Automating Financial Analysis Using Python (2024)

Financial analysis can be defined as the process of evaluating projects, businesses, budgets, and other finance-related transactions to work out their performance and appropriateness. In short, financial analysis is used to analyze whether an entity is stable, solvent, liquid, or profitable enough to warrant a monetary investment.

Understanding Financial Analysis

Financial analysis is employed to gauge economic trends, set financial policy, build long-term plans for commercial activity as well as identify projects or companies for investment. This is done through the synthesis of monetary data and numbers. A security analyst will thoroughly inspect a company’s financial statements—the earnings report , record, and income statement. Financial analysis is often conducted in both namely — finance and investment finance. One of the most common ways to analyze financial data and to calculate ratios from the data provided in the financial statements to compare against those of other companies or against the company’s own historical fulfilment.

Types of Financial Analysis

There are two sorts of financial analysis namely : fundamental analysis and technical analysis.

Fundamental Analysis

Fundamental analysis is that analysis which uses ratios gathered from data within the financial statements, like a company’s earnings per share (EPS), in order to work out with the business’s value. Using ratio analysis with a radical review of economic and financial situations surrounding the corporation , the analyst is in a position to reach an intrinsic value for safety. The utmost goal is to reach a height that an investor can compare with a security’s current price so as to assure whether the safety is undervalued or overvalued. This method of stock analysis is taken into account to be in contrast to technical analysis, which forecasts the direction of costs through an analysis of historical market data like volume and price.

Technical Analysis

Technical analysis is the analysis which uses statistical trends gathered from trading activity, like Moving Averages (MA). The most vital part of technical analysis is that it deems that a security’s price so far reflects all openly available information and instead pressurizes on the statistical analysis of price movements. Technical analysis tries to know the market sentiment behind price trends by attempting to find out patterns and trends instead of analyzing a security’s fundamental attributes.

Understanding Technical Analysis

In contrast to fundamental analysis, which attempts to scale a security’s value to support business results like sales and earnings, technical analysis prioritizes the study of price and volume. Technical analysis tools want to scrutinize the ways supply and demand for a security will affect changes in price, volume, and implied volatility.

Technical analysis generally wants to synthesize short-term trading signals from various charting tools, but also can help improve the consideration of a security’s strength or weakness respective to the broader market or one among its zones. This information helps analysts improve their overall quotation estimate.

Technical analysis is majorly used on any security with historical trading data. This includes stocks, futures, commodities, fixed-income, currencies, and other securities. In fact, technical analysis is way more widespread in commodities and forex markets where traders work exclusively in short-term price movements.

In general, technical analysts check out the subsequent broad sorts of indicators:

• Chart patterns
• Oscillators
• Moving averages
• Support and resistance levels
• Price trends
• Volume and momentum indicators

Technical Analysis vs. Fundamental Analysis

Fundamental analysis and technical analysis, the main schools of thought when it involves approaching the markets, are at opposite ends of the spectrum. Both methods are used for examining and predicting the future trends available prices and all investment plans or convictions, both have their advice and adversaries. Fundamental analysis may be a method of estimating securities by attempting to live the inherent value of a stock. Fundamental analysts study everything from the general economy and industry situation to the economic situation and also management of companies. Earnings, expenses, assets, and liabilities are all essential attributes to fundamental analysts.

Python

Python came into existence in the 1990s. Originally, it had been one among Guido van Rossum’s side projects. At that point, none of us would have said that this “side project” will rise one day to be one among the leading programming languages ever. The main reason why this language has achieved such far-fetched popularity is that, from the very start, the main idea behind Python was to make a language that might lower the hurdle to programming. Python was always claimed to be a programming language that’s entirely focused on the developer’s experience. It had been claimed to be so effortless that even school kids could learn it and write fully-fledged code and applications. Truly, Python was destined for greatness.

This language is an interactive, object-oriented programming solution that consists of all the relevant elements:

• Modules
• High-level dynamic data types
• Classes
• Exceptions
• Dynamic typing

Python for finance

First of all, let’s think about why Python is good for finance? There are a few reasons:

Python handles most elements automatically

Let’s think about memory management. Many of the lower-level programming languages require a deep understanding of the difficulties of how the computer’s memory is laid out and released, additionally to the time spent and features of code required to handle tasks. Python handles many of those details automatically. This way, programmers can narrow-down in their goals.

If you run a financial or monetary company and are interested in how Python is employed in finance, have a look at these packages that are mainly used for finance and monetary modeling:

1. NumPy is a critical open-source package used for scientific and mathematical computing in Python
2. SciPy comes in picture when a repository consisting of more advanced statistical tools and operations is needed
3. Pandas package is designed primarily for financial analysis and model building
4. Statsmodels package is built on SciPy and Pandas and implements more advanced testing of different statistical models
5. (If you work in the financial industry, Quandl is one of your mandatory packages). It gives access to an amazing collection of economic, financial, and market data that comes from banks, governments, and other international organizations
6. Matplotlib provides data visualization, which is particularly important when it comes to financial modeling

PYTHON ANALYTICS TOOLS

Python is extensively used in quantitative finance and monetary. That’s because this approach consists of solutions that processes and analyzes large datasets and financial big data. Some of the above mentioned libraries (such as Pandas) simplify the procedure of creating complicated statistical calculations, and other libraries (like Matplotlib) help with data visualization. Also, many Python-based solutions make use of machine learning (ML) algorithms that are extensively helpful in financial data analytics and predictive analytics.

The script makes use of standard Python packages as well as the yfinance API, which is used to download the S&P 500 stock prices. The API is free to use and it’s public, meaning that the user does not need an individual API key.

In the first section of the code, the user needs to define a variable called depth to define the status of detail of the analysis. This can either be calibrate to sector or sub_sector. In the last case, the user has to also mention the filter, that is the sector of interest.

All the other inputs such as the list of S&P 500 stocks and the date to compare the current market performance against (beginning of 2020) are automatically set.

At this point, the data are downloaded and all the calculations are performed. As specified, the finance API is used to gather the financial or monetary data.

The end section is where all plots are created.