csindera Goto Github PK
Type: User
Type: User
Exercises of the book: Advances in Financial Machine Learning by Marcos Lopez de Prado
Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]
Algorithmic trading framework for cryptocurrencies.
In this repo I conduct anomaly detection in the US equity market using deep learning and the price and volume of three major index etfs: SPY (S&P 500 etf), QQQ (NASDAQ etf), and DIA (Dow etf).
Plotting library for IPython/Jupyter Notebooks
my first repository
Introductory Applied Machine Learning (INFR09029)
With real market data using Black Scholes and Brentq
Machine Learning in Asset Management (by @firmai)
Various python scripts to introduce mean reversion concepts.
MlFinlab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
Developing Options Trading Strategies using Technical Indicators and Quantitative Methods
PyTrendFollow - systematic futures trading using trend following
Example of adaptive trend following strategy based on Renko
Notebooks based on financial machine learning.
Quantitative research and educational materials
Notebook for <Advances in Financial Machine Learning> using Python 3.7
three stochastic volatility model: Heston, SABR, SVI
Use Machine Learning to create a stock price predictor. Improved my technical analysis skills in the process. This is my capstone project for Udacity's Data Scientist Nanodegree Program.
SVI volatility surface model and an example of China 50ETF option
tick价差套利(参考vnpy网友资料、vnpy论坛资料、windquant): 1、按被动腿时间戳对齐 2、profile函数展示(需要py3) 3、平稳性检验 4、对冲手数计算 5、2sigma开仓,3sigma止损(或者赌价差扩散?)6、连续止损后cool down一段时间
This project used GARCH type models to estimate volatility and used delta hedging method to make a profit.
A statistical arbitrage strategy on treasury futures using mean-reversion property and meanwhile insensitive to the yield change
• Conducted a volatility study to develop pairs trading strategy by writing web crawlers that automated extracting 30 equity and ETF spot and options prices data from CBOE and Yahoo Finance • Utilized NumPy, Pandas, and SciPy packages to calculate implied volatility, realized volatility, and risk premiums to measure how the market prices risk • Gathered and plotted daily VIX futures data with Selenium, Seaborn and Matplotlib to study volatility term structure • Examined volatility clustering and built forecasting tools for market risk using correlations of daily absolute returns and volatility at different time lags
A complete set of volatility estimators based on Euan Sinclair's Volatility Trading
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.