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109a-final-project

2021-cs109b

american_option_valuation

Valuation of American options using binomial trees, and numerical approximation of the exercise boundaries for various underlying.

arima-and-garch

Applying the time series models of ARIMA and GARCH to model stock returns and volatility respectively

arima_garch

ARIMA & GARCH models for stock price prediction

arimax

Understanding ARIMAX modeling in Python.

bermuda_callable

bermudan_swaption_nn

Price Bermudan style swaptions using String Market Interest rate model and Neural Network

causalitytools.jl

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.

csci-e-96

Repository for CSCI-E-96 Data Mining and Exploration Course

data

Data and code behind the articles and graphics at FiveThirtyEight

deep-hedging

Deep Hedging Demo - An Example of Using Machine Learning for Derivative Pricing.

deep-learning-machine-learning-stock

Stock for Deep Learning and Machine Learning

deep-learning-model-for-stock-price-prediction

A simple deep learning model for predicting SP500 stock price using TensorFlow

deep-stock

Deep Learning for Stock Market

filterpy

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'.

high-frequency-trading-model-with-ib

A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion in Python

ib_insync

Python sync/async framework for Interactive Brokers API

interestrateswapriskladders

Interest Rate Swap Pricing and Risk Ladder module using Python

kalman-and-bayesian-filters-in-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.

lch

machinelearning

My blogs and code for machine learning. http://cnblogs.com/pinard

market-risk-management-with-time-series-prediction-of-stock-market-trends-

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.

mva2-pca

Principal Component Analysis of Yield Curves

options

Binomial Option Tree: American Calls and Puts

pcaapplied_and_european_yield_curve

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.

pset1_test

pyesn

Echo State Networks in Python

pymc3

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

pytorch-mnist-celeba-gan-dcgan

Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets