In electronic trading, the profitability of a particular trading strategy often decreases over time because more market participants mimic successful strategies or because the market regime has changed. This is commonly referred to as Alpha Decay. As a result, new trading strategies are constantly being developed, and the trading industry rushes to the next innovation to gain the next advantage over the competition.
Two decades ago, speed became the deciding factor to differentiate from the competition. The race was on with ever-shorter response times to incoming market data. As more high-frequency traders entered the market, companies began to develop new strategies based on machine learning. These use AI and machine learning algorithms to analyze patterns in market data and automatically predict short-term price and liquidity changes.
A robust method for short-term prediction of financial time series is of great value, but traditionally a difficult endeavor because their statistical characteristics change over time. Machine learning methods have the advantage that they can be more precisely adapted to the current market situation by regularly retraining the machine learning model.
For example, machine learning methods are used to model the Limit Order Book to gain insights into market dynamics: Based on historical data, short-term price movements can be predicted more accurately than with traditional methods. A machine learning model helps decipher the intricate details of order flow, bid-ask spreads, and liquidity dynamics to help predict price fluctuations. Machine learning is also used in option pricing for risk mitigation, hedging, speculative strategies, and volatility estimation. More generally, it enables a systematic analysis of financial data to identify complex patterns, correlations, and trends in price movements.
The impact of machine learning on trading is already significant. A representative survey in 2020 found that 44% of capital markets professionals use AI and machine learning; another 17% expected to use machine learning in the first 1-2 years after the survey.
As ML-enabled trading strategies proliferate, companies must find ways to differentiate themselves. One trend we've seen over the past two years is trading companies moving machine learning into the hot path of the ultra-low latency trading cycle - a task that remains challenging as most machine learning does not operate at microsecond latency. We are seeing a new speed race for ML-based trading methods that has just begun.
In the next article, we will take a closer look at low-latency machine learning for high-frequency trading. We will also discuss how Xelera Silva solves the machine learning latency dilemma. Stay tuned!
In the era of data-driven decisions, every microsecond counts. In this article "Ultra-low Latency XGBoost with Xelera Silva", we discuss the optimization of xgboost, lightgbm and catboost for lightning-fast machine learning inference.
As electronic trading strategies that support machine learning proliferate, the speed at which machine learning algorithms can make decisions is once again becoming one of the critical factors in differentiating oneself from the competition.
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