Lifetimes Part 2: Gamma Spend Model and Financial Valuation

See here for the full iPython Notebook code.  Some of the descriptions are outdated but the code is almost the same. After getting to know what lifetimes can provide, I started applying it from a financial perspective.  I wanted to answer the most important question for Zakka Canada:  Leveraging our customer analytic models, how can I estimate the firm value that Zakka Canada is worth as of today? The rest of this post is divided into two parts: 1) modelling the monetary value of our customer base and 2) estimating the price of Zakka Canada through a simple present value cash flow valuation […]

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Lifetimes Part 1: Customer Analytics

What is Customer Analysis?   Customer analysis, being such a vague phrase, can mean a lot of different things whether that's from businesses, financial analysts or everyday ordinary people.  The work I will be drawing upon comes from Peter Fader, Bruce Hardie as well as Cameron Pilon.  The research by Fader and Hardie matches the math with the customer behavioral story.  This is often referred to as Customer Lifetime Value (CLV), Recency, Frequency, Monetary Value (RFM) or Customer Probability Models, etc. etc.  These models focus exclusively on how customers make repeat purchases over their own lifetime relationship with the company.  Cameron Pilon later transformed their work into an easily implementable […]

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Optimal Alphadraft Bankroll Management with Beta-Beta Model and Kelly Criterion

For context, you can read my previous post on alphadraft betting for CS:GO here. After we have developed a concrete model for drafting our line-ups, we want to focus more on the bettor's bankroll management over time to minimize risk, maximize return and reduce our probability of ruin.  In this blog post, I will first describe the typical scenarios behind Alphadraft contests and how that can be translated to our probability model for win/loss (Beta-Beta Model).  We can then modify the standard Kelly Criterion to fit our case and calculate the optimal % bankroll to bet per game. Note that the concepts […]

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Guns, Bombs and eSports: Applying Data and Portfolio Analytics to Counter-Strike Gambling

Since the publication of Bill James' seminal work, Baseball Abstract, and the rise to stardom for the Oakland A's, Sports Analytics - the application of statistics to competitive sports - has been (and still is) a prominent topic within the industry.  Thus, it is only reasonable for practitioners to apply this movement to the new and upcoming playing field called eSports, which has gained a large following over the years with many online games such as League of Legends, Dota 2 and Counter-Strike: Global Offensive (CSGO).  I would like to argue that the data drawn from eSports is definitely more abundant and easier to acquire whereas, real life sporting data requires physical measurements, whether it's measured by a person […]

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Volatility Models and Backtests on Quantopian

In this blog post, I will present some backtest results on volatility models.  The list I present here are not exhaustive and there are still a gargantuan set of papers focusing on this issue (a good place to start is on vlab).  In the next section, I present some simple notations to define financial volatility and then define each model and show general backtest results with risk attributes.  The premise of the backtest is as follows: financial volatility of an investment portfolio is able to be minimized globally through allocating the correct amount of dollar toward each asset within the portfolio. […]

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A Portfolio Dashboard for SSIF (and perhaps you!)

I've recently been coding a dashboard project for the Sprott student fund (SSIF) in a Pythonian Framework called Django that someone recommended.  The project is subject to change at anytime and this post will be updated accordingly.   The reason I decided to use Python rather than traditional PHP that I'm familiar with is cause that seems to be where the industry is moving toward (at least for finance, development wise).   Furthermore, the pandas, numpy and scipy libraries are handy as well.   The current dashboard can be found here and github repo can be found here.  The purpose of the dashboard […]

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Creating Algorithmic Trading Portfolios with Quantopian (PART II)

In this post, I will be documenting a few of my strategies. The Two Divide in Universe Selection From my personal experience of hacking up strategies and browsing the forums for interesting topics/ideas, I found that there are often two divides in setting up the universe of stocks to trade.  The first being that a specific subset of stocks are hardcoded in the initialize phase, with most securities being a type of ETF that track some broad market.  This method has particular advantages such that it provides low commission, large diversification benefits and global exposure.  Disadvantages can include lack of alpha, high […]

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Creating Algorithmic Trading Portfolios with Quantopian (PART I)

Catching up My goals for this summer are to firstly, keep studying portfolio management and start reading Meucci Risk and Allocation; secondly, develop trading strategies and find some cheap way to implement them.  My current focus is on FX, due to its cheap spreads, however that seems to be changing as I realize a lot of the limitations of Metatrader. Moving onto API-based systems also present itself a problem since Questrade is currently stocks and options only... Side objective this summer is to casually read sports analytics cause it's extremely interesting (Did you know that the inferred probability of England […]

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