Automated trading using deep learning and machine learning, building a trading agent using reinforcement learning Implementing a trading agent that learns autonomously using reinforcement learning techniques.

1. Introduction

The cryptocurrency market, such as Bitcoin, is highly volatile, and various technologies are being researched to automate trading. Deep Learning and Machine Learning techniques are effective in building such automated trading systems. This post explains how to build a self-learning trading agent using Reinforcement Learning techniques.

2. Basics of Machine Learning and Deep Learning

Machine Learning is a methodology for learning patterns from data and creating predictive models. Deep Learning is a subfield of Machine Learning that uses artificial neural networks to learn the structure of complex data. Their advantage is the ability to process large amounts of data.

2.1. Understanding Reinforcement Learning

Reinforcement Learning is a method where an agent learns the optimal actions through interaction with the environment. The agent selects specific actions from a given state and receives rewards as a result. Through this reward, the agent improves its actions.

3. Building a Trading Agent Based on Reinforcement Learning

3.1. Configuring the Environment

Configuring the environment for the trading agent is very important. To this end, we define the market environment based on OHLC (Open, High, Low, Close) data.

3.2. Installing OpenAI Gym

You can use OpenAI’s Gym library to create a reinforcement learning environment. Installation can be done via the following command.

pip install gym

3.3. Implementing the Trading Environment

Below is a code that implements a simple trading environment.


import gym
from gym import spaces
import numpy as np

class CryptoTradingEnv(gym.Env):
    def __init__(self, data):
        super(CryptoTradingEnv, self).__init__()
        self.data = data
        self.current_step = 0
        self.action_space = spaces.Discrete(3)  # 0: Hold, 1: Buy, 2: Sell
        self.observation_space = spaces.Box(low=0, high=np.inf, shape=(len(data[0]),), dtype=np.float32)

    def reset(self):
        self.current_step = 0
        return self.data[self.current_step]

    def step(self, action):
        self.current_step += 1
        if self.current_step >= len(self.data):
            self.current_step = len(self.data) - 1
        
        prev_state = self.data[self.current_step - 1]
        current_state = self.data[self.current_step]

        reward = 0
        if action == 1:  # Buy
            reward = current_state[3] - prev_state[3]  # Close price
        elif action == 2:  # Sell
            reward = prev_state[3] - current_state[3]

        done = self.current_step == len(self.data) - 1
        return current_state, reward, done, {}

3.4. Building the Deep Learning Model

Now we implement a deep learning model to train the reinforcement learning agent. Here, we use a simple Multi-layer Perceptron (MLP).


import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

def create_model(input_shape):
    model = keras.Sequential()
    model.add(layers.Dense(24, activation='relu', input_shape=input_shape))
    model.add(layers.Dense(24, activation='relu'))
    model.add(layers.Dense(3, activation='linear'))  # 3 actions
    model.compile(optimizer='adam', loss='mse')
    return model

3.5. Training the Agent

The agent learns its policy through multiple episodes. Here, we apply a simple Q-learning algorithm.


import random

class DQNAgent:
    def __init__(self, state_size):
        self.state_size = state_size
        self.memory = []
        self.gamma = 0.95  # discount rate
        self.epsilon = 1.0  # exploration rate
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.model = create_model((state_size,))

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return random.randrange(3)  # exploration
        q_values = self.model.predict(state)
        return np.argmax(q_values[0])  # exploitation

    def replay(self, batch_size):
        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target += self.gamma * np.amax(self.model.predict(next_state)[0])
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
        
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

4. Conclusion

This post explained the automatic trading system for Bitcoin using reinforcement learning techniques. We built a simple trading environment and a deep learning model, and covered the approach of learning using Q-learning. More data and hyperparameter tuning are needed to predict actual Bitcoin prices and establish trading strategies. Lastly, exchange API integration will be necessary for real trading.