diff --git a/index.html b/index.html index 5b2800b..6a4f7f6 100644 --- a/index.html +++ b/index.html @@ -485,7 +485,7 @@ ul.task-list li input[type="checkbox"] { diff --git a/projects.html b/projects.html index c9acfa3..134a7f8 100644 --- a/projects.html +++ b/projects.html @@ -154,8 +154,8 @@ ul.task-list li input[type="checkbox"] { Table 1: Projects
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diff --git a/search.json b/search.json index b2a49da..5fc7676 100644 --- a/search.json +++ b/search.json @@ -88,7 +88,7 @@ "href": "index.html#r", "title": "Awesome Quant", "section": "R", - "text": "R\n\nNumerical Libraries & Data Structures\n\nxts - eXtensible Time Series: Provide for uniform handling of R’s different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.\ndata.table - Extension of data.frame: Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns and a fast file reader (fread). Offers a natural and flexible syntax, for faster development.\nsparseEigen - Sparse pricipal component analysis.\nTSdbi - Provides a common interface to time series databases.\ntseries - Time Series Analysis and Computational Finance.\nzoo - S3 Infrastructure for Regular and Irregular Time Series (Z’s Ordered Observations).\ntis - Functions and S3 classes for time indexes and time indexed series, which are compatible with FAME frequencies.\ntfplot - Utilities for simple manipulation and quick plotting of time series data.\ntframe - A kernel of functions for programming time series methods in a way that is relatively independently of the representation of time.\n\n\n\nData Sources\n\nIBrokers - Provides native R access to Interactive Brokers Trader Workstation API.\nRblpapi - An R Interface to ‘Bloomberg’ is provided via the ‘Blp API’.\nQuandl - Get Financial Data Directly Into R.\nRbitcoin - Unified markets API interface (bitstamp, kraken, btce, bitmarket).\nGetTDData - Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto.\nGetHFData - Downloads and aggregates high frequency trading data for Brazilian instruments directly from Bovespa ftp site.\nReddit WallstreetBets API - Provides daily top 50 stocks from reddit (subreddit) Wallstreetbets and their sentiments via the API.\ntd - Interfaces the ‘twelvedata’ API for stocks and (digital and standard) currencies.\nrbcb - R interface to Brazilian Central Bank web services.\nrb3 - A bunch of downloaders and parsers for data delivered from B3.\nsimfinapi - Makes ‘SimFin’ data (https://simfin.com/) easily accessible in R.\n\n\n\nFinancial Instruments and Pricing\n\nRQuantLib - RQuantLib connects GNU R with QuantLib.\nquantmod - Quantitative Financial Modelling Framework.\nRmetrics - The premier open source software solution for teaching and training quantitative finance.\n\nfAsianOptions - EBM and Asian Option Valuation.\nfAssets - Analysing and Modelling Financial Assets.\nfBasics - Markets and Basic Statistics.\nfBonds - Bonds and Interest Rate Models.\nfExoticOptions - Exotic Option Valuation.\nfOptions - Pricing and Evaluating Basic Options.\nfPortfolio - Portfolio Selection and Optimization.\n\nportfolio - Analysing equity portfolios.\nsparseIndexTracking - Portfolio design to track an index.\ncovFactorModel - Covariance matrix estimation via factor models.\nriskParityPortfolio - Blazingly fast design of risk parity portfolios.\nsde - Simulation and Inference for Stochastic Differential Equations.\nYieldCurve - Modelling and estimation of the yield curve.\nSmithWilsonYieldCurve - Constructs a yield curve by the Smith-Wilson method from a table of LIBOR and SWAP rates.\nycinterextra - Yield curve or zero-coupon prices interpolation and extrapolation.\nAmericanCallOpt - This package includes pricing function for selected American call options with underlying assets that generate payouts.\nVarSwapPrice - Pricing a variance swap on an equity index.\nRND - Risk Neutral Density Extraction Package.\nLSMonteCarlo - American options pricing with Least Squares Monte Carlo method.\nOptHedging - Estimation of value and hedging strategy of call and put options.\ntvm - Time Value of Money Functions.\nOptionPricing - Option Pricing with Efficient Simulation Algorithms.\ncredule - Credit Default Swap Functions.\nderivmkts - Functions and R Code to Accompany Derivatives Markets.\nFinCal - Package for time value of money calculation, time series analysis and computational finance.\nr-quant - R code for quantitative analysis in finance.\noptions.studies - options trading studies functions for use with options.data package and shiny.\nPortfolioAnalytics - Portfolio Analysis, Including Numerical Methods for Optimizationof Portfolios.\nfmbasics - Financial Market Building Blocks.\nR-fixedincome - Fixed income tools for R.\n\n\n\nTrading\n\nbacktest - Exploring Portfolio-Based Conjectures About Financial Instruments.\npa - Performance Attribution for Equity Portfolios.\nTTR - Technical Trading Rules.\nQuantTools - Enhanced Quantitative Trading Modelling.\nblotter - Transaction infrastructure for defining instruments, transactions, portfolios and accounts for trading systems and simulation. Provides portfolio support for multi-asset class and multi-currency portfolios. Actively maintained and developed.\n\n\n\nBacktesting\n\nquantstrat - Transaction-oriented infrastructure for constructing trading systems and simulation. Provides support for multi-asset class and multi-currency portfolios for backtesting and other financial research.\n\n\n\nRisk Analysis\n\nPerformanceAnalytics - Econometric tools for performance and risk analysis.\n\n\n\nFactor Analysis\n\nFactorAnalytics - The FactorAnalytics package contains fitting and analysis methods for the three main types of factor models used in conjunction with portfolio construction, optimization and risk management, namely fundamental factor models, time series factor models and statistical factor models.\nExpected Returns - Solutions for enhancing portfolio diversification and replications of seminal papers with R, most of which are discussed in one of the best investment references of the recent decade, Expected Returns: An Investors Guide to Harvesting Market Rewards by Antti Ilmanen.\n\n\n\nTime Series\n\ntseries - Time Series Analysis and Computational Finance.\nfGarch - Rmetrics - Autoregressive Conditional Heteroskedastic Modelling.\ntimeSeries - Rmetrics - Financial Time Series Objects.\nrugarch - Univariate GARCH Models.\nrmgarch - Multivariate GARCH Models.\ntidypredict - Run predictions inside the database https://tidypredict.netlify.com/.\ntidyquant - Bringing financial analysis to the tidyverse.\ntimetk - A toolkit for working with time series in R.\ntibbletime - Built on top of the tidyverse, tibbletime is an extension that allows for the creation of time aware tibbles through the setting of a time index.\nmatrixprofile - Time series data mining library built on top of the novel Matrix Profile data structure and algorithms.\ngarchmodels - A parsnip backend for GARCH models.\n\n\n\nCalendars\n\ntimeDate - Chronological and Calendar Objects\nbizdays - Business days calculations and utilities" + "text": "R\n\nNumerical Libraries & Data Structures\n\nxts - eXtensible Time Series: Provide for uniform handling of R’s different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.\ndata.table - Extension of data.frame: Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns and a fast file reader (fread). Offers a natural and flexible syntax, for faster development.\nsparseEigen - Sparse principal component analysis.\nTSdbi - Provides a common interface to time series databases.\ntseries - Time Series Analysis and Computational Finance.\nzoo - S3 Infrastructure for Regular and Irregular Time Series (Z’s Ordered Observations).\ntis - Functions and S3 classes for time indexes and time indexed series, which are compatible with FAME frequencies.\ntfplot - Utilities for simple manipulation and quick plotting of time series data.\ntframe - A kernel of functions for programming time series methods in a way that is relatively independently of the representation of time.\n\n\n\nData Sources\n\nIBrokers - Provides native R access to Interactive Brokers Trader Workstation API.\nRblpapi - An R Interface to ‘Bloomberg’ is provided via the ‘Blp API’.\nQuandl - Get Financial Data Directly Into R.\nRbitcoin - Unified markets API interface (bitstamp, kraken, btce, bitmarket).\nGetTDData - Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto.\nGetHFData - Downloads and aggregates high frequency trading data for Brazilian instruments directly from Bovespa ftp site.\nReddit WallstreetBets API - Provides daily top 50 stocks from reddit (subreddit) Wallstreetbets and their sentiments via the API.\ntd - Interfaces the ‘twelvedata’ API for stocks and (digital and standard) currencies.\nrbcb - R interface to Brazilian Central Bank web services.\nrb3 - A bunch of downloaders and parsers for data delivered from B3.\nsimfinapi - Makes ‘SimFin’ data (https://simfin.com/) easily accessible in R.\n\n\n\nFinancial Instruments and Pricing\n\nRQuantLib - RQuantLib connects GNU R with QuantLib.\nquantmod - Quantitative Financial Modelling Framework.\nRmetrics - The premier open source software solution for teaching and training quantitative finance.\n\nfAsianOptions - EBM and Asian Option Valuation.\nfAssets - Analysing and Modelling Financial Assets.\nfBasics - Markets and Basic Statistics.\nfBonds - Bonds and Interest Rate Models.\nfExoticOptions - Exotic Option Valuation.\nfOptions - Pricing and Evaluating Basic Options.\nfPortfolio - Portfolio Selection and Optimization.\n\nportfolio - Analysing equity portfolios.\nsparseIndexTracking - Portfolio design to track an index.\ncovFactorModel - Covariance matrix estimation via factor models.\nriskParityPortfolio - Blazingly fast design of risk parity portfolios.\nsde - Simulation and Inference for Stochastic Differential Equations.\nYieldCurve - Modelling and estimation of the yield curve.\nSmithWilsonYieldCurve - Constructs a yield curve by the Smith-Wilson method from a table of LIBOR and SWAP rates.\nycinterextra - Yield curve or zero-coupon prices interpolation and extrapolation.\nAmericanCallOpt - This package includes pricing function for selected American call options with underlying assets that generate payouts.\nVarSwapPrice - Pricing a variance swap on an equity index.\nRND - Risk Neutral Density Extraction Package.\nLSMonteCarlo - American options pricing with Least Squares Monte Carlo method.\nOptHedging - Estimation of value and hedging strategy of call and put options.\ntvm - Time Value of Money Functions.\nOptionPricing - Option Pricing with Efficient Simulation Algorithms.\ncredule - Credit Default Swap Functions.\nderivmkts - Functions and R Code to Accompany Derivatives Markets.\nFinCal - Package for time value of money calculation, time series analysis and computational finance.\nr-quant - R code for quantitative analysis in finance.\noptions.studies - options trading studies functions for use with options.data package and shiny.\nPortfolioAnalytics - Portfolio Analysis, Including Numerical Methods for Optimizationof Portfolios.\nfmbasics - Financial Market Building Blocks.\nR-fixedincome - Fixed income tools for R.\n\n\n\nTrading\n\nbacktest - Exploring Portfolio-Based Conjectures About Financial Instruments.\npa - Performance Attribution for Equity Portfolios.\nTTR - Technical Trading Rules.\nQuantTools - Enhanced Quantitative Trading Modelling.\nblotter - Transaction infrastructure for defining instruments, transactions, portfolios and accounts for trading systems and simulation. Provides portfolio support for multi-asset class and multi-currency portfolios. Actively maintained and developed.\n\n\n\nBacktesting\n\nquantstrat - Transaction-oriented infrastructure for constructing trading systems and simulation. Provides support for multi-asset class and multi-currency portfolios for backtesting and other financial research.\n\n\n\nRisk Analysis\n\nPerformanceAnalytics - Econometric tools for performance and risk analysis.\n\n\n\nFactor Analysis\n\nFactorAnalytics - The FactorAnalytics package contains fitting and analysis methods for the three main types of factor models used in conjunction with portfolio construction, optimization and risk management, namely fundamental factor models, time series factor models and statistical factor models.\nExpected Returns - Solutions for enhancing portfolio diversification and replications of seminal papers with R, most of which are discussed in one of the best investment references of the recent decade, Expected Returns: An Investors Guide to Harvesting Market Rewards by Antti Ilmanen.\n\n\n\nTime Series\n\ntseries - Time Series Analysis and Computational Finance.\nfGarch - Rmetrics - Autoregressive Conditional Heteroskedastic Modelling.\ntimeSeries - Rmetrics - Financial Time Series Objects.\nrugarch - Univariate GARCH Models.\nrmgarch - Multivariate GARCH Models.\ntidypredict - Run predictions inside the database https://tidypredict.netlify.com/.\ntidyquant - Bringing financial analysis to the tidyverse.\ntimetk - A toolkit for working with time series in R.\ntibbletime - Built on top of the tidyverse, tibbletime is an extension that allows for the creation of time aware tibbles through the setting of a time index.\nmatrixprofile - Time series data mining library built on top of the novel Matrix Profile data structure and algorithms.\ngarchmodels - A parsnip backend for GARCH models.\n\n\n\nCalendars\n\ntimeDate - Chronological and Calendar Objects\nbizdays - Business days calculations and utilities" }, { "objectID": "index.html#matlab", @@ -186,6 +186,6 @@ "href": "index.html#reproducing-works-training-books", "title": "Awesome Quant", "section": "Reproducing Works, Training & Books", - "text": "Reproducing Works, Training & Books\n\nDerman Papers - Notebooks that replicate original quantitative finance papers from Emanuel Derman.\nML-Quant - Top Quant resources like ArXiv (sanity), SSRN, RePec, Journals, Podcasts, Videos, and Blogs.\nvolatility-trading - A complete set of volatility estimators based on Euan Sinclair’s Volatility Trading.\nquant - Quantitative Finance and Algorithmic Trading exhaust; mostly ipython notebooks based on Quantopian, Zipline, or Pandas.\nfecon235 - Open source project for software tools in financial economics. Many jupyter notebook to verify theoretical ideas and practical methods interactively.\nQuantitative-Notebooks - Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy\nQuantEcon - Lecture series on economics, finance, econometrics and data science; QuantEcon.py, QuantEcon.jl, notebooks\nFinanceHub - Resources for Quantitative Finance\nPython_Option_Pricing - An libary to price financial options written in Python. Includes: Black Scholes, Black 76, Implied Volatility, American, European, Asian, Spread Options.\npython-training - J.P. Morgan’s Python training for business analysts and traders.\nStock_Analysis_For_Quant - Different Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau.\nalgorithmic-trading-with-python - Source code for Algorithmic Trading with Python (2020) by Chris Conlan.\nMEDIUM_NoteBook - Repository containing notebooks of cerlymarco’s posts on Medium.\nQuantFinance - Training materials in quantitative finance.\nIPythonScripts - Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning.\nComputational-Finance-Course - Materials for the course of Computational Finance.\nMachine-Learning-for-Asset-Managers - Implementation of code snippets, exercises and application to live data from Machine Learning for Asset Managers (Elements in Quantitative Finance) written by Prof. Marcos López de Prado.\nPython-for-Finance-Cookbook - Python for Finance Cookbook, published by Packt.\nmodelos_vol_derivativos - “Modelos de Volatilidade para Derivativos” book’s Jupyter notebooks\nNMOF - Functions, examples and data from the first and the second edition of “Numerical Methods and Optimization in Finance” by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658).\npy4fi2nd - Jupyter Notebooks and code for Python for Finance (2nd ed., O’Reilly) by Yves Hilpisch.\naiif - Jupyter Notebooks and code for the book Artificial Intelligence in Finance (O’Reilly) by Yves Hilpisch.\npy4at - Jupyter Notebooks and code for the book Python for Algorithmic Trading (O’Reilly) by Yves Hilpisch.\ndawp - Jupyter Notebooks and code for Derivatives Analytics with Python (Wiley Finance) by Yves Hilpisch.\ndx - DX Analytics | Financial and Derivatives Analytics with Python.\nQuantFinanceBook - Quantitative Finance book.\nrough_bergomi - A Python implementation of the rough Bergomi model.\nfrh-fx - A python implementation of the fast-reversion Heston model of Mechkov for FX purposes.\nValue Investing Studies - A collection of data analysis studies that examine the performance and characteristics of value investing over long periods of time.\nMachine Learning Asset Management - Machine Learning in Asset Management (by @firmai).\nDeep Learning Machine Learning Stock - Deep Learning and Machine Learning stocks represent a promising long-term or short-term opportunity for investors and traders.\nTechnical Analysis and Feature Engineering - Feature Engineering and Feature Importance of Machine Learning in Financial Market.\nDifferential Machine Learning and Axes that matter by Brian Huge and Antoine Savine - Implement, demonstrate, reproduce and extend the results of the Risk articles ‘Differential Machine Learning’ (2020) and ‘PCA with a Difference’ (2021) by Huge and Savine, and cover implementation details left out from the papers.\nsystematictradingexamples - Examples of code related to book Systematic Trading and blog\npysystemtrade_examples - Examples using pysystemtrade for Robert Carver’s blog.\nML_Finance_Codes - Machine Learning in Finance: From Theory to Practice Book\nHands-On Machine Learning for Algorithmic Trading - Hands-On Machine Learning for Algorithmic Trading, published by Packt\nfinancialnoob-misc - Codes from @financialnoob’s posts\nMesoSim Options Trading Strategy Library - Free and public Options Trading strategy library for MesoSim.\nQuant-Finance-With-Python-Code - Repo for code examples in Quantitative Finance with Python by Chris Kelliher\nQuantFinanceTraining - This repository contains codes that were executed during my training in the CQF (Certificate in Quantitative Finance). The codes are organized by class, facilitating navigation and reference.\nStatistical-Learning-based-Portfolio-Optimization - This R Shiny App utilizes the Hierarchical Equal Risk Contribution (HERC) approach, a modern portfolio optimization method developed by Raffinot (2018).\nbook_irds3 - Code repository for Pricing and Trading Interest Rate Derivatives.\nAutoencoder-Asset-Pricing-Models - Reimplementation of Autoencoder Asset Pricing Models (GKX, 2019).\nFinance - 150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data.\n101_formulaic_alphas - Implemention of 101 formulaic alphas using qstrader." + "text": "Reproducing Works, Training & Books\n\nDerman Papers - Notebooks that replicate original quantitative finance papers from Emanuel Derman.\nML-Quant - Top Quant resources like ArXiv (sanity), SSRN, RePec, Journals, Podcasts, Videos, and Blogs.\nvolatility-trading - A complete set of volatility estimators based on Euan Sinclair’s Volatility Trading.\nquant - Quantitative Finance and Algorithmic Trading exhaust; mostly ipython notebooks based on Quantopian, Zipline, or Pandas.\nfecon235 - Open source project for software tools in financial economics. Many jupyter notebook to verify theoretical ideas and practical methods interactively.\nQuantitative-Notebooks - Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy\nQuantEcon - Lecture series on economics, finance, econometrics and data science; QuantEcon.py, QuantEcon.jl, notebooks\nFinanceHub - Resources for Quantitative Finance\nPython_Option_Pricing - An library to price financial options written in Python. Includes: Black Scholes, Black 76, Implied Volatility, American, European, Asian, Spread Options.\npython-training - J.P. Morgan’s Python training for business analysts and traders.\nStock_Analysis_For_Quant - Different Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau.\nalgorithmic-trading-with-python - Source code for Algorithmic Trading with Python (2020) by Chris Conlan.\nMEDIUM_NoteBook - Repository containing notebooks of cerlymarco’s posts on Medium.\nQuantFinance - Training materials in quantitative finance.\nIPythonScripts - Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning.\nComputational-Finance-Course - Materials for the course of Computational Finance.\nMachine-Learning-for-Asset-Managers - Implementation of code snippets, exercises and application to live data from Machine Learning for Asset Managers (Elements in Quantitative Finance) written by Prof. Marcos López de Prado.\nPython-for-Finance-Cookbook - Python for Finance Cookbook, published by Packt.\nmodelos_vol_derivativos - “Modelos de Volatilidade para Derivativos” book’s Jupyter notebooks\nNMOF - Functions, examples and data from the first and the second edition of “Numerical Methods and Optimization in Finance” by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658).\npy4fi2nd - Jupyter Notebooks and code for Python for Finance (2nd ed., O’Reilly) by Yves Hilpisch.\naiif - Jupyter Notebooks and code for the book Artificial Intelligence in Finance (O’Reilly) by Yves Hilpisch.\npy4at - Jupyter Notebooks and code for the book Python for Algorithmic Trading (O’Reilly) by Yves Hilpisch.\ndawp - Jupyter Notebooks and code for Derivatives Analytics with Python (Wiley Finance) by Yves Hilpisch.\ndx - DX Analytics | Financial and Derivatives Analytics with Python.\nQuantFinanceBook - Quantitative Finance book.\nrough_bergomi - A Python implementation of the rough Bergomi model.\nfrh-fx - A python implementation of the fast-reversion Heston model of Mechkov for FX purposes.\nValue Investing Studies - A collection of data analysis studies that examine the performance and characteristics of value investing over long periods of time.\nMachine Learning Asset Management - Machine Learning in Asset Management (by @firmai).\nDeep Learning Machine Learning Stock - Deep Learning and Machine Learning stocks represent a promising long-term or short-term opportunity for investors and traders.\nTechnical Analysis and Feature Engineering - Feature Engineering and Feature Importance of Machine Learning in Financial Market.\nDifferential Machine Learning and Axes that matter by Brian Huge and Antoine Savine - Implement, demonstrate, reproduce and extend the results of the Risk articles ‘Differential Machine Learning’ (2020) and ‘PCA with a Difference’ (2021) by Huge and Savine, and cover implementation details left out from the papers.\nsystematictradingexamples - Examples of code related to book Systematic Trading and blog\npysystemtrade_examples - Examples using pysystemtrade for Robert Carver’s blog.\nML_Finance_Codes - Machine Learning in Finance: From Theory to Practice Book\nHands-On Machine Learning for Algorithmic Trading - Hands-On Machine Learning for Algorithmic Trading, published by Packt\nfinancialnoob-misc - Codes from @financialnoob’s posts\nMesoSim Options Trading Strategy Library - Free and public Options Trading strategy library for MesoSim.\nQuant-Finance-With-Python-Code - Repo for code examples in Quantitative Finance with Python by Chris Kelliher\nQuantFinanceTraining - This repository contains codes that were executed during my training in the CQF (Certificate in Quantitative Finance). The codes are organized by class, facilitating navigation and reference.\nStatistical-Learning-based-Portfolio-Optimization - This R Shiny App utilizes the Hierarchical Equal Risk Contribution (HERC) approach, a modern portfolio optimization method developed by Raffinot (2018).\nbook_irds3 - Code repository for Pricing and Trading Interest Rate Derivatives.\nAutoencoder-Asset-Pricing-Models - Reimplementation of Autoencoder Asset Pricing Models (GKX, 2019).\nFinance - 150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data.\n101_formulaic_alphas - Implementation of 101 formulaic alphas using qstrader." } ] \ No newline at end of file