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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.

Sourcehuggingface.co/ibtsam/nq-fvg-detector/tree/45d52629e6260f40b4161292307b721ff5dc5586
TypeOtherVersionRevision 45d5262BClasse

Source du modèle

Type
Other
Taille du fichier
42,0 MB
Version
vRevision 45d5262
Mis à jour
16 sept. 2026
Téléchargements
16
Source
Hugging Face

Description de la source

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

Sources

1 source
Hugging FacePrincipalVersion 45d52629e6260f40b4161292307b721ff5dc5586
huggingface.co/ibtsam/nq-fvg-detector/tree/45d52629e6260f40b4161292307b721ff5dc5586

Vérifié 2 sept.

Artefacts du modèle

1 artefact

best.pt

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

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Extraits de sources

2 extraits
Readme2 sept.huggingface.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 sept.huggingface.co/ibtsam/nq-fvg-detector

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