README.md

metadata

license: apache-2.0

The project on GitHub : https://github.com/reuniware/CryptoForex-Trader-Framework/tree/main/CCXT_ICHIMOKU/julie_scanner

How to Use bluewenne8.py

1. Install Dependencies: Ensure you have the required libraries installed:

   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:

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:

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:

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:

   python bluewenne8.py --timeframe 1d
   

   This will fetch data for all available USDT pairs, analyze it, and save results.

2. Train Model:

   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:

   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.