Automated Trading Using Deep Learning and Machine Learning, Trading Prediction Using XGBoost How to Generate High-Performance Trading Signals Using XGBoost.

Recently, automated trading systems through artificial intelligence (AI), deep learning, and machine learning have rapidly developed in financial markets. These technologies are powerful tools that can learn patterns from data and make trading decisions based on that learning. In this blog post, we will take an in-depth look at how to automatically trade cryptocurrencies like Bitcoin using XGBoost (Extreme Gradient Boosting).

What is Automated Trading?

An automated trading system is software that conducts trades through pre-set algorithms. Emotional decisions by humans are excluded, and decisions are made based on data. Such automated trading predicts market trends through high-frequency trading, pattern recognition, and technical analysis like Bollinger Bands.

What is XGBoost?

XGBoost is an extension of the Gradient Boosting algorithm, and it is a powerful predictive model often used in machine learning competitions. The reasons for its superior performance are as follows:

• Accuracy: It creates better models through regularization with the loss function.
• Scalability: It is efficient at handling large datasets.
• Parallel Processing: It utilizes multiple CPU cores to enhance learning speed.

Generating Trading Signals Using XGBoost

The goal of automated trading is to generate buy or sell signals. XGBoost can learn from historical data to predict future prices. Here is the signal generation process using XGBoost.

Step 1: Data Collection

First, we need to collect Bitcoin price data. Here, we will show an example of fetching data via the Binance API.

import numpy as np
import pandas as pd
import requests

def fetch_data(symbol, interval, start, end):
    url = f'https://api.binance.com/api/v3/klines?symbol={symbol}&interval={interval}&startTime={start}&endTime={end}'
    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['close'] = df['close'].astype(float)
    return df

# Example: Fetching daily data for BTCUSDT.
data = fetch_data('BTCUSDT', '1d', '1609459200000', '1640995200000')  # From January 1, 2021, to January 1, 2022.

Step 2: Data Preprocessing

Extract the necessary features from the collected data. For example, technical indicators such as moving averages, RSI, and MACD can be calculated.

def compute_features(df):
    df['MA5'] = df['close'].rolling(window=5).mean()
    df['MA20'] = df['close'].rolling(window=20).mean()
    df['RSI'] = compute_rsi(df['close'])
    df['MACD'] = compute_macd(df['close'])
    return df.dropna()

def compute_rsi(series, period=14):
    delta = series.diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

def compute_macd(series):
    exp1 = series.ewm(span=12, adjust=False).mean()
    exp2 = series.ewm(span=26, adjust=False).mean()
    return exp1 - exp2

data = compute_features(data)

Step 3: Splitting Training and Testing Data

To train the model, split the data into training and testing sets. Typically, 70% to 80% of the data is used for training.

from sklearn.model_selection import train_test_split

X = data[['MA5', 'MA20', 'RSI', 'MACD']].values
y = np.where(data['close'].shift(-1) > data['close'], 1, 0)[:-1]  # If the price rises the next day

X_train, X_test, y_train, y_test = train_test_split(X[:-1], y, test_size=0.2, random_state=42)

Step 4: Training the XGBoost Model

Now we will train the XGBoost model. XGBoost creates high-performance predictors.

from xgboost import XGBClassifier

model = XGBClassifier()
model.fit(X_train, y_train)

Step 5: Generating Trading Signals

Use the trained model to generate trading signals. Based on the prediction results, we can assign buy and sell signals.

predictions = model.predict(X_test)
predictions_proba = model.predict_proba(X_test)

buy_signals = np.where(predictions == 1, 1, 0)  # Buy signal
sell_signals = np.where(predictions == 0, -1, 0)  # Sell signal

signals = buy_signals + sell_signals

Step 6: Strategy Validation

Compare the generated trading signals with actual price data to validate the strategy’s performance. This process is called backtesting and is an important step in evaluating the model’s validity.

def backtest(signals, prices):
    initial_capital = 10000
    shares = 0
    capital = initial_capital

    for i in range(len(signals)):
        if signals[i] == 1:  # Buy signal
            shares += capital // prices[i]
            capital -= (capital // prices[i]) * prices[i]
        elif signals[i] == -1:  # Sell signal
            capital += shares * prices[i]
            shares = 0

    return capital + (shares * prices[-1])

strategy_return = backtest(signals, data['close'].values[len(X_train):])
print('Strategy Return:', strategy_return)

Conclusion

Automated trading systems utilizing deep learning and machine learning technologies can enable data-driven decision-making, thereby maximizing investors’ profitability. Among these, XGBoost shows outstanding performance and is effective in generating trading signals for highly volatile assets like Bitcoin.

Based on this material, we encourage you to improve your algorithm and apply it to various assets. Continuous learning and experimentation are necessary to succeed in the world of automated trading.