quant-platform.com

python-quants.com

The Python Quants Group

http://www.python-quants.com/index.html

The PYTHON QUANTS GROUP. Making the Best of Open Source. Your Partner for Python in Quant finance. On delivering Python- and Open Source-based financial analytics solutions. SIGN UP FOR OUR FREE PYTHON FOR FINANCE EMAIL COURSE. FOR PYTHON QUANTS CONFERENCE NYC—MAY 2016. ONLINE TRAININGS IN DATA SCIENCE, ALGO TRADING and QUANT FINANCE. PYTHON PODCAST WITH YVES HILPISCH ON QUANT FINANCE. WEBINAR ON AUTOMATED TRADING WITH PYTHON. Presentations and other resources by Yves Hilpisch. Python, R, Julia. Experien...

dx-analytics.com

Mean-Variance Portfolio Class — DX Analytics (Derivatives Analytics with Python)

http://dx-analytics.com/11_dx_mean_variance_portfolio.html

Without doubt, the Markowitz (1952) mean-variance portfolio theory. Is a cornerstone of modern financial theory. This section illustrates the use of the. Class to implement this approach. Market Environment and Portfolio Object. We start by instantiating a. Object which in particular contains a list of ticker symbols. In which we are interested in. Using pandas under the hood, the class retrieves historial stock price data. CPU times: user 236 ms, sys: 3 ms, total: 239 ms Wall time: 1.2 s. Portfolio am t...

dx-analytics.com

Framework Classes and Functions — DX Analytics (Derivatives Analytics with Python)

http://dx-analytics.com/01_dx_frame.html

Framework Classes and Functions. This section explains the usage of some basic framework classes and functions of DX Analytics. Mainly some helper functions, the discounting classes and the market environment class used to store market data and other parameters/data needed to model, value and risk manage derivative instruments. There are two helper functions used regulary:. Get year deltas: get a list of year deltas (decimal fractions) relative to first value in time list. Suppose we have a. Class repres...

dx-analytics.com

Multi-Risk Derivatives Portfolios — DX Analytics (Derivatives Analytics with Python)

http://dx-analytics.com/05_dx_portfolio_multi_risk.html

The step from multi-risk derivatives instruments to multi-risk derivatives instrument portfolios is not a too large one. This part of the tutorial shows how to model an economy with three risk factors. This sub-section models the single risk factors. We start with definition of the risk-neutral discounting object. Probability for jump p.a. Expected jump size [%]. Finally, the unifying valuation assumption for the valuation environment. These are added to the single. Objects of the risk factors. Time stat...

dx-analytics.com

Quickstart — DX Analytics (Derivatives Analytics with Python)

http://dx-analytics.com/00_dx_quickstart.html

This brief first part illustrates—without much explanation—the usage of the DX Analytics library. It models two risk factors, two derivatives instruments and values these in a portfolio context. The first step is to define a model for the risk-neutral discounting. We then define a market environment. Containing the major parameter specifications needed,. Starting value of simulated processes. Next, the model object for the first risk factor. In general two (. Parameters to the market environments. To ill...

dx-analytics.com

Multi-Risk Derivatives Valuation — DX Analytics (Derivatives Analytics with Python)

http://dx-analytics.com/04_dx_valuation_multi_risk.html

A specialty of DX Analytics is the valuation of derivatives instruments defined on multiple risk factors and portfolios composed of such derivatives. This section of the documentation illustrates the usage of the dedicated multi-risk valuation classes. There are the following multiple risk factor valuation classes. Valuation mcs european multi. For the valuation of multi-risk derivatives with European exercise. Valuation mcs american multi. Market environments for the risk factors. Are the starting point.

dx-analytics.com

Derivatives Portfolio Risk Statistics — DX Analytics (Derivatives Analytics with Python)

http://dx-analytics.com/07_dx_portfolio_risk.html

Derivatives Portfolio Risk Statistics. From a risk management perspective it is important to know how sensitive derivatives portfolios are. With regard to certain parameter values (market quotes, model assumptions, etc.). This part illustrates how to generate certain risk reports. The example is based on two risk factors. Both modeled as geometric Brownian motions. Add valuation environment to market environments. Market with two risk factors. We are going to model total of 6 derivatives positions. Portf...

hilpisch.com

Python_Financial_Community slides

http://hilpisch.com/Python_Financial_Community.html

Python and the Financial Community ¶. A Subjective and Biased Overview. Dr Yves J. Hilpisch. The Python Quants GmbH. The Python Language ¶. Of geometric Brownian motion. DS t = rS tdt sigma S t dZ t $. Draw $I$ standard normally distributed random number $z t i$ and apply them to the following by Euler disctretization scheme to simulate $I$ end values of the GBM:. S {T} = S 0 exp left( left( r - frac{1}{2} sigma 2 right) T sigma sqrt{T} z T right) $. Description of Euler discretization. CPU times: user 1...

hilpisch.com

Browser_based_Financial_Analytics slides

http://hilpisch.com/Browser_based_Financial_Analytics.html

Browser-based, Collaborative Financial and Derivatives Analytics ¶. Python Quant Platform and DX Analytics. Dr Yves J. Hilpisch The Python Quants GmbH. For Python Quants Conference, 28. November 2014. The Python Language ¶. Of geometric Brownian motion. The Hello World example of Quant Finance. DS t = rS tdt sigma S t dZ t $. Draw $I$ standard normally distributed random number $z t i$ and apply them to the following by Euler disctretization scheme to simulate $I$ end values of the GBM:. The rpy2.ipy...