{"id":747,"date":"2017-05-19T21:03:41","date_gmt":"2017-05-19T21:03:41","guid":{"rendered":"http:\/\/quantpedia.com\/?p=747"},"modified":"2025-06-04T14:13:27","modified_gmt":"2025-06-04T12:13:27","slug":"an-example-of-trading-model-design-by-richard-olsen-founder-of-oanda","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/an-example-of-trading-model-design-by-richard-olsen-founder-of-oanda\/","title":{"rendered":"An Example of Trading Model Design by Richard Olsen (Founder of OANDA)"},"content":{"rendered":"

\n\tA very interesting example of FX trading strategy created by Richard Olsen (Founder of OANDA):<\/strong><\/p>\n

\n\tAuthors:<\/strong> Golub, Glattfelder, Olsen<\/p>\n

\n\tTitle: <\/strong>The Alpha Engine: Designing an Automated Trading Algorithm<\/p>\n

\n\tLink:<\/strong> https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2951348<\/a><\/p>\n

\n\tAbstract:<\/strong><\/p>\n

\n\tWe introduce a new approach to algorithmic investment management that yields profitable automated trading strategies. This trading model design is the result of a path of investigation that was chosen nearly three decades ago. Back then, a paradigm change was proposed for the way time is defined in financial markets, based on intrinsic events. This definition lead to the uncovering of a large set of scaling laws. An additional guiding principle was found by embedding the trading model construction in an agent-base framework, inspired by the study of complex systems. This new approach to designing automated trading algorithms is a parsimonious method for building a new type of investment strategy that not only generates profits, but also provides liquidity to financial markets and does not have a priori restrictions on the amount of assets that are managed.<\/p>\n

\n\tNotable quotations from the academic research paper:<\/strong><\/p>\n

\n\t"To summarize, our aim is to develop trading models based on parsimonious, self-similar, modular, and agent-based behavior, designed for multiple time horizons and not purely driven by trend following action. The intellectual framework unifying these angles of attack is outlined in Section 3 of source research paper. The result of this endeavor are interacting systems that are highly dynamic, robust, and adaptive. In other words, a type of trading model that mirrors the dynamic and complex nature of financial markets. The code can be download from GitHub [The Alpha Engine: Designing an Automated Trading Algorithm Code. https:\/\/github.com\/AntonVonGolub\/Code\/blob\/master\/code.java<\/a>. Accessed: 2017-01-04. 2017]<\/p>\n

\n\tThe Alpha Engine is a counter-trending trading model algorithm that provides liquidity by opening a position when markets overshoot, and manages positions by cascading and de-cascading during the evolution of the long coastline of prices, until it closes in a pro t. The building blocks of the trading model are:<\/p>\n

\n\t– an endogenous time scale called intrinsic time that dissects the price curve into directional changes and overshoots;
\n\t- patterns, called scaling laws that hold over several orders of magnitude, providing an analytical relationship between price overshoots and directional change reversals;
\n\t- coastline trading agents operating at intrinsic events, defi ned by the event based language;
\n\t- a probability indicator that determines the sizing of positions, by identifying periods of market activity that deviate from normal behavior;
\n\t- skewing of cascading and de-cascading designed to mitigate the accumulation of large inventory sizes during trending markets;
\n\t- the splitting of directional change and, consequently, overshoot thresholds into upwards and downwards components, i.e., the introduction of asymmetric thresholds."<\/p>\n\n

\n\n\n\n

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\n\tA very interesting example of FX trading strategy created by Richard Olsen (Founder of OANDA):<\/strong><\/p>\n

\n\tAuthors:<\/strong> Golub, Glattfelder, Olsen<\/p>\n

\n\tTitle: <\/strong>The Alpha Engine: Designing an Automated Trading Algorithm<\/p>\n

\n\tLink:<\/strong> https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2951348<\/a><\/p>\n

\n\tAbstract:<\/strong><\/p>\n

\n\tWe introduce a new approach to algorithmic investment management that yields profitable automated trading strategies. This trading model design is the result of a path of investigation that was chosen nearly three decades ago. Back then, a paradigm change was proposed for the way time is defined in financial markets, based on intrinsic events. This definition lead to the uncovering of a large set of scaling laws. An additional guiding principle was found by embedding the trading model construction in an agent-base framework, inspired by the study of complex systems. This new approach to designing automated trading algorithms is a parsimonious method for building a new type of investment strategy that not only generates profits, but also provides liquidity to financial markets and does not have a priori restrictions on the amount of assets that are managed.<\/p>\n

\n\tNotable quotations from the academic research paper:<\/strong><\/p>\n

\n\t"To summarize, our aim is to develop trading models based on parsimonious, self-similar, modular, and agent-based behavior, designed for multiple time horizons and not purely driven by trend following action. The intellectual framework unifying these angles of attack is outlined in Section 3 of source research paper. The result of this endeavor are interacting systems that are highly dynamic, robust, and adaptive. In other words, a type of trading model that mirrors the dynamic and complex nature of financial markets. The code can be download from GitHub [The Alpha Engine: Designing an Automated Trading Algorithm Code. https:\/\/github.com\/AntonVonGolub\/Code\/blob\/master\/code.java<\/a>. Accessed: 2017-01-04. 2017]<\/p>\n

\n\tThe Alpha Engine is a counter-trending trading model algorithm that provides liquidity by opening a position when markets overshoot, and manages positions by cascading and de-cascading during the evolution of the long coastline of prices, until it closes in a pro t. The building blocks of the trading model are:<\/p>\n

\n\t– an endogenous time scale called intrinsic time that dissects the price curve into directional changes and overshoots;
\n\t- patterns, called scaling laws that hold over several orders of magnitude, providing an analytical relationship between price overshoots and directional change reversals;
\n\t- coastline trading agents operating at intrinsic events, defi ned by the event based language;
\n\t- a probability indicator that determines the sizing of positions, by identifying periods of market activity that deviate from normal behavior;
\n\t- skewing of cascading and de-cascading designed to mitigate the accumulation of large inventory sizes during trending markets;
\n\t- the splitting of directional change and, consequently, overshoot thresholds into upwards and downwards components, i.e., the introduction of asymmetric thresholds."<\/p>\n

\n

\n\tAre you looking for more strategies to read about? Check http:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/Screener<\/a><\/strong><\/p>\n

\n\tDo you want to see performance of trading systems we described? Check<\/strong> http:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/Chart\/Performance<\/a><\/strong><\/p>\n

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