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OpenFintech

OpenFintech is a financial analysis library designed for Python developers and financial analysts. It provides powerful tools for conducting both trend following and mean reversion analyses, utilizing financial market data. This project aims to make complex financial algorithms accessible and easy to use.

Install / Use

/learn @Laurier-Fintech/OpenFintech
About this skill

Quality Score

0/100

Category

Design

Supported Platforms

Universal

README

OpenFintech

Introduction

OpenFintech is a financial analysis library designed for Python developers and financial analysts. It provides powerful tools for conducting both trend following and mean reversion analyses, utilizing financial market data. This project aims to make complex financial algorithms accessible and easy to use.

Installation

To install OpenFintech, follow these steps:

  1. Ensure you have Python installed on your system.
  2. Install the OpenFintech PyPi pckage:
    pip install OpenFintech
    
  3. Update OpenFintech if already installed:
    pip install OpenFintech --upgrade
    

Usage

Here's a quick example of how to use OpenFintech to run financial algorithms:

from OpenFintech import *

# Initialize and get data
data_acq = DataAcquisition("your-api-key")
tickerData = data_acq.requestDataFromAPI('AAPL', 'daily')

# Convert to FinancialInstrument
ticker_finInst = data_acq.convertDataToFinancialInstrument(tickerData)

# Run algorithms
tr_algo = TrendFollowing()
tr_backtest_data = tr_algo.runAlgorithmOnCandleContainer(...)
mr_algo = MeanReversion()
mr_backtest_data = mr_algo.runAlgorithmOnCandleContainer(...)

print(tr_backtest_data)
print(mr_backtest_data)

Replace "your-api-key" with your actual API key.

Components

Model.py

  • Algorithm: Base class for trading algorithms.
  • MeanReversion: Implements the mean reversion strategy.
  • TrendFollowing: Implements the trend following strategy.

Data.py

  • Candle, CandleContainer: Represent market data.
  • FinancialInstrument: Represents a financial instrument with associated market data.
  • Indicator, BollingerBands, NormalizedPrices, SMA: Various financial indicators.
  • DataAcquisition: Handles data acquisition from external sources.

Contributing

Contributions are welcome! Please read our contributing guidelines to get started.

License

This project is licensed under the Apache License - see the LICENSE file for details.

FAQs/Contact Information

For any queries, please reach out to us at team@wlufintech.com.

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View on GitHub
GitHub Stars11
CategoryDesign
Updated2mo ago
Forks2

Languages

Python

Security Score

95/100

Audited on Jan 14, 2026

No findings