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Quantocracy’s Daily Wrap for 04/15/2020

This is a summary of links featured on Quantocracy on Wednesday, 04/15/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Discounted expectations [OSM]

    After our little detour into GARCHery, were back to discuss capital market expectations. In Mean expectations, we examined using the historical average return to set return expectations when constructing a portfolio. We noted hurdles to this approach due to factors like non-normal distributions, serial correlation, and ultra-wide confidence intervals. While we highlighted these obstacles and
  • Generic Octave_Oanda_API Function [Dekalog Blog]

    My last two posts have shown Octave functions that use the Oanda API to access and download data. In the first of these posts I said that I would post more code for further functions as and when I write them. However, on further reflection this would be unnecessary as the generic form of any such function is: 1) create the required headers ## set up the headers query = [ 'curl -s –compressed
  • Curse of Dimensionality part 4: Distance Metrics [Eran Raviv]

    Many machine learning algorithms rely on distances between data points as their input, sometimes the only input, especially so for clustering and ranking algorithms. The celebrated k-nearest neighbors (KNN) algorithm is our example chief, but distances are also frequently used as an input in the natural language processing domain; You shall know a word by the company it keeps (Firth, J. R.
  • A primer on embedded currency risk [Quant Dare]

    In a previous post, we showed that unhedged currency exposure adds unrewarded risk to our investment, hurting risk-adjusted-performance. This risk should either be neutralized through passive hedging; or mitigated and turned into profit with an active overlay, the latter being what ETS has been doing for the last 20 years. Now, lets say we dont want to get involved in currency matters and we
  • Dual Momentum & Vortex Indicator: Trading Strategy Review [Oxford Capital]

    Developer: Etienne Botes and Douglas Siepman (Vortex Indicator). Concept: Dual momentum trading strategy based on Vortex Indicator. Research Goal: Performance verification of dual momentum signals. Specification: Table 1. Results: Figure 1-2. Trade Filter: Long Filter: Slow Positive Vortex Indicator (+VI1) is above Slow Negative Vortex Indicator (VI1). Short Filter: Slow Negative Vortex

Quantocracy’s Daily Wrap for 04/14/2020

This is a summary of links featured on Quantocracy on Tuesday, 04/14/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Inverting Differentiated Time-Series in pandas for Deep Learning Prediction Analysis [Quant at Risk]

    A differentiation of the time-series is a common transformation used when we want to get a stationary time-series given a non-stationary one. The latter usually displays time-dependent relationships like trends, seasonality, quasi-cyclic patterns, and their Fourier power spectrum is characterised by the colour noise. On the other hand, stationary time-series summary statistics are not dependent on
  • Trading and investing performance – year six [Investment Idiocy]

    Time for the annual review post, as my reviews follow the UK tax year which ended on the 5th April. And what a year it has been; well 10 months or so of fairly normal stuff, followed by several weeks of stomach churning market chaos. Previous updates can be found here, here, here, here and here. This post will follow the format of previous posts, but there will be some extra stuff related to the
  • Trend Following Reality: You Need Trends to Trend-Follow [Alpha Architect]

    Trend Following, as an investing strategy has delivered strong performance during market chaos (e.g., Global Financial Crisis of 20072009), but the strategy has gone through a significant drawdown (save the last few months where things are perking up!). We have seen dismal returns in the recent decade relative to the historical record (this article refers to the period from 2010-2018). In some

Quantocracy’s Daily Wrap for 04/13/2020

This is a summary of links featured on Quantocracy on Monday, 04/13/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Low Vol-Momentum vs Value-Momentum Portfolios [Factor Research]

    Low Vol-Momentum & Value-Momentum portfolios outperformed stock markets since 1989 Low factor correlations contributed to the attractive risk-return profiles Excess returns have been lower in the most recent than in previous decades INTRODUCTION If an investor would state today that in ten or twenty years most portfolios would include an allocation to cryptocurrencies, he would likely be
  • Macro trading and macroeconomic trend indicators [SR SV]

    Macroeconomic trends are powerful asset return factors because they affect risk aversion and risk-neutral valuations of securities at the same time. The influence of macroeconomics appears to be strongest over longer horizons. A macro trend indicator can be defined as an updatable time series that represents a meaningful economic trend and that can be mapped to the performance of tradable assets

Quantocracy’s Daily Wrap for 04/09/2020

This is a summary of links featured on Quantocracy on Thursday, 04/09/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Fermi’s Intuition on Models [Falkenblog]

    In this video snippet, Freeman Dyson talks about an experience he had with Enrico Fermi in 1951. Dyson was originally a mathematician who had just shown how two different formulations of quantum electrodynamics (QED), Feynman diagrams and Schwinger-Tomonoga's operator method, were equivalent. Fermi was a great experimental and theoretical physicist who built the first nuclear reactor and
  • How Do Investment Strategies Perform After Publication? [Quantpedia]

    In many academic fields like physics, chemistry or natural sciences in general, laws do not change. While economics and theory of investing try to find rules that would be true and always applicable, it is not that simple, there is a complication human. Psychology of humans is very complex. In the one hand, it creates anomalies in the market, that academics study and practitioners use.

Quantocracy’s Daily Wrap for 04/08/2020

This is a summary of links featured on Quantocracy on Wednesday, 04/08/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The other way around: from correlations to returns [Quant Dare]

    In one way or another, most quantitative models somehow seek to find and exploit relationships between two or more series of returns. Therefore, the usual pipeline has a time-series go through mathematical procedures which condensate in a couple of figures meaningful information: the expected mean, volatility, drawdowns, runups, correlations, among others. That is, the space of returns, large and
  • Daily vs. Monthly Trend-Following Rules…Plus Some DIY Tools! [Alpha Architect]

    Trend-following strategies are a lot like stock-picking strategies there are endless approaches and varying levels of complexity. In this short piece, we explore the decision related to implementing basic trend-following strategies on either a daily or a monthly basis. Many traders intuitively believe that daily data is better than monthly data. Is this belief justified? Like most things in

Quantocracy’s Daily Wrap for 04/06/2020

This is a summary of links featured on Quantocracy on Monday, 04/06/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Volatility, Risk Management, and Market Chaos: Research that Might Help [Alpha Architect]

    Given the recent market decline, we thought it would be helpful to review some of our blog posts from the past that may be relevant to the current crisis atmosphere. These posts focus on research that explores investment strategies that are believed to help investors manage risk and diversify their portfolios. Short Selling Bans Generally Dont Work! Most regulators around the world reacted to
  • Factor Olympics Q1 2020 [Factor Research]

    We present the performance of five well-known factors on an annual basis for the last 10 years. We only present factors where academic research highlights positive excess returns across market cycles and asset classes. Other strategies like Growth might be widely-followed investment styles, but lack academic support and are therefore excluded. METHODOLOGY The factors are created by constructing
  • A L-U-V-Wy Recovery [Flirting with Models]

    There has been considerable speculation as to the shape of the markets recovery. Practitioners have taken to using letters as short hand for the recovery they forecast. Whether the market makes a fast V-shaped recovery, a slower U-based formation, a W-style double-bottom, or an L-shaped reset is heavily debated. As a path dependent strategy, trend following will behave differently in each of
  • First Octave Function using Oanda API [Dekalog Blog]

    As part of my on-going code revision I have written my first Octave function to use the Oanda API. This is just a simple "proof of concept" function which downloads an account summary. ## Copyright (C) 2020 dekalog ## ## This program is free software: you can redistribute it and/or modify it ## under the terms of the GNU General Public License as published by ## the Free Software

Quantocracy’s Daily Wrap for 04/05/2020

This is a summary of links featured on Quantocracy on Sunday, 04/05/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • GARCHery [OSM]

    In our last post, we discussed using the historical average return as one method for setting capital market expectations prior to constructing a satisfactory portfolio. We glossed over setting expectations for future volatility, mainly because it is such a thorny issue. However, we read an excellent tutorial on GARCH models that inspired us at least to take a stab at it. The tutorial hails from

Quantocracy’s Daily Wrap for 04/04/2020

This is a summary of links featured on Quantocracy on Saturday, 04/04/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pandemics and Factor Investing: A Glimpse into the Past [Alpha Architect]

    When I was in the Marines we were voluntold to read a lot on the history of warfare. This mandate came from General Mattis desire that we lean on the 5,000+ years of fighting experience amongst us illustrious humans. Of course, history never tells you exactly what will happen in the future, but the perspective of history can be useful for preparing ourselves for the future. In a similar
  • Accelerating Python for Exotic Option Pricing (h/t @PyQuantNews) [Nvidia Developer]

    In finance, computation efficiency can be directly converted to trading profits sometimes. Quants are facing the challenges of trading off research efficiency with computation efficiency. Using Python can produce succinct research codes, which improves research efficiency. However, vanilla Python code is known to be slow and not suitable for production. In this post, I explore how to use Python
  • A statistical learning workflow for macro trading strategies [SR SV]

    Statistical learning for macro trading involves model training, model validation and learning method testing. A simple workflow [1] determines form and parameters of trading models, [2] chooses the best of these models based on past out-of-sample performance, and [3] assesses the value of the deployed learning method based on further out-of-sample results. A convenient technology is the

Quantocracy’s Daily Wrap for 04/03/2020

This is a summary of links featured on Quantocracy on Friday, 04/03/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Portfolio Optimization for Efficient Stock Portfolios [Invest Resolve]

    Its time to rethink passive stock investing. While capitalization weighted U.S. stock indices have delivered good performance over the past decade and the long-term, many investors dont realize that they can achieve similar returns with much less risk by employing risk-efficient portfolio construction. Risk-efficient portfolios avoid active stock picking and instead focus on achieving
  • Managing Expectations: Comparing S&P 500 s Deepest Drawdowns [Capital Spectator]

    In a previous post, I simulated S&P 500 drawdowns for perspective on what the current market correction may dispense in the weeks and months ahead. Lets supplement that analysis by visually comparing the current and ongoing peak-to-market decline with the ten deepest drawdowns since 1950. History doesnt repeat, at least not exactly when it comes to stock market trends. But you can still

Quantocracy’s Daily Wrap for 04/02/2020

This is a summary of links featured on Quantocracy on Thursday, 04/02/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Predict Bitcoin Price with Deep Learning LSTM Network – Part 1 [Quant at Risk]

    You cant predict the future unless you have a crystal ball but you can predict an assets trading price in next time step if you have a right tool and enough confidence in your model. With the development of a new class of forecasting models employing Deep Learning neural networks, we gained new opportunities in foreseeing near future. A rebirth of Long Short Term Memory (LSTM) artificial
  • How fast should we trade? [Investment Idiocy]

    This is the final post in a series aimed at answering three fundamental questions in trading: How should we control risk (first post) How much risk should we take? (previous post) How fast should we trade? (this post) Understanding these questions will allow you to avoid the two main mistakes made when trading: taking on too much risk and trading too frequently. Incidentally, systematic traders
  • Volatility Expectations and Returns [Alpha Architect]

    A large body of research, including the 2017 study Tail Risk Mitigation with Managed Volatility Strategies by Anna Dreyer and Stefan Hubrich, demonstrates that while past returns do not predict future returns, past volatility largely predicts future near-term volatility, i.e., volatility is persistent (it clusters). High (low) volatility over the recent past tends to be followed by high