Market News API

Overview

The Market News API retrieves the latest market news articles with sentiment analysis for individual stocks. This endpoint provides up to 100 news items per stock with overall sentiment classification (positive, negative, neutral). Source File: fetch_market_news.py

Endpoint Details

URL

string

required

https://news-live.dhan.co/v2/news/getLiveNews

Method

string

required

POST

Content-Type

string

required

application/json

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, */*"
}

Request Payload

Example Payload

{
  "categories": ["ALL"],
  "page_no": 0,
  "limit": 50,
  "first_news_timeStamp": 0,
  "last_news_timeStamp": 0,
  "news_feed_type": "live",
  "stock_list": ["INE002A01018"],
  "entity_id": ""
}

Example Request

curl -X POST https://news-live.dhan.co/v2/news/getLiveNews \
  -H "Content-Type: application/json" \
  -H "User-Agent: Mozilla/5.0" \
  -d '{
    "categories": ["ALL"],
    "page_no": 0,
    "limit": 50,
    "first_news_timeStamp": 0,
    "last_news_timeStamp": 0,
    "news_feed_type": "live",
    "stock_list": ["INE002A01018"],
    "entity_id": ""
  }'

Response Structure

data.latest_news

array

Array of news item objects

News Item Fields

news_object.title

string

News headline/title

news_object.text

string

News summary/content

news_object.overall_sentiment

string

Sentiment classification: “positive”, “negative”, or “neutral”

publish_date

integer

Publish timestamp (Unix epoch in milliseconds)

Example Response

{
  "data": {
    "latest_news": [
      {
        "news_object": {
          "title": "Reliance Industries Reports Strong Q3 Results",
          "text": "Reliance Industries posted consolidated net profit of Rs 18,500 crore for Q3 FY24, up 12% YoY...",
          "overall_sentiment": "positive"
        },
        "publish_date": 1705320000000,
        "category": "Business News"
      },
      {
        "news_object": {
          "title": "RIL to Invest $10B in Green Energy",
          "text": "Reliance announces major investment in renewable energy sector...",
          "overall_sentiment": "positive"
        },
        "publish_date": 1705233600000,
        "category": "Market News"
      }
    ]
  }
}

Implementation Details

Configuration

Max Threads

integer

default:"15"

Concurrent threads for parallel processing (conservative for news API)

Timeout

integer

default:"10"

Request timeout in seconds

News Limit

integer

default:"50"

News items per stock (max tested: 100)

Input File

string

required

master_isin_map.json

Output Directory

string

market_news/{SYMBOL}_news.json

Processing Flow

Load Master Map: Read ISINs from master_isin_map.json
Parallel Fetch: Use ThreadPoolExecutor with 15 workers
Extract News: Parse news_object for title, text, sentiment
Structure Data: Create clean news objects with metadata
Save Per Stock: Write individual files per symbol
Track Progress: Log success/empty/error counts

Code Implementation

import requests
import json
from concurrent.futures import ThreadPoolExecutor, as_completed

url = "https://news-live.dhan.co/v2/news/getLiveNews"
NEWS_LIMIT = 50

def fetch_market_news(item):
    symbol = item.get("Symbol")
    isin = item.get("ISIN")
    
    output_path = f"market_news/{symbol}_news.json"
    
    payload = {
        "categories": ["ALL"],
        "page_no": 0,
        "limit": NEWS_LIMIT,
        "first_news_timeStamp": 0,
        "last_news_timeStamp": 0,
        "news_feed_type": "live",
        "stock_list": [isin],
        "entity_id": ""
    }
    
    try:
        response = requests.post(url, json=payload, headers=headers, timeout=10)
        
        if response.status_code == 200:
            data = response.json()
            news_items = data.get("data", {}).get("latest_news", [])
            
            if news_items:
                processed_news = []
                for news in news_items:
                    news_obj = news.get("news_object", {})
                    processed_news.append({
                        "Title": news_obj.get("title", ""),
                        "Summary": news_obj.get("text", ""),
                        "Sentiment": news_obj.get("overall_sentiment", "neutral"),
                        "PublishDate": news.get("publish_date", 0),
                        "Source": news.get("category", "")
                    })
                
                final_output = {
                    "Symbol": symbol,
                    "ISIN": isin,
                    "News": processed_news
                }
                
                with open(output_path, "w") as f:
                    json.dump(final_output, f, indent=4)
                
                return "success"
            else:
                return "empty"
        elif response.status_code == 429:
            time.sleep(2)  # Rate limit backoff
            return "rate_limit"
        else:
            return f"http_{response.status_code}"
    except:
        return "error"

# Load master list
with open("master_isin_map.json", "r") as f:
    stock_list = json.load(f)

# Parallel processing
with ThreadPoolExecutor(max_workers=15) as executor:
    future_to_stock = {executor.submit(fetch_market_news, item): item["Symbol"] 
                       for item in stock_list}
    
    for future in as_completed(future_to_stock):
        result = future.result()
        # Track success/empty/error counts

Output Structure

Each stock gets its own JSON file:

market_news/
├── RELIANCE_news.json
├── TCS_news.json
└── HDFCBANK_news.json

File format:

{
  "Symbol": "RELIANCE",
  "ISIN": "INE002A01018",
  "News": [
    {
      "Title": "Reliance Industries Reports Strong Q3 Results",
      "Summary": "Reliance Industries posted consolidated net profit...",
      "Sentiment": "positive",
      "PublishDate": 1705320000000,
      "Source": "Business News"
    }
  ]
}

Performance Metrics

Total Stocks: ~2,775
Threads: 15 concurrent requests
Time per Stock: ~2-3 seconds
Total Time: ~6-8 minutes for full market
Avg News per Stock: 10-30 (varies significantly)
Success Rate: ~85% (some stocks have no news)
Empty Response Rate: ~15%

Progress Tracking

[50/2775] | Success: 42 | Empty: 7 | Errors: 1 | Elapsed: 85.3s
[100/2775] | Success: 83 | Empty: 15 | Errors: 2 | Elapsed: 165.1s
...
News Found: 2350 stocks | No News: 420 | Errors: 5

Pagination Support

For stocks with >100 news items, use pagination:

def fetch_all_news(isin):
    all_news = []
    page_no = 0
    limit = 100
    
    while True:
        payload = {
            "categories": ["ALL"],
            "page_no": page_no,
            "limit": limit,
            "news_feed_type": "live",
            "stock_list": [isin]
        }
        
        response = requests.post(url, json=payload, headers=headers)
        news_items = response.json().get("data", {}).get("latest_news", [])
        
        if not news_items:
            break
        
        all_news.extend(news_items)
        page_no += 1
    
    return all_news

Sentiment Analysis

positive

string

Bullish news - earnings beats, new contracts, positive outlook

negative

string

Bearish news - earnings misses, regulatory issues, downgrades

neutral

string

Informational news - general updates, announcements

Sentiment Distribution Examples

# Analyze sentiment distribution
with open("market_news/RELIANCE_news.json", "r") as f:
    data = json.load(f)

sentiments = [item["Sentiment"] for item in data["News"]]
positive = sentiments.count("positive")
negative = sentiments.count("negative")
neutral = sentiments.count("neutral")

print(f"Positive: {positive}, Negative: {negative}, Neutral: {neutral}")

Rate Limiting

The API implements rate limiting:

429 Response: Too many requests
Backoff Strategy: Sleep 2 seconds on 429
Thread Limit: 15 concurrent (conservative)
Retry Logic: Automatic retry on rate limit

if response.status_code == 429:
    time.sleep(2)  # Backoff
    return "rate_limit"

Use Cases

News Aggregation: Build comprehensive news database per stock
Sentiment Analysis: Track sentiment trends over time
Event Detection: Identify major news events impacting stocks
Portfolio Monitoring: Get news alerts for portfolio holdings
Earnings Coverage: Track quarterly result announcements
Market Research: Analyze news flow for stock screening

Filtering Examples

Recent News Only

from datetime import datetime, timedelta

# Last 7 days
seven_days_ago = (datetime.now() - timedelta(days=7)).timestamp() * 1000

recent_news = [
    item for item in news_list
    if item["PublishDate"] >= seven_days_ago
]

Positive Sentiment Only

positive_news = [
    item for item in news_list
    if item["Sentiment"] == "positive"
]

By Source

business_news = [
    item for item in news_list
    if item["Source"] == "Business News"
]

Notes

News availability varies by stock (large caps have more coverage)
Small/micro caps may have limited or no news
Sentiment is auto-generated using NLP algorithms
News sources include major business publications and wire services
The API updates in real-time as news is published
Historical news depth: typically 30-90 days
Limit=100 is maximum tested; higher values may not work
Use per-stock files for efficient incremental updates

Endpoints

Output Fields

Overview

Endpoint Details

Request Headers

Request Payload

Example Payload

Example Request

Response Structure

News Item Fields

Example Response

Implementation Details

Configuration

Processing Flow

Code Implementation

Output Structure

Performance Metrics

Progress Tracking

Sentiment Analysis

Sentiment Distribution Examples

Rate Limiting

Use Cases

Filtering Examples

Recent News Only

Positive Sentiment Only

By Source

Notes

Build docs developers (and LLMs) love

Endpoints

Output Fields

​Overview

​Endpoint Details

​Request Headers

​Request Payload

​Example Payload

​Example Request

​Response Structure

​News Item Fields

​Example Response

​Implementation Details

​Configuration

​Processing Flow

​Code Implementation

​Output Structure

​Performance Metrics

​Progress Tracking

​Pagination Support

​Sentiment Analysis

​Sentiment Distribution Examples

​Rate Limiting

​Use Cases

​Filtering Examples

​Recent News Only

​Positive Sentiment Only

​By Source

​Notes

Build docs developers (and LLMs) love

Overview

Endpoint Details

Request Headers

Request Payload

Example Payload

Example Request

Response Structure

News Item Fields

Example Response

Implementation Details

Configuration

Processing Flow

Code Implementation

Output Structure

Performance Metrics

Progress Tracking

Pagination Support

Sentiment Analysis

Sentiment Distribution Examples

Rate Limiting

Use Cases

Filtering Examples

Recent News Only

Positive Sentiment Only

By Source

Notes