The YOLOv8s Stock Market Pattern Detection model enables real-time detection of crucial chart patterns within stock market screen captures. As stock markets evolve rapidly, this model's capabilities empower users with timely insights, allowing them to make informed decisions with speed and accuracy.

It presents two common patterns, the method used to build the training set, the neural networks architectures and the accuracies obtained. Patterns are recurring sequences found in OHLC1 candle-stick charts which traders have historically used as buy and sell signals.

Feb 19, 2024 · In this paper, to comprehensively and rigorously benchmark the ability of the off-the-shelf MLLMs in the chart domain, we construct ChartX, a multi-modal evaluation set covering 18 chart types, 7 chart tasks, 22 disciplinary topics, and high-quality chart data.

Jan 4, 2024 · Charts play a vital role in data visualization, understanding data patterns, and informed decision-making. However, their unique combination of graphical elements (e.g., bars, lines) and textual components (e.g., labels, legends) poses challenges for general-purpose multimodal models.

1455 open source Chart-patterns images plus a pre-trained Chart-pattern model and API. Created by ebtihel.

This chapter introduces the graph neural network model in various computer vision tasks, including specific tasks for image, video and cross-media (cross- modal) (Zhuang et al, 2017).

Leverages OpenRouter's Vision API to provide AI-powered analysis of chart images, including pattern recognition, trend analysis, and trading recommendations. Combines results from both traditional ML models and AI vision models to provide comprehensive analysis with enhanced confidence scoring.

Oct 23, 2022 · In this work, we address this outcome and propose a new model that jointly learns classification and regression. Our language-vision setup uses co-attention transformers to capture the complex real-world interactions between the question and the textual elements.

Oct 25, 2020 · In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are...

Mar 30, 2022 · In this paper, we describe the design and implementation of one-dimensional convolutional neural networks (1D CNNs) for the classification of chart patterns from financial time series. The proposed 1D CNN model is compared against support vector machine, extreme learning machine, long short-term memory, rule-based and dynamic time warping.