aadhaargoel/trading-analyser

Trading Data Analyzer - OCR Version

A Python tool that extracts trading data from images using OCR (Optical Character Recognition), performs calculations, and generates professional Excel reports.

Features

✅ Image OCR: Automatically extracts trading data from JPG, PNG, JPEG, TIFF images ✅ Data Calculation: Computes (Quantity ÷ 250) × Market Rate for all trades ✅ Group Analysis: Separates and sums BUY and SELL transactions ✅ Excel Reports: Generates formatted Excel files with detailed analysis ✅ Multiple Documents: Processes multiple images and creates individual sheets ✅ Professional Output: Color-coded summaries with borders and formatting

Installation

Step 1: Install Python Dependencies

pip install -r requirements.txt

Or install manually:

pip install pytesseract pillow openpyxl opencv-python

Step 2: Install Tesseract OCR

Windows:

1. 1.Download installer from: https://github.com/UB-Mannheim/tesseract/wiki
2. 2.Run the installer (default location: C:\Program Files\Tesseract-OCR)
3. 3.If installed in non-default location, update the script:

   pytesseract.pytesseract.pytesseract_cmd = r'C:\path\to\tesseract.exe'

macOS:

brew install tesseract

Linux (Ubuntu/Debian):

sudo apt-get install tesseract-ocr

Linux (Fedora/RedHat):

sudo yum install tesseract

Usage

Method 1: Interactive Mode (Recommended)

Simply run the script and follow the prompts:

python trading_data_analyzer.py

The script will:

1. 1.Ask you to input image file paths
2. 2.Process each image and extract trading data
3. 3.Generate an Excel file with analysis
4. 4.Display summary statistics

Example:

╔══════════════════════════════════════════════════════════════╗
║                                                              ║
║     TRADING DATA ANALYSIS TOOL - OCR VERSION                ║
║                                                              ║
╚══════════════════════════════════════════════════════════════╝

Enter image file paths (one per line, press Enter twice to finish):
(Supported formats: JPG, PNG, JPEG, TIFF)

Image 1: /path/to/image1.jpg
Image 2: /path/to/image2.jpg
Image 3: 

============================================================
TRADING DATA EXTRACTION
============================================================
Processing: /path/to/image1.jpg
  ✓ Found 28 trades
Processing: /path/to/image2.jpg
  ✓ Found 26 trades

✓ Total trades extracted: 54

============================================================
GENERATING EXCEL REPORT
============================================================

✓ Excel report generated: trading_analysis_20240522_145030.xlsx

============================================================
ANALYSIS SUMMARY
============================================================
BUY Total:  118.4000
SELL Total: 181.6000
NET (BUY - SELL): -63.2000
============================================================

✓ Analysis complete! File saved as: trading_analysis_20240522_145030.xlsx

Method 2: Batch Processing (Advanced)

Modify the script to process a folder of images:

from pathlib import Path
from trading_data_analyzer import TradingDataExtractor

# Create extractor
extractor = TradingDataExtractor()

# Process all JPG files in a folder
image_folder = Path("./trading_images")
image_paths = list(image_folder.glob("*.jpg")) + list(image_folder.glob("*.png"))

# Process images
if extractor.process_images(image_paths):
    extractor.generate_excel("batch_analysis.xlsx")

How It Works

1. Image Processing

• —Converts image to grayscale
• —Applies thresholding for better contrast
• —Denoises the image for cleaner text

2. Text Extraction (OCR)

• —Uses Tesseract to recognize text in images
• —Extracts trading records (BUY/SELL, Quantity, Rate)

3. Data Parsing

• —Identifies BUY and SELL transactions
• —Extracts quantities and market rates
• —Validates data for accuracy

4. Calculation

• —Applies formula: (Quantity ÷ 250) × Market Rate
• —Groups transactions by type (BUY/SELL)
• —Calculates totals and net values

5. Excel Generation

• —Creates summary sheet with overall totals
• —Creates individual sheets for each document
• —Applies professional formatting and styling

Output

The script generates an Excel file with:

Summary Sheet:

• —Overall BUY total
• —Overall SELL total
• —NET (BUY - SELL) calculation
• —Transaction counts

Detail Sheets (one per document):

• —All transactions with calculated values
• —Document-specific summaries
• —BUY and SELL subtotals

Supported Image Formats

• —✅ JPG / JPEG
• —✅ PNG
• —✅ TIFF / TIF
• —✅ BMP

Tips for Best Results

1. 1.Image Quality: Use clear, high-resolution images (300+ DPI recommended)
2. 2.Lighting: Ensure good lighting - avoid shadows and glare
3. 3.Orientation: Images should be upright and properly aligned
4. 4.Contrast: High contrast between text and background works best
5. 5.Resolution: Larger images generally produce better OCR results

Troubleshooting

"Tesseract is not installed" error:

• —Make sure Tesseract OCR is installed (see Installation Step 2)
• —Windows users: Verify the installation path

"No trades extracted" error:

• —Check image quality and clarity
• —Ensure the image contains clear trading table data
• —Try preprocessing the image in an image editor first

"Could not read image" error:

• —Verify the file path is correct
• —Check that the image file is not corrupted
• —Supported formats are JPG, PNG, JPEG, TIFF

Inaccurate data extraction:

• —Improve image quality and resolution
• —Ensure proper lighting and contrast
• —Try different image preprocessing techniques

Example Output

Summary Sheet Output:

TRADING ANALYSIS SUMMARY

OVERALL TOTALS
Type        Total Value    Count
BUY         118.4000       30
SELL        181.6000       28
NET         -63.2000

Calculation Examples:

• —BUY 250 @ 2.75 → (250÷250) × 2.75 = 2.75
• —SELL 500 @ 3.30 → (500÷250) × 3.30 = 6.60
• —BUY 1000 @ 2.35 → (1000÷250) × 2.35 = 9.40

Performance

• —Single image: ~2-5 seconds
• —10 images: ~20-50 seconds
• —Processing time depends on image resolution and system performance

Advanced Usage

Custom Image Processing

Modify preprocessing parameters in the script:

def preprocess_image(self, image_path):
    # Adjust threshold value (150 by default)
    _, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)
    
    # Adjust denoising strength (h=10 by default)
    denoised = cv2.fastNlMeansDenoising(thresh, h=10)

Custom Output Path

extractor.generate_excel("/custom/path/output.xlsx")

License

This script is provided as-is for personal and commercial use.

Support

For issues or improvements, check:

• —Tesseract documentation: https://github.com/tesseract-ocr/tesseract
• —OpenPyXL documentation: https://openpyxl.readthedocs.io/
• —Python OCR guides: https://realpython.com/pytesseract-ocr-python/

Version: 1.0 Last Updated: 2026-05-22 Python Version: 3.7+