Timing means everything in the intraday trading world. Given the volatile nature of the transaction, trading decisions don’t have very much in common with regular purchasing decisions, whereby offers are carefully chosen, dissected and weighed. The fact that trading windows close as quickly as they do underscores the limitations of human capability in this space. Algorithms are essential tools in navigating the trading field, but no two computations are the same, and in the end, the fastest platform generates the most sustainable profit.
A method used to ascertain the viability of a potential trading strategy based on existing data, backtesting constitutes one of the most crucial steps in the development of a trading system, especially one that incorporates AI. Unsurprisingly, processing the sheer volume of data required to develop and continually optimise such an algorithmic system does not come without difficulty. Rapidity plays a key part in backtesting as adequate speed is the only way to properly analyse the billions of data points collected from the market so that associated conclusions can be projected onto live trading. Without sufficient speed, a single backtest would take ages to complete, rendering the millions of necessary test runs impossible. It can take years for a trading strategy to reach ideal proportions, which is why we sped up the training process for our algorithmic models significantly from conventional durations.
In sports, playing for fun is all good and well, but if you want to go professional, you need to put a lot of time and effort into your training. Practice is the only way to reach your full potential — to reliably perform well rather than by a fluke. And once you know the sport inside out, every inch of the playing field, that's when you've gone pro. Our algorithms make this progress happen in strategic trading terms. The only difference? It goes much faster than the road to the NBA or Champion's League.
At enspired, backtesting is a standardized mechanism to determine the value of an asset and the potential gains to be made from it - this is how we ensure the performance of our algorithms. The most important prerequisite for effective backtesting is the use of a wide range of data. The data we source directly from relevant markets includes the order book and the exact proceedings of every trade recorded in it. In addition, asset or region-specific information such as updated forecasts for wind farms, marginal costs, and battery constraints are used to further fine-tune the algorithm. This volume of information - we currently use 7.3 billion data points – allows us to replicate past markets 1:1 to maximise asset profitability in the future. The backtesting process itself is similar to live trading - the difference is in the source material. While live trading uses current data, backtesting uses historical data. In practice, the process chain looks like this:
With this approach, we can guarantee that our models perform at their best from the beginning rather than after two years of ongoing manual enhancements by a trader.
A few years ago, we set out to build the fastest AI trading platform on the European power market. That is why we designed its architecture, language and infrastructure around speed as the driving force behind backtesting. This enables us to reduce imported data to what is relevant for a given use case and train our trading models in as precise and quick a manner as state-of-the-art technology allows. At this point, it should be noted that algorithmic speed goes hand in hand with reparameterisation, which tends to the pace of adaptation to adjust for changes in the market. After all, a fast algorithm will perform poorly if ill-attuned to current market conditions.
In the landscape of energy trading, a product value similar to what enspired offers is hard to find because competitors lack the resources and know-how we have in-house. This often leads to the acquisition of generic, ready-to-use system solutions, which – in turn – exhibit numerous shortcomings external users are unfamiliar with. In actual numbers, under the same market conditions, the efficiency brought about by our in-house expertise equates to 3,000 executed trades per hour instead of just a handful.
Properly setting up an algorithm through backtesting is a time investment of about a month. Once the algorithm is live, performance improvements become noticeable within days. Without having the knowledge and infrastructure to expedite backtests in place, the same process can take years. Our platform works in milliseconds, running tests millions of times to ensure maximum proficiency. Other than unprecedented speed, the platform comes with several additional advantages:
Our service is always active. The platform runs constantly – 24 hours a day, 7 days a week. There is no risk of unscheduled downtime.
Our system isn’t restricted to one specific energy exchange but runs on multiple pan-European ones to ensure a cross-market presence for assets in our service. This feature also acts as a type of fail-safe in case one exchange goes down.
At enspired, we don’t rely on third-party authorisations – every task in the trading chain is completed in-house. We have complete control over the performance and scalability of our platform and, by extension, your assets, which allows us to keep you in the loop throughout.
Our trading approach is 100% customer-centric in that we react swiftly and flexibly to market changes by continuously optimizing both our algorithms and our platform in accordance with updated market conditions, thus achieving PnL improvements for our clients.
We bank on artificial intelligence, and statistics over manual trading for the simple reason that the commercialisation of flexibility isn’t a matter of experience but of mathematics. For instance, battery cycles, roundtrip efficiencies and maximum (dis)charge capacities– all these are properties algorithms can understand, interpret and capitalise based on absorbed information.
Our experts write algorithms to be fully automated and to execute trades automatically at opportune moments relative to current market conditions. The lack of human intervention results in improved accuracy and reduced vulnerability to mistakes.
Strategising for the long term, our platform aims to connect backtesting with live operations to ensure the models continue to be trained and reparameterised while active.
Through sharing findings with our clients and continuously learning more about their assets, we are able to train and enhance models more customer-specifically to get the most out of future market positionings. Moreover, our service includes providing clients with the knowledge to optimise their own trading activities in-house.
Trading strategies are like templates – the general logic the system follows in performing the tasks it is given. An algorithm is the application of a strategy in the market, taking into consideration the results of the training models. All our algos have different parameters that determine how they behave, when positions are opened or closed and at what price point a trade occurs. This goes to show that the need for speed in developing the ideal trading strategy through backtesting with millions of available data references is a great one. It boils down to a simple guiding principle: the higher the speed, the greater the profit.