np.array([Finance, Python, Streamlit, Cloud Platform]) == http://18.219.211.47:8501/

The journey of building a financial web app…

In this article, I would like to show you how to design a CPPI (Constant proportion portfolio investment) strategy and publish it on the Internet. The result is http://18.219.211.47:8501/ (much more fun if you open the link on a big screen rather in a mobile browser)

Here are the steps.

First, let’s remember what CPPI is.

DIVERSIFICATION allows you to eliminate specific or idiosyncratic risks. It cannot help you deal with systematic risk as in 2008 crisis, when systematic risk impacted all the assets simultaneously.

The Bermuda Triangle of Investing

HEDGING, as an alternative, helps you with systematic risks but it is purely symmetric: you…


Analyzing the path of EURUSD derived from the market vol surface by using the Heston Model

There are three main volatility models in the finance: constant volatility, local volatility and stochastic volatility models.

Before the stock market crash of 1987, the Black-Scholes (B-S) model which was built on geometric Brownian motion (GBM) with constant volatility and drift was the dominant model. In this model, stock price is the only source of randomness and it can be hedged with the underlying stock with a return distribution as log-normal. In the B-S model, the stock price S is described by the following stochastic differential equation (SDE), where W is a standard Brownian motion:

where μ (return, drift, or riskless rate) and σ ( volatility) are constants.

The above Stochastic Differential Equation…


Endgame for “AI Winter”

How a competition, ImageNet, along with a noisy algorithm, Stochastic Gradient Descent, changed the fate of AI?

Picture from The Elders Scroll | Skyrim

In the early 1980s, Winter was coming for Artificial Intelligence (AI) with a period of reduced funding and interest in AI research, which will later be called the “AI Winter”!

During this cold-weather period which lasted until the mid-2000s, almost no research paper on Neural Nets was published because of the lost interest in the field. The reason was simple: no effective algorithms had been put forward against the traditional ones.


Karger’s algorithm back in town (with Python code)

Divide and conquer! A widely used strategy for years in history. But how to divide? Generally, most intuitive way is to look for segments with lowest levels of affinity or links within systems, networks, and even for populations! So, let’s start!

Are you ready for dividing and slicing? Let’s start with networks. We like dense networks, otherwise even if a very single edge or wire is disconnected, a large portion of the network can be down — a situation definetely not desired ! There are generally clusters, unevenly distributed segments, within networks. While dealing with separation of these segments or “clusters” efficiently, we look for edges between them such that the number of edges are minimum.

Eyup Gulsun

Quant Asset Allocation & Strategy manager in Asset Management, former Goldman Sachs & Merrill Lynch Trader - UC Berkeley & Bogazici Univ Alum

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