Quantocracy

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Quantocracy’s Daily Wrap for 08/05/2016

This is a summary of links featured on Quantocracy on Friday, 08/05/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Simple Moving Average Filter | Trading Strategy [Oxford Capital]

    I. Trading Strategy Source: Kaufman, P. J. (2013). Trading Systems and Methods. New Jersey: John Wiley & Sons, Inc. Concept: Trend following trading strategy based on Simple Moving Average (SMA) filters. Research Goal: To benchmark the Simple Moving Average (SMA) against the Hull Moving Average (HMA). Specification: Table 1. Results: Figure 1-2. Trade Filter: Long Trades: Fast_SMA[i ? 1]
  • SEBI Releases Paper on Algorithmic Trading & Co-Location [Quant Insti]

    SEBI issued a discussion paper today with inputs from all stakeholders such as investors, infrastructure institutions and intermediary to understand how Algorithmic Trading has led to fairness, concerns and changes in market quality in recent years. It states that more than 80% of the orders placed on most of the exchange traded products are generated by algorithms and such orders contribute to

Quantocracy’s Daily Wrap for 08/04/2016

This is a summary of links featured on Quantocracy on Thursday, 08/04/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Most Useful Investment Blogs [Dual Momentum]

    As with many people these days, most of my investment information comes from the internet. It has taken me years to compile a group of research-oriented blogs and websites that I have found most useful. Here is my list: Investment Blogs Quantocracy: This is an aggregator of quantitative trading links to blog posts and research articles. It covers a broad range of ideas from coding to theory. So
  • How Do VelocityShares’ BSWN, LSVX, & XIVH Work? [Six Figure Investing]

    The indexes that power VelocityShares new BSWN, LSVX, and XIVH funds have been live since 2011, but they havent been directly accessible via exchange traded products until July 2016. The goals of these new funds are pretty straightforward, on the long side BSWN & LSVX track upside volatility with some fidelity while minimizing decay costs, while XIVH captures the premium typically available
  • 50 Years Of Sharpe Ratio Analysis: Useful But Easily Abused [Capital Spectator]

    The Sharpe ratio was introduced half a century ago and its still going strong. Although the world is now awash with competitors, the granddaddy of quantitative risk metrics endures. Its longevity and widespread use drives some analysts batty, but for good or ill the SR is deeply embedded into the fabric of risk management discussions and analytics. Part of its appeal is its simplicity, but that
  • Flat and Slightly Down for Trend Following in July [Wisdom Trading]

    June 2016 Trend Following: UP -1.34% / YTD: -0.92% Not much volatility for the index in the month of July. It started around +1% and finished around -1% with little amplitude in the middle. The YTD just turned slightly negative. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for July: Wisdom State of Trend Following – July 2016 And the

Quantocracy’s Daily Wrap for 08/03/2016

This is a summary of links featured on Quantocracy on Wednesday, 08/03/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Practical Ethereum Arbitrage Experiments [Koppian Adventures]

    Introduction Inspired by another blogpost I decided to experiment with trading arbitrage between different exchanges. Not so long ago there was a hardfork in the blockchain-based cryptocurrency Ethereum. This means that we now have two Ethereums: Ethereum (ETH) and Ethereum Classic (ETC). The exchanges decided to also support the original cryptocurrency, i.e., ETC, and so we can now trade ETH
  • Podcast: Interview with Yves Hilpisch of @PythonQuants [Chat With Traders]

    This episode features Dr. Yves Hilpischthe founder of The Python Quants. TPQ do a lot of good for those involved in quantitative finance, they; frequently host meet-ups and workshops, have developed platforms and analytics libraries, and often contract to exchanges, banks and hedge funds for custom Python development. Yves is also a three-time published author, with his most notable title
  • Start Dates, Correlation and Random Strategy [Alvarez Quant Trading]

    In my last post I showed research on how optimization results can be mean reverting. Sometimes, my research keeps getting side tracked as I think of random ideas to look at. In this post, we look at the random walk my research took starting from my mean reverting optimization research. I will show how changing the start date can have a big change in the results, correlation of 1990s to now, and

Quantocracy’s Daily Wrap for 08/02/2016

This is a summary of links featured on Quantocracy on Tuesday, 08/02/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mailbag: How Do You Move From Quant Developer To Quant Trader? [Quant Start]

    I was emailed recently with a career-related question about jumping from one quant role to another. The question posed was "How can I make the jump from being a quant/software developer to a quant trader/researcher in a fund or investment bank?". This is certainly possible and does happen occasionally. However, it will require some extra-curricular work, and some initiative, in order to
  • Fine Wine is a Fine Addition to Your Investment Portfolio [Alpha Architect]

    Here we are in August, a great time to drinkand thinkabout wine. Of course, as a research-focused finance blog, our angle on wine is a bit different than that of Dr. Vino. A summary of the discussion: we estimate a real financial return to wine investment (net of storage costs) of 4.1%, which exceeds bonds, art, and stamps. Get your attention? Lets dive right in, but grab a glass of
  • Cassandra as a Historical Finance DB [Ryan Kennedy]

    While the explosion of noSQL database offerings of late can be daunting, each of them is typically suited for a particular purpose. Most CRUD web-applications can be comfortably done with either noSQL or a RDBMS, however for true high performance applications, the choice of database is of make-or-break importance. In this post I'll explain what we typically want from a database to store

Quantocracy’s Daily Wrap for 08/01/2016

This is a summary of links featured on Quantocracy on Monday, 08/01/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Using System Parameter Randomization To Estimate Future Returns [System Trader Success]

    You just spent a ton of time creating a trading system and being very careful not to over-optimize. You then tested it on the out-of-sample data segment and the performance looks good. What's next? Jump right into the live market? Maybe. But instead, you would like to perform one more test called System Parameter Randomization. The article, System Parameter Permutation a better
  • Paper: Stock Portfolio Design and Backtest Overfitting (h/t Abnormal Returns)

    We demonstrate a computer program that designs a portfolio consisting of common securities, such as the constituents of the S&P 500 index, that achieves any desired profile via in-sample backtest optimization. Unfortunately, the program also shows that these portfolios typically perform erratically on more recent, out-of-sample data, which is symptomatic of selection bias. One implication of
  • Empirical Analysis of Limit Order Books [Quant Insti]

    What is an Order book? With the growing popularity of Algorithmic and High Frequency Trading, study of order books has grown manifolds. Order book is essentially an electronic list of all Buy and Sell orders, arranged as per price time priority. This means that a person having higher price on the buy side or lower price on the sell side will get priority over others to execute the trade. If
  • Can Dividend (Swaps) Replace Bonds? [Flirting with Models]

    Summary As a stand-alone asset class, dividends may make an interesting alternative to fixed income: they offer low volatility, are generally robust to market crises, and may serve as an inflation hedge. Accessing dividend strips was previously restricted to institutional investors, using over-the-counter swaps or exchange traded futures. For retail investors today, the ETF DIVY enables access to

Quantocracy’s Daily Wrap for 07/29/2016

This is a summary of links featured on Quantocracy on Friday, 07/29/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Case for Hedge Funds / Creating an Ideal Liquid Alt [EconomPic]

    A hedge fund is simply a go anywhere investment vehicle that attempts to provide excess returns to cash with a low correlation to traditional asset classes (i.e. vehicles that provide alpha). Hedge funds and liquid alternatives have taken a lot of heat recently, much of it deserved, but in this post I'll outline the bull case. Specifically, this post will outline the benefit of a hedge fund
  • Clustering: “Two’s company, three’s a crowd” [Quant Dare]

    Its hard enough deciding which Machine Learning technique to use, but after selecting an appropriate clustering algorithm the next challenges begin: how good is the separation and into how many groups should you divide the data? Maybe three is not always a crowd First, lets set the scene We want to create a diversified investment strategy using a set of predictions and making use of

Quantocracy’s Daily Wrap for 07/28/2016

This is a summary of links featured on Quantocracy on Thursday, 07/28/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Asset Class Risk Premiums Explained by Skewness [Quantpedia]

    We present extensive evidence that "risk premium" is strongly correlated with tail-risk skewness but very little with volatility. We introduce a new, intuitive definition of skewness and elicit an approximately linear relation between the Sharpe ratio of various risk premium strategies (Equity, Fama-French, FX Carry, Short Vol, Bonds, Credit) and their negative skewness.
  • Momentum on Individual Stocks vs Asset Classes [Sharpe Returns]

    I had the pleasure of finally meeting Gary Antonacci earlier this year. Gary is the creator of the momentum strategy that I follow and have been discussing on this blog. I first came across his work in 2011 on the blog Abnormal Returns (which should be a daily read for investors). Gary and I have been e-mailing each other ever since. After over 4 years, it was nice to finally see him in person.
  • Beginner’s Guide to Unsupervised Learning [Quant Start]

    The majority of machine learning posts to date on QuantStart have all been about supervised learning. In this post we are going to take a look at unsupervised learning, which is a far more challenging area of machine learning. Supervised learning involves taking a number of data observations, each of which contains a feature, or predictor, vector as well as an associated output, or response. The
  • Look at Data with a Discerning Eye [Flirting with Models]

    I recently came across a graph similar to the following while doing some market research. 1 Source: Yahoo! Finance. Analysis by Newfound Research. Data from January 1951 December 2015. The argument was that the markets are getting more volatile. While this certainly looks to be the case based on the upward trend of the histogram, let's investigate the data more thoroughly and ask
  • Trading Ethereum: Making 10% every 20 minutes [Jon.IO]

    This is more of a "How to build your own algotrading strategy – the Ethereum edition" and not a "make money fast" blog post. It is also a real example with real returns (and real production errors that cost me money) where you can see how to identify opportunities, why algotrading is awesome and why risk management can save your ass. This is the another post of the series: How
  • Tight Consolidations After New Highs [Quantifiable Edges]

    The range over the last week has been extremely tight. On 7/20/16 SPY closed at a 50-day high. Every SPY close in the 5 days since 7/20 has been within the intraday range of that 7/20/16 bar. (And it wasnt even that big of a range.) It is said that consolidations are often resolved in the direction of the trend. This guideline suggests that were more likely to see another leg up from here