Candlestick Chart CNN Prediction for A-Share Market

Research on whether candlestick charts contain implicit price trend information in the Chinese stock market.

Problem: Can image-based deep learning extract predictive signals from candlestick charts that traditional quantitative features miss?

Method: Built an attention-based CNN model that visualizes A-share price, volume, and moving averages as candlestick images for binary classification prediction. Compared CNN prediction signals with classic factors and conducted empirical analysis using logistic regression.

Result: The Attention-CNN performed best at the 20-day window for both weekly and monthly frequencies, with long-short Sharpe ratios of 3.30 and 1.50 respectively, improving annualized return by 2% over baseline CNN. CNN prediction signals showed independent pricing factor characteristics, with stronger predictive power in small-cap and high-turnover stocks, validating the effectiveness of image deep learning for A-share trend prediction.