capm

capm's Stars

• TencentARC/GFPGAN

  GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.

  Language:Python35.6k5044725.9k

• rlabbe/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.

  Language:Jupyter Notebook16.5k4633234.2k

• cuemacro/finmarketpy

  Python library for backtesting trading strategies & analyzing financial markets (formerly pythalesians)

  Language:Python3.4k21528494

• dwcoder/QuantitativePrimer

  An Interview Primer for Quantitative Finance

  Language:TeX1.2k386158

• svaksha/pythonidae

  Curated decibans of scientific programming resources in Python.

  Language:Julia956744187

• deshpandenu/Time-Series-Forecasting-of-Amazon-Stock-Prices-using-Neural-Networks-LSTM-and-GAN-

  Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.

  Language:Jupyter Notebook3961411121

• robjhyndman/ETC3550Slides

  Slides for a forecasting course based on "Forecasting: Principles and Practice"

  Language:R168129168

• sofienkaabar/the-book-of-trading-strategies

  Source Codes for the Book of Trading Strategies

  Language:Python16011097

• lakshmiDRIP/DROP

  Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics

  Language:HTML117987845

• unbalancedparentheses/data_science_in_julia_for_hackers

  Data Science in Julia With Hackers

  Language:Julia112108525

• danielmangrum/jobmarkethacker

  A tool to create application packets for the academic economics job market

  Language:TeX1055055

• Ncohen10/StockAlgorithm

  A profitable mean reversion stock trading algorithm

  Language:Python9111231

• bendgame/MediumFinance

  Language:Python476226

• Harkishan-99/Alternative-Bars

  Generate various Alternative Bars both historically and at real-time.

  Language:Jupyter Notebook343219

• OscarJHernandez/qc_portfolio_optimization

  A program that implements the portfolio optimization experiments using a hybrid quantum computing algorithm from arXiv:1911.05296. The code was developed as part of the 2020 Quantum mentorship program. Many thanks to my mentor Guoming Wang from Zapata Computing!

  Language:Python265313

• rladies/meetup-presentations_buenosaires

  R-Ladies Buenos Aires recursos

  Language:HTML2414012

• dmadeka/PyDataNYC2017

  This repository contains the slides and notebooks for the bqplot talk at PyData NYC 2017

  Language:Jupyter Notebook20309

• austinlasseter/flying-dog-beers

  Language:Python1820546

• letsgoexploring/yield-curve-animation

  An animated visualization of the US Treasury yield curve from January 1965 to the present.

  Language:Jupyter Notebook15508

• 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.

  Language:Jupyter Notebook143013

• howardbaek/mastering-shiny-book-exercises-solutions-book

  Repo for Mastering Shiny Solutions

  Language:HTML11213

• luphord/yield_curve_dynamics

  A cursory look at the dynamics of zero coupon bond yield curves.

  Language:Jupyter Notebook11008

• asitav-sen/IFRSassistant

  Shiny app for IFRS provisioning and estimated loss report

  Language:R10442

• MichaelKono/Yahoo-Scraper

  A simple Yahoo option chain scraping library

  Language:C#8100

• nathanwilthomas/UK-Yield-Curve

  PCA analysis on UK spot rates to model attributes of the yield curve

  Language:Jupyter Notebook8114

• ltiao/notebooks

  A collection of my IPython Notebooks

  Language:Jupyter Notebook5403

• KyleMFE/PCA

  PCA Applications in Yield Curve Structure

  Language:Jupyter Notebook4101

• mbonillacr/R4XCL

  Se incluye la documentación necesaria para ejecutar R4XCL

  41

• VVVleng/Bond-Yield-Curve-Construction

  Language:Jupyter Notebook30

• oisinkenny/ustreasurycurve

  Pulls the historical nominal and real yield curve from the US Treasury's website into a Pandas dataframe

  Language:Python2