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Strategies Articles from Automated Trader

An in depth examination of a technology or technique used in automated or algorithmic trading written by an industry practitioner.

  • Machine Learning + Regime Switching = Profitability? Machine Learning + Regime Switching = Profitability?

    SUBSCRIBERS ONLYThe concept of regimes – such as bull and bear markets – is elemental to financial markets. The desire to predict regime switches, commonly known as turning points, is similarly elemental. Ernest Chan, CEO of E. P. Chan & Associates, examines a possible technique for this most demanding of tasks. full story

  • Transition Management: Best Execution through Algorithmic Trading Transition Management: Best Execution through Algorithmic Trading

    SUBSCRIBERS ONLYThe ability to restructure investment portfolios with minimal cost, risk or information leakage is a critical skill in today’s competitive fund management industry. Joseph Sidibe, Vice President, Execution Sales Desk, EMEA, Merrill Lynch, explains how use of algorithmic trading techniques can help ensure best execution in transaction management. full story

  • Adapting Algorithms to Realities of Asian Markets Adapting Algorithms to Realities of Asian Markets

    SUBSCRIBERS ONLYIf execution algorithms are to be adopted widely in the Asian markets, both providers and users must be aware of the unique characteristics of trading in the region, says Dr Usman Malik of P. E. Lynch LLP. full story

  • The Role of Advanced Models in Performance Boosting The Role of Advanced Models in Performance Boosting

    SUBSCRIBERS ONLYIn the second part of a two-part article, David Aronson, President of Hood River Research, examines the modelling techniques used in arriving at a valuable predictor set for boosting ‘raw’ trading model performance. full story

  • Using Trading Dynamics to Boost Strategy Performance Using Trading Dynamics to Boost Strategy Performance

    QUALIFIED REGISTRANTS & SUBSCRIBERSIn the first part of a two-part article, David Aronson, President of Hood River Research, introduces the concept of performance boosting strategies and explains the selection process for their predictor inputs. full story

  • Backtesting: Best practice principles for beating the market Backtesting: Best practice principles for beating the market

    REGISTERED VIEWERSIn an increasingly crowded market, traders need comprehensive, integrated backtesting capabilities to ensure their algorithms stay ahead of the competition. Jorin Daleanes, Sales and Account Manager, RTD Tango and Backtester, and Steffen Gemuenden, Co CEO, RTS Realtime Systems Group, lay out the key principles. full story

  • Using Order Book Data to Improve Automated Model Performance Using Order Book Data to Improve Automated Model Performance

    REGISTERED VIEWERSAutomated traders now have access to unprecedented levels of market data. Thom Hartle, Director of Marketing, CQG, conducts a theoretical comparison between two trading systems to explore how order book data can be leveraged for optimal trade performance. full story

  • High frequency data analysis High frequency data analysis

    FREE ARTICLEAs the requirements for storing, manipulating and deriving intelligence from ever larger data sets continue to expand, techniques and technology have to keep pace. Brian Sentance, CEO of Xenomorph, outlines some of the prerequisites. full story

  • Strategies: Waiting for the Iceberg Strategies: Waiting for the Iceberg

    FREE ARTICLEAlgorithmic trading has radically changed trading patterns in capital markets. Transaction volumes have increased and in the equities markets in particular, individual transaction sizes have plummeted. Debbie Williams, Group Vice President of the Capital Markets and Risk Management Practices at Financial Insights examines the implications of this for risk management and how market participants are (or aren’t) responding to the challenge. full story

  • Strategies: Building a Better Bear Trap Strategies: Building a Better Bear Trap

    FREE ARTICLEOne of the most critical elements in algorithmic trading lies in accurately modelling trading costs, yet this still remains a rather inexact science. While certain cost elements are relatively stable and/or easy to predict, others are not. As a result, models for estimating trading costs have tended to be reasonably predictive when viewed across a very large sample of trades, but decidedly indifferent performers on individual ones. This has in turn made the task of minimising these costs through the selection, tuning and scheduling of appropriate execution algorithms difficult. Dan diBartolomeo, president of Northfield Information Services, discusses the current limitations and suggests some additional elements that can be used to improve forecasting of trading costs and trade scheduling. full story

  • Strategies: Optimisation Algorithms for Automated Trading Strategies: Optimisation Algorithms for Automated Trading

    FREE ARTICLEAutomation opens up the possibility of trading multiple models or the same/similar model with multiple parameter sets. However, that raises the question of how best to optimise those parameter sets. Chris Donnan, who works in equity derivatives trading technology at a top Wall Street firm, answers it. full story