Single Stock Analysis - ChartsMaze EDL Pipeline

Overview

The single_stock_analyzer.py script provides detailed fundamental analysis for any stock in the ChartsMaze database. It extracts quarterly results, ratios, and ownership data directly from fundamental_data.json. Use Case: Quick inspection of a single stock’s fundamentals without processing the entire dataset.

Quick Start

Ensure fundamental_data.json exists

The analyzer requires fundamental_data.json from the pipeline:

cd ~/workspace/source/DO\ NOT\ DELETE\ EDL\ PIPELINE/
ls -lh fundamental_data.json

If missing, fetch it:

python3 fetch_dhan_data.py
python3 fetch_fundamental_data.py

Run the analyzer

python3 single_stock_analyzer.py SYMBOL

Example:

python3 single_stock_analyzer.py RELIANCE

Review output

The script prints comprehensive fundamental metrics to console (see Output Format below).

Usage

Basic Syntax

python3 single_stock_analyzer.py <SYMBOL>

Parameters:

<SYMBOL>: NSE stock symbol (e.g., RELIANCE, TCS, INFY, HDFCBANK)

Examples

# Analyze Reliance Industries
python3 single_stock_analyzer.py RELIANCE

# Analyze TCS
python3 single_stock_analyzer.py TCS

# Analyze HDFC Bank
python3 single_stock_analyzer.py HDFCBANK

# Analyze Infosys
python3 single_stock_analyzer.py INFY

Error Handling

Missing file:

python3 single_stock_analyzer.py RELIANCE

Error: fundamental_data.json not found.

Invalid symbol:

python3 single_stock_analyzer.py INVALID123

Stock 'INVALID123' not found in database.

No arguments:

python3 single_stock_analyzer.py

Usage: python3 single_stock_analyzer.py <SYMBOL>
Example: python3 single_stock_analyzer.py RELIANCE

Output Format

Sample Output (RELIANCE)

--- Analysis for RELIANCE ---

Net Profit Latest Quarter: 18200.0
Net Profit Previous Quarter: 17250.0
Net Profit 2 Quarters Back: 16800.0
Net Profit 3 Quarters Back: 16200.0
Net Profit Last Year Quarter: 16100.0
QoQ % Net Profit Latest: 5.51%
YoY % Net Profit Latest: 13.04%

EPS Latest Quarter: 27.5
EPS Previous Quarter: 26.1
EPS 2 Quarters Back: 25.4
EPS 3 Quarters Back: 24.5
EPS Last Year Quarter: 24.3
QoQ % EPS Latest: 5.36%
YoY % EPS Latest: 13.17%
EPS Last Year: 102.3
EPS 2 Years Back: 95.7

Sales Latest Quarter: 235000.0
Sales Previous Quarter: 228000.0
Sales 2 Quarters Back: 221000.0
Sales 3 Quarters Back: 218000.0
Sales Last Year Quarter: 215000.0
QoQ % Sales Latest: 3.07%
YoY % Sales Latest: 9.30%
Sales Growth 5 Years(%): 8.45%

OPM Latest Quarter: 12.5
OPM Previous Quarter: 12.1
OPM 2 Quarters Back: 11.8
OPM 3 Quarters Back: 11.5
OPM Last Year Quarter: 11.2
QoQ % OPM Latest: 3.31%
YoY % OPM Latest: 11.61%

Latest Quarter: Dec 2025
ROE(%): 15.2
ROCE(%): 18.7
D/E: 0.58
OPM TTM(%): 12.0
P/E: 28.5
FII % change QoQ: 1.25%
DII % change QoQ: -0.35%
PEG: 2.16
Forward P/E: 26.3
Historical P/E 5: 0.0 (Data Unavailable)

Metrics Explained

1. Net Profit Analysis

Metric	Description
Net Profit Latest Quarter	Net profit for most recent quarter (in Crores)
Net Profit Previous Quarter	Net profit for Q-1
Net Profit 2/3 Quarters Back	Net profit for Q-2, Q-3
Net Profit Last Year Quarter	Net profit for same quarter last year (for YoY comparison)
QoQ % Net Profit Latest	Quarter-over-Quarter growth: `(Latest - Previous) / Previous * 100`
YoY % Net Profit Latest	Year-over-Year growth: `(Latest - Last Year Q) / Last Year Q * 100`

Interpretation:

Positive QoQ: Sequential improvement
Positive YoY: Year-over-year growth
Consistent growth across quarters: Strong momentum

Metric	Description
EPS Latest Quarter	Earnings per share for latest quarter (₹)
EPS Previous/2Q/3Q Back	Historical quarterly EPS
EPS Last Year Quarter	Same quarter last year (for YoY)
QoQ % EPS Latest	Sequential EPS growth
YoY % EPS Latest	Year-over-year EPS growth
EPS Last Year	Full-year EPS for previous fiscal year
EPS 2 Years Back	EPS from 2 years ago

Interpretation:

Rising EPS: Improving profitability per share
YoY > 15%: High growth stock
Compare with P/E to assess valuation

3. Sales (Revenue)

Metric	Description
Sales Latest Quarter	Total revenue for latest quarter (in Crores)
Sales Previous/2Q/3Q Back	Historical quarterly revenue
Sales Last Year Quarter	Same quarter last year
QoQ % Sales Latest	Sequential revenue growth
YoY % Sales Latest	Year-over-year revenue growth
Sales Growth 5 Years(%)	5-year CAGR: `((Current / 5Y Ago)^(1/5) - 1) * 100`

Interpretation:

Consistent sales growth: Market share expansion
5Y CAGR > 10%: Strong long-term growth
Compare with profit growth to assess margin trends

4. Operating Profit Margin (OPM)

Metric	Description
OPM Latest Quarter	Operating margin % for latest quarter
OPM Previous/2Q/3Q Back	Historical quarterly margins
OPM Last Year Quarter	Same quarter last year
QoQ % OPM Latest	Sequential margin change
YoY % OPM Latest	Year-over-year margin change
OPM TTM(%)	Trailing Twelve Months operating margin

Interpretation:

Rising OPM: Improving operational efficiency
OPM > 15%: High-margin business
Declining OPM with rising sales: Volume growth at cost of margins

5. Valuation Ratios

Metric	Description	Formula
P/E	Price-to-Earnings ratio	`Stock Price / TTM EPS`
Forward P/E	Forward Price-to-Earnings	`Price / (Latest Q EPS * 4)`
PEG	Price/Earnings to Growth	`P/E / YoY EPS Growth %`
Historical P/E 5	5-year average P/E	Not available in current data

Interpretation:

P/E < 15: Potentially undervalued (or low growth)
P/E 15-25: Fairly valued
P/E > 25: Premium valuation (growth stock) or overvalued
Forward P/E < Current P/E: Earnings expected to grow
PEG < 1: Undervalued relative to growth
PEG > 2: Overvalued relative to growth

6. Return Ratios

Metric	Description	Benchmark
ROE(%)	Return on Equity	> 15% is good
ROCE(%)	Return on Capital Employed	> 15% is good
D/E	Debt-to-Equity ratio	< 1.0 is healthy

Interpretation:

ROE > 15%: Efficient use of shareholder equity
ROCE > ROE: Efficient use of total capital
D/E < 0.5: Conservative leverage
D/E 0.5-1.0: Moderate leverage
D/E > 1.0: High debt (check industry norms)

7. Ownership Changes

Metric	Description
FII % change QoQ	Foreign Institutional Investor holdings change (percentage points)
DII % change QoQ	Domestic Institutional Investor holdings change (percentage points)

Interpretation:

FII increase: Positive institutional sentiment
DII increase: Domestic confidence
Both increasing: Strong accumulation
Both decreasing: Institutional selling pressure

Data Source Structure

The analyzer reads from fundamental_data.json, which contains:

Data Sections

{
  "Symbol": "RELIANCE",
  "incomeStat_cq": {  // Quarterly Income Statement (Current Quarter)
    "NET_PROFIT": "18200|17250|16800|16200|16100",  // Latest|Q-1|Q-2|Q-3|Last Year Q
    "EPS": "27.5|26.1|25.4|24.5|24.3",
    "SALES": "235000|228000|221000|218000|215000",
    "OPM": "12.5|12.1|11.8|11.5|11.2",
    "YEAR": "Dec 2025|Sep 2025|Jun 2025|Mar 2025|Dec 2024"
  },
  "incomeStat_cy": {  // Annual Income Statement (Current Year)
    "EPS": "102.3|95.7|88.2|82.1|75.6",  // FY2025|FY2024|FY2023|...
    "SALES": "902000|850000|780000|720000|650000"
  },
  "TTM_cy": {  // Trailing Twelve Months
    "OPM": "12.0",
    "EPS": "105.8"
  },
  "CV": {  // Current Valuation
    "STOCK_PE": "28.5"
  },
  "roce_roe": {
    "ROE": "15.2",
    "ROCE": "18.7"
  },
  "sHp": {  // Shareholding Pattern
    "FII": "24.5|23.25",  // Latest|Previous
    "DII": "18.2|18.55"
  },
  "bs_c": {  // Balance Sheet Consolidated
    "NON_CURRENT_LIABILITIES": "185000|...",
    "TOTAL_EQUITY": "320000|..."
  }
}

Pipe-Separated Values

Most fields use pipe (|) separation for time series:

"NET_PROFIT": "18200|17250|16800|16200|16100"
                 ^      ^      ^      ^      ^
              Latest  Q-1    Q-2    Q-3   Last Year Q

Indexing:

index 0 = Latest quarter
index 1 = Previous quarter (Q-1)
index 2 = 2 quarters back (Q-2)
index 3 = 3 quarters back (Q-3)
index 4 = Last year same quarter (for YoY)

Use Cases

1. Pre-Investment Due Diligence

# Quick fundamental check before buying
python3 single_stock_analyzer.py TATAMOTORS

Look for:

Consistent QoQ and YoY growth in sales and profits
Improving or stable OPM
ROE > 15%, ROCE > 15%
PEG ratio < 1.5
Positive FII/DII changes

2. Quarterly Results Analysis

# After company announces Q4 results
python3 single_stock_analyzer.py INFY

Check:

Latest quarter vs previous quarter (QoQ growth)
Latest quarter vs last year same quarter (YoY growth)
Margin expansion or compression

3. Peer Comparison

# Compare IT sector stocks
python3 single_stock_analyzer.py TCS > tcs_analysis.txt
python3 single_stock_analyzer.py INFY > infy_analysis.txt
python3 single_stock_analyzer.py WIPRO > wipro_analysis.txt

# Compare key metrics manually or with diff
diff tcs_analysis.txt infy_analysis.txt

4. Tracking Portfolio Stocks

#!/bin/bash
# Daily fundamental check for portfolio

for stock in RELIANCE TCS HDFCBANK INFY ICICIBANK
do
    echo "\n=== $stock ==="
    python3 single_stock_analyzer.py $stock | grep -E "(YoY|ROE|P/E)"
done

Output:

=== RELIANCE ===
YoY % Net Profit Latest: 13.04%
YoY % EPS Latest: 13.17%
YoY % Sales Latest: 9.30%
ROE(%): 15.2
P/E: 28.5

=== TCS ===
YoY % Net Profit Latest: 8.2%
...

Scripting & Automation

Extract Specific Metrics

# Get only ROE and ROCE
python3 single_stock_analyzer.py RELIANCE | grep -E "(ROE|ROCE)"

Output:

ROE(%): 15.2
ROCE(%): 18.7

Batch Processing

#!/usr/bin/env python3
import subprocess
import re

stocks = ['RELIANCE', 'TCS', 'INFY', 'HDFCBANK', 'ICICIBANK']

for symbol in stocks:
    result = subprocess.run(
        ['python3', 'single_stock_analyzer.py', symbol],
        capture_output=True,
        text=True
    )
    
    # Extract ROE
    roe_match = re.search(r'ROE\(%\): ([0-9.]+)', result.stdout)
    roe = float(roe_match.group(1)) if roe_match else 0
    
    # Extract P/E
    pe_match = re.search(r'P/E: ([0-9.]+)', result.stdout)
    pe = float(pe_match.group(1)) if pe_match else 0
    
    print(f"{symbol:12} | ROE: {roe:5.1f}% | P/E: {pe:5.1f}")

Output:

RELIANCE     | ROE:  15.2% | P/E:  28.5
TCS          | ROE:  42.3% | P/E:  30.2
INFY         | ROE:  31.5% | P/E:  27.8
HDFCBANK     | ROE:  17.8% | P/E:  19.5
ICICIBANK    | ROE:  18.2% | P/E:  18.3

JSON Output Conversion

Convert console output to JSON for programmatic use:

#!/usr/bin/env python3
import json
import sys
from single_stock_analyzer import analyze_stock

# Modify single_stock_analyzer.py to return dict instead of print
# Or parse stdout with regex

def analyze_to_json(symbol):
    # This requires modifying single_stock_analyzer.py
    # to return a dictionary instead of printing
    data = {
        "symbol": symbol,
        "net_profit_latest": 18200.0,
        "qoq_np": 5.51,
        "yoy_np": 13.04,
        # ... etc
    }
    return data

if __name__ == "__main__":
    symbol = sys.argv[1]
    result = analyze_to_json(symbol)
    print(json.dumps(result, indent=2))

Limitations

No Technical Indicators: This script only analyzes fundamental data. For technical analysis (RSI, SMA, RVOL), use the full pipeline output.
No OHLCV Data: Price history, returns, and ATH are not available. Use ohlcv_data/{SYMBOL}.csv for price data.
Historical P/E Not Available: The script shows “Data Unavailable” for 5-year historical P/E.
Static Snapshot: Analyzes data from fundamental_data.json - run fetch_fundamental_data.py to refresh.
No News/Events: Corporate actions, announcements, and news are not included. See full pipeline output for these.

Advanced Usage

Modify Calculation Logic

Edit single_stock_analyzer.py to add custom metrics:

# Add Price-to-Book ratio
book_value_per_share = get_value_from_pipe_string(bs_c.get("TOTAL_EQUITY"), 0) / total_shares
price = pe * ttm_eps  # Back-calculate price from PE
pb_ratio = price / book_value_per_share if book_value_per_share > 0 else 0.0

print(f"P/B Ratio: {pb_ratio:.2f}")

Integration with Full Pipeline

Use as a validation step after pipeline completion:

#!/bin/bash
# Run pipeline then spot-check a few stocks

python3 run_full_pipeline.py

echo "\n=== Spot Check: Top 5 by Market Cap ==="
for stock in RELIANCE TCS HDFCBANK INFY BHARTIARTL
do
    echo "\n--- $stock ---"
    python3 single_stock_analyzer.py $stock | grep -E "(Latest Quarter|YoY|ROE)"
done

Troubleshooting

”fundamental_data.json not found”

Solution: Fetch the data first:

python3 fetch_dhan_data.py
python3 fetch_fundamental_data.py

“Stock ‘XYZ’ not found in database”

Causes:

Incorrect symbol (check NSE symbol, not company name)
Stock recently listed (not in database yet)
Stock delisted

Solution: Verify symbol on NSE website or run:

python3 -c "import json; data=json.load(open('fundamental_data.json')); print([s['Symbol'] for s in data if 'HDFC' in s['Symbol']])"

Zero Values for All Metrics

Cause: Stock exists but has no fundamental data (e.g., newly listed or suspended) Solution: Check if stock is actively traded and has filed quarterly results.

Next Steps

Running Full Pipeline - Generate complete dataset with all metrics
Incremental Updates - Keep data fresh with daily updates
Data Fields Reference - Complete list of 86 output fields

Usage

Data Management

Advanced

Documentation Index

​Overview

​Quick Start

​Usage

​Basic Syntax

​Examples

​Error Handling

​Output Format

​Sample Output (RELIANCE)

​Metrics Explained

​1. Net Profit Analysis

​2. Earnings Per Share (EPS)

​3. Sales (Revenue)

​4. Operating Profit Margin (OPM)

​5. Valuation Ratios

​6. Return Ratios

​7. Ownership Changes

​Data Source Structure

​Data Sections

​Pipe-Separated Values

​Use Cases

​1. Pre-Investment Due Diligence

​2. Quarterly Results Analysis

​3. Peer Comparison

​4. Tracking Portfolio Stocks

​Scripting & Automation

​Extract Specific Metrics

​Batch Processing

​JSON Output Conversion

​Limitations

​Advanced Usage

​Modify Calculation Logic

​Integration with Full Pipeline

​Troubleshooting

​”fundamental_data.json not found”

​“Stock ‘XYZ’ not found in database”

​Zero Values for All Metrics

​Next Steps

Build docs developers (and LLMs) love

Overview

Quick Start

Usage

Basic Syntax

Examples

Error Handling

Output Format

Sample Output (RELIANCE)

Metrics Explained

1. Net Profit Analysis

2. Earnings Per Share (EPS)

3. Sales (Revenue)

4. Operating Profit Margin (OPM)

5. Valuation Ratios

6. Return Ratios

7. Ownership Changes

Data Source Structure

Data Sections

Pipe-Separated Values

Use Cases

1. Pre-Investment Due Diligence

2. Quarterly Results Analysis

3. Peer Comparison

4. Tracking Portfolio Stocks

Scripting & Automation

Extract Specific Metrics

Batch Processing

JSON Output Conversion

Limitations

Advanced Usage

Modify Calculation Logic

Integration with Full Pipeline

Troubleshooting

”fundamental_data.json not found”

“Stock ‘XYZ’ not found in database”

Zero Values for All Metrics

Next Steps