Synthetic Indices Trading: How Technology Is Shaping the Future of Market Access

Finance is continually evolving at a pace that is incomparable to anything that has come before. While there have always been new developments and ways of trading, what we are seeing today is an exponential acceleration in terms of complexity, versatility, and usability. The combination of seemingly never-ending improvements in computational power and the ubiquitous nature of instant information transfer means that technology has reshaped the future of market access. 

In this article, we’ll be taking a look at the emerging area of synthetic indices to see how they are leading the way in terms of fintech progress and financial innovation. Not only that, but they are leading to the creation of new trading environments that progressive traders are beginning to explore in greater numbers. 

What is synthetic indices trading?

If we take a look at the world of synthetic indices trading, we see a world in which tech-driven systems and tech-savvy traders look to come together to make progress. Rather than being tied down to the behavior of traditional assets and the standard ways in which their prices and values evolve over time, this new class of investors is looking to profit from market-like movements that have been algorithmically generated. 

Synthetic indices are created based on computer simulations of financial markets that are designed to reflect real-world volatility levels. This is not to say that they are live records of exactly what is happening in real markets, only that their overall behavior is comparable to how markets tend to behave across time. 

Because there are no underlying assets to support synthetic indices, every data point is created by an algorithm. While this initially sounds like nothing more than an interesting problem for a computer scientist, there are several benefits once you take a closer look: 

• The potential for 24/7 availability means that traders can work at times that suit their other constraints, regardless of their time zone 
• Synthetic indices are not impacted by breaking news, making them somewhat more focused than traditional real-world indices
• A diverse range of volatility levels means strategies can be systematically tested, and traders can speculate in several different ways 

Many synthetic indices use random number generators to pick sets of realistic data, creating a level of volatility and a type of behavior that closely aligns with real-world performance. The idea is not to replicate the price evolution of real-world indices across a certain timespan, but rather to emulate the kinds of behavior that one would typically see. 

Why do traders use synthetic indices?

Being able to control the level of volatility and work 24/7 means that traders can hone their skills and optimize strategies, ready for real-world applications in the near future. There is no impact from external noise (often described as noise on top of a price action line), so the trader can focus solely on the technical analysis side of things. This is ideal for improving chart pattern recognition skills and the ability to put forward in-depth interpretations. 

Many traders will train their expert advisors (EAs) on synthetic indices so that they can see how the bot performs when rolled out at scale. The ability to fine-tune the market conditions means that traders can come to an informed judgment as to how well their EA performs in a given set of conditions. Given the shift towards automated, high-volume trading in recent years, you quickly see how synthetic indices have become an important resource for traders of all abilities. 

The simulation of market conditions

Synthetic indices simulate market conditions by using a combination of probability models and random number generators to create price movements. The size, frequency, and timeframes are all chosen so that they closely resemble those produced by a real market. As there is no underlying asset that creates the value traded in the market, the price is set by a series of predefined rules in a way that remains statistically consistent over time. This is in contrast to a real-world market, where value comes from the interplay of supply and demand. 

Developers will assign a volatility coefficient to each index so that they can set the size and frequency of the price shifts. Traders can then select the volatility level that they would like to work with, allowing them to fine-tune the trading environment to match the risk-reward equation that they feel most comfortable with. 

In addition, there may be sudden events coded into the algorithm that create things like booms and busts, recessions, and periods of extended stability. These are included so that traders can select broader market conditions with a more global approach than would otherwise be possible if they only had the option of adjusting the level of volatility. 

Synthetic indices as market simulation tools

Once you have the ability to simulate a market in a hyper-realistic way, you can use it for testing EAs without having to think about the impact of external news. Alternatively, you can use the simulated data to test new manual strategies before deciding to shift to live markets and real capital. There are also some advanced traders who decide to work with real-world capital and diversify their portfolio by adopting positions with synthetic indices.

In the interests of balance, we should also say that there are some traders who prefer real-world markets over broker-generated synthetic indices. Commodities, shares, forex, and traditional indices are all alternatives, with many traders using historical data from such markets to train their EAs and optimize their own custom trading strategies. However, one of the real advantages of the synthetic approach is that it allows traders to eliminate external noise from economic announcements and global news, while at the same time tightly defining the volatility level. 

Being able to set the volatility means that a trader can figure out where the overlap between two similar strategies lies and at what point a shift from one to the other is needed. It’s these gray areas that can often cause issues, as traders may be less willing to suddenly shift strategies the longer they have been executing a given strategy. 

Balancing risk and reward with synthetic indices

It’s very important to note that while we have talked about synthetic indices as training resources at points in this guide, you can also trade real money, and therefore potentially lose real money. The complex nature of these digital instruments means that they come with a high associated risk of losing money quickly with poor strategic choices, particularly if you adopt a leveraged position. 

While it may go against your nature, it is important to put to one side any knowledge of real-world events and external economics that you have. None of it will influence the prices of the synthetic indices you are trading, meaning that any analysis you do in this regard will be, at best, redundant. If in doubt, taking a disciplined approach built around the consistent deployment of stop-losses and conservative position sizing will help you to build a level of consistent performance. 

Going further with synthetic indices trading

If you are interested in going further, the good news is that there is plenty of help out there, provided you know where to look. A regulated broker like ThinkMarkets will be able to connect you to the functionality you need to trade synthetic indices. Even if you are a long way away from training an EA, looking at synthetic data can help improve your knowledge of real-world markets. 

No matter what you choose to do, it’s important to only ever trade in a way that is healthy and sustainable. By taking time to assess the risk-reward equation and to take breaks when you need to, you will be able to ensure that you always trade within your means. After all, the last thing you want to do is develop negative habits while trading synthetic indices, which you then carry over to other components of your portfolio.