Output Schema - ChartsMaze EDL Pipeline

Overview

The EDL Pipeline produces a unified JSON file (all_stocks_fundamental_analysis.json.gz) containing 86 fields per stock across 2,775 stocks. This schema is the result of combining data from 12+ endpoints and processing through 4 pipeline phases. Output File:

Compressed: all_stocks_fundamental_analysis.json.gz (~8-10 MB)
Uncompressed: all_stocks_fundamental_analysis.json (~40 MB)
Records: 2,775 stocks
Fields per stock: 86

Schema Visualization

1. Identity & Classification

Fields (6)

Field	Type	Source	Example
`Symbol`	string	dhan_data_response.json	`"RELIANCE"`
`Name`	string	fundamental_data.json	`"Reliance Industries Ltd"`
`Listing Date`	string	nse_equity_list.csv	`"29-Nov-1977"`
`Basic Industry`	string	fundamental_data.json (CV.INDUSTRY_NAME)	`"Refineries"`
`Sector`	string	fundamental_data.json (CV.SECTOR)	`"Energy"`
`Index`	string	dhan_data_response.json (idxlist)	`"NIFTY 50, NIFTY 100, NIFTY ENERGY"`

Index Membership

The Index field contains comma-separated index names. The pipeline tracks 37 major indices:

"Index": "NIFTY 50, NIFTY 100, NIFTY ENERGY"

Tracked Index IDs: 13, 51, 38, 17, 18, 19, 20, 37, 1, 442, 443, 22, 5, 3, 444, 7, 14, 25, 27, 28, 447, 35, 41, 46, 44, 16, 43, 42, 45, 39, 466, 34, 32, 15, 33, 31, 30, 29

2. Fundamentals (Quarterly)

Fields (45)

Latest Quarter

Field	Type	Calculation	Example
`Latest Quarter`	string	cq.YEAR (split pipe, index 0)	`"Q3-FY24"`
`Net Profit Latest Quarter`	float	cq.NET_PROFIT (index 0)	`18500.0`
`Net Profit Previous Quarter`	float	cq.NET_PROFIT (index 1)	`17200.0`
`Net Profit 2 Quarters Back`	float	cq.NET_PROFIT (index 2)	`16800.0`
`Net Profit 3 Quarters Back`	float	cq.NET_PROFIT (index 3)	`15900.0`
`Net Profit Last Year Quarter`	float	cq.NET_PROFIT (index 4)	`17800.0`
`QoQ % Net Profit Latest`	float	((Latest - Previous) /	Previous	) * 100	`7.56`
`YoY % Net Profit Latest`	float	((Latest - Last Year) /	Last Year	) * 100	`3.93`

Field	Type	Calculation	Example
`EPS Latest Quarter`	float	cq.EPS (index 0)	`27.5`
`EPS Previous Quarter`	float	cq.EPS (index 1)	`25.6`
`EPS 2 Quarters Back`	float	cq.EPS (index 2)	`25.0`
`EPS 3 Quarters Back`	float	cq.EPS (index 3)	`23.7`
`EPS Last Year Quarter`	float	cq.EPS (index 4)	`26.5`
`QoQ % EPS Latest`	float	((Latest - Previous) /	Previous	) * 100	`7.42`
`YoY % EPS Latest`	float	((Latest - Last Year) /	Last Year	) * 100	`3.77`
`EPS Last Year`	float	cy.EPS (index 0) — Annual	`98.5`
`EPS 2 Years Back`	float	cy.EPS (index 1) — Annual	`92.3`

Sales (Revenue)

Field	Type	Calculation	Example
`Sales Latest Quarter`	float	cq.SALES (index 0)	`234000.0`
`Sales Previous Quarter`	float	cq.SALES (index 1)	`228000.0`
`Sales 2 Quarters Back`	float	cq.SALES (index 2)	`225000.0`
`Sales 3 Quarters Back`	float	cq.SALES (index 3)	`220000.0`
`Sales Last Year Quarter`	float	cq.SALES (index 4)	`232000.0`
`QoQ % Sales Latest`	float	((Latest - Previous) /	Previous	) * 100	`2.63`
`YoY % Sales Latest`	float	((Latest - Last Year) /	Last Year	) * 100	`0.86`
`Sales Growth 5 Years(%)`	float	((Current / 5Y Ago)^(1/5) - 1) * 100	`8.45`

OPM (Operating Profit Margin)

Field	Type	Calculation	Example
`OPM Latest Quarter`	float	cq.OPM (index 0)	`12.5`
`OPM Previous Quarter`	float	cq.OPM (index 1)	`11.8`
`OPM 2 Quarters Back`	float	cq.OPM (index 2)	`12.1`
`OPM 3 Quarters Back`	float	cq.OPM (index 3)	`11.5`
`OPM Last Year Quarter`	float	cq.OPM (index 4)	`12.3`
`QoQ % OPM Latest`	float	((Latest - Previous) /	Previous	) * 100	`5.93`
`YoY % OPM Latest`	float	((Latest - Last Year) /	Last Year	) * 100	`1.63`
`OPM TTM(%)`	float	ttm_cy.OPM	`12.2`

Pipe-Separated Values: All quarterly data from fundamental_data.json comes as pipe-separated strings:

"NET_PROFIT": "18500|17200|16800|15900|17800"

Indexing: 0=Latest, 1=Previous, 2=2Q Back, 3=3Q Back, 4=Same Quarter Last Year

3. Valuation Ratios

Fields (9)

Field	Type	Calculation	Example
`Market Cap(Cr.)`	float	CV.MARKET_CAP	`1658234.5`
`Stock Price(₹)`	float	dhan_tech.Ltp	`2456.75`
`P/E`	float	CV.STOCK_PE	`24.5`
`Forward P/E`	float	P/E * (TTM EPS / Annualized Current EPS)	`23.8`
`Historical P/E 5`	float	Reserved for future use	`0.0`
`PEG`	float	P/E / YoY EPS Growth	`6.5`
`ROE(%)`	float	roce_roe.ROE	`14.8`
`ROCE(%)`	float	roce_roe.ROCE	`16.2`
`D/E`	float	Non-Current Liabilities / Total Equity	`0.29`

Calculation Details

Forward P/E

if eps_latest > 0 and pe > 0:
    annualized_eps = eps_latest * 4
    ttm_eps = get_float(ttm_cy.get("EPS"))
    if annualized_eps > 0:
        forward_pe = pe * (ttm_eps / annualized_eps)

PEG Ratio

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

D/E Ratio

non_current_liab = bs_c.NON_CURRENT_LIABILITIES[0]
total_equity = bs_c.TOTAL_EQUITY[0]
de_ratio = non_current_liab / total_equity if total_equity != 0 else 0.0

4. Ownership & Float

Fields (4)

Field	Type	Calculation	Example
`FII % change QoQ`	float	sHp.FII[0] - sHp.FII[1]	`0.7`
`DII % change QoQ`	float	sHp.DII[0] - sHp.DII[1]	`0.2`
`Free Float(%)`	float	100 - sHp.PROMOTER[0]	`49.6`
`Float Shares(Cr.)`	float	(Market Cap / LTP) * (Free Float / 100)	`3345.2`

Shareholding Pattern Logic

fii_latest = get_value_from_pipe_string(shp.get("FII"), 0)
fii_prev = get_value_from_pipe_string(shp.get("FII"), 1)
fii_change_qoq = fii_latest - fii_prev

promoter_latest = get_value_from_pipe_string(shp.get("PROMOTER"), 0)
free_float_pct = 100.0 - promoter_latest

5. Technical Indicators

Fields (7)

Field	Type	Source	Example
`RSI (14)`	float	dhan_tech.DayRSI14CurrentCandle	`62.5`
`SMA Status`	string	advanced_indicators (calculated)	`"SMA 20: Above (4.9%) \| SMA 50: Above (24.1%)"`
`EMA Status`	string	advanced_indicators (calculated)	`"EMA 20: Above (6.3%) \| EMA 200: Above (72.6%)"`
`Technical Sentiment`	string	advanced_indicators.TechnicalIndicators	`"RSI: Neutral \| MACD: Bearish"`
`Pivot Point`	string	advanced_indicators.Pivots[0].Classic.PP	`"245.50"`
`Gap Up %`	float	Reserved (calculated from intraday data)	`0.0`

SMA/EMA Status Calculation

For each SMA (20, 50, 200):

for s in smas:
    ind_name = s.get("Indicator").replace("-SMA", "")
    val = get_float(s.get("Value"))
    
    if ind_name in ["20", "50", "200"] 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)}%)")

sma_status = " | ".join(sma_signals)

Output:

"SMA Status": "SMA 20: Above (4.9%) | SMA 50: Below (-2.3%) | SMA 200: Above (15.7%)"

6. Price Performance

Fields (9)

Field	Type	Source	Example
`1 Day Returns(%)`	float	dhan_tech.PPerchange	`1.23`
`1 Week Returns(%)`	float	dhan_tech.PricePerchng1week	`2.1`
`1 Month Returns(%)`	float	dhan_tech.PricePerchng1mon	`-3.5`
`3 Month Returns(%)`	float	dhan_tech.PricePerchng3mon	`8.2`
`6 Month Returns(%)`	float	Reserved	`0.0`
`1 Year Returns(%)`	float	dhan_tech.PricePerchng1year	`15.6`
`% from 52W High`	float	((LTP - High1Yr) / High1Yr) * 100	`-12.3`
`% from 52W Low`	float	Reserved	`0.0`
`% from ATH`	float	Calculated from ohlcv_data/*.csv	`-18.5`
`Day Range(%)`	float	Reserved	`0.0`

Fields marked as “Reserved” are placeholders for future enhancements. They currently return 0.0.

7. Volume & Liquidity

Fields (6)

Field	Type	Source Phase	Example
`RVOL`	float	Phase 4 (advanced_metrics_processor.py)	`1.45`
`200 Days EMA Volume`	float	Phase 4 (advanced_metrics_processor.py)	`12500000.0`
`% from 52W High 200 Days EMA Volume`	float	Phase 4	`-15.2`
`Daily Rupee Turnover 20(Cr.)`	float	Phase 4	`3450.5`
`Daily Rupee Turnover 50(Cr.)`	float	Phase 4	`3280.2`
`Daily Rupee Turnover 100(Cr.)`	float	Phase 4	`3120.8`
`30 Days Average Rupee Volume(Cr.)`	float	Phase 4	`3350.0`

Calculation Logic

RVOL (Relative Volume)

current_volume = today's volume from OHLCV
avg_20_day_volume = mean(last 20 days volume)
RVOL = current_volume / avg_20_day_volume

Interpretation:

RVOL > 1.5 — High volume (50% above average)
RVOL = 1.0 — Average volume
RVOL < 0.5 — Low volume (50% below average)

Daily Rupee Turnover

turnover_20 = mean(price * volume for last 20 days) / 10^7  # Convert to Crores

8. Volatility

Fields (4)

Field	Type	Source Phase	Example
`5 Days MA ADR(%)`	float	Phase 4 (advanced_metrics_processor.py)	`2.3`
`14 Days MA ADR(%)`	float	Phase 4	`2.5`
`20 Days MA ADR(%)`	float	Phase 4	`2.4`
`30 Days MA ADR(%)`	float	Phase 4	`2.6`

ADR (Average Daily Range) Calculation

for each day in period:
    daily_range = ((high - low) / low) * 100

ADR = mean(daily_range for period)

Example:

# Day 1: Low=2400, High=2480 → Range = 3.33%
# Day 2: Low=2440, High=2475 → Range = 1.43%
# ...
# 20 Days MA ADR = mean([3.33, 1.43, ...]) = 2.4%

Interpretation:

ADR > 5% — High volatility
ADR 2-5% — Normal volatility
ADR < 2% — Low volatility

9. Circuit & Price Bands

Fields (1)

Field	Type	Source Phase	Example
`Circuit Limit`	string	Phase 4 (add_corporate_events.py)	`"20%"`

Circuit Limit Values

20% — Standard circuit (most stocks)
10% — Restricted circuit
5% — Highly restricted (ASM/GSM stocks)
2% — Extreme restriction

Source: Matched from complete_price_bands.json (NSE CSV)

10. Earnings Tracking

Fields (3)

Field	Type	Source Phase	Example
`Quarterly Results Date`	string	Phase 4 (process_earnings_performance.py)	`"03-Mar-2024"`
`Returns since Earnings(%)`	float	Phase 4	`8.5`
`Max Returns since Earnings(%)`	float	Phase 4	`12.3`

Calculation Logic

# Step 1: Find latest results filing from company_filings/*.json
results_filings = [f for f in filings if "Results" in f.caption]
latest_results = results_filings[0]  # Most recent
results_date = latest_results.news_date

# Step 2: Get price from OHLCV on results_date - 1 (pre-earnings close)
pre_earnings_close = ohlcv[results_date - 1].close

# Step 3: Calculate returns
returns_since_earnings = ((current_price - pre_earnings_close) / pre_earnings_close) * 100

# Step 4: Calculate max returns (peak since results date)
max_price_since_results = max(ohlcv[results_date:].high)
max_returns_since_earnings = ((max_price_since_results - pre_earnings_close) / pre_earnings_close) * 100

Example:

Results Date: Mar 3, 2024
Pre-Earnings Close: ₹2,265
Current Price: ₹2,456 → Returns = +8.5%
Max Price Since Results: ₹2,543 → Max Returns = +12.3%

11. Event Markers

Field (1)

Field	Type	Source Phase	Example
`Event Markers`	array	Phase 4 (add_corporate_events.py)	See below

Event Marker Structure

"Event Markers": [
  {"icon": "★", "name": "STASM", "trigger": "Stock in ASM Stage 1"},
  {"icon": "📊", "name": "Results Recently Out", "trigger": "Released 5 days ago"},
  {"icon": "💸", "name": "Dividend (15-Mar)", "trigger": "Ex-date approaching"}
]

Complete Event Icons Reference

Icon	Name	Trigger Condition
★	LTASM / STASM	Stock in ASM surveillance groups
📊	Results Recently Out	Results released in last 7 days
🔑	Insider Trading	SEBI Reg 7(2) / Form C filing in last 15 days
📦	Block Deal	Bulk/Block deal in last 7 days
#	+/- Revision	Price band revision detected (incremental_price_bands.json)
⏰	Results (DD-Mon)	Upcoming results announcement (with date)
🎁	Bonus (DD-Mon)	Upcoming bonus issue (with date)
✂️	Split (DD-Mon)	Upcoming stock split (with date)
💸	Dividend (DD-Mon)	Upcoming dividend (with ex-date)
📈	Rights (DD-Mon)	Upcoming rights issue (with date)

Trigger Logic Examples

ASM/GSM Surveillance

if symbol in nse_asm_list:
    stage = nse_asm_list[symbol].surveillance_stage
    event_markers.append({
        "icon": "★",
        "name": stage,  # "STASM" or "LTASM"
        "trigger": f"Stock in {stage}"
    })

Results Recently Out

results_filings = [f for f in filings if "Results" in f.caption]
if results_filings:
    latest_date = parse_date(results_filings[0].news_date)
    days_ago = (today - latest_date).days
    if days_ago <= 7:
        event_markers.append({
            "icon": "📊",
            "name": "Results Recently Out",
            "trigger": f"Released {days_ago} days ago"
        })

Upcoming Dividend

upcoming_actions = [a for a in upcoming_corporate_actions if a.Symbol == symbol]
dividend_actions = [a for a in upcoming_actions if a.ActionType == "Dividend"]
if dividend_actions:
    ex_date = dividend_actions[0].ExDate
    amount = dividend_actions[0].Details  # e.g., "Rs 9 per share"
    event_markers.append({
        "icon": "💸",
        "name": f"Dividend ({ex_date})",
        "trigger": f"{amount}, Ex-date: {ex_date}"
    })

12. Recent Announcements (Regulatory)

Field (1)

Field	Type	Source Phase	Example
`Recent Announcements`	array	Phase 4 (add_corporate_events.py)	See below

Structure

Top 5 most recent regulatory filings:

"Recent Announcements": [
  {
    "Date": "03-Mar-2024",
    "Headline": "Outcome of Board Meeting - Quarterly Results",
    "URL": "https://www.bseindia.com/xml-data/corpfiling/AttachHis/abc123.pdf"
  },
  {
    "Date": "28-Feb-2024",
    "Headline": "Insider Trading - SEBI Reg 7(2)",
    "URL": "https://www.bseindia.com/xml-data/corpfiling/AttachHis/xyz789.pdf"
  },
  {
    "Date": "15-Feb-2024",
    "Headline": "Intimation of Board Meeting",
    "URL": "https://www.bseindia.com/xml-data/corpfiling/AttachHis/def456.pdf"
  }
]

Data Source

Files: company_filings/{SYMBOL}_filings.json Sorting: By news_date descending (newest first) Limit: Top 5 announcements

13. News Feed (Media)

Field (1)

Field	Type	Source Phase	Example
`News Feed`	array	Phase 4 (add_corporate_events.py)	See below

Structure

Top 5 most recent news items with AI sentiment:

"News Feed": [
  {
    "Title": "Reliance Q3 Results Beat Estimates",
    "Sentiment": "positive",
    "Date": "2024-03-03T14:30:00Z"
  },
  {
    "Title": "Jio Subscriber Growth Slows in Q3",
    "Sentiment": "neutral",
    "Date": "2024-03-02T10:15:00Z"
  },
  {
    "Title": "Reliance Retail Plans Aggressive Expansion",
    "Sentiment": "positive",
    "Date": "2024-03-01T16:45:00Z"
  }
]

Data Source

Files: market_news/{SYMBOL}_news.json Sorting: By published_date descending (newest first) Limit: Top 5 news items

Sentiment Values

positive — Bullish news (🟢)
neutral — Factual reporting (⚪)
negative — Bearish news (🔴)

Complete Schema Example

Single Stock Record

{
  "Symbol": "RELIANCE",
  "Name": "Reliance Industries Ltd",
  "Listing Date": "29-Nov-1977",
  "Basic Industry": "Refineries",
  "Sector": "Energy",
  "Index": "NIFTY 50, NIFTY 100, NIFTY ENERGY",
  "Market Cap(Cr.)": 1658234.5,
  "Stock Price(₹)": 2456.75,
  "Latest Quarter": "Q3-FY24",
  "Net Profit Latest Quarter": 18500.0,
  "Net Profit Previous Quarter": 17200.0,
  "Net Profit 2 Quarters Back": 16800.0,
  "Net Profit 3 Quarters Back": 15900.0,
  "Net Profit Last Year Quarter": 17800.0,
  "QoQ % Net Profit Latest": 7.56,
  "YoY % Net Profit Latest": 3.93,
  "EPS Latest Quarter": 27.5,
  "EPS Previous Quarter": 25.6,
  "EPS 2 Quarters Back": 25.0,
  "EPS 3 Quarters Back": 23.7,
  "EPS Last Year Quarter": 26.5,
  "QoQ % EPS Latest": 7.42,
  "YoY % EPS Latest": 3.77,
  "EPS Last Year": 98.5,
  "EPS 2 Years Back": 92.3,
  "Sales Latest Quarter": 234000.0,
  "Sales Previous Quarter": 228000.0,
  "Sales 2 Quarters Back": 225000.0,
  "Sales 3 Quarters Back": 220000.0,
  "Sales Last Year Quarter": 232000.0,
  "QoQ % Sales Latest": 2.63,
  "YoY % Sales Latest": 0.86,
  "Sales Growth 5 Years(%)": 8.45,
  "OPM Latest Quarter": 12.5,
  "OPM Previous Quarter": 11.8,
  "OPM 2 Quarters Back": 12.1,
  "OPM 3 Quarters Back": 11.5,
  "OPM Last Year Quarter": 12.3,
  "QoQ % OPM Latest": 5.93,
  "YoY % OPM Latest": 1.63,
  "OPM TTM(%)": 12.2,
  "P/E": 24.5,
  "Forward P/E": 23.8,
  "Historical P/E 5": 0.0,
  "PEG": 6.5,
  "ROE(%)": 14.8,
  "ROCE(%)": 16.2,
  "D/E": 0.29,
  "FII % change QoQ": 0.7,
  "DII % change QoQ": 0.2,
  "Free Float(%)": 49.6,
  "Float Shares(Cr.)": 3345.2,
  "1 Day Returns(%)": 1.23,
  "1 Week Returns(%)": 2.1,
  "1 Month Returns(%)": -3.5,
  "3 Month Returns(%)": 8.2,
  "6 Month Returns(%)": 0.0,
  "1 Year Returns(%)": 15.6,
  "% from 52W High": -12.3,
  "% from 52W Low": 0.0,
  "% from ATH": -18.5,
  "Gap Up %": 0.0,
  "Day Range(%)": 0.0,
  "RSI (14)": 62.5,
  "SMA Status": "SMA 20: Above (4.9%) | SMA 50: Above (24.1%) | SMA 200: Above (15.7%)",
  "EMA Status": "EMA 20: Above (6.3%) | EMA 50: Above (18.2%) | EMA 200: Above (72.6%)",
  "Technical Sentiment": "RSI: Neutral | MACD: Bullish",
  "Pivot Point": "2456.0",
  "RVOL": 1.45,
  "200 Days EMA Volume": 12500000.0,
  "% from 52W High 200 Days EMA Volume": -15.2,
  "Daily Rupee Turnover 20(Cr.)": 3450.5,
  "Daily Rupee Turnover 50(Cr.)": 3280.2,
  "Daily Rupee Turnover 100(Cr.)": 3120.8,
  "30 Days Average Rupee Volume(Cr.)": 3350.0,
  "5 Days MA ADR(%)": 2.3,
  "14 Days MA ADR(%)": 2.5,
  "20 Days MA ADR(%)": 2.4,
  "30 Days MA ADR(%)": 2.6,
  "Circuit Limit": "20%",
  "Quarterly Results Date": "03-Mar-2024",
  "Returns since Earnings(%)": 8.5,
  "Max Returns since Earnings(%)": 12.3,
  "Event Markers": [
    {"icon": "📊", "name": "Results Recently Out", "trigger": "Released 5 days ago"},
    {"icon": "💸", "name": "Dividend (15-Mar)", "trigger": "Rs 9 per share, Ex-date: 15-Mar-2024"}
  ],
  "Recent Announcements": [
    {
      "Date": "03-Mar-2024",
      "Headline": "Outcome of Board Meeting - Quarterly Results",
      "URL": "https://www.bseindia.com/xml-data/corpfiling/AttachHis/abc123.pdf"
    },
    {
      "Date": "28-Feb-2024",
      "Headline": "Insider Trading - SEBI Reg 7(2)",
      "URL": "https://www.bseindia.com/xml-data/corpfiling/AttachHis/xyz789.pdf"
    }
  ],
  "News Feed": [
    {
      "Title": "Reliance Q3 Results Beat Estimates",
      "Sentiment": "positive",
      "Date": "2024-03-03T14:30:00Z"
    },
    {
      "Title": "Jio Subscriber Growth Slows in Q3",
      "Sentiment": "neutral",
      "Date": "2024-03-02T10:15:00Z"
    }
  ]
}

Field Count Summary

Category	Field Count
Identity & Classification	6
Fundamentals (Quarterly)	45
Valuation Ratios	9
Ownership & Float	4
Technical Indicators	7
Price Performance	9
Volume & Liquidity	7
Volatility	4
Circuit & Price Bands	1
Earnings Tracking	3
Event Markers	1
Recent Announcements	1
News Feed	1
Total	86

Get Started

Core Concepts

Pipeline Scripts

Standalone Scripts

Configuration

​Overview

​Schema Visualization

​1. Identity & Classification

​Fields (6)

​Index Membership

​2. Fundamentals (Quarterly)

​Fields (45)

​Latest Quarter

​EPS (Earnings Per Share)

​Sales (Revenue)

​OPM (Operating Profit Margin)

​3. Valuation Ratios

​Fields (9)

​Calculation Details

​Forward P/E

​PEG Ratio

​D/E Ratio

​4. Ownership & Float

​Fields (4)

​Shareholding Pattern Logic

​5. Technical Indicators

​Fields (7)

​SMA/EMA Status Calculation

​6. Price Performance

​Fields (9)

​7. Volume & Liquidity

​Fields (6)

​Calculation Logic

​RVOL (Relative Volume)

​Daily Rupee Turnover

​8. Volatility

​Fields (4)

​ADR (Average Daily Range) Calculation

​9. Circuit & Price Bands

​Fields (1)

​Circuit Limit Values

​10. Earnings Tracking

​Fields (3)

​Calculation Logic

​11. Event Markers

​Field (1)

​Event Marker Structure

​Complete Event Icons Reference

​Trigger Logic Examples

​ASM/GSM Surveillance

​Results Recently Out

​Upcoming Dividend

​12. Recent Announcements (Regulatory)

​Field (1)

​Structure

​Data Source

​13. News Feed (Media)

​Field (1)

​Structure

​Data Source

​Sentiment Values

​Complete Schema Example

​Single Stock Record

​Field Count Summary

​Next Steps

Pipeline Architecture

Data Sources

Build docs developers (and LLMs) love

Overview

Schema Visualization

1. Identity & Classification

Fields (6)

Index Membership

2. Fundamentals (Quarterly)

Fields (45)

Latest Quarter

EPS (Earnings Per Share)

Sales (Revenue)

OPM (Operating Profit Margin)

3. Valuation Ratios

Fields (9)

Calculation Details

Forward P/E

PEG Ratio

D/E Ratio

4. Ownership & Float

Fields (4)

Shareholding Pattern Logic

5. Technical Indicators

Fields (7)

SMA/EMA Status Calculation

6. Price Performance

Fields (9)

7. Volume & Liquidity

Fields (6)

Calculation Logic

RVOL (Relative Volume)

Daily Rupee Turnover

8. Volatility

Fields (4)

ADR (Average Daily Range) Calculation

9. Circuit & Price Bands

Fields (1)

Circuit Limit Values

10. Earnings Tracking

Fields (3)

Calculation Logic

11. Event Markers

Field (1)

Event Marker Structure

Complete Event Icons Reference

Trigger Logic Examples

ASM/GSM Surveillance

Results Recently Out

Upcoming Dividend

12. Recent Announcements (Regulatory)

Field (1)

Structure

Data Source

13. News Feed (Media)

Field (1)

Structure

Data Source

Sentiment Values

Complete Schema Example

Single Stock Record

Field Count Summary

Next Steps