Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 09/05/2016

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

  • Systematic risk management [Investment Idiocy]

    As the casual reader of this blog (or my book) will be aware, I like to delegate my trading to systems, since humans aren't very good at it (well, I'm not). This is quite a popular thing to do; many systematic investment funds are out there competing for your money; from simple passive tracking funds like ETF's to complex quantitative hedge funds. Yet most of these employ people to
  • How to Learn Advanced Mathematics Without Heading to University – Part 3 [Quant Start]

    In the first and second articles in the series we looked at the courses that are taken in the first half of a four-year undergraduate mathematics degree – and how to learn these modules on your own. In the first year we discussed the basics – Linear Algebra, Ordinary Differential Equations, Real Analysis and Probability. In the second year we built on those basics, studying Metric Spaces, the
  • Effect of Maturity Structure of Roll Yields in Commodity Futures Strategies [Quantpedia]

    We investigate the maturity-structure of roll strategy returns in the energy futures markets. Our innovation is to report and analyze the risk/return profile, the Sharpe ratio, and the asset pricing loadings of rollover strategies based on futures contracts of the same underlying commodity but with maturities between two and 12 months. We find that a conditional rollover strategy, which takes a
  • Trading on Sentiment with Richard Peterson [Better System Trader]

    Trading algorithmically based on sentiment data is a relatively new field compared to more established approaches. With the explosion of social media and computing power, the analysis of sentiment data has also increased, with some hedge funds committing considerable resources to researching the applications of sentiment data in trading. However, there is also some skepticism of the value of

Quantocracy’s Daily Wrap for 09/03/2016

This is a summary of links featured on Quantocracy on Saturday, 09/03/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Backtesting With Zipline Ii [Koppian Adventures]

    In this post, we play again little bit around with python and the pandas-library. You may want to read the first part of this series. There we have backtested a simple crossing moving average strategy in pandas. We had a long/slow moving average over the last 40 days and a fast/short moving average over the last 20 days. When the stock price rockets skywards, the short moving average is above the
  • AllocateSmartly [TrendXplorer]

    Launched only recently, AllocateSmartly.com tracks the industrys best tactical asset allocation strategies with thorough, up-to-date backtests. As of writing 16 (sub) strategies are tracked and benchmarked on near real-time basis. All of the tracked strategies are both quantitative and systematic, meaning well-defined mathematical rules govern exactly when and what to trade. Among the featured
  • Possible Addition of NARX Network to Conditional Restricted Boltzmann Machine [Dekalog Blog]

    It has been over three months since my last post, due to working away from home for some of the summer, a summer holiday and moving home. However, during this time I have continued with my online reading and some new thinking about my conditional restricted boltzmann machine based trading system has developed, namely the use of a nonlinear autoregressive exogenous model in the bottom layer

Quantocracy’s Daily Wrap for 09/02/2016

This is a summary of links featured on Quantocracy on Friday, 09/02/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • When Academics Disagree on Momentum Investing [Alpha Architect]

    The academic standard for intermediate-term momentum measurement is 12_2 momentum: simply sort all stocks based on a stocks total return over the past twelve months, ignoring the last month. (a discussion is here and here) However, a few years ago Robert Novy-Marx wrote a paper titled Is Momentum Really Momentum? Given its provocative title, this paper caught our attention! Why
  • State of Trend Following in August: Sharp Down Move [Au Tra Sy]

    It is Fall in Summer! The trend following index had a big move to the downside last month, taking the YTD performance to a negative level as well. The results are similar, over on the Wisdom State of Trend Following, a sort of version 2.0 of this report, which I write for them too. Please check below for more details. Detailed Results The figures for the month are: August return: -6.59% YTD
  • August Fall for Trend Following [Wisdom Trading]

    August 2016 Trend Following: UP -7.32% / YTD: -7.95% August was mostly one-sided, sliding down to a strong negative performance, and taking with it the Year-To-Date performance to a similar level. Interesting to note the shorter timeframes weighing on the index while the longer timeframes are still positive/neutral on a 12-month horizon (we do offer trading systems with long-term timeframes). Note

Quantocracy’s Daily Wrap for 09/01/2016

This is a summary of links featured on Quantocracy on Thursday, 09/01/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation Performance in August [Allocate Smartly]

    This is a summary of the recent performance of a number of excellent asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. They range from simple, static portfolio allocations, to complex and dynamic portfolio optimization. Read more about our backtests and what we do. Recent Performance of Asset Allocation Strategies Use the Arrows to Sort
  • No Signal [Automated Trader]

    NO SIGNAL is a regular column where we examine various snafus in the trading, particularly the automated trading, world. We look at errors in application logic, mistakes by overzealous co-workers, failures in technology and temporary losses of power to both infrastructure as well as craniums. These all make for good stories that everyone can alternatively either learn from or be amused by. If you
  • Multivariate Volatility Forecast Evaluation [Eran Raviv]

    The evaluation of volatility models is gracefully complicated by the fact that, unlike other time series, even the realization is not observable. Two researchers would never disagree about what was yesterdays stock price, but they can easily disagree about what was yesterdays stock volatility. Because we dont observe volatility directly, each of us uses own proxy of choice. There are many
  • What is Quantler? [Quantler]

    WHAT IS QUANTLER? Quantler is an open source cloud-based trade automation software designed for individual traders of FX and CFDs. Our goal is to help individual traders optimize their trading performance through innovative but simple-to-use trading technology. Quantler makes it easy to build your own trading algorithms or work from a number of preset trading strategy templates. Unique is our

Quantocracy’s Daily Wrap for 08/31/2016

This is a summary of links featured on Quantocracy on Wednesday, 08/31/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Time Series Momentum and Volatility Scaling [Alpha Architect]

    There is a new paper published in the Journal of Financial Markets that digs a bit deeper into the Moskowitz, Ooi, and Pedersen Time Series Momentum paper (some background here). ts paper The paper is behind a pay firewall, but luckily there is a 4 part lecture by the authors explaining the key results: #1: https://www.youtube.com/watch?v=2akXA5y2Abw #2:

Quantocracy’s Daily Wrap for 08/30/2016

This is a summary of links featured on Quantocracy on Tuesday, 08/30/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Dynamic Hedge Ratio Between ETF Pairs Using the Kalman Filter [Quant Start]

    A common quant trading technique involves taking two assets that form a cointegrating relationship and utilising a mean-reverting approach to construct a trading strategy. This can be carried out by performing a linear regression between the two assets (such as a pair of ETFs) and using this to determine how much of each asset to long and short at particular thresholds. One of the major concerns

Quantocracy’s Daily Wrap for 08/29/2016

This is a summary of links featured on Quantocracy on Monday, 08/29/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Uncertain Alpha [Flirting with Models]

    SUMMARY We have previously discussed many problems associated with the measurement of alpha. Measurement uncertainty, the choice of model risk factors, and the analysis timeframe can all have significant impacts on the calculation and applicability of alpha, and investors are often hard-pressed to obtain sufficient information surrounding the calculation methods. These problems can be exacerbated