https://github.com/erfaniaa/financial-dataset-generator

Financial Dataset Generator
https://github.com/erfaniaa/financial-dataset-generator

algo-trading crypto cryptocurrency dataset finance financial-data machine-learning python time-series time-series-analysis trading yfinance yfinance-api

Last synced: 16 days ago
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Financial Dataset Generator

• Host: GitHub
• URL: https://github.com/erfaniaa/financial-dataset-generator
• Owner: Erfaniaa
• License: gpl-3.0
• Created: 2022-10-31T04:32:07.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-03-03T17:24:57.000Z (8 months ago)
• Last Synced: 2024-10-13T20:41:46.684Z (about 1 month ago)
• Topics: algo-trading, crypto, cryptocurrency, dataset, finance, financial-data, machine-learning, python, time-series, time-series-analysis, trading, yfinance, yfinance-api
• Language: Python
• Homepage:
• Size: 34.2 KB
• Stars: 28
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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](https://data.nasdaq.com/) 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](https://github.com/erfaniaa/binance-futures-trading-bot)

- [Binance Spot Trading Bot](https://github.com/smzerehpoush/binance-spot-trading-bot)

- [Crypto Trading Strategy Backtester](https://github.com/Erfaniaa/crypto-trading-strategy-backtester)

## Credits

[Erfan Alimohammadi](https://github.com/Erfaniaa) and [Amir Reza Shahmiri](https://github.com/Amirrezashahmiri)