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GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data

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Yuki Hayashi
Yuki Hayashi

Since most of previous studies about pattern recognition on FX time series data were done by handcrafted features which were not easy to program and lacking flexibility. We propose the LSTM based deep neural network and its training method which helps traders to duplicate their strategies by just feeding what they see on the chart as training samples for AI.

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GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series data

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