Robert Bach

Robert Bach

I am a Quantitative Researcher, specialized in Algorithmic Trading and Portfolio Management. If you are interested in mathematics and programing, please connect with me.

Blog posts

2024

In-Depth Guide to Simulating Paths with the Euler-Maruyama Scheme for Exotic Options Pricing

7 minute read

Published:

In the world of finance, exotic options offer unique payoff structures that are highly sensitive to the path taken by the underlying asset. Traditional pricing models like Black-Scholes are not sufficient for such path-dependent options, so we turn to simulation methods. One of the most efficient methods for simulating asset prices is the Euler-Maruyama scheme, which is used to approximate solutions for stochastic differential equations (SDEs) like the geometric Brownian motion (GBM) that models asset prices.

Mastering QuantConnect’s Algorithm Framework for Efficient Trading Strategy Development

6 minute read

Published:

QuantConnect is an innovative, browser-based platform designed for algorithmic trading. Whether you’re a seasoned quant or just beginning, QuantConnect offers a robust environment to develop, test, and execute trading strategies using modern programming languages like Python and C#. The platform’s intuitive interface and vast data resources make it a go-to choice for traders at all levels.

How devdocs.io Revolutionized My Workflow in Data Science and Quantitative Finance

3 minute read

Published:

As a data scientist and quant developer, I constantly juggle between various libraries and frameworks. Scikit-learn, Pandas, and NumPy are the pillars of my work, whether I’m building predictive models, analyzing financial data, or optimizing trading strategies. However, keeping track of the vast amount of documentation for each library used to slow me down. That’s where devdocs.io comes in, and it has completely transformed my workflow.

Enhancing Algorithmic Trading Backtesting with Custom OHLC Resampling

4 minute read

Published:

In algorithmic trading, the accuracy of backtesting can make or break a strategy. The ability to simulate how your trading algorithm would have performed historically is crucial for refining and optimizing your approach. A key aspect of this process is how time series data, particularly OHLC (Open, High, Low, Close) data, is handled. While pandas provides a built-in method for resampling, it often falls short when dealing with the unique demands of financial data. In this post, we’ll explore a custom OHLC resampling function that offers greater precision, allowing for more reliable backtesting results.