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Quantocracy’s Daily Wrap for 06/16/2016

This is a summary of links featured on Quantocracy on Thursday, 06/16/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Some harmless data-mining: Testing individual words in EDGAR filings [Greg Harris]

    Everyone knows about the perils of data-mining and multiple testing. So, dont take this post too seriously. I recently made an inverted index into all 11 million regulatory filings disseminated online by the SEC. This means that for each string of three or more letters I have a list of all documents that contain it. I did this to facilitate full text search. But, now that I have it, I decided

Quantocracy’s Daily Wrap for 06/15/2016

This is a summary of links featured on Quantocracy on Wednesday, 06/15/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Lasso applied in Portfolio Management [Quant Dare]

    There are a wide variety of Machine Learning techniques that help us to solve Big Data problems. In this post we talk about how to apply Lasso Regression in Portfolio Management. You may have heard of this technique in the past, for that reason Ill try to explain it in a brief introduction. Lasso definition Least Absolute Shrinkage and Selection Operator or Lasso is a regression analysis method
  • Write Covered Call Strategy in Python [Quant Insti]

    Traders in the derivative market often exercise one of the following: Call option or Put Option. Call option is a financial contract between a buyer and seller, whereby the buyer has the right, but not the obligation, to buy an agreed quantity of a financial instrument from the seller of the option at a certain time for a certain price (the strike price). The Pull Option serves the

Quantocracy’s Daily Wrap for 06/14/2016

This is a summary of links featured on Quantocracy on Tuesday, 06/14/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Differential equation that models a support and resistance strategy [Tulip Quant]

    Support and resistance indicators are widely used in technical analysis. What I tried to do is to model this strategy using a suitable differential equation, in order to test it with historical data. Support-resistance_binary_options A simple model could be the following: S'(t)=\gamma_t \displaystyle{\min_{s\in[0, N]} }\left ( |S'(t-s)|^\alpha + |S(t-s)-S(t)|^\beta \right ) Where S(t)
  • Cointegrated Augmented Dickey Fuller Test for Pairs Trading Evaluation in R [Quant Start]

    In the previous article on cointegration in R we simulated two non-stationary time series that formed a cointegrated pair under a specific linear combination. We made use of the statistical Augmented Dickey-Fuller, Phillips-Perron and Phillips-Ouliaris tests for the presence of unit roots and cointegration. A problem with the ADF test is that it does not provide us with the necessary ? regression
  • The Brutal Math of a 60/40 Portfolio [EconomPic]

    Think only a bear market can keep returns of a 60/40 near 0%… think again. Given the huge opportunity cost of allocating to cash or bonds at current yield levels, even generally optimistic return assumptions for stocks are enough to keep portfolio level returns near 0% real. The goal of this post is to set the stage for a future post where I hope to share potential solutions that may improve
  • Trading strategy for the S&P 500 index based on ARIMA models [Tulip Quant]

    ARIMA models are a family of models for time series that are used to forecast future behaviour. It can be (and it is) used in finance, and in particular in trading. In this post I will try to show a specific use for a trading strategy based on these models, and it will be applied to the S&P 500 index. ARIMA models are denoted by ARIMA(p,d,q), where: p is the order of the autorregresive factor.
  • Mini-Meucci : Applying The Checklist – Steps 6-7 [Return and Risk]

    Today we'll be visiting 2 sites along Via Meucci, Evaluation and Attribution. Evaluation We need some way to measure the goodness of the ex-ante portfolio across the scenarios from the Aggregation step, and for this Meucci introduces the concept of a Satisfaction index. Given the distribution of the ex-ante performance we can compute the satisfaction index (or its opposite, the risk index)

Quantocracy’s Daily Wrap for 06/13/2016

This is a summary of links featured on Quantocracy on Monday, 06/13/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Smart Beta Is Still Beta [Dual Momentum]

    Some say that bull markets climb a wall of worry. This is good news for those already in the market. Worriers will help the market go higher later when they finally decide to jump on the bandwagon. Herding, representativeness, and regret aversion (fear of losing out on future profits) can eventually overtake loss aversion. Investors Skeptical The iconic investor and money manager, Sir John
  • Forecast combinations in R [Eran Raviv]

    Few weeks back I gave a talk in the R/Finance 2016 conference, about forecast combinations in R. Here are the slides.
  • Want to Know the Secret to Inefficient Prices? Lazy Prices. [Alpha Architect]

    How do you handle repetitive tasks? If youre like most people, you work through a task in a variety of ways, find the most efficient approach, and then stick to that workflow. Consider email address autofill, automatic payment plans, or automatic renewal of magazine subscriptions. Because of behavior bias and the power of inertia (also known as the, Yeah, whatever, heuristic), it takes
  • Diversification opportunities in fixed income [Flirting with Models]

    Summary Many investors look at fixed income as the diversifying sleeve of their portfolio, helping to safeguard capital against losses in more volatile equity positions. Traditional fixed income indices are very heavily weighted towards U.S. Treasuries and agency mortgage-backed securities, offering very little internal diversification. There are numerous extended sectors now available as ETFs
  • Sharp Drops From Intermediate-Term Highs Short Term Bullish [Quantifiable Edges]

    Thursday and Friday saw relatively large selloffs in SPX. After closing at a 50-day high on Wednesday it closed at a 10-day low on Friday. This triggered an interesting study from the Quantifinder that looked at relatively sharp selloffs that made at least 8-day lows. I have updated that study below. 2016-06-13 image1 The stats all suggest an upside edge over the next 1-5 days. Traders may want to
  • FTSE 100 around FOMC announcements [UK Stock Market Almanac]

    The Federal Open Market Committee (FOMC) is the monetary policy-making body of the U.S. Federal Reserve System. Since 1981 the FOMC has had eight scheduled meetings per year, the timing of which is quite irregular, The schedule of meetings for a particular year is announced ahead of time [calendar here]. Starting in 1994, the FOMC began to issue a policy statement (FOMC statement) after the

Quantocracy’s Daily Wrap for 06/12/2016

This is a summary of links featured on Quantocracy on Sunday, 06/12/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Best Links of the Last Two Weeks [Quantocracy]

    The best quant mashup links for the two weeks ending Saturday, 06/11 as voted by our readers: A Survey of Deep Learning Techniques Applied to Trading [Greg Harris] Diversification Will Always Disappoint [Flirting with Models] Capital correction (pysystemtrade) [Investment Idiocy] Will Bonds Deliver Crisis Alpha in the Next Crisis? [Alpha Architect] The Internal Bar Strength Indicator [Jonathan
  • System Parameter Permutation – a better alternative? [Better System Trader]

    When I wrote my Wagner Award winning paper "Know your System! Turning Data Mining from Bias to Benefit," I had two goals in mind: Introduce a new method to reasonably estimate the long-run expected performance of a trading system, and Provide a simple method for the average system trader to understand and employ the method. I've subsequently realized that the paper's focus
  • Strategy Evaluation with Dave Walton [Better System Trader]

    Today we're covering a topic which can really be a concern for traders of all levels, from beginner to pro, and that is the topic of strategy evaluation. Have you ever found that real-life performance does not match expected results? Or perhaps you have a strategy that is stuck in a drawdown and wondering if it's actually broken? I'm sure we've all heard of data mining bias,

Quantocracy’s Daily Wrap for 06/11/2016

This is a summary of links featured on Quantocracy on Saturday, 06/11/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • PDF: To Win With Smart Beta Ask If the Price Is Right [Research Affiliates]

    This is the second of a series on the future of smart beta. In our first article in this seriesHow Can Smart Beta Go Horribly Wrong? 1we show, using U.S. data, that the relative valuation of a strategy (in comparison with its own historical norms) is correlated with the strategys subsequent return at a five-year horizon. The high past performance of many of the increasingly

Quantocracy’s Daily Wrap for 06/10/2016

This is a summary of links featured on Quantocracy on Friday, 06/10/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: Fundamentals of Machine Learning for Predictive Data Analytics [Amazon]

    Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in
  • Mini-Meucci : Applying The Checklist – Steps 3-5 [Return and Risk]

    "In the future, instead of striving to be right at a high cost, it will be more appropriate to be flexible and plural at a lower cost. If you cannot accurately predict the future then you must flexibly be prepared to deal with various possible futures." Edward de Bono, author and thinker extraordinaire (born 1933) In this third leg of The Checklist tour, we will take 3 more steps,
  • State of Trend Following in May [Au Tra Sy]

    A strong down month in May for the state of trend following index, which solidifies the downtrend from the last two months and takes the YTD performance in the red, after the strong start to the year. Please check below for more details. Detailed Results The figures for the month are: May return: -6.17% YTD return: -4.52% Below is the chart displaying individual system results throughout May:

Quantocracy’s Daily Wrap for 06/09/2016

This is a summary of links featured on Quantocracy on Thursday, 06/09/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Markov Switching Regimes say bear or bullish? [Quant Dare]

    We continue with our last OBSSESION trying to capture an index trend but at the moment, not playing with future information. Markov Switching RegimesWe are going to introduce the Markov Switching Regimes (MSR) model which, as its name indicates, tries to capture when a regimen has changed to another one. This would be a change between opposite trends or it could consist in passing from being
  • Simple Machine Learning Model to Trade SPY (h/t AlgoTrading Reddit) [Alpha Plot]

    I have created a quantitative trading strategy that incorporates a simple machine learning model to trade the SPY as part of my ongoing research in quantitative trading. The focus here was not on creating a strategy with alpha but rather to develop a framework both in my mind and in code to develop more advanced models in the future. 1. Does SPY Exhibit Short-Term Mean Reversion or Momentum?
  • Trend Following carries on with downtrend in May [Wisdom Trading]

    May 2016 Trend Following: DOWN -7.37% / YTD: -1.71% This time, the negative performance for the index last month takes the Year-To-Date performance in the red, for the first time in 2016. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for May: Wisdom State of Trend Following – May 2016 And the 12-month chart: Wisdom State of Trend Following
  • Your best strategy in 2016 so far [Quant Investing]

    I am sure you also don't run after the most recent best performing investment strategy. I stopped doing this, a long time ago, after I (quite a few times) discovered I was the last to jump on the strategy just as it stopped working. But I suspect you also find it interesting to see what has worked well so far this year. That is why I decided to take a look at what investment strategy would

Quantocracy’s Daily Wrap for 06/08/2016

This is a summary of links featured on Quantocracy on Wednesday, 06/08/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Capital correction (pysystemtrade) [Investment Idiocy]

    This post is about how should you adjust the trading capital you have at risk given the profitability (or not) of your trading account. I'm posting this for three reasons. Firstly it's a pretty important topic. I address, in some detail, how to set your risk target for a given amount of trading capital in chapter 9 of my book. I only briefly discuss what you should do thereafter, once
  • Random Asset Allocation in the ASX200 [Ryan Kennedy]

    To paraphrase the old adage; "a monkey throwing darts will outperform most fund managers". I have seen this concept explored several times in relation to the SP500, but I was interested to see if it had any relevance to the ASX200. Our monkey with darts will be a random number generator, selecting 10 stocks to buy from the XJO in equal weight. We test with $100,000 of capital. Benchmark
  • Trend Model via Difference Between Long and Short-Term Variance [Quantpedia]

    We relate the performance of trend following strategy to the difference between a long-term and a short-term variance. We show that this result is rather general, and holds for various definitions of the trend. We use this result to explain the positive convexity property of CTA performance and show that it is a much stronger effect than initially thought. This result also enable us to highlight

Quantocracy’s Daily Wrap for 06/07/2016

This is a summary of links featured on Quantocracy on Tuesday, 06/07/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Will Bonds Deliver Crisis Alpha in the Next Crisis? [Alpha Architect]

    Bonds are often viewed as being great diversifiers due to the perception that they perform well during tough times for stocks. Historically this has been a true statement. But will it continue? Our answer: unclear. Most investors use correlation to measure the diversification benefit an investment might provide an existing portfolio. However, this article uses a slightly different approach to