本文档提供执行首次覆盖技能中任务4（图表生成）的逐步说明。
目的：为报告生成25-35张专业金融图表。
先决条件：⚠️ 开始前请验证
⚠️ 关键：除非任务1、2、3均已完成，否则不要开始此任务
本任务需要前三个任务的全部输出。在缺少这些输出的情况下开始将导致图表不完整。
如果任务1、2或3中的任何一项未完成：立即停止并告知用户需要首先完成哪些任务。具体要求为：
不要尝试创建占位图表或由于数据缺失而跳过图表。
输出：25-35张专业图表文件（PNG/JPG，300 DPI）
开始前——检查所有先决条件：
如果任何验证失败：
重要：任务5（报告组装）将在整个报告中嵌入所有创建的图表。报告需要密集的视觉内容（每200-300字一张图表），因此要创建全面的图表覆盖。
以下4张图表是关键可视化元素，必须存在：
创建以下全部25张图表。每张在任务5中都有特定用途：
投资摘要部分（1张图表）：
财务表现部分（6张图表）：
公司101部分（7张图表）：
竞争与市场部分（2张图表）：
情景分析部分（2张图表）：
估值部分（7张图表）：
总计：25张必需图表
添加以下图表以获得更大的视觉密度和叙事效果（达到26-35张总计）：
总计范围：25-35张图表（25张必需 + 0-10张可选）
了解每张图表数据的来源：
重要：需要全部三个任务（1、2、3）完成加上外部数据访问才能创建所有25张必需图表。
安装所需库：
pip install matplotlib seaborn pandas numpy plotly创建 Python 脚本头部：
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# 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]# 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]# 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]
])# 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 = 55def 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 ($M)', fontsize=12, fontweight='bold')
ax.set_title('Figure 3 - Revenue by Product/Segment (2020-2029E)',
fontsize=14, fontweight='bold', pad=20)
# Legend
ax.legend(loc='upper left', frameon=False, fontsize=10)
# Grid
ax.grid(axis='y', alpha=0.3, linestyle='--')
ax.set_axisbelow(True)
# Remove top and right spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
# Add vertical line to separate historical from projected
ax.axvline(x=2024, color='gray', linestyle='--', linewidth=1, alpha=0.5)
ax.text(2024.2, ax.get_ylim()[1]*0.95, 'Projected →',
fontsize=9, color='gray', ha='left')
# Source line
fig.text(0.12, 0.02, 'Source: Company data, [Firm] estimates',
fontsize=9, style='italic', color='gray')
# Save
plt.tight_layout()
plt.savefig('chart_03_revenue_by_product_stacked_area.png',
dpi=300, bbox_inches='tight', facecolor='white')
plt.close()
print("Created: chart_03_revenue_by_product_stacked_area.png")
create_revenue_by_product_chart()def create_revenue_by_geography_chart():
"""Create revenue by geography stacked bar chart"""
years_labels = ['2020', '2021', '2022', '2023', '2024',
'2025E', '2026E', '2027E', '2028E', '2029E']
fig, ax = plt.subplots(figsize=(10, 6))
# Create stacked bar chart
width = 0.6
x = np.arange(len(years_labels))
p1 = ax.bar(x, north_america, width, label='North America', color='#1f77b4')
p2 = ax.bar(x, europe, width, bottom=north_america,
label='Europe', color='#ff7f0e')
p3 = ax.bar(x, asia_pacific, width,
bottom=np.array(north_america) + np.array(europe),
label='Asia-Pacific', color='#2ca02c')
p4 = ax.bar(x, rest_of_world, width,
bottom=np.array(north_america) + np.array(europe) + np.array(asia_pacific),
label='Rest of World', color='#d62728')
# Formatting
ax.set_xlabel('Year', fontsize=12, fontweight='bold')
ax.set_ylabel('Revenue ($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()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()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()完成25张必需图表，从必需列表中创建所有剩余图表。每张图表在任务5中都有特定用途。
# 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# 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)# 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# chart_17: Market Share Breakdown
# chart_18: Competitive Benchmarking# chart_13: Operating Metrics Dashboard# 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在所有图表中使用一致的格式：
可选：从此列表中添加1-10张附加图表以获得更大的视觉密度：
# 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这些可选图表提供额外的视觉叙事，并有助于在任务5中实现"每200-300字一张图表"的密度目标。
创建一个记录所有图文的文本文件：
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()运行验证检查：
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" OK {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()折线图：时间序列趋势（营收、利润率、股价）
堆叠面积图：按产品的营收 ⭐、市场规模构成
堆叠柱状图：按地域的营收 ⭐、季度分解
热力图：DCF 敏感性 ⭐、相关性矩阵
水平柱状图：估值足球场图 ⭐、同业排名
瀑布图：营收桥接、利润率分析、DCF 构建
散点/气泡图：增长 vs. 估值、竞争定位
2×2 矩阵图：竞争定位、产品组合
始终使用以下格式：
chart_[编号]_[描述].png
示例：
chart_01_stock_price_performance.png
chart_03_revenue_by_product_stacked_area.png
chart_28_dcf_sensitivity_heatmap.png按图表在报告中的位置顺序编号，而非创建顺序。
问题：图表看起来像素化
解决方案：确保在 plt.savefig() 中使用
dpi=300
问题：标签或标题在边缘处被截断
解决方案：在 plt.savefig() 中使用
bbox_inches='tight'
问题：颜色看起来不专业 解决方案：使用成熟色板如 Tableau10，或定义自定义企业颜色
问题：轴标签重叠
解决方案：旋转标签（例如
rotation=45）或减小字号
问题：图表周围空白过多
解决方案：在保存前使用
plt.tight_layout()
一个成功的图表包应：
请记住：任务5将把所有创建的图表（25-35张）嵌入到整个报告中以实现视觉密度。
完成任务4后，交付物包括：
25张必需图表文件（最低要求）：
10张可选图表文件（用于达到26-35张总计）：
图表索引（1个文本文件）：
所有图表文件必须：
最后一步：打包所有图表
创建包含所有图表文件和图表索引的 zip 文件：
[公司]_Charts_[日期].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
├── ...（全部25-35个图表文件）
├── chart_28_dcf_sensitivity_heatmap.png ⭐
├── chart_32_valuation_football_field.png ⭐
├── chart_34_historical_valuation_multiples.png
└── chart_index.txt示例：Tesla_Charts_2024-10-28.zip
为何重要：任务5将把所有创建的图表（25-35张）嵌入到整个报告中。报告需要视觉密度（每200-300字一张图表），因此所有图表都有其用途——无论是用于特定的分析部分，还是用于视觉叙事和页面密度。
完成任务4后，zip 文件将用于：
4张强制图表对于报告的估值和财务分析部分至关重要。