Financial Dataset Generator

Easy-to-use dataset generator for applying machine learning on financial markets

Features

• You can run it fast, and it is easy to use.
• There are no complexities and no database usage in this project. Even dependencies are a few.
• It is easy to modify and customize.
• This project generates practical datasets for data scientists.
• You can read the code for educational purposes.

Run

1. Clone the repository.
2. Run pip3 install -r requirements.txt.
3. Put your Nasdaq Data Link API key in the API_KEY file.
4. Run python3 main.py.

This will generate train set and test set for you.

Config

For the configuration, you can:

• Change config.py constants.
• Define new indicators in indicators.py.

Config.py Description

• PAIR_NAMES_LIST_WITH_SOURCE: What's your machine learning model input?
• TARGET_PAIR_NAME_WITH_SOURCE: What's your machine learning model output?
• SMA_LENGTHS_LIST: Do you want to generate a dataset with some moving averages?
• APPLY_FLIP_AUGMENTATION and APPLY_NOISE_AUGMENTATION: Using data augmentations
• AUGMENTATION_NOISE_INTERVAL: Set the amount of augmentation noise
• TRAIN_DATASET_NEW_SIZE_COEFFICIENT: How much augmented data do you want?
• START_TIME and END_TIME: The time interval for the dataset
• FORECAST_DAYS: How many days is your target?
• USE_WMA_FOR_FORECAST_DAYS: Do you want to use linear weighted moving average for your target?
• NUMBER_OF_CANDLES: Number of candles your machine learning model needs as its input
• TRAIN_CSV_FILE_PATH, TEST_CSV_FILE_PATH, and PREDICT_CSV_FILE_PATH: Output CSV file paths
• TEST_SET_SIZE_RATIO: Test set size to whole dataset size ratio
• CSV_DELIMITER: The delimiter in every generated CSV file
• API_KEY_FILE_PATH: Path to the Nasdaq Data Link API key file

See Also

• Binance Futures Trading Bot
• Binance Spot Trading Bot
• Crypto Trading Strategy Backtester

Credits

Erfan Alimohammadi and Amir Reza Shahmiri