NEURAL NETWORK-BASED AUTOMATED FINANCIAL TRADING SYSTEM

Abstract

The invention discloses a neural network-based automated financial trading system designed to enhance predictive accuracy and trading efficiency in dynamic market environments. The system integrates a multi-layer neural network model, combining supervised and reinforcement learning, to analyze real-time market data, technical indicators, and sentiment analysis. It features modules for data acquisition, feature extraction, decision-making, trade execution, and adaptive risk management. By continuously learning from market feedback, the system adapts its trading strategies to evolving conditions, providing a comprehensive, end-to-end solution for automated trading across various financial markets, including stocks, forex, and cryptocurrencies.

Specification

Description:The present invention relates to the domain of automated financial trading systems, specifically focusing on the use of neural network models for predicting market trends and executing trades in real-time. It leverages advanced machine learning algorithms, including deep learning and reinforcement learning, to analyze vast datasets from financial markets and make data-driven decisions with minimal human intervention, aiming to enhance trading efficiency, accuracy, and profitability.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

Automated trading systems, also known as algorithmic trading systems, have , Claims:1. A neural network-based automated financial trading system comprising:
• A data acquisition module configured to collect real-time market data from multiple sources;
• A feature extraction module designed to process the collected data and extract relevant features;
• A neural network model trained on the extracted features, utilizing supervised learning and reinforcement learning algorithms for predictive analysis;
• A decision-making engine that generates trading signals based on the predictions of the neural network model;
• An execution module that places trades based on the generated trading signals through an integrated brokerage API; and
• A risk management module that dynamically adjusts trade parameters based on predicted market volatility and user-defined risk preferences.

2. The system of Claim 1, wherein the neural network model comprises convolutional layers for spatial feature extraction and recurrent layers for time-series data analysis.

3. The system of Claim 1, wherein the feature extracti

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