arodman's Stars
PlamenStilyianov/FinMathematics
Books
uzairlol/InflationForecastML
Machine learning-based inflation forecasting project using Random Forest, XGBoost, and ensemble modeling. Includes feature engineering, hyperparameter tuning, and recursive forecasting for 2024 predictions.
JerBouma/AlgorithmicTrading
This repository contains three ways to obtain arbitrage which are Dual Listing, Options and Statistical Arbitrage. These are projects in collaboration with Optiver and have been peer-reviewed by staff members of Optiver.
rachellin0/econ_shifts
Support Vector Machines (SVM), Random Forest, and Bayesian Network to predict economic changes and causality between economic changes and 14 industries
jrfiedler/causal_inference_python_code
Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
py-why/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
EricPlekon/Nasdaq-Data-Link-API
quandl/quandl-python
PacktPublishing/Building-Trading-Bots
Building Trading Algorithms with Python, published by Packt
PacktPublishing/Hands-On-Genetic-Algorithms-with-Python-Second-Edition
Hands-On Genetic Algorithms with Python, Second Edition, published by Packt
PacktPublishing/Building-Trading-Algorithms-with-Python-
PacktPublishing/Hands-On-Financial-Trading-with-Python
Hands-On Financial Trading with Python, published by Packt
PacktPublishing/Getting-Started-with-Forex-Trading-Using-Python
Forex Algorithmic Trading using Python, published by Packt
PacktPublishing/Causal-Inference-and-Discovery-in-Python
Causal Inference and Discovery in Python by Packt Publishing
PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Second-Edition
Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.
PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading-sample
Hands-On Machine Learning for Algorithmic Trading, published by Packt
PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Bots-with-Python
PacktPublishing/Python-for-Algorithmic-Trading-Cookbook
Python for Algorithmic Trading Cookbook, published by Packt
TopherF22/PCA_Analysis
Using PCA to deconstruct yield curves
KyleMFE/PCA
PCA Applications in Yield Curve Structure
Qery-Data/Norway_Industry_Tracker
Key economic indicators for different industries in Norway
fipelle/replication-hasenzagl-et-al-2020
Replication code for "A Model of the Fed's View on Inflation".
rangikagmg/Empirical-Analysis-and-Forecasting-of-yield-curves.
Principal Component Analysis(PCA), Nelson-Siegel(NS) model, and Gaussian Regression Process(GPR) are used to fit and forecast the European yield curve with different maturities.
robcarrick/Visualisations-Australian-Economy
Creating a 3D yield curve with Australian bond data.
supreeth8/Term_structure_modeling
Yield curve Interpolation using cubic spline and nelson Seigel model
bernhard-pfann/pca-yield-curve-analytics
Predictive yield curve modeling in reduced dimensionality
letsgoexploring/fredpy
A Python module for easily retrieving and manipulating data series from Federal Reserve Economic Data (FRED)
tomasrubin/yield-curve-forecasting
This repository provides the implementation of a handful of forecasting methods in yield curve modelling.
epogrebnyak/data-ust
US Treasuries Yield Curve Data
fabriziobasso/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.