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Quantocracy’s Daily Wrap for 12/08/2019

This is a summary of links featured on Quantocracy on Sunday, 12/08/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Lessons learned building ML trading system that turned $5k into $200k (h/t @PyQuantNews) [Tradient]

    One of my recent side projects was building an automated trading system for the crypto markets. To be fair, I probably spent more time on this than on my full-time job, so calling it a side project may not be completely accurate. The internet is full of people ready to teach you about trading. Most are trying to sell you something, and many are mistaking random chance for skill. Coming from a
  • Marcos @LopezDePrado testifies before U.S. Congress [Mathematical Investor]

    Famed quantitative financial mathematician Marcos Lopez de Prado, who was recently featured as Master of the Robots by Bloomberg, testified today (6 December 2019) before the U.S. Congress, together with four other panelists. The topic for the panel, organized by the U.S. House Committee on Financial Services, was Robots on Wall Street: The Impact of AI on Capital Markets and Jobs in the Financial
  • Equity return anomalies and their causes [SR SV]

    The vast range of academically researched equity return anomalies can be condensed into five categories: [1] return momentum, [2] outperformance of high valuation, [3] underperformance of high investment growth, [4] outperformance of high profitability, and [5] outperformance of stocks subject to trading frictions. A new empirical analysis suggests that these return anomalies are related to market

Quantocracy’s Daily Wrap for 12/06/2019

This is a summary of links featured on Quantocracy on Friday, 12/06/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Yield Curve Empirics [Scalable Capital]

    Interest rates measure the level of compensation that financial market participants get for lending money. The level of compensation for lending varies over time and is related to several other economic factors. Most important ones are rates set by central banks, inflation rates and underlying credit risks. Bonds with non-negligible credit risks trade at a spread compared to risk-free bonds, but
  • Using Kalman filters to derive predictive factors from limit order book data [Alex Botsula]

    This post is based on the experience I have got while taking part in a very interesting forecasting competition hosted by XTX. Participants were challenged by the task to forecast the future return of a (presumably) Forex asset based on the limit order book (LOB) data. No details of the asset or limit order book dates were disclosed as part of the competition. As part of the competition, XTX
  • Tactical Asset Allocation in November [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
  • Global Impact of Investor Home Country Bias [Alpha Architect]

    A large body of research demonstrates that familiarity breeds investment. For example, a study by Gur Huberman found that shortly after AT&T was broken up and shareholders were given shares in each of what were called the Baby Bells, the residents of each region held a disproportionate number of shares of their regional Bell. Each group of regional investors was confident their regional

Quantocracy’s Daily Wrap for 12/05/2019

This is a summary of links featured on Quantocracy on Thursday, 12/05/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Experiments with GANs for Simulating Returns [EP Chan]

    Simulating returns using either the traditional closed-form equations or probabilistic models like Monte Carlo has been the standard practice to match them against empirical observations from stock, bond and other financial time-series data. (See Chan and Ng, 2017 and Lopez de Prado, 2018.) Some of the stylised facts of return distributions are as follows: The tails of an empirical return
  • Leveraged Trading [Following the Trend]

    I tend to be a little skeptical when I see books aimed at retail traders with low amount of trading capital, focusing on leveraged trading on FX, CFDs and the like. The very mention of retail forex trading means that theres a near certainty that whatever comes next is misinformed at best and a scam at worst. But seeing my favorite investment author release a book on the topic got my attention.
  • The 60/40 Benchmark Portfolio [Quant Start]

    In a recent article we introduced systematic tactical asset allocation (TAA) as a low-frequency example of quantitative trading strategy. For those who are taking their first steps in systematic trading, are wanting to consider systematic trading in the context of their retirement planning or are simply wishing to minimise the day-to-day work of running a strategy TAA can be a great choice.

Quantocracy’s Daily Wrap for 12/04/2019

This is a summary of links featured on Quantocracy on Wednesday, 12/04/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Adaptive VIX Moving Average with Ehlers Alpha Formula [CSS Analytics]

    In the last post I described a relatively simply method to incorporate the VIX into the well-known AMA or Adaptive Moving Average framework. The alpha formula requires two separate parameters- a short and a long-term constant which requires greater specification by the user. Ideally the fewer parameters you have to specify the better (although it is important to note that logical requirements for
  • Mitigating overfitting on Trading Strategies [Quant Dare]

    According to Wikipedia in finance, a trading strategy is a fixed plan that is designed to achieve a profitable return by going long or short in markets. The main reasons that a properly researched trading strategy helps are its verifiability, quantifiability, consistency, and objectivity. For every trading strategy, one needs to define assets to trade, entry/exit points, and money management

Quantocracy’s Daily Wrap for 12/03/2019

This is a summary of links featured on Quantocracy on Tuesday, 12/03/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Jim Simons: The man who solved the market [Mathematical Investor]

    Gregory Zuckerman, author of The Greatest Trade Ever, has published a new book highlighting the life and work of Jim Simons, who, at the age of 40, walked away from a very successful career as a research mathematician and cryptologist to try his hand at the financial markets, and ultimately revolutionized the field. Zuckermans new book is titled The Man Who Solved the Market: How Jim Simons
  • How to Choose the Best Period for Indicators [Quantpedia]

    Academic literature recognizes a large set of indicators or factors that are connected with the various assets. These indicators can be utilized in a variety of trading strategies, which means that such indicators are popular among practitioners who seek to invest their funds. Usually, the indicators are connected with some evaluation period. For example, we have a trend following strategy using a

Quantocracy’s Daily Wrap for 12/02/2019

This is a summary of links featured on Quantocracy on Monday, 12/02/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Refresher: Integration, Co-Integration Stationarity [Auquan]

    When working with time series financial data, stationarity (or lack thereof!) is going to be a defining aspect of how you conduct your analyses. In this article, we're going to give you a quick refresher of what these terms mean and how they affect your data. Let's start with importing the basics! import numpy as np import pandas as pd import statsmodels import statsmodels.api as sm from
  • Quantamental Investing – Change vs Patterns [Two Centuries Investments]

    As readers would know, discussing ways to combine quant and fundamental investing has been a topic I care about (see some prior posts here, here and here). Perhaps Im biased. But I believe that proper collaboration between quant and fundamental approaches is still a largely unexplored area. Yes, many fundamental shops rely on quant screens and many quant shops rely on fundamental justifications
  • Employing Human-Order in pandas DataFrame Sorting: Risk Factors and Tenors [Quant At Risk]

    There are various Python projects which require sorting but not the ones that employ a default alphanumeric functionality. We talk about manually specified order or human-order, in short. One of such examples is the case study presented below. Imagine that your risk system provides you with a list of various risk factors and the corresponding risk tenors. The latter may differ among risk factors
  • Myth-busting: Fed Actions and Stock Prices [Alpha Architect]

    Since the global financial crisis, the financial press has periodically asserted that the Federal Reserves actions were the driving force behind rising stock prices. This study investigates this assertion by asking the following research question: Is there a relationship between stock prices and the level of central bank influence in interest rates (where influence= difference between the
  • Why Pension Funds & Millennials Should Avoid ESG [Factor Research]

    ESG ETFs underperformed the stock market since 2005 Likely explained by higher fees, a constrained stock universe, and sector bets Financially-impaired investors like public pension funds and Millennials should avoid ESG investing INTRODUCTION If investors would be looking for the ETF flavor of the year, then it would likely be ESG as highlighted by the large number of product launches in 2019. In

Quantocracy’s Daily Wrap for 12/01/2019

This is a summary of links featured on Quantocracy on Sunday, 12/01/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How You Measure Months Matters A Lot. A Look At Two Implementations of KDA [QuantStrat TradeR]

    This post will detail a rather important finding I found while implementing a generalized framework for momentum asset allocation backtests. Namely, that when computing momentum (and other financial measures for use in asset allocation, such as volatility and correlations), measuring formal months, from start to end, has a large effect on strategy performance. So, first off, I am in the job
  • RSI Hellfire Heatmap Indicator [Philipp Kahler]

    Chart analysis is all about visualizing data. The RSI hellfire indicator uses a heat-map to visualizes how overbought or oversold the market is on a broad scale. This helps to get a broad picture of the current market setup. Multiple Time-frame Relative Strength Index Wells Wilders RSI is an old timer of technical indicators. It tries to find out if markets are overbought or oversold. Usually

Quantocracy’s Daily Wrap for 11/29/2019

This is a summary of links featured on Quantocracy on Friday, 11/29/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Contribute to The Alpha Scientist blog! [Alpha Scientist]

    You may have noticed that this blog has been mostly inactive in 2019. Earlier this year, I took a full-time position with one of my buy-side consulting clients focused on researching methodologies for application of ML / data science to alpha research. While I'm thoroughly enjoying the work, it creates constraints on my bandwidth and flexibility to share research here. However, despite a lack
  • Automated Trading Systems: Architecture, Protocols, Types of Latency [Quant Insti]

    The automated trading system or Algorithmic Trading has been at the centre-stage of the trading world for more than a decade now. A trading system, more commonly referred as a trading strategy is nothing but a set of rules, which is applied to the given input data to generate entry and exit signals (buy/sell). Although formulating a trading strategy seems like an easy task, in reality,
  • Financial Models Numerical Methods in Jupyter Notebooks (h/t @PyQuantNews)

    This is a collection of Jupyter notebooks based on different topics in the area of quantitative finance. Is this a tutorial? Almost! 🙂 This is just a collection of topics and algorithms that in my opinion are interesting. It contains several topics that are not so popular nowadays, but that can be very powerful. Usually, topics such as PDE methods, Lvy processes, Fourier methods or Kalman
  • When the Wednesday Before Thanksgiving Closes at a New High [Quantifiable Edges]

    Thanksgiving has some seasonal tendencies, with Wednesday and Friday often being bullish, and the Monday after being bearish. This year not only did Wednesday perform well, but it left the SPX at a new high heading into the holiday. So I decided to look back at other times SPX closed at a 50-day high on the day before Thanksgiving. 2019-11-28 Results here show a bearish inclination over the next 2

Quantocracy’s Daily Wrap for 11/27/2019

This is a summary of links featured on Quantocracy on Wednesday, 11/27/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Black & Scholes for Puts/Calls in a Single Excel Cell [Six Figure Investing]

    Sometimes an online option calculator isnt enough and youd like to implement the Black & Scholes (B&S) option pricing equations in Excel. If youre just playing around it doesnt matter how you structure the calculation. In fact, for claritys sake, its probably a good idea to spread out the calculation across multiple cells. However, if youre planning to do some serious
  • Forbidden Knowledge: Long-Only Academic Factors are Also Cool [Alpha Architect]

    The standard academic approach to factor analysis is through the lens of long-short portfolios (which often confuses practitioners!). For example, a researcher may take the universe of the largest 1,000 stocks and sort them on value, as measured via book-to-market. The value factor portfolio may go long the top third cheapest stocks (value leg) and go short the bottom third most

Quantocracy’s Daily Wrap for 11/26/2019

This is a summary of links featured on Quantocracy on Tuesday, 11/26/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Adaptive VIX Moving Average [CSS Analytics]

    One of the challenges with technical or quantitative analysis is to identify strategies that can adapt to different market regimes. The most obvious is a change in the forecast or implied volatility as proxied by the VIX. During more volatile periods we would expect more signal noise and during less volatile periods we would expect less signal noise. But how do we capture this in a strategy? One
  • Enterprise Multiples and Equity Country Allocations [Alpha Architect]

    The use of valuation multiples in selecting equity securities is well established in the literature, and weve covered the research on enterprise multiples here (here is a recent JPM on the topic). However, there are relevant questions as to the effectiveness of multiples when applied to national indexes in the service of country allocation. Contrary to popular opinion, studies show that not
  • Diversification: More Than “What” [Flirting with Models]

  • Are Earnings Forecasts of Sell-side Analysts Biased? [Alpha Architect]

    There is a substantial body of evidence linking various accounting ratios to expected stock returns. One explanation of the links is that they could be explained by the accounting ratios being associated with systematic sources of risk. Alternatively, they could be associated with mispricing that arises from systematically biased investor expectations (see here for a discussion on this topic).