Kaylah072001/Stock_Graphs_Dataset

Dataset Card for Stock Graph Data

Bullish and Bearish Stock Data Graphs

Dataset Details

Dataset Description

This dataset can be valuable for:

• Training machine learning models to automatically identify market trends
• Educating finance students on pattern recognition
• Assisting traders in making informed decisions
• Conducting research on stock market behavior and trend analysis2

Uses

Training machine learning models: The primary purpose is likely to train image classification models to automatically identify bullish vs bearish stock patterns. This could be used to develop automated trading algorithms or analysis tools. Pattern recognition: The dataset allows models to learn visual features that distinguish upward vs downward price trends in stock charts. This trains the model to recognize key technical analysis patterns. Backtesting strategies: By applying trained models to historical stock chart data, traders can backtest and evaluate potential trading strategies based on bullish/bearish pattern recognition. Market sentiment analysis: Aggregating predictions across many stocks could provide insights into overall market sentiment and trends. Educational purposes: The labeled dataset could be used to teach students and new traders how to visually identify bullish and bearish patterns in stock charts. Benchmark dataset: It may serve as a standardized dataset for comparing different machine learning approaches to stock chart pattern recognition. Feature extraction: Beyond binary classification, more advanced models could potentially extract specific features from the charts that correlate with bullish or bearish outcomes. -->

Dataset Structure

Data Structure Image Data Format: Likely PNG or JPEG images Size: Possibly standardized (e.g., 150x150 pixels) Channels: 3 (RGB) or 1 (grayscale) Content: Candlestick charts representing stock price movements

Labels: Binary classification: 0 for bearish, 1 for bullish Stored alongside corresponding images

Dataset Splits: Training set: Largest portion, used for model training Validation set: Smaller portion, used for hyperparameter tuning Test set: Held-out data for final model evaluation

Source Data

yfinance open source API