diff --git a/index.html b/index.html index 0c22ba2..a021bcd 100644 --- a/index.html +++ b/index.html @@ -789,7 +789,9 @@ ul.task-list li input[type="checkbox"] {
  • QuantFinanceTraining - 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.
  • Statistical-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).
  • book_irds3 - Code repository for Pricing and Trading Interest Rate Derivatives.
    • -
  • Autoencoder-Asset-Pricing-Models - Reimplementation of Autoencoder Asset Pricing Models (GKX, 2019)
    • +
  • Autoencoder-Asset-Pricing-Models - Reimplementation of Autoencoder Asset Pricing Models (GKX, 2019).
    • +
  • Finance - 150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data.
    • +
  • 101_formulaic_alphas - Implemention of 101 formulaic alphas using qstrader.
    • diff --git a/projects.html b/projects.html index 9b75d89..78451f0 100644 --- a/projects.html +++ b/projects.html @@ -153,8 +153,8 @@ ul.task-list li input[type="checkbox"] { Table 1: Projects
    -
    - +
    +
    diff --git a/search.json b/search.json index db267d6..e3ebaac 100644 --- a/search.json +++ b/search.json @@ -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)" + "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." } ] \ No newline at end of file