#!/opt/anaconda3/envs/fidelity/bin/python3
"""
Deep Research Phase using Claude API
Reads research_report.md and performs comprehensive deep analysis
using Claude Sonnet 4.5 with extended thinking.
Usage:
./skills/research_deep.py SYMBOL [--work-dir DIR]
Output:
- 08_deep_research/deep_research_output.md
- 08_deep_research/deep_research_thinking.md
"""
import os
import sys
import argparse
from datetime import datetime
from anthropic import Anthropic
import dotenv
def load_research_report(work_dir):
"""Load the initial research report as context."""
report_path = os.path.join(work_dir, 'research_report.md')
if not os.path.exists(report_path):
raise FileNotFoundError(f"Research report not found: {report_path}")
with open(report_path, 'r') as f:
content = f.read()
# Extract company name from report (first line after "Company:")
company_name = None
for line in content.split('\n'):
if line.startswith('**Company:**'):
company_name = line.replace('**Company:**', '').strip()
break
return content, company_name
def run_deep_research(symbol, company_name, report_content):
"""
Call Claude API with extended thinking to perform deep research.
Returns:
tuple: (analysis_output, thinking_process)
"""
dotenv.load_dotenv()
api_key = os.environ.get('ANTHROPIC_API_KEY')
if not api_key:
raise ValueError("ANTHROPIC_API_KEY environment variable not set")
client = Anthropic(api_key=api_key)
# Use company_name if available, otherwise use symbol
company_display = company_name if company_name else symbol
prompt = f"""I have collected information on {company_display} (symbol {symbol}) from various tools in the research_report.md file.
Using everything found so far and everything you can find by using tools and doing deep research, write a report on {symbol} in the straightforward factual style of a Wall Street equity research analyst, in 9 sections:
1. Short summary overall assessment:
One-sentence summary of section 12 with evaluation of risk/reward profile
2. Extended Profile:
• History with origin story and key historical milestones
• Core business and competitors
• Recent major news
3. Business Model:
• Describe their core businesses, products and services.
• Outline their key revenue streams, customer segments, and monetization strategies.
• Analyze key characteristics of markets it operates in:
- customer acquisition costs
- retention metrics
- sales cycles
- seasonal or cyclical business patterns
- margins, market size, growth trajectory and factors affecting them
• Explain sources of competitive advantage such as network effects, switching costs, brands, intellectual property, regulatory moats, and other barriers to entry.
4. Competitive Landscape:
• Identify their main competitors, including direct, adjacent, and emerging competitors.
• Compare key metrics such as market share, product differentiation, pricing power, and growth trajectories.
5. Supply Chain Positioning:
• Describe their role in the upstream (supplier-side) and downstream (customer/distribution) parts of the supply chain.
• Identify key suppliers, partners, distributors, and any major dependencies or concentrations.
6. Financial and Operating Leverage:
• Analyze the company's use of financial leverage (debt levels, interest obligations, credit ratings).
• Analyze operating leverage (fixed vs. variable cost structure, scalability, margin sensitivity to revenue changes).
• Analyze cash flow generation and working capital dynamics.
• Analyze capital allocation strategy (dividends, buybacks, reinvestment).
7. Valuation:
• Identify appropriate valuation methodologies, including income-based (e.g., DCF), asset-based (e.g. book value and sum of parts), market-based (e.g. peer multiples and comparisons), and LBO analysis
• Highlight important valuation inputs and metrics (growth rates, margins, discount rates, terminal value assumptions).
• Summarize current ratings and analyst opinions, including recent changes.
• Note the stock's volatility, liquidity, if it is widely covered and owned, if it is a hedge fund story stock or meme stock, what macro factors it is sensitive to
8. Recent developments, News Search and Risk Factors:
• Conduct a deep news search for significant positive and negative news items over the last 12 months, including:
- company + "analyst report" OR "research note" downgrade OR upgrade
- use perplexity to search for company + "profile" or "executive profile" and ask: "What are the most significant investigative reports and executive profiles about {company_display} published in 2023-2024?"
- Revenue and earnings trends
- Management changes
- New product launches
- Restructurings, mergers, acquisitions, divestitures, strategic partnerships
- Short-seller reports or allegations.
- Regulatory investigations or lawsuits.
- Product failures, operational issues, or supply chain disruptions.
- Major wins (e.g., partnerships, large customer wins, successful product launches).
- Insider trading activity and institutional ownership changes
• Summarize key themes from recent media coverage, analyst reports, and public filings.
• Note controversies, reputational risks, and governance concerns if any.
• Discuss what companies might be potential acquisition targets or acquirers of the company, based on overlapping or complementary customer bases, product offerings, and technical capabilities.
• Note any other key themes or trends that you think are important.
9. Conclusion:
• Summarize the company's strategic position and the stock's investment risk/reward profile
• Strengths, weaknesses, opportunities, threats (SWOT)
• Bear case and bull case
• The level of risk
• Any critical "watch points" for further due diligence and ongoing monitoring.
Use a straightforward, factual tone throughout the analysis. Focus on data and observable facts rather than speculation."""
print(f"\nCalling Claude API...")
print(f"Model: claude-sonnet-4-5-20250929")
print(f"Extended thinking: Enabled (10K token budget)")
try:
message = client.messages.create(
model="claude-sonnet-4-5",
max_tokens=16000,
thinking={
"type": "enabled",
"budget_tokens": 10000
},
messages=[
{
"role": "user",
"content": f"{prompt}\n\n---\n\nHere is the research report:\n\n{report_content}"
}
]
)
# Extract thinking and text content
thinking_blocks = []
text_blocks = []
for block in message.content:
if block.type == "thinking":
thinking_blocks.append(block.thinking)
elif block.type == "text":
text_blocks.append(block.text)
analysis = "\n\n".join(text_blocks)
thinking = "\n\n---\n\n".join(thinking_blocks)
return analysis, thinking
except Exception as e:
print(f"\n❌ Error calling Claude API: {e}")
raise
def save_outputs(work_dir, analysis, thinking):
"""Save deep research outputs."""
output_dir = os.path.join(work_dir, '08_deep_research')
os.makedirs(output_dir, exist_ok=True)
# Save analysis
analysis_path = os.path.join(output_dir, 'deep_research_output.md')
with open(analysis_path, 'w') as f:
f.write(analysis)
print(f"✓ Saved: {analysis_path}")
# Save thinking process
thinking_path = os.path.join(output_dir, 'deep_research_thinking.md')
with open(thinking_path, 'w') as f:
f.write(f"# Extended Thinking Process\n\n{thinking}")
print(f"✓ Saved: {thinking_path}")
return True
def main():
parser = argparse.ArgumentParser(description='Deep research phase using Claude API')
parser.add_argument('symbol', help='Stock ticker symbol')
parser.add_argument('--work-dir', required=True, help='Work directory path')
args = parser.parse_args()
symbol = args.symbol.upper()
print("="*60)
print("Deep Research Phase - Claude API with Extended Thinking")
print("="*60)
print(f"Symbol: {symbol}")
print(f"Work Directory: {args.work_dir}")
print("="*60)
try:
# Load research report
print(f"\nLoading research report...")
report_content, company_name = load_research_report(args.work_dir)
print(f"✓ Loaded research report")
if company_name:
print(f"✓ Company: {company_name}")
# Run deep research
print(f"\nAnalyzing {symbol} with Claude Sonnet 4.5...")
print("This may take 30-60 seconds with extended thinking enabled...")
analysis, thinking = run_deep_research(symbol, company_name, report_content)
print(f"✓ Analysis complete ({len(analysis)} characters)")
print(f"✓ Thinking process captured ({len(thinking)} characters)")
# Save outputs
print(f"\nSaving outputs...")
save_outputs(args.work_dir, analysis, thinking)
print("\n" + "="*60)
print("SUCCESS: Deep research completed!")
print("="*60)
return 0
except FileNotFoundError as e:
print(f"\n❌ Error: {e}")
print("\nMake sure research_report.md has been generated first.")
print("Run with --phases report before running deep research.")
return 1
except ValueError as e:
print(f"\n❌ Error: {e}")
print("\nMake sure ANTHROPIC_API_KEY is set in your .env file.")
return 1
except Exception as e:
print(f"\n❌ Error in deep research: {e}")
import traceback
traceback.print_exc()
return 1
if __name__ == '__main__':
sys.exit(main())
