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Output Formats

The Systematic Investment Analysis Framework generates results in multiple formats. This guide explains each format and how to use them effectively.

Available Output Formats

1. Text Report (Default)

  • File: {config_name}_{timestamp}_report.txt
  • Content: Human-readable comprehensive analysis
  • Use Case: Executive summary and detailed stock analysis

2. CSV Export (--save-csv)

  • File: {config_name}_{timestamp}_results.csv
  • Content: Structured data with all metrics
  • Use Case: Spreadsheet analysis, data manipulation

3. JSON Export (--save-json)

  • File: {config_name}_{timestamp}_data.json
  • Content: Complete raw data and metadata
  • Use Case: API integration, custom analysis tools

Text Report Format

Executive Summary Section

EXECUTIVE SUMMARY
================
Analysis: sp500_full_screen
Date: 2024-08-18 12:32
Description: A screen of all S&P 500 companies.

RESULTS OVERVIEW
---------------
• Total Universe Screened: 503 stocks
• Passed Initial Screening: 45 stocks  
• Final Recommendations: 25 stocks
• Success Rate: 5.0%

AVERAGE QUALITY METRICS
----------------------
• Quality Score: 78.2/100
• Value Score: 65.4/100
• Growth Score: 58.9/100
• Risk Score: 42.1/100

TOP RECOMMENDATIONS
------------------
1. GOOGL - Score: 98.9 (Communication Services)
2. MSFT - Score: 94.2 (Technology)
3. AAPL - Score: 87.6 (Technology)
4. JNJ - Score: 85.3 (Healthcare)
5. PG - Score: 82.9 (Consumer Defensive)

SECTOR BREAKDOWN
---------------
• Technology: 8 stocks
• Healthcare: 6 stocks
• Financial Services: 5 stocks
• Consumer Defensive: 4 stocks
• Communication Services: 2 stocks

KEY INSIGHTS
-----------
• Found 25 stocks meeting criteria
• Average composite score: 79.8
• Most represented sector: Technology

Detailed Stock Analysis

For each stock that passes filters:

DETAILED ANALYSIS: GOOGL
==================================================

COMPANY OVERVIEW
---------------
• Sector: Communication Services
• Industry: Internet Content & Information
• Market Cap: $2462.65B
• Current Price: $203.34

COMPOSITE SCORING
• Overall Score: 98.9/100
• Quality Score: 100.0/100
• Value Score: 100.0/100  
• Growth Score: 100.0/100
• Risk Score: 7.2/100

QUALITY ASSESSMENT
-----------------
• Return on Equity: 34.8%
• Return on Invested Capital: 31.2%
• Current Ratio: 1.90
• Debt to Equity: 11.5%
• Quality Rating: EXCELLENT

VALUATION ANALYSIS
-----------------
• P/E Ratio: 21.7
• P/B Ratio: 6.80
• EV/EBITDA: 17.1
• Valuation Rating: ATTRACTIVE

GROWTH EVALUATION
----------------
• Revenue Growth: 13.8%
• Earnings Growth: 22.3%
• Growth Quality: STRONG
• Growth Sustainability: HIGH

RISK ASSESSMENT
--------------
• Risk Level: LOW
• Beta: 1.05 (estimated)
• Financial Risk: VERY LOW
• Business Risk: LOW
• Sector Risk: MODERATE

CSV Format Structure

Column Definitions

Ticker,Sector,Market_Cap_B,Current_Price,Passes_Filters,Composite_Score,Quality_Score,Value_Score,Growth_Score,Risk_Score,P_E,P_B,ROE,ROIC,Revenue_Growth,Debt_Equity

Data Types and Formats

Column Data Type Format Example
Ticker String Text AAPL
Sector String Text Technology
Market_Cap_B Float Billions USD 3436.89
Current_Price Float USD 231.59
Passes_Filters String Y/N Y
Composite_Score Float 0-100 95.4
Quality_Score Float 0-100 75.0
Value_Score Float 0-100 0.0
Growth_Score Float 0-100 100.0
Risk_Score Float 0-100 30.6
P_E Float Ratio 35.1
P_B Float Ratio 52.27
ROE Float Decimal (0.15 = 15%) 1.498
ROIC Float Decimal 0.589
Revenue_Growth Float Decimal 0.096
Debt_Equity Float Percentage 154.5

Sample CSV Data

Ticker,Sector,Market_Cap_B,Current_Price,Passes_Filters,Composite_Score,Quality_Score,Value_Score,Growth_Score,Risk_Score,P_E,P_B,ROE,ROIC,Revenue_Growth,Debt_Equity
GOOGL,Communication Services,2471.45,203.90,Y,98.9,100.0,100.0,100.0,7.2,21.7,6.80,0.348,0.312,0.138,11.5
AAPL,Technology,3436.89,231.59,N,57.9,75.0,0.0,100.0,30.6,35.1,52.27,1.498,0.589,0.096,154.5
TSLA,Consumer Cyclical,1066.20,330.56,N,28.6,50.0,0.0,0.0,9.0,195.6,13.78,0.082,0.070,-0.118,16.8

JSON Format Structure

Complete Data Export

{
  "config": {
    "name": "sp500_full_screen",
    "description": "A screen of all S&P 500 companies.",
    "universe": {
      "pre_screening_universe": "sp500",
      "region": "US",
      "min_market_cap": 100
    },
    "quality": {
      "min_roic": 0.08,
      "min_roe": 0.10,
      "max_debt_equity": 2.0
    }
  },
  "summary": {
    "top_picks": [
      {
        "ticker": "GOOGL",
        "composite_score": 98.9,
        "sector": "Communication Services"
      }
    ],
    "average_scores": {
      "quality": 78.2,
      "value": 65.4,
      "growth": 58.9,
      "risk": 42.1
    },
    "sector_breakdown": {
      "Technology": 8,
      "Healthcare": 6,
      "Financial Services": 5
    },
    "key_insights": [
      "Found 25 stocks meeting criteria",
      "Average composite score: 79.8",
      "Most represented sector: Technology"
    ]
  },
  "stocks": [
    {
      "ticker": "GOOGL",
      "basic_data": {
        "ticker": "GOOGL",
        "market_cap": 2471450000000,
        "sector": "Communication Services",
        "current_price": 203.90
      },
      "scores": {
        "composite": 98.9,
        "quality": 100.0,
        "value": 100.0,
        "growth": 100.0,
        "risk": 7.2
      },
      "passes_filters": true,
      "quality": {
        "quality_score": 100.0,
        "quality_metrics": {
          "roe": 0.348,
          "roic": 0.312,
          "debt_to_equity": 11.5,
          "current_ratio": 1.90
        }
      }
    }
  ],
  "total_universe": 503,
  "passed_screening": 45,
  "final_results": 25
}

Working with Output Files

Excel/Spreadsheet Analysis

Loading CSV Data

  1. Excel: File → Open → Select CSV file → Import with comma delimiter
  2. Google Sheets: File → Import → Upload file → Separator: Comma
  3. LibreOffice Calc: File → Open → Select CSV → Delimiter: Comma

Useful Excel Formulas

Filter for passing stocks only: 

=FILTER(A:P, E:E="Y")

Calculate sector averages: 

=AVERAGEIF(B:B, "Technology", F:F)

Rank stocks by composite score: 

=RANK(F2, F:F, 0)

Python Data Analysis

Loading CSV with Pandas

import pandas as pd

# Load results
df = pd.read_csv('sp500_full_screen_20240818_123456_results.csv')

# Filter for passing stocks only
passing_stocks = df[df['Passes_Filters'] == 'Y']

# Top 10 by composite score
top_stocks = df.nlargest(10, 'Composite_Score')

# Sector analysis
sector_stats = df.groupby('Sector').agg({
    'Composite_Score': ['mean', 'count'],
    'Passes_Filters': lambda x: (x == 'Y').sum()
})

Loading JSON Data

import json
import pandas as pd

# Load complete results
with open('sp500_full_screen_20240818_123456_data.json', 'r') as f:
    results = json.load(f)

# Extract stock data
stocks_df = pd.json_normalize(results['stocks'])

# Get configuration used
config = results['config']

R Data Analysis

library(readr)
library(dplyr)

# Load CSV data
results <- read_csv("sp500_full_screen_20240818_123456_results.csv")

# Filter and analyze
passing_stocks <- results %>%
  filter(Passes_Filters == "Y") %>%
  arrange(desc(Composite_Score))

# Sector breakdown
sector_analysis <- results %>%
  group_by(Sector) %>%
  summarize(
    avg_score = mean(Composite_Score, na.rm = TRUE),
    count = n(),
    passing_count = sum(Passes_Filters == "Y")
  )

Output File Management

File Naming Convention

Files follow this pattern: 

{config_name}_{timestamp}_{type}.{extension}

Examples: - sp500_full_screen_20240818_123456_report.txt - sp500_full_screen_20240818_123456_results.csv - sp500_full_screen_20240818_123456_data.json

Organizing Results

Recommended directory structure: 

investment_analysis/
├── configs/
│   ├── my_strategy.yaml
│   └── backup_configs/
├── results/
│   ├── 2024-08/
│   │   ├── sp500_analysis/
│   │   ├── sector_analysis/
│   │   └── custom_screens/
│   └── archive/
└── analysis/
    ├── python_scripts/
    ├── excel_workbooks/
    └── reports/

Batch Processing Results

# Create organized results structure
mkdir -p results/$(date +%Y-%m)/sp500_analysis

# Run analysis with organized output
poetry run python scripts/systematic_analysis.py \
  configs/sp500_full.yaml \
  --save-csv --save-json \
  --output results/$(date +%Y-%m)/sp500_analysis/

Custom Output Processing

Creating Summary Reports

import pandas as pd
from datetime import datetime

def create_summary_report(csv_file):
    """Create custom summary from CSV results."""
    df = pd.read_csv(csv_file)

    summary = {
        'analysis_date': datetime.now().strftime('%Y-%m-%d'),
        'total_stocks': len(df),
        'passing_stocks': len(df[df['Passes_Filters'] == 'Y']),
        'top_5_stocks': df.nlargest(5, 'Composite_Score')[['Ticker', 'Composite_Score']].to_dict('records'),
        'sector_breakdown': df['Sector'].value_counts().to_dict(),
        'avg_scores': {
            'composite': df['Composite_Score'].mean(),
            'quality': df['Quality_Score'].mean(),
            'value': df['Value_Score'].mean(),
            'growth': df['Growth_Score'].mean(),
            'risk': df['Risk_Score'].mean()
        }
    }

    return summary

Automated Reporting

import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

def email_results_summary(results_file, recipient):
    """Email analysis results summary."""
    summary = create_summary_report(results_file)

    # Create email content
    message = f"""
    Investment Analysis Results - {summary['analysis_date']}

    Total Stocks Analyzed: {summary['total_stocks']}
    Stocks Passing Filters: {summary['passing_stocks']}
    Success Rate: {summary['passing_stocks']/summary['total_stocks']*100:.1f}%

    Top 5 Recommendations:
    {chr(10).join([f"{i+1}. {stock['Ticker']} ({stock['Composite_Score']:.1f})" for i, stock in enumerate(summary['top_5_stocks'])])}

    Average Scores:
    Composite: {summary['avg_scores']['composite']:.1f}
    Quality: {summary['avg_scores']['quality']:.1f}
    Value: {summary['avg_scores']['value']:.1f}
    Growth: {summary['avg_scores']['growth']:.1f}
    Risk: {summary['avg_scores']['risk']:.1f}
    """

    # Send email (configure SMTP settings)
    # Implementation details depend on email provider

Integration with External Tools

Power BI Integration

  1. Data Source: Use CSV output as data source
  2. Refresh Schedule: Set up automatic data refresh
  3. Visualizations: Create dashboards for sector analysis, scoring trends

Tableau Integration

  1. Connect: File → Connect → Text file → Select CSV
  2. Data Prep: Clean and structure data as needed
  3. Visualizations: Build interactive analysis dashboards

Database Integration

-- Create table for results
CREATE TABLE investment_analysis (
    analysis_date DATE,
    ticker VARCHAR(10),
    sector VARCHAR(50),
    market_cap_b DECIMAL(10,2),
    current_price DECIMAL(8,2),
    passes_filters BOOLEAN,
    composite_score DECIMAL(5,2),
    quality_score DECIMAL(5,2),
    value_score DECIMAL(5,2),
    growth_score DECIMAL(5,2),
    risk_score DECIMAL(5,2)
);

-- Load data from CSV
LOAD DATA INFILE 'results.csv' 
INTO TABLE investment_analysis
FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'
IGNORE 1 ROWS;

Best Practices

File Management

  1. Consistent naming - Use descriptive configuration names
  2. Regular archiving - Move old results to archive folders
  3. Version control - Track configuration changes
  4. Backup strategy - Protect important analysis results

Data Analysis Workflow

  1. Start with CSV - Most versatile for initial analysis
  2. Use JSON for integration - API development and custom tools
  3. Text reports for presentation - Executive summaries and documentation
  4. Combine formats - Leverage strengths of each format

Quality Control

  • Validate data completeness before analysis
  • Check for obvious errors in results
  • Compare results across different time periods
  • Verify calculations with manual spot checks

Next Steps