Quantocracy

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Quantocracy’s Daily Wrap for 04/13/2021

This is a summary of links featured on Quantocracy on Tuesday, 04/13/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trading and investing performance – year seven [Investment Idiocy]

    It's April, which means the birds are singing, the trees are leafing, and I'm doing my annual review of my investing and trading performance. The format will be familiar from previous years, but I'm going to be using the fact I've upgraded my live trading system to include a lot more detail about my futures trading performance. TLDR: Last year my futures trading bailed me out

Quantocracy’s Daily Wrap for 04/12/2021

This is a summary of links featured on Quantocracy on Monday, 04/12/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Time Machines for Investors [Factor Research]

    Investors are challenged when evaluating investment opportunities with limited track records Factor exposure analysis can be used to create replication portfolios These empower investors to walk backward and forward in time, enhancing the investment decision process INTRODUCTION Investing is all about time traveling. Most investors travel forward a few days, months, or even years, in order to take
  • Where You Can Trade Cryptocurrencies using Fiat Currencies? [Quant at Risk]

    With a myriad of new crypto-exchanges popping up every quarter, lots of newcomers to this fields can be overwhelmed by their number. Big names can quickly stand out if you filter the list according to daily trading volume or the total number of cryptocurrencies available for trading. Some offer decent liquidity but some are newly born rising stars. In this quick note, we will show a few-liner in
  • Cryptocurrency Volatility Indexes [Only VIX]

    Last week I wrote about BVOL – bitcoin volatility index launch on Deribit. However this is not the first crypto volatility index. In fact last year T3 Indexes – the folks behind SPIKES volatility index launched both Bitcoin and Etherium volatility indexes, and already executed trades tied to their BitVol index on LedgerX platform. The first trade executed about a month ago was a call spread; the
  • The Definitive Guide to Pairs Trading [Hudson and Thames]

    Born at Morgan Stanley in the late 1980s, under the wing of Nunzio Tartaglia and his team, who later split up to start several of the worlds best hedge funds, namely PDT Partners and D.E. Shaw (which then lead to Two Sigma). Pairs trading has proven to be a popular and sophisticated trading strategy, often taught in advanced MSc Financial Engineering programs. A special mention to Gerry
  • Trend-Following Filters Part 3 [Alpha Architect]

    This is the third article in a series of three, the first two are available here and here. Those articles focus on examining from a digital signal processing (DSP) perspective 1 various types of digital filters that are designed to model trends in time series, in order to illustrate their properties and limitations. This article discusses a different signal processing tool called a filter
  • Research Review | 9 April 2021 | Bitcoin [Capital Spectator]

    How Much Bitcoin Should I Own? A Mathematical Answer Adam Grealish (Betterment) March 9, 2021 It goes without saying that this is a hard question to answer. But we can borrow a page from modern quantitative finance to help us arrive at a potential answer. For years, Wall Street quants have used a mathematical framework to manage their portfolios called the Black-Litterman model. Yes, the

Quantocracy’s Daily Wrap for 04/08/2021

This is a summary of links featured on Quantocracy on Thursday, 04/08/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What P&L Swings Can I Expect as a Trader? [Robot Wealth]

    Many beginner traders dont realize how variable the p&l of a high-performing trading strategy really is. Heres an example I simulated ten different 5 year GBM processes with expected annual returns of 20% and annualized volatility of 10%. (If you speak Sharpe Ratios, Im simulating a strategy within known Sharpe 2 characteristics.) I plotted the path with the highest ending equity

Quantocracy’s Daily Wrap for 04/07/2021

This is a summary of links featured on Quantocracy on Wednesday, 04/07/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Adding candlesticks to mean reversion setup in a portfolio [Alvarez Quant Trading]

    In my previous post, Adding candlesticks to mean reversion setup, we looked at how various candle patterns could help individual trades. Now we will see how those results translate to a portfolio. And why I usually only do portfolio level testing. The Strategy Setup Rules Stock is a member or was a member of the Russell 3000 The Dollar Volume of stock is greater than $500,000 Close is above $1

Quantocracy’s Daily Wrap for 04/05/2021

This is a summary of links featured on Quantocracy on Monday, 04/05/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Estimating the Stock-Bond Correlation [Alpha Architect]

    The correlation between stock and bond returns is an integral component of hedging strategies, risk assessment, and minimization of risk in allocation decisions. In the context of those strategies, the stock-bond correlation is typically estimated using monthly return data over a recent previous period. This is a reasonable approach but has turned out to be an unreliable indicator for forecasting

Quantocracy’s Daily Wrap for 04/02/2021

This is a summary of links featured on Quantocracy on Friday, 04/02/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Not so soft softmax [OSM]

    Our last post examined the correspondence between a logistic regression and a simple neural network using a sigmoid activation function. The downside with such models is that they only produce binary outcomes. While we argued (not very forcefully) that if investing is about assessing the probability of achieving an attractive risk-adjusted return, then it makes sense to model investment decisions

Quantocracy’s Daily Wrap for 04/01/2021

This is a summary of links featured on Quantocracy on Thursday, 04/01/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bitcoin: An Asset Allocation Perspective [Light Finance]

    Its no secret that 2021 has started off well for Bitcoin. Having breached a new all time high of $61,788.45 on March 13th it seems that each passing month brings with it a new milestone, new players, and greater acceptance. Recently, significant news has focused on the pace of institutional adoption. In fact, in a survey of 800 institutional clients, investment giant Fidelity reported that a
  • Conditional Parameter Optimization: Adapting Parameters to Changing Market Regimes via Machine Learning [EP Chan]

    Every trader knows that there are market regimes that are favorable to their strategies, and other regimes that are not. Some regimes are obvious, like bull vs bear markets, calm vs choppy markets, etc. These regimes affect many strategies and portfolios (unless they are market-neutral or volatility-neutral portfolios) and are readily observable and identifiable (but perhaps not predictable).
  • Fixed Income when you re Between a Rock and a Hard Place – Part 1/2 [Alpha Architect]

    Investors are stuck between a rock and a hard place. On one hand, it is painful to buy bonds that deliver paltry yields near all-time lows (Figure 2). On the other hand, many investors risk tolerance, compliance guidelines or liabilities preclude them from reducing their fixed income allocations. While there is likely no panacea for this dilemma, fixed income factors may offer incremental
  • What is Mutual Information? [Quant Dare]

    In the field of machine learning, when it comes to extracting relationships between variables, we often use Pearson correlation. The problem is that this measure only finds linear relationships, which can lead sometimes to a bad interpretation of the relation between two variables. Nevertheless, other statistics measure non-linear relationships, such as mutual information. Therefore, in this post,

Quantocracy’s Daily Wrap for 03/30/2021

This is a summary of links featured on Quantocracy on Tuesday, 03/30/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Minimum Profit Optimization: Mean-reversion Trading [Hudson and Thames]

    In my previous articles, I introduced how to construct long-short asset pairs according to the concept of cointegration and how to build a sparse mean-reverting multi-asset portfolio. Now that we are able to answer the question what to trade with confidence, it is time to get down to the nitty-gritty of the implementation of a mean-reversion strategy. The crux of implementing a

Quantocracy’s Daily Wrap for 03/29/2021

This is a summary of links featured on Quantocracy on Monday, 03/29/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • An Investigation of R&D Risk Premium Strategies [Quantpedia]

    A firm as an independent entity is engaged in a wide range of activities that affect its value. While the impact of some activities on the firms value is immediate and indisputable, there also exists a variety of activities that might impact the firms value in the future, while their outcome is yet uncertain. A similar logical approach can be used when evaluating the firms assets. The
  • How Active Mutual Funds Use ETFs [Alpha Architect]

    As of 2017, and in spite of the documented negative relationship between fund performance and use of ETFs, approximately one-third of US-domiciled, actively managed mutual funds held ETFs at one time or another. Active managers justifiably make use of ETFs to improve their portfolio management operations. But is their use for cash management, liquidity, improved returns, or risk reduction? The

Quantocracy’s Daily Wrap for 03/28/2021

This is a summary of links featured on Quantocracy on Sunday, 03/28/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Market Consequences of Investment Advice on Reddit’s Wallstreetbets [SSRN]

    We examine the market consequences of due diligence (DD) reports on Reddits Wallstreetbets (WSB) platform. We find average buy recommendations result in two-day announcement returns of 1.1%. Further, the returns drift upwards by 2% over the subsequent month and nearly 5% over the subsequent quarter. Retail trading increases sharply in the intraday window following publication, and retail
  • Market/Volume Profile and Matrix Profile [Dekalog Blog]

    A quick preview of what I am currently working on: using Matrix Profile to search for time series motifs, using the R tsmp package. The exact motifs I'm looking for are the various "initial balance" set ups of Market Profile charts. To do so, I'm concentrating the investigation around both the London and New York opening times, with a custom annotation vector (av). Below is a