OHLCV Data API - ChartsMaze EDL Pipeline

Overview

The OHLCV Data API retrieves historical and current-day daily candlestick data for NSE equities. It uses a hybrid approach combining historical tick data API with live market snapshots to provide complete OHLCV coverage including today’s data. Source File: fetch_all_ohlcv.py

Endpoint Details

Historical Tick Data API

URL

string

required

https://openweb-ticks.dhan.co/getDataH

Method

string

required

POST

Live Snapshot API

URL

string

required

https://ow-scanx-analytics.dhan.co/customscan/fetchdt

Method

string

required

POST

Request Headers

{
  "Content-Type": "application/json",
  "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
  "Accept": "application/json, text/plain, */*",
  "Origin": "https://scanx.dhan.co",
  "Referer": "https://scanx.dhan.co/"
}

Request Payloads

Historical OHLCV Request

EXCH

string

required

Exchange code (“NSE” for NSE equities)

SYM

string

required

Trading symbol

SEG

string

required

Segment (“E” for equities)

INST

string

required

Instrument type (“EQUITY”)

SEC_ID

string

required

Security ID (Sid from master_isin_map.json)

EXPCODE

integer

default:"0"

Expiry code (0 for equities)

INTERVAL

string

required

Timeframe - “D” for daily, “W” for weekly, “M” for monthly

START

integer

required

Start timestamp (Unix epoch seconds)

END

integer

required

End timestamp (Unix epoch seconds)

Example Historical Payload

{
  "EXCH": "NSE",
  "SYM": "RELIANCE",
  "SEG": "E",
  "INST": "EQUITY",
  "SEC_ID": "11536",
  "EXPCODE": 0,
  "INTERVAL": "D",
  "START": 1672531200,
  "END": 1705449600
}

Live Snapshot Request

{
  "data": {
    "sort": "Volume",
    "sorder": "desc",
    "count": 5000,
    "fields": ["Sym", "Open", "High", "Low", "Ltp", "Volume"],
    "params": [{"field": "Exch", "op": "", "val": "NSE"}]
  }
}

Example Requests

Historical OHLCV

curl -X POST https://openweb-ticks.dhan.co/getDataH \
  -H "Content-Type: application/json" \
  -H "Origin: https://scanx.dhan.co" \
  -H "Referer: https://scanx.dhan.co/" \
  -d '{
    "EXCH": "NSE",
    "SYM": "RELIANCE",
    "SEG": "E",
    "INST": "EQUITY",
    "SEC_ID": "11536",
    "EXPCODE": 0,
    "INTERVAL": "D",
    "START": 1672531200,
    "END": 1705449600
  }'

Live Snapshot

curl -X POST https://ow-scanx-analytics.dhan.co/customscan/fetchdt \
  -H "Content-Type: application/json" \
  -H "Origin: https://scanx.dhan.co" \
  -d '{
    "data": {
      "sort": "Volume",
      "sorder": "desc",
      "count": 5000,
      "fields": ["Sym", "Open", "High", "Low", "Ltp", "Volume"],
      "params": [{"field": "Exch", "op": "", "val": "NSE"}]
    }
  }'

Response Structure

Historical OHLCV Response

data.Time

array

Array of timestamps or date strings

data.o

array

Array of open prices

data.h

array

Array of high prices

data.l

array

Array of low prices

data.c

array

Array of close prices

data.v

array

Array of volumes

Example Historical Response

{
  "data": {
    "Time": ["2024-01-15", "2024-01-16", "2024-01-17"],
    "o": [2590.0, 2600.5, 2595.0],
    "h": [2610.0, 2615.5, 2608.0],
    "l": [2585.0, 2595.0, 2590.0],
    "c": [2605.0, 2598.5, 2602.0],
    "v": [5234567, 4876543, 5123456]
  }
}

Live Snapshot Response

{
  "data": [
    {
      "Sym": "RELIANCE",
      "Open": 2598.0,
      "High": 2615.5,
      "Low": 2590.0,
      "Ltp": 2605.0,
      "Volume": 5234567
    }
  ]
}

Implementation Details

Hybrid Multi-Chunk Strategy

import json
import requests
import csv
import time
from datetime import datetime
from concurrent.futures import ThreadPoolExecutor, as_completed

TICK_API_URL = "https://openweb-ticks.dhan.co/getDataH"
SCANX_URL = "https://ow-scanx-analytics.dhan.co/customscan/fetchdt"
CHUNK_DAYS = 180  # Fetch in 6-month chunks
MAX_THREADS = 15

def get_live_snapshots():
    """Fetch live OHLCV snapshot for all stocks (Today's data)"""
    payload = {
        "data": {
            "sort": "Volume", "sorder": "desc", "count": 5000,
            "fields": ["Sym", "Open", "High", "Low", "Ltp", "Volume"],
            "params": [{"field": "Exch", "op": "", "val": "NSE"}]
        }
    }
    try:
        response = requests.post(SCANX_URL, json=payload, headers=headers, timeout=15)
        if response.status_code == 200:
            return {i['Sym']: i for i in response.json().get('data', [])}
    except:
        pass
    return {}

def fetch_history_chunk(payload):
    """Fetch a single chunk of historical data"""
    try:
        response = requests.post(TICK_API_URL, json=payload, headers=headers, timeout=15)
        if response.status_code == 200:
            data = response.json().get("data", {})
            times = data.get("Time", [])
            if times:
                o, h, l, c, v = data.get("o", []), data.get("h", []), data.get("l", []), data.get("c", []), data.get("v", [])
                rows = []
                for i in range(len(times)):
                    t = times[i]
                    dt_str = t if isinstance(t, str) else datetime.fromtimestamp(t).strftime("%Y-%m-%d")
                    rows.append({
                        'Date': dt_str,
                        'Open': o[i],
                        'High': h[i],
                        'Low': l[i],
                        'Close': c[i],
                        'Volume': v[i]
                    })
                return rows
    except:
        pass
    return []

def fetch_single_stock(sym, details, live_snapshot=None):
    output_path = f"ohlcv_data/{sym}.csv"
    today_str = datetime.now().strftime("%Y-%m-%d")
    
    # Determine starting point
    global_start_ts = int(time.time()) - (2 * 365 * 86400)  # 2 years back
    target_start = global_start_ts
    
    existing_rows = []
    if os.path.exists(output_path):
        try:
            with open(output_path, "r") as f:
                existing_rows = list(csv.DictReader(f))
                if existing_rows:
                    last_date = existing_rows[-1]["Date"]
                    last_dt = datetime.strptime(last_date, "%Y-%m-%d")
                    target_start = int(last_dt.timestamp()) + 86400  # Next day
        except:
            pass
    
    # Fetch missing history in chunks
    new_rows = []
    current_end = int(time.time())
    
    if target_start < current_end - 86400:
        chunk_ptr = current_end
        while chunk_ptr > target_start:
            c_start = max(target_start, chunk_ptr - (CHUNK_DAYS * 86400))
            payload = {
                "EXCH": details["Exch"],
                "SYM": sym,
                "SEG": details["Seg"],
                "INST": details["Inst"],
                "SEC_ID": details["Sid"],
                "EXPCODE": 0,
                "INTERVAL": "D",
                "START": int(c_start),
                "END": int(chunk_ptr)
            }
            chunk_rows = fetch_history_chunk(payload)
            if chunk_rows:
                new_rows.extend(chunk_rows)
            
            chunk_ptr = c_start - 86400
    
    # Add today's data from live snapshot
    if live_snapshot:
        s = live_snapshot
        today_row = {
            'Date': today_str,
            'Open': s.get('Open', 0),
            'High': s.get('High', 0),
            'Low': s.get('Low', 0),
            'Close': s.get('Ltp', 0),
            'Volume': s.get('Volume', 0)
        }
        new_rows.append(today_row)
    
    if not new_rows:
        return "uptodate"
    
    # Merge and deduplicate
    merged = {r['Date']: r for r in existing_rows + new_rows}
    final_rows = sorted(merged.values(), key=lambda x: x['Date'])
    
    if not final_rows:
        return "uptodate"
    
    # Save to CSV
    with open(output_path, "w", newline='') as f:
        writer = csv.DictWriter(f, fieldnames=['Date', 'Open', 'High', 'Low', 'Close', 'Volume'])
        writer.writeheader()
        writer.writerows(final_rows)
    
    return "success"

Configuration

Max Threads

integer

default:"15"

Concurrent threads for parallel processing

Chunk Size

integer

default:"180"

Days per chunk (6 months)

Default History

string

2 years back from today (ensures 200-day MA calculation)

Timeout

integer

default:"15"

Request timeout in seconds

Input File

string

required

dhan_data_response.json (requires Sid field)

Output Directory

string

ohlcv_data/{SYMBOL}.csv

Output Structure

Each stock gets its own CSV file:

ohlcv_data/
├── RELIANCE.csv
├── TCS.csv
└── HDFCBANK.csv

CSV Format

Date,Open,High,Low,Close,Volume
2024-01-15,2590.0,2610.0,2585.0,2605.0,5234567
2024-01-16,2600.5,2615.5,2595.0,2598.5,4876543
2024-01-17,2595.0,2608.0,2590.0,2602.0,5123456

Performance Metrics

Total Stocks: ~2,775
Threads: 15 concurrent requests
History Depth: 2 years (default)
Chunk Size: 180 days
Time per Stock: 5-10 seconds (initial), 1-2 seconds (incremental)
Total Time: ~30-45 minutes (initial full sync), ~5-10 minutes (daily updates)
Success Rate: >95%
Output Size: ~300-500 KB per stock (2 years daily data)

Progress Tracking

Syncing OHLCV for 2775 stocks (Hybrid Multi-Chunk Mode)...
Done! Updated: 2650 | UpToDate: 100 | Errors: 25

Incremental Updates

The implementation supports efficient incremental updates:

Check Existing File: Read last date from CSV
Calculate Gap: Determine missing date range
Fetch Gap Only: Request only missing data
Add Today: Append live snapshot for current day
Merge & Save: Combine with existing data

Incremental Example

# Existing file has data up to 2024-01-10
# Today is 2024-01-17
# Script fetches only 2024-01-11 to 2024-01-16 from tick API
# Adds 2024-01-17 from live snapshot
# Merges all and saves

Use Cases

Technical Analysis: Calculate moving averages, RSI, MACD
Backtesting: Test trading strategies on historical data
Pattern Recognition: Identify chart patterns
Volatility Analysis: Calculate ATR, Bollinger Bands
Volume Analysis: Track volume trends and spikes
Price Action: Study candlestick patterns
Data Exports: Build custom analytics dashboards

Data Quality

Adjustments

Data is NOT adjusted for splits/bonuses by default
Manual adjustment required for corporate actions
Use corporate actions API to identify adjustment dates

Gaps

Market holidays: No data (expected gaps)
Stock suspensions: No data during suspension
Newly listed: Data starts from listing date
Delisted: Data ends at delisting date

Technical Indicators Examples

Simple Moving Average (SMA)

import pandas as pd

df = pd.read_csv("ohlcv_data/RELIANCE.csv")
df['SMA_50'] = df['Close'].rolling(window=50).mean()
df['SMA_200'] = df['Close'].rolling(window=200).mean()

RSI (Relative Strength Index)

def calculate_rsi(df, period=14):
    delta = df['Close'].diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

df['RSI_14'] = calculate_rsi(df)

Volume Analysis

# Average volume
df['VolMA_20'] = df['Volume'].rolling(window=20).mean()

# Volume spikes
df['VolSpike'] = df['Volume'] > (df['VolMA_20'] * 2)

Error Handling

Missing Sid

if not sid:
    return "error"  # Skip stocks without Sid

API Timeout

try:
    response = requests.post(url, json=payload, headers=headers, timeout=15)
except requests.Timeout:
    return "error"  # Timeout after 15 seconds

Empty Response

if not times:
    return []  # No data for this chunk (market holiday, etc.)

Notes

Sid (Security ID) is mandatory - stocks without Sid are skipped
Data includes both historical and current-day (live) candles
Current day data updates during market hours (9:15 AM - 3:30 PM IST)
Post-market, current day becomes historical next trading day
Chunk size of 180 days balances API limits and efficiency
CSV format allows easy import into Excel, pandas, or TA-Lib
Date format is YYYY-MM-DD for universal compatibility
Volume is in number of shares (not value)
All prices are in INR
Run daily after market close for up-to-date data
Initial sync takes 30-45 minutes; incremental updates take 5-10 minutes
Consider running incrementally (only fetch new data) for daily updates

Endpoints

Output Fields

​Overview

​Endpoint Details

​Historical Tick Data API

​Live Snapshot API

​Request Headers

​Request Payloads

​Historical OHLCV Request

​Example Historical Payload

​Live Snapshot Request

​Example Requests

​Historical OHLCV

​Live Snapshot

​Response Structure

​Historical OHLCV Response

​Example Historical Response

​Live Snapshot Response

​Implementation Details

​Hybrid Multi-Chunk Strategy

​Configuration

​Output Structure

​CSV Format

​Performance Metrics

​Progress Tracking

​Incremental Updates

​Incremental Example

​Use Cases

​Data Quality

​Adjustments

​Gaps

​Technical Indicators Examples

​Simple Moving Average (SMA)

​RSI (Relative Strength Index)

​Volume Analysis

​Error Handling

​Missing Sid

​API Timeout

​Empty Response

​Notes

Build docs developers (and LLMs) love

Overview

Endpoint Details

Historical Tick Data API

Live Snapshot API

Request Headers

Request Payloads

Historical OHLCV Request

Example Historical Payload

Live Snapshot Request

Example Requests

Historical OHLCV

Live Snapshot

Response Structure

Historical OHLCV Response

Example Historical Response

Live Snapshot Response

Implementation Details

Hybrid Multi-Chunk Strategy

Configuration

Output Structure

CSV Format

Performance Metrics

Progress Tracking

Incremental Updates

Incremental Example

Use Cases

Data Quality

Adjustments

Gaps

Technical Indicators Examples

Simple Moving Average (SMA)

RSI (Relative Strength Index)

Volume Analysis

Error Handling

Missing Sid

API Timeout

Empty Response

Notes