Gold Price Trend Prediction from Candlestick Chart Images Using Multi-Time Frame Analysis and Machine Learning
Bibliographic record
Abstract
Candlestick chart patterns are a cornerstone of technical analysis, offering visual cues for trend identification in financial markets. This paper introduces a novel image-based classification framework for detecting daily gold (XAU/USD) trends using a multi-time frame candlestick encoding. Specifically, each sample comprises a 24-hour sequence of hourly candlesticks rendered as a 96×96 RGB image. The corresponding trend label—Uptrend, Downtrend, or Sideways—is determined by analyzing six overlapping 4-hour candles based on directional dominance, average body size, and overall price slope. Using this labeling logic, we generated over 100,000 labeled samples and evaluated three classification models: Random Forest with PCA, a custom 6-layer convolutional neural network (CNN), and MobileNetV2 with transfer learning. All models were trained using stratified 5-fold cross-validation, with CNN-based models enhanced by data augmentation and early stopping. In the binary classification task (Uptrend vs. Downtrend), the custom CNN achieved the highest performance, with an F1-score of 99.24%, followed closely by MobileNetV2 and Random Forest. To assess model performance under more realistic market conditions, we extended the best CNN architecture to a 3-class setting by adding the Sideways label. While overall accuracy declined due to class imbalance and visual ambiguity, the model still demonstrated robust trend differentiation. These results confirm that combining multi-time frame labeling with visual encoding enables effective and scalable financial trend classification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".