Python libraries for financial calculation

Python is a powerful programming language that is widely used in the financial industry for data analysis, modeling, and automation. There are many Python libraries that provide useful tools and functionality for financial calculations, and in this article, we will introduce seven of the most popular and useful ones.

7 most useful Python libraries for financial calculation

1. 1. PyAlgoTrade: PyAlgoTrade is a Python library for backtesting and live trading of financial strategies. It allows users to test their trading ideas and algorithms against historical data, and then implement them in live trading. PyAlgoTrade provides a wide range of tools for analyzing and visualizing the performance of trading strategies, including technical indicators, charting, and risk management tools.

1. 1. Pyfolio: Pyfolio is a Python library for performance and risk analysis of financial portfolios. It provides tools for calculating portfolio returns, risk metrics, and performance attribution, as well as visualizing the performance and risk characteristics of portfolios. Pyfolio is particularly useful for portfolio managers and analysts who want to track and analyze the performance of their portfolios over time.

1. 1. Zipline: Zipline is an open-source Python library for building and executing trading strategies. It provides a wide range of tools for backtesting and live trading, including technical indicators, event-based rules, and order management. Zipline is particularly popular among quant traders and financial engineers who want to build and test complex trading strategies.

1. 1. SciPy: SciPy is a Python library for scientific and technical computing. It provides a wide range of tools for numerical and statistical analysis, optimization, and signal processing. SciPy is particularly useful for financial analysts and data scientists who need to perform complex calculations and statistical analysis on financial data.

1. 1. Scikit-learn: Scikit-learn is a Python library for machine learning and data analysis. It provides a wide range of tools for supervised and unsupervised learning, including classification, regression, clustering, and dimensionality reduction. Scikit-learn is particularly useful for financial analysts and data scientists who want to build predictive models and forecast financial markets.

1. 1. Finmarketpy: Finmarketpy is a Python library for financial market data analysis and modeling. It provides tools for fetching and analyzing financial market data from various sources, including stock prices, options, futures, and FX rates. Finmarketpy is particularly useful for financial analysts and data scientists who want to analyze and model financial markets and securities.

1. NumPy: NumPy is a Python library for scientific computing. It provides tools for numerical and statistical analysis, including linear algebra, random number generation, and fast array operations. NumPy is a fundamental library for many other scientific and financial Python libraries, and it is essential for anyone working with numerical data in Python.

Conclusion 

In conclusion, Python is a powerful programming language that provides a wide range of libraries for financial calculation and analysis. These seven libraries – PyAlgoTrade, Pyfolio, Zipline, SciPy, Scikit-learn, Finmarketpy, and NumPy – are some of the most popular and useful tools for financial professionals working with Python.

Example code:

from pyalgotrade import strategy
from pyalgotrade.technical import ma
from pyalgotrade.technical import cross

class SMATrategy(strategy.BacktestingStrategy):
    def __init__(self, feed, instrument, smaPeriod):
        super().__init__(feed)
        self.__instrument = instrument
        self.__position = None
        # We'll use adjusted close values instead of regular close values.
        self.setUseAdjustedValues(True)
        self.__prices = feed[instrument].getPriceDataSeries()
        self.__sma = ma.SMA(self.__prices, smaPeriod)

    def onEnterOk(self, position):
        execInfo = position.getEntryOrder().getExecutionInfo()
        self.info("BUY at $%.2f" % (execInfo.getPrice()))

    def onEnterCanceled(self, position):
        self.__position = None

    def onExitOk(self, position):
        execInfo = position.getExitOrder().getExecutionInfo()
        self.info("SELL at $%.2f" % (execInfo.getPrice()))
        self.__position = None

    def onExitCanceled(self, position):
        # If the exit was canceled, re-submit it.
        self.__position.exitMarket()

    def onBars(self, bars):
        # If a position was not opened, check if we should enter a long position.
        if self.__position is None:
            if cross.cross_above(self.__prices, self.__sma) > 0:
                self.__position = self.enterLong(self.__instrument, 10, True)
        # Check if we have to exit the position.
        elif not self.__position.exitActive() and cross.cross_below(self.__prices, self.__sma) > 0:
            self.__position.exitMarket()

• • https://gbeced.github.io/pyalgotrade/

• • https://github.com/quantopian/pyfolio

• • https://zipline.ml4trading.io/

• • https://scipy.org/

• • https://scikit-learn.org/stable/

• • https://github.com/cuemacro/finmarketpy

• • https://numpy.org/