Output Schema

Complete reference for the 86 fields in all_stocks_fundamental_analysis.json

The final output all_stocks_fundamental_analysis.json.gz contains 86 fields per stock across 2,775 stocks, organized into 13 logical categories. This page documents every field, its data type, calculation method, and source.

Overview

Total Stocks

2,775

Fields per Stock

86

Total Data Points

238,650

File Size: ~7.5 MB compressed (38 MB uncompressed)

Format: JSON array of objects (gzipped)

Field Categories

Identity (6)

1. Identity & Classification

Basic identifying information and classification for each stock.

Field	Type	Source	Description	Example
Symbol	String	master_isin_map.json	Stock ticker symbol	"RELIANCE"
Name	String	fundamental_data.json	Full company name	"Reliance Industries Limited"
Listing Date	String	nse_equity_list.csv	Date stock was listed on NSE	"29-NOV-1977"
Basic Industry	String	fundamental_data.json → CV.INDUSTRY_NAME	Industry classification	"Refineries"
Sector	String	fundamental_data.json → CV.SECTOR	Sector classification	"Energy"
Index	String	dhan_data_response.json → idxlist	Comma-separated list of indices	"NIFTY 50, NIFTY 100"

Index Filtering Logic

The pipeline filters for 37 specific index IDs:

requested_indices = {
    13,  # NIFTY 50
    51,  # NIFTY BANK
    38,  # NIFTY IT
    17,  # NIFTY NEXT 50
    # ... (33 more indices)
}

indices_found = []
for idx_obj in tech['idxlist']:
    if idx_obj['Indexid'] in requested_indices:
        indices_found.append(idx_obj['Name'])

stock['Index'] = ", ".join(indices_found)

If a stock is not part of any tracked index, this field shows "N/A".

Fundamentals (37)

2. Fundamentals (Quarterly Data)

Financial performance metrics from quarterly results. All pipe-delimited values (|) are parsed from fundamental_data.json.

Net Profit (5 fields + 2 growth)

Field	Type	Calculation	Example
Latest Quarter	String	incomeStat_cq.YEAR (first value)	"Dec-2023"
Net Profit Latest Quarter	Float	incomeStat_cq.NET_PROFIT (index 0)	18500.0
Net Profit Previous Quarter	Float	incomeStat_cq.NET_PROFIT (index 1)	17600.0
Net Profit 2 Quarters Back	Float	incomeStat_cq.NET_PROFIT (index 2)	16800.0
Net Profit 3 Quarters Back	Float	incomeStat_cq.NET_PROFIT (index 3)	16200.0
Net Profit Last Year Quarter	Float	incomeStat_cq.NET_PROFIT (index 4)	16500.0
QoQ % Net Profit Latest	Float	`((Latest - Previous) /	Previous	) × 100`	5.11
YoY % Net Profit Latest	Float	`((Latest - LastYr) /	LastYr	) × 100`	12.12

EPS (7 fields + 2 growth)

Field	Type	Calculation	Example
EPS Latest Quarter	Float	incomeStat_cq.EPS (index 0)	12.5
EPS Previous Quarter	Float	incomeStat_cq.EPS (index 1)	11.8
EPS 2 Quarters Back	Float	incomeStat_cq.EPS (index 2)	11.2
EPS 3 Quarters Back	Float	incomeStat_cq.EPS (index 3)	10.8
EPS Last Year Quarter	Float	incomeStat_cq.EPS (index 4)	11.0
EPS Last Year	Float	incomeStat_cy.EPS (index 0) - Annual	45.2
EPS 2 Years Back	Float	incomeStat_cy.EPS (index 1) - Annual	38.5
QoQ % EPS Latest	Float	`((Latest - Previous) /	Previous	) × 100`	5.93
YoY % EPS Latest	Float	`((Latest - LastYr) /	LastYr	) × 100`	13.64

Sales (6 fields + 2 growth + 5yr CAGR)

Field	Type	Calculation	Example
Sales Latest Quarter	Float	incomeStat_cq.SALES (index 0)	185000.0
Sales Previous Quarter	Float	incomeStat_cq.SALES (index 1)	178000.0
Sales 2 Quarters Back	Float	incomeStat_cq.SALES (index 2)	172000.0
Sales 3 Quarters Back	Float	incomeStat_cq.SALES (index 3)	168000.0
Sales Last Year Quarter	Float	incomeStat_cq.SALES (index 4)	165000.0
QoQ % Sales Latest	Float	`((Latest - Previous) /	Previous	) × 100`	3.93
YoY % Sales Latest	Float	`((Latest - LastYr) /	LastYr	) × 100`	12.12
Sales Growth 5 Years(%)	Float	((Current_Annual / 5Yr_Ago)^(1/5) - 1) × 100	8.7

5-Year Sales CAGR Formula

sales_current_annual = incomeStat_cy.SALES[0]
sales_5_years_ago = incomeStat_cy.SALES[5]

if sales_5_years_ago > 0:
    sales_growth_5y = ((sales_current_annual / sales_5_years_ago) ** (1/5) - 1) * 100

This gives the Compound Annual Growth Rate (CAGR) of sales over 5 years.

OPM (Operating Profit Margin) (6 fields + 2 growth + TTM)

Field	Type	Calculation	Example
OPM Latest Quarter	Float	incomeStat_cq.OPM (index 0)	11.5
OPM Previous Quarter	Float	incomeStat_cq.OPM (index 1)	11.2
OPM 2 Quarters Back	Float	incomeStat_cq.OPM (index 2)	10.8
OPM 3 Quarters Back	Float	incomeStat_cq.OPM (index 3)	10.5
OPM Last Year Quarter	Float	incomeStat_cq.OPM (index 4)	10.2
OPM TTM(%)	Float	TTM_cy.OPM - Trailing Twelve Months	11.0
QoQ % OPM Latest	Float	`((Latest - Previous) /	Previous	) × 100`	2.68
YoY % OPM Latest	Float	`((Latest - LastYr) /	LastYr	) × 100`	12.75

Financial Ratios (4 fields)

Field	Type	Calculation	Example
ROE(%)	Float	roce_roe.ROE	15.2
ROCE(%)	Float	roce_roe.ROCE	12.8
D/E	Float	bs_c.NON_CURRENT_LIABILITIES[0] / bs_c.TOTAL_EQUITY[0]	0.45

D/E Calculation Details

non_current_liab = bs_c['NON_CURRENT_LIABILITIES'].split('|')[0]
total_equity = bs_c['TOTAL_EQUITY'].split('|')[0]

de_ratio = float(non_current_liab) / float(total_equity) if total_equity != 0 else 0.0

This uses the most recent balance sheet data (index 0).

Valuation (7)

3. Valuation Ratios

Market valuation metrics.

Field	Type	Calculation	Example
Market Cap(Cr.)	Float	CV.MARKET_CAP	1700000.0
Stock Price(₹)	Float	dhan_data_response.Ltp	2540.75
P/E	Float	CV.STOCK_PE	28.5
Forward P/E	Float	P/E × (TTM_EPS / Annualized_Latest_EPS)	26.2
Historical P/E 5	Float	Reserved (currently 0.0)	0.0
PEG	Float	P/E / YoY_EPS_Growth	2.31
% from 52W High	Float	((LTP - High_52W) / High_52W) × 100	-8.5

Forward P/E Calculation

if eps_latest > 0 and pe > 0:
    annualized_eps = eps_latest * 4  # Quarterly to annual
    ttm_eps = TTM_cy['EPS']
    
    if annualized_eps > 0:
        forward_pe = pe * (ttm_eps / annualized_eps)

This estimates the P/E based on annualized latest quarter EPS vs. TTM EPS.

PEG Calculation

if yoy_eps > 0 and pe > 0:
    peg = pe / yoy_eps

Example:

• P/E = 28.5
• YoY EPS Growth = 12.3%
• PEG = 28.5 / 12.3 = 2.32

Interpretation:

• PEG < 1: Potentially undervalued
• PEG > 1: Potentially overvalued

Ownership (4)

4. Ownership & Float

Shareholding pattern changes and free float availability.

Field	Type	Calculation	Example
FII % change QoQ	Float	sHp.FII[0] - sHp.FII[1]	1.2
DII % change QoQ	Float	sHp.DII[0] - sHp.DII[1]	-0.5
Free Float(%)	Float	100 - sHp.PROMOTER[0]	49.5
Float Shares(Cr.)	Float	(Market_Cap / LTP) × (Free_Float / 100)	336.5

Float Shares Calculation

if mcap_cr > 0 and ltp > 0:
    total_shares_cr = mcap_cr / ltp
    float_shares_cr = total_shares_cr * (free_float_pct / 100.0)

Example (Reliance):

• Market Cap = ₹17,00,000 Cr
• LTP = ₹2,540.75
• Total Shares = 17,00,000 / 2,540.75 = 669.04 Cr
• Free Float % = 49.5%
• Float Shares = 669.04 × 0.495 = 331.17 Cr

Performance (6)

5. Price Performance

Historical return percentages over various time periods.

Field	Type	Source	Example
1 Day Returns(%)	Float	dhan_data_response.PPerchange	0.8
1 Week Returns(%)	Float	dhan_data_response.PricePerchng1week	2.5
1 Month Returns(%)	Float	dhan_data_response.PricePerchng1mon	4.2
3 Month Returns(%)	Float	dhan_data_response.PricePerchng3mon	8.5
6 Month Returns(%)	Float	Calculated from ohlcv_data/ (~126 days back)	15.3
1 Year Returns(%)	Float	dhan_data_response.PricePerchng1year	28.7

6 Month Returns Calculation

# From advanced_metrics_processor.py
price_6m_ago = df['Close'].iloc[-126] if len(df) >= 126 else df['Close'].iloc[0]
returns_6m = ((latest_close - price_6m_ago) / price_6m_ago) * 100

Uses OHLCV data to calculate exact 6-month (126 trading days) return.

Technical (6)

6. Technical Indicators

Technical analysis signals and levels.

Field	Type	Source/Calculation	Example
RSI (14)	Float	dhan_data_response.DayRSI14CurrentCandle	62.5
SMA Status	String	Parsed from advanced_indicator_data.SMA	"SMA 20: Above (4.9%) | SMA 50: Above (24.1%)"
EMA Status	String	Parsed from advanced_indicator_data.EMA	"EMA 20: Above (6.3%) | EMA 200: Above (72.6%)"
Technical Sentiment	String	Parsed from advanced_indicator_data.TechnicalIndicators	"RSI: Neutral | MACD: Bearish"
Pivot Point	String	advanced_indicator_data.Pivots[0].Classic.PP	"2485.50"
Gap Up %	Float	((Open - Prev_Close) / Prev_Close) × 100 from OHLCV	0.3

SMA/EMA Status Parsing

# From bulk_market_analyzer.py
target_smas = ["20", "50", "200"]
sma_signals = []

for s in advanced_indicator_data['SMA']:
    ind_name = s['Indicator'].replace("-SMA", "")  # "20-SMA" → "20"
    val = float(s['Value'])
    
    if ind_name in target_smas and val > 0 and ltp > 0:
        diff = ((ltp - val) / val) * 100
        status = "Above" if diff > 0 else "Below"
        sma_signals.append(f"SMA {ind_name}: {status} ({round(diff, 1)}%)")

stock['SMA Status'] = " | ".join(sma_signals)

Example Output:

• Price: ₹2,540.75
• SMA 20: ₹2,420 → “Above (4.9%)”
• SMA 50: ₹2,050 → “Above (24.1%)”
• Result: "SMA 20: Above (4.9%) | SMA 50: Above (24.1%)"

Technical Sentiment Parsing

tech_inds = advanced_indicator_data['TechnicalIndicators']
sentiment_summary = []

for t in tech_inds:
    name = t['Indicator']
    action = t['Action']  # "Bullish", "Bearish", "Neutral"
    
    if "RSI" in name:
        sentiment_summary.append(f"RSI: {action}")
    elif "MACD" in name:
        sentiment_summary.append(f"MACD: {action}")

stock['Technical Sentiment'] = " | ".join(sentiment_summary)

Volatility (11)

7. Volatility & Volume

Volatility metrics (ADR) and volume analysis. All calculated from ohlcv_data/{SYMBOL}.csv.

Average Daily Range (ADR)

Field	Type	Calculation	Example
5 Days MA ADR(%)	Float	mean((High - Low) / Low × 100) last 5 days	2.1
14 Days MA ADR(%)	Float	mean((High - Low) / Low × 100) last 14 days	2.3
20 Days MA ADR(%)	Float	mean((High - Low) / Low × 100) last 20 days	2.4
30 Days MA ADR(%)	Float	mean((High - Low) / Low × 100) last 30 days	2.5
Day Range(%)	Float	((High - Low) / Low) × 100 for latest day	1.8

ADR Calculation Code

# From advanced_metrics_processor.py
df['Daily_Range_Pct'] = ((df['High'] - df['Low']) / df['Low']) * 100

adr_5 = df['Daily_Range_Pct'].tail(5).mean()
adr_14 = df['Daily_Range_Pct'].tail(14).mean()
adr_20 = df['Daily_Range_Pct'].tail(20).mean()
adr_30 = df['Daily_Range_Pct'].tail(30).mean()

Use Case: Helps identify volatility patterns and potential breakout candidates.

Volume Metrics

Field	Type	Calculation	Example
RVOL	Float	Latest_Volume / Avg_Volume_20_Days	1.25
200 Days EMA Volume	Float	EMA(Volume, 200) latest value	8500000
% from 52W High 200 Days EMA Volume	Float	((Latest_EMA - 52W_High_EMA) / 52W_High_EMA) × 100	-15.2
Daily Rupee Turnover 20(Cr.)	Float	mean(Close × Volume / 10M) last 20 days	1250
Daily Rupee Turnover 50(Cr.)	Float	mean(Close × Volume / 10M) last 50 days	1180
Daily Rupee Turnover 100(Cr.)	Float	mean(Close × Volume / 10M) last 100 days	1150
30 Days Average Rupee Volume(Cr.)	Float	mean(Close × Volume / 10M) last 30 days	1200

RVOL Calculation

avg_vol_20 = df['Volume'].tail(21).iloc[:-1].mean()  # Exclude latest day
rvol = latest_volume / avg_vol_20 if avg_vol_20 > 0 else 0

Interpretation:

• RVOL > 1.5: Unusually high volume (potential breakout)
• RVOL < 0.5: Low volume (potential consolidation)
• RVOL ≈ 1.0: Normal volume

Turnover Calculation

df['Turnover_Cr'] = (df['Close'] * df['Volume']) / 10000000

turnover_20 = df['Turnover_Cr'].tail(20).mean()
turnover_50 = df['Turnover_Cr'].tail(50).mean()
turnover_100 = df['Turnover_Cr'].tail(100).mean()

Note: Turnover is in Crores (₹10 Million units).

Historical (3)

8. Historical Price Metrics

All-time and 52-week benchmarks from OHLCV data.

Field	Type	Calculation	Example
ATH	Float	max(High) across all history	2975.50
% from ATH	Float	((ATH - Latest_Close) / ATH) × 100	-14.6
% from 52W Low	Float	((Latest_Close - Low_52W) / Low_52W) × 100	35.8

ATH & 52W Calculations

# All-Time High
ath = df['High'].max()  # Across entire OHLCV history
pct_from_ath = ((ath - latest_close) / ath) * 100

# 52-Week Low
low_52w = df['Low'].tail(252).min()  # ~252 trading days = 1 year
pct_from_52w_low = ((latest_close - low_52w) / low_52w) * 100

Use Cases:

• ATH: Identify stocks near all-time highs (momentum)
• 52W Low: Find stocks recovering from lows (reversal)

Earnings (3)

9. Earnings Tracking

Post-earnings price performance metrics.

Field	Type	Calculation	Example
Quarterly Results Date	String	Latest “Quarterly Results” filing from company_filings/	"15-Jan-2024"
Returns since Earnings(%)	Float	((Current_Price - Results_Day_Close) / Results_Day_Close) × 100	3.5
Max Returns since Earnings(%)	Float	((Max_Price_Since - Results_Day_Close) / Results_Day_Close) × 100	8.2

Earnings Performance Calculation

# From process_earnings_performance.py

# 1. Find latest quarterly results filing
filings = load_filings(f"company_filings/{symbol}_filings.json")
results_filing = [f for f in filings if "Quarterly Results" in f['caption']][0]
results_date = results_filing['news_date']

# 2. Get closing price on results date
ohlcv = load_csv(f"ohlcv_data/{symbol}.csv")
results_close = ohlcv.loc[results_date, 'Close']
current_price = ohlcv.iloc[-1]['Close']

# 3. Calculate returns
returns = ((current_price - results_close) / results_close) * 100

# 4. Find max price since results
max_price_since = ohlcv[results_date:]['High'].max()
max_returns = ((max_price_since - results_close) / results_close) * 100

Use Case: Track how stocks perform after earnings announcements.

F&O (3)

10. F&O Data

Futures & Options availability and contract details.

Field	Type	Source	Example
FNO Flag	String	enrich_fno_data.py checks if symbol in F&O list	"Yes" or "No"
Lot Size	Integer	fno_lot_sizes_cleaned.json lookup	250
Next Expiry	String	fno_expiry_calendar.json lookup	"28-Mar-2024"

If FNO Flag = "No", the Lot Size and Next Expiry fields will be empty or show default values.

Circuit (1)

11. Circuit & Price Bands

Current price movement limits.

Field	Type	Source	Example
Circuit Limit	String	complete_price_bands.json lookup	"20%", "10%", "5%"

Circuit Limit Values

Common Circuit Limits:

• "20%": Standard for most liquid stocks
• "10%": Stocks with lower liquidity
• "5%": Stocks under surveillance or with very low liquidity
• "2%": Stocks under ASM/GSM (Additional Surveillance Measure)

Source: NSE’s daily price band file parsed by fetch_complete_price_bands.py

Events (3)

12. Event Markers

Array of icons/labels indicating recent or upcoming events.

Field	Type	Source	Example
Event Markers	Array[String]	Aggregated from multiple sources	["📊: Results Recently Out", "💸: Dividend (15-Mar)"]

Event Types

Surveillance

Icon: "★: LTASM" or "★: STASM"

Source: nse_asm_list.json

Trigger: Stock is in Long-Term or Short-Term Additional Surveillance Measure

Results Recently Out

Icon: "📊: Results Recently Out"

Source: company_filings/ (checks for “Quarterly Results” filing in last 7 days)

Trigger: Quarterly results released within 7 days

Insider Trading

Icon: "🔑: Insider Trading"

Source: company_filings/ (checks for “Reg 7(2)” or “Form C” in last 15 days)

Trigger: Insider trading disclosure filed within 15 days

Block Deal

Icon: "📦: Block Deal"

Source: bulk_block_deals.json (checks for deal in last 7 days)

Trigger: Bulk or block deal executed within 7 days

Circuit Revision

Icon: "#: +/- Revision"

Source: incremental_price_bands.json

Trigger: Price band change detected (e.g., 20% → 10%)

Upcoming Events

Icons:

• "⏰: Results (DD-Mon)" - Upcoming quarterly results
• "💸: Dividend (DD-Mon)" - Upcoming dividend
• "🎁: Bonus (DD-Mon)" - Upcoming bonus issue
• "✂️: Split (DD-Mon)" - Upcoming stock split
• "📈: Rights (DD-Mon)" - Upcoming rights issue

Source: upcoming_corporate_actions.json

Trigger Windows:

• Results: Within 14 days
• Other actions: Within 30 days

13. Recent Announcements

Top 5 most recent regulatory filings.

Field	Type	Source	Example
Recent Announcements	Array[Object]	company_filings/{SYMBOL}_filings.json (sorted by date, top 5)	See below

Object Structure:

{
  "Date": "2024-01-15",
  "Headline": "Outcome of Board Meeting - Quarterly Results",
  "URL": "https://www.bseindia.com/xml-data/corpfiling/..."
}

14. News Feed

Top 5 most recent news items with sentiment.

Field	Type	Source	Example
News Feed	Array[Object]	market_news/{SYMBOL}_news.json (sorted by date, top 5)	See below

Object Structure:

{
  "Title": "Reliance announces new green energy initiative",
  "Sentiment": "positive",  // "positive" | "negative" | "neutral"
  "Date": "2024-03-02"
}

Complete Field List (Alphabetical)

All 86 Fields (Click to Expand)

1. 1 Day Returns(%)
2. 1 Month Returns(%)
3. 1 Week Returns(%)
4. 1 Year Returns(%)
5. 3 Month Returns(%)
6. 5 Days MA ADR(%)
7. 6 Month Returns(%)
8. 14 Days MA ADR(%)
9. 20 Days MA ADR(%)
10. 30 Days Average Rupee Volume(Cr.)
11. 30 Days MA ADR(%)
12. 200 Days EMA Volume
13. % from 52W High
14. % from 52W High 200 Days EMA Volume
15. % from 52W Low
16. % from ATH
17. ATH
18. Basic Industry
19. Circuit Limit
20. D/E
21. Daily Rupee Turnover 100(Cr.)
22. Daily Rupee Turnover 20(Cr.)
23. Daily Rupee Turnover 50(Cr.)
24. Day Range(%)
25. DII % change QoQ
26. EMA Status
27. EPS 2 Quarters Back
28. EPS 2 Years Back
29. EPS 3 Quarters Back
30. EPS Last Year
31. EPS Last Year Quarter
32. EPS Latest Quarter
33. EPS Previous Quarter
34. Event Markers
35. FII % change QoQ
36. Float Shares(Cr.)
37. FNO Flag
38. Forward P/E
39. Free Float(%)
40. Gap Up %
41. Historical P/E 5
42. Index
43. Latest Quarter
44. Listing Date
45. Lot Size
46. Market Cap(Cr.)
47. Max Returns since Earnings(%)
48. Name
49. Net Profit 2 Quarters Back
50. Net Profit 3 Quarters Back
51. Net Profit Last Year Quarter
52. Net Profit Latest Quarter
53. Net Profit Previous Quarter
54. News Feed
55. Next Expiry
56. OPM 2 Quarters Back
57. OPM 3 Quarters Back
58. OPM Last Year Quarter
59. OPM Latest Quarter
60. OPM Previous Quarter
61. OPM TTM(%)
62. P/E
63. PEG
64. Pivot Point
65. QoQ % EPS Latest
66. QoQ % Net Profit Latest
67. QoQ % OPM Latest
68. QoQ % Sales Latest
69. Quarterly Results Date
70. Recent Announcements
71. Returns since Earnings(%)
72. ROCE(%)
73. ROE(%)
74. RSI (14)
75. RVOL
76. Sales 2 Quarters Back
77. Sales 3 Quarters Back
78. Sales Growth 5 Years(%)
79. Sales Last Year Quarter
80. Sales Latest Quarter
81. Sales Previous Quarter
82. Sector
83. SMA Status
84. Stock Price(₹)
85. Symbol
86. Technical Sentiment
87. YoY % EPS Latest
88. YoY % Net Profit Latest
89. YoY % OPM Latest
90. YoY % Sales Latest

Field Dependencies by Phase

If FETCH_OHLCV = False, 15 fields from Phase 4.1 (Advanced Metrics) and 3 fields from Phase 4.2 (Earnings Performance) will be zero or empty, resulting in 68 meaningful fields instead of 86.

Usage Examples

Load & Explore

import json
import gzip

# Load the compressed file
with gzip.open('all_stocks_fundamental_analysis.json.gz', 'rt') as f:
    stocks = json.load(f)

print(f"Total stocks: {len(stocks)}")
print(f"Fields per stock: {len(stocks[0])}")

# Find stocks with high RVOL
high_rvol = [s for s in stocks if s.get('RVOL', 0) > 2.0]
print(f"Stocks with RVOL > 2.0: {len(high_rvol)}")

Next Steps

Pipeline Architecture

Understand how these fields are generated

Data Flow

Trace data transformations across phases

Quick Start

Run your first pipeline

Field Reference

Complete reference of all 86 fields

Advanced Usage

Learn advanced filtering and analysis techniques