Modules

Module Documentation

correlation.py

This script calculates financial correlations and stores the results in a PostgreSQL database.

Modules:

psycopg2: For connecting to and interacting with a PostgreSQL database.
pandas: For handling and processing tabular data.
dotenv: For loading environment variables from a .env file.
os: For accessing environment variables.
numpy: For numerical operations.

Environment Variables:

POSTGRES_USER: PostgreSQL username (default: ‘myuser’).
POSTGRES_PASSWORD: PostgreSQL password (default: ‘mypassword’).
POSTGRES_DB: PostgreSQL database name (default: ‘mydatabase’).
POSTGRES_HOST: PostgreSQL host (default: ‘localhost’).
POSTGRES_PORT: PostgreSQL port (default: ‘5432’).

Functions:

fetch_data(query): Fetches data from the PostgreSQL database based on the provided SQL query.
calculate_codes(data): Calculates codes based on percentage changes in the data.
- Code 1: Positive change.
- Code 0: Small negative or no change (-7% to 0%).
- Code -1: Large negative change (less than -7%).
calculate_correlation(revenue, market_cap, roic): Calculates the correlation between revenue, market cap, and ROIC codes.
- Correlation is incremented when all three codes are either 1 or -1.
calculate_consecutive_ones(data): Calculates the number of periods with more than two consecutive code 1 values.
main(): Main function that fetches data, processes it, and stores the results in a PostgreSQL table.

Database Schema:

Table: company_correlation
- symbol (TEXT): Primary key representing the company symbol.
- revenue (INTEGER[]): Array of calculated revenue codes.
- market_cap (INTEGER[]): Array of calculated market cap codes.
- roic (INTEGER[]): Array of calculated ROIC codes.
- correlation (FLOAT): Correlation value between revenue, market cap, and ROIC.
- consecutive_ones (INT): Number of periods with more than two consecutive code 1 values.

Usage:

Ensure the required environment variables are set in a .env file.
Run the script to calculate financial correlations and store the results in the database.

advisor.correlation.calculate_codes(data)

advisor.correlation.calculate_consecutive_ones(data)

advisor.correlation.calculate_correlation_all(revenue, market_cap, roic)

advisor.correlation.calculate_correlation_rev_cap(revenue, market_cap)

advisor.correlation.calculate_correlation_rev_roic(revenue, roic)

advisor.correlation.calculate_correlation_roic_cap(roic, market_cap)

advisor.correlation.fetch_data(query)

advisor.correlation.main()

fastapp.py

This module implements a FastAPI application for visualizing and analyzing financial data stored in a PostgreSQL database. It provides endpoints for rendering HTML pages, fetching and filtering data, and generating visualizations using Plotly. Modules and Libraries:

fastapi: Framework for building APIs.
psycopg2: PostgreSQL database adapter for Python.
pandas: Data manipulation and analysis library.
dotenv: For loading environment variables from a .env file.
numpy: Library for numerical computations.
plotly: Library for creating interactive visualizations.
uvicorn: ASGI server for running FastAPI applications. Environment Variables:
POSTGRES_USER: PostgreSQL username.
POSTGRES_PASSWORD: PostgreSQL password.
POSTGRES_DB: PostgreSQL database name.
POSTGRES_HOST: PostgreSQL host address.
POSTGRES_PORT: PostgreSQL port. Functions:
fetch_data(query): Executes a SQL query on the PostgreSQL database and returns the result as a pandas DataFrame.
categorize_company(data): Categorizes companies based on the average change in their financial data.
get_item_color(data): Determines the color for each value in a dataset based on percentage change.
generate_plot(data, title): Generates a Plotly bar chart for the given data and returns it as an HTML string. Endpoints:
GET “/”: Renders the main HTML page using Jinja2 templates.
GET “/data”: Fetches and filters financial data based on market capitalization and ROIC thresholds.
GET “/plot/{symbol}”: Generates a Plotly visualization for a specific company’s financial metric. Usage: Run the application using the command uvicorn fastapp:app –host 0.0.0.0 –port 8001.

ana_report.fastapp.categorize_company(data)

ana_report.fastapp.fetch_data(query: str, params: Tuple | None = None) → DataFrame

ana_report.fastapp.generate_plot(data, title)

ana_report.fastapp.get_data()

ana_report.fastapp.get_item_color(data)

ana_report.fastapp.get_plot(symbol: str, metric: str)

async ana_report.fastapp.get_symbol_data(symbol: str)

async ana_report.fastapp.get_symbol_market_cap_normalized(symbol: str)

async ana_report.fastapp.get_symbol_revenue(symbol: str)

async ana_report.fastapp.get_symbol_revenue_normalized(symbol: str)

async ana_report.fastapp.get_symbol_roic(symbol: str)

async ana_report.fastapp.get_symbol_roic_normalized(symbol: str)

ana_report.fastapp.normalize_array(data)

Normalize a list of numbers to the range [0, 1] using min-max normalization.

ana_report.fastapp.read_root(request: Request)

report.py

This script generates a PDF report with financial metrics for companies stored in a PostgreSQL database. It includes functionalities for fetching data, categorizing companies, generating plots, and creating a PDF report. Modules and Libraries: