Financial institutions such as banks, hedge funds and mutual funds use quantitative analysis to trade stocks. An Investopedia article states:Quantitative trading consists of trading strategies based on quantitative analysis, which rely on mathematical calculations and crunching numbers to identify trading opportunities. Price and volume are two of the most common data inputs used in quantitative analysis as the main inputs for mathematical models.”
It is critical for financial services firms to stay ahead of the competition and maintain maximum profitability when trading stocks. To achieve this goal, financial firms are developing their own algorithmic trading models that are considered protected intellectual property that is not shared. The trading models use computers to analyze a mix of proprietary data, statistical and risk analysis, and external data.
Trading strategies have traditionally been developed by financial quantitative analysts (quants) using ‘what if rules’ to determine the best and most profitable trading opportunities. After the trading strategies were refined, the trading criteria were hard-coded into computer programs used in making real-time stock exchange trades. Trading programs were often executed from computers in financial services data centers using central processing units for calculation. The enormous amounts of data to be processed put a strain on the data center infrastructure. In addition, quantitative analysts have been unable to keep up with the analysis needed to update their trading models to reflect the constantly changing market and economic conditions. Algorithmic trading was created to help financial services firms meet the fast-paced stock trading needs.
What is algorithmic trading?
Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions that take into account variables such as time, price and volume. This type of trading attempts to utilize the speed and computing power of computers as compared to human traders.
Evolution of algorithmic trading
Financial services firms are increasingly building highly automated algorithmic trading systems using artificial intelligence (AI) for quantitative trading analysis. According to SG Analytics“Algorithmic trading accounts for nearly 60 – 73% of all US stock trading – stock market data analysis.”
Algorithmic trading involves building unique computer models that find patterns or trends not normally observed by people scanning charts or ticker (price) movements. The algorithms use quantitative analysis to execute trades when the conditions are met. A simple example would be, if the oil price reaches $130 and the US dollar falls 5% in the past two weeks, then sell oil and buy gold in a ratio of 20:1. Mathematical statistics such as standard deviation and correlation would be added to the model to determine when to execute a trade.
Machine learning (ML) is especially valuable in algorithmic trading because ML models can identify patterns in data and automatically update training algorithms based on changes in data patterns without human intervention or relying on hard-coded rules. According to an Finextra article“With the hiring of data scientists, advances in cloud computing, and access to open source frameworks for training machine learning models, AI is transforming the trading desk. The largest banks have already rolled out machine learning algorithms for stock trading.”
How cloud-based, GPU-accelerated AI is meeting the needs of algorithmic trading
The complexity and infrastructure requirements of algorithmic trading make it important for financial organizations to partner with technology providers. Many of today’s algorithmic trading systems are driven by advances in GPUs and cloud computing.
Microsoft and NVIDIA have long partnered to support financial institutions by providing cloud, hardware, platforms and software to support algorithmic trading. Microsoft Azure cloud, NVIDIA GPUs and NVIDIA AI provide scalable, accelerated resources, routines, and libraries for automating quantitative analysis and stock trading.
The partnership between Microsoft and NVIDIA makes NVIDIA’s powerful GPU acceleration available to financial institutions. Azure supports NVIDIA’s T4 Tensor Core Graphics Processing Units (GPUs), which are optimized for the cost-effective deployment of machine learning inferencing or quantitative analytical workloads. The Azure Machine Learning service integrates the NVIDIA open source GEARS software library that allows machine learning users to accelerate their pipelines with NVIDIA GPUs.
Tools needed to create and maintain trading algorithms
In addition to Microsoft Azure Cloud solutions, Microsoft also provides tools that help developers and quantitative analysts develop and modify trading algorithms.
Microsoft Research developed Microsoft Qlib which is an AI-focused quantitative investment platform with the full ML pipeline of data processing, model training and back-testing – it covers the full automatic workflow of quantitative investments. Other features include risk modeling, portfolio optimization, alpha search, and order execution.
Microsoft Azure Stream Analytics
Microsoft Azure Stream Analytics is a fully managed, real-time analytics service designed to analyze and process large amounts of high-speed streaming data from multiple sources simultaneously. Azure Stream Analytics on Azure provides large-scale analytics in the cloud. The service is a fully managed (PaaS) offering on Azure.
Financial institutions using outdated data centers can no longer keep up with the massive amounts of data and analytics required for today’s fast-paced stock trading. Algorithmic trading with AI and ML that does not require human analysis is becoming the norm for stock trading. Microsoft and NVIDIA provide advanced hardware, cloud, AI and software solutions for algorithmic trading to meet the needs of the digital age.