Quantocracy’s Daily Wrap for 01/28/2019

This is a summary of links featured on Quantocracy on Monday, 01/28/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tightening the Uncertain Payout of Trend-Following [Flirting with Models]

    Long/flat trend-following strategies have historically delivered payout profiles similar to those of call options, with positive payouts for larger positive underlying asset returns and slightly negative payouts for near-zero or negative underlying returns. However, this functional relationship contains a fair amount of uncertainty for any given trend-following model and lookback period. In
  • HFT-like Trading Algorithm in 300 Lines of Code You Can Run Now [Alpaca]

    Commission Free Trading API Trading with commission free API opened up many interesting ideas. Lots of people liked the idea of trading stocks using Google Spreadsheet, and some people have been building their own Slack integrations. You can even build a robo advisor that automates longer-term investment strategies. Manage Your Stocks from Google Spreadsheet Using API You might think API trading
  • The Failure of Factor Investing was Predictable [Alpha Architect]

    In a recent ETF column, Allan Roth listed five investment lessons. While I agreed with much of what he wrote, one claimfactor investing has failed miserably called for examination of the facts. But first, a little background. William Sharpe, Jack Treynor and John Linter are typically given most of the credit for introducing the capital asset pricing model (CAPM). The CAPM was the first
  • Cross Validation in Machine Learning Trading Models [Quant Insti]

    The application of the machine learning models is to learn from the existing data and use that knowledge to predict the future unseen events. The model needs to be thoroughly tested and cross-validated to profitably trade in live trading. After reading this, you will be able to: Cross validate whether your model is good in predicting buy signal and/or sell signal Demonstrate the performance of
  • Value, Momentum and Carry Across Asset Classes [Factor Research]

    Cross-asset multi-factor exposure might be an attractive diversifier for an equity portfolio Factors share trends across asset classes, indicating common drivers However, relationships are time-varying, increasing complexity and risks INTRODUCTION There is a 72% probability of the San Franciso Bay Area getting hit by at least one earthquake of a magnitude of 6.7 or stronger between today and 2043
  • Last Chance for Early Bird Pricing: AI and Data Science in Trading Conference, NYC March 2019

    There is so much hype and confusion surrounding AI and alt data at the moment. The AI & Data Science in Trading conference separates the hype from the reality Professor David Hand, Imperial College, London Finding alpha has always required asset managers to raise the bar in terms of technology. Today, the combination of endless new data sources, cheap computing and new AI techniques

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