With more than 5 trillion in assets under management at the end of 2023, the hedge fund industry has reached another milestone with constant growth since the end of the 2008 financial crisis. It has more than doubled in the last seven years and more than quadrupled since 2008. With such an amount before leverage,  hedge funds are now playing a pivotal role in what is commonly called ‘shadow banking’.

With such growth and complexification, there is a critical need for hedge funds to have a comprehensive data platform covering multiple use cases, for several reasons. First, it facilitates compliance by providing accurate and accessible data for reporting. Second, it enables robust hedge fund risk management by integrating data from various sources to monitor exposures effectively. Lastly, it enhances operational efficiency by streamlining data workflows and analysis processes, helping hedge funds adapt to evolving regulatory demands while maintaining a competitive edge.

By combining scalable data management and real-time analytics in a unified platform, Opensee allows hedge funds to aggregate, explore, simulate, analyze, and visualize complete datasets. Users can access unprecedented volumes of data, at any level of granularity and history, at speed, with a significant reduction of the infrastructure cost.

This is why Opensee’s platform serves many key use cases for hedge funds, notably market risk, leverage optimization, execution analysis, and building and backtesting quantitative strategies.

Market risk

Risk management quality and expertise are key for hedge funds. Their market risk frameworks include several industry and proprietary indicators.

The market risk dataset could be huge, depending on, for example, the type and number of strategies and the historical needs for the indicators.

 Through its full scalability, Opensee allows Risk and Trading teams to get: 

• Direct access from the aggregated metrics to the most granular ones
• Immediate explainability of metric variations, including data quality, thanks to the embedded AI analytical toolkit
• Specific metric calculations using pre-packaged Python UDF (User Defined Functions) with interactive visualization
• Immediate effect from simulation of positions and metric adjustments, while being able to track and audit any changes

Leverage and collateral optimization

Leverage is key for hedge funds to achieve their target performance. Negotiating the level of leverage and collateral to be put in place with different prime brokers can be lengthy and complex.

Once these guidelines have been agreed, it's important to be able to analyze the figures that prime brokers require and study whether any optimizations are possible. This issue will become even more complex as the Federal Stability Board (FSB) implements its recommendations on the collateral buffer needed to anticipate potential market stress.

With Opensee, the ability to get granular data on any axes (prime brokers, counterparts, assets…) is very useful to understand and challenge figures given by the prime brokers. Opensee’s AI toolkit also provides explanations for all metrics and proposes collateral optimization and stress testing (assets, counterparts, deals allocations…).

Execution analysis

Quality of execution is also a key part of hedge fund performance.

Opensee allows users to centralize fragmented data sources and aggregate and analyze data in real-time, with no volume limitation.

Opensee's solution is particularly suited to transaction data and analyzing execution performance both with pre- and post-trade angles across all asset classes.

Opensee allows traders, quants, and analysts to:

• have a full representation of their data sources (trade repository with full granularity, from Parent order to Child order, RFQ, Market order, Market execution...)
• navigate the microstructure by calculating price deviation metrics at every order level and comparing them with all available market data sources (Primary Exchange, MTFs, SIs, Streaming Prices...) and others post-trade analytics
• have pre-trade intelligence via analytics like “natural” counterparts or venue selection

Quantitative strategies

To build and backtest quantitative strategies, quants need not only a huge amount of  historical data but also direct, simplified access to them.

Opensee allows quant and trading teams to:

• Get full scalability, no compromise on speed nor on volume and history
• Have direct and immediate access to the entire dataset through low-code rest API 
• Build backtesting and hedging strategies through re-packaged Python UDF (User Defined Functions), with results on the fly

In most of the current infrastructures, the quant team is victim of a long and frustrating cycle of: 

• Defining the strategy 
• Formalizing its specifications to their IT department
• Waiting for IT to provide the outputs with uncertain timing depending on their budget and priorities  
• Analyzing the results, and if modifications or enhancements are needed (which they usually are), going back to step one…

With Opensee, as the quant is completely autonomous (data access, strategy building) and gets the results “on the fly”, the cycle is reduced to near zero. This allows the quant to be much more efficient by staying focused on strategy building without the stop and go due to IT timing constraints.

Whether serving a single or multiple use cases, Opensee brings measurable and quick added value to even the most sophisticated hedge funds. By providing immediate access to granular metrics, user defined functions, and AI-driven insights, Opensee enhances the efficiency and accuracy of Hedge Funds’ multiple needs. As the financial landscape continues to evolve in multiple aspects (strategies, products, regulatory….), embracing innovative solutions like Opensee becomes paramount for hedge funds looking to maintain a competitive edge.