Category: Deep learning Automated trading

In this course, we will explore the process of building an automated trading system for Bitcoin using deep learning and machine learning. In particular, we will explain in detail the importance of hyperparameter tuning for maximizing performance and the methods to achieve this. We will provide an introduction to the data and machine learning models we will use, along with practical code examples of hyperparameter tuning techniques.

1. Overview of the Bitcoin Automated Trading System

An automated trading system is an algorithm used to trade assets such as stocks and cryptocurrencies. These systems make decisions through data analysis, pattern recognition, and predictive modeling. Because Bitcoin is particularly volatile, machine learning and deep learning models can effectively automate trading.

2. Importance of Hyperparameter Tuning

Hyperparameters are parameters that must be set during the training process of machine learning models. These include learning rate, batch size, regularization coefficient, and more, and the model’s performance can vary significantly based on these values. Finding the appropriate hyperparameters is one of the most critical parts of improving a model.

3. Hyperparameter Tuning Techniques

There are several methods for hyperparameter tuning. Here, we will introduce two representative methods: Grid Search and Random Search.

3.1 Grid Search

Grid Search is a method that searches all combinations of predefined hyperparameter values to find the optimal combination. This method is straightforward but can be computationally expensive.

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier

# Hyperparameter grid
param_grid = {
    'n_estimators': [10, 50, 100],
    'max_features': ['auto', 'sqrt', 'log2'],
    'max_depth': [None, 10, 20, 30],
}

grid_search = GridSearchCV(estimator=RandomForestClassifier(), param_grid=param_grid, cv=3)
grid_search.fit(X_train, y_train)
best_params = grid_search.best_params_

3.2 Random Search

Random Search is a method that selects random combinations from the hyperparameter space to evaluate performance. It can find the optimal combination faster than Grid Search, but there is no theoretical guarantee of finding the appropriate combinations.

from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import randint

# Hyperparameter distribution
param_dist = {
    'n_estimators': randint(10, 200),
    'max_features': ['auto', 'sqrt', 'log2'],
    'max_depth': [None] + list(range(10, 31)),
}

random_search = RandomizedSearchCV(estimator=RandomForestClassifier(), param_distributions=param_dist, n_iter=100, cv=3)
random_search.fit(X_train, y_train)
best_params = random_search.best_params_

4. Building the Bitcoin Automated Trading Model

This time, we will collect Bitcoin price data and build a deep learning model for automated trading based on this data, along with an example of hyperparameter tuning.

4.1 Data Collection

Bitcoin price data can be collected through various data service providers via APIs. For example, data can be obtained through the Binance API.

import pandas as pd
import requests

def get_bitcoin_data():
    url = 'https://api.binance.com/api/v3/klines?symbol=BTCUSDT&interval=1d&limit=100'
    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)
    df['Open time'] = pd.to_datetime(df['Open time'], unit='ms')
    return df[['Open time', 'Close']]

bitcoin_data = get_bitcoin_data()

4.2 Data Preprocessing

Preprocessing is required for the collected data. This includes handling missing values, scaling, and splitting the data into training and testing sets.

from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split

# Data preprocessing
scaler = MinMaxScaler()
bitcoin_data['Close'] = scaler.fit_transform(bitcoin_data['Close'].values.reshape(-1, 1))

X = bitcoin_data['Close'].shift(1).dropna().values.reshape(-1, 1)
y = bitcoin_data['Close'].iloc[1:].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

4.3 Model Building

We will use an LSTM (Long Short-Term Memory) deep learning model to build a Bitcoin price prediction model.

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

model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(1, 1)))
model.add(Dropout(0.2))
model.add(LSTM(units=50))
model.add(Dropout(0.2))
model.add(Dense(units=1))

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

4.4 Model Training

Hyperparameter tuning is necessary to train the model. The following is an example of adjusting the learning rate and batch size.

from keras.callbacks import EarlyStopping

early_stopping = EarlyStopping(monitor='loss', patience=3)

model.fit(X_train.reshape((X_train.shape[0], 1, 1)), y_train, epochs=100, batch_size=1, callbacks=[early_stopping])

4.5 Prediction and Evaluation

We perform predictions on the test data using the trained model and evaluate them.

import numpy as np

predicted_prices = model.predict(X_test.reshape((X_test.shape[0], 1, 1)))
predicted_prices = scaler.inverse_transform(predicted_prices)

# Model evaluation
from sklearn.metrics import mean_squared_error

mse = mean_squared_error(y_test, predicted_prices)
print('Mean Squared Error:', mse)

5. Conclusion

In this article, we have explored the process of building a Bitcoin automated trading system using deep learning and machine learning, detailing the importance of hyperparameter tuning and the methods to achieve it. By tuning hyperparameters, we can enhance the model’s performance, significantly increasing the efficiency of the Bitcoin automated trading system.

6. Additional Resources

For more information and resources on hyperparameter tuning, please refer to the following links:

• Grid Search Document
• Random Search Document
• Keras Sequential Model Guide

Feature Engineering: Extracting features such as trading volume, moving averages, and RSI to apply to machine learning models

In recent years, the cryptocurrency market has grown rapidly, with Bitcoin being the most well-known digital asset. Automated trading of such assets has become an attractive choice for many investors. This article will explain how to extract various features that can be used for Bitcoin trading using machine learning and deep learning, and how to build and evaluate models based on them.

1. Understanding Bitcoin Data

The first step for automated trading is data collection. To collect data from the Bitcoin market, several factors should be included:

• Open Price
• Close Price
• High Price
• Low Price
• Volume

This data changes over time, so it should be stored in a time-ordered format.

2. Importance of Feature Engineering

Feature Engineering is a crucial process that determines the performance of model training. It is essential to extract useful information from time series data like Bitcoin and prepare it for the learning model. Here, we will look at how to extract features using indicators such as trading volume, moving averages, and Relative Strength Index (RSI).

2.1 Trading Volume

Trading volume is an indicator of market activity, with high volume possibly indicating strong buying or selling pressure. Therefore, adding volume as a feature can enhance the predictive power of the model.

2.2 Moving Average

Moving averages calculate the average price over a given period, smoothing out price movements. Commonly used moving averages include short-term and long-term moving averages. For example, calculating the 5-day and 20-day moving averages and analyzing their crossover can generate trading signals.

2.3 Relative Strength Index (RSI)

RSI calculates the ratio of price increases to decreases over a given period, represented as a value between 0 and 100. Values above 70 are interpreted as overbought, while values below 30 are interpreted as oversold, making them useful for trading signals.

3. Data Collection and Feature Engineering Using Python

Now we will actually collect Bitcoin data and extract features using the indicators mentioned above. The following code demonstrates how to process Bitcoin data using the pandas and numpy libraries in Python.

import pandas as pd
import numpy as np
import pandas_datareader.data as web
import datetime

# Data collection
start = datetime.datetime(2020, 1, 1)
end = datetime.datetime.now()

btc_data = web.DataReader('BTC-USD', 'yahoo', start, end)

# Calculate moving averages
btc_data['MA5'] = btc_data['Close'].rolling(window=5).mean()
btc_data['MA20'] = btc_data['Close'].rolling(window=20).mean()

# Calculate RSI
def compute_rsi(data, window):
    delta = data['Close'].diff(1)
    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

btc_data['RSI'] = compute_rsi(btc_data, 14)

# Add volume
btc_data['Volume'] = btc_data['Volume']

# Final data check
print(btc_data.tail())
    

4. Building and Predicting with Machine Learning Models

Once the features are prepared, we can build a machine learning model to predict whether the price of Bitcoin will rise or fall. Below is an example code using the scikit-learn library.

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, accuracy_score

# Handle missing values
btc_data.dropna(inplace=True)

# Define features and labels
features = btc_data[['MA5', 'MA20', 'RSI', 'Volume']]
labels = (btc_data['Close'].shift(-1) > btc_data['Close']).astype(int)  # Whether the next day's close price rises

# Split into training and test datasets
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)

# Train the model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Predict and evaluate
predictions = model.predict(X_test)
print(classification_report(y_test, predictions))
print('Accuracy:', accuracy_score(y_test, predictions))
    

5. Additional Considerations

To operate an automated trading system, several additional considerations are needed:

• Risk management: You should set investment amounts and loss limits to manage risk.
• Data accessibility: The quality and quantity of data greatly affect the performance of the model, so reliable data sources must be secured.
• Continuous model improvement: It is necessary to periodically retrain the model with new data to improve performance.

6. Conclusion

Building an automated trading system for Bitcoin using deep learning and machine learning starts with understanding the data and extracting useful indicators. This process maximizes trading efficiency and allows well-designed models to continuously evolve. I hope the processes presented in this article will help readers build their own automated trading systems.

In the future, I hope to develop a proactive automated trading system that responds to market changes using various techniques.

The market for cryptocurrencies like Bitcoin is highly volatile, and countless transactions occur every day. To generate profits in such a market environment, sophisticated trading strategies are necessary. Recently, with advancements in artificial intelligence (AI) technology, automated trading systems utilizing deep learning and machine learning have gained increasing attention. In this article, we will provide an in-depth explanation of a Bitcoin automated trading strategy based on ensemble learning and provide example code for it.

1. What is Ensemble Learning?

Ensemble Learning is a technique that combines multiple machine learning models to achieve better predictive performance. By combining the results of each model, which learns and predicts individually, we can reduce the errors that may occur in a single model and enhance generalization performance.

Major methods of ensemble learning include Bagging, Boosting, and Stacking.

1.1 Bagging

Bagging involves dividing the data into several subsets and independently training a model on each subset. The final prediction is determined by averaging the predictions of each model or by majority vote. Random Forest is a representative bagging algorithm.

1.2 Boosting

Boosting is a technique for training the next model to correct the errors of the previous model. Each model is trained sequentially, combining several weak learners to create a strong learner. AdaBoost and XGBoost are well-known boosting algorithms.

1.3 Stacking

Stacking involves training several models and then using a new model (meta-model) to learn the predictions from each model to perform the final prediction. This allows for the creation of a model with stronger predictive power by aggregating the advantages of various models.

2. Designing a Bitcoin Automated Trading System

In this section, we will design an example system that combines CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) models. This automated trading system will predict Bitcoin price fluctuations based on historical price data and generate trading signals based on the results.

2.1 Data Collection

Bitcoin price data can be collected through several APIs. In this example, we will use the yfinance library to fetch historical price data.

2.2 Data Preprocessing

The collected data needs to be preprocessed to fit the model. It is common to handle missing values and normalize the price data.

2.3 Model Training

The model to be trained will combine CNN and LSTM. CNN helps extract important features from time-series data, while LSTM is effective in learning sequence information and long-term dependencies in time-series data.

2.4 Generating Trading Signals

Using the trained model, predictions are made, and buy or sell signals are generated based on specific thresholds. For example, if the predicted price is higher than the current price, a buy signal can be generated; if lower, a sell signal can be sent.

3. Example Code

Now let’s implement the above explanations through an actual code example.

    
import numpy as np
import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import LSTM, Conv1D, Dense, Flatten, Dropout

# Collecting Bitcoin data
data = yf.download('BTC-USD', start='2020-01-01', end='2023-10-01')
data = data['Close'].values

# Data preprocessing
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data.reshape(-1, 1))

# Function to create sequence data
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)

time_step = 60
X, y = create_dataset(scaled_data, time_step)
X = X.reshape(X.shape[0], X.shape[1], 1)

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create LSTM model
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], 1)))
model.add(Dropout(0.2))
model.add(LSTM(50, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(50))
model.add(Dropout(0.2))
model.add(Dense(1))

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

# Train the model
model.fit(X_train, y_train, epochs=50, batch_size=32)

# Prediction
predicted_price = model.predict(X_test)
predicted_price = scaler.inverse_transform(predicted_price)

# Visualization of results
plt.figure(figsize=(10,6))
plt.plot(scaler.inverse_transform(y_test.reshape(-1, 1)), color='blue', label='Actual Price')
plt.plot(predicted_price, color='red', label='Predicted Price')
plt.title('Bitcoin Price Prediction')
plt.xlabel('Time')
plt.ylabel('Price')
plt.legend()
plt.show()
    
    

The above code is an example of a simple LSTM model implemented to predict Bitcoin prices. For it to become an actual trading system, additional logic is needed to generate trading signals.

4. Implementing Trading Strategy

After the model is trained, we move on to the stage of establishing a trading strategy based on the predicted prices. Here, we will generate trading signals based on the differences between predicted prices and actual prices as a simple strategy.

    
# Generate trading signals
def generate_signals(predicted_prices, actual_prices):
    signals = np.zeros(len(predicted_prices))
    for i in range(1, len(predicted_prices)):
        if predicted_prices[i] > actual_prices[i-1]:
            signals[i] = 1  # Buy signal
        elif predicted_prices[i] < actual_prices[i-1]:
            signals[i] = -1  # Sell signal
    return signals

signals = generate_signals(predicted_price.flatten(), scaler.inverse_transform(y_test.reshape(-1, 1)).flatten())
    
    

5. Performance Evaluation

Evaluating the performance of the completed trading system is crucial. Several metrics can indicate the success of the system. Metrics such as return, maximum drawdown, and Sharpe ratio can be used for evaluation.

    
# Performance evaluation
def evaluate_performance(signals, actual_prices):
    returns = np.zeros(len(signals))
    for i in range(len(signals)-1):
        if signals[i] == 1:  # Buy
            returns[i+1] = actual_prices[i+1] / actual_prices[i] - 1
        elif signals[i] == -1:  # Sell
            returns[i+1] = -1 * (actual_prices[i+1] / actual_prices[i] - 1)
    return np.cumprod(1 + returns) - 1

cumulative_returns = evaluate_performance(signals, scaler.inverse_transform(y_test.reshape(-1, 1)).flatten())

# Visualization of results
plt.figure(figsize=(10,6))
plt.plot(cumulative_returns, color='green', label='Cumulative Returns')
plt.title('Bitcoin Automated Trading System Performance')
plt.xlabel('Time')
plt.ylabel('Returns')
plt.legend()
plt.show()
    
    

6. Conclusion

In this post, we explored how to design and implement a Bitcoin automated trading system using deep learning and machine learning based on the principles of ensemble learning. Ensemble learning is a useful technique that combines the strengths of various models to enhance predictive performance. In actual trading environments, more precise trading strategies are needed, and various advanced algorithms and techniques can also be utilized.

It is essential to conduct more experiments and research to improve and advance the Bitcoin automated trading system. I encourage readers to develop their own trading strategies and experiment with them.

Moreover, the cryptocurrency market is extremely volatile and high-risk. Therefore, it is crucial to conduct thorough research and review before engaging in actual trading.

Pattern Recognition-Based Trading Strategy Using CNN

Due to the rapid price fluctuations and high trading volumes in cryptocurrencies, Bitcoin trading has become an attractive market for many investors and trading algorithms. In particular, algorithmic trading strategies that analyze past price patterns and predict future price movements using machine learning and deep learning technologies are gaining attention. This course will explain a trading strategy based on pattern recognition using Convolutional Neural Networks (CNN) and implement it through practical example code.

1. Understanding CNN and Deep Learning

Convolutional Neural Networks (CNN) are a deep learning architecture that demonstrates excellent performance in image recognition and vision-related tasks. CNNs can analyze multiple images using filters (or kernels) with the same weights to learn important features. Thanks to these characteristics, they can recognize patterns in chart images and support trading decisions based on them.

2. Use Cases of Deep Learning in Bitcoin Trading

Deep learning can be effectively used for data analysis and predictions in Bitcoin trading. It helps in making trading decisions through automatic exploration of data, pattern recognition, and predictive algorithms. CNN converts time-series data of Bitcoin price fluctuations (e.g., price recorded every hour) into images for training.

2.1 Data Collection

Bitcoin price data can be collected through various public APIs, among which the Binance API is widely utilized. The following example shows how to collect Bitcoin price data using Python from the Binance API.

import requests
import pandas as pd
import datetime

def fetch_binance_data(symbol='BTCUSDT', interval='1h', 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_volume', 'taker_buy_quote_asset_volume', 'ignore'])
    df['open_time'] = pd.to_datetime(df['open_time'], unit='ms')
    df['close'] = df['close'].astype(float)
    
    return df[['open_time', 'close']]

btc_data = fetch_binance_data()
print(btc_data.head())

2.2 Data Preprocessing and Image Generation

The collected price data needs to be transformed into a form suitable for CNN through a data preprocessing process. For example, additional features can be generated by calculating technical indicators like moving averages or Bollinger bands. Subsequently, the transformed data can be visualized as charts and saved as image files for use as input data for the CNN.

import matplotlib.pyplot as plt
import numpy as np

def plot_price_chart(data):
    plt.figure(figsize=(10, 5))
    plt.plot(data['open_time'], data['close'], label='Close Price', color='blue')
    plt.title('Bitcoin Price Chart')
    plt.xlabel('Time')
    plt.ylabel('Price (USDT)')
    plt.legend()
    plt.grid()
    plt.savefig('btc_price_chart.png')
    plt.close()

plot_price_chart(btc_data)

3. Building and Training the CNN Model

Now, the data needs to be structured for input into the CNN model. The TensorFlow/Keras library can be utilized to build and train the CNN model.

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Defining the CNN model
def create_cnn_model():
    model = Sequential()

    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(64, 64, 3)))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(2, activation='softmax'))  # Classifying into two classes (Buy/Sell)

    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

cnn_model = create_cnn_model()
cnn_model.summary()

3.1 Model Training

To train the images, data augmentation can be performed using ImageDataGenerator, and the model can be trained.

from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical

# Custom function to load image data (assumption)
def load_images_and_labels():
    # Logic to load images and labels
    return images, labels

images, labels = load_images_and_labels()
X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2)

# One-hot encoding the labels
y_train = to_categorical(y_train, num_classes=2)
y_test = to_categorical(y_test, num_classes=2)

# Data augmentation setup
datagen = ImageDataGenerator(
    rotation_range=10,
    width_shift_range=0.1,
    height_shift_range=0.1,
    shear_range=0.1,
    zoom_range=0.1,
    horizontal_flip=True,
    fill_mode='nearest')

# Training the model
cnn_model.fit(datagen.flow(X_train, y_train, batch_size=32), 
               validation_data=(X_test, y_test), 
               epochs=50) 

4. Trading Decision and Strategy Implementation

Once the model has finished training, a strategy can be implemented to make Bitcoin trading decisions. Predictions can be made on new data, and buy or sell signals can be generated if they exceed or fall below a certain threshold.

def make_trade_decision(image):
    # Transforming the image into the input shape for CNN
    processed_image = preprocess_image(image)
    prediction = cnn_model.predict(np.expand_dims(processed_image, axis=0))

    return 'Buy' if prediction[0][0] > 0.5 else 'Sell'

latest_chart_image = 'latest_btc_price_chart.png'
decision = make_trade_decision(latest_chart_image)
print(f'Trade Decision: {decision}') 

5. Conclusion

In this course, we explored how to implement an automatic Bitcoin trading strategy using deep learning and CNNs. Through the processes of data collection, preprocessing, and image generation, building and training the CNN model, and making trading decisions, we were able to execute the application of machine learning in trading. This process can be further developed into more sophisticated strategies by integrating various data and technical indicators.

Finally, there are always risks associated with Bitcoin trading, and as the model’s predictions are based on past data, a cautious approach is necessary.

The cryptocurrency market, like Bitcoin, poses significant risks to investors due to high volatility and uncertainty. To manage these risks, automated trading systems utilizing deep learning and machine learning techniques are gaining attention. In particular, Autoencoder has established itself as a useful tool for risk management by detecting anomalous movements in data. This article will explain the concept of Autoencoder, its theoretical background, an application example of outlier detection in Bitcoin price data, and how to integrate this into an automated trading system.

1. What is an Autoencoder?

An Autoencoder is an unsupervised learning model that compresses and reconstructs input data. The input and output share the same structure, with a low-dimensional representation known as latent space in between. An Autoencoder is divided into two main components:

• Encoder: Converts input data into the latent space.
• Decoder: Restores the original input data from the latent space.

The goal of an Autoencoder is to make the input data and output data as similar as possible. Typically, the Mean Squared Error is used as the loss function.

2. Structure of an Autoencoder

The basic structure of an Autoencoder is as follows:

class Autoencoder(nn.Module):
    def __init__(self):
        super(Autoencoder, self).__init__()
        self.encoder = nn.Sequential(
            nn.Linear(3, 2),
            nn.ReLU(True)
        )
        self.decoder = nn.Sequential(
            nn.Linear(2, 3),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

3. Bitcoin Price Data and Outlier Detection

The price data of Bitcoin is influenced by various factors, which can lead to abnormal price fluctuations. The process of detecting outliers using an Autoencoder can be broadly divided into three stages:

1. Price Data Preprocessing
2. Training the Autoencoder Model
3. Outlier Detection

3.1 Price Data Preprocessing

The process of loading and preprocessing Bitcoin price data is as follows.

import pandas as pd

# Load data
data = pd.read_csv('bitcoin_price.csv')
data['Date'] = pd.to_datetime(data['Date'])
data.set_index('Date', inplace=True)

# Select necessary columns
price_data = data['Close'].values.reshape(-1, 1)

# Normalization
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
normalized_data = scaler.fit_transform(price_data)

3.2 Training the Autoencoder Model

After preparing the data, we create and train the Autoencoder model.

import torch
import torch.nn as nn
import torch.optim as optim

# Hyperparameters
num_epochs = 100
learning_rate = 0.001

# Prepare dataset
tensor_data = torch.FloatTensor(normalized_data)

# Initialize model
model = Autoencoder()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# Training
for epoch in range(num_epochs):
    model.train()
    optimizer.zero_grad()
    output = model(tensor_data)
    loss = criterion(output, tensor_data)
    loss.backward()
    optimizer.step()

    if epoch % 10 == 0:
        print(f'Epoch [{epoch}/{num_epochs}], Loss: {loss.item():.4f}')

3.3 Outlier Detection

Using the trained model, we calculate the reconstruction error of the input data and detect data as outliers if they exceed a certain threshold.

# Model evaluation
model.eval()
with torch.no_grad():
    reconstructed = model(tensor_data)
    reconstruction_loss = criterion(reconstructed, tensor_data)

# Outlier detection
reconstruction_loss_values = torch.sum((tensor_data - reconstructed) ** 2, axis=1).numpy()
threshold = 0.1  # Example threshold
anomalies = reconstruction_loss_values > threshold

# Outlier indices
anomaly_indices = [i for i, x in enumerate(anomalies) if x]
print(f'Outlier indices: {anomaly_indices}')

4. Integration into Automated Trading System

If anomalous movements are detected at specific points in time through outlier detection, the automated trading system can generate buy or sell signals. To do this, it is necessary to define trading strategies based on the detected outliers.

4.1 Example Trading Strategy

Let’s consider a simple strategy to take a sell position when an outlier is detected:

# Trading strategy
for index in anomaly_indices:
    price = price_data[index][0]
    # Sell about abnormal price fluctuation
    print(f'Outlier detected - Sell: Price {price} at index {index}')

5. Conclusion

Outlier detection using deep learning and machine learning techniques, particularly Autoencoders, is an effective tool for risk management of highly volatile assets such as Bitcoin. In this article, we explained how to implement an Autoencoder in Python to detect outliers and integrate it into an automated trading system. This system allows investors to make more data-driven decisions and contributes to reducing uncertainty.

Future areas for improvement include experimenting with various algorithms, adding more input variables, and optimizing trading strategies to enhance performance. This will lead to the development of smarter and more effective automated trading systems.