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Quantitative Content Analysis of Bitcoin Facebook Groups

Abstract

Social media sites such as Facebook allow people across the globe to converse and
share posts without borders. Users can form groups to interact about a variety
of topics, and cryptocurrency is no exception. The focus of this study was
to determine the demographics and activity of groups focused on both Bitcoin
specifically and cryptocurrencies more broadly, and how they compare to one
another. Results indicated that groups found in search results for “bitcoin” were
typically larger and more active than those for “cryptocurrency”. However,
the major finding of this study was that many of the groups served primarily
to promote dubious money-making opportunities and in some cases outright
scams.

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Contract-Specific Trading Costs and Optimal Execution Strategy

Intro

There are as many strategies for extracting alpha from the markets as there are traders. Unfortunately, this article will be discussing none of them. If that’s what you’re looking for, I suggest you check out the very sophisticated techniques covered in this video.

OK. If you’re still reading, you probably take trading at least somewhat seriously. When setting up your trading business (and it is a business, like any other), one of the most important things to consider is the impact that costs will have on your bottom line. And just like any other business, traders deal with costs in many forms. This article will discuss the explicit and implicit costs that are incurred on every transaction in global markets. We’ll apply this primer to a real world application for the cryptocurrency derivatives exchange Bitmex, with its unique properties. Finally, we’ll develop a simple execution algorithm that should help to reduce the impact costs have on our PnL. If any of that sounds remotely interesting or useful, read on!

Notebook for this article can be found here.

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Position Sizing for Practitioners [Part 3: A Portfolio Approach]

“Diversification is the Only Free Lunch”

I’m sure everyone has heard this old adage at some point in their trading career. Most people probably shrug it off and go back to watching The Big Short and dreaming of putting on that one career-making trade. Or maybe they’re still trying to figure out how to pick every single top and bottom on one instrument, thinking all they need is that one perfect strategy. The idea of concentrated bets and being right is just so sexy. Compare this to trading a basket of strategies across styles, time frames, and asset classes. Hopefully the thought alone hasn’t bored you to sleep. Because what I hope to prove with this post is that diversification can combine multiple small edges into a portfolio with both reduced risk AND increased reward . This was a true turning point in how I thought about trading, and hopefully it will be for you too.

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Trading Metrics that Actually Matter

Jupyter Notebook for this post can be found here

Traders love their performance metrics. Anyone who’s used their platform’s backtesting features has probably come across a few dozen of them, and everyone’s got their favorite. Anybody who’s anybody in the finance world has one named after them: Sharpe, Sortino, Calmar, Treynor, Gartman, etc. (OK, maybe not the last one). But which ones are the most important? There should be some kind of objective answer to this, right? If you look for the answer to this question on Google, you’ll be quickly overwhelmed. Most people want to give you 5+ different metrics you should be focusing on, all at once. But they can’t all be equally important; what we really want is a fitness function, or one statistic that can be optimized to compare all strategies against one another.

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Position Sizing for Practitioners [Part 2: Dealing with Drawdown]

The Problem with Optimal f

What does “optimal” mean, anyway? In the first part of this series, we discovered that the staked fraction of capital that yields the greatest compounded returns also yields a less-than-optimal level of drawdown.  To realize the greatest return on capital, an investor in SPY since its inception should have used over 3x leverage to buy in. This would have yielded the greatest compounded rate of return, but would have induced a 97% (!!!) max drawdown along the way. Since a 20% retracement from peak equity causes most investors to start tossing in their sleep, this approach doesn’t seem very realistic. This post will help traders maximize their gains while still getting their beauty rest!

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Position Sizing for Practitioners [Part 1: Beyond Kelly]

Background

Albert Einstein once proclaimed that “compound interest is the eighth wonder of the world” (allegedly, at least; people attribute all kinds of sayings to that guy). Let’s just assume that he did. This is the single most important reason why people participate in the markets. The magic of compounding interest turns time into an exponential money multiplier, and the greater the rate the more dramatic the results. This is the single most important concept that traders need to understand.

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Volume and Volatility: A Tale of Two Vols

Background

Outside of price action, two of the most popular market characteristics analyzed are volume and volatility. Volatility is often used to determine market regime, while the traditional use for volume is to confirm price movement.  This post will investigate the relationship between these two characteristics, and whether using both of them may be redundant.

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