Energy Commodities Trading...
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Researchers at Dublin Institute of Technology (DIT) have developed new algorithms designed for automated commodities and financial trading. The software is based on a combination of models and methods that are underpinned by the Fractal Market Hypothesis (FMH), evolutionary computing and the application of Artificial Neural Networks. The FMH is an economic model that takes rare and extreme events into account, and uses fractional dynamics to understand the trending characteristics of financial time series data. Coupled with a range of statistical models, the software assesses the future volatility and dynamic behaviour of commodities and financial securities. The software can provide companies involved in commodities and financials trading with: • trend analysis of financial time series data; • short term prediction using evolutionary computing methods; and • buy/sell decision making support. Overview Current trading and risk management tools rely strongly on models which assume that signals (e.g. price data) are normally distributed. Normal (or Gaussian) statistics in reality provide a poor representation of actual financial data (specifically the price differences) and therefore result in poor accuracy when used for forecasting. In contrast, the FMH provides a more realistic interpretation of historical data as it takes extreme events into account. This is particularly relevant to markets which show increased volatility when there is minimal supply capability to meet variations in natural demand. Long lead times and large capital requirements are factors in energy production and supply; therefore energy markets are more susceptible to supply and demand imbalances. This results in price volatility. Electricity markets, for example, may be susceptible because of the unpredictable fluctuations in the weather. This software takes into account these fluctuations in a theoretically and computationally unified way and helps companies involved in commodities or financial market trading to; improve their transaction management and procurement strategies, understand their market, volume and financial exposure in real-time, and optimise their cost structures. The software is in the form of a set of algorithms that can be implemented on a range of trading platforms. Advantages • Accuracy – enhanced forecasting accuracy in the commodities and financial markets; • Integration – software can integrate with expert systems (e.g. existing trading platforms); • Time-scale independent – applicable to data over any time scale; • Financial prediction – predictive trading of financial and physically based securities; • Supply prediction – estimation of supply relating to wind, solar, tidal and wave energy. Commodities & Financial Markets Trading System New Technology from DIT DIT Aungier Street, Dublin 2 T: +353 1 402 7179 E: [email protected] W: www.dit.ie/hothouse Hothouse Docklands Innovation Park 128 – 130 East Wall Road, Dublin 3 T: +353 1 240 1300 W: www.dit.ie/hothouse ICT
Transcript of Energy Commodities Trading...
Researchers at Dublin Institute of Technology (DIT) have developed new algorithms designed for automated commodities and financial trading.
The software is based on a combination of models and methods that are underpinned by the Fractal Market Hypothesis (FMH), evolutionary computing and the application of Artificial Neural Networks. The FMH is an economic model that takes rare and extreme events into account, and uses fractional dynamics to understand the trending characteristics of financial time series data. Coupled with a range of statistical models, the software assesses the future volatility and dynamic behaviour of commodities and financial securities.
The software can provide companies involved in commodities and financials trading with:• trend analysis of financial time series data;• short term prediction using evolutionary computing methods; and• buy/sell decision making support.
OverviewCurrent trading and risk management tools rely strongly on models which assume that signals (e.g. price data) are normally distributed. Normal (or Gaussian) statistics in reality provide a poor representation of actual financial data (specifically the price differences) and therefore result in poor accuracy when used for forecasting. In contrast, the FMH provides a more realistic interpretation of historical data as it takes extreme events into account. This is particularly relevant to markets which show increased volatility when there is minimal supply capability to meet variations in natural demand. Long lead times and large capital requirements are factors in energy production and supply; therefore energy markets are more susceptible to supply and demand imbalances. This results in price volatility. Electricity markets, for example, may be susceptible because of the unpredictable fluctuations in the weather.
This software takes into account these fluctuations in a theoretically and computationally unified way and helps companies involved in commodities or financial market trading to; improve their transaction management and procurement strategies, understand their market, volume and financial exposure in real-time, and optimise their cost structures. The software is in the form of a set of algorithms that can be implemented on a range of trading platforms.
Advantages• Accuracy – enhanced forecasting accuracy in the commodities and financial markets;• Integration – software can integrate with expert systems (e.g. existing trading platforms);• Time-scale independent – applicable to data over any time scale;• Financial prediction – predictive trading of financial and physically based securities;• Supply prediction – estimation of supply relating to wind, solar, tidal and wave energy.
Commodities & Financial Markets Trading System
New Technology from DIT
DIT Aungier Street, Dublin 2T: +353 1 402 7179E: [email protected]: www.dit.ie/hothouse
HothouseDocklands Innovation Park128 – 130 East Wall Road, Dublin 3T: +353 1 240 1300W: www.dit.ie/hothouse
ICT
HOTHOUSE INFO SHEETS_Energy Commodities Trading System 07/05/2013 11:06 Page 1
Technology DescriptionThe software uses various non-Gaussian statistical parameters to assess the dynamic behaviour of time seriesdata. The system interprets the many signals generated through application of the FMH into a single Buy/Selldecision. Based on significant back-testing and validation with various Irish, UK, and US investment specialists,the software provides a statistically significant improvement over current models and delivers exit/entryefficiency of the order of 70—80%. The model used by the software incorporates metrics such as the stochasticvolatility and the ‘volume’ traded using a hypothesis which is not taken into account by other models, for example,those based on the Efficient Market Hypothesis upon which many current risk management tools are based.
Stage of DevelopmentBeta versions of the software have been developed that have been implemented using MATLAB and MetaTrader.DIT is seeking partners and collaborators to license the technology, or work to develop it further to meet specific commercial applications. The model and software derived thereof has already been commercialised forFOREX market analysis by a DIT spin out company which has been supported by Enterprise Ireland’sCompetitive Start Fund and is now fully operational and trading on the FOREX markets in real time.
Project Team and Collaborators
The technology was developed through research undertaken at DIT’s Dublin Energy Lab (DEL) funded by the Science Foundation Ireland. DEL is a principal leader in science and engineering energy research in Ireland conducting research across a range of disciplines organised into the themes of; renewable energy: energy demand analysis and forecasting; electrical power; solar energy; and zero emissions buildings. For details see: http://dit.ie/dublinenergylab.
The project team includes:Prof. Jonathan Blackledge – Principal investigator. Jonathan is the Science Foundation Ireland StokesProfessor at DIT and Director of the Information & Communications Security Research Group:http://blackledge.sharepoint.com
• Prof. Eugene Coyle – Head of Research Innovation and Partnerships at DIT. Eugene was formerlyHead of Electrical Engineering Systems School: www.dit.ie/researchandenterprise/meettheteam/staffprofiles
DIT Aungier Street, Dublin 2T: +353 1 402 7179E: [email protected]: www.dit.ie/hothouse
HothouseDocklands Innovation Park128 – 130 East Wall Road, Dublin 3T: +353 1 240 1300W: www.dit.ie/hothouse
DIT Hothouse is the award winning Innovation and Technology TransferCentre at Dublin Institute of Technology. DIT Hothouse draws inentrepreneurial and academic talent, ignites creativity and provides a dynamic environment to fast-track businesses and technologies to commercial success.
ICT
HOTHOUSE INFO SHEETS_Energy Commodities Trading System 07/05/2013 11:06 Page 2
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New Technology from DIT
Commodities & Financial Markets Trading System
