Stock Research Report Template: Live Stock Dashboard

Purpose of the stock research report template

This Stock research report template provides a dynamic, single-sheet dashboard for comprehensive equity analysis. It consolidates live pricing, historical financials, technical indicators, and recent news into one unified view. By utilizing automated Python data fetches, the template eliminates manual data gathering and keeps your equity research up to date.

How the dynamic dashboard works

The entire sheet operates from a single user-editable input cell located at B2, which contains a stock ticker symbol. Eight distinct Python code cells connect to this input. This setup establishes a strict dependency chain. When the ticker changes, all sections instantly re-execute.

Behind the scenes, the Python code utilizes dictionary comprehensions to map financial statement tags. This allows for fast, key-based data lookups. Additionally, the template applies automated formatting to monetary values by appending billion and million suffixes for readability.

Core template components

Company profile and valuation metrics

The top tier of the dashboard surfaces company metadata and real-time price data in a clean two-column DataFrame. To ensure data availability, the Python logic fetches 52-week price history and financial statements using a year-fallback loop.

The template automatically computes several derived valuation metrics:

• Computes P/E ratio, P/B ratio, ROE, and ROA.
• Estimates market capitalization by inferring shares from net income and diluted EPS.
• Calculates financial health indicators such as gross, operating, and net margins alongside liquidity ratios.
• Computes free cash flow, utilizing a fallback calculation of operating cash flow plus capital expenditure if direct data is unavailable.

Price charts and technical indicators

The second visual tier contains interactive visualizations built with Plotly. It generates a candlestick chart displaying one year of adjusted daily prices and overlays 20-day and 50-day simple moving averages directly onto the chart. This section also includes a dedicated volume subplot featuring color-coded bars.

Next to the price chart, a two-panel technical indicator subplot renders RSI and MACD data. The RSI chart plots static overbought and oversold threshold lines at 70 and 30. The MACD panel dynamically detects column names for the MACD line, signal line, and histogram to ensure accurate rendering.

Revenue trends and quarterly financials

The third tier focuses on historical performance. A Python script loops through recent fiscal years to build a grouped bar chart of revenue, gross profit, and net income.

For a more granular view, another cell iterates through up to eight recent quarters to extract key income statement line items. This section employs tag-name fallback chains to guarantee data retrieval if primary tags are missing. The result is a formatted six-column DataFrame for rapid quarterly financial review.

Recent company news

The final tier retrieves company-specific news articles published within the last 60 days. To maintain a clean dashboard layout, the output is filtered to display only the top ten most recent articles. All article fields are truncated to 120 characters.

How to interact with the dashboard

Using this template requires minimal setup:

• Locate the designated ticker input cell at B2.
• Replace the default ticker with any valid stock symbol.
• Wait briefly as the Python dependency chain updates all four visual tiers automatically.
• Reference the validation message on B2 for specific usage instructions.
• Review the static disclaimer row regarding financial data usage.

Who this stock research report template is for

This workspace is designed for professionals and investors who need an automated, code-driven tool for equity analysis.

• Financial analysts conducting rapid fundamental and technical evaluations of public companies.
• Retail investors seeking an automated tear sheet for individual equities.
• Data analysts looking for practical examples of Python-based financial data retrieval and visualization.
• Portfolio managers requiring a standardized, repeatable format for equity research.

Use Quadratic to create a live stock dashboard for comprehensive equity analysis

• Automate fetches for live pricing, historical financials, and recent news with Python.
• Update the entire dashboard instantly by changing a single stock ticker symbol.
• Automatically compute derived valuation metrics like P/E, ROE, and free cash flow.
• Visualize interactive candlestick charts, moving averages, RSI, and MACD indicators.
• Review detailed revenue trends and granular quarterly income statement line items.
• Access the top ten most recent company-specific news articles within your report.

Duplicate this stock research report template file