Update README.md

README.md

@@ -1,3 +1,97 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+Thanks for letting me know! Here's a concise guide on how to use `bluewenne8.py`, incorporating its specific name and usage scenarios:
+
+### How to Use `bluewenne8.py`
+
+1. **Install Dependencies**:
+   Ensure you have the required libraries installed:
+   ```sh
+   pip install ccxt pandas scikit-learn joblib argparse pytz
+   ```
+
+2. **Script Overview**:
+   `bluewenne8.py` performs cryptocurrency data analysis, trains a machine learning model, and makes predictions. 
+
+### Command-Line Usage
+
+You run the script from the command line with various arguments to control its behavior:
+
+#### 1. **Fetch Data and Analyze Symbols**
+This command will fetch data for symbols, analyze the greatest candles, and save the results:
+
+```sh
+python bluewenne8.py --timeframe 1d
+```
+
+- **`--timeframe`**: Required. Defines the candlestick timeframe, e.g., '1d' for daily candles, '1h' for hourly candles.
+
+#### 2. **Train the Model**
+If you want to train a model on historical data, use the following command:
+
+```sh
+python bluewenne8.py --timeframe 1d --train
+```
+
+- **`--train`**: Optional. If included, the script will train a machine learning model using existing historical data.
+
+#### 3. **Use Existing Model to Make Predictions**
+To make predictions using an existing model:
+
+```sh
+python bluewenne8.py --timeframe 1d --use-existing
+```
+
+- **`--use-existing`**: Optional. If included, the script will use the pre-trained model to make predictions based on existing historical data.
+
+### Detailed Steps for Each Mode
+
+#### A. **Fetch Data and Analyze Symbols**
+
+1. **Fetch Markets**: The script retrieves a list of available markets from the Binance exchange.
+2. **Fetch OHLCV Data**: Collects candlestick data for each symbol based on the provided timeframe.
+3. **Save Data**: Saves the fetched historical data to CSV files in the `downloaded_history` directory.
+4. **Analyze Symbols**: Identifies and logs the greatest candle for each symbol, including current prices.
+
+#### B. **Train the Model**
+
+1. **Load Historical Data**: Reads data from CSV files in the `downloaded_history` directory.
+2. **Preprocess Data**: Prepares data by formatting timestamps, setting indices, and splitting features and target variables.
+3. **Train Model**: Uses a RandomForestRegressor to train on the historical data.
+4. **Save Model**: Saves the trained model and scaler to disk (`model.pkl` and `scaler.pkl`).
+
+#### C. **Use Existing Model to Make Predictions**
+
+1. **Load Model and Data**: Loads the saved model and scaler, and reads historical data.
+2. **Predict Next Candle**: Uses the trained model to predict future price movements based on the latest data.
+3. **Save Predictions**: Writes predictions to a results file.
+
+### File Structure and Directories
+
+- **`downloaded_history/`**: Directory where historical data CSV files are saved.
+- **`scan_results_bluewenne8/`**: Directory where results and prediction files are saved. Created based on the script name.
+- **Model Files**: `model.pkl` and `scaler.pkl` are saved in the script's working directory when training.
+
+### Example Use Case
+
+1. **Fetch and Analyze Data**:
+   ```sh
+   python bluewenne8.py --timeframe 1d
+   ```
+   This will fetch data for all available USDT pairs, analyze it, and save results.
+
+2. **Train Model**:
+   ```sh
+   python bluewenne8.py --timeframe 1d --train
+   ```
+   This will train the model on data from files matching the filter `BTC_USDT`.
+
+3. **Predict with Existing Model**:
+   ```sh
+   python bluewenne8.py --timeframe 1d --use-existing
+   ```
+   This uses the pre-trained model to make predictions based on the latest historical data.
+
+Feel free to adjust the timeframe and filters as needed for your specific analysis or training tasks.