Collection of notebooks about quantitative finance, with interactive python code.

Retrieved from cantaro86 under GNU Affero General Public License v3.0 for personal uses and modification. Notebooks for quantitative finance, with interactive Python code. Repo source: https://github.com/cantaro86/Financial-Models-Numerical-Methods

This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.

I try learn a course that focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.

Superman/woman - Programming Guru Needed! http://careers.interfacefinancial.com We need a Super Hero! It really does depend on your definition, though. If you are into great people, a startup mentality with no startup issues and you enjoy writing code using Python and C# or C++ in a Linux environment, then you'll love working at IFG! Join the company that’s bringing technological advancement to the commercial finance industry! We're looking for a Software Engineer to join our awesome development team. We like working with nice people who are excited to learn. :) Here's what we need... Software Architect/Financial Engineer The Role Financial Engineer/Software Data Architect is responsible for the strategic architecture and deployment of the data infrastructure ecosystems. As a key member of the technology team you will be responsible for architecting, designing and developing major components of a next generation stream and batch processing lending platform. Qualifications *Strong experience with object-oriented design, coding and testing patterns *Experience in architecting, building and maintaining (commercial or open source) software platforms and large-scale data infrastructures *Experience building big data solution *2+ years software development experience *Experience with other technology such as Amazon EC2 is a plus *A strong team player, ability to quickly triage and troubleshoot complex problems Responsibilities and Duties: *Develop and execute various methods for data collection and acquisition. *Analyze statistics, recommend ways to improve various outlining risk model. *Develop and write various functional requirement documents to assist various software developers. *Coordinate with various software developers and perform various tests on software requirements. *Administer technical performance and monitor up gradation process for various businesses. *Monitor all account related issues and provide expert advice on various methods and processes for existing systems. *Perform and evaluate all functional requirements and perform required calculations such as algorithms. *Perform research on various products, gather knowledge on user requirement and develop plans to improve products. *Design and execute various pricing models for financial products and services. *Monitor all lending models, establish capacity parameters and recommend improvements on same. *Administer all risk calculations for customer and evaluate appropriate tools for same. *Manage and resolve all complex financial issues and develop effective mathematical and statistical methods to resolve issues. *Coordinate with software developers and develop effective implementation methods for various functional products. Preferred skills: *Numerical/financial algorithms, XML, JSON, C#, Java *Database relationship and data schemas *RESTFUL API knowledge *Software testing methodology *financial analysis skills *FRM (Financial Risk Manager) Certification Education *Bachelor's Degree required *Master Degree preferred 2 positions open Salary: $90K-$100K

Open-sourced Technical Indicator library with 120+ indicators, 30+ Financial Numerical Methods, solvers of 21 different differential equations with GPU support and efficient backpropagation with PyTorch; realizations & models for 40+ stochastic processes

This project implements a comprehensive set of tools for pricing financial options using both analytical and numerical methods, alongside calculating key risk metrics (Greeks). It includes implementations for European and American options pricing and supports the calculation of Greeks using the Black-Scholes model and Monte Carlo simulation.

Implementations of Various Financial Pricing Models and Numerical Methods

A Python-based implementation for pricing American options using numerical methods, including trinomial approximation and Geske-Johnson method. Includes examples, code, and explanations for financial modeling.

This repository contains several assignments from my Financial Computation course, focusing on numerical methods and stochastic models for option pricing. Topics include Martingale Pricing Method, Black-Scholes Pricing, CRR Binomial Tree, Lookback Options, and Asian Options

Offering scalable, efficient, and data-driven approaches. Neural ODEs model continuous dynamics, outperforming traditional numerical methods in complex systems. Applications span physics simulations, biological modeling, and financial predictions. ML-based solutions reduce computational costs while improving accuracy and adaptability.

Numerical Schemes for Differential Equations: Python implementations of explicit/implicit Euler, shooting, and finite difference methods for ODEs/PDEs. Solved damped harmonic oscillators (stability analysis) and 2D heat equation on [0,100]² domain with NumPy/SciPy/Matplotlib. Error orders O(Δt²); applications to financial stochastic modeling.

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This is just a collection of topics and algorithms that in my opinion are interesting.

Collection of notebooks about quantitative finance, with interactive python code.

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Implementations of financial models using various numerical and machine learning methods

Implementation of classical and numerical methods for pricing financial derivatives under arbitrage-free models.

Numerical simulation of deterministic and stochastic financial models using RK4 and Monte Carlo methods in Python.

#Python code of #financial_models with various #options_price and #numerical_methods

A repository showcasing numerical methods and computational techniques applied to financial data analysis and modeling.

This Repository is a collection of code devoloped during Financial Engineering course. The projects cover a wide range of topics related to financial derivatives, pricing models, and numerical methods.

This Java library implements numerical methods for pricing financial derivatives, with a focus on the binomial tree model for option pricing.

Developed a quantitative model to simulate and validate option prices using Monte Carlo and Black–Scholes methods. Demonstrated strong command of stochastic modeling, probability, and numerical analysis relevant to financial risk modeling.

Market Volatility Simulator explores financial volatility using stochastic processes such as the Ornstein–Uhlenbeck and CIR models. Combining stochastic calculus, numerical methods, and simulation, it bridges theory with empirical market analysis.

A collection of advanced models in financial engineering bridging theory and computation. Covers stochastic calculus, derivative pricing, term structure modeling, and numerical methods, offering transparent, research grade implementations for risk, valuation, and portfolio applications.

An Interactive Monte Carlo simulation dashboard for financial modelling. Runs thousands of asset price paths, calculates risk metrics like VaR and Expected Shortfall, and visualises probability distributions in real time using efficient numerical methods.

This project is a Flask-based web application for pricing European options using advanced financial models and numerical methods. Users can input parameters to calculate option prices using the Black-Scholes model, Monte Carlo simulations (with GBM discretization via Euler-Maruyama and Milstein schemes), and the Heston stochastic volatility model.

PACS Project: The project aims to develop a fully implicit, unconditionally monotone numerical scheme for solving the two-factor uncertain volatility model, ensuring stability and convergence to the viscosity solution. It focuses on pricing financial derivatives under worst-case volatility scenarios using a hybrid discretization method.