Essay On Deep Learning

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ABSTRACT
Can we use Deep Learning to replicate the activities of traders in the Automated
Systems?
This research considers the use of Deep Learning techniques for generating artificial traders and test their efficiency in the Automated Systems. These traders are developed using Keras, a high level neural network toolkit wrapped up by Tensorflow.
The data is generated from Bristol Stock Exchange developed by Cliff[21] which is an
Automated market. This generated data is then trained and tested using deep learning neural network traders.
This research also gives a comprehensive knowledge of Neural Networks especially
Multi Layer Perceptron Neural Network, LSTM (Long Short-Term Memory) Recurrent
Neural Network, and BSE. Moreover, it also
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2.3.2 Market Demand and Supply . . . . . . . . . . . . . . . . . . . . . . . 8
2.3.3 Market Equilibrium Price . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3.4 Auction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.5 Continuous Double Auction . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.6 Limit Order Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3 Keras and Models 13
3.1 Multi-Layer Perceptron Regression . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 Convolutional Neural Network Using Keras . . . . . . . . . . . . . . . . . . 17
3.3 LSTM Recurrent Neural Network . . . . . . . . . . . . . . . . . . . . . . . . 20
4 Data Used 25
4.1 Bristol Stock Exchange . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.1.1 Traders in BSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.2 Applied Trading Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2.1 ZIP Trading Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.3 Data Modification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5 Models for Training and Testing 33 ix TABLE OF CONTENTS
5.1 Multi Layer Perceptron Trader . . . . . . . . . . . . . . . . . . . . . . . . . . 34
5.1.1 Load Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
5.1.2 MLP Network Model . . . . . . . . . . . . . . . . . . . . .
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