TradingView Market Data & Community Ideas API: Complete Developer Guide

Executive Summary

Last Updated: March 14, 2026

This comprehensive guide teaches developers how to use TradingView Market Data API and Community Ideas API to build professional financial analysis applications. Covers 15 market data categories (financial statements, analyst recommendations, enterprise value, etc.) and 5 community insight features (hot ideas, editor picks, market sentiment, etc.) with complete Python and JavaScript code examples.

Key Statistics:

• Market Data Coverage: 15 data categories, 160,000+ stocks, 353+ exchanges
• Community Source: 50 million+ TradingView users, real-time updates
• Update Frequency: Quarterly financial updates, real-time community ideas
• API Performance: Average response time < 200ms, 99.9% uptime
• Pricing: Free tier 500 requests/month, paid plans from $9.99/month
• Learning Time: 45-90 minutes to complete first integration

What You’ll Learn: Fetch complete financial statements, analyze analyst recommendations, calculate enterprise valuations, track community sentiment, build fundamental analysis systems, implement quantitative stock screening strategies.

Want to build a stock screener, fundamental analysis tool, or track market sentiment? This guide provides production-ready code and best practices. Try the API instantly in our interactive playground or explore the complete API documentation. For advanced quantitative strategies, see our Quantitative Investment Framework.

What is TradingView Market Data API?

TradingView Market Data API is a RESTful API providing comprehensive stock fundamental data and financial information. It integrates 15 categories of market data including company information, financial statements, technical indicators, analyst recommendations, enterprise value, credit ratings, and more, covering 160,000+ global stocks and ETFs.

Core Features:

📊 Market Data Categories (15 Types)

1. Company Information - Company name, industry, country, exchange, employee count
2. IPO Information - Shares issued, IPO market cap, listing date
3. Technical Indicators - P/E, P/B, EPS, ROE, ROA, dividend yield, 20+ metrics
4. TTM Data - Trailing twelve months rolling financial data
5. Current Metrics - Latest quarterly key financial indicators
6. Quarterly Financials - Balance sheet, income statement, cash flow statement
7. Annual Financials - Complete annual financial statements
8. Quarterly History - Multiple quarters financial data time series
9. Annual History - Multi-year financial data time series
10. Dividend Data - Dividend yield, payout ratio, ex-dividend dates, dividend history
11. Analyst Recommendations - Buy/hold/sell ratings, price targets, analyst count
12. Enterprise Value - EV, EV/EBITDA, EV/Sales valuation metrics
13. Credit Ratings - S&P, Fitch, Moody’s rating agency scores
14. Cash Flow Analysis - Operating, investing, financing activities detailed cash flow
15. Comprehensive Data - Retrieve all categories in one request

💡 Community Ideas Features (5 Types)

1. Hot Ideas - Get trending trading ideas and analysis
2. Editor’s Picks - TradingView editor-curated high-quality analysis
3. Stock Sentiment - Community opinion distribution for specific stocks (bullish/bearish/neutral)
4. Trading Ideas List - Detailed trading strategies filtered by stock
5. Idea Details - Complete chart analysis, technical indicators, price targets

Why Choose TradingView Market Data API?

Compared to other financial data providers (Bloomberg, FactSet, Refinitiv), TradingView API offers:

1. Data Completeness

• 15 data categories covering all dimensions needed for fundamental analysis
• Complete historical quarterly and annual financial statements
• Analyst recommendations from major Wall Street investment banks

2. Global Market Coverage

• Major markets: US, China, Europe, Japan
• 353+ exchanges, 160,000+ stocks
• Unified API interface, no need to integrate multiple data sources

3. Cost Effectiveness

• Free tier: 500 requests/month
• Basic plan: $9.99/month (10,000 requests)
• Pro plan: $49.99/month (100,000 requests)
• 99% cost savings vs Bloomberg Terminal ($24,000/year)

4. Developer Friendly

• RESTful API design, standard JSON responses
• Complete OpenAPI documentation
• Batch request support to reduce API calls
• Detailed error messages and status codes

5. Community Insights

• Collective wisdom of 50 million+ TradingView users
• Real-time market sentiment indicators
• Professional trader chart analysis

Quick Start

Prerequisites

1. RapidAPI Account - Register free account at RapidAPI
2. Subscribe to API - Subscribe to TradingView Data API (free tier available)
3. Get API Key - Copy your X-RapidAPI-Key from API page
4. Programming Environment - Python 3.7+ or Node.js 14+

Install Dependencies

Python:

pip install requests pandas

JavaScript/Node.js:

npm install axios

💡 Quick Tip: Before writing code, try the API in our interactive playground to test endpoints and see live responses. For detailed endpoint references, check the complete documentation.

Market Data API Usage Guide

1. Get Comprehensive Market Data

Retrieve all 15 data categories for a stock in one request:

Python Example:

import requests

def get_all_market_data(symbol):
    """Get all market data for a stock"""
    url = f"https://tradingview-data1.p.rapidapi.com/api/market-data/{symbol}"

    headers = {
        "X-RapidAPI-Key": "YOUR_API_KEY",
        "X-RapidAPI-Host": "tradingview-data1.p.rapidapi.com"
    }

    try:
        response = requests.get(url, headers=headers)
        response.raise_for_status()
        data = response.json()

        if data['success']:
            return data['data']
        else:
            print(f"Error: {data.get('error', 'Unknown error')}")
            return None

    except requests.exceptions.RequestException as e:
        print(f"Request failed: {e}")
        return None

# Usage
symbol = "NASDAQ:AAPL"
market_data = get_all_market_data(symbol)

if market_data:
    print(f"\n{symbol} Market Data Overview:")
    print(f"Categories: {len(market_data)}")
    print(f"Available: {', '.join(market_data.keys())}")

JavaScript Example:

const axios = require('axios');

async function getAllMarketData(symbol) {
    const url = `https://tradingview-data1.p.rapidapi.com/api/market-data/${symbol}`;

    const headers = {
        'X-RapidAPI-Key': 'YOUR_API_KEY',
        'X-RapidAPI-Host': 'tradingview-data1.p.rapidapi.com'
    };

    try {
        const response = await axios.get(url, { headers });

        if (response.data.success) {
            return response.data.data;
        } else {
            console.error(`Error: ${response.data.error}`);
            return null;
        }
    } catch (error) {
        console.error(`Request failed: ${error.message}`);
        return null;
    }
}

// Usage
(async () => {
    const symbol = 'NASDAQ:AAPL';
    const marketData = await getAllMarketData(symbol);

    if (marketData) {
        console.log(`\n${symbol} Market Data Overview:`);
        console.log(`Categories: ${Object.keys(marketData).length}`);
        console.log(`Available: ${Object.keys(marketData).join(', ')}`);
    }
})();

2. Get Financial Statements

Retrieve quarterly or annual financial statements (balance sheet, income statement, cash flow):

Python Example:

def get_financial_statements(symbol, period='quarterly'):
    """
    Get financial statements
    period: 'quarterly' or 'annual'
    """
    endpoint = f"financials-{period}"
    url = f"https://tradingview-data1.p.rapidapi.com/api/market-data/{symbol}/{endpoint}"

    headers = {
        "X-RapidAPI-Key": "YOUR_API_KEY",
        "X-RapidAPI-Host": "tradingview-data1.p.rapidapi.com"
    }

    response = requests.get(url, headers=headers)
    data = response.json()

    if data['success']:
        financials = data['data']
        print(f"\n{symbol} {period.upper()} Financial Statements:")

        # Balance Sheet
        if 'balance_sheet' in financials:
            bs = financials['balance_sheet']
            print(f"\nBalance Sheet:")
            print(f"  Total Assets: {bs.get('total_assets', 'N/A')}")
            print(f"  Total Liabilities: {bs.get('total_liabilities', 'N/A')}")
            print(f"  Total Equity: {bs.get('total_equity', 'N/A')}")

        # Income Statement
        if 'income_statement' in financials:
            inc = financials['income_statement']
            print(f"\nIncome Statement:")
            print(f"  Revenue: {inc.get('revenue', 'N/A')}")
            print(f"  Net Income: {inc.get('net_income', 'N/A')}")
            print(f"  EPS: {inc.get('eps', 'N/A')}")

        # Cash Flow Statement
        if 'cash_flow' in financials:
            cf = financials['cash_flow']
            print(f"\nCash Flow Statement:")
            print(f"  Operating Cash Flow: {cf.get('operating_cash_flow', 'N/A')}")
            print(f"  Investing Cash Flow: {cf.get('investing_cash_flow', 'N/A')}")
            print(f"  Financing Cash Flow: {cf.get('financing_cash_flow', 'N/A')}")

        return financials
    else:
        print(f"Error: {data.get('error')}")
        return None

# Usage
symbol = "NASDAQ:AAPL"
quarterly = get_financial_statements(symbol, 'quarterly')
annual = get_financial_statements(symbol, 'annual')

3. Get Analyst Recommendations

View Wall Street analyst ratings and price targets:

Python Example:

def get_analyst_recommendations(symbol):
    """Get analyst recommendations"""
    url = f"https://tradingview-data1.p.rapidapi.com/api/market-data/{symbol}/analyst-recommendations"
    
    headers = {
        "X-RapidAPI-Key": "YOUR_API_KEY",
        "X-RapidAPI-Host": "tradingview-data1.p.rapidapi.com"
    }
    
    response = requests.get(url, headers=headers)
    data = response.json()
    
    if data['success']:
        recs = data['data']
        
        print(f"\n{symbol} Analyst Recommendations:")
        print(f"  Buy Ratings: {recs.get('buy', 0)}")
        print(f"  Hold Ratings: {recs.get('hold', 0)}")
        print(f"  Sell Ratings: {recs.get('sell', 0)}")
        print(f"  Target Price: ${recs.get('target_price', 'N/A')}")
        print(f"  Analyst Count: {recs.get('analyst_count', 0)}")
        
        return recs
    else:
        print(f"Error: {data.get('error')}")
        return None

# Usage
symbol = "NASDAQ:TSLA"
analyst_data = get_analyst_recommendations(symbol)

4. Calculate Valuation Metrics

Get key valuation metrics for fundamental analysis:

Python Example:

def analyze_valuation(symbol):
    """Analyze stock valuation"""
    url = f"https://tradingview-data1.p.rapidapi.com/api/market-data/{symbol}/indicators"
    
    headers = {
        "X-RapidAPI-Key": "YOUR_API_KEY",
        "X-RapidAPI-Host": "tradingview-data1.p.rapidapi.com"
    }
    
    response = requests.get(url, headers=headers)
    data = response.json()
    
    if data['success']:
        indicators = data['data']
        
        print(f"\n{symbol} Valuation Analysis:")
        
        # P/E Ratio Analysis
        pe = indicators.get('price_earnings_ttm')
        if pe:
            print(f"  P/E Ratio: {pe:.2f}")
            if pe < 15:
                print("    → Undervalued")
            elif pe > 30:
                print("    → Overvalued")
            else:
                print("    → Fair Value")
        
        # P/B Ratio
        pb = indicators.get('price_book_fq')
        if pb:
            print(f"  P/B Ratio: {pb:.2f}")
        
        # Dividend Yield
        div_yield = indicators.get('dividends_yield')
        if div_yield:
            print(f"  Dividend Yield: {div_yield:.2f}%")
        
        # ROE
        roe = indicators.get('return_on_equity')
        if roe:
            print(f"  ROE: {roe:.2f}%")
        
        return indicators
    else:
        print(f"Error: {data.get('error')}")
        return None

# Usage
symbol = "NASDAQ:AAPL"
valuation = analyze_valuation(symbol)

Community Ideas API Usage Guide

1. Get Hot Trading Ideas

View currently trending trading ideas:

Python Example:

def get_hot_ideas(page=1, lang='en'):
    """Get hot trading ideas"""
    url = "https://tradingview-data1.p.rapidapi.com/api/ideas/hot"
    
    headers = {
        "X-RapidAPI-Key": "YOUR_API_KEY",
        "X-RapidAPI-Host": "tradingview-data1.p.rapidapi.com"
    }
    
    params = {'page': page, 'lang': lang}
    
    response = requests.get(url, headers=headers, params=params)
    data = response.json()
    
    if data['success']:
        ideas = data['data']
        
        print(f"\nHot Trading Ideas (Page {page}):")
        for idx, idea in enumerate(ideas[:5], 1):
            print(f"\n{idx}. {idea.get('name', 'N/A')}")
            print(f"   Author: {idea.get('user', {}).get('username', 'N/A')}")
            print(f"   Symbol: {idea.get('symbol', {}).get('name', 'N/A')}")
            print(f"   Likes: {idea.get('likes_count', 0)}")
            print(f"   Comments: {idea.get('comments_count', 0)}")
        
        return ideas
    else:
        print(f"Error: {data.get('error')}")
        return None

# Usage
hot_ideas = get_hot_ideas(page=1, lang='en')

JavaScript Example:

async function getHotIdeas(page = 1, lang = 'en') {
    const url = 'https://tradingview-data1.p.rapidapi.com/api/ideas/hot';
    
    const headers = {
        'X-RapidAPI-Key': 'YOUR_API_KEY',
        'X-RapidAPI-Host': 'tradingview-data1.p.rapidapi.com'
    };
    
    const params = { page, lang };
    
    try {
        const response = await axios.get(url, { headers, params });
        
        if (response.data.success) {
            const ideas = response.data.data;
            
            console.log(`\nHot Trading Ideas (Page ${page}):`);
            ideas.slice(0, 5).forEach((idea, idx) => {
                console.log(`\n${idx + 1}. ${idea.name || 'N/A'}`);
                console.log(`   Author: ${idea.user?.username || 'N/A'}`);
                console.log(`   Symbol: ${idea.symbol?.name || 'N/A'}`);
                console.log(`   Likes: ${idea.likes_count || 0}`);
            });
            
            return ideas;
        }
    } catch (error) {
        console.error(`Error: ${error.message}`);
        return null;
    }
}

// Usage
getHotIdeas(1, 'en');

2. Get Stock Sentiment

View community opinion distribution for specific stocks:

Python Example:

def get_stock_sentiment(symbol, lang='en'):
    """Get stock market sentiment"""
    url = f"https://tradingview-data1.p.rapidapi.com/api/ideas/{symbol}/minds"
    
    headers = {
        "X-RapidAPI-Key": "YOUR_API_KEY",
        "X-RapidAPI-Host": "tradingview-data1.p.rapidapi.com"
    }
    
    params = {'lang': lang}
    
    response = requests.get(url, headers=headers, params=params)
    data = response.json()
    
    if data['success']:
        sentiment = data['data']
        
        print(f"\n{symbol} Market Sentiment:")
        
        bullish = sentiment.get('bullish', 0)
        bearish = sentiment.get('bearish', 0)
        neutral = sentiment.get('neutral', 0)
        total = bullish + bearish + neutral
        
        if total > 0:
            print(f"  Bullish: {bullish} ({bullish/total*100:.1f}%)")
            print(f"  Bearish: {bearish} ({bearish/total*100:.1f}%)")
            print(f"  Neutral: {neutral} ({neutral/total*100:.1f}%)")
            
            # Sentiment assessment
            if bullish > bearish * 2:
                print("  → Sentiment: Strongly Bullish")
            elif bearish > bullish * 2:
                print("  → Sentiment: Strongly Bearish")
            else:
                print("  → Sentiment: Neutral")
        
        return sentiment
    else:
        print(f"Error: {data.get('error')}")
        return None

# Usage
symbol = "NASDAQ:AAPL"
sentiment = get_stock_sentiment(symbol)

3. Get Editor’s Picks

View TradingView editor-curated high-quality analysis:

Python Example:

def get_editors_picks(page=1, lang='en'):
    """Get editor's picks"""
    url = "https://tradingview-data1.p.rapidapi.com/api/ideas/editors-picks"
    
    headers = {
        "X-RapidAPI-Key": "YOUR_API_KEY",
        "X-RapidAPI-Host": "tradingview-data1.p.rapidapi.com"
    }
    
    params = {'page': page, 'lang': lang}
    
    response = requests.get(url, headers=headers, params=params)
    data = response.json()
    
    if data['success']:
        picks = data['data']
        
        print(f"\nEditor's Picks (Page {page}):")
        for idx, idea in enumerate(picks[:3], 1):
            print(f"\n{idx}. {idea.get('name', 'N/A')}")
            print(f"   Symbol: {idea.get('symbol', {}).get('name', 'N/A')}")
            print(f"   Author: {idea.get('user', {}).get('username', 'N/A')}")
            print(f"   Published: {idea.get('created_at', 'N/A')}")
        
        return picks
    else:
        print(f"Error: {data.get('error')}")
        return None

# Usage
editors_picks = get_editors_picks(page=1)

Real-World Example: Build a Stock Screener

Combine market data and community sentiment to build an intelligent stock screening system.

For advanced quantitative analysis strategies and professional trading frameworks, check out our TradingView Quantitative Investment Guide.

Complete Python Example:

import requests
import pandas as pd
from typing import List, Dict

class StockScreener:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://tradingview-data1.p.rapidapi.com"
        self.headers = {
            "X-RapidAPI-Key": api_key,
            "X-RapidAPI-Host": "tradingview-data1.p.rapidapi.com"
        }
    
    def get_market_data(self, symbol: str) -> Dict:
        """Get market data"""
        url = f"{self.base_url}/api/market-data/{symbol}"
        response = requests.get(url, headers=self.headers)
        data = response.json()
        return data['data'] if data['success'] else None
    
    def get_sentiment(self, symbol: str) -> Dict:
        """Get market sentiment"""
        url = f"{self.base_url}/api/ideas/{symbol}/minds"
        response = requests.get(url, headers=self.headers)
        data = response.json()
        return data['data'] if data['success'] else None
    
    def screen_stocks(self, symbols: List[str]) -> pd.DataFrame:
        """Screen stocks"""
        results = []
        
        for symbol in symbols:
            print(f"Analyzing {symbol}...")
            
            market_data = self.get_market_data(symbol)
            if not market_data:
                continue
            
            indicators = market_data.get('indicators', {})
            analyst = market_data.get('analyst_recommendations', {})
            sentiment = self.get_sentiment(symbol)
            
            # Calculate composite score
            score = 0
            
            # Valuation score (0-30)
            pe = indicators.get('price_earnings_ttm', 999)
            if pe < 15:
                score += 30
            elif pe < 25:
                score += 20
            elif pe < 35:
                score += 10
            
            # Profitability score (0-25)
            roe = indicators.get('return_on_equity', 0)
            if roe > 20:
                score += 25
            elif roe > 15:
                score += 20
            elif roe > 10:
                score += 15
            
            # Dividend score (0-15)
            div_yield = indicators.get('dividends_yield', 0)
            if div_yield > 3:
                score += 15
            elif div_yield > 2:
                score += 10
            elif div_yield > 1:
                score += 5
            
            # Analyst score (0-20)
            buy_rating = analyst.get('buy', 0)
            total_rating = analyst.get('analyst_count', 1)
            if buy_rating / total_rating > 0.6:
                score += 20
            elif buy_rating / total_rating > 0.4:
                score += 10
            
            # Sentiment score (0-10)
            if sentiment:
                bullish = sentiment.get('bullish', 0)
                total_sentiment = sentiment.get('total', 1)
                if bullish / total_sentiment > 0.6:
                    score += 10
                elif bullish / total_sentiment > 0.5:
                    score += 5
            
            results.append({
                'symbol': symbol,
                'score': score,
                'pe_ratio': pe,
                'roe': roe,
                'dividend_yield': div_yield,
                'analyst_buy': buy_rating,
                'sentiment_bullish': sentiment.get('bullish', 0) if sentiment else 0
            })
        
        df = pd.DataFrame(results)
        df = df.sort_values('score', ascending=False)
        return df

# Usage
screener = StockScreener("YOUR_API_KEY")

symbols = [
    "NASDAQ:AAPL",
    "NASDAQ:MSFT",
    "NASDAQ:GOOGL",
    "NYSE:JPM",
    "NYSE:JNJ"
]

results = screener.screen_stocks(symbols)

print("\nStock Screening Results:")
print(results.to_string(index=False))

print("\nTop 3 Recommendations:")
for idx, row in results.head(3).iterrows():
    print(f"\n{idx + 1}. {row['symbol']}")
    print(f"   Score: {row['score']}/100")
    print(f"   P/E: {row['pe_ratio']:.2f}")
    print(f"   ROE: {row['roe']:.2f}%")

Best Practices

1. API Call Optimization

Use comprehensive endpoint to reduce calls:

# ❌ Not recommended: Multiple calls
indicators = get_indicators(symbol)
financials = get_financials(symbol)
analyst = get_analyst_recommendations(symbol)
# Total: 3 API calls

# ✅ Recommended: Get all data at once
all_data = get_all_market_data(symbol)
indicators = all_data['indicators']
financials = all_data['financials_quarterly']
analyst = all_data['analyst_recommendations']
# Total: 1 API call

Implement smart caching:

import time

class CachedAPI:
    def __init__(self, api_key):
        self.api_key = api_key
        self.cache = {}
        self.cache_ttl = {
            'market_data': 86400,  # 24 hours
            'sentiment': 3600,      # 1 hour
            'ideas': 1800           # 30 minutes
        }
    
    def get_with_cache(self, key, fetch_func, ttl):
        """Get data with cache"""
        now = time.time()
        
        if key in self.cache:
            data, timestamp = self.cache[key]
            if now - timestamp < ttl:
                return data
        
        data = fetch_func()
        self.cache[key] = (data, now)
        return data

2. Error Handling

Implement retry mechanism:

from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

def create_session_with_retry():
    """Create session with retry"""
    session = requests.Session()
    
    retry = Retry(
        total=3,
        backoff_factor=1,
        status_forcelist=[429, 500, 502, 503, 504]
    )
    
    adapter = HTTPAdapter(max_retries=retry)
    session.mount('http://', adapter)
    session.mount('https://', adapter)
    
    return session

# Usage
session = create_session_with_retry()
response = session.get(url, headers=headers)

3. Batch Processing

Concurrent fetching for multiple stocks:

import asyncio
import aiohttp

async def fetch_market_data_async(session, symbol, headers):
    """Async fetch market data"""
    url = f"https://tradingview-data1.p.rapidapi.com/api/market-data/{symbol}"
    async with session.get(url, headers=headers) as response:
        return await response.json()

async def batch_fetch_stocks(symbols, api_key):
    """Batch fetch stocks"""
    headers = {
        "X-RapidAPI-Key": api_key,
        "X-RapidAPI-Host": "tradingview-data1.p.rapidapi.com"
    }
    
    async with aiohttp.ClientSession() as session:
        tasks = [
            fetch_market_data_async(session, symbol, headers)
            for symbol in symbols
        ]
        results = await asyncio.gather(*tasks)
        return results

# Usage
symbols = ["NASDAQ:AAPL", "NASDAQ:MSFT", "NASDAQ:GOOGL"]
results = asyncio.run(batch_fetch_stocks(symbols, "YOUR_API_KEY"))

FAQ

Q1: How often is financial data updated?

Financial statement data is updated 24-48 hours after companies release quarterly or annual reports. Technical indicators (P/E, P/B) are updated daily.

Q2: How to handle missing data?

Some stocks may lack specific data (analyst recommendations, credit ratings). Use .get() method with default values:

pe_ratio = indicators.get('price_earnings_ttm', None)
if pe_ratio is not None:
    # Process data
else:
    # Handle missing case

Q3: How reliable is community sentiment data?

Community sentiment reflects market mood but shouldn’t be the sole investment basis. Combine with fundamental analysis, technical analysis, and professional analyst recommendations.

Q4: Are there rate limits?

Yes, different subscription tiers have monthly request limits. Implement caching and batch requests to optimize usage.

Q5: Which programming languages are supported?

The API is a standard RESTful interface supporting any language capable of HTTP requests: Python, JavaScript, Java, C#, PHP, Ruby, Go, etc.

Summary

TradingView Market Data & Community Ideas API provides developers with all data needed to build professional financial analysis applications:

Market Data API Core Value:

• 15 data categories covering all fundamental analysis dimensions
• Complete historical financial statement data
• Wall Street analyst recommendations and price targets
• 160,000+ global stock coverage

Community Ideas API Core Value:

• Collective wisdom of 50 million+ users
• Real-time market sentiment indicators
• Professional trader chart analysis
• Multi-language support

Get Started:

1. Register and subscribe to TradingView Data API on RapidAPI
2. Copy code examples from this article to your project
3. Replace YOUR_API_KEY with your actual API key
4. Start building your financial analysis application

Related Resources:

• Complete API Documentation
• API Playground
• RapidAPI Hub

Start using TradingView Market Data & Community Ideas API today to build next-generation financial analysis tools!

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Keywords: TradingView API, market data API, financial statements API, analyst recommendations, community ideas, stock fundamental analysis, financial data API, trading ideas, market sentiment analysis, quantitative investing, stock screener, Python finance API, JavaScript stock API

Tags: #TradingView #MarketData #FinancialData #CommunityIdeas #FundamentalAnalysis #APIDevelopment #QuantitativeInvesting #StockAnalysis

Frequently Asked Questions

What types of financial data does the TradingView Market Data API provide?

The TradingView Market Data API provides 15 categories of comprehensive financial data: (1) Company Information - company name, industry, country, exchange, and basic details; (2) IPO Information - shares issued, IPO market cap, listing date; (3) Technical Indicators - P/E ratio, P/B ratio, EPS, ROE, ROA, and 20+ valuation metrics; (4) TTM Data - trailing twelve months rolling financial data; (5) Current Metrics - latest quarterly financial indicators; (6) Quarterly Financials - balance sheet, income statement, cash flow statement; (7) Annual Financials - complete annual financial statements; (8) Quarterly History - multiple quarters financial data arrays; (9) Annual History - multi-year financial data arrays; (10) Dividend Data - dividend yield, payout ratio, ex-dividend dates; (11) Analyst Recommendations - buy/hold/sell ratings, price targets, analyst count; (12) Enterprise Value - EV, EV/EBITDA, EV/Sales valuation metrics; (13) Credit Ratings - S&P, Fitch, and other rating agency scores; (14) Cash Flow Analysis - operating, investing, financing activities cash flow; (15) Comprehensive Data - retrieve all categories in one request. Coverage: 160,000+ global stocks, ETFs, funds. Update frequency: 24-48 hours after quarterly/annual report releases. Data source: institutional-grade financial data from TradingView platform. Use cases: fundamental analysis, valuation models, financial modeling, investment research, quantitative stock screening.

How do I use the Market Data API for fundamental stock analysis?

Complete fundamental analysis workflow using Market Data API: (1) Get Valuation Metrics - GET /api/market-data/{symbol}/indicators to retrieve P/E, P/B, PEG, dividend yield and assess if stock is undervalued or overvalued; (2) Analyze Financial Statements - GET /api/market-data/{symbol}/financials-quarterly for latest balance sheet, income statement, cash flow to evaluate financial health; (3) Review Historical Trends - GET /api/market-data/{symbol}/history-annual for multi-year data to analyze revenue growth, profit margin trends; (4) Assess Profitability - use TTM data to calculate ROE, ROA, net margin; (5) Check Cash Flow - GET /api/market-data/{symbol}/cash-flow to analyze operating cash flow, free cash flow, and cash generation ability; (6) Reference Analyst Opinions - GET /api/market-data/{symbol}/analyst-recommendations for Wall Street analyst ratings and price targets; (7) Calculate Enterprise Value - GET /api/market-data/{symbol}/enterprise-value for EV/EBITDA and other enterprise valuation metrics. Real-world example: Build value investing screener with criteria: P/E < 15, P/B < 2, ROE > 15%, dividend yield > 3%, positive operating cash flow, analyst rating >= hold. Implementation: batch fetch multiple stocks, apply screening criteria, rank by composite score. Performance optimization: use batch requests to reduce API calls, cache financial data (TTL 24 hours).

How does the TradingView Community Ideas API help with investment decisions?

TradingView Community Ideas API provides market insights from global traders and analysts to support investment decisions: (1) Hot Ideas Tracking - GET /api/ideas/hot retrieves trending trading ideas to understand market focus and hot sectors; (2) Editor's Picks - GET /api/ideas/editors-picks gets high-quality analysis curated by TradingView editors, filtering noise for professional insights; (3) Stock Sentiment Analysis - GET /api/ideas/{symbol}/minds shows community opinion distribution (bullish/bearish/neutral) to quantify market sentiment; (4) Trading Strategy Learning - GET /api/ideas/list/{symbol} retrieves detailed trading ideas for specific stocks to learn technical analysis methods and trading logic; (5) Idea Details Research - GET /api/ideas/{imageUrl} views complete chart analysis, support/resistance levels, price targets. Application scenarios: (1) Contrarian indicator - consider profit-taking when extremely bullish, look for bottom when extremely bearish; (2) Trend confirmation - combine technical analysis with community sentiment to verify trading signals; (3) Learning resource - study expert analysis and chart annotation methods; (4) Sentiment monitoring - track discussion heat changes for specific stocks. Data features: real-time updates, multi-language support (English, Chinese, etc.), includes chart screenshots, author reputation scores. Important note: community opinions are for reference only, not investment advice - combine with your own analysis.

What is the pricing for Market Data and Community Ideas APIs?

Market Data and Community Ideas APIs are included in TradingView Data API unified pricing with no additional fees: (1) Free tier - $0/month, 500 requests/month (~16/day), suitable for testing and small personal projects; (2) Basic plan - $9.99/month, 10,000 requests/month (~333/day), suitable for hobby projects and small applications; (3) Pro plan - $49.99/month, 100,000 requests/month (~3,333/day), suitable for production applications and active trading tools; (4) Ultra plan - $199.99/month, 1,000,000 requests/month (~33,333/day), suitable for high-volume commercial applications. Billing details: (1) charged by API call count, not data volume; (2) batch requests count as 1 call (e.g., fetching all market data for one stock); (3) all plans access identical complete data with no feature restrictions; (4) monthly quota resets on subscription anniversary; (5) exceeding quota returns HTTP 429 error until next billing cycle. Cost optimization tips: (1) use comprehensive endpoint /api/market-data/{symbol} to fetch all data at once instead of multiple separate calls; (2) cache financial data (recommended TTL 24 hours) to reduce duplicate requests; (3) batch process multiple stocks to avoid loop calls; (4) monitor usage and consider upgrading at 80% threshold. Value comparison: compared to Bloomberg Terminal ($24,000/year), FactSet ($12,000/year), TradingView API provides 95%+ core data at only 1-2% of the cost.

How do I integrate Market Data API into a quantitative trading system?

Best practices for integrating Market Data API into quantitative trading systems: (1) Data Acquisition Layer - create unified data interface class to encapsulate all API calls, implement auto-retry, error handling, rate limiting; use async requests (Python aiohttp, JavaScript axios) to improve concurrent performance; implement request queue management to avoid exceeding rate limits. (2) Data Caching Layer - use Redis or in-memory cache to store financial data (TTL 24 hours), technical indicators (TTL 1 hour), real-time quotes (TTL 60 seconds); implement cache warming mechanism to preload common stock data before market open; use LRU cache strategy to prioritize hot stock data. (3) Data Processing Layer - convert API JSON responses to Pandas DataFrame or database tables; calculate derived metrics (e.g., PEG ratio, free cash flow yield); data cleaning and outlier handling. (4) Strategy Execution Layer - build stock selection strategies based on financial metrics (value investing, growth investing, dividend investing); combine technical analysis and fundamental analysis to generate trading signals; use Community Ideas API as sentiment indicator to assist decisions. (5) Backtesting Validation - use historical financial data to backtest strategy performance; calculate Sharpe ratio, maximum drawdown and other risk metrics; optimize parameters to improve strategy stability. Technical architecture example: Python + FastAPI (API service) + Celery (async tasks) + Redis (cache) + PostgreSQL (data storage) + Pandas (data analysis). Performance optimization: (1) batch requests reduce API calls by 90%; (2) smart caching reduces duplicate requests by 80%; (3) async concurrency improves throughput by 10x. Monitoring alerts: implement API call counting, error rate monitoring, quota usage alerts to ensure system stability.

Which countries and regions does the Market Data API support?

TradingView Market Data API supports stocks from major global markets, covering 160,000+ financial instruments: (1) United States - New York Stock Exchange (NYSE), NASDAQ, American Stock Exchange (AMEX), covering all US stocks, ETFs, ADRs; (2) China - Shanghai Stock Exchange (SSE), Shenzhen Stock Exchange (SZSE), Hong Kong Stock Exchange (HKEX), supporting A-shares, Hong Kong stocks, Chinese ADRs; (3) Europe - London Stock Exchange (LSE), Euronext, Frankfurt Stock Exchange (XETR), Swiss Exchange (SIX); (4) Asia-Pacific - Tokyo Stock Exchange (TSE), Korea Exchange (KRX), Singapore Exchange (SGX), Australian Securities Exchange (ASX), National Stock Exchange of India (NSE); (5) Other Markets - Canada (TSX), Brazil (BOVESPA), Mexico (BMV), South Africa (JSE), and 50+ country exchanges. Data completeness: major markets (US, China, Europe, Japan) provide complete 15 categories including financial statements, analyst recommendations, enterprise value; emerging markets may have partial data gaps (e.g., analyst recommendations, credit ratings). Symbol format: unified 'EXCHANGE:SYMBOL' format, e.g., 'NASDAQ:AAPL' (Apple), 'HKEX:700' (Tencent), 'SSE:600519' (Kweichow Moutai), 'TSE:7203' (Toyota). Language support: API returns data in multiple languages (via lang parameter), including English, Simplified Chinese, Traditional Chinese, Japanese, Korean. Query exchange list: GET /api/metadata/exchanges retrieves detailed information for all 353+ supported exchanges.