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Valuation of American options using binomial trees, and numerical approximation of the exercise boundaries for various underlying.
Applying the time series models of ARIMA and GARCH to model stock returns and volatility respectively
ARIMA & GARCH models for stock price prediction
Understanding ARIMAX modeling in Python.
Price Bermudan style swaptions using String Market Interest rate model and Neural Network
Collection of algorithms related to the detection of underlying causal structure from time series data, and for the approximation of the transfer operator and invariant measures.
Repository for CSCI-E-96 Data Mining and Exploration Course
Data and code behind the articles and graphics at FiveThirtyEight
Deep Hedging Demo - An Example of Using Machine Learning for Derivative Pricing.
Stock for Deep Learning and Machine Learning
A simple deep learning model for predicting SP500 stock price using TensorFlow
Deep Learning for Stock Market
Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.
A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion in Python
Python sync/async framework for Interactive Brokers API
Interest Rate Swap Pricing and Risk Ladder module using Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
My blogs and code for machine learning. http://cnblogs.com/pinard
Market Risk Management with Time Series Prediction of Stock Market Trends using ARMA, ARIMA, GARCH regression models and RNN for time series analysis and prediction of short-term tends in stock prices.
Principal Component Analysis of Yield Curves
Binomial Option Tree: American Calls and Puts
This paper aims to explore the time series’ proprieties of the features extracted by using the Principal Component Analysis (PCA) technique on the European AAA-rated Government Bond Yield curve. The PCA can greatly simplify the problem of modelling the yield curve by massively reducing its dimensionality to a small set of uncorrelated features. It finds several applications in finance and in the fixed income particularly from risk management to trade recommendation. After selecting a subset of Principal Components (PCs), this paper first analyzes their nature in comparison to the original rates and the implications in terms of information retained and lost. Then the time-series characteristics of each PC are studied and, when possible, Auto-Regressive Moving-Average (ARMA) models will be fitted on the data. One hundred observations of the original dataset are set aside as a test set to evaluate the predictive power of these models. Eventually, further analyses are performed on the PCs to evaluate the presence of heteroscedasticity and GARCH-ARCH models are fitted when possible. Tests are performed on the fitted coefficient to investigate the real nature of the conditional variance process.
Echo State Networks in Python
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano
Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.