tradingview-scraper 0.4.20

Tradingview scraper tool

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:

1. Open your terminal: Launch your preferred command line interface.

2. Install the package: Run the following command to install the TradingView Scraper:

   pip install tradingview-scraper
   

3. 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:

1. Open https://www.tradingview.com/symbols/BTCUSD/ideas/ in your browser
2. Open Developer Tools (F12) and go to Network tab
3. Verify the captcha if prompted
4. Refresh the page and find the GET request at the top of the list with URL https://www.tradingview.com/symbols/BTCUSD/ideas/
5. 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

Supported Categories

• gainers: Top gaining stocks
• losers: Top losing stocks
• most-active: Most actively traded stocks
• penny-stocks: Stocks trading below $5
• pre-market-gainers: Pre-market gainers
• pre-market-losers: Pre-market losers
• after-hours-gainers: After-hours gainers
• after-hours-losers: After-hours losers

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]