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JerBouma / repository
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.
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This repository contains three ways to obtain arbitrage:
These are projects in collaboration with Optiver and have been peer-reviewed by staff members of Optiver. Therefore, much of the analysis are correct and give an indication how these methods work.
Please note that these methods can only be effective when written in C++ as speed is of utmost performance. Next to that it requires a lightning fast connection (talking in nanoseconds) which is not feasible for the retail investor. Any profits made by using these strategies is therefore by pure chance.
I suggest using my FinanceDatabase to do fundamental analysis and base your investment decisions on that since it has proven to be profitable.
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Dhiren-Pragallapati /
This repository contains algorithmic trading strategies for the S&P 500 index, focusing on value investing approaches. It includes Jupyter notebooks with Python code for data analysis, strategy implementation, and backtesting. The project aims to identify undervalued stocks in the S&P 500 using financial metrics and market data.
43/100 healthThis repository contains the code for a Jupyter notebook that combine new algorithmic trading skills with existing skills in financial Python programming and machine learning to create an algorithmic trading bot that learns and adapts to new data and evolving markets.
37/100 health