With the rise of AI, traditional Quant Workflow is simply primitive.

The traditional research workflow is increasingly inadequate in today’s data-rich environment. It often begins with a dataset containing hundreds of dimensions, where manually designing features is not only time-consuming but also inefficient. In contrast, AI-driven approaches leverage machine learning algorithms to automatically generate these features, significantly speeding up the process.

Reinforcement Learning (RL) takes this a step further by optimizing trading strategies through interaction with a trading simulator, rather than relying on the outdated back-testing functions of traditional methodologies.

AI technologies offer unparalleled flexibility and capabilities that far exceed those of existing traditional research tools. However, building an AI-centric research workflow from scratch can be time-consuming. It’s clear that the reliance on traditional research workflows must be replaced with advanced AI methodologies to keep pace with the demands of modern finance.

There are several offerings in this arena: QuantLib, a free and open-source library for modeling, trading, and risk management in finance; FinRobot, an open-source AI agent platform for financial tasks that supports multiple AI agents powered by LLMs; and Qlib, developed by Microsoft, which is an AI-oriented quantitative investment platform.

in this blog, I will explore QuantLib codes, as the previous bog was about Qlib, and I already have dived deep to FinRobot earlier.

Overall, Qlib is about equity investment, while QuantLib has a lot more on fixed income and alternative or derivative financial products. In the example folder, examples on how to use QuantLib.

Basket Losses
————-

This example uses a default basket losses portfolio construction to mitigate risk
with additional models such as binomial model, Inhomogeneous model, and Random model.


BermudanSwaption
—————-

This example prices a few Bermudan swaptions using different short-rate
models calibrated to market swaptions.


Bonds
—–

This example shows how to set up a term structure and then price some
simple bonds. The last part is dedicated to peripheral computations
such as “Yield to Price” or “Price to Yield”


CallableBonds
————-

This example prices a number of callable bonds and compares the
results to known good data.


CDS
—

This example bootstraps a default-probability curve over a number of
CDS and reprices them.


CVAIRS
——

This example reproduces Table 2 on page 11 of
A Formula for Interest Rate Swaps Valuation under
Counterparty Risk in presence of Netting Agreements.


ConvertibleBonds
—————-

This example evaluates convertible bond values.


DiscreteHedging
—————

This is an example on using QuantLib Montecarlo framework.

It computes profit and loss of a discrete interval hedging strategy and compares
with the results of Derman & Kamal’s (Goldman Sachs Equity Derivatives Research)
Research Note: “When You Cannot Hedge Continuously: The Corrections to
Black-Scholes”
http://www.ederman.com/emanuelderman/GSQSpapers/when_you_cannot_hedge.pdf


EquityOption
————

This example calculates equity option values with a number of methods.


FRA
—

Forward-rate agreement valuation example.


FittedBondCurve
—————

This example fits a discount curve over a set of bonds with a number
of methods.


Gaussian1dModels
—————-

This example calibrates models using Gaussian short rate (GSR) and
Markov Functional Model.


GlobalOptimizer
—————

Examples showing how to use the global optimizers in QuantLib.


Latent Model
————

This sample code shows basic usage of a Latent variable model.


Market Models
————-

This example explores various market models’ delta and vega computes.
Additional features include lower, upper bound, and standard error.


MulticurveBootstrapping
———————–

This example shows how to set up a term structure and then price a simple swap.


Replication
———–

This example uses the CompositeInstrument class to build a static
replication of a down-and-out barrier option.


Repo
—-

Fixed-coupon bond repo valuation example.

While QuantLib is powerful, there are reasons it may not always be directly applied in real-time trading systems:

1. Performance Constraints
   • QuantLib is written in C++ but is not specifically optimized for ultra-low latency trading.
   • High-frequency trading (HFT) systems often require custom-built, highly optimized libraries.
2. Operational Complexity
   • QuantLib’s flexibility comes with complexity, which might be overkill for some simpler trading strategies.
   • Managing the library in production systems can be cumbersome without experienced developers.
3. Commercial Alternatives
   • Some firms prefer commercial solutions (e.g., Numerix, FINCAD, Bloomberg, or proprietary systems) that come with vendor support and specialized features.
   • These may offer better integration or compliance with specific regulatory requirements.