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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
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Model source

Kind
Other
File size
42.0 MB
Version
vRevision 45d5262
Updated
Sep 16, 2026
Downloads
16
Source
Hugging Face

Source description

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 FacePrimaryVersion 45d52629e6260f40b4161292307b721ff5dc5586
huggingface.co/ibtsam/nq-fvg-detector/tree/45d52629e6260f40b4161292307b721ff5dc5586

Verified Sep 2

Model artifacts

1 artifact

best.pt

pt · 42.0 MB · SHA-256 8691c95f6c47…af19 · Hugging Face

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Source excerpts

2 excerpts
ReadmeSep 2huggingface.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) ---

NoteSep 2huggingface.co/ibtsam/nq-fvg-detector

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