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Quantocracy’s Daily Wrap for 11/29/2017

This is a summary of links featured on Quantocracy on Wednesday, 11/29/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Better Way to Model the VIX [Six Figure Investing]

    Models are useful. They help us understand the world around us and aid us in predicting what will happen next. But its important to remember that models dont necessarily reflect the underlying reality of the thing were modeling. The Ptolemaic model of the solar system assumed the Earth was the center of everything but in spite of that spectacular error, it did a good job of predicting the
  • Combine Market Trend and Economic Trend Signals? [CXO Advisory]

    A subscriber requested review of an analysis concluding that combining economic trend and market trend signals enhances market timing performance. Specifically, per the example in the referenced analysis, we look at combining: The 10-month simple moving average (SMA10) for the broad U.S. stock market. The trend is positive (negative) when the market is above (below) its SMA10. The 12-month simple
  • Myth Busting: Stocks Correlations and Active Investment Opportunities [Alpha Architect]

    Many investors, investment professionals, and pundits make comments regarding the relationship between stock correlations and opportunities for active stock pickers. For example, here is a recent example from the Financial Times: Correlation crash clears way for stockpickers. The basic (albeit flawed) intuition behind the statement is that when correlations are low, the variation in returns is

Quantocracy’s Daily Wrap for 11/28/2017

This is a summary of links featured on Quantocracy on Tuesday, 11/28/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Investing and Trading Costs [Alpha Architect]

    Factor investing, and the associated intellectual battles, have raged for decades in academic finance journals. However, now that factor investing has gone mainstream via ETFs, the debate has broader interest among the investing public. Some investors question the very existence of factor premiums. We are sympathetic to this viewpoint given the noise around poor factor replication and the
  • SPY s 2-Day Pattern Suggesting A Bullish Tendency For Tuesday [Quantifiable Edges]

    SPY gapped up and closed lower Monday after leaving an unfilled up gap on Friday. This triggered the study below that examined similar price action in SPY with regards to how it gapped and finished
  • More About Meta: The Best Asset Allocation Strategies Over Time [Allocate Smartly]

    Last month we launched Meta Strategy, our own smart approach to combining the 40+ tactical asset allocation strategies tracked on our site. Each month, Meta selects 10 strategies and then trades their combined asset allocation. Members can follow Meta in near-real time. Each months 10 strategies are selected based on a number of factors (read more), including how well each strategy plays with

Quantocracy’s Daily Wrap for 11/27/2017

This is a summary of links featured on Quantocracy on Monday, 11/27/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Are Market Implied Probabilities Useful? [Flirting with Models]

    Using historical data from the options market along with realized subsequent returns, we can translate risk-neutral probabilities into real-world probabilities. Market implied probabilities are risk-neutral probabilities derived from the derivatives market. They incorporate both the probability of an event happening and the equilibrium cost associated with it. Since investors have the flexibility
  • Computing Option Skews with Dask [Black Arbs]

    This article series provides an opportunity to move towards more interactive analysis. My plan is to integrate more Jupyter notebooks and Github repos into my research/publishing workflow. For datasets that are too big to share through github I will provide a download link both here and in the github readme. I will be posting the notebooks into this blog using iframes. If you experience any issues
  • Factor Construction: Portfolio Scenarios [Factor Research]

    Most researchers create factor portfolios by taking the top & bottom 30% of stocks, which results in large portfolios Portfolios can be reduced, but firm risks start influencing factor returns with too few stocks Most investors are likely better of buying factor products then building factor portfolios themselves INTRODUCTION Investors glancing at the Wilshire 5000 Total Market Index would
  • Algorithmic Options Trading, Part 3 [Financial Hacker]

    In this article well look into a real options trading strategy, like the strategies that we code for clients. This one however is based on a system from a trading book. As mentioned before, options trading books often contain systems that really work which can not be said about stock or forex trading books. The system that well examine here is indeed able to produce profits. Even extreme
  • Do Short Selling Costs Affect the Profitability of Stock Anomalies [Quantpedia]

    Short selling frictions cannot explain the persistence of seven prominent stock anomalies. Long-only investing is robust and profitable and can be further enhanced by using a synthetic short. Moreover, portfolios restricted to stocks that are easy to short sell continue to have large and significant short anomaly alphas. I derive cost bounds for switching between implementation methods and show

Quantocracy’s Daily Wrap for 11/26/2017

This is a summary of links featured on Quantocracy on Sunday, 11/26/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Investing: Implementation Costs Really Do Matter [Dual Momentum]

    One of the tenets of modern portfolio theory is that you cannot generally beat the market after transaction costs. Yet academic researchers have shown that momentum consistently beats the market. Other factors besides momentum have also cast doubt on the efficacy of the efficient market hypothesis. There is one way though that academics can still hold on to the efficient market hypothesis. It is
  • QSTrader: November 2017 Update [Quant Start]

    Last month I presented a detailed roadmap for the redevelopment of QSTrader, our open-source systematic trading simulation engine. Today I want to discuss our progress in the month since that article was published and what still remains to be completed prior to the initial 0.1.0 alpha release. Progress To Date The internal QSTrader development version is now mature enough to carry out simple

Quantocracy’s Daily Wrap for 11/24/2017

This is a summary of links featured on Quantocracy on Friday, 11/24/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • From Potential to Proven: Why AI is Taking Off in the Finance World [Robot Wealth]

    This article is a departure from the quantitative research that usually appears on the Robot Wealth blog. Until recently, I was working as a machine learning consultant to financial services organizations and trading firms in Australia and the Asia Pacific region. A few months ago, I left that world behind to join an ex-clients proprietary trading firm. I thought Id jot down a few thoughts

Quantocracy’s Daily Wrap for 11/22/2017

This is a summary of links featured on Quantocracy on Wednesday, 11/22/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How To Get Free Intraday Options Data With Pandas-DataReader [Black Arbs]

    This is a simple reference article for readers that might wonder where I get/got my options data from. In this regard I would like to shout out the contributors to the pandas-datareader, without their efforts this process would be much more complex. Intuitive Explanation So this code consists of three components. The first is the actual script that wraps the pandas-datareader functions and
  • Volume Filters (Part 3) | Trading Strategy (Entry & Exit) [Oxford Capital]

    Developer: Larry Williams (All in one: Price, volume and open interest); R. D. Donchian (Breakout Channels). Concept: Trading strategy based on price breakouts confirmed by POIV (Price, Open Interest, and Volume) filters. Research Question: Can combined filters improve price breakouts? Specification: Table 1. Results: Figure 1-2. Trade Setup: Long Entry Setup: High[i] >
  • A Few Tips for Volatility Trading [Quantpedia]

    We present some empirical evidence for short volatility strategies and for the cyclical pattern of their P&L. The cyclical pattern of the short volatility strategies produces an alpha in good times but collapses to the beta in bad times. We introduce a factor model with risk-aversion to explain the risk-premium of short volatility strategies as a compensation to bear losses in bad market
  • Asset allocation with constraints using Backtracking [Quant Dare]

    Assigning weights to portfolio assets is challenging when we have to consider multiple constraints. Asset allocation may be seen as a constraint satisfaction problem (CSP), and some algorithms allow us to define our own restrictions and look for an optimal weight distribution. In this post, we will show how to define a CSP for your portfolio and how to use the Backtracking algorithm to obtain an

Quantocracy’s Daily Wrap for 11/20/2017

This is a summary of links featured on Quantocracy on Monday, 11/20/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Risk Parity: How Much Data Should We Use When Estimating Volatilities and Correlations? [Flirting with Models]

    Risk parity portfolios attempt to diversify across asset classes and strategies by risk contribution as opposed to dollar allocation. Implementing a risk parity strategy requires making a number of important construction decisions. A key question we have to answer is How are we going to measure risk? One approach is to use historical data to estimate risk. When using this approach, we have
  • Sector Rotation with Fama-French Alphas [Allocate Smartly]

    Allocate Smartly tests and tracks asset allocation strategies sourced from books, academic papers and other publications. Most of the strategies that we test though never make it on to this site. There are a variety of reasons that might be, but often its simply because theyre not very good. Usually we just let those strategies slip gently into the good night, but this one took a lot of work
  • Candlestick Plotting Function for Octave [Dekalog Blog]

    I have long been frustrated by the lack of an "out of the box" solution for plotting OHLC candlestick charts natively in Octave, the closest solution I know being the highlow plot function from the financial package ( which does not yet implement a candle function ) over at Octave Sourceforge. This being the case, I decided to write my own candlestick plotting functions, the codes for
  • Quant Strategies in the Cryptocurrency Space [Factor Research]

    The year 2017 might be regarded as the year where cryptocurrencies became mainstream. Investment funds focused on cryptocurrencies were launched, the CBOE announced Bitcoin futures for the end of the year and some everyday expenses like booking flights at Expedia can be paid in Bitcoins. Institutional investors have been cautious entering the space, but are slowly getting more active given that

Quantocracy’s Daily Wrap for 11/19/2017

This is a summary of links featured on Quantocracy on Sunday, 11/19/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Transforming a QP Problem to an SOCP Problem [Numerical Method]

    A Quadratic Programming problem (QP) in the form of \begin{aligned} & \underset{x}{\text{minimize}} & & \frac{1}{2} x^T H x + p^T x \\ & \text{subject to} \\ & & & A_{eq} x = b_{eq} \\ & & & A x \geq b \end{aligned} where H \in \Re^{n \times n}, A \in \Re^{m \times n}, p, x \in \Re^n, b \in \Re^m, can be transformed to a Second-Order Cone Programming (SOCP)
  • Recalibrating Expected Shortfall to Match Value-at-Risk for Discrete Distributions [Quant at Risk]

    By considering the same risk measure, , applied to two or more portfolios (credit loss distributions, profit-and-loss distributions, etc.) one desires to have a subadditivity property in place: (X1+X2)(X1)+(X2) i.e. meaning that two combined portfolios should never be more risky than the sum of the risk of two portfolios separately. Unfortunately, the Value-at-Risk risk measure does not

Quantocracy’s Daily Wrap for 11/17/2017

This is a summary of links featured on Quantocracy on Friday, 11/17/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Optimizing trading strategies without overfitting [EP Chan]

    Optimizing the parameters of a trading strategy via backtesting has one major problem: there are typically not enough historical trades to achieve statistical significance. Whatever optimal parameters one found are likely to suffer from data snooping bias, and there may be nothing optimal about them in the out-of-sample period. That's why parameter optimization of trading strategies often
  • Monetary Momentum [Alpha Architect]

    On most mainstream finance websites, a good chunk of the stories discuss the FED and where interest rates are going. Intuitively, this makes sense: The FED is arguably an extremely influential component of U.S. economy. But how do markets respond to the FED? Is the response rational, irrational, or something in between? This is a good question, and one discussed in a working paper which we examine

Quantocracy’s Daily Wrap for 11/16/2017

This is a summary of links featured on Quantocracy on Thursday, 11/16/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Adaptive Volatility [CSS Analytics]

    One of the inherent challenges in designing strategies is the need to specify certain parameters. Volatility parameters tend to work fairly well regardless of lookback, but there are inherent trade-offs to using short-term versus longer-term volatility. The former is more responsive to current market conditions while the latter is more stable. One approach is to use a range of lookbacks which