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DESCRIPTION:A profitable algorithmic stock trading algorithm benefits from 
 a forecasting system that can produce accurate short-term forecasts. Based
  on this premise\, we proposed to build up our previous experience in buil
 ding short-term forecasting models using machine learning models. The rese
 arch project aims to develop effective algorithmic trading algorithms base
 d on accurate short-term forecasts for financial time series using machine
  learning models. The project focuses on three research activities: (1) bu
 ilding a data warehouse containing the targeted financial time series and 
 other time series considered valuable as input to the machine learning mod
 els. Data acquisition\, processing\, and staging routines that are require
 d to feed the data warehouse dynamically are evaluated and developed. A da
 ta visualization and business intelligence layer is also built on top of t
 he data warehouse\; (2) a short-term forecasting model based on machine le
 arning is developed using the various time-series data from the data wareh
 ouse. Machine learning algorithms such as neural network\, random forecast
 \, support vector regression\, XGBoost\, and long short-term memory (LSTM)
  are evaluated in conjunction with several performance criteria to identif
 y the most accurate model from a short-term trading perspective\; (3) an a
 utomated evaluation system is developing to assess the effectiveness of ex
 isting algorithmic short-term trading strategies. Novel algorithms are als
 o developed and evaluated using the evaluation system and the short-term f
 orecast machine learning model.\n\nCo-sponsored by: Langara College\n\nSpe
 aker(s): Dr. Youry Khmelevsky\, Albert Wong\, Ph.D.\n\nRoom: L318\, 100 We
 st 49th Avenue\, Langara College\, Vancouver\, British Columbia\, Canada\,
  V5Y 2Z6\, Virtual: https://events.vtools.ieee.org/m/309786
LOCATION:Room: L318\, 100 West 49th Avenue\, Langara College\, Vancouver\, 
 British Columbia\, Canada\, V5Y 2Z6\, Virtual: https://events.vtools.ieee.
 org/m/309786
ORGANIZER:youry@ieee.org
SEQUENCE:4
SUMMARY:Algorithmic Trading and Short-term Forecast for Financial Time Seri
 es with Machine Learning Models
URL;VALUE=URI:https://events.vtools.ieee.org/m/309786
X-ALT-DESC:Description: <br /><p>A profitable algorithmic stock trading alg
 orithm benefits from a forecasting system that can produce accurate short-
 term forecasts. Based on this premise\, we proposed to build up our previo
 us experience in building short-term forecasting models using machine lear
 ning models. The research project aims to develop effective algorithmic tr
 ading algorithms based on accurate short-term forecasts for financial time
  series using machine learning models. The project focuses on three resear
 ch activities: (1) building a data warehouse containing the targeted finan
 cial time series and other time series considered valuable as input to the
  machine learning models. Data acquisition\, processing\, and staging rout
 ines that are required to feed the data warehouse dynamically are evaluate
 d and developed. A data visualization and business intelligence layer is a
 lso built on top of the data warehouse\; (2) a short-term forecasting mode
 l based on machine learning is developed using the various time-series dat
 a from the data warehouse. Machine learning algorithms such as neural netw
 ork\, random forecast\, support vector regression\, XGBoost\, and long sho
 rt-term memory (LSTM) are evaluated in conjunction with several performanc
 e criteria to identify the most accurate model from a short-term trading p
 erspective\; (3) an automated evaluation system is developing to assess th
 e effectiveness of existing algorithmic short-term trading strategies. Nov
 el algorithms are also developed and evaluated using the evaluation system
  and the short-term forecast machine learning model.</p>
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