Update README.md

README.md

@@ -11,4 +11,34 @@ CNN with 3 convolutional layers, max pooling, and 2 dense layers
 Trained on custom stock chart image dataset
 Binary classification (bullish vs bearish)
 datasets:
-Custom stock chart image dataset (train/val/test splits)
+Custom stock chart image dataset (train/val/test splits)
+
+Model Overview
+This Convolutional Neural Network (CNN) classifies stock chart images as bullish or bearish, using a binary classification approach to predict market trends based on visual patterns.
+
+Model Details
+Architecture: Sequential CNN with 3 Convolutional layers, MaxPooling, Flatten, and 2 Dense layers
+Input: RGB images (150x150 pixels)
+Output: Binary classification (0 for bearish, 1 for bullish)
+Training Data: Custom dataset of stock chart images, split into train/validation/test sets
+Metrics: Accuracy, Precision, Recall, F1-Score
+
+Intended Use
+Primary Use: Automated classification of stock chart patterns
+Intended Users: Financial analysts, traders, automated trading systems
+Out-of-Scope Uses: Real-time trading decisions without human oversight, long-term market predictions
+Performance and Limitations
+Metrics: Accuracy: 0.57
+
+Limitations:
+Binary classification only
+May not capture complex market dynamics
+Performance depends on training data quality
+Ethical Considerations
+Potential for dataset-based biases
+Not to be used as sole basis for financial decisions
+May perpetuate existing market trends
+Caveats and Recommendations
+Regular retraining with recent data recommended
+Use alongside other analysis tools and human expertise
+Consider expanding to multi-class classification