Displaying 1 to 3 from 3 results

Pyfolio - Portfolio and risk analytics in Python

  •    Python

pyfolio is a Python library for performance and risk analysis of financial portfolios developed by Quantopian Inc. It works well with the Zipline open source backtesting library.Also see slides of a talk about pyfolio.

scram - Probabilistic Risk Analysis Tool (fault tree analysis, event tree analysis, etc.)

  •    C++

SCRAM is a Command-line Risk Analysis Multi-tool. This project aims to build a command line tool for probabilistic risk analysis. SCRAM is capable of performing event tree analysis, static fault tree analysis, analysis with common cause failure models, probability calculations with importance analysis, and uncertainty analysis with Monte Carlo simulations. This tool can handle non-coherent fault trees, containing NOT logic.

financial-asset-comparison-tool - R Shiny app to compare the historical performance of crypto-assets and equities

  •    R

Welcome! The Financial Asset Comparison Tool is an R Shiny App that facilitates the comparison of a myriad of assets--both traditional and crypto--across time. The idea for this tool came to me when I was trading crypto-currencies actively, and spending a decent amount of time in investor telegram chats and forums. A common argument I would see was over what asset one should have invested in a short while ago, but it was clear that most such discussions were fueled by emotion--primarily "FOMO"--as opposed to testable metrics. This isn't just a popular type of discussion in the crypto investing space; in fact, it may be even more common in traditional finance. I wanted to create a tool that would be able to settle all such asset performance comparison questions, regardless of whether the question was about traditional assets such as equities, crypto-assets like Bitcoin and Ethereum, or some combination of both. The tools made available via this app allow for analysis of varying degrees of complexity, as can be seen in the visualization below. This scaling of metric complexity is also intuitively integrated into the UI design of the app itself, as illustrated by the screenshot below.