Task 4: Chart Generation - Detailed Workflow

This document provides step-by-step instructions for executing Task 4 (Chart Generation) of the initiating-coverage skill.

Task Overview

Purpose: Generate 25-35 professional financial charts for the report.

Prerequisites: ⚠️ Verify before starting

⚠️ CRITICAL: DO NOT START THIS TASK UNLESS TASKS 1, 2, AND 3 ARE COMPLETE

This task requires outputs from all three previous tasks. Starting without them will result in incomplete charts.

IF ANY OF TASKS 1, 2, OR 3 ARE NOT COMPLETE: Stop immediately and inform the user which tasks need to be completed first. The specific requirements are:

Do not attempt to create placeholder charts or skip charts due to missing data.

Output: 25-35 Professional Chart Files (PNG/JPG, 300 DPI)

Input Verification

BEFORE STARTING - CHECK ALL PREREQUISITES:

Task 1 Verification (Company Research)

Task 2 Verification (Financial Model)

Task 3 Verification (Valuation)

External Data Verification

IF ANY VERIFICATION FAILS:

Chart Requirements: 25 Required + 10 Optional

IMPORTANT: Task 5 (Report Assembly) will embed ALL charts created throughout the report. The report requires dense visual content (1 chart every 200-300 words), so create comprehensive chart coverage.

4 MANDATORY Charts (Non-Negotiable) ⭐

These 4 charts are critical visualizations that MUST be present:

  1. chart_03: Revenue by Product/Segment - Stacked Area Chart ⭐
  2. chart_04: Revenue by Geography - Stacked Bar Chart ⭐
  3. chart_28: DCF Sensitivity Analysis - 2-Way Heatmap ⭐
  4. chart_32: Valuation Football Field - Horizontal Bar Chart ⭐

25 REQUIRED Charts (Complete Set)

Create all 25 of these charts. Each has a specific purpose in Task 5:

Investment Summary Section (1 chart):

Financial Performance Section (6 charts):

Company 101 Section (7 charts):

Competitive & Market Section (2 charts):

Scenario Analysis Section (2 charts):

Valuation Section (7 charts):

Total: 25 Required Charts

10 OPTIONAL Charts (For 30-35 Range)

Add these for greater visual density and storytelling (reach 26-35 total):

Total Range: 25-35 Charts (25 required + 0-10 optional)

Data Source Mapping for Required Charts

Understanding where each chart's data comes from:

From Task 1 (Company Research) - 9 charts

From Task 2 (Financial Model) - 8 charts

From Task 3 (Valuation) - 6 charts

From External Sources - 2 charts

IMPORTANT: Require ALL three tasks (1, 2, 3) complete PLUS external data access to create all 25 required charts.

Step-by-Step Chart Generation Workflow

Step 1: Set Up Environment

Install required libraries:

pip install matplotlib seaborn pandas numpy plotly

Create Python script header:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from matplotlib.patches import Rectangle
import warnings
warnings.filterwarnings('ignore')

# Set global style
plt.style.use('seaborn-v0_8-darkgrid')
sns.set_palette("husl")

# Global settings
DPI = 300
FIGURE_WIDTH = 10
FIGURE_HEIGHT = 6
TITLE_FONT_SIZE = 14
AXIS_FONT_SIZE = 12
LABEL_FONT_SIZE = 10

Step 2: Extract Data from Model and Valuation

A. Extract Revenue Data

# Revenue by Product (from Task 2 model)
years = [2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029]

# Extract from Excel or define manually from model
product_a = [100, 120, 145, 175, 210, 252, 302, 363, 435, 522]
product_b = [80, 95, 115, 138, 165, 198, 238, 285, 342, 411]
product_c = [50, 62, 78, 98, 122, 153, 191, 239, 299, 374]
product_d = [30, 38, 48, 61, 77, 97, 122, 153, 191, 239]

# Revenue by Geography
north_america = [150, 180, 220, 265, 320, 384, 461, 553, 664, 797]
europe = [80, 95, 115, 140, 170, 204, 245, 294, 353, 423]
asia_pacific = [40, 50, 63, 80, 101, 127, 159, 199, 249, 311]
rest_of_world = [20, 25, 32, 40, 51, 64, 80, 100, 125, 156]

B. Extract Margin Data

# Margin evolution
gross_margin = [58.0, 59.2, 60.5, 61.8, 63.0, 64.5, 66.0, 67.0, 67.5, 68.0]
ebitda_margin = [12.0, 15.5, 18.8, 22.0, 25.0, 28.0, 30.5, 32.0, 33.0, 34.0]
fcf_margin = [8.0, 11.0, 14.5, 18.0, 21.0, 24.0, 26.5, 28.0, 29.0, 30.0]

C. Extract DCF Sensitivity Data

# DCF Sensitivity (from Task 3 valuation)
wacc_values = [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
terminal_growth = [1.5, 2.0, 2.5, 3.0, 3.5]

# Price per share matrix (rows = WACC, columns = terminal growth)
dcf_sensitivity = np.array([
    [66, 71, 76, 82, 89],
    [58, 62, 67, 72, 78],
    [52, 55, 59, 63, 68],
    [47, 50, 53, 56, 60],
    [42, 45, 48, 51, 54],
    [39, 41, 44, 46, 49]
])

D. Extract Valuation Ranges

# Valuation Football Field (from Task 3)
valuation_methods = ['DCF Analysis', 'Trading Comps\n(NTM)', 'Precedent\nTransactions']
valuation_low = [48, 45, 52]
valuation_high = [62, 57, 66]
current_price = 50
target_price = 55

Step 3: Create Mandatory Charts

Chart 1: Revenue by Product - Stacked Area ⭐ MANDATORY

def create_revenue_by_product_chart():
    """Create revenue by product stacked area chart"""

    fig, ax = plt.subplots(figsize=(10, 6))

    # Create stacked area chart
    ax.stackplot(years, product_a, product_b, product_c, product_d,
                 labels=['Product A', 'Product B', 'Product C', 'Product D'],
                 colors=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728'],
                 alpha=0.8)

    # Formatting
    ax.set_xlabel('Year', fontsize=12, fontweight='bold')
    ax.set_ylabel('Revenue ($M)', fontsize=12, fontweight='bold')
    ax.set_title('Figure 3 - Revenue by Product/Segment (2020-2029E)',
                 fontsize=14, fontweight='bold', pad=20)

    # Legend
    ax.legend(loc='upper left', frameon=False, fontsize=10)

    # Grid
    ax.grid(axis='y', alpha=0.3, linestyle='--')
    ax.set_axisbelow(True)

    # Remove top and right spines
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

    # Add vertical line to separate historical from projected
    ax.axvline(x=2024, color='gray', linestyle='--', linewidth=1, alpha=0.5)
    ax.text(2024.2, ax.get_ylim()[1]*0.95, 'Projected →',
            fontsize=9, color='gray', ha='left')

    # Source line
    fig.text(0.12, 0.02, 'Source: Company data, [Firm] estimates',
             fontsize=9, style='italic', color='gray')

    # Save
    plt.tight_layout()
    plt.savefig('chart_03_revenue_by_product_stacked_area.png',
                dpi=300, bbox_inches='tight', facecolor='white')
    plt.close()
    print("✓ Created: chart_03_revenue_by_product_stacked_area.png")

create_revenue_by_product_chart()

Chart 2: Revenue by Geography - Stacked Bar ⭐ MANDATORY

def create_revenue_by_geography_chart():
    """Create revenue by geography stacked bar chart"""

    years_labels = ['2020', '2021', '2022', '2023', '2024',
                    '2025E', '2026E', '2027E', '2028E', '2029E']

    fig, ax = plt.subplots(figsize=(10, 6))

    # Create stacked bar chart
    width = 0.6
    x = np.arange(len(years_labels))

    p1 = ax.bar(x, north_america, width, label='North America', color='#1f77b4')
    p2 = ax.bar(x, europe, width, bottom=north_america,
                label='Europe', color='#ff7f0e')
    p3 = ax.bar(x, asia_pacific, width,
                bottom=np.array(north_america) + np.array(europe),
                label='Asia-Pacific', color='#2ca02c')
    p4 = ax.bar(x, rest_of_world, width,
                bottom=np.array(north_america) + np.array(europe) + np.array(asia_pacific),
                label='Rest of World', color='#d62728')

    # Formatting
    ax.set_xlabel('Year', fontsize=12, fontweight='bold')
    ax.set_ylabel('Revenue ($M)', fontsize=12, fontweight='bold')
    ax.set_title('Figure 4 - Revenue by Geography (2020-2029E)',
                 fontsize=14, fontweight='bold', pad=20)
    ax.set_xticks(x)
    ax.set_xticklabels(years_labels, rotation=45, ha='right')

    # Legend
    ax.legend(loc='upper left', frameon=False, fontsize=10)

    # Grid
    ax.grid(axis='y', alpha=0.3, linestyle='--')
    ax.set_axisbelow(True)

    # Remove top and right spines
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

    # Source line
    fig.text(0.12, 0.02, 'Source: Company data, [Firm] estimates',
             fontsize=9, style='italic', color='gray')

    # Save
    plt.tight_layout()
    plt.savefig('chart_04_revenue_by_geography_stacked_bar.png',
                dpi=300, bbox_inches='tight', facecolor='white')
    plt.close()
    print("✓ Created: chart_04_revenue_by_geography_stacked_bar.png")

create_revenue_by_geography_chart()

Chart 3: DCF Sensitivity - Heatmap ⭐ MANDATORY

def create_dcf_sensitivity_heatmap():
    """Create DCF sensitivity analysis heatmap"""

    # Create DataFrame
    df = pd.DataFrame(dcf_sensitivity,
                      index=[f'{w}%' for w in wacc_values],
                      columns=[f'{g}%' for g in terminal_growth])

    fig, ax = plt.subplots(figsize=(8, 6))

    # Create heatmap
    sns.heatmap(df, annot=True, fmt='d', cmap='RdYlGn',
                cbar_kws={'label': 'Price per Share ($)'},
                linewidths=0.5, linecolor='white',
                ax=ax, vmin=35, vmax=95)

    # Formatting
    ax.set_xlabel('Terminal Growth Rate', fontsize=12, fontweight='bold')
    ax.set_ylabel('WACC', fontsize=12, fontweight='bold')
    ax.set_title('Figure 28 - DCF Sensitivity Analysis ($/share)',
                 fontsize=14, fontweight='bold', pad=20)

    # Rotate y-axis labels
    plt.yticks(rotation=0)

    # Source line
    fig.text(0.12, 0.02, 'Source: [Firm] estimates',
             fontsize=9, style='italic', color='gray')

    # Save
    plt.tight_layout()
    plt.savefig('chart_28_dcf_sensitivity_heatmap.png',
                dpi=300, bbox_inches='tight', facecolor='white')
    plt.close()
    print("✓ Created: chart_28_dcf_sensitivity_heatmap.png")

create_dcf_sensitivity_heatmap()

Chart 4: Valuation Football Field ⭐ MANDATORY

def create_valuation_football_field():
    """Create valuation football field chart"""

    fig, ax = plt.subplots(figsize=(10, 5))

    # Create horizontal bars
    y_positions = np.arange(len(valuation_methods))
    colors = ['#1f77b4', '#ff7f0e', '#2ca02c']

    for i, (method, low, high, color) in enumerate(
            zip(valuation_methods, valuation_low, valuation_high, colors)):
        ax.barh(i, high - low, left=low, height=0.6,
                color=color, alpha=0.7, label=method)

        # Add value labels at ends
        ax.text(low - 1, i, f'${low}', va='center', ha='right', fontsize=10)
        ax.text(high + 1, i, f'${high}', va='center', ha='left', fontsize=10)

    # Add current price line
    ax.axvline(x=current_price, color='red', linestyle='--', linewidth=2,
               label=f'Current: ${current_price}', alpha=0.7)

    # Add target price line
    ax.axvline(x=target_price, color='black', linestyle='-', linewidth=2,
               label=f'Target: ${target_price}')

    # Formatting
    ax.set_yticks(y_positions)
    ax.set_yticklabels(valuation_methods, fontsize=11)
    ax.set_xlabel('Price Per Share ($)', fontsize=12, fontweight='bold')
    ax.set_title('Figure 32 - Valuation Football Field',
                 fontsize=14, fontweight='bold', pad=20)

    # Set x-axis limits
    ax.set_xlim(40, 70)

    # Remove spines
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['left'].set_visible(False)

    # Grid
    ax.grid(axis='x', alpha=0.3, linestyle='--')
    ax.set_axisbelow(True)

    # Legend
    ax.legend(loc='upper right', frameon=False, fontsize=9)

    # Source line
    fig.text(0.12, 0.02, 'Source: [Firm] estimates',
             fontsize=9, style='italic', color='gray')

    # Save
    plt.tight_layout()
    plt.savefig('chart_32_valuation_football_field.png',
                dpi=300, bbox_inches='tight', facecolor='white')
    plt.close()
    print("✓ Created: chart_32_valuation_football_field.png")

create_valuation_football_field()

Step 4: Create Remaining Required Charts (Charts 1-34)

Complete the 25 REQUIRED charts by creating all remaining charts from the required list. Each chart has a specific purpose in Task 5.

Investment Summary (1 chart)

# chart_01: Stock Price Performance (12-24 months)
# - Line chart showing stock price over time vs. market index
# - Used on Page 1 of final report

Financial Performance (5 additional charts beyond chart_03 and chart_04)

# chart_02: Revenue Growth Trajectory
# chart_10: Gross Margin Evolution
# chart_11: EBITDA Margin Progression
# chart_12: Free Cash Flow Trend
# chart_14: Scenario Comparison (Bull/Base/Bear)

Company 101 Section (7 charts)

# chart_05: Company Overview/Timeline
# chart_06: Key Milestones Timeline
# chart_07: Organizational Structure
# chart_08: Product Portfolio Overview
# chart_09: Customer Segmentation
# chart_15: Market Size Evolution (TAM)
# chart_16: Competitive Positioning Matrix

Competitive & Market (2 charts)

# chart_17: Market Share Breakdown
# chart_18: Competitive Benchmarking

Scenario Analysis (1 chart)

# chart_13: Operating Metrics Dashboard

Valuation Section (6 additional charts beyond chart_28 and chart_32)

# chart_29: DCF Valuation Waterfall
# chart_30: Trading Comps Scatter Plot
# chart_31: Peer Multiples Comparison
# chart_33: Price Target Scenarios
# chart_34: Historical Valuation Multiples

Use consistent formatting across all charts:

Step 4B: Create Optional Charts (For 26-35 Total)

Optional: Add 1-10 additional charts from this list for greater visual density:

# chart_19: Customer Acquisition Trends
# chart_20: Unit Economics Evolution
# chart_21: Product Roadmap Timeline
# chart_22: Geographic Expansion Map
# chart_23: R&D Investment Trends
# chart_24: Sales & Marketing Efficiency
# chart_25: Working Capital Trends
# chart_26: Debt Maturity Schedule
# chart_27: Ownership Structure
# chart_35: Analyst Price Target Distribution

These optional charts provide additional visual storytelling and help achieve the "1 chart per 200-300 words" density target in Task 5.

Step 5: Create Chart Index

Create a text file documenting all charts:

def create_chart_index():
    """Create index of all charts"""

    # 25 REQUIRED CHARTS
    required_charts = [
        "chart_01_stock_price_performance.png - Stock Price Performance (12-24M)",
        "chart_02_revenue_growth_trajectory.png - Revenue Growth Trajectory",
        "chart_03_revenue_by_product_stacked_area.png - Revenue by Product [MANDATORY]",
        "chart_04_revenue_by_geography_stacked_bar.png - Revenue by Geography [MANDATORY]",
        "chart_05_company_overview.png - Company Overview/Timeline",
        "chart_06_key_milestones_timeline.png - Key Milestones Timeline",
        "chart_07_organizational_structure.png - Organizational Structure",
        "chart_08_product_portfolio.png - Product Portfolio Overview",
        "chart_09_customer_segmentation.png - Customer Segmentation",
        "chart_10_gross_margin_evolution.png - Gross Margin Evolution",
        "chart_11_ebitda_margin_progression.png - EBITDA Margin Progression",
        "chart_12_free_cash_flow_trend.png - Free Cash Flow Trend",
        "chart_13_operating_metrics_dashboard.png - Operating Metrics Dashboard",
        "chart_14_scenario_comparison.png - Scenario Comparison (Bull/Base/Bear)",
        "chart_15_market_size_evolution.png - Market Size Evolution (TAM)",
        "chart_16_competitive_positioning.png - Competitive Positioning Matrix",
        "chart_17_market_share.png - Market Share Breakdown",
        "chart_18_competitive_benchmarking.png - Competitive Benchmarking",
        "chart_28_dcf_sensitivity_heatmap.png - DCF Sensitivity Heatmap [MANDATORY]",
        "chart_29_dcf_waterfall.png - DCF Valuation Waterfall",
        "chart_30_trading_comps_scatter.png - Trading Comps Scatter Plot",
        "chart_31_peer_multiples_comparison.png - Peer Multiples Comparison",
        "chart_32_valuation_football_field.png - Valuation Football Field [MANDATORY]",
        "chart_33_price_target_scenarios.png - Price Target Scenarios",
        "chart_34_historical_valuation_multiples.png - Historical Valuation Multiples",
    ]

    # 10 OPTIONAL CHARTS (for 26-35 range)
    optional_charts = [
        "chart_19_customer_acquisition_trends.png - Customer Acquisition Trends [OPTIONAL]",
        "chart_20_unit_economics_evolution.png - Unit Economics Evolution [OPTIONAL]",
        "chart_21_product_roadmap_timeline.png - Product Roadmap Timeline [OPTIONAL]",
        "chart_22_geographic_expansion_map.png - Geographic Expansion Map [OPTIONAL]",
        "chart_23_rd_investment_trends.png - R&D Investment Trends [OPTIONAL]",
        "chart_24_sales_marketing_efficiency.png - Sales & Marketing Efficiency [OPTIONAL]",
        "chart_25_working_capital_trends.png - Working Capital Trends [OPTIONAL]",
        "chart_26_debt_maturity_schedule.png - Debt Maturity Schedule [OPTIONAL]",
        "chart_27_ownership_structure.png - Ownership Structure [OPTIONAL]",
        "chart_35_analyst_price_targets.png - Analyst Price Target Distribution [OPTIONAL]",
    ]

    with open('chart_index.txt', 'w') as f:
        f.write("CHART INDEX FOR [COMPANY] EQUITY RESEARCH REPORT\n")
        f.write("=" * 60 + "\n\n")

        f.write("4 MANDATORY CHARTS (Must be present):\n")
        f.write("- chart_03: Revenue by Product (Stacked Area) ⭐\n")
        f.write("- chart_04: Revenue by Geography (Stacked Bar) ⭐\n")
        f.write("- chart_28: DCF Sensitivity (Heatmap) ⭐\n")
        f.write("- chart_32: Valuation Football Field ⭐\n\n")

        f.write("25 REQUIRED CHARTS:\n")
        for chart in required_charts:
            f.write(f"  {chart}\n")

        f.write("\n10 OPTIONAL CHARTS (for 26-35 total):\n")
        for chart in optional_charts:
            f.write(f"  {chart}\n")

        f.write("\n" + "=" * 60 + "\n")
        f.write("NOTE: Task 5 will embed ALL charts created (25-35) throughout\n")
        f.write("the report for visual density (1 chart every 200-300 words).\n")

    print("✓ Created: chart_index.txt")

create_chart_index()

Step 6: Quality Check

Run verification checks:

import os

def verify_charts():
    """Verify all charts were created successfully"""

    mandatory_charts = [
        'chart_03_revenue_by_product_stacked_area.png',
        'chart_04_revenue_by_geography_stacked_bar.png',
        'chart_28_dcf_sensitivity_heatmap.png',
        'chart_32_valuation_football_field.png'
    ]

    print("\n" + "="*60)
    print("CHART GENERATION VERIFICATION")
    print("="*60)

    # Check mandatory charts
    print("\n1. MANDATORY CHARTS:")
    all_mandatory_present = True
    for chart in mandatory_charts:
        if os.path.exists(chart):
            size = os.path.getsize(chart) / 1024  # KB
            print(f"   ✓ {chart} ({size:.1f} KB)")
        else:
            print(f"   ✗ MISSING: {chart}")
            all_mandatory_present = False

    # Count total charts
    chart_files = [f for f in os.listdir('.') if f.startswith('chart_') and f.endswith('.png')]
    print(f"\n2. TOTAL CHARTS: {len(chart_files)}")
    print(f"   Target: 25-35 charts")
    print(f"   Status: {'✓ PASS' if 25 <= len(chart_files) <= 35 else '⚠ WARNING'}")

    # Check file sizes (should be > 50KB for 300 DPI)
    print("\n3. FILE SIZE CHECK:")
    small_files = []
    for chart in chart_files[:5]:  # Sample first 5
        size = os.path.getsize(chart) / 1024
        if size < 50:
            small_files.append(chart)
        print(f"   {chart}: {size:.1f} KB")

    if small_files:
        print(f"   ⚠ WARNING: {len(small_files)} files may be low resolution")
    else:
        print(f"   ✓ All sampled files have adequate size")

    # Final verdict
    print("\n" + "="*60)
    if all_mandatory_present and 25 <= len(chart_files) <= 35:
        print("✓ VERIFICATION PASSED - Ready for Task 5")
    else:
        print("✗ VERIFICATION FAILED - Review missing charts")
    print("="*60 + "\n")

verify_charts()

Quality Standards

Visual Quality

Data Accuracy

Formatting Quality

Completeness

Chart Types Reference

When to Use Each Chart Type

Line Charts: Time series trends (revenue, margins, stock price)

Stacked Area: Revenue by product ⭐, market size composition

Stacked Bar: Revenue by geography ⭐, quarterly breakdowns

Heatmap: DCF sensitivity ⭐, correlation matrices

Horizontal Bar: Valuation football field ⭐, peer rankings

Waterfall: Revenue bridges, margin analysis, DCF build-up

Scatter/Bubble: Growth vs. valuation, competitive positioning

2×2 Matrix: Competitive positioning, product portfolio

File Naming Convention

Always use this format:

chart_[NUMBER]_[DESCRIPTION].png

Examples:
chart_01_stock_price_performance.png
chart_03_revenue_by_product_stacked_area.png
chart_28_dcf_sensitivity_heatmap.png

Number charts sequentially based on their position in the report, not creation order.

Common Chart Generation Issues

Issue 1: Low Resolution

Problem: Chart looks pixelated Solution: Ensure dpi=300 in plt.savefig()

Issue 2: Text Cutoff

Problem: Labels or titles cut off at edges Solution: Use bbox_inches='tight' in plt.savefig()

Issue 3: Poor Colors

Problem: Colors don't look professional Solution: Use established palettes like Tableau10 or define custom corporate colors

Issue 4: Overlapping Labels

Problem: Axis labels overlap Solution: Rotate labels (e.g., rotation=45) or reduce font size

Issue 5: White Space

Problem: Too much white space around chart Solution: Use plt.tight_layout() before saving

Success Criteria

A successful chart package should:

  1. Include all 4 mandatory charts (verified) ⭐
  2. Create 25 required charts minimum (verified)
  3. Optional: 1-10 additional charts for 26-35 total
  4. Have consistent professional styling across all charts
  5. Be high resolution (300 DPI) for print quality
  6. Have clear labels, legends, and titles on every chart
  7. Include proper figure numbers and source citations
  8. Be ready for immediate embedding in Word
  9. Cover all key financial metrics and analyses
  10. Tell a visual story complementing the written analysis
  11. Be accurate and auditable to source data (model/valuation)
  12. All charts packaged in zip file with chart index

Remember: Task 5 will embed ALL charts created (25-35) throughout the report for visual density.

Output Files

After completing Task 4, deliverables include:

25 REQUIRED Chart Files (Minimum):

  1. chart_01_stock_price_performance.png
  2. chart_02_revenue_growth_trajectory.png
  3. chart_03_revenue_by_product_stacked_area.png ⭐ MANDATORY
  4. chart_04_revenue_by_geography_stacked_bar.png ⭐ MANDATORY
  5. chart_05_company_overview.png
  6. chart_06_key_milestones_timeline.png
  7. chart_07_organizational_structure.png
  8. chart_08_product_portfolio.png
  9. chart_09_customer_segmentation.png
  10. chart_10_gross_margin_evolution.png
  11. chart_11_ebitda_margin_progression.png
  12. chart_12_free_cash_flow_trend.png
  13. chart_13_operating_metrics_dashboard.png
  14. chart_14_scenario_comparison.png
  15. chart_15_market_size_evolution.png
  16. chart_16_competitive_positioning.png
  17. chart_17_market_share.png
  18. chart_18_competitive_benchmarking.png 19-27. Reserved for optional charts if created
  19. chart_28_dcf_sensitivity_heatmap.png ⭐ MANDATORY
  20. chart_29_dcf_waterfall.png
  21. chart_30_trading_comps_scatter.png
  22. chart_31_peer_multiples_comparison.png
  23. chart_32_valuation_football_field.png ⭐ MANDATORY
  24. chart_33_price_target_scenarios.png
  25. chart_34_historical_valuation_multiples.png
  26. Reserved for optional chart if created

10 OPTIONAL Chart Files (For 26-35 Total):

Chart Index (1 text file):

All chart files must be:

Final Step: Package All Charts

Create a zip file containing all chart files and the chart index:

[Company]_Charts_[Date].zip
├── chart_01_stock_price_performance.png
├── chart_02_revenue_growth_trajectory.png
├── chart_03_revenue_by_product_stacked_area.png ⭐
├── chart_04_revenue_by_geography_stacked_bar.png ⭐
├── chart_05_company_overview.png
├── ... (all 25-35 chart files)
├── chart_28_dcf_sensitivity_heatmap.png ⭐
├── chart_32_valuation_football_field.png ⭐
├── chart_34_historical_valuation_multiples.png
└── chart_index.txt

Example: Tesla_Charts_2024-10-28.zip

Why this matters: Task 5 will embed ALL charts created (25-35) throughout the report. The report requires visual density (1 chart per 200-300 words), so all charts serve a purpose—either for specific analytical sections or for visual storytelling and page density.

Next Steps

After completing Task 4, the zip file will be used for:

The 4 mandatory charts are critical for the valuation and financial analysis sections of the report.