Top Ten Financial Functions in Python

If you didn’t know Python has a host of function that cover most domains. Pandas is the premier data analysist and data manipulation library in Python. It is an incredibly versatile tool for financial analysis. With its rich set of functions tailored for data wrangling and analysis, it has become the go-to library for financial data science in Python. Whether you’re a seasoned quant or a finance enthusiast beginning your Python journey, Pandas has got you covered. However, this tutorial won’t be limited to a single library we will explore regression and forecasting which require different libraries.

For forecasting and more indept analysis we may need to lean on a few other libraries.

Lets explore the Python Essentials for Finance

1. Reading in Financial Data

Whether your data in a Excel workbooks, CSVs or PDF, Pandas can be used to readin in the data into a notebook or environment for analysis.

import pandas as pd

# Reading stock data from a CSV file
data = pd.read_csv('stock_data.csv')
data  = pd.read_excel('stock_data.xlsx')
data = pd.read_clipboard()

As you can see we can use Pandas to ingest a hot of different data format easily. Once this data loaded, we can easily use Pandas to clean, analyze and visualize. We will delve into this in a bit.

2. Calculating Returns

Calculating returns is also fairly simple with Pandas. Of course with financial data, this is going to be the most important parts of any financial analysis. We can look at calculating returns by analyzing the line items in row of monthly data.

data['Returns'] = data['Close'].pct_change()

3. Moving Averages and Rolling Operations

Moving averages, whether simple or exponential, are frequently used in financial analysis. Here’s how you can compute them with Python Pandas. You can see by changing the window parameter we are able to increase the moving average length under evaluation.

# Simple Moving Average (SMA)
data['SMA_50'] = data['Close'].rolling(window=50).mean()

# Exponential Moving Average (EMA)
data['EMA_50'] = data['Close'].ewm(span=50, adjust=False).mean()

4. Shifting Data LAG and LEAD indicators

Lagging or leading your data can be achieved with the shift() function. This is especially useful for calculating day-over-day changes. This is the primary operation for percent change.

data['Previous Close'] = data['Close'].shift(1)

5. Correlation and Covariance

Analyzing the relationship between two financial instruments is crucial. Pandas provides direct methods for this.

# Correlation
correlation = data['Close'].corr(data['Volume'])

# Covariance
covariance = data['Close'].cov(data['Volume'])

6. Grouping and Aggregation

If you have data spanning multiple years and you wish to analyze yearly performance, the groupby() function is invaluable. Those familiar with SQL know this function extremally well. The group by function aggregates the data into an object. Then we can pass new function such as mean, sum or utlize the agg function to for more complex calculation.

mean_annual_data = data.groupby(data['year']).mean()
summed_annual_data = data.groupby(data['year']).sum()
annual_data = data.groupby(data['year']).agg({'Close': 'last'})

5. Handling Missing Data

Financial datasets might have missing values. Pandas provides methods to handle them with ease.

# Forward fill

# Backward fill

# Drop missing values

7. Pivot Tables

Pivot tables are useful for summarizing data. For example, if you have data on multiple assets and you want to see a summary of the average return by asset, you can use the pivot_table() function.

pivot_table = data.pivot_table(values='Returns', index='Date', columns='Asset')

8. Linear Regression for Predictive Analysis

Linear regression is a basic and commonly used type of predictive analysis. Although it has its limitations, linear regression is a good starting point for financial forecasting. We can use Scikit learn library.

from sklearn.linear_model import LinearRegression

# Features and target
X = data.index.values.reshape(-1, 1)
y = data['Close']

# Model and forecast
model = LinearRegression(), y)
forecast = model.predict(X)

9. Exponential Smoothing

Exponential Smoothing is a time series forecasting method for univariate data that can be used to forecast data points by considering the trend and seasonality of the data. It’s also available in the Statsmodels library.

from statsmodels.tsa.holtwinters import ExponentialSmoothing

model = ExponentialSmoothing(data['Close'])
model_fit =
forecast = model_fit.forecast(steps=10)

10. AutoRegressive Integrated Moving Average (ARIMA)

ARIMA is a forecasting technique that utilizes the inherent characteristics of the data like seasonality, trend, and noise. It’s available in the Statsmodels library.

from statsmodels.tsa.arima.model import ARIMA

model = ARIMA(data['Close'], order=(5,1,0))
model_fit =
forecast = model_fit.forecast(steps=10)

Gaelim Holland

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