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Quantocracy’s Daily Wrap for 05/18/2016

This is a summary of links featured on Quantocracy on Wednesday, 05/18/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Optimising weights with costs (pysystemtrade) [Investment Idiocy]

    In a previous post I showed you how to use my open source python backtesting package, pysystemtrade, to estimate forecast weights and instrument weights. At the time I hadn't included code to calculate costs. Now that has been remedied I thought I should also write some code to demonstrate the different ways you can optimise in the presence of costs. You can see what else has been included in
  • A Stunning New Finding: Return Seasonalities are Everywhere [Alpha Architect]

    Weve discussed return seasonalities in the past, especially as they pertain to our approach to momentum. Turns out seasonality effects arent confined to momentum they are literally everywhere and they are incredibly strong. This paper will blow your mind once you let the results settle in a bit. Source paper Slides Turns out stock returns are lumpy across the calendar. Stocks dont
  • Machine Beats Human: Machine Learning in Forex [Jon.IO]

    Machine learning and trading is a very interesting subject. It is also a subject where you can spend tons of time writing code and reading papers and then a kid can beat you while playing Mario Kart. In the nexts posts, we are going to talk about: Optimize entries and exits. This and only this could make a ton of difference in your bank roll. Calculate position size (in case you don't like
  • Which Institution Has The Best Asset Allocation Model? [Meb Faber]

    If youre like most investors, youre asking the wrong questions. I was chatting with a group of advisors this week down in La Jolla and a question arose. Ill paraphrase: Meb, thanks for the talk. We get a steady stream of salespeople and consultants in here hawking their various asset allocation models. Frankly, it can be overwhelming. Some will send us a 50-page report, all to explain
  • The State of Risk Management [Flirting with Models]

    How effective is your method of managing portfolio risk? We compare and contrast different approaches including fixed income, managed futures, low volatility equities, and tactical to explore the relative protection they can deliver versus the return drag they can create.
  • World s Simplest Trading System [UK Stock Market Almanac]

    Heres the system: At the end of every month, if the index is above its 10-month simple moving average: the portfolio is 100% in the market if the index is below its 10-month simple moving average: the portfolio is 100% in cash And thats it. So, if we take the FTSE 100 Index as an example, if at the end of a month the FTSE 100 is above its 10-month simple moving average then either, the

Quantocracy’s Daily Wrap for 05/17/2016

This is a summary of links featured on Quantocracy on Tuesday, 05/17/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Where Do All the Clicks Go? [Quantocracy]

    Its been about a year since we launched Quantocracy. Over that time, weve sent about 450,000 clickthroughs to sites in the quant community, and that doesnt even include RSS, Twitter, StockTwits and Facebook. To all of the denizens of Quantocracy: a big mahalo, gracias, ?? and thank you for helping this community to grow. But where are all those clicks going? I view this site as more
  • Not so Simple: Valuations and Low Volatility Strategies [Alpha Architect]

    Low volatility funds are everywhere. The reasons for their proliferation are clear: Who wouldnt want to own something with the label low volatility and Recent performance has been great. Open the AUM floodgates! But perhaps not all is well in low volatility land. A recent snippet by Josh Brown hints at the idea that perhaps low volatility is overdone. Charles Bilello at Pension Partners
  • Can We Predict Forward Alternative Investment Performance? [EconomPic]

    My friend Ben from A Wealth of Common Sense poses the interesting question, How Should Alternative Investments Be Benchmarked? Please go read his post for a number of interesting thoughts on that topic. In this post, rather than rehash his arguments, I'll go a different direction and will try to articulate what drives the performance of alternatives (i.e. hedge funds / liquid alts) to see if
  • Update on Attilio Meucci’s The Checklist and ARPM Bootcamp / Code [Return and Risk]

    Last week I organised for Attilio Meucci to give a webinar to members of CFA Singapore and NUS Risk Management Institute on an Introduction to The Checklist Ten Steps for Advanced Risk and Portfolio Management. It served the dual-purpose of a professional development talk and marketing of the upcoming ARPM Bootcamp on 15-20 August in NYC. The slides can be viewed online at
  • Giving Up on Recursive Sine Formula for Period Calculation [Dekalog Blog]

    I have spent the last few weeks trying to get my recursive sine wave formula for period calculations to work, but try as I might I can only get it to do so under ideal theoretical conditions. Once any significant noise, trend or combination thereof is introduced the calculations explode and give meaningless results. In light of this, I am no longer going to continue this work. Apart from the above
  • The Fine Art of Opening Range Breakout Trading [Milton FMR]

    The goal of this research is to find various set-ups and exit strategies that could be used for trading the opening range breakouts. The time frames we will be looking at are 10min, 15min and 30min opening range breakouts. We will focus our attention on the very liquid futures markets in particular we will analyze the S&P500 futures. We would like to encourage you as the reader to participate
  • Trading With Indices [Jonathan Kinlay]

    In this post I want to discuss ways to make use of signals from relevant market indices in your trading. These signals can add value regardless of whether you trade algorithmically or manually. The techniques described here are one of the most widely applicable in the quantitative analyst's arsenal. Let's motivate the discussion by looking an example of a simple trading system trading

Quantocracy’s Daily Wrap for 05/14/2016

This is a summary of links featured on Quantocracy on Saturday, 05/14/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 52 Pick-Up and factor investing [Factor Investor]

    Remember 52 Pick-Up? A dubious sibling, usually older, would ask if you wanted to play a game while holding a deck of cards. Emphatically, you agree, only to be showered with the deck of playing cards and told to pick them up. Wikipedia in its infinite wisdom defines 52 pick up as "a game of picking up thrown cards." In a lot of ways, the market is that devious sibling. The cards are

Quantocracy’s Daily Wrap for 05/13/2016

This is a summary of links featured on Quantocracy on Friday, 05/13/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How Portfolio Construction Affects Value Funds [Alpha Architect]

    Value investing is an investment philosophy that has been extensively discussed and examined at least since the days of Ben Graham, who popularized it as a discipline in the 20s and 30s. While there are some who are dismissive of its advantages as a long-term strategy, the historical evidence is compellingly clear: Cheap stocks beat expensive stocks over time (see our simulation study as an

Quantocracy’s Daily Wrap for 05/12/2016

This is a summary of links featured on Quantocracy on Thursday, 05/12/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Deep Learning with Theano – Part 1: Logistic Regression [Quant Start]

    Over the last ten years the subject of deep learning has been one of the most discussed fields in machine learning and artificial intelligence. It has produced state-of-the-art results in areas as diverse as computer vision, image recognition, natural language processing and speech recognition. However it has also been widely hyped – the answer to all machine learning problems – and is often
  • Heatmaps in R [Quant Finance Academy]

    In exploratory data analysis, we often need to visualize our data in different formats, in order to gain more understanding about the numbers and the relationship between the parameters. One such wonderful and informative representation is the Heatmap, which is basically a colored image, the colors explain the strength of the relationship between two parameters. It normally has a dendogram

Quantocracy’s Daily Wrap for 05/11/2016

This is a summary of links featured on Quantocracy on Wednesday, 05/11/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Cliff Asness’s (AQR) View on Factor Timing [Quantpedia]

    Everyone seems to want to time factors. Often the first question after an initial discussion of factors is ok, whats the current outlook? And the common answer, the same as usual, is often unsatisfying. There is powerful incentive to oversell timing ability. Factor investing is often done at fees in between active management and cap-weighted indexing and these fees have been falling
  • How To Compute Turnover With Return.Portfolio in R [QuantStrat TradeR]

    This post will demonstrate how to take into account turnover when dealing with returns-based data using PerformanceAnalytics and the Return.Portfolio function in R. It will demonstrate this on a basic strategy on the nine sector SPDRs. So, first off, this is in response to a question posed by one Robert Wages on the R-SIG-Finance mailing list. While there are many individuals out there with a
  • State of Trend Following in April [Au Tra Sy]

    The state of trend following was negative last month, as it was in March. The index is now just above the zero-line for the year, back from nearly the +20% mark a month and a half ago. Please check below for more details. Detailed Results The figures for the month are: April return: -2.35% YTD return: 2.54% Below is the chart displaying individual system results throughout April:

Quantocracy’s Daily Wrap for 05/10/2016

This is a summary of links featured on Quantocracy on Tuesday, 05/10/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 05/09/2016

This is a summary of links featured on Quantocracy on Monday, 05/09/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Motivation: Why Do I Blog? [Quantocracy]

    A common concern I hear from many in our community of quantitative bloggers is defining their motivation to write for the long-term. Most begin writing without knowing what to expect, just happy to take a break from crunching numbers to interact with actual humans. Sometimes that optimism wanes though when the realities of lifes other responsibilities begin to pull at their time. Throw in a
  • Alternative Beta can be Great: But Beware of Data-Mining! [Alpha Architect]

    We investigate the biases in the backtested performance of alternative beta strategies using a sample of 215 commercially promoted trading strategies across five asset classes. Our results lend support to the cautions in recent literature regarding backtest overfitting and lack of robustness in trading strategy performance during the live period (out of sample). We report a median 73%
  • The two sources of outperformance [Flirting with Models]

    This blog post is available for download here. Summary When a manager outperforms, it implies that other investors have underperformed. In understanding an investment process, we believe it is critical to understand the source of this outperformance to determine whether it is sustainable or not. We believe there are two key sources of outperformance: exploiting investor behavior and being

Quantocracy’s Daily Wrap for 05/07/2016

This is a summary of links featured on Quantocracy on Saturday, 05/07/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Relative Strength Index (RSI) Model | Trading Strategy (Entry) [Oxford Capital]

    I. Trading Strategy Developer: Larry Connors (The 2-Period RSI Trading Strategy), Welles Wilder (RSI Momentum Oscillator). Source: (i) Connors, L., Alvarez, C. (2009). Short Term Trading Strategies That Work. Jersey City, NJ: Trading Markets; (ii) Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research. Concept: The long equity trading system based on the