DancewithChuckPrince

DancewithChuckPrince's Stars

• psf/requests

  A simple, yet elegant, HTTP library.

  Language:Python52.3k9.3k

• Prograsaur/ib-historical-data

  Interactive Brokers TWS API -- Historical data downloader

  Language:Python5317

• konscodes/tradelog_importer

  Python script to work with IB Third-Party TradeLog files

  Language:Python31

• Glebeserker/IB_to_XCEL_Python

  To utilize Interactive Brokers API to send portfolio information onto Excel spreadsheet to make models.

  Language:Python31

• 1kc2/Long-Short-Stress-Test

  📊 Long/Short Equity Portfolio Stress Test

  Language:Python71

• 1kc2/Minimal-Correlation-Portfolio

  🏦 Building a Minimally Correlated Portfolio with Data Science

  Language:Python71

• santoshlite/EigenLedger

  An Open Source Portfolio Backtesting Engine for Everyone | 面向所有人的开源投资组合回测引擎

  Language:Python958128

• Kiran-Sawant/Bridge

  This project consists of 2 scripts & 2 excel templates that help in preemptive risk management

  Language:Python1

• dcajasn/OpenBBTerminal

  Investment Research for Everyone.

  Language:Python31

• dcajasn/cvxpy

  A Python-embedded modeling language for convex optimization problems.

  Language:C++41

• prashantbhuyan/Investment-Portfolio-Performance-Attribution

  The purpose of this project is to measure how much of the performance of a diversified quantitative investment portfolio is significantly impacted by random market behavior, if at all. If successful, the results of this analysis will lay the groundwork for a broader analysis pertaining to the separation of alpha and beta across the investment portfolio. If the "luck" portion of the portfolio can be measured dynamically (accounting for lags etc) then a hedging tool could potentially eliminate random market risk without eroding portfolio returns in times of erratic market behavior. The methodology is to obtain historical performance data from 11 different trading models (mean reversion, pairs, market making, momentum, statistical arbitrage, etc) that together form a diversified investment portfolio over a particularly volatile trading period. I will explore the data by analyzing the distribution of performance across symbols and across time periods to reveal the structure of the performance data and how it relates to and is impacted by market behavior. I will then model the data to measure how much of the performance is explained by the market and market volatility, its clustering tendencies and its correlation to the predictor variables. Finally, I will interpret the results and reconcile the results with my original hypothesis to determine if it makes sense to continue work to create a hedging instrument for the portfolio.

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• dcajasn/Riskfolio-Lib

  Portfolio Optimization and Quantitative Strategic Asset Allocation in Python

  Language:C++3.1k528

• blaahhrrgg/equity-risk-model

  Attribution and optimisation using a multi-factor equity risk model.

  Language:Jupyter Notebook306

• MBKraus/Python_Portfolio__VaR_Tool

  Python-based portfolio / stock widget which sources data from Yahoo Finance and calculates different types of Value-at-Risk (VaR) metrics and many other (ex-post) risk/return characteristics both on an individual stock and portfolio-basis, stand-alone and vs. a benchmark of choice (constructed with wxPython)

  Language:Python11637

• SharmaVidhiHaresh/Portfolio-Risk-Analysis-with-Python

  Using a dataset of hedge fund indices, I had computed various risk parameters, explicitly Value at risk (VaR), drawdown and deviation from normality with Python. Using different models, I had computed non-parametric VaR, Parametric Gaussian Model VaR and Cornish-Fisher VaR, as well as plotted the VaR of all hedge fund indices.

  Language:Jupyter Notebook215

• ranaroussi/quantstats

  Portfolio analytics for quants, written in Python

  Language:Python5.1k873