Automated trading using deep learning and machine learning, data collection and preprocessing, real-time price data collection using exchange APIs, preprocessing techniques such as data cleaning and normalization.

Introduction

As the volatility of cryptocurrency markets like Bitcoin increases, automated trading systems utilizing machine learning and deep learning are gaining attention. These systems are designed to analyze real-time price data and automatically make buy or sell decisions. In this article, we will detail the preprocessing techniques used to organize and normalize the collected data alongside the real-time price data collection using exchange APIs.

1. Real-Time Price Data Collection Using Exchange APIs

Cryptocurrency exchanges provide APIs that allow users to collect real-time price data. Here, we will take Binance, one of the representative exchanges, as an example to explain how to collect real-time price data.

1.1 Obtaining Binance API Key

To use the Binance API, you first need to obtain an API key. Follow the steps below to create an API key:

1. Log in to your Binance account.
2. Click on ‘API Management’ from the top menu.
3. Create a new API key and store it in a safe place.
4. Access the API using the API key and secret key.

1.2 Using Binance API in Python

To access the Binance API using Python, install the ccxt library. This library is a useful tool that integrates and manages APIs from multiple exchanges.

pip install ccxt

The following code is an example of collecting real-time Bitcoin (BTC) price data from Binance.

import ccxt
import time

# Create a Binance API object
binance = ccxt.binance({'enableRateLimit': True})

def fetch_btc_price():
    # Collect Bitcoin price data
    ticker = binance.fetch_ticker('BTC/USDT')
    return ticker['last']

while True:
    price = fetch_btc_price()
    print(f'Current Bitcoin Price: {price} USDT')
    time.sleep(5)  # Updates the price every 5 seconds.

2. Data Collection and Storage

We use the pandas library to store the collected data. This allows us to create a data frame and save it as a CSV file.

2.1 Installing the Pandas Library

pip install pandas

2.2 Example Code for Creating a Data Frame and Saving as CSV

The code below shows how to convert the collected Bitcoin price data into a data frame and save it as a CSV file.

import pandas as pd

# Create an empty data frame
df = pd.DataFrame(columns=["timestamp", "price"])

while True:
    price = fetch_btc_price()
    timestamp = pd.Timestamp.now()
    
    # Add data
    df = df.append({"timestamp": timestamp, "price": price}, ignore_index=True)
    
    # Save to file every 5 minutes
    if len(df) % 60 == 0:  # Collect one data point every 5 minutes
        df.to_csv('btc_price_data.csv', index=False)
        print("Data has been saved to CSV file.")
    
    time.sleep(5)  # Updates the price every 5 seconds.

3. Preprocessing Collected Data

After data collection, it is essential to preprocess the data before training the machine learning model. The preprocessing aims to improve data quality and maximize learning effectiveness.

3.1 Data Cleaning

Data cleaning involves tasks such as handling missing values and removing duplicates.

3.2 Handling Missing Values

# Handling missing values
df = df.fillna(method='ffill')  # Fill missing values with the previous value

3.3 Removing Duplicates

# Remove duplicates
df = df.drop_duplicates(subset=["timestamp"], keep='last')

3.4 Data Normalization

To enhance the efficiency of machine learning models, we normalize the data. Here, we will use Min-Max normalization.

# Min-Max normalization
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
df['normalized_price'] = scaler.fit_transform(df[['price']])

4. Applying Machine Learning Models

Based on the preprocessed data, we can train a machine learning model. Here, we will implement a price prediction model using a simple LSTM (Long Short-Term Memory) model.

4.1 Data Transformation for LSTM Model

The LSTM model is suitable for time series data. The data must be split into a consistent temporal order for model input. The code below shows how to create the dataset.

import numpy as np

def create_dataset(data, time_step=1):
    X, Y = [], []
    for i in range(len(data)-time_step-1):
        X.append(data[i:(i+time_step), 0])
        Y.append(data[i + time_step, 0])
    return np.array(X), np.array(Y)

# Convert to normalized data
data = df['normalized_price'].values
data = data.reshape(-1, 1)

# Create dataset
X, Y = create_dataset(data, time_step=10)
X = X.reshape(X.shape[0], X.shape[1], 1)  # LSTM input shape

4.2 Building and Training the LSTM Model

from keras.models import Sequential
from keras.layers import LSTM, Dense, Dropout

# Create LSTM model
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)))
model.add(Dropout(0.2))
model.add(LSTM(units=50, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(units=1))  # Predict next price

model.compile(optimizer='adam', loss='mean_squared_error')

# Train the model
model.fit(X, Y, epochs=50, batch_size=32)

Conclusion

This article provided a detailed explanation of the components of an automated Bitcoin trading system utilizing deep learning and machine learning, specifically focusing on data collection and preprocessing. We explored the process of collecting real-time price data using the Binance API, structuring the data with pandas, and learning an LSTM model through normalization and time series dataset creation. This process is a fundamental aspect of building a basic automated trading system.

In the future, this model can be improved for better predictive performance through more complex strategies, feature tuning, and hyperparameter adjustments. Implementing a Bitcoin automated trading system is a time- and effort-intensive process, and continuous data collection and model improvement are essential.