Building a Profit and Loss (PNL) Report with Atoti: A Step-by-Step Guide

Building a Profit and Loss (PNL) Report with Atoti: A Step-by-Step Guide

Introduction:

Profit and Loss (PNL) reporting is a critical function for financial institutions, providing insights into the financial performance of trading portfolios, investments, and other financial instruments. Accurate and effective PNL reporting is essential for decision-making, risk management, and financial analysis. Atoti, a Python-based data analytics library, offers a powerful and flexible solution for building PNL reports. In this blog, we will provide a step-by-step guide on how to build a PNL report using Atoti, showcasing its features and capabilities.

What is Atoti?

Atoti is a Python-based data analytics library that provides a high-performance, in-memory analytical platform for data analysis, visualization, and reporting. It offers a wide range of features, including multidimensional data modeling, data aggregation, calculations, and visualization, making it ideal for building financial analytics applications, including PNL reporting.

Step 1: Install and Import Atoti The first step in building a PNL report with Atoti is to install the Atoti library. Atoti can be installed using pip, the Python package manager, by running the following command:

Copy codepip install atoti

Once Atoti is installed, it can be imported into your Python script or Jupyter Notebook using the following import statement:

pythonCopy codeimport atoti as tt

Step 2: Create a Session The next step is to create a session in Atoti, which serves as the entry point for building the PNL report. The session provides the connection to the Atoti engine and allows you to create and manipulate data cubes, which are the building blocks for multidimensional data modeling in Atoti.

pythonCopy code# Create a session
session = tt.create_session()

Step 3: Load Data The PNL report requires data to be loaded into Atoti for analysis. Data can be loaded from various sources, such as CSV files, Excel files, or databases. Atoti provides a convenient way to load data using its create_store function, which allows you to define the schema of the data and specify the data source.

pythonCopy code# Load data into Atoti
store = session.read_csv("path/to/data.csv", store_name="my_store")

Step 4: Define Data Cubes Data cubes are the multidimensional data structures in Atoti that allow you to analyze data from multiple dimensions, such as time, product, and geography. To build a PNL report, you need to define data cubes that represent the relevant dimensions and measures for your report. Atoti provides a Cube class that allows you to define data cubes and their dimensions and measures.

pythonCopy code# Define data cubes
cube = session.create_cube(store, name="my_cube")

Step 5: Define Dimensions Dimensions represent the different attributes or characteristics of your data, such as time, product, and geography. You can define dimensions in Atoti using the Dimension class, specifying the dimension type and hierarchy.

pythonCopy code# Define time dimension
time = cube.dimension("Time", hierarchy="Date")

# Define product dimension
product = cube.dimension("Product", hierarchy=True)

Step 6: Define Measures Measures represent the quantitative values that you want to analyze, such as revenue, cost, and profit. You can define measures in Atoti using the Measure class, specifying the measure type and the aggregation function.

pythonCopy code# Define revenue measure
revenue = cube.measure("Revenue", "SUM", format="currency")

# Define cost measure
cost = cube.measure("Cost", "SUM", format="currency")

# Define profit measure