Event-driven Serverless Architecture with Bayesian Priority Queue for Osu! User Tracking

September 2020    Uncategorized

If you're interested in using the app, click here to check it out! Osu! is a rhythm game that I've been playing for the past few months.  The goal of the game is just to simply click circles on your screen with the right timing.  The game also contains a rich competitive performance system where each completed play rewards players with performance points (pp for short) and players are given global rankings based on their average pp.  One cool feature that the user base has been asking for is to announce to discord channels whenever the user has made a new […]

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Hierarchical bayesian rating model in PyMC3 with application to eSports

November 2017    eSports, Machine Learning, Python

Suppose you are interested in measuring how strong a counterstrike eSports team is relative to other teams.  This measure will need to be able to predict the outcome of a  heads-up matches between two teams. We can use bayesian analysis to do this through inferring a latent rating variable for each team that predicts match results.  This rating variable should reflect the relative strength and consistency of a team since all sports outcomes are inherently probabilistic. Quick debrief on Counterstrike Professional counterstrike consists of two teams of 5 players playing head to head on one of the seven available maps with first to reach 16 rounds […]

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Lifetimes Part 2: Gamma Spend Model and Financial Valuation

July 2016    Customer Analysis, Finance, Python, Quantitative Analysis, Time-series Analysis

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  Some of the descriptions are outdated but the code is almost the same.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

March 2016    Customer Analysis, Finance, Programming, Python

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

March 2016    eSports, Programming, Python

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

October 2015    eSports, Portfolio, Portfolio Management, Python, Statistics

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

September 2015    Finance, GARCH, Python, Quantitative Analysis, Quantopian, Time-series Analysis, Volatility

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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Creating Algorithmic Trading Portfolios with Quantopian (PART II)

June 2015    Finance, Forex, Python, Quantopian, Stocks, Trading

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)

June 2015    Finance, Programming, Python, Quantopian, Stocks, Technical Analysis, Trading

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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