Intraday Decision Support for Traders: Explainable CNN-Based Directional Price Forecasting from Candlestick Chart Images

Short-horizon trader-support systems in financial markets should be judged not only by predictive skill but also by auditability, inspectability, and suitability for decision support under uncertainty. This paper studies an auditable 20-bar chart-image branch built from a retained MOEX Si futures artifact bundle. Starting from 14 contract-level one-minute candle files plus an auxiliary participant-position file, the pipeline resamples to 15-minute candles, selects the daily front contract by realized volume, applies a conservative liquidity filter, constructs a clean continuous-front series of 29,926 bars across 502 trading days, and generates 10,350 same-day, same-contract 20-bar windows rendered as 64 x 60 grayscale candlestick images. A three-block convolutional neural network (CNN) and a Grad-CAM-style local explanation layer are then embedded in a dashboard-centered inspection workflow. On the held-out test split (1,646 windows), the model attains 0.552 accuracy, 0.530 balanced accuracy, 0.408 F1, 0.557 ROC-AUC, and 0.063 MCC. Performance is modest and, at the default 0.50 threshold, trails the naive majority-class baseline on raw accuracy, while remaining better than random in threshold-free ranking terms. Results are contract-concentrated: SiZ5 materially outperforms SiU5. Quantitative explanation summaries show that heat mass is concentrated mainly in the price panel (0.807) rather than volume (0.193), but remains broad and only weakly focused on the final bar (0.043). The contribution is therefore bounded: not novelty of chart-image CNN forecasting or Grad-CAM in futures, both of which already exist, but the integration of chronology-aware futures engineering, auditable chart-image modeling, local explanation outputs, and row-level dashboard inspection in a trader-facing decision-support artifact. Fee-aware economic validation and formal user evaluation remain separated as pending next-stage work.