Automated trading using deep learning and machine learning, Trading prediction based on Support Vector Machine (SVM) Generate buy and sell signals using SVM.

In recent years, the utilization of Artificial Intelligence (AI) and Machine Learning (ML) in financial markets has been detected. Among these, there is a growing interest in asset price prediction and the development of automated trading systems in the cryptocurrency market, such as Bitcoin. This article provides a step-by-step guide for building a Bitcoin automated trading prediction system using Support Vector Machine (SVM).

1. Understanding Support Vector Machine (SVM)

SVM is a powerful machine learning algorithm used for classification and regression analysis. The core idea of this algorithm is to find the optimal hyperplane that separates the data in N-dimensional space. SVM has the following features:

  • Provides kernel functions for non-linear data classification
  • Maximizes the margin between classes based on the maximum margin principle
  • Can prevent overfitting for the given data

2. Collecting Bitcoin Price Data

Bitcoin price data can be collected from various platforms. Here, we will use pandas to load Bitcoin price data from a CSV file.

import pandas as pd

# Load Bitcoin price data
data = pd.read_csv('bitcoin_price.csv')
data.head()

Here, ‘bitcoin_price.csv’ should contain the date and price information for Bitcoin. The main columns consist of date and close price.

3. Data Preprocessing

Preprocessing the collected data significantly affects the performance of the machine learning model. We will generate buy/sell signals based on the price data.

3.1. Feature Generation

Additional features will be generated based on the price data. For example, we can create Moving Averages and Relative Strength Index (RSI).

import numpy as np

# Moving Average
data['SMA_30'] = data['close'].rolling(window=30).mean()
data['SMA_100'] = data['close'].rolling(window=100).mean()

# Calculate Relative Strength Index (RSI)
def calculate_rsi(data, window=14):
    delta = data['close'].diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
    rs = gain / loss
    return 100 - (100 / (1 + rs))

data['RSI'] = calculate_rsi(data)

3.2. Creating Target Labels

It is necessary to create target labels to generate buy and sell signals for Bitcoin. For example, if the closing price of the next day is higher than today's closing price, it will be labeled as buy (1); otherwise, it will be labeled as sell (0).

data['Target'] = np.where(data['close'].shift(-1) > data['close'], 1, 0)

4. Splitting Data and Training the Model

After splitting the data into training and testing sets, we will train the SVM model. We will be using scikit-learn for this purpose.

from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import classification_report, confusion_matrix

# Set features and target
features = data[['SMA_30', 'SMA_100', 'RSI']].dropna()
target = data['Target'][features.index]

# Split data
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)

# Train SVM model
model = SVC(kernel='rbf')
model.fit(X_train, y_train)

5. Model Evaluation

To evaluate the trained model, predictions will be made using the test set, and performance will be checked.

# Make predictions
y_pred = model.predict(X_test)

# Evaluate performance
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))

6. Implementing Automated Trading Strategy

An automated trading system will be implemented to generate actual trading signals based on prediction results. The API of a Bitcoin exchange can be used to execute orders. The following is an example using the Binance API.

from binance.client import Client

# Set up Binance API client
api_key = 'YOUR_API_KEY'
api_secret = 'YOUR_API_SECRET'
client = Client(api_key, api_secret)

def place_order(signal):
    if signal == 1: # Buy signal
        client.order_market_buy(symbol='BTCUSDT', quantity=0.001) # Adjust quantity as needed
    elif signal == 0: # Sell signal
        client.order_market_sell(symbol='BTCUSDT', quantity=0.001) # Adjust quantity as needed

# Execute order based on predicted signal
latest_data = features.iloc[-1]
predicted_signal = model.predict(latest_data.values.reshape(1, -1))[0]
place_order(predicted_signal)

Conclusion

An automated trading system can be a good way to maximize profit in Bitcoin trading. The trading prediction system utilizing SVM is built through a series of steps including data collection, preprocessing, model training, and evaluation. However, it is essential to always consider market volatility and risks, and thorough testing and validation are required before using this system.

In implementing such automated trading systems, it is important to analyze the data thoroughly and try various algorithms. Besides SVM, there are many machine learning techniques, so it is advisable to find the most suitable method for the situation.