Stockmarket Pattern Detection Yolov8

🚀 YOLOv8s股票市場即時圖案檢測模型

該模型基於YOLO（You Only Look Once）框架，能夠從屏幕捕獲的股票市場交易數據中即時檢測各種圖表模式。它通過自動分析圖表模式，為交易者和投資者提供及時的見解，輔助他們做出明智的決策。該模型在多樣化的數據集上進行了微調，在即時交易場景中能夠高精度地檢測和分類股票市場模式。

🚀 快速開始

要開始使用YOLOv8s股票市場即時圖案檢測模型，你需要安裝必要的庫：

pip install mss==10.0.0 opencv-python==4.11.0.86 numpy ultralytics==8.3.94 openpyxl==3.1.5

💻 使用示例

基礎用法

import os
import mss # type: ignore
import cv2
import numpy as np
import time
import glob
from ultralytics import YOLO
from openpyxl import Workbook

# Get the user's home directory
home_dir = os.path.expanduser("~")

# Define dynamic paths
save_path = os.path.join(home_dir, "yolo_detection")
screenshots_path = os.path.join(save_path, "screenshots")
detect_path = os.path.join(save_path, "runs", "detect")

# Ensure necessary directories exist
os.makedirs(screenshots_path, exist_ok=True)
os.makedirs(detect_path, exist_ok=True)

# Define pattern classes
classes = ['Head and shoulders bottom', 'Head and shoulders top', 'M_Head', 'StockLine', 'Triangle', 'W_Bottom']

# Load YOLOv8 model
model_path = "model.pt"
if not os.path.exists(model_path):
    raise FileNotFoundError(f"Model file not found: {model_path}")
model = YOLO(model_path)

# Define screen capture region
monitor = {"top": 0, "left": 683, "width": 683, "height": 768}

# Create an Excel file
excel_file = os.path.join(save_path, "classification_results.xlsx")
wb = Workbook()
ws = wb.active
ws.append(["Timestamp", "Predicted Image Path", "Label"])  # Headers

# Initialize video writer
video_path = os.path.join(save_path, "annotated_video.mp4")
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
fps = 0.5  # Adjust frames per second as needed
video_writer = None

with mss.mss() as sct:
    start_time = time.time()
    last_capture_time = start_time  # Track the last capture time
    frame_count = 0
    
    while True:
        # Continuously capture the screen
        sct_img = sct.grab(monitor)
        img = np.array(sct_img)
        img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)

        # Check if 60 seconds have passed since last YOLO prediction
        current_time = time.time()
        if current_time - last_capture_time >= 60:
            # Take screenshot for YOLO prediction
            timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
            image_name = f"predicted_images_{timestamp}_{frame_count}.png"
            image_path = os.path.join(screenshots_path, image_name)
            cv2.imwrite(image_path, img)

            # Run YOLO model and get save directory
            results = model(image_path, save=True)
            predict_path = results[0].save_dir if results else None

            # Find the latest annotated image inside predict_path
            if predict_path and os.path.exists(predict_path):
                annotated_images = sorted(glob.glob(os.path.join(predict_path, "*.jpg")), key=os.path.getmtime, reverse=True)
                final_image_path = annotated_images[0] if annotated_images else image_path
            else:
                final_image_path = image_path  # Fallback to original image

            # Determine predicted label
            if results and results[0].boxes:
                class_indices = results[0].boxes.cls.tolist()
                predicted_label = classes[int(class_indices[0])]
            else:
                predicted_label = "No pattern detected"

            # Insert data into Excel (store path instead of image)
            ws.append([timestamp, final_image_path, predicted_label])

            # Read the image for video processing
            annotated_img = cv2.imread(final_image_path)
            if annotated_img is not None:
                # Add timestamp and label text to the image
                font = cv2.FONT_HERSHEY_SIMPLEX
                cv2.putText(annotated_img, f"{timestamp}", (10, 30), font, 0.7, (0, 255, 0), 2, cv2.LINE_AA)
                cv2.putText(annotated_img, f"{predicted_label}", (10, 60), font, 0.7, (0, 255, 255), 2, cv2.LINE_AA)
                
                # Initialize video writer if not already initialized
                if video_writer is None:
                    height, width, layers = annotated_img.shape
                    video_writer = cv2.VideoWriter(video_path, fourcc, fps, (width, height))
                
                video_writer.write(annotated_img)

            print(f"Frame {frame_count}: {final_image_path} -> {predicted_label}")
            frame_count += 1

            # Update the last capture time
            last_capture_time = current_time

        # Save the Excel file periodically
        wb.save(excel_file)

        # If you want to continuously display the screen, you can add this line
        cv2.imshow("Screen Capture", img)

        # Break if 'q' is pressed (you can exit the loop this way)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

# Release video writer
if video_writer is not None:
    video_writer.release()
    print(f"Video saved at {video_path}")

# Remove all files in screenshots directory
for file in os.scandir(screenshots_path):
    os.remove(file.path)
os.rmdir(screenshots_path)

print(f"Results saved to {excel_file}")

# Close OpenCV window
cv2.destroyAllWindows()

📚 詳細文檔

模型詳情

模型描述

YOLOv8s股票市場圖案檢測模型能夠即時檢測股票市場屏幕截圖中的關鍵圖表模式。由於股票市場變化迅速，該模型的功能使用戶能夠及時獲取信息，從而快速準確地做出明智決策。

該模型旨在處理股票市場交易圖表的屏幕截圖，能夠檢測諸如“頭肩底”、“頭肩頂”、“M頭”、“股票線”、“三角形”和“W底”等模式。交易者可以優化他們的策略，自動做出交易決策，並即時響應市場趨勢。

若要將此模型集成到即時交易系統或進行定製諮詢，請通過info@foduu.com與我們聯繫。

支持的標籤

['Head and shoulders bottom', 'Head and shoulders top', 'M_Head', 'StockLine', 'Triangle', 'W_Bottom']

用途

• 直接使用：該模型可用於對屏幕捕獲的股票市場圖表進行即時圖案檢測。它可以記錄檢測到的圖案，對檢測到的圖像進行註釋，將結果保存到Excel文件中，並生成一段時間內檢測到的圖案的視頻。
• 下游使用：該模型的即時功能可用於自動化交易策略，為特定圖案生成警報，並提高整體交易表現。
• 訓練數據：該股票市場模型在一個自定義數據集上進行訓練，該數據集包含9000張訓練圖像和800張驗證圖像。
• 適用範圍外的使用：該模型不適用於與股票市場圖案檢測無關的目標檢測任務或場景。

偏差、風險和侷限性

• 性能可能會受到圖表樣式、屏幕分辨率和市場條件變化的影響。
• 快速的市場波動和交易數據中的噪聲可能會影響準確性。
• 訓練數據中未充分體現的特定市場模式可能會給檢測帶來挑戰。

建議

用戶應瞭解該模型的侷限性和潛在偏差。在將模型用於實際交易決策之前，建議使用歷史數據和即時市場條件進行測試和驗證。

📄 許可證

文檔中未提及相關許可證信息。

📞 模型聯繫信息

如有諮詢和貢獻需求，請通過info@foduu.com與我們聯繫。

引用信息

@ModelCard{
  author    = {Nehul Agrawal,
               Pranjal Singh Thakur, Priyal Mehta and Arjun Singh},
  title     = {YOLOv8s Stock Market Pattern Detection from Live Screen Capture},
  year      = {2023}
}