Automated trading using deep learning and machine learning, market state classification using unsupervised learning K-means clustering to classify market states (bull market, bear market, etc.).

1. Introduction

Bitcoin is one of the most volatile assets and the most popular cryptocurrency in financial markets. Therefore, building a system for automatic trading provides many advantages to traders. In particular, advances in deep learning and machine learning have made this possible.

In this course, we will learn how to classify market conditions as “bull market”, “bear market”, or “sideways market” using K-means clustering, which is one of the unsupervised learning techniques. By accurately understanding market conditions, we can design automated trading strategies more effectively.

2. Bitcoin Data Collection

To build a deep learning model, it is necessary to have sufficient and reliable data. Bitcoin price data can be collected from various APIs, and for example, you can use the Binance API. Below is a sample code for data collection using Python:

                
import requests
import pandas as pd

# Collect Bitcoin price data from Binance
def fetch_bitcoin_data(symbol='BTCUSDT', interval='1d', limit='1000'):
    url = f'https://api.binance.com/api/v3/klines?symbol={symbol}&interval={interval}&limit={limit}'
    response = requests.get(url)
    data = response.json()
    df = pd.DataFrame(data, columns=['Open Time', 'Open', 'High', 'Low', 'Close', 'Volume', 
                                      'Close Time', 'Quote Asset Volume', 'Number of Trades', 
                                      'Taker Buy Base Asset Volume', 'Taker Buy Quote Asset Volume', 'Ignore'])
    df['Open Time'] = pd.to_datetime(df['Open Time'], unit='ms')
    df['Close'] = pd.to_numeric(df['Close'])
    return df[['Open Time', 'Close']]

# Load Bitcoin price data
bitcoin_data = fetch_bitcoin_data()
bitcoin_data.set_index('Open Time', inplace=True)
print(bitcoin_data.head())
                
            

The above code collects daily price data for Bitcoin from the Binance API and returns it in the form of a DataFrame.

3. Data Preprocessing

Before performing K-means clustering, we need to preprocess the data. The main data preprocessing steps are as follows:

• Handling missing values
• Scaling
• Feature creation

To achieve this, we will proceed with the following steps:

                
from sklearn.preprocessing import MinMaxScaler

# Check and handle missing values
bitcoin_data.dropna(inplace=True)

# Scale the data
scaler = MinMaxScaler()
bitcoin_data['Close'] = scaler.fit_transform(bitcoin_data[['Close']])

# Feature creation: Price change rate
bitcoin_data['Price Change'] = bitcoin_data['Close'].pct_change()
bitcoin_data.dropna(inplace=True)

print(bitcoin_data.head())
                
            

The above code handles missing values and uses MinMaxScaler to scale the data, allowing the K-means algorithm to cluster data with different distributions effectively. Additionally, it calculates the price change rate to create a new feature.

4. K-means Clustering

K-means clustering is an unsupervised learning algorithm that divides a given set of data points into K clusters. The process of this algorithm is as follows:

1. Randomly select K cluster centers.
2. Assign each data point to the nearest cluster center.
3. Update the cluster center by calculating the average of the assigned data points.
4. Repeat the above steps until the cluster centers do not change anymore.

An example of K-means clustering is shown below:

                
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

# Perform K-means clustering
kmeans = KMeans(n_clusters=3, random_state=0)
bitcoin_data['Cluster'] = kmeans.fit_predict(bitcoin_data[['Close', 'Price Change']])

# Visualize the clusters
plt.scatter(bitcoin_data['Close'], bitcoin_data['Price Change'], c=bitcoin_data['Cluster'], cmap='viridis')
plt.xlabel('Scaled Close Price')
plt.ylabel('Price Change')
plt.title('K-means Clustering of Bitcoin Market States')
plt.show()
                
            

The above code performs K-means clustering and visualizes the clusters for each price state. Each cluster is displayed with a different color.

5. Cluster Interpretation and Market Condition Classification

After clustering, we can define market conditions by interpreting the characteristics of each cluster. For example:

• Cluster 0: Bear Market
• Cluster 1: Bull Market
• Cluster 2: Sideways Market

By analyzing the averages and distributions of each cluster, we can clarify these definitions. This allows us to establish trading strategies for each market condition.

6. Establishing Automated Trading Strategies

We develop automated trading strategies that vary according to each market condition. For example:

• Bear Market: Sell signal
• Bull Market: Buy signal
• Sideways Market: Maintain neutrality

These strategies can be easily integrated into the algorithm based on the state of each cluster. For implementing a real automated trading system, it is also necessary to consider how to automatically send buy/sell signals using the exchange API.

7. Conclusion and Future Research Directions

This article discussed the method of classifying Bitcoin market states using K-means clustering, an unsupervised learning technique. Each cluster can reflect actual market trends and contribute to establishing trading strategies.

Future research will focus on:

• Applying various clustering algorithms beyond K-means
• Developing hybrid models incorporating deep learning techniques
• Experimenting with different feature sets

This work will help build a more advanced automated trading system through further in-depth research.