TradingView Scraper

This is a Python library for scraping ideas and indicators from TradingView.com. The goal is to develop this package to scrape anything on TradingView.com with real-time responses.
Thanks to contributors!

To-Do List

Export
- Export as a CSV file
- Export as JSON
Scrape Symbol Subpages:
- Ideas
- Indicators
- Overview
- News
- Earning-calendar
- Minds
- Technical
- Symbol-Market
- Markets
- Markets-Quotes
- Screener
- Fundamental-Graphs
- Get 'OHLC', 'Indicators' using TradingView WebSocket
- Export historical OHLC candle and Indicator values
Additional suggestions welcome!

To be aware of the latest changes, go to the end of this page.

Features

Idea page Scraping
- Title
- Paragraph
- Preview Image
- Author
- Comments Count
- Boosts Count
- Publication Datetime
- Is Updated
- Idea Strategy
News page Scraping
- Breadcrumbs
- Title
- Published datetime
- Related symbols
- Body
- Tags
Webpage Scraping Options
- Scrape All Pages
- Scrape a Specific Range of Pages
Indicator Extraction
- Extract values for indicators like RSI, Stoch.K, etc.
- Full list of indicators
Real-Time data Extraction
- OHLCV
- Watchlist
- Indicators
Market Movers Scraping
- Gainers
- Losers
- Most Active
- Penny Stocks
- Pre-market Gainers/Losers
- After-hours Gainers/Losers
- Multiple markets support (USA, UK, India, Australia, Canada, Crypto, Forex, Bonds, Futures)
Screener
- Custom filters (price, volume, market cap, technical indicators, etc.)
- Multiple filter operations (greater, less, equal, in_range, crosses, etc.)
- Custom column selection
- Sorting by any field
- Support for 18 markets (America, Canada, Germany, India, UK, Crypto, Forex, CFD, Futures, Bonds, etc.)
Symbol Markets
- Find all exchanges/markets where a symbol is traded
- Global, regional, and market-specific scanners
- Support for stocks, crypto, forex, CFD markets
- Detailed market information (price, volume, exchange, currency, etc.)
Markets Overview
- Top stocks by market cap, volume, change, price, volatility
- Multiple market support (America, Australia, Canada, Germany, India, UK, Crypto, Forex, Global)
- Customizable sorting and column selection
- Real-time market data
Symbol Overview
- Comprehensive symbol data (profile, statistics, financials, performance, technicals)
- Support for stocks, crypto, forex, and other instruments
- Categorized data fields (basic, price, market, valuation, dividends, financials, performance, volatility, technical)
- Helper methods for specific data categories (profile, statistics, financials, performance, technicals)
Minds Community Discussions
- Community-generated discussions, questions, and trading ideas
- Multiple sort options (recent, popular, trending)
- Pagination support for retrieving all discussions
- User information (username, profile, likes, comments)
- Symbol mentions and engagement metrics
Fundamental Financial Data
- Comprehensive fundamental metrics for financial analysis
- Income statement data (revenue, profit, earnings, EBITDA)
- Balance sheet data (assets, debt, equity, cash)
- Cash flow statement data (operating, investing, financing activities)
- Profitability ratios (ROE, ROA, ROI)
- Liquidity ratios (current ratio, quick ratio)
- Leverage ratios (debt-to-equity, debt-to-assets)
- Margin metrics (gross, operating, net, EBITDA margins)
- Valuation metrics (market cap, enterprise value, P/E, P/B, P/S)
- Dividend information (yield, per share, payout ratio)
- Multi-symbol comparison support
Export Formats
- CSV
- JSON
Output Format
- Returns data in JSON format

Here’s a revised version of the Installation section that enhances clarity and encourages user engagement:

Installation

To get started with the TradingView Scraper library, follow these simple steps:

Open your terminal: Launch your preferred command line interface.
Install the package: Run the following command to install the TradingView Scraper:
```
pip install tradingview-scraper
```
Upgrade if necessary: If you already have the library installed and want to upgrade to the latest version, use:
```
pip install --upgrade --no-cache tradingview-scraper
```

Here’s a revised version of the Examples section, focusing on clarity, ease of understanding, and providing essential information about default values:

Examples

1. Fast Running (Default Parameters)

To quickly scrape ideas using default settings, use the following code:

from tradingview_scraper.symbols.ideas import Ideas

# Initialize the Ideas scraper with default parameters
ideas_scraper = Ideas()  # Default: export_result=False, export_type='json'
ideas = ideas_scraper.scrape()  # Default symbol: 'BTCUSD'
print("Ideas:", ideas)

Default Parameters:

export_result: False (no file will be saved)
export_type: 'json' (output format)

2. Getting Ideas for a Specific Symbol, Export Type, and Pages

To scrape ideas for a specific symbol and export them as a CSV file, you can specify the parameters:

from tradingview_scraper.symbols.ideas import Ideas

# Initialize the Ideas scraper with custom parameters
ideas_scraper = Ideas(
  export_result=True,  # Set to True to save the results
  export_type='csv'    # Specify the export type (json or csv)
)

# Scrape ideas for the ETHUSD symbol, from page 1 to page 2
ideas = ideas_scraper.scrape(
  symbol="ETHUSD",
  startPage=1,
  endPage=2,
  sort="popular"  # Could be 'popular' or 'recent'
)
print("Ideas:", ideas)

Using Cookie for Captcha Bypass

To avoid captcha challenges, set your TradingView cookie as an environment variable:

Open https://www.tradingview.com/symbols/BTCUSD/ideas/ in your browser
Open Developer Tools (F12) and go to Network tab
Verify the captcha if prompted
Refresh the page and find the GET request at the top of the list with URL https://www.tradingview.com/symbols/BTCUSD/ideas/
Copy the cookie value and add it to your .env file:
```
TRADINGVIEW_COOKIE=your_cookie_here
```

from tradingview_scraper.symbols.ideas import Ideas

ideas_scraper = Ideas()
ideas = ideas_scraper.scrape(symbol="BTCUSD")

Output Format: The output will always be a list of Python dictionaries, structured as follows:

[
  {
    "title": "Bitcoin Analysis: Bullish Momentum Building",
    "description": "Technical analysis showing strong bullish signals...",
    "preview_image": ["https://s3.tradingview.com/logo.png"],
    "chart_url": "https://www.tradingview.com/chart/BTCUSD/...",
    "comments_count": 25,
    "views_count": 1250,
    "author": "CryptoAnalyst",
    "likes_count": 45,
    "timestamp": 1672531200
  }
]

When export_result=True, the default export_type is 'json', and a JSON file will be saved in the /export directory. To save as CSV, set export_type='csv'.

3. Getting Indicators Status

To scrape the status of specific indicators, use the following code:

from tradingview_scraper.symbols.technicals import Indicators

# Initialize the Indicators scraper with export options
indicators_scraper = Indicators(export_result=True, export_type='json')

# Scrape indicators for the BTCUSD symbol from the BINANCE exchange
indicators = indicators_scraper.scrape(
    exchange="BINANCE",
    symbol="BTCUSD",
    timeframe="1d",
    indicators=["RSI", "Stoch.K"]
)
print("Indicators:", indicators)

Output:

{
  "status": "success",
  "data": {
    "RSI": "46.34926112",
    "Stoch.K": "40.40173723"
  }
}

4. Getting All Indicators

If you want to retrieve all available indicators for a symbol, set allIndicators=True:

from tradingview_scraper.symbols.technicals import Indicators

# Scrape all indicators for the BTCUSD symbol
indicators_scraper = Indicators(export_result=True, export_type='json')
indicators = indicators_scraper.scrape(
    symbol="BTCUSD",
    timeframe="4h",
    allIndicators=True
)
print("All Indicators:", indicators)

5. Getting News Headlines/Content

from tradingview_scraper.symbols.news import NewsScraper

# Create an instance of the NewsScraper with export options
news_scraper = NewsScraper(export_result=True, export_type='json')

# Retrieve news headlines from a specific provider
news_headlines = news_scraper.scrape_headlines(
    symbol='BTCUSD',      # Uncomment and specify if needed
    exchange='BINANCE', # Uncomment and specify if needed
    # provider='newsbtc',  # Specify the news provider
    # area='world',  # Specify the geographical area
    # section='all',  # Specify the section of news
    sort='latest'
)

# Retrieve detailed news content for a specific story
news_content = news_scraper.scrape_news_content(
    story_path=news_headlines['data'][0]['storyPath']  # Specify the story path from scraped headlines
)

Retrieve news by symbol:
- Both symbol and exchange are required parameters
Filter result by:
- area, provider and section can be specified to refine the news results.

Output (news headline):

{
  "status": "success",
  "data": [
    {
      "id": "tag:reuters.com,2024:newsml_L1N3KM09S:0",
      "title": "Goldman Sachs sees biggest boost to US economy from Harris win",
      "provider": "reuters",
      "sourceLogoId": "reuters",
      "published": 1725443676,
      "source": "Reuters",
      "urgency": 2,
      "permission": "preview",
      "relatedSymbols": [
        {
          "symbol": "BITMEX:XBTETH.P",
          "currency-logoid": "country/US",
          "base-currency-logoid": "crypto/XTVCBTC"
        },
        {
          "symbol": "ICEUS:DXY",
          "logoid": "indices/u-s-dollar-index"
        }
      ],
      "storyPath": "/news/reuters.com,2024:newsml_L1N3KM09S:0-goldman-sachs-sees-biggest-boost-to-us-economy-from-harris-win/"
    }
  ],
  "total": 1
}

Output (news content):

{
  "status": "success",
  "data": {
    "breadcrumbs": "News > U.Today > Bitcoin ETFs Record Enormous Outflows",
    "title": "Bitcoin ETFs Record Enormous Outflows",
    "published_datetime": "Wed, 04 Sep 2024 07:55:38 GMT",
    "related_symbols": [
      {
        "name": "BTCUSDT",
        "logo": "https://s3-symbol-logo.tradingview.com/crypto/XTVCUSDT.svg"
      }
    ],
    "body": ["<List of text page content>"],
    "tags": ["Crypto", "U.Today"]
  }
}

6. Fetching Real-Time Trading Data

The RealTimeData class offers functionality to fetch real-time trading data from various exchanges. This section provides usage examples demonstrating how to retrieve the latest trade information and OHLCV (Open, High, Low, Close, Volume) data.

Retrieve OHLCV Data:

Timestamp
Open
High
Low
Close
Volume

Example:

Method 1: Simple OHLCV Retrieval

This method is straightforward and streams only OHLC data.

from tradingview_scraper.symbols.stream import RealTimeData
# Create an instance of the RealTimeData class
real_time_data = RealTimeData()

# Retrieve OHLCV data for a specific symbol
data_generator = real_time_data.get_ohlcv(exchange_symbol="BINANCE:BTCUSDT")

Method 2: Streaming OHLC and Indicators Simultaneously

Streams both OHLC data and indicators
Exports historical data in specific timeframe (price candles and indicator history).
Specifies the number of OHLCV historical candles to export.
Requires JWT token for indicator access.

from tradingview_scraper.symbols.stream import Streamer
# Create an instance of the Streamer class
streamer = Streamer(
    export_result=False,
    export_type='json',
    websocket_jwt_token="Your-Tradingview-Websocket-JWT"
    )

data_generator = streamer.stream(
    exchange="BINANCE",
    symbol="BTCUSDT",
    timeframe="4h",
    numb_price_candles=100,
    indicator_id="STD;RSI",
    indicator_version="31.0"
    )

Important Notes

Export Historical Data: Set export_result=True if only historical data is needed. (returns json instead of generator)
Stream Only OHLCV: Do not include indicator_id or indicator_version.

Indicator Search

For assistance in finding your preferred indicator, visit: TradingView Indicator Search.

Retrieve Watchlist Market Info

You can send a list of exchange:symbol to get real-time market information, including:
volume
lp_time (Last Price Time)
lp (Last Price)
ch (Change in Price)
chp (Change in Percent)

Example:

from tradingview_scraper.symbols.stream import RealTimeData
# Create an instance of the RealTimeData class
real_time_data = RealTimeData()

# Define the exchange symbols for which to fetch data
exchange_symbol = ["BINANCE:BTCUSDT", "BINANCE:ETHUSDT", "FXOPEN:XAUUSD"]

# Retrieve the latest trade information for a specific symbol
data_generator = real_time_data.get_latest_trade_info(exchange_symbol=exchange_symbol)

Printing Results

To display the real-time data packets, iterate over the generator as follows:

for packet in data_generator:
    print('-' * 50)
    print(packet)

Output Examples

Output (OHLCV):

{'m': 'du', 'p': ['cs_qgmbtglzdudl', {'sds_1': {'s': [{'i': 9, 'v': [1734082440.0, 100010.0, 100010.01, 100006.27, 100006.27, 1.3242]}], 'ns': {'d': '', 'indexes': 'nochange'}, 't': 's1', 'lbs': {'bar_close_time': 1734082500}}}]}

Output (Watchlist market info):

{'m': 'qsd', 'p': ['qs_folpuhzgowtu', {'n': 'BINANCE:BTCUSDT', 's': 'ok', 'v': {'volume': 6817.46425, 'lp_time': 1734082521, 'lp': 99957.9, 'chp': -0.05, 'ch': -46.39}}]}

7. Getting Calendar events

Scraping Earnings events

from tradingview_scraper.symbols.cal import CalendarScraper

calendar_scraper = CalendarScraper()

# Scrape earnings from all markets.
res = calendar_scraper.scrape_earnings(
  values=["logoid", "name", "earnings_per_share_fq"]
)


# Scrape upcoming week earnings from the american market
from datetime import datetime, timedelta

timestamp_now = datetime.now().timestamp()
timestamp_in_7_days = (datetime.now() + timedelta(days=7)).timestamp()

res = calendar_scraper.scrape_earnings(
  timestamp_now,
  timestamp_in_7_days,
  ["america"],
  values=["logoid", "name", "earnings_per_share_fq"]
  )

Scraping Dividend events

from tradingview_scraper.symbols.cal import CalendarScraper

calendar_scraper = CalendarScraper()

# Scrape dividends from all markets.
res = calendar_scraper.scrape_dividends(
  values=["logoid", "name", "dividends_yield"]
)


# Scrape upcoming week dividends from the american market
from datetime import datetime, timedelta

timestamp_now = datetime.now().timestamp()
timestamp_in_7_days = (datetime.now() + timedelta(days=7)).timestamp()

res = calendar_scraper.scrape_dividends(
  timestamp_now,
  timestamp_in_7_days,
  ["america"],
  values=["logoid", "name", "dividends_yield"]
  )

8. Getting Market Movers Data

The MarketMovers class allows you to scrape market movers data such as gainers, losers, most active stocks, penny stocks, and pre-market/after-hours movers.

Scraping Top Gainers

from tradingview_scraper.symbols.market_movers import MarketMovers

# Create an instance of MarketMovers
market_movers = MarketMovers(export_result=True, export_type='json')

# Get top gainers from US stock market
gainers = market_movers.scrape(
    market='stocks-usa',
    category='gainers',
    limit=20
)

print("Top Gainers:", gainers['data'])

Scraping Penny Stocks

from tradingview_scraper.symbols.market_movers import MarketMovers

market_movers = MarketMovers()

# Get penny stocks (stocks trading below $5)
penny_stocks = market_movers.scrape(
    market='stocks-usa',
    category='penny-stocks',
    limit=50
)

print("Penny Stocks:", penny_stocks['data'])

Scraping Pre-Market Gainers

from tradingview_scraper.symbols.market_movers import MarketMovers

market_movers = MarketMovers()

# Get pre-market gainers
premarket_gainers = market_movers.scrape(
    market='stocks-usa',
    category='pre-market-gainers',
    limit=30
)

print("Pre-Market Gainers:", premarket_gainers['data'])

Scraping with Custom Fields

from tradingview_scraper.symbols.market_movers import MarketMovers

market_movers = MarketMovers()

# Specify only the fields you need
custom_fields = ['name', 'close', 'change', 'volume', 'market_cap_basic']

losers = market_movers.scrape(
    market='stocks-usa',
    category='losers',
    fields=custom_fields,
    limit=10
)

print("Top Losers:", losers['data'])

Supported Markets

stocks-usa: US Stock Market
stocks-uk: UK Stock Market
stocks-india: Indian Stock Market
stocks-australia: Australian Stock Market
stocks-canada: Canadian Stock Market
crypto: Cryptocurrency Market
forex: Forex Market
bonds: Bonds Market
futures: Futures Market

Output Example:

{
  "status": "success",
  "data": [
    {
      "symbol": "NASDAQ:AAPL",
      "name": "Apple Inc.",
      "close": 150.25,
      "change": 2.5,
      "change_abs": 3.75,
      "volume": 50000000,
      "market_cap_basic": 2500000000000,
      "price_earnings_ttm": 25.5,
      "earnings_per_share_basic_ttm": 6.0,
      "logoid": "apple-logo-id",
      "description": "Technology company"
    }
  ],
  "total": 1
}

9. Using the Screener

The Screener allows you to filter and screen financial instruments based on custom criteria across multiple markets.

Basic Screening

from tradingview_scraper.symbols.screener import Screener

screener = Screener()

# Screen US stocks with default settings
results = screener.screen(market='america', limit=50)
print("Results:", results['data'])

Screening with Filters

from tradingview_scraper.symbols.screener import Screener

screener = Screener()

# Screen stocks with price > $100 and volume > 1M
filters = [
    {'left': 'close', 'operation': 'greater', 'right': 100},
    {'left': 'volume', 'operation': 'greater', 'right': 1000000}
]

results = screener.screen(
    market='america',
    filters=filters,
    sort_by='volume',
    sort_order='desc',
    limit=20
)

print("Filtered Results:", results['data'])

Screening Crypto by Market Cap

from tradingview_scraper.symbols.screener import Screener

screener = Screener()

# Screen crypto with market cap > $1B
crypto_filters = [
    {'left': 'market_cap_calc', 'operation': 'greater', 'right': 1000000000}
]

crypto_results = screener.screen(
    market='crypto',
    filters=crypto_filters,
    columns=['name', 'close', 'market_cap_calc', 'change'],
    limit=50
)

print("Top Crypto by Market Cap:", crypto_results['data'])

Price Range Filtering

from tradingview_scraper.symbols.screener import Screener

screener = Screener()

# Screen stocks in price range $50-$200
range_filters = [
    {'left': 'close', 'operation': 'in_range', 'right': [50, 200]}
]

range_results = screener.screen(
    market='america',
    filters=range_filters,
    limit=30
)

print("Stocks in Range:", range_results['data'])

Supported Markets

america: US Stock Market
australia: Australian Stock Market
canada: Canadian Stock Market
germany: German Stock Market
india: Indian Stock Market
israel: Israeli Stock Market
italy: Italian Stock Market
luxembourg: Luxembourg Stock Market
mexico: Mexican Stock Market
spain: Spanish Stock Market
turkey: Turkish Stock Market
uk: UK Stock Market
crypto: Cryptocurrency Market
forex: Forex Market
cfd: CFD Market
futures: Futures Market
bonds: Bonds Market
global: Global Market

Supported Filter Operations

greater: Greater than
less: Less than
egreater: Equal or greater than
eless: Equal or less than
equal: Equal to
nequal: Not equal to
in_range: Within range [min, max]
not_in_range: Outside range
above: Above (for crosses)
below: Below (for crosses)
crosses: Crosses
crosses_above: Crosses above
crosses_below: Crosses below
has: Has (for categorical fields)
has_none_of: Has none of

Output Example:

{
  "status": "success",
  "data": [
    {
      "symbol": "NASDAQ:AAPL",
      "name": "Apple Inc.",
      "close": 150.25,
      "change": 2.5,
      "change_abs": 3.75,
      "volume": 50000000,
      "Recommend.All": 0.8,
      "market_cap_basic": 2500000000000,
      "price_earnings_ttm": 25.5,
      "earnings_per_share_basic_ttm": 6.0
    }
  ],
  "total": 1,
  "totalCount": 1500
}

10. Finding Symbol Markets

The Symbol Markets feature allows you to discover all exchanges and markets where a specific symbol is traded.

Basic Symbol Market Search

from tradingview_scraper.symbols.symbol_markets import SymbolMarkets

markets = SymbolMarkets()

# Find all markets where AAPL is traded
aapl_markets = markets.scrape(symbol='AAPL')

print(f"AAPL is traded on {aapl_markets['total']} markets")
print("Markets:", aapl_markets['data'])

Regional Market Search

from tradingview_scraper.symbols.symbol_markets import SymbolMarkets

markets = SymbolMarkets()

# Find only US markets
us_markets = markets.scrape(
    symbol='AAPL',
    scanner='america'
)

print("US Markets:", us_markets['data'])

Crypto Symbol Markets

from tradingview_scraper.symbols.symbol_markets import SymbolMarkets

markets = SymbolMarkets()

# Find all crypto exchanges trading Bitcoin
btc_markets = markets.scrape(
    symbol='BTCUSD',
    scanner='crypto',
    limit=100
)

print(f"Bitcoin is traded on {btc_markets['total']} crypto exchanges")

Custom Columns

from tradingview_scraper.symbols.symbol_markets import SymbolMarkets

markets = SymbolMarkets()

# Get specific information only
custom_markets = markets.scrape(
    symbol='TSLA',
    columns=['name', 'close', 'volume', 'exchange', 'currency'],
    limit=50
)

for market in custom_markets['data']:
    print(f"{market['exchange']}: {market['close']} {market.get('currency', 'N/A')}")

Supported Scanners

global: Search across all markets worldwide (default)
america: US stock markets
crypto: Cryptocurrency exchanges
forex: Forex markets
cfd: CFD markets

Output Example:

{
  "status": "success",
  "data": [
    {
      "symbol": "NASDAQ:AAPL",
      "name": "AAPL",
      "close": 150.25,
      "change": 2.5,
      "change_abs": 3.75,
      "volume": 50000000,
      "exchange": "NASDAQ",
      "type": "stock",
      "description": "Apple Inc.",
      "currency": "USD",
      "market_cap_basic": 2500000000000
    },
    {
      "symbol": "GPW:AAPL",
      "name": "AAPL",
      "close": 148.50,
      "change": 1.2,
      "change_abs": 1.80,
      "volume": 1000000,
      "exchange": "GPW",
      "type": "stock",
      "description": "Apple Inc.",
      "currency": "PLN",
      "market_cap_basic": 2500000000000
    }
  ],
  "total": 2,
  "totalCount": 87
}

Section 11: Markets Overview

Get top stocks by various criteria from different markets:

Get Top Stocks by Market Cap

from tradingview_scraper.symbols.markets import Markets

markets = Markets()

# Get top 20 stocks by market capitalization from US market
top_by_cap = markets.get_top_stocks(
    market='america',
    by='market_cap',
    limit=20
)

print("Top Stocks by Market Cap:")
for stock in top_by_cap['data']:
    print(f"{stock['name']}: ${stock['market_cap_basic']:,.0f}")

Get Most Active Stocks

from tradingview_scraper.symbols.markets import Markets

markets = Markets()

# Get top 30 stocks by trading volume
most_active = markets.get_top_stocks(
    market='america',
    by='volume',
    limit=30
)

print("Most Active Stocks:")
for stock in most_active['data']:
    print(f"{stock['name']}: Volume {stock['volume']:,.0f}")

Get Biggest Movers

from tradingview_scraper.symbols.markets import Markets

markets = Markets()

# Get top 25 stocks by price change
biggest_movers = markets.get_top_stocks(
    market='america',
    by='change',
    limit=25
)

print("Biggest Movers:")
for stock in biggest_movers['data']:
    print(f"{stock['name']}: {stock['change']:.2f}%")

Get Top Stocks with Custom Columns

from tradingview_scraper.symbols.markets import Markets

markets = Markets()

# Get top stocks with specific data fields
custom_columns = [
    'name',
    'close',
    'volume',
    'market_cap_basic',
    'price_earnings_ttm',
    'sector',
    'industry'
]

custom_results = markets.get_top_stocks(
    market='america',
    by='market_cap',
    columns=custom_columns,
    limit=10
)

print("Top Stocks with Custom Data:")
for stock in custom_results['data']:
    print(f"{stock['name']}: ${stock['close']} | P/E: {stock.get('price_earnings_ttm', 'N/A')}")

Supported Markets

america: US Stock Market
australia: Australian Stock Market
canada: Canadian Stock Market
germany: German Stock Market
india: Indian Stock Market
uk: UK Stock Market
crypto: Cryptocurrency Market
forex: Forex Market
global: Global Market

Supported Sort Criteria

market_cap: Sort by market capitalization
volume: Sort by trading volume
change: Sort by price change percentage
price: Sort by current price
volatility: Sort by volatility

Output Example:

{
  "status": "success",
  "data": [
    {
      "symbol": "NASDAQ:NVDA",
      "name": "NVDA",
      "close": 875.50,
      "change": 3.25,
      "change_abs": 27.50,
      "volume": 45000000,
      "Recommend.All": 0.8,
      "market_cap_basic": 2200000000000,
      "price_earnings_ttm": 45.5,
      "earnings_per_share_basic_ttm": 19.25,
      "sector": "Technology",
      "industry": "Semiconductors"
    }
  ],
  "total": 1,
  "totalCount": 5000
}

Section 12: Symbol Overview

Get comprehensive overview data for any symbol:

Get Complete Overview Data

from tradingview_scraper.symbols.overview import Overview

overview = Overview()

# Get full overview for Apple stock
aapl = overview.get_symbol_overview(symbol='NASDAQ:AAPL')

print("Company:", aapl['data']['description'])
print("Price:", aapl['data']['close'])
print("Market Cap:", aapl['data']['market_cap_basic'])
print("P/E Ratio:", aapl['data']['price_earnings_ttm'])
print("Sector:", aapl['data']['sector'])

Get Profile Information

from tradingview_scraper.symbols.overview import Overview

overview = Overview()

# Get basic profile for a symbol
profile = overview.get_profile(symbol='NASDAQ:AAPL')

print("Profile Data:")
print(f"Name: {profile['data']['name']}")
print(f"Description: {profile['data']['description']}")
print(f"Exchange: {profile['data']['exchange']}")
print(f"Sector: {profile['data']['sector']}")
print(f"Industry: {profile['data']['industry']}")
print(f"Country: {profile['data']['country']}")

Get Statistics and Valuation

from tradingview_scraper.symbols.overview import Overview

overview = Overview()

# Get market statistics and valuation ratios
stats = overview.get_statistics(symbol='NASDAQ:AAPL')

print("Statistics:")
print(f"Market Cap: ${stats['data']['market_cap_basic']:,.0f}")
print(f"P/E Ratio: {stats['data']['price_earnings_ttm']:.2f}")
print(f"P/B Ratio: {stats['data']['price_book_fq']:.2f}")
print(f"EPS: ${stats['data']['earnings_per_share_basic_ttm']:.2f}")
print(f"Dividend Yield: {stats['data']['dividends_yield']:.2%}")

Get Financial Metrics

from tradingview_scraper.symbols.overview import Overview

overview = Overview()

# Get financial data
financials = overview.get_financials(symbol='NASDAQ:AAPL')

print("Financials:")
print(f"Revenue: ${financials['data']['total_revenue']:,.0f}")
print(f"Net Income: ${financials['data']['net_income_fy']:,.0f}")
print(f"Operating Margin: {financials['data']['operating_margin_ttm']:.2f}%")
print(f"ROE: {financials['data']['return_on_equity_fq']:.2f}%")
print(f"ROA: {financials['data']['return_on_assets_fq']:.2f}%")
print(f"Debt/Equity: {financials['data']['debt_to_equity_fq']:.2f}")

Get Performance Metrics

from tradingview_scraper.symbols.overview import Overview

overview = Overview()

# Get performance data
performance = overview.get_performance(symbol='NASDAQ:AAPL')

print("Performance:")
print(f"1 Week: {performance['data']['Perf.W']:.2f}%")
print(f"1 Month: {performance['data']['Perf.1M']:.2f}%")
print(f"3 Months: {performance['data']['Perf.3M']:.2f}%")
print(f"6 Months: {performance['data']['Perf.6M']:.2f}%")
print(f"1 Year: {performance['data']['Perf.Y']:.2f}%")
print(f"YTD: {performance['data']['Perf.YTD']:.2f}%")

Get Technical Indicators

from tradingview_scraper.symbols.overview import Overview

overview = Overview()

# Get technical indicators
technicals = overview.get_technicals(symbol='NASDAQ:AAPL')

print("Technical Indicators:")
print(f"RSI: {technicals['data']['RSI']:.2f}")
print(f"MACD: {technicals['data']['MACD.macd']:.2f}")
print(f"ADX: {technicals['data']['ADX']:.2f}")
print(f"Recommendation: {technicals['data']['Recommend.All']:.2f}")
print(f"Daily Volatility: {technicals['data']['Volatility.D']:.2f}%")
print(f"Beta: {technicals['data']['beta_1_year']:.2f}")

Get Crypto Overview

from tradingview_scraper.symbols.overview import Overview

overview = Overview()

# Get Bitcoin overview
btc = overview.get_symbol_overview(symbol='BITSTAMP:BTCUSD')

print("Bitcoin Overview:")
print(f"Price: ${btc['data']['close']:,.2f}")
print(f"24h Change: {btc['data']['change']:.2f}%")
print(f"Volume: ${btc['data']['volume']:,.0f}")
print(f"Market Cap: ${btc['data']['market_cap_basic']:,.0f}")

Get Custom Fields

from tradingview_scraper.symbols.overview import Overview

overview = Overview()

# Get specific fields only
custom_fields = [
    'name',
    'close',
    'volume',
    'market_cap_basic',
    'price_earnings_ttm',
    'dividends_yield',
    'Perf.Y',
    'RSI'
]

result = overview.get_symbol_overview(
    symbol='NASDAQ:AAPL',
    fields=custom_fields
)

print("Custom Data:", result['data'])

Supported Symbols

All symbols must include exchange prefix:

Stocks: NASDAQ:AAPL, NYSE:TSLA, LSE:VOD
Crypto: BITSTAMP:BTCUSD, BINANCE:BTCUSDT
Forex: FX:EURUSD, OANDA:GBPUSD
Indices: SP:SPX, DJ:DJI
Commodities: COMEX:GC1!, NYMEX:CL1!

Available Field Categories

BASIC_FIELDS: name, description, type, exchange, country, sector, industry
PRICE_FIELDS: close, change, high, low, open, volume, 52-week highs/lows
MARKET_FIELDS: market cap, shares outstanding, float, diluted shares
VALUATION_FIELDS: P/E, P/B, P/S, P/FCF, EPS ratios
DIVIDEND_FIELDS: yield, per share, payout ratio
FINANCIAL_FIELDS: revenue, income, margins, returns, ratios, EBITDA, employees
PERFORMANCE_FIELDS: weekly, monthly, quarterly, yearly returns
VOLATILITY_FIELDS: daily/weekly/monthly volatility, beta
TECHNICAL_FIELDS: RSI, MACD, ADX, CCI, Stoch.K, recommendations

Output Example:

{
  "status": "success",
  "data": {
    "symbol": "NASDAQ:AAPL",
    "name": "AAPL",
    "description": "Apple Inc.",
    "close": 267.66,
    "change": -0.78,
    "volume": 26882593,
    "market_cap_basic": 3955022105550,
    "price_earnings_ttm": 35.88,
    "earnings_per_share_basic_ttm": 7.49,
    "dividends_yield": 0.39,
    "sector": "Electronic Technology",
    "industry": "Telecommunications Equipment",
    "country": "United States",
    "Perf.Y": 19.16,
    "RSI": 61.90,
    "Recommend.All": 0.22,
    "beta_1_year": 1.19
  }
}

Section 13: Minds Community Discussions

Get community discussions, questions, and trading ideas:

Get Recent Discussions

from tradingview_scraper.symbols.minds import Minds

minds = Minds()

# Get recent discussions for Apple
aapl_minds = minds.get_minds(
    symbol='NASDAQ:AAPL',
    sort='recent',
    limit=20
)

print(f"Found {aapl_minds['total']} discussions")
for discussion in aapl_minds['data']:
    print(f"\n{discussion['author']['username']}: {discussion['text'][:100]}...")
    print(f"Likes: {discussion['total_likes']}, Comments: {discussion['total_comments']}")

Get Popular Discussions

from tradingview_scraper.symbols.minds import Minds

minds = Minds()

# Get popular discussions sorted by engagement
popular = minds.get_minds(
    symbol='NASDAQ:TSLA',
    sort='popular',
    limit=15
)

print("Popular Discussions:")
for item in popular['data']:
    print(f"\nAuthor: {item['author']['username']}")
    print(f"Text: {item['text']}")
    print(f"Engagement: {item['total_likes']} likes, {item['total_comments']} comments")
    print(f"Created: {item['created']}")

Get Trending Discussions

from tradingview_scraper.symbols.minds import Minds

minds = Minds()

# Get trending discussions
trending = minds.get_minds(
    symbol='BITSTAMP:BTCUSD',
    sort='trending',
    limit=25
)

print("Trending Bitcoin Discussions:")
for discussion in trending['data']:
    print(f"\n{discussion['text']}")
    print(f"Author: {discussion['author']['username']}")
    print(f"Profile: {discussion['author']['profile_url']}")

Get All Discussions with Pagination

from tradingview_scraper.symbols.minds import Minds

minds = Minds()

# Get all available discussions (up to max_results)
all_discussions = minds.get_all_minds(
    symbol='NASDAQ:AAPL',
    sort='popular',
    max_results=100
)

print(f"Retrieved {all_discussions['total']} discussions across {all_discussions['pages']} pages")

# Analyze sentiment
total_likes = sum(d['total_likes'] for d in all_discussions['data'])
print(f"Total community likes: {total_likes}")

Access Symbol Information

from tradingview_scraper.symbols.minds import Minds

minds = Minds()

result = minds.get_minds(symbol='NASDAQ:AAPL', limit=10)

# Symbol information included in response
symbol_info = result['symbol_info']
print(f"Symbol: {symbol_info.get('short_name')}")
print(f"Exchange: {symbol_info.get('exchange')}")
print(f"Type: {symbol_info.get('type')}")
print(f"Name: {symbol_info.get('instrument_name')}")

Filter by User Activity

from tradingview_scraper.symbols.minds import Minds

minds = Minds()

# Get discussions
discussions = minds.get_minds(symbol='NASDAQ:AAPL', limit=50)

# Filter high-engagement posts
high_engagement = [
    d for d in discussions['data']
    if d['total_likes'] > 10 or d['total_comments'] > 5
]

print(f"High engagement posts: {len(high_engagement)}")
for post in high_engagement:
    print(f"\n{post['text'][:100]}...")
    print(f"Engagement: {post['total_likes']} likes, {post['total_comments']} comments")

Export Discussions

from tradingview_scraper.symbols.minds import Minds

# Enable export to JSON
minds = Minds(export_result=True, export_type='json')

# Get and export discussions
discussions = minds.get_minds(
    symbol='NASDAQ:AAPL',
    sort='popular',
    limit=100
)

print(f"Exported {discussions['total']} discussions to JSON file")

Supported Symbols

All symbols must include exchange prefix:

Stocks: NASDAQ:AAPL, NYSE:TSLA, LSE:VOD
Crypto: BITSTAMP:BTCUSD, BINANCE:BTCUSDT
Forex: FX:EURUSD, OANDA:GBPUSD
Indices: SP:SPX, DJ:DJI

Sort Options

recent: Most recent discussions (default)
popular: Sorted by total engagement (likes + comments)
trending: Currently trending discussions

Output Example:

{
  "status": "success",
  "data": [
    {
      "uid": "abc123xyz",
      "text": "$AAPL looking strong above 270. Potential breakout incoming!",
      "url": "https://www.tradingview.com/symbols/NASDAQ-AAPL/minds/?mind=abc123xyz",
      "author": {
        "username": "trader123",
        "profile_url": "https://www.tradingview.com/u/trader123/",
        "is_broker": false
      },
      "created": "2025-11-07 16:47:45",
      "symbols": ["NASDAQ:AAPL"],
      "total_likes": 15,
      "total_comments": 8,
      "modified": false,
      "hidden": false
    }
  ],
  "total": 1,
  "symbol_info": {
    "short_name": "AAPL",
    "exchange": "NASDAQ",
    "type": "stock",
    "instrument_name": "Apple Inc"
  },
  "next_cursor": "cursor_string_for_pagination"
}

14. Fundamental Financial Data

The FundamentalGraphs class provides comprehensive fundamental financial data for stocks and other securities. Access income statements, balance sheets, cash flow data, profitability ratios, and valuation metrics.

Get Complete Fundamental Data

from tradingview_scraper.symbols.fundamental_graphs import FundamentalGraphs

fundamentals = FundamentalGraphs()

# Get all fundamental data for Apple
aapl_data = fundamentals.get_fundamentals(symbol='NASDAQ:AAPL')

if aapl_data['status'] == 'success':
    data = aapl_data['data']
    print(f"Revenue: ${data.get('total_revenue', 'N/A'):,.0f}")
    print(f"Net Income: ${data.get('net_income', 'N/A'):,.0f}")
    print(f"EBITDA: ${data.get('EBITDA', 'N/A'):,.0f}")
    print(f"Market Cap: ${data.get('market_cap_basic', 'N/A'):,.0f}")
    print(f"P/E Ratio: {data.get('price_earnings_ttm', 'N/A')}")

Get Income Statement Data

from tradingview_scraper.symbols.fundamental_graphs import FundamentalGraphs

fundamentals = FundamentalGraphs()

# Get income statement for Apple
income_data = fundamentals.get_income_statement(symbol='NASDAQ:AAPL')

data = income_data['data']
print(f"Total Revenue: ${data.get('total_revenue', 'N/A'):,.0f}")
print(f"Gross Profit: ${data.get('gross_profit', 'N/A'):,.0f}")
print(f"Operating Income: ${data.get('operating_income', 'N/A'):,.0f}")
print(f"Net Income: ${data.get('net_income', 'N/A'):,.0f}")
print(f"EPS (Basic): ${data.get('earnings_per_share_basic_ttm', 'N/A'):.2f}")
print(f"EPS (Diluted): ${data.get('earnings_per_share_diluted_ttm', 'N/A'):.2f}")

Get Balance Sheet Data

from tradingview_scraper.symbols.fundamental_graphs import FundamentalGraphs

fundamentals = FundamentalGraphs()

# Get balance sheet for Microsoft
balance_data = fundamentals.get_balance_sheet(symbol='NASDAQ:MSFT')

data = balance_data['data']
print(f"Total Assets: ${data.get('total_assets', 'N/A'):,.0f}")
print(f"Cash & Short-term Investments: ${data.get('cash_n_short_term_invest', 'N/A'):,.0f}")
print(f"Total Debt: ${data.get('total_debt', 'N/A'):,.0f}")
print(f"Stockholders Equity: ${data.get('stockholders_equity', 'N/A'):,.0f}")
print(f"Book Value per Share: ${data.get('book_value_per_share_fq', 'N/A'):.2f}")

Get Cash Flow Data

from tradingview_scraper.symbols.fundamental_graphs import FundamentalGraphs

fundamentals = FundamentalGraphs()

# Get cash flow for Google
cash_flow_data = fundamentals.get_cash_flow(symbol='NASDAQ:GOOGL')

data = cash_flow_data['data']
print(f"Operating Activities: ${data.get('cash_f_operating_activities', 'N/A'):,.0f}")
print(f"Investing Activities: ${data.get('cash_f_investing_activities', 'N/A'):,.0f}")
print(f"Financing Activities: ${data.get('cash_f_financing_activities', 'N/A'):,.0f}")
print(f"Free Cash Flow: ${data.get('free_cash_flow', 'N/A'):,.0f}")

Get Profitability Ratios

from tradingview_scraper.symbols.fundamental_graphs import FundamentalGraphs

fundamentals = FundamentalGraphs()

# Get profitability metrics
profitability = fundamentals.get_profitability(symbol='NASDAQ:AAPL')

data = profitability['data']
print(f"Return on Equity (ROE): {data.get('return_on_equity_fq', 'N/A'):.2%}")
print(f"Return on Assets (ROA): {data.get('return_on_assets_fq', 'N/A'):.2%}")
print(f"Return on Investment (ROI): {data.get('return_on_investment_ttm', 'N/A'):.2%}")

Get Margin Metrics

from tradingview_scraper.symbols.fundamental_graphs import FundamentalGraphs

fundamentals = FundamentalGraphs()

# Get margin data
margins = fundamentals.get_margins(symbol='NASDAQ:AAPL')

data = margins['data']
print(f"Gross Margin: {data.get('gross_margin_percent_ttm', 'N/A'):.2%}")
print(f"Operating Margin: {data.get('operating_margin_ttm', 'N/A'):.2%}")
print(f"Net Margin: {data.get('net_margin_percent_ttm', 'N/A'):.2%}")
print(f"EBITDA Margin: {data.get('EBITDA_margin', 'N/A'):.2%}")

Get Liquidity & Leverage Ratios

from tradingview_scraper.symbols.fundamental_graphs import FundamentalGraphs

fundamentals = FundamentalGraphs()

# Get liquidity ratios
liquidity = fundamentals.get_liquidity(symbol='NASDAQ:AAPL')
print(f"Current Ratio: {liquidity['data'].get('current_ratio_fq', 'N/A'):.2f}")
print(f"Quick Ratio: {liquidity['data'].get('quick_ratio_fq', 'N/A'):.2f}")

# Get leverage ratios
leverage = fundamentals.get_leverage(symbol='NASDAQ:AAPL')
print(f"Debt-to-Equity: {leverage['data'].get('debt_to_equity_fq', 'N/A'):.2f}")
print(f"Debt-to-Assets: {leverage['data'].get('debt_to_assets', 'N/A'):.2f}")

Get Valuation Metrics

from tradingview_scraper.symbols.fundamental_graphs import FundamentalGraphs

fundamentals = FundamentalGraphs()

# Get valuation data
valuation = fundamentals.get_valuation(symbol='NASDAQ:AAPL')

data = valuation['data']
print(f"Market Cap: ${data.get('market_cap_basic', 'N/A'):,.0f}")
print(f"Enterprise Value: ${data.get('enterprise_value_fq', 'N/A'):,.0f}")
print(f"P/E Ratio: {data.get('price_earnings_ttm', 'N/A'):.2f}")
print(f"P/B Ratio: {data.get('price_book_fq', 'N/A'):.2f}")
print(f"P/S Ratio: {data.get('price_sales_ttm', 'N/A'):.2f}")
print(f"P/FCF Ratio: {data.get('price_free_cash_flow_ttm', 'N/A'):.2f}")

Get Dividend Information

from tradingview_scraper.symbols.fundamental_graphs import FundamentalGraphs

fundamentals = FundamentalGraphs()

# Get dividend data
dividends = fundamentals.get_dividends(symbol='NASDAQ:AAPL')

data = dividends['data']
print(f"Dividend Yield: {data.get('dividends_yield', 'N/A'):.2%}")
print(f"Dividends per Share: ${data.get('dividends_per_share_fq', 'N/A'):.2f}")
print(f"Payout Ratio: {data.get('dividend_payout_ratio_ttm', 'N/A'):.2%}")

Compare Multiple Symbols

from tradingview_scraper.symbols.fundamental_graphs import FundamentalGraphs

fundamentals = FundamentalGraphs()

# Compare fundamental metrics across tech giants
comparison = fundamentals.compare_fundamentals(
    symbols=['NASDAQ:AAPL', 'NASDAQ:MSFT', 'NASDAQ:GOOGL'],
    fields=['total_revenue', 'net_income', 'EBITDA', 'market_cap_basic', 'price_earnings_ttm']
)

if comparison['status'] == 'success':
    # Access comparison data
    for field, values in comparison['comparison'].items():
        print(f"\n{field}:")
        for symbol, value in values.items():
            print(f"  {symbol}: {value:,.0f}" if isinstance(value, (int, float)) else f"  {symbol}: {value}")

Get Custom Fields

from tradingview_scraper.symbols.fundamental_graphs import FundamentalGraphs

fundamentals = FundamentalGraphs()

# Get only specific fundamental fields
custom_data = fundamentals.get_fundamentals(
    symbol='NASDAQ:AAPL',
    fields=[
        'total_revenue',
        'net_income',
        'free_cash_flow',
        'return_on_equity_fq',
        'debt_to_equity_fq',
        'price_earnings_ttm'
    ]
)

print("Selected Metrics:")
for field, value in custom_data['data'].items():
    if field != 'symbol':
        print(f"{field}: {value}")

Export Fundamental Data

from tradingview_scraper.symbols.fundamental_graphs import FundamentalGraphs

# Enable export to JSON
fundamentals = FundamentalGraphs(export_result=True, export_type='json')

# Get and export fundamental data
data = fundamentals.get_fundamentals(symbol='NASDAQ:AAPL')

print(f"Exported fundamental data for {data['data']['symbol']}")

Supported Symbols

All symbols must include exchange prefix:

Stocks: NASDAQ:AAPL, NYSE:JPM, LSE:VOD
No support for crypto, forex, or other non-stock symbols

Available Field Categories

Income Statement: revenue, profit, earnings, EBITDA
Balance Sheet: assets, debt, equity, cash
Cash Flow: operating, investing, financing activities, free cash flow
Profitability: ROE, ROA, ROI
Liquidity: current ratio, quick ratio
Leverage: debt-to-equity, debt-to-assets
Margins: gross, operating, net, EBITDA margins
Valuation: market cap, enterprise value, P/E, P/B, P/S
Dividends: yield, per share, payout ratio

Output Example:

{
  "status": "success",
  "data": {
    "symbol": "NASDAQ:AAPL",
    "total_revenue": 394328000000,
    "net_income": 100913000000,
    "EBITDA": 130541000000,
    "total_assets": 365725000000,
    "total_debt": 101304000000,
    "stockholders_equity": 62146000000,
    "cash_f_operating_activities": 116433000000,
    "free_cash_flow": 106496000000,
    "return_on_equity_fq": 1.6233,
    "current_ratio_fq": 0.94,
    "debt_to_equity_fq": 1.63,
    "gross_margin_percent_ttm": 0.4607,
    "operating_margin_ttm": 0.3096,
    "net_margin_percent_ttm": 0.2559,
    "market_cap_basic": 3550000000000,
    "price_earnings_ttm": 35.19,
    "dividends_yield": 0.0044
  }
}

Changes:

Release 0.4.19: Fix raise error while fetching ideas for pages greater than 1
Release 0.4.17: Add Fundamental Graphs feature for comprehensive financial data Support 9 field categories: income statement, balance sheet, cash flow, profitability, liquidity, leverage, margins, valuation, dividends Helper methods for specific financial statements (get_income_statement, get_balance_sheet, get_cash_flow, etc.) Multi-symbol comparison with compare_fundamentals() method Support for 60+ fundamental metrics per symbol
Release 0.4.16: Add Minds feature for community discussions and trading ideas Support recent, popular, and trending sort options Pagination support with get_all_minds() method User engagement metrics (likes, comments) and author information
Release 0.4.15: Add Symbol Overview feature for comprehensive symbol data Support for profile, statistics, financials, performance, and technical data 9 field categories with 70+ data points per symbol Helper methods for specific data categories
Release 0.4.14: Add Markets Overview feature for top stocks analysis Sort by market cap, volume, change, price, volatility Support 9 markets (America, Australia, Canada, Germany, India, UK, Crypto, Forex, Global)
Release 0.4.13: Add Symbol Markets feature to find all exchanges/markets where a symbol is traded Support global, regional (America, Crypto, Forex, CFD) market scanners Discover stocks, crypto, derivatives across 100+ exchanges worldwide
Release 0.4.12: Add Screener functionality with custom filters, sorting, and column selection Support 18 markets (America, Canada, Germany, India, UK, Crypto, Forex, CFD, Futures, Bonds, etc.) Support 15+ filter operations (greater, less, equal, in_range, crosses, etc.)
Release 0.4.11: Add Market Movers scraper (Gainers, Losers, Penny Stocks, Pre-market/After-hours movers) Support multiple markets (USA, UK, India, Australia, Canada, Crypto, Forex, Bonds, Futures)
Release 0.4.9: Add documentation
Release 0.4.8: Fix bug while fetching ADX+DI indicators Add timeframe param for streamer export data
Release 0.4.7: Fix bug undefined RealTimeData class
Release 0.4.6: Add value argument to specify calander fields Add Streamer class for getting OHLCV and indicator simultaneously Integrate realtime data and historical exporter into Streamer class
Release 0.4.2: Add calander (Dividend, Earning) Make requirements non-explicit Lint fix Add tests (ideas, realtime_price, indicators) Add reconnection method for realtime price scraper
Release 0.4.0: Update exchange list Add real-time price streaming
Release 0.3.2:
Support timeframe to get Indicators
Release 0.3.0:
Add news scraper
Release 0.2.9:
Refactor for new TradingView structure
Release 0.1.0:
The name of ClassA changed to Ideas

License:

[MIT]

Name		Name	Last commit message	Last commit date
Latest commit History 260 Commits
.github/workflows		.github/workflows
docs		docs
tests		tests
tradingview_scraper		tradingview_scraper
.env.example		.env.example
.gitignore		.gitignore
.pylintrc		.pylintrc
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
MANIFEST		MANIFEST
README.md		README.md
requirements.txt		requirements.txt
setup.cfg		setup.cfg
setup.py		setup.py

License

mnwato/tradingview-scraper

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TradingView Scraper

To-Do List

To be aware of the latest changes, go to the end of this page.

Features

Installation

Examples

1. Fast Running (Default Parameters)

2. Getting Ideas for a Specific Symbol, Export Type, and Pages

Using Cookie for Captcha Bypass

3. Getting Indicators Status

4. Getting All Indicators

5. Getting News Headlines/Content

Output (news headline):

Output (news content):

6. Fetching Real-Time Trading Data

Retrieve OHLCV Data:

Example:

Method 1: Simple OHLCV Retrieval

Method 2: Streaming OHLC and Indicators Simultaneously

Important Notes

Indicator Search

Retrieve Watchlist Market Info

Example:

Printing Results

Output Examples

Output (OHLCV):

Output (Watchlist market info):

7. Getting Calendar events

Scraping Earnings events

Scraping Dividend events

8. Getting Market Movers Data

Scraping Top Gainers

Scraping Penny Stocks

Scraping Pre-Market Gainers

Scraping with Custom Fields

Supported Markets

Supported Categories

Output Example:

9. Using the Screener

Basic Screening

Screening with Filters

Screening Crypto by Market Cap

Price Range Filtering

Supported Markets

Supported Filter Operations

Output Example:

10. Finding Symbol Markets

Basic Symbol Market Search

Regional Market Search

Crypto Symbol Markets

Custom Columns

Supported Scanners

Output Example:

Section 11: Markets Overview

Get Top Stocks by Market Cap

Get Most Active Stocks

Get Biggest Movers

Get Top Stocks with Custom Columns

Supported Markets

Supported Sort Criteria

Output Example:

Section 12: Symbol Overview

Get Complete Overview Data

Get Profile Information

Get Statistics and Valuation

Get Financial Metrics

Get Performance Metrics

Get Technical Indicators

Get Crypto Overview

Get Custom Fields

Supported Symbols

Available Field Categories

Output Example:

Section 13: Minds Community Discussions

Packages