This articles demonstrates how to measure the risk adjusted performance of financial portfolios.

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Source code of this article can be downloaded from Github: Link

A financial portfolio is a collection of different assets such as stocks, bonds, ETFs, mutual funds, etc. The performance of any portfolio directly correlates to it’s constituents. Each asset within the portfolio has different return. …

This articles demonstrates how to measure the correlation of financial portfolios to build diversified portfolios.

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Source code of this article can be downloaded from Github: Link

In the context of finance, a portfolio is a collection of different financial assets (securities) such as stocks, bonds, exchange traded funds (ETFs), mutual funds, and/or cash. …

Part 3 of this multiple-part series on the most popular convolutional neural network (CNN) architectures with reproducible Python notebooks

Convolutional neural networks are a special type of neural network that is used for modeling data with strong spatial correlations such as images, multivariate time-series, earth science studies (seismic classification and regression), among many other applications. Convolutional networks have gone under significant changes since 1998 and in this series of…

Multipart series on time series analysis with Python applied to financial datasets

Source code of this article can be downloaded from Github: Link

Time Series

A time series is a series of data points indexed in time order. Time series resolution is the frequency that data is recorded. For example, heart rate measurements (in units of beats per minute) occur at 0.5 second intervals…

Part 2 of the multiple-part series on the most popular convolutional neural network (CNN) architectures with reproducible Python notebooks

Convolutional neural networks are special type of neural network that is used for modeling data with strong spatial correlations such as images, multivariate time-series, earth science studies (seismic classification and regression) among many other applications. Convolutional networks have gone under significant changes since 1998 and in this series of articles…

Part 1 of the multiple-part series on the most popular convolutional neural network (CNN) architectures with reproducible Python notebooks.

Convolutional neural networks are a special type of neural network that is used for modeling data with strong spatial correlations such as images, multivariate time-series, earth science studies (seismic classification and regression) among many other applications. Convolutional networks have gone under significant changes since 1998 and in this series of…

Using Python to explore seasonal effects on stock market and it’s different components

Code

Script to create this study is stored at my Github Page.

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Fama (1970) introduced efficient market hypothesis (EMH), stating the prices of securities fully reflect available information. Therefore, investors buying securities in an efficient market should expect to obtain an equilibrium rate of return. Later he introduced three different form…

Amir Nejad

PhD. Engineer | Data Scientist | Problem Solver | Solution Oriented (twitter: @Dr_Nejad)

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