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Quantocracy’s Daily Wrap for 10/26/2016

This is a summary of links featured on Quantocracy on Wednesday, 10/26/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Framework for a Short VIX Allocation [EconomPic]

    It has historically paid to be a seller of volatility for at least two reasons… 1) Volatility is typically overpriced relative to realized volatility The chart on the left shows the VIX index (predicted volatility) relative to the forward realized volatility of the S&P 500, while the chart on the right shows the variance between the two (anything > 0 means the VIX index was higher than
  • Interactive Brokers API in Docker [Ryan Kennedy]

    While Interactive Brokers provide arguably the most extensive retail-level API available for trading, the software is quite frustrating to work with. Rather than IB offering you an API endpoint on their server to interact with, you must run their Gateway or TWS program on your host and interact with this program, which acts as an intermediary server. Further, both of these programs are
  • VXX & XIV Strategies [Alvarez Quant Trading]

    My recent research has been on the volatility Exchange Traded Products. My focus has been on long trades using VXX and XIV. Although VXX has a very strong downtrend, I am not a fan of developing short strategies on it due to the huge upside risk. I wrote about XIV here and expressed some of the dangers of trading these ETFs. Issues XIV has an inception date of 11/30/2010 and VXX inception date is

Quantocracy’s Daily Wrap for 10/25/2016

This is a summary of links featured on Quantocracy on Tuesday, 10/25/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Turn an Engineer into a Quantitative Investor [Alpha Architect]

    We receive multiple requests from readers looking to break into the finance industry. Quite often the reader is currently working in a traditional engineering job and looking to make a career switch. The question we often hear is How does an engineer become a quantitative finance geek? To answer this question we decided to ask someone who recently made the switchKris Longmore at newly
  • Is My Diversified Commodity Index Just Oil? [Flirting with Models]

    The benefit of including commodities in individual investor portfolios is often up for debate, focusing on aspects such as expected returns, volatility, access, and diversification. While we think that commodities can add value if the risks are understood, many passive ETFs that offer commodity exposure rely on indices that are significantly exposed to oil prices and energy, in general. One
  • Strategic and Equal Weighted ETF Portfolios in QSTrader [Quant Start]

    In a previous article the monthly rebalance feature of the open-source backtesting library QSTrader was demonstrated on a simplistic equities/bonds ETF mix portfolio. In this article new streamlined code will be presented to allow straightforward modification of the portfolio weightings. In particular two new portfolios of ETFs will be presented, influenced by by a recent post[1] at The Capital
  • Spot Price Patterns with the COT Report [Milton FMR]

    This post goes into an in depth analysis of the commitment of traders report and its usefulness for predicting price movements. The CFTC collects data on the daily positions of large participants in the commodity markets. The data is aggregated in the weekly COT report which is published every Friday at 3;30 PM EST. So the one million dollar question is whether we can use this weekly report to

Quantocracy’s Daily Wrap for 10/24/2016

This is a summary of links featured on Quantocracy on Monday, 10/24/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Published Results Impact Future Results [Larry Swedroe]

    Financial research has uncovered many relationships between investment factors and stock returns. For investors, an important question is whether the publication of this research can impact the future size of factor premiums. Asking this question is crucial on two fronts. First, if anomalies are the result of behavioral errors, or even investor preferences, and the publication of research into
  • Connecting FXCM over FIX (QuickFix engine) [Quant Insti]

    We talked about the defacto standard for message communication in our previous article on FIX protocol. The Financial Information Exchange (FIX) Protocol is a message standard developed to facilitate the electronic exchange of information related to securities transactions. It is intended for use between trading partners wishing to automate communications.[1] In this article, we are going to

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