Speed equals profits and latency means losses for financial institutions, especially traders. Fast processing speeds are not only required for trade reconciliation but also for machine learning systems to analyze the risk of trading positions and to help predict future stock prices.
Speed, scalability and real-time analytics are crucial to execute trades in microseconds, and to complete more transactions quicker for increased profitability.
In-memory computing, where analytics are co-located with the data, provides the boost in speed that is critical to complete analysis and execute positions at almost zero latency.
Here are five ways faster processing can increase profitability for financial trading.
Every trader wants a crystal ball that can forecast market changes. To help predict when a stock price is going to go up or down, machine learning models ingest various types of data including raw, unstructured data from social media and news feeds that are analyzed to glean insights. The success of the model is largely dependent on the variety of data sources, the volume of data, and the speed that data can be ingested and analyzed. More complex and detailed models are continuously being developed which are dependent on larger samples that can be analyzed faster.
2: Risk Analysis
The race is on to devise the best trading algorithm that will outperform the competition. One important component of the algorithm is its ability to estimate risk with a high level of accuracy in order to devise the best strategy, to maximize returns. Without having a system architecture that can support fast processing speeds, the time to store, filter, and organize the data required to perform risk analysis can become a bottleneck.
The faster the trading platform, the more profitable the trades. High-frequency trading can spot new trends across global financial markets and act before the rest of the market has even had a chance to identify the opportunity. Empowered by complex algorithms, HFT can analyze multiple markets at once and make a trading decision in under a millionth of a second. High frequency trading applications demand higher throughput and lower latency to process and act quickly on terabytes of data.
Backtesting allows traders to analyze risk and profitability by simulating a trading strategy using historical data. The accuracy of the test is directly impacted by the amount of data that can be fed into the analysis including world events, gross domestic product (GDP), interest rates, levels of employment and consumer spending, etc. Often complex analyses need to be performed involving multiple scenarios until a strategy with the desired outcome is identified. The ability to query data lakes faster enriches real-time data analysis for faster and smarter insights. If a large amount of data is available, and the data can be analyzed within a suitable time frame, the chances of a pending trade meeting the client’s objectives go up.
The reliance on trusted data, in real time is increasing. Post-trade processing is important in that it verifies the details of a transaction. To reduce errors it is vital to complete high-volume, equities and fixed income reconciliation, as well as complex derivatives reconciliations in shrinking time frames. Speed is of the essence since markets and prices move fast and transactions are executed quickly, often instantaneously, and are based on real time pricing. For example, in forex trading where firms trade in huge volumes to reap modest returns, traders can experience huge losses due to a small degree of latency — even milliseconds. In addition, regulations require trades to be executed within specific short time frames that need to be monitored and maintained.
Efficiency by Design
Every stage of the life-cycle of a trade from predicting prices to confirming the details of a transaction is dependent on speed. Traditional computing methods can fall short in their ability to process large amounts of data, analyze and perform transactions while maintaining data validity or ACID (atomicity, consistency, isolation, and durability) within microseconds.
Complex architectures can involve multiple clusters and a proliferation of network hops that can’t handle fast processing and analysis of massive volumes of real-time data.
In-memory computing works with data and indexes entirely in-memory eliminating disk and file I/O as well as caching to provide faster data access. By building distributed processing and clustered processing into memory, performance levels can be achieved to support transaction speeds required for high frequency trading.
In-memory computing also provides elasticity to meet the growing demands of big data to support smarter machine learning models that feed on larger and larger data samples. During periods of peak load or through seasons of increased traffic, in-memory platforms dynamically expands applications onto additional physical resources – automatically and on-demand. In addition, intelligent multi-tiered data storage can enables financial organizations to prioritize access to more important data to reduce TCO and maintain system performance.
Using in-memory computing to keep data in memory instead of on disk provides massive improvements in performance and extreme scalability to handle massive sets of data needed to maximize profitability for financial trading. The unification of microservices and transactional applications with machine learning models in an event-driven architecture can fuel the race for more profits, faster.
This article is from the CBROnline archive: some formatting and images may not be present.
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