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Convolutional Neural Network-Based Pattern Recognition in Candlestick Charts for Automated Technical Analysis and Trading Signals.

Narcisa Zlatan
Volume 5

Abstract

Candlestick chart analysis is widely used in financial markets to interpret price behavior, identify trend continuation or reversal patterns, and generate trading signals; however, traditional technical analysis often depends on manually defined rules, subjective visual interpretation, and fixed pattern templates that may fail under noisy, volatile, and rapidly changing market conditions. This paper proposes a Convolutional Neural Network-based pattern recognition framework for automated technical analysis and trading signal generation using candlestick chart representations. The proposed framework transforms historical open, high, low, close, and volume data into image-based candlestick sequences, enabling deep convolutional models to learn discriminative visual patterns associated with future price movements. Advanced CNN architectures, including VGGNet, ResNet, EfficientNet, and custom lightweight CNN models, are employed to extract spatial features from candlestick formations, trend structures, support and resistance zones, volatility patterns, and price momentum signals. The framework was evaluated using historical financial data from multiple asset classes, including equities, foreign exchange pairs, commodities, and cryptocurrencies, across different time horizons and market regimes. A comprehensive preprocessing pipeline was applied, including OHLCV data normalization, sliding-window chart generation, image resizing, noise reduction, class labeling based on future returns, and chronological train-validation-test splitting to avoid look-ahead bias. To improve robustness and reduce overfitting, data augmentation, dropout regularization, batch normalization, transfer learning, and class-weighted loss functions were incorporated during model training. Experimental results demonstrate that the proposed CNN-based framework achieves superior pattern recognition and trading signal prediction performance compared with rule-based candlestick strategies, traditional technical indicators, Support Vector Machine, Random Forest, and recurrent neural network baselines. The proposed model records higher classification accuracy, precision, recall, F1-score, AUC-ROC, and directional prediction performance, while generating more reliable buy, sell, and hold signals under volatile market conditions. Furthermore, Grad-CAM visualization was integrated to highlight the most influential chart regions contributing to each prediction, enhancing interpretability by revealing how the model focuses on relevant candlestick structures and price formations. The findings indicate that CNN-based candlestick pattern recognition provides an adaptive, scalable, and interpretable approach for automated technical analysis, offering practical value for algorithmic trading systems, financial analysts, quantitative traders, and decision-support platforms operating in complex and dynamic financial markets.

Keywords

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