Candlestick pattern detector (YOLO11n)
Detects 8 candlestick patterns in 640x640 renderings of 20-candle daily charts.
The headline finding
Adding this detector's predictions to a plain price-feature baseline changed next-day direction accuracy by +0.0036 (95% bootstrap CI -0.0041 to +0.0114, McNemar p = 0.443) over 1936 out-of-sample days on SPY. The interval contains zero. Neither variant beats the majority-class rate of 0.5537, and both have a ROC AUC near 0.496.
So: this model detects the patterns well, and detecting them does not help predict the next day. That is the result, reported as found.
Labels are rule-based, not human-verified
Training boxes came from TA-Lib's CDLxxx functions. Nobody annotated these
charts by hand, so this model imitates a published heuristic. Where TA-Lib and
an experienced trader would disagree about the same candles, this model follows
TA-Lib. Detection metrics below therefore measure agreement with a rule, not
correctness.
Detection, held-out test split
| class | instances | precision | recall | mAP@50 |
|---|---|---|---|---|
| Hammer | 557 | 0.665 | 0.720 | 0.757 |
| ShootingStar | 570 | 0.801 | 0.798 | 0.893 |
| BullishEngulfing | 1178 | 0.999 | 0.983 | 0.995 |
| BearishEngulfing | 1368 | 0.908 | 0.997 | 0.976 |
| MorningStar | 198 | 0.602 | 0.773 | 0.772 |
| EveningStar | 180 | 0.773 | 0.550 | 0.669 |
| Doji | 5261 | 0.893 | 0.948 | 0.975 |
| Harami | 2350 | 0.824 | 0.946 | 0.944 |
| aggregate | 0.808 | 0.839 | 0.872 |
Reported per class because the classes are very unevenly represented. The pattern in those numbers is not random: classes defined by a relationship between whole candle bodies (Engulfing, Harami) are close to solved, while those defined by a proportion inside a single candle (Hammer, Shooting Star, the Stars) are much harder -- at this resolution a candle body is only a few pixels tall, so the measurement the rule depends on is the one the image barely resolves.
Usage
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
model = YOLO(hf_hub_download("rohanjain2312/candlestick-pattern-recognition-system-yolo", "best.pt"))
results = model.predict("chart.png", conf=0.25)
Charts must be rendered the same way the training data was: 20
candles, 640x640, no axes, no gridlines, no volume
panel. Use src/data_pipeline/render_charts.py from the repo -- a differently
drawn chart is an input distribution this model has never seen. Note also that
Ultralytics expects BGR arrays while most renderers emit RGB.
Splits
Chronological with an embargo gap, never random. Consecutive windows overlap in 19 of 20 candles, so the first 20 windows after each boundary are discarded and no test chart shares a candle with a training chart.
- Code: https://github.com/rohanjain2312/candlestick-pattern-recognition-system
- Dataset: https://huggingface.co/datasets/rohanjain2312/candlestick-pattern-recognition-system-data
- Demo: https://huggingface.co/spaces/rohanjain2312/candlestick-pattern-recognition-system-demo
Not investment advice.