Deep Trading Agent

Deep Reinforcement Learning based Trading Agent for Bitcoin using DeepSense Network for Q function approximation.

For complete details of the dataset, preprocessing, network architecture and implementation, refer to the Wiki of this repository.

Requirements

Python 2.7
Tensorflow
TA-Lib (for processing Bitcoin Price Series)
Pandas (for pre-processing Bitcoin Price Series)
tqdm (for displaying progress of training)

To setup a ubuntu virtual machine with all the dependencies to run the code, refer to assets/vm.

Trading Model

is inspired by Deep Q-Trading where they solve a simplified trading problem for a single asset.
For each trading unit, only one of the three actions: neutral(1), long(2) and short(3) are allowed and a reward is obtained depending upon the current position of agent. Deep Q-Learning agent is trained to maximize the total accumulated rewards.
Current Deep Q-Trading model is modified by using the Deep Sense architecture for Q function approximation.

Dataset

Per minute Bitcoin series is obtained by modifying the procedure mentioned in this repository. Transactions in the Coinbase exchange are sampled to generate the Bitcoin price series.
Refer to assets/dataset to download the dataset.

Preprocessing

Basic Preprocessing
Completely ignore missing values and remove them from the dataset and accumulate blocks of continuous values using the timestamps of the prices.
All the accumulated blocks with number of timestamps lesser than the combined history length of the state and horizon of the agent are then filtered out since they cannot be used for training of the agent.
In the current implementation, past 3 hours (180 minutes) of per minute Bitcoin prices are used to generate the representation of the current state of the agent.
With the existing dataset (at the time of writing), following are the logs generated while preprocessing the dataset:

INFO:root:Number of blocks of continuous prices found are 58863
INFO:root:Number of usable blocks obtained from the dataset are 887
INFO:root:Number of distinct episodes for the current configuration are 558471

Advanced Preprocessing
Process missing values and concatenate smaller blocks to increase the sizes of continuous price blocks
(To be implemented)

Implementation

Tensorflow "1.1.0" version is used for the implementation of the Deep Sense network.

Deep Sense

Implementation is adapted from this Github repository with a few simplifications in the network architecture to incorporate learning over a single time series of the Bitcoin data.

Deep Q Trading

Implementation and preprocessing is inspired from this Medium post. The actual implementation of the Deep Q Network is adapted from DQN-tensorflow.

canerdogan / deep-trading-agent Goto Github PK