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Stock Chart Pattern recognition with Deep Learning

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arxiv 1808.00418 v1 pith:D2ZV2PG5 submitted 2018-08-01 cs.LG stat.ML

Marc Velay , Fabrice Daniel This is my paper · ORCID
classification cs.LGstat.ML
keywords commonpatternsstockaccuraciesarchitecturesbuildchartcharts
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This study evaluates the performances of CNN and LSTM for recognizing common charts patterns in a stock historical data. It presents two common patterns, the method used to build the training set, the neural networks architectures and the accuracies obtained.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Exploring Diffusion Models for Generative Forecasting of Financial Charts

    cs.AI 2025-09 conditional novelty 5.0 of 10

    A Stable Diffusion model fine-tuned on paired Bitcoin chart images can generate plausible next-candle charts, but its marker-based accuracy of 68.9% is below the majority-class baseline of 85.9%.

  2. Investigating Market Strength Prediction with CNNs on Candlestick Chart Images

    cs.CV 2025-01 reject novelty 3.0 of 10

    A CNN trained on raw candlestick chart images predicts market strength at roughly 0.7 accuracy, and adding YOLO-detected candlestick patterns does not reliably improve that performance.

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