Build real-world financial dashboards, portfolio analytics, and automated reporting workflows using Haijun's Excel, PowerPoint, and PDF tracks.

💡 Real-world Impact: These are the same Tracks that power Haijun Creates Files, enabling Haijun to create professional financial documents directly in the interface.

What you'll learn:

Automate multi-format reporting pipelines

#Table of Contents

Portfolio Analytics Excel

Investment Committee Deck

Use Case 3: Automated Reporting Pipeline

#Prerequisites

;financial"

OUTPUT_DIR.mkdir(parents=True, exist_ok=True)

DATA_DIR = Path.cwd().parent / "sample_data"

print("✓ Environment configured")

print(f"✓ Output directory: {OUTPUT_DIR}")

print(f"✓ Data directory: {DATA_DIR}")

#Load Financial Data

def format_financial_value(value, is_currency=True, decimals=0):

"""Format financial values for display."""

if is_currency:

return f"${value:,.{decimals}f}"

else:

return f"{value:,.{decimals}f}"

print("✓ Helper functions defined")

#2. Use Case 1: Financial Dashboard Creation

print("This creates a focused 2-sheet portfolio analysis optimized for the Tracks API.")

print("\n⏱️ Generation time: 1-2 minutes\n")

Prepare portfolio data for the prompt

top_holdings = portfolio_df.nlargest(5, "market_value")

sector_allocation = portfolio_data["sector_allocation"]

portfolio_excel_prompt = f"""

Create a portfolio analysis Excel workbook with 2 sheets:

Sheet 1 - "Portfolio Overview":

Create a comprehensive holdings and performance table:

Section 1 - Holdings (top of sheet):

{portfolio_df[["ticker", "name", "shares", "current_price", "market_value", "unrealized_gain", "allocation_percent"]].head(10).to_string()}

Section 2 - Portfolio Summary:

• Total portfolio value: ${portfolio_data["total_value"]:,.2f}

• Total unrealized gain: ${portfolio_df["unrealized_gain"].sum():,.2f}

• Total Return: {portfolio_data["performance_metrics"]["total_return_percent"]:.1f}%

• YTD Return: {portfolio_data["performance_metrics"]["year_to_date_return"]:.1f}%

• Sharpe Ratio: {portfolio_data["performance_metrics"]["sharpe_ratio"]:.2f}

• Portfolio Beta: {portfolio_data["performance_metrics"]["beta"]:.2f}

Apply conditional formatting: green for gains, red for losses.

Add a bar chart showing top 5 holdings by value.

Sheet 2 - "Sector Analysis & Risk":

Create sector allocation and risk metrics:

Section 1 - Sector Allocation:

{json.dumps(sector_allocation, indent=2)}

Include a pie chart of sector allocation.

Section 2 - Key Risk Metrics:

• Portfolio Beta: {portfolio_data["performance_metrics"]["beta"]:.2f}

• Standard Deviation: {portfolio_data["performance_metrics"]["standard_deviation"]:.1f}%

• Value at Risk (95%): $62,500

• Maximum Drawdown: -12.3%

• Sharpe Ratio: {portfolio_data["performance_metrics"]["sharpe_ratio"]:.2f}

Section 3 - Rebalancing Recommendations:

• Reduce Technology from 20% to 18%

• Increase Healthcare from 8.7% to 10%

• Maintain current diversification

Apply professional formatting with clear sections and headers.

"""

Create portfolio analysis Excel

portfolio_response, portfolio_results = create_skills_message(

client,

portfolio_excel_prompt,

[{"type": "juglow", "skill_id": "xlsx", "version": "latest"}],

prefix="portfolio_analysis_",

)

print("\n" + "=" * 60)

print_download_summary(portfolio_results)

if len(portfolio_results) > 0 and portfolio_results[0]["success"]:

print("\n✅ Portfolio analysis Excel created successfully!")

#3.2 Investment Committee Presentation