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Develop Advanced Financial Solutions With IMSL Financial Algorithms
From asset management to investment banking, financial organizations around the world rely on IMSL Numerical Libraries for advanced data analysis and visualization.
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Create data-driven forecasting and modeling for equities, currencies, and commodities.
Optimize your trading and portfolio strategies by identifying patterns, opportunities, and limitations.
See the best, worst, and most likely outcomes to help inform financial decision making.
Accurately model interest and exchange rates for more informed financial strategies.
Analyze interest rate risk, credit risk, and expected price behavior for security trading.
Efficiently calculate options and derivatives pricing for timely and relevant trading insights.
With financial forecasting algorithms like GARCH, ARMA, Auto_ARIMA, and advanced forecasting techniques like Feed Forward Neural Networks, quantitative analysts and researches can create data-driven forecasting for equities, fixed income, currency, and commodities.
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With IMSL financial algorithms, quantitative analysts and researchers can use data collection, processing, visualization, and modeling to predict market volatility.
Quickly add application functionality for prediction, simulation, optimization, and other financial modeling techniques with IMSL financial modeling algorithms.
What risk modeling methods are used today? How can you effectively use them?
IMSL libraries feature financial algorithms, functions, and techniques that help quantitative analysts deliver high-performance, risk-averse, and high-ROI trading portfolio management strategies.
Explore Credit Risk Modeling
With linear, non-linear, quadratic programming, and other options within the IMSL libraries, asset managers and quantitative analysts can quickly develop versatile portfolio optimization applications.
IMSL financial risk management algorithms can calculate information about range of outcomes, such as best / worst-case, the chances of reaching target goals, and the most likely outcomes.
IMSL financial algorithms like the Genetic algorithm help quantitative analysts create trading strategy optimization applications that identify patterns, opportunities, and limitations in existing strategies.
Quantitative financial analysts rely on data-based interest and exchange rate metrics to make accurate assessments and value propositions for their clients. But parsing high volume data to get actionable insights requires robust financial analysis tools.
IMSL algorithms help developers quickly add interest and exchange rate analysis functionality to financial applications so financial analysts can make accurate and data-driven decisions.
In fixed income analysis, analysts determine whether to buy, sell, hold, hedge or stay out of securities based on analysis of their interest rate risk, credit risk and likely price behavior in hedging portfolios.
Using the linear and non-linear optimization functions in IMSL Libraries can help drive fixed income analysis functionality.
Because many significant problems in financial modeling can be expressed as particular choices of coefficients, initial conditions, and boundary values. The IMSL C library includes a function for solving a generalized version of the Feynman-Kac partial differential equation, allowing applications to efficiently calculate price options on stocks via quantitative financial equations like the Black-Scholes equation.
For large financial institutions, IMSL algorithms can add immediate functionality and value while significantly decreasing costs on code development and maintenance.
The IMSL libraries provide users with the software and technical expertise needed to develop and execute scalable, high-performance numerical financial quantitative analysis applications. IMSL libraries save development time by providing pre-written mathematical and statistical algorithms that can be embedded into C, Java, Fortran, and Python applications.
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With embeddable algorithms in C, Fortran, Java, and Python, developers can seamlessly integrate IMSL algorithms in a fraction of the time it would take to develop algorithms from scratch.
Because IMSL has libraries in C, Fortran, Java and Python, developers can write a prototype in Python or Fortran, then use the same algorithms for production in C or Java without wrapping.
Trusted by customers for over five decades, the IMSL libraries are reliable, accurate, and proven to deliver value on numerical applications across all industries.
Want to see how IMSL Financial Algorithms can work with your application? Request an evaluation today!
Want an overview of the IMSL libraries? Download our datasheet.
Have questions about the IMSL Numerical Libraries? Reach out!