Technical Documentation: Financial Analysis Function Using NodeJS and Python
The performAnalysis function takes raw financial data and performs a series of calculations to derive key financial metrics. It leverages the capabilities of Python's pandas library for data manipulation, called from a Node.js environment.
Function Signature:
performAnalysis(financialData: string, stockPrice: number, sharesOutstanding: number): Promise<object>Parameters:
financialData: A string of raw financial data that will be converted into a pandas DataFrame using a Python script.stockPrice: The current price of a single share of stock.sharesOutstanding: The total number of outstanding shares.
Returns:
A Promise that resolves to an object containing the following calculated financial metrics:
revenues: Total revenues.expenses: Total operating expenses.netIncome: Net income, calculated as revenues minus expenses.grossMargin: Gross margin as a percentage, calculated as (revenues - expenses) / revenues.ebitda: Earnings before interest, taxes, depreciation, and amortization.earningsPerShare: Earnings per share, calculated as net income divided by outstanding shares.peRatio: Price to earnings ratio, calculated as stock price divided by earnings per share.
Description:
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Data Conversion: The function first invokes a Python script ('convert_to_dataframe.py') via PythonShell to convert the raw financial data into a pandas DataFrame. The DataFrame is then serialized to JSON and returned back to the Node.js environment.
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Financial Calculations: Using the DataFrame data, the function calculates several key financial metrics. These calculations leverage standard mathematical operations.
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Formatting Results: The results are then formatted into a JavaScript object. Each calculated metric is rounded to two decimal places, and percentages (like gross margin) are expressed as such.
This function stands as an example of how to bridge Node.js and Python for efficient data analysis, combining the strengths of JavaScript's asynchronous capabilities and Python's data manipulation power.