Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 12/07/2022

This is a summary of links featured on Quantocracy on Wednesday, 12/07/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Building a Raspberry Pi Cluster for QSTrader Using SLURM – Part 5 [Quant Start]

    In the previous article we created a virtual environment and installed QSTrader on all our secondary nodes. We then carried out a test of the sixty forty strategy across all secondary nodes to make sure our installation had been successful. Now that we have successfully paralellised QSTrader we can start to carry out parameter sweeps for strategies. In this article we are going to carry out just
  • Volume and Mean Reversion [Alvarez Quant Trading]

    Overall, I have had very little success integrating volume into any of my strategies. Either volume would have no predictive value or if it did, using it reduced the number of trades too much to be worthwhile. It has been a long while since I have looked into this and I had some new ideas. The Rules Test date range 1/1/2007 to 10/31/2022. I wanted to keep the rules simple. Buy Rules Stock is a
  • Why I prefer probabilistic forecasts – hitting time probabilities [Sarem Seitz]

    Probabilistic forecasts are a more comprehensive way to predict future events compared to point forecasts. Probabilistic forecasts involve creating a model that predicts the entire probability distribution for a given future period, providing insight into all likely outcomes. This allows for the derivation of both point and interval forecasts. Point forecasts are easier to communicate to
  • Doubling Down: Double Deep Q-networks for trading [Quant Dare]

    In previous posts, we have seen the basic RL algorithm, Deep Q learning (DQN). We have also seen it applied, using Neural Networks as the Agent, to an investment strategy. We finally even used it for a cryptocurrency investment strategy. This time, we will implement a slightly more advanced technique, Double Deep Q-networks (DDQN), and create a trading strategy using this algorithm. DQN revisited

Quantocracy’s Daily Wrap for 12/06/2022

This is a summary of links featured on Quantocracy on Tuesday, 12/06/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Investing in Deflation, Inflation, and Stagflation Regimes [Alpha Architect]

    Spikes in inflation and fear of stagflation prompted this study which answers the following question: 1. How do risk premiums and investment strategies behave across inflationary regimes like periods of high inflation, deflation, or stagflation? What are the Academic Insights? By utilizing the deepest sample available for a relatively broad cross-section of asset class and factor portfolio
  • Are Alternative ETFs Good Diversifiers? [Finominal]

    Alternative products with uncorrelated returns do not necessarily provide diversification benefits Out of 10 alternative ETFs, only one product improved the Sharpe ratio of a 60/40 portfolio Correlations should be regarded carefully in fund selection INTRODUCTION Given the demise of the traditional 60/40 portfolio comprised of equities and bonds in 2022, investors are desperate for diversifying

Quantocracy’s Daily Wrap for 12/03/2022

This is a summary of links featured on Quantocracy on Saturday, 12/03/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Vol-of-Vol for Crypto-Derivative Products [Quant at Risk]

    In quantitative finance, the Volatility of Volatility (also referred to as Vol-of-Vol or VoV) is an important parameter for pricing various derivative products (e.g. Volatility Dispersion Swaps) and its correct estimation is frequently desired. VoV is usually a single number treated an an input parameter. One can make a couple of assumptions to start working within VoV framework. Namely, (1) there
  • How Much Are Bitcoin Returns Driven by News? [Quantpedia]

    The main theme of these days in the crypto world is unmistakenly clear, its the mayhem connected with the collapse of the FTX empire, insolvencies of various lenders, and questions about underlying holdings in GBTC OTC ETF and reserves of exchanges and Tether (or other stablecoins as well). With new information, nothing does paint a bright picture of this industry in the financial world now and
  • Trend Following and Relative Sentiment: Complementary Factors [Alpha Architect]

    Trend following (time series momentum) is one of the most well-documented and well-known factors in investing, demonstrating persistence, pervasiveness, robustness, and implementability (survives transaction costs). Lesser well-known is relative sentimentan indicator that measures the positions, flows, and attitudes of institutional investors compared to those of individual investors. The

Quantocracy’s Daily Wrap for 11/29/2022

This is a summary of links featured on Quantocracy on Tuesday, 11/29/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Weekly rebalancing Sector ETFs using Structural Entropy [Pravin Bezwada]

    Based on previous article Structural Entropy, this article attempts to test the performance of a long short strategy on US sectors using structural entropy. Ideally it should use liquid US sector futures but since data is unavailable (not for free), it uses ETFs as proxy. The assumption in previous article (referenced above), was that when entropy is low there is contagion in the market and we

Quantocracy’s Daily Wrap for 11/28/2022

This is a summary of links featured on Quantocracy on Monday, 11/28/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Slava Ukraini! Latest from Quantocracy contributor in Ukraine: VIX and Expected Range [Only VIX]

    Continuing on trying to fight disinformation about VIX. Everyone knows that VIX index the square root of the expected 30-day variance, and if we drop mathematical precision – 30 day expected volatility of S&P index. Scott Bauer, on CBO's website – "the VIX Index tells us the level of expected volatility of the S&P 500 Index for the next 30 days, with a 68% confidence level",
  • Binance API Python Tutorial [Analyzing Alpha]

    This tutorial explains how to call Binance API endpoints in Python using the python-binance library and the Python requests function. The Binance API provides extensive documentation on how to call its various endpoints. Furthermore, multiple online blogs explain how to call the Binance API using a Python client. However, I find that existing resources merely explain how to call a particular
  • Replicating a CTA via Factor Exposures [Finominal]

    Strategies can be copied by recreating them from scratch or using factor exposures CTAs can be replicated via factor exposure analysis by utilizing only 4 asset classes Not a perfect replication, but surprisingly good given the limited input INTRODUCTION Our two most recent research articles focused on creating a CTA, also known as managed futures or trend following strategies, from scratch (read
  • Identifying the drivers of the commodity market [SR SV]

    Commodity futures returns are correlated across many different raw materials and products. Research has identified various types of factors behind this commonality: [i] macroeconomic changes, [ii] financial market trends, and [iii] shifts in general uncertainty. A new paper proposes to estimate the strength and time horizon of these influences through mixed-frequency vector autoregression.

Quantocracy’s Daily Wrap for 11/25/2022

This is a summary of links featured on Quantocracy on Friday, 11/25/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Creating a CTA from Scratch – II [Finominal]

    CTAs pursue trends across asset classes, regardless if long or short 2022 is exceptional as CTAs have more short positions than during the GFC No single long bond index position remains, fixed income is one big short INTRODUCTION In our last research article (read Creating a CTA from Scratch I) we demonstrated that building a CTA strategy is not particularly difficult. All it takes is a broad
  • Option Momentum: does it work? [Alpha Architect]

    Several studies show momentum works in global equities, corporate bonds, currencies, and commodities. This paper asks the following research question: Does momentum work within option markets? What are the Academic Insights? By focusing on the returns of delta-neutral straddles (from 1996 to 2019) on individual equities to form a strategy whose returns are approximately invariant to the
  • Why bother with unbiasedness? [Quant Dare]

    For every quantity to be estimated (estimand), theres a plethora of ways to estimate it (estimators). This raises the question of what properties we should be looking for so as to make a sensible choice. Often highlighted as one of such properties is unbiasedness, which we will discuss below with a particular focus on its shortcomings. Introducing unbiasedness In estimation, we want to obtain

Quantocracy’s Daily Wrap for 11/20/2022

This is a summary of links featured on Quantocracy on Sunday, 11/20/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trend-Following Rules in Two-State Regime-Switching Models [Alpha Architect]

    Academic research on trend-following investing has almost exclusively been focused on testing the profitability of various trading rules. Most of these rules are based on moving averages of past prices. The most popular is the Simple Moving Average (SMA). Less commonly used types of moving averages are the Linear Moving Average (LMA) and Exponential Moving Average (EMA). Each moving average is
  • Active manager bias is it the same as for individual investors? [Alpha Architect]

    The empirical research on the ability of actively managed funds, including such studies as the 2002 paper Mutual Fund Performance: An Empirical Decomposition into Stock-Picking Talent, Style, Transactions Costs, and Expenses by Russ Wermers, has found that they do have the ability to identify stocks that go on to outperform appropriate benchmarksthey are skilled. But are active managers

Quantocracy’s Daily Wrap for 11/16/2022

This is a summary of links featured on Quantocracy on Wednesday, 11/16/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Reviewing Patent-to-Market Trading Strategies [Quantpedia]

    The following article is a short distillation of the research paper Leveraging the Technical Competence of a Stock for the Purpose of Trading written by Rishabh Gupta. The author spent a summer internship at Quantpedia, investigating the Patent-to-Market (PTM) ratio developed by Jiaping Qiu, Kevin Tseng, and Chao Zhang. The PTM ratio uses public information about the number and dates of patents
  • New Quant Podcast: So, you want to be a Quant? [Quant at Risk]

    Here is the first episode in a new series of podcasts entitled Break into Finance. We will be talking about what it takes to launch your career in finance, what does it mean to become a quant, and where to start. Any questions welcomed!
  • Beyond linear II: the Unscented Kalman Filter [Quant Dare]

    The Unscented Kalman Filter allows to deal with nonlinear systems in a different way than the Extended Kalman Filter. Find how it works in this post. This is not the first time we talk about the Kalman Filter (and it probably wont be the last); I recommend you check this and this posts to understand the standard and extended versions of this algorithm and the notation we are going to use. The

Quantocracy’s Daily Wrap for 11/14/2022

This is a summary of links featured on Quantocracy on Monday, 11/14/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • If you’re so smart, how come you’re not Sam Bankman-Fried? [Investment Idiocy]

    There has been a very interesting discussion on twitter, relating to some stuff said by Sam Bankman-Fried (SBF), who at the time of writing has just completely vaporized billions of dollars in record time via the medium of his crypto exchange FTX, and provided a useful example to future school children of the meaning of the phrase nominative determinism*. * Sam, Bank Man: Fried. Geddit? Read the
  • Impact of Dataset Selection on the Performance of Trading Strategies [Quantpedia]

    We have previously mentioned that not all models (such as CAPM) that work well for developed markets (DM, such as the U.S. and Europe) are suited to be applicable in other world parts. The following article is a short analysis that shows that investing in Emerging Markets (EM) has its peculiarities. Especially investing in Chinese equities can sometimes be complicated with its mix of mainland
  • Creating a CTA from Scratch [Finominal]

    CTAs have become popular again given their positive returns in 2022 However, they are typically difficult to grasp given their ever-changing portfolios Building a CTA from scratch is not complicated and reduces the opaqueness INTRODUCTION 2022 has been a long year of disappointments for investors. Growth stocks do not go up forever. TIPS do not protect against high inflation. And cryptocurrencies
  • Industry and factor momentum: is there a theoretical foundation? [Alpha Architect]

    This post is the second and final portion of the review on momentum published on Momentum literature. The seminal article on momentum was published by Jegadeesh and Titman in 1993. Although the Jegadeesh article foreshadowed much of the research on cross-sectional and time series momentum at the stock level, it wasnt until the mid-to-late-2000s that investigators turned to study momentum at the

Quantocracy’s Daily Wrap for 11/13/2022

This is a summary of links featured on Quantocracy on Sunday, 11/13/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Simple Approach to Market-Timing Strategy Replication [Quantpedia]

    In previous articles, we discussed the ideas behind portfolio replication with market factors and presented Quantpedias approach to Multi-Factor Regression. Additionally, we examined the methods of market factor data extension used in construction of our historic factor universe we utilize to mimic portfolios during the past century (Extending Historical Daily Bond Data to 100 Years, Extending
  • Top 7 blogs on Sentiment Trading | 2022 [Quant Insti]

    The intelligent investor is a realist who sells to optimists and buys from pessimists. – Benjamin Graham Sentiment Trading strategies work on market sentiment and the trends around them. The strategies are often determined by the price and value of an asset that may fluctuate. Market sentiments influence the behavior of investors for a specific asset or financial market. It is the emotional
  • Macro factors of the risk-parity trade [SR SV]

    Risk-parity positioning in equity and (fixed income) duration has been a popular and successful investment strategy in past decades. However, part of that success is owed to a supportive macro environment, with accommodative refinancing conditions and slow, disinflationary, or even deflationary economies. Financial and economic shocks, as opposed to inflation shocks, dominated markets, leading to