Add repository overview

README.md

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+# Detect Chart Pattern
+
+This repository contains tools and data for detecting classical chart patterns from OHLCV time series.
+
+## Repository structure
+
+- `algo_dataset/` – Automatically generated CSV datasets for six chart patterns.
+- `create_dataset/` – Python package for creating synthetic chart pattern datasets.
+- `backtest/` – Notebook demonstrating model predictions on real data.
+- `train/` – Notebook for training a CNN‑LSTM classifier.
+- `save_model/` – Pretrained model (`chart_pattern_model.h5`).
+- `notepad.txt` – Notes about external Kaggle dataset and pattern codes.
+
+## Supported patterns
+
+1. Ascending Triangle
+2. Ascending Wedge
+3. Descending Triangle
+4. Descending Wedge
+5. Double Top
+6. Double Bottom
+
+## Dataset generation
+
+Synthetic datasets are produced using the utilities in `create_dataset`. Example usage:
+
+```python
+from create_dataset import create_dataset
+create_dataset(generation_count=1000, n_min=50, n_max=120)
+```
+
+This generates random OHLCV sequences for each pattern type and saves them under `algo_dataset/`.
+
+## Training
+
+The notebook `train/train.ipynb` loads the generated CSV files, constructs sliding windows and trains a CNN‑LSTM model to classify the six patterns. The resulting model can be found in `save_model/chart_pattern_model.h5`.
+
+Required libraries include:
+
+- TensorFlow / Keras
+- pandas
+- numpy
+- scikit-learn
+- mplfinance
+
+## Backtesting
+
+`backtest/backtest.ipynb` shows how to load the trained model and run predictions on OHLCV data fetched from `pykrx`. The notebook visualizes each segment with the predicted pattern and probability.
+
+## Notes
+
+The repository includes over 600 MB of synthetic CSV data under `algo_dataset`. Original Kaggle data references are listed in `notepad.txt`.
+