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Apprendre et Progresser
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A Propos

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Démarrer en Finance Quantitative avec Python

Auteur

Vincent D.

Tracking Error of QQQ vs SPY: A Practical Guide with Python

In the world of finance and investing, understanding how closely an investment tracks its benchmark index is crucial for evaluating performance and risk. This concept is…

Correlation of returns: why it matters, and how to analyse it with Python

Understanding the correlation of returns between different asset classes is a key concept in finance and investment. For instance, analysing the correlation between QQQ…

Maximum Drawdown: A Practical Approach with Python

Maximum Drawdown (MDD) is a critical risk metric for investors. It quantifies the largest peak-to-trough decline in an investment’s value over a specified period, providing…

Sharpe Ratio: A Must-Know for Every Investor

As a financial engineer with over 20 years of experience, I understand just how crucial the Sharpe Ratio is for evaluating investments. This essential metric provides a way…

Understanding Historical Volatility

If you’re a budding financial engineer, investor, or market enthusiast, understanding historical volatility is crucial. Historical volatility, by referencing past price…

Portfolio Optimisation with Python

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Setting up a Python Development Workstation (Windows)

In this article we set up our Windows workstation and install all the relevant tools to be efficient in coding in Python.

Setting up a Python Development Workstation (MacOs)

In this article, we start our journey into Finance and Python coding. Let’s set up our MacOs Workstation and install a few key tools to code efficiently.

Getting and charting data with Matplotlib

In this article, we start our data analysis journey with Python by getting and charting data, in this case the performance of an ETF using freely available data.

Visualising Historical Data

“A quick introduction on how to gather data using Python, Pandas and Pandas_DataReader in order to easily gather historical times from Yahoo Finance.”

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