Machine Learning and Deep Learning Algorithm Trading, Univariate Time Series Model

1. Introduction

In recent years, there has been a growing interest in algorithmic trading using machine learning (ML) and deep learning (DL) technologies in the financial markets.
This course will provide a detailed explanation of how to build univariate time series models by applying these technologies.
Univariate time series data consists of values of a single variable measured over time. For example, this includes stock prices,
exchange rates, or demand for a specific product. By leveraging machine learning and deep learning, it is possible to predict these patterns and
build systems that support investment decisions.

2. Understanding Time Series Data

Time series data refers to data that occurs over time.
In the financial markets, data such as stock prices, exchange rates, and trading volumes are collected, and analyzing this data to predict future
trends is crucial. Time series data possesses the following characteristics.

  • Trend: A tendency for time series data to increase or decrease over time.
  • Seasonality: Patterns that occur periodically.
  • Noise: Unpredictable irregular fluctuations.

Understanding these characteristics is the first step toward effective modeling.

3. Univariate Time Series Modeling

Univariate time series modeling is a technique for analyzing time series data composed of a single variable.
In machine learning and deep learning, various models can be used, including ARIMA and LSTM.

3.1 ARIMA Model

ARIMA stands for AutoRegressive Integrated Moving Average, a model that combines the autoregressive component, differencing component, and moving average component of a time series.
The ARIMA model consists of the following three elements:

  • AR(p): The autoregressive part, which uses p past observations to predict the present value.
  • I(d): The number of differencing operations applied to stabilize the time series.
  • MA(q): The moving average part, which uses q past error terms to predict the present value.

To build an ARIMA model, one must first check the stationarity of the data.
This stationarity can be verified through ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) graphs. 

import pandas as pd
import numpy as np
from statsmodels.tsa.arima.model import ARIMA
import matplotlib.pyplot as plt

# Load data
data = pd.read_csv('financial_data.csv')
ts = data['price']

# Fit model
model = ARIMA(ts, order=(p, d, q))
model_fit = model.fit()

# Forecast
forecast = model_fit.forecast(steps=10)
print(forecast)

3.2 LSTM Model

The LSTM (Long Short-Term Memory) model is a type of recurrent neural network (RNN) architecture that
is very effective for processing time series data. LSTM is designed to solve the long-term dependency problem and uses
multiple gates to regulate the process of remembering and forgetting information. 

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import LSTM, Dense

# Data preprocessing
data = pd.read_csv('financial_data.csv')
data = data['price'].values
data = data.reshape(-1, 1)

# Build LSTM model
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(timesteps, 1)))
model.add(LSTM(50, return_sequences=False))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mean_squared_error')

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

4. Building an Algorithmic Trading System

The process of building an algorithmic trading system using machine learning and deep learning models consists of the following steps.

  • Step 1: Data Collection – Collect necessary data using financial data APIs.
  • Step 2: Data Preprocessing – Perform tasks such as handling missing values and normalization.
  • Step 3: Model Selection and Training – Select and train the ARIMA or LSTM model.
  • Step 4: Develop Trading Strategy – Develop strategies for buy/sell decisions based on predictive results.
  • Step 5: Perform Backtesting – Validate and improve the model’s performance using historical data.
  • Step 6: Real-time Trading – Receive real-time data and apply the model to execute trades automatically.

5. Conclusion

Algorithmic trading using machine learning and deep learning is becoming increasingly important in modern financial markets.
The univariate time series modeling techniques described in this course can be effective tools for improving predictions of financial data.
However, when applying these techniques, various risk management and performance validation measures are necessary, and it is crucial to build a reliable automated trading system based on this.