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Nq Fvg Detector

YOLOv26m model trained to detect Bullish and Bearish Fair Value Gaps (FVGs) on NQ Micro E-mini Nasdaq-100 Futures charts.

Fuentehuggingface.co/ibtsam/nq-fvg-detector/tree/45d52629e6260f40b4161292307b721ff5dc5586
TipoOtherVersiónRevision 45d5262Bcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
42,0 MB
Versión
vRevision 45d5262
Actualizado
16 sept 2026
Descargas
16
Fuente
Hugging Face

Descripción de la fuente

NQ Fair Value Gap (FVG) Detector (try it out )

YOLOv26m model trained to detect Bullish and Bearish Fair Value Gaps (FVGs) on NQ Micro E-mini Nasdaq-100 Futures charts.

Test Set Results

MetricScore
mAP@5092.3%
mAP@50-9558.2%
Precision83.9%
Recall89.4%
F186.6%

Usage

from ultralytics import YOLO

model = YOLO("ibtsam/nq-fvg-detector")
results = model("chart.png", conf=0.25)
results[0].show()

Classes

  • bullish_fvg: gap created during upward impulse move
  • bearish_fvg: gap created during downward impulse move

Dataset

nq-fvg-dataset on Kaggle

Details

  • Instrument: NQ1! Futures
  • Timeframes: 1m, 5m
  • Input size: 640x640
  • Framework: Ultralytics YOLOv26m

Limitations

Trained on white background TradingView charts only. Not financial advice.

Author

Ibtsam Sadiq — GitHub · LinkedIn

Fuentes

1 fuente
Hugging FacePrincipalVersión 45d52629e6260f40b4161292307b721ff5dc5586
huggingface.co/ibtsam/nq-fvg-detector/tree/45d52629e6260f40b4161292307b721ff5dc5586

Verificado 2 sept

Artefactos del modelo

1 artefacto

best.pt

pt · 42,0 MB · SHA-256 8691c95f6c47…af19 · Hugging Face

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Extractos de fuentes

2 extractos
Readme2 septhuggingface.co/ibtsam/nq-fvg-detector

--- license: agpl-3.0 tags: - object-detection - computer-vision - yolo - yolov26m - finance - trading - ict - fair-value-gap library_name: ultralytics pipeline_tag: object-detection --- # NQ Fair Value Gap (FVG) Detector ([try it out](https://huggingface.co/spaces/ibtsam/nq-fvg-detector) ) YOLOv26m model trained to detect **Bullish and Bearish Fair Value Gaps (FVGs)** on NQ Micro E-mini Nasdaq-100 Futures charts. ## Test Set Results | Metric | Score | |--------|-------| | mAP@50 | 92.3% | | mAP@50-95 | 58.2% | | Precision | 83.9% | | Recall | 89.4% | | F1 | 86.6% | ## Usage ```python from ultralytics import YOLO model = YOLO("ibtsam/nq-fvg-detector") results = model("chart.png", conf=0.25) results[0].show() ``` ## Classes - `bullish_fvg`: gap created during upward impulse move - `bearish_fvg`: gap created during downward impulse move ## Dataset [nq-fvg-dataset on Kaggle](https://www.kaggle.com/datasets/ibtsamsadiq/nq-fvg-dataset) ## Details - **Instrument:** NQ1! Futures - **Timeframes:** 1m, 5m - **Input size:** 640x640 - **Framework:** Ultralytics YOLOv26m ## Limitations Trained on white background TradingView charts only. Not financial advice. ## Author **Ibtsam Sadiq** — [GitHub](https://github.com/ibi111) · [LinkedIn](https://www.linkedin.com/in/ibtsamsadiq01) ---

Note2 septhuggingface.co/ibtsam/nq-fvg-detector

Source context: 16 downloads · 1 likes · Pipeline object-detection · Library ultralytics · Repo ibtsam/nq-fvg-detector