A mid-sized bank is looking for a cost-effective solution that will allow them to better compete.
DataMine Machine Learning Service
Traders at a mid-sized bank are facing increasing competition from larger banks and hedge funds that have access to more data, computing power, and a deeper talent pool. They need to find a way to improve their trading strategies to stay ahead of the competition.
The bank has limited resources, so traders are often bogged down with more manual processes for analyzing data and building effective models, leading to inaccurate predictions and missed opportunities. They need a solution that will supplement their lack of resources and help them gain a competitive edge.
The bank decides to leverage automated machine learning to build and back-test trading strategies. Machine learning can automate many tasks involved in building and training learning models, such as data preparation, feature engineering, and model selection. This can enable the bank’s traders to leverage technology that would otherwise be inaccessible and free up traders to focus on execution and risk management.
Implementation: The bank implements DataMine Machine Learning Service for building trading models via the cloud-based automated machine learning platform. This allows the bank to access the forefront of automated machine learning algorithms and an extensive data library without having to make expensive up-front investments in specialized technical personnel and complex hardware and software deployments.
Optimizing trading strategies: The bank uses DataMine Machine Learning Service to identify trading opportunities by analyzing historical market data and detecting patterns. This solution is used to help optimize trading strategies by testing different model configurations and hyper-parameters within a library of algorithms.
Managing risk: The bank utilizes DataMine Machine Learning Service to help identify and mitigate potential risks. The bank customizes their model to include 50+ indicators to align with their investment and risk management objectives. The model is trained on an extensive scope of global market data, including futures, derivatives, commodities, currencies, macroeconomics, and more. This breadth and depth of data informs the analytics, helping the bank manage risk effectively across their portfolio and avoid market risks.
The bank was able to utilize automated machine learning to employ a technology that it would otherwise not have access to, due to limited resources or technical expertise.
With DataMine Machine Learning Service, the bank was able to deploy custom models and integrate the outputs and insights into their daily routines and trading strategies. Furthermore, with access to more data, the bank can maintain a wider net of awareness to help manage and mitigate global risks.
Overall, with DataMine Machine Learning Service, the bank can go toe-to-toe with leading hedge funds and prop trading firms and maintain their position in an increasingly competitive marketplace.
U.S. 2023 Disclaimer
Neither futures trading nor swaps trading are suitable for all investors, and each involves the risk of loss. Swaps trading should only be undertaken by investors who are Eligible Contract Participants (ECPs) within the meaning of Section 1a(18) of the Commodity Exchange Act. Futures and swaps each are leveraged investments and, because only a percentage of a contract’s value is required to trade, it is possible to lose more than the amount of money deposited for either a futures or swaps position. Therefore, traders should only use funds that they can afford to lose without affecting their lifestyles and only a portion of those funds should be devoted to any one trade because traders cannot expect to profit on every trade.
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