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Quantocracy’s Daily Wrap for 10/23/2016

This is a summary of links featured on Quantocracy on Sunday, 10/23/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Evolving Neural Networks through Augmenting Topologies Part 4 of 4 [Gekko Quant]

    This post explores applying NEAT to trading the S&P. The learned strategy significantly out performs buying and holding both in and out of sample. Features: A key part of any machine learning problem is defining the features and ensuring that theyre normalised in some fashion. The features will be rolling percentiles of the following economic data, a rolling percentile takes the last n data
  • Flexing VBA For Quants (And Everyone Else) [TrendXplorer]

    Would it not be great to have the models for Protective Asset Allocation (PAA) and Global Protective Momentum (GPM) in Excel, so you can run your own backtests without AmiBroker? And not being limited to a pre-defined universe? Actually, now you can. Based on a foundation by InvestExel, Denis Bergemann from Germany collaborated with me in developing an Excel spreadsheet that allows you to select

Quantocracy’s Daily Wrap for 10/22/2016

This is a summary of links featured on Quantocracy on Saturday, 10/22/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tail Protection of Trend-Following Strategies [Quantpedia]

    The performance of trend following strategies can be ascribed to the difference between long-term and short-term realized variance. We revisit this general result and show that it holds for various definitions of trend strategies. This explains the positive convexity of the aggregate performance of Commodity Trading Advisors (CTAs) which — when adequately measured — turns out to be much stronger
  • Algorithmic Trading Basics for New Algorithmic Traders [Quant Insti]

    With more than 70% of the trading volumes in the US markets being automated, the rise of the algorithms seem more inevitable than ever before. The mechanical jobs are shifting to computers and only those who can tame the machines can rule the trade markets. Equipping oneself with the skills of Algorithmic trading is one of the best ways prepare for the changing face of financial markets. As we

Quantocracy’s Daily Wrap for 10/20/2016

This is a summary of links featured on Quantocracy on Thursday, 10/20/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Stoken’s Active Combined Asset Strategy [Allocate Smartly]

    This is a test of Dick Stokens Active Combined Asset (ACA) strategy from his book Survival of the Fittest for Investors. This tactical asset allocation strategy uses price channel breakouts to choose between pairs of opposing risk and defensive asset classes. Results from 1988, net of transaction costs, follow. Read more about our backtests or let AllocateSmartly help you follow this strategy
  • Reflexivity and the Feedback Effect in Financial Markets [Alpha Architect]

    Eugene Famas Efficient Market Hypothesis argues that because stock prices follow a random walk, future price behavior cannot be predicted. In his seminal paper, Random Walks in Stock Market Prices, he explains the relationship between prices and fundamentals: If the random-walk theory is valid and if security exchanges are efficient markets, then stock prices at any point in
  • Zero Lag Moving Average Filter | Trading Strategy (Entry & Exit) [Oxford Capital]

    I. Trading Strategy Developer: John Ehlers and Ric Way. Source: Ehlers, J., Way, R. (2010). Zero Lag (Well, Almost). Concept: Trend following trading strategy based on moving average filters. Research Goal: To verify performance of the Zero Lag Moving Average Filter. Specification: Table 1. Results: Figure 1-2. Trade Filter: Long Trades: Zero Lag Moving Average (ZLMA) crosses over Exponential

Quantocracy’s Daily Wrap for 10/17/2016

This is a summary of links featured on Quantocracy on Monday, 10/17/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Capital Efficiency in Multi-factor Portfolios [Flirting with Models]

    The debate for the best way to build a multi-factor portfolio mixed or integrated rages on. Last week we explored whether the argument held that integrated portfolios are more capital efficient than mixed portfolios in realized return data for several multi-factor ETFs. This week we explore whether integrated portfolios are more capital efficient than mixed portfolios in theory. We find
  • Book Review of Quantitative Momentum [Dual Momentum]

    I have been looking forward to Wes Gray and Jack Vogel's new book, Quantitative Momentum. It is the only book besides my own Dual Momentum that relies on academic research to develop systematic momentum strategies. My book uses a macro approach of applying momentum to indices and asset classes. Wes and Jack (W&J) take a more common approach and apply momentum to individual stocks. W&J
  • Algorithmic Trading in Indian Markets using Python [Quant Insti]

    Algorithmic Trading in Indian Markets using Python We have told you why Python is one of the preferred languages to do algo trading in this article. We have also told you how algorithmic trading in India. Since we are gearing up for our webinar on Trading in Indian Markets using Python (Not registered yet? Click here to register your seat), we ought to give you a prelude to the trading platform
  • Monthly Rebalancing of ETFs with Fixed Initial Weights in QSTrader [Quant Start]

    Many institutional global asset managers are constrained by the need to invest in long-only strategies with zero or minimal leverage. This means that their strategies are often highly correlated to "the market" (usually the S&P500 index). While it is difficult to minimise this correlation without applying a short market hedge, it can be reduced by investing in non-equities based
  • October Opex Week Has Historically Been Bullish [Quantifiable Edges]

    From a seasonal standpoint option expiration week is often a pretty good week for the market. October is one of those months where it has been especially good over the years. The study below examines performance during October op-ex week. 2016-10-17 image1 I decided to exclude 2008 because action that week was such an incredible outlier that it greatly skewed all the stats. (The week started with

Quantocracy’s Daily Wrap for 10/16/2016

This is a summary of links featured on Quantocracy on Sunday, 10/16/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Podcast: Strategy Optimization with Robert Pardo [Better System Trader]

    Why is it that some traders can create trading strategies that perform well in real-time trading while other strategies fall apart? How do some traders keep their trading strategies fresh and adaptive to market conditions while other strategies just stop working altogether? Robert Pardo, president of Pardo Capital, author of the book The Evaluation and Optimization of Trading Strategies and

Quantocracy’s Daily Wrap for 10/15/2016

This is a summary of links featured on Quantocracy on Saturday, 10/15/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Measure Momentum? [Alpha Architect]

    Since weve released our new book, Quantitative Momentum, weve received a handful of basic questions related to momentumspecifically as it relates to stock selection. At this point, the so-called momentum effect has occupied academic researchers for several decades. Researchers have found that, on average, stocks with strong recent performance relative to other stocks in the cross
  • Client -III- [Algorythmn Trader]

    In my previous post, I started the implementation of WPF program entry point and the View ViewModel interaction basics. The goal of this Client chapter is to get a client application which connects to the basic server application I covered earlier. This post continues the basic infrastructure where the Views and ViewModels become integrated. So lets start with some auxiliary entities

Quantocracy’s Daily Wrap for 10/14/2016

This is a summary of links featured on Quantocracy on Friday, 10/14/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • So You Want to Build Your Own Algo Trading System? [Robot Wealth]

    Unlike any other business, algorithmic trading has the advantage of being independent of marketing, sales, customers and all those things that need the pretty people to make it run. Also, you get almost instant feedback on how good you are in your business. For anyone who is numerically inclined (and more often than not falls into a particular demographic in terms of their social
  • Zero-Crossing Variant of Pairs Trading Strategy [Quantpedia]

    Pairs trading is a venerable trading strategy. There is agreement that it worked fine in the far past. But it is less clear if it still profitable today. In this working paper the universe of eligible pairs is defined by the holdings of a given ETF. It is shown that the stocks must be from ETFs which select high-quality, low-volatility stocks. The usual closeness measure presented in the

Quantocracy’s Daily Wrap for 10/13/2016

This is a summary of links featured on Quantocracy on Thursday, 10/13/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Reverse Engineering AQR’s Risk Parity Strategy [Signal Plot]

    Im going to start this post by saying that it makes no sense for anyone to pay management fees to get a return stream that is highly correlated to any existing asset class. Unfortunately, many actively managed funds fall in this category. Theres two reasons for this. One, you can replicate this return stream by just investing in that asset class yourself, likely through low-cost ETFs. Two,
  • What Is The Best “Risk Off” Asset for Trend Followers? [Alpha Architect]

    So youre a trend-follower. Great. But here is a question: What do you invest in when your rules suggest risk off? Many investors suggest low duration cash or t-bills. Seems reasonable. But is it optimal? Perhaps we should invest in longer duration risk-off assets like 10-yr bonds? We investigate these questions and come to the conclusion that keeping it simple is probably the best
  • The illusion of choice in ETF’s [Factor Investor]

    A search for all equity ETF's available to U.S. investors in Bloomberg leads to a list of 969 candidates, a surprisingly large number of options for a relatively new investment vehicle. Given that most focus on large capitalization stocks here in the U.S. (not all, but most), this means that there has to be overlap in the underlying stock holdings…in some cases a lot of overlap. The
  • A Review of @AlphaArchitect Quantitative Momentum book [QuantStrat TradeR]

    This post will be an in-depth review of Alpha Architects Quantitative Momentum book. Overall, in my opinion, the book is terrific for those that are practitioners in fund management in the individual equity space, and still contains ideas worth thinking about outside of that space. However, the system detailed in the book benefits from nested ranking (rank along axis X, take the top decile,