title: ÐÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÐºÐ¾ÑÐ¸ÑÐ¾Ð²Ð¾Ðº Ð°ÐºÑÐ¸Ð¹ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Forecasting stock prices using a neural network
Creators: name: Ð§Ð¸Ð³Ð°ÑÐ¾Ð²Ð°, ÐÐ°ÑÐ¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐ¾Ð½Ð´Ð¾Ð²ÑÐ¹ ÑÑÐ½Ð¾Ðº
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Ð±Ð¸ÑÐ¶ÐµÐ²Ð¾Ð¹ Ð°Ð½Ð°Ð»Ð¸Ð·
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
neural networks
stock market
financial time series forecasting
market analysis
machine learning

description: Ð Ð°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¸Ð½Ð°Ð½ÑÐ¾Ð²ÑÑ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð², Ð° ÑÐ°ÐºÐ¶Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ ÑÑÑÐ°ÑÐµÐ³Ð¸Ð¸ ÑÐ¾ÑÐ³Ð¾Ð²Ð»Ð¸ Ð´Ð»Ñ Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ°ÑÐ¸Ð¸ Ð¿ÑÐ°ÐºÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ. Ð¡ÑÑÐµÑÑÐ²ÑÐµÑ Ð¼Ð½Ð¾Ð¶ÐµÑÑÐ²Ð¾ ÑÐ¿Ð¾ÑÐ¾Ð±Ð¾Ð² Ð±Ð¸ÑÐ¶ÐµÐ²Ð¾Ð³Ð¾ Ð°Ð½Ð°Ð»Ð¸Ð·Ð°, Ð¾Ð´Ð½Ð°ÐºÐ¾ ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¼ÐµÑÐ¾Ð´Ñ Ð² Ð¿Ð¾ÑÐ»ÐµÐ´Ð½Ð¸Ðµ Ð³Ð¾Ð´Ñ Ð½Ð°ÑÐ¸Ð½Ð°ÑÑ ÑÑÑÑÐ¿Ð°ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐ¼ ÑÐµÑÑÐ¼, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð¸Ð¼ÐµÑÑ Ð±Ð¾Ð»ÑÑÐ¾Ðµ Ð¿ÑÐµÐ¸Ð¼ÑÑÐµÑÑÐ²Ð¾ Ð¿Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ñ Ð´ÑÑÐ³Ð¸Ð¼Ð¸ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸ â ÑÑÐ¾ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð ÑÐ°Ð¼ÐºÐ°Ñ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐµ ÑÐ¿Ð¾ÑÐ¾Ð±Ñ Ð±Ð¸ÑÐ¶ÐµÐ²Ð¾Ð³Ð¾ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¸ ÑÐ°ÐºÐ¸Ðµ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ ÐºÐ°Ðº ÑÐµÐºÑÑÑÐµÐ½ÑÐ½Ð°Ñ, ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð°Ñ, Ð¼Ð½Ð¾Ð³Ð¾ÑÐ»Ð¾Ð¹Ð½ÑÐ¹ Ð¿ÐµÑÑÐµÐ¿ÑÑÐ¾Ð½ Ð¸ ÑÐµÑÑ Ð´Ð¾Ð»Ð³Ð¾Ð¹ ÐºÑÐ°ÑÐºÐ¾ÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð¿Ð°Ð¼ÑÑÐ¸ (LSTM). ÐÑÐ»Ð¾ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¾, ÑÑÐ¾ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐ»ÐµÐ´ÑÐµÑ Ð¾Ð¶Ð¸Ð´Ð°ÑÑ Ð¾Ñ ÑÐµÑÐ¸ Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ¾Ð¹ LSTM. ÐÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð°Ñ ÑÐ°ÑÑÑ ÑÐ°Ð±Ð¾ÑÑ, Ð·Ð°ÐºÐ»ÑÑÐ°ÑÑÐ°ÑÑÑ Ð² ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ, ÑÑÑÐ°ÑÐµÐ³Ð¸Ð¸ ÑÐ¾ÑÐ³Ð¾Ð²Ð»Ð¸ Ð¸ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ¾Ð³Ð¾ Ð¾ÐºÐ¾Ð½Ð½Ð¾Ð³Ð¾ Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ, Ð±ÑÐ»Ð° Ð½Ð°Ð¿Ð¸ÑÐ°Ð½Ð° Ð½Ð° ÑÐ·ÑÐºÐµ Python Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ°ÐºÐ¸Ñ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐº ÐºÐ°Ðº Keras, Pandas, Backtrader Ð¸ PyQt5. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð° Ð´Ð¾ÑÑÐ°ÑÐ¾ÑÐ½Ð¾ Ð²ÑÑÐ¾ÐºÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ, Ð° Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÑÐ°ÑÐµÐ³Ð¸Ð¸ ÑÐ¾ÑÐ³Ð¾Ð²Ð»Ð¸ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¾ Ð¿ÑÐ¸ÑÐ¾ÑÑ ÑÑÐ¾Ð¸Ð¼Ð¾ÑÑÐ¸ Ð¿Ð¾ÑÑÑÐµÐ»Ñ Ð² Ð¿Ð¾Ð»ÑÐ¾ÑÐ° ÑÐ°Ð·Ð° Ð·Ð° Ð¿Ð¾Ð»ÑÐ¾ÑÐ° Ð³Ð¾Ð´Ð°.
description: The work is devoted to the creation of a forecasting model for financial time series and the development of a trading strategy to demonstrate the practical application of forecasting results. There are many ways of stock analysis, however, in recent years, classical methods have been gaining popularity - neural networks, which have the main advantage over other methods - the ability to learn. As part of this work, various methods of stock analysis and such architectures of neural networks as recurrent, convolutional, multilayer perceptron and a network of long short-term memory were analyzed. It was found that the most effective results should be expected from a network with a long short-term memory architecture. The software part of the work, which consists in the implementation of the forecasting model, trading strategy and custom window application, was written in Python using libraries such as Keras, Pandas, Backtrader and PyQt5. As a result, the model showed a high forecasting accuracy, and the use of the trading strategy showed an increase in the value of the portfolio by one and a half times in a year and a half.