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Quantocracy’s Daily Wrap for 07/04/2019

This is a summary of links featured on Quantocracy on Thursday, 07/04/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Flexible Returns Distribution- Part I (Generalized Lambda Distribution) [Asm Quant]

    It is commonly known that financial returns exhibit characteristics that are not captured by the widely applied normal and log-normal distributions. In a series of posts I want to present some flexible distributions that are well suited to model financial returns. We will work our way through quick modelling exercises in R that show how easy it is to use these alternative distributions. To begin,

Quantocracy’s Daily Wrap for 07/03/2019

This is a summary of links featured on Quantocracy on Wednesday, 07/03/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Deep Trading with TensorFlow V [Todo Trader]

    o you want to know how to build a multi-layered neural network? As deep as you want? In the next post, we will use real market data. In this one, we will still use non-trading data, because we are looking for a well-established knowledge of the basic concepts of Tensorflow. But we will use data used in other very real and current problems. OK, remember to keep in mind our other posts that make up
  • Graph Theory in portfolio analysis. Part I [Quant Dare]

    Have you ever thought about the bias of your portfolio to specific countries or asset types? Do you know that high concentration in one region implies a riskier path for your portfolio? If you want to know how to improve your portfolio using Graph Theory, first youll need to understand the basics. We discussed this topic in previous posts: Graph theory: connections in the market. To understand

Quantocracy’s Daily Wrap for 07/02/2019

This is a summary of links featured on Quantocracy on Tuesday, 07/02/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in June [Allocate Smartly]

    This is a summary of the recent performance of a wide range of excellent Tactical Asset Allocation (TAA) strategies, net of transaction costs. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Learn more about what we do or let AllocateSmartly help
  • Investment Portfolio Optimisation with Python Revisited [Python For Finance]

    In this post I am going to be looking at portfolio optimisation methods, touching on both the use of Monte Carlo, brute force style optimisation and then the use of Scipys optimize function for minimizing (or maximizing) objective functions, possibly subject to constraints, as it states in the official docs (https://docs.scipy.org/doc/scipy/reference/optimize.html). I have to

Quantocracy’s Daily Wrap for 07/01/2019

This is a summary of links featured on Quantocracy on Monday, 07/01/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The average is better than average [Spring Valley]

    Researchers often devote a significant amount of time trying to determine the optimal, or best performing, configuration of a trading model. With the proliferation of data and advances in high-performance computing, it is trivial to optimize millions, even billions, of trading models and parameter sets. While these developments are undoubtedly powerful, researchers are virtually guaranteed to
  • Factor Olympics 1H 2019 [Factor Research]

    Most factors generated positive returns in 1H 2019 Low Volatility produced the best and Value the worst performance Factor performance is comparable in the US & Europe, but markedly different in Japan INTRODUCTION 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
  • Debunking myths about stock buybacks [Alpha Architect]

    What are the research questions? The authors present 4 MYTHs regarding stock buybacks popular in the financial press. MYTH 1: Companies are self-liquidating using share repurchases at a historically high rate. MYTH 2: Share repurchases have come at the expense of profitable investment. MYTH 3: The recent run-up in prices is the result of share repurchases. MYTH 4: Companies that repurchase shares
  • Value and the Credit Spread [Flirting with Models]

    We continue our exploration of quantitative signals in fixed income. We use a measure of credit curve steepness as a valuation signal for timing exposure between corporate bonds and U.S. Treasuries. The value signal generates a 0.84% annualized return from 1950 to 2019 but is highly regime dependent with meaningful drawdowns. Introducing a nave momentum strategy significantly improves the
  • 12 Reasons Why Traditional Asset Allocation Doesn t Work [Two Centuries Investments]

    1. Crashes and Low Returns (link) Static asset allocation locks in the Two Risks that Ruin Investing – crashes and low returns. If you accept a static asset allocation strategy, you accept its history repeating in the future. For example, a 60/40 strategy drawdown of -63% in the 1930s. 2. Low Conviction (link) Data shows most people cannot stick with their static asset allocation
  • Bitcoin Swing Trading [Philipp Kahler]

    I published a bitcoin swing trading strategy in 2015 over here (German only). Time to review the methodology of swing trading and have a look on the performance. Can a rational strategy get an edge in an irrational market? Have a look and be surprised! Swing Point Trading Technique Swing trading is a short term, trend following trading technique which focuses on the local highs and lows of the
  • State of Trend Following in June [Au Tra Sy]

    Positive month for the Wizards which lifts the YTD performance further up in the positive territory at the halfway mark. Please check below for more details. Detailed Results The figures for the month are: June return: 1.46% YTD return: 4.52% Below is the chart displaying individual system results throughout June: StateTF June And in tabular format: System June Return YTD Return BBO-20 1.07% 7.59%

Quantocracy’s Daily Wrap for 06/29/2019

This is a summary of links featured on Quantocracy on Saturday, 06/29/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Models, Little Green Men, And Machine Learning [Alex Chinco]

    Economists use machine learning (ML) to study asset prices in two different ways. Approach #1: use these techniques to predict the cross-section of expected returnsi.e., to predict which stocks are most likely to have high or low future returns. e.g., see here, here, or here. Approach #2: use them to try to uncover the true asset-pricing modela.k.a., the set of priced risk
  • Bad and good beta in FX strategies [SR SV]

    Bad beta means market exposure that is expensive to hedge. Good beta is market exposure that is cheap to hedge. Distinguishing between these is crucial for FX trading strategies. The market sensitivity of FX positions can be decomposed into a risk premium beta (bad beta) and a real rate beta (good beta). FX positions with risk premium betas are associated with a positive price of risk

Quantocracy’s Daily Wrap for 06/27/2019

This is a summary of links featured on Quantocracy on Thursday, 06/27/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Ichimoku Trading Strategy With Python Part 2 [Python For Finance]

    This is part 2 of the Ichimoku Strategy creation and backtest with part 1 having dealt with the calculation and creation of the individual Ichimoku elements (which can be found here), we now move onto creating the actual trading strategy logic and subsequent backtest. The Ichimoku approach concerns itself with two major elements firstly the signals and insights produced by the cloud

Quantocracy’s Daily Wrap for 06/26/2019

This is a summary of links featured on Quantocracy on Wednesday, 06/26/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Graph algorithms and currency arbitrage, part 2 [Reasonable Deviations]

    In the previous post (which should definitely be read first!) we explored how graphs can be used to represent a currency market, and how we might use shortest-path algorithms to discover arbitrage opportunities. Today, we will apply this to real-world data. It should be noted that we are not attempting to build a functional arbitrage bot, but rather to explore how graphs could potentially be used
  • Trend Following: The Epitome of No Pain, No Gain [Alpha Architect]

    One of the recurring themes we see in our research is the concept of no pain; no gain. Or as Corey Hoffstein says, No pain, no premium. Cliff Asness may put it best when he says that some strategies require that you hold on to them like grim death. Bottom line: nothing is easy in financial markets. Pain is viewed slightly differently for academic economists versus the rest of us.
  • Large-Cap Price-to-Book Investing: What is Dead May Never Die [Alpha Architect]

    In the great book and series Game of Thrones, the inhabitants of the Iron Islands have a saying What is Dead May Never Die which is to be replied with But rises again harder and stronger. I am reminded of this saying as more and more market commentators and practitioners declare that value investing is dead and cite its terrible performance over the last 10 years (and even longer for
  • Ichimoku Trading Strategy With Python [Python For Finance]

    I thought it was about time for another blog post, and this time I have decided to take a look at the Ichimoku Kinko Hyo trading strategy, or just Ichimoku strategy for short. The Ichimoku system is a Japanese charting and technical analysis method and was published in 1969 by a reporter in Japan. I thought I would spend this post on the creation of the indicator elements themselves,

Quantocracy’s Daily Wrap for 06/24/2019

This is a summary of links featured on Quantocracy on Monday, 06/24/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Backtesting a sentiment analysis strategy for Bitcoin [Augmento]

    TL;DR: We developed a strategy using Augmento sentiment signals, and backtested it on Bitmex XBTUSD to generate a positive return between 2017 and 2019. Creating algorithms to trade Bitcoin is hard, and finding good data that is independent of the price but still correlated with the market is even harder. Sentiment data could be the answer, but its often hard to use for algorithmic trading, and
  • Generating Financial Series with Generative Adversarial Networks Part 2 [Quant Dare]

    This is a follow-up post to a recent post in which we discussed how to generate 1-dimensional financial time series with Generative Adversarial Networks. If you havent read that post yet we suggest you to do so, since it introduces the building blocks used in this one. Here we will go over the process behind generating multidimensional time series with GANs, the challenges behind this task and
  • Flirting with Models – Season 2 [Flirting with Models]

    With a 5-star rating on iTunes, we are proud to say that Season 1 of our podcast Flirting with Models received a tremendously warm welcome. And so were happy to announce that Season 2 is now available! You can listen to the new season on: iTunes Stitcher Google Play TuneIn Android The Interviews S2E1 Daniel Grioli Thinking like a Fox S2E2 Benn Eifert Volatility Investing
  • Mapping My Mind: Value Factor [Factor Research]

    There is consistency in the performance of the Value factor across markets and asset classes Allows to create a coherent framework of how to think about Value Suggests a global driver of factor performance INTRODUCTION Our research aims to educate investors by bridging the gap between academic literature and practical investing. In contrast to our usual research notes, this one is more personal as

Quantocracy’s Daily Wrap for 06/23/2019

This is a summary of links featured on Quantocracy on Sunday, 06/23/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • DeepTrading with Tensorflow IV [Todo Trader]

    fter you have trained a neural network (NN), you would want to save it for future calculation and eventually deploying to production. So, what is a Tensorflow model? Tensorflow model contains the network design or graph and values of the network parameters that we have trained. Important Note: I know that the reader is impatient to use real data from the financial markets. Please be patient, I
  • Post Opex Weakness Typical in June [Quantifiable Edges]

    In March I discussed how the weeks following options expiration in March, June, and September have been the worst 3 weeks of the year. Below I have updated the June stats and profit, which I also showed last June. 2019-06-23 The strong, steady downslope and bearish numbers suggest we are entering a very weak seasonal period. It will be interesting to see how the market holds up this week, and
  • Process Noise Covariance Matrix Q for a Kalman Filter [Dekalog Blog]

    Since my last post I have been working on the process noise covariance matrix Q, with a view to optimising both the Q and R matrices for an Extended Kalman filter to model the cyclic component of price action as a Sine wave. However, my work to date has produced unsatisfactory results and I have decided to give up trying to make it work. The reasons for this failure are unclear to me, and I

Quantocracy’s Daily Wrap for 06/21/2019

This is a summary of links featured on Quantocracy on Friday, 06/21/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bond. Treasury Bond [Robot Wealth]

    The Federal Reserve publishes the yield-to-maturity of US Treasury bonds. However, the actual returns earned by investors are not publicly available. Nor are they readily and intuitively discerned from historical yields, since a bonds return equals its yield only if its yield stays constant and if all coupons (cash payments) are reinvested at that same yield (Tuckman and Angel, 2013,
  • Trading China A-Share Stocks Based On Social Media Data Analysis In Python [Quant Insti]

    In this article, we will understand how natural language processing, sentiment analysis and social media play a role in the share markets with the help of Python. This would be explained with respect to the trading in China markets A-share stocks. This article is the final project submitted by the authors as a part of their coursework in the Executive Programme in Algorithmic Trading (EPAT) at