter if the results are no longer matching up with the market.
That’s why you want to look at other areas of your algorithm’s execution aspects, including whether it’s linking up with your brokerage properly, whether it’s carrying out new parameters the right way and more.
- Manage Risks
You should always manage risks efficiently and effectively, as one overlooked item could mean the difference between success and disaster. Risk management helps to maximize your profits and minimize your losses.
This process starts by defining your own risk tolerance. This is important in any trading scenario, but especially in algorithmic trading, in which you will build your risk tolerance right into the actual computer code. Your risk comfort level should be reflected in the algorithm itself.
One of the most common ways traders will manage their risk is by diversifying their portfolios. This is relatively easy to do from the outset if you’re investing in a retirement account such as a 401(k), for instance.
However, it takes some finesse for algorithmic trading, Dan Calugar points out, for all of the reasons discussed above. That’s why you need to constantly monitor the performance of your algorithms to see what assets you’re buying and what assets you’re selling.
If you find that one algorithm is performing better than another — or if one scenario is presenting itself more than others — you can adjust your algorithms to purchase more of one type of asset and less of another, for example.
Risk management can be done in plenty of other ways, such as through stop-loss orders. These will automatically sell an asset once its price drops below a pre-defined level. This helps to mitigate losses from one particular asset.
The key to risk management is to approach issues systematically and proactively so that you can protect your algorithms’ performance and your capital. And, like everything else in algo trading, this can and should be done in real-time.
About Daniel Calugar
Daniel Calugar is a versatile and experienced investor with a background in computer science, business and law. While working as a pension lawyer, he developed a passion for investing and leveraged his technical capabilities to write computer programs that helped him identify more profitable investment strategies. When Dan Calugar is not working, he enjoys working out, being with friends and family, and volunteering with Angel Flight.
