Artificial intelligence, machine learning, and reinforcement learning play a very important role in the current financial markets. In particular, automated trading systems in cryptocurrency markets, such as Bitcoin, are gaining great popularity, and various algorithms are being researched to develop these systems. Among them, the PPO (Proximal Policy Optimization) algorithm is a state-of-the-art technology widely used in the field of reinforcement learning. This article will detail how to implement an automated trading agent for Bitcoin using the PPO algorithm.
1. Overview of the PPO (Proximal Policy Optimization) Algorithm
PPO is a reinforcement learning algorithm proposed by OpenAI that has good characteristics of stability and convergence speed. PPO is a policy-based method that updates the policy in a direction that maximizes rewards based on the agent’s experiences in the environment. The core idea of PPO is to optimize the policy’s output while limiting the changes from the previous policy to maintain stability during training.
1.1 Key Features of PPO
- Conservative Updates: Limits changes between the old policy and the new policy to improve training stability.
- Clipping: Adjusts the loss function to prevent ‘wrong updates’.
- Sample Efficiency: Allows for more efficient learning by utilizing the existing policy.
2. Structure of the Bitcoin Automated Trading Agent
To implement a Bitcoin automated trading system, the following key components are required.
- Environment: Bitcoin market data that the agent interacts with.
- State: A feature set reflecting the current market situation.
- Action: Buy, sell, or hold actions that the agent can choose from.
- Reward: The economic outcome of the agent’s actions.
2.1 Implementing the Environment
To implement the environment, Bitcoin price data must be collected, and based on this data, states and rewards must be defined. Typically, various technical indicators (TA) are used to define the state. For example, indicators such as moving averages, Relative Strength Index (RSI), and MACD can be used.
2.1.1 Example of Implementing the Environment Class
import numpy as np
import pandas as pd
class BitcoinEnv:
def __init__(self, data):
self.data = data
self.current_step = 0
self.current_balance = 1000 # Initial capital
self.holdings = 0 # Bitcoin holdings
def reset(self):
self.current_step = 0
self.current_balance = 1000
self.holdings = 0
return self._get_state()
def _get_state(self):
return self.data.iloc[self.current_step].values
def step(self, action):
price = self.data.iloc[self.current_step]['Close']
# Calculate reward and new state based on the action
if action == 1: # Buy
self.holdings += 1
self.current_balance -= price
elif action == 2: # Sell
if self.holdings > 0:
self.holdings -= 1
self.current_balance += price
self.current_step += 1
done = self.current_step >= len(self.data) - 1
reward = self.current_balance + self.holdings * price - 1000 # Reward based on initial capital
return self._get_state(), reward, done
3. Implementing the PPO Algorithm
To implement the PPO policy optimization algorithm, a neural network must be used to model the policy. A commonly used neural network architecture is as follows.
3.1 Defining Neural Network Architecture
import tensorflow as tf
class PPOAgent:
def __init__(self, state_size, action_size, lr=0.001):
self.state_size = state_size
self.action_size = action_size
self.lr = lr
self.gamma = 0.99 # Discount factor
self.epsilon = 0.2 # Clipping ratio
self.model = self._create_model()
def _create_model(self):
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(64, activation='relu', input_shape=(self.state_size,)))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(self.action_size, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(lr=self.lr))
return model
def act(self, state):
state = state.reshape([1, self.state_size])
probabilities = self.model.predict(state)[0]
return np.random.choice(self.action_size, p=probabilities)
3.2 Implementing the Policy Update Function
class PPOAgent:
# ... (same as previous code)
def train(self, states, actions, rewards):
states = np.array(states)
actions = np.array(actions)
discounted_rewards = self._discount_rewards(rewards)
actions_one_hot = tf.keras.utils.to_categorical(actions, num_classes=self.action_size)
# Calculate policy loss
with tf.GradientTape() as tape:
probabilities = self.model(states)
advantages = discounted_rewards - tf.reduce_mean(discounted_rewards)
policy_loss = -tf.reduce_mean(actions_one_hot * tf.math.log(probabilities) * advantages)
gradients = tape.gradient(policy_loss, self.model.trainable_variables)
self.model.optimizer.apply_gradients(zip(gradients, self.model.trainable_variables))
def _discount_rewards(self, rewards):
discounted = np.zeros_like(rewards)
running_add = 0
for t in reversed(range(len(rewards))):
running_add = running_add * self.gamma + rewards[t]
discounted[t] = running_add
return discounted
4. Training and Evaluating the Agent
To train the agent, the environment and the agent must continuously interact. Through a training loop, the agent selects actions in the environment, receives rewards, and updates its policy.
4.1 Implementing the Agent Training Function
def train_agent(env, agent, episodes=1000):
for episode in range(episodes):
state = env.reset()
done = False
states, actions, rewards = [], [], []
while not done:
action = agent.act(state)
next_state, reward, done = env.step(action)
states.append(state)
actions.append(action)
rewards.append(reward)
state = next_state
agent.train(states, actions, rewards)
total_reward = sum(rewards)
print(f'Episode: {episode + 1}, Total Reward: {total_reward}')
4.2 Implementing the Evaluation Function
def evaluate_agent(env, agent, episodes=10):
total_rewards = []
for episode in range(episodes):
state = env.reset()
done = False
total_reward = 0
while not done:
action = agent.act(state)
next_state, reward, done = env.step(action)
state = next_state
total_reward += reward
total_rewards.append(total_reward)
print(f'Average Reward over {episodes} episodes: {np.mean(total_rewards)}')
5. Conclusion
We explored how to build a Bitcoin automated trading agent using the PPO algorithm. The PPO algorithm is a stable and effective method for policy optimization, demonstrating its potential in the financial markets. Through this project, I hope you were able to understand the basic concepts of reinforcement learning and the implementation method using PPO. Going forward, I recommend experimenting with and developing various AI-based trading strategies.
The code used in this article is provided as an example and will require more considerations in actual trading environments. For instance, various evaluation criteria, more features, and refined state management must be included. Moreover, the process of collecting and processing data is also a very important part, and through this, more effective and stable trading systems can be developed.
6. References
- PIE: Proximal Policy Optimization Algorithms (OpenAI)
- Example code and tutorials: Gym, TensorFlow, Keras
- Bitcoin and cryptocurrency related data: Yahoo Finance, CoinMarketCap