investgo 1.0.9

A Python library for fetching financial data from Investing.com

InvestGo

A Python library for fetching financial data from Investing.com, including historical stock prices, ETF holdings, technical indicators, and market info.

Features

• 📈 Historical Data: Fetch historical stock prices with automatic date range chunking
• 🏢 Holdings Data: Get ETF/fund holdings, asset allocation, and sector breakdowns
• 📊 Technical Analysis: Access pivot points, moving averages, technical indicators, and trading signals across 8 timeframes
• 📰 Market Info: Get comprehensive overview data including price, volume, fundamentals, sentiment, and technical summaries
• 🔍 Symbol Search: Find pair IDs by ticker symbols
• ⚡ Concurrent Processing: Fast data retrieval using multithreading
• 🐼 Pandas Integration: Returns data as pandas DataFrames for easy analysis

Installation

pip install investgo

Quick Start

from investgo import get_pair_id, get_historical_prices, get_holdings, get_info

# Get pair ID for a ticker
pair_id = get_pair_id(['AAPL'])[0]

# Get market info (price, fundamentals, sentiment)
info = get_info(pair_id)
print(info)

# Fetch historical data
df = get_historical_prices(pair_id, "01012021", "01012024")
print(df.head())

# Get ETF holdings
qqq_id = get_pair_id(['QQQ'])[0]
holdings = get_holdings(qqq_id, "top_holdings")
print(holdings)

API Reference

Historical Data

get_historical_prices(pair_id, date_from, date_to)

Fetch historical price data for a given stock.

Parameters:

• pair_id (str): The Investing.com pair ID
• date_from (str): Start date in "DDMMYYYY" format
• date_to (str): End date in "DDMMYYYY" format

Returns: pandas.DataFrame with columns: price, open, high, low, vol, perc_chg

# Example
data = get_historical_prices("1075", "01012023", "31122023")

get_multiple_historical_prices(pair_ids, date_from, date_to)

Fetch historical data for multiple stocks concurrently.

Parameters:

• pair_ids (list): List of Investing.com pair IDs
• date_from (str): Start date in "DDMMYYYY" format
• date_to (str): End date in "DDMMYYYY" format

Returns: pandas.DataFrame with concatenated data

Search Functions

get_pair_id(stock_ids, display_mode="first", name="no")

Search for stock pair IDs by ticker symbols.

Parameters:

• stock_ids (str or list): Ticker symbol(s) to search
• display_mode (str): "first" for first match, "all" for all matches
• name (str): "yes" to return names along with IDs

Returns: List of pair IDs or DataFrame (depending on parameters)

# Get pair ID for Apple
apple_id = get_pair_id('AAPL')[0]

# Get IDs and names for multiple tickers
ids, names = get_pair_id(['AAPL', 'MSFT'], name='yes')

# Get all search results
all_results = get_pair_id('AAPL', display_mode='all')

Holdings Data

get_holdings(pair_id, holdings_type="all")

Get holdings and allocation data for ETFs and funds.

Parameters:

• pair_id (str): The Investing.com pair ID
• holdings_type (str): Type of data to retrieve:
  • "top_holdings": Top holdings by weight
  • "assets_allocation": Asset class breakdown (stocks, bonds, cash)
  • "stock_sector": Sector allocation
  • "stock_region": Geographic allocation
  • "all": All holdings data types

Returns: pandas.DataFrame or list of DataFrames

# Get top holdings for QQQ ETF
qqq_id = get_pair_id('QQQ')[0]
top_holdings = get_holdings(qqq_id, "top_holdings")

# Get asset allocation
allocation = get_holdings(qqq_id, "assets_allocation")

# Get all holdings data
all_data = get_holdings(qqq_id, "all")

Market Info

get_info(pair_id)

Get comprehensive market overview data for any financial instrument.

Parameters:

• pair_id (str): The Investing.com pair ID

Returns: pandas.DataFrame with comprehensive market data including:

Instrument Identity:

• symbol, name, full_name, exchange, currency, pair_type, is_crypto

Current Price Data:

• last, bid, ask, change, change_percent, open, high, low, previous_close

Volume:

• volume, avg_volume_3m

Performance:

• 52w_high, 52w_low, one_year_return

Technical & Sentiment:

• technical_summary (Strong Buy/Sell), bullish, bearish (sentiment percentages)

Stock-Specific (when available):

• eps, pe_ratio, market_cap, shares_outstanding, beta, revenue, dividend, dividend_yield, next_earnings_date

Index-Specific:

• number_of_components

Market Status:

• exchange_is_open, last_timestamp

# Get comprehensive info for Apple stock
apple_id = get_pair_id('AAPL')[0]
info = get_info(apple_id)

print(f"Symbol: {info['symbol'].iloc[0]}")
print(f"Price: {info['last'].iloc[0]}")
print(f"Change: {info['change_percent'].iloc[0]}%")
print(f"Market Cap: {info['market_cap'].iloc[0]}")
print(f"P/E Ratio: {info['pe_ratio'].iloc[0]}")
print(f"Technical Signal: {info['technical_summary'].iloc[0]}")
print(f"Sentiment - Bullish: {info['bullish'].iloc[0]}% / Bearish: {info['bearish'].iloc[0]}%")

Technical Analysis

get_technical_data(pair_id, tech_type='pivot_points', interval='daily')

Get technical analysis data and indicators.

Parameters:

• pair_id (str): The Investing.com pair ID
• tech_type (str): Type of technical data:
  • 'pivot_points': Support and resistance levels (classic & fibonacci)
  • 'ti': Technical indicators
  • 'ma': Moving averages (simple & exponential)
  • 'summary': Technical summary with overall signal
• interval (str): Time interval:
  • '5min', '15min', '30min': Intraday intervals
  • 'hourly', '5hourly': Hourly intervals
  • 'daily', 'weekly', 'monthly': Long-term intervals

Returns: pandas.DataFrame with technical indicators

Pivot Points Columns: level, classic, fibonacci

Moving Averages Columns: period, simple_ma, simple_signal, exponential_ma, exponential_signal

Technical Indicators Columns: indicator, value, signal

Summary Columns: type, signal, action, buy, sell, neutral, value

# Example - Daily pivot points
spy_id = get_pair_id('SPY')[0]
pivot_data = get_technical_data(spy_id, 'pivot_points', 'daily')
print(pivot_data)
#       level  classic  fibonacci
#          R3   709.80     688.57
#          R2   688.57     676.19
# Pivot Point   656.15     656.15

# Example - Weekly moving averages
weekly_ma = get_technical_data(spy_id, 'ma', 'weekly')

# Example - Technical summary
summary = get_technical_data(spy_id, 'summary', 'daily')
print(summary)
#                    type         signal     action      buy      sell     neutral   value
#                 Overall     Strong Buy strong_buy      NaN       NaN         NaN     NaN
#         Moving Averages     Strong Buy        NaN Buy (10) Sell (2)         NaN     NaN
#    Technical Indicators     Strong Buy        NaN  Buy (8)  Sell (2) Neutral (1)     NaN
#        ATR (Volatility) High Volatility        NaN      NaN       NaN         NaN  1.3011

Complete Example

from investgo import get_pair_id, get_historical_prices, get_holdings, get_info
import matplotlib.pyplot as plt

# Search for QQQ ETF
pair_ids = get_pair_id(['QQQ'])
qqq_id = pair_ids[0]

# Get market info
info = get_info(qqq_id)
print(f"\n{info['name'].iloc[0]} ({info['symbol'].iloc[0]})")
print(f"Price: {info['last'].iloc[0]} {info['change_percent'].iloc[0]}%")
print(f"Technical Signal: {info['technical_summary'].iloc[0]}")

# Get 1 year of historical data
historical_data = get_historical_prices(qqq_id, "01012023", "31122023")

# Get top holdings
holdings = get_holdings(qqq_id, "top_holdings")

# Plot price chart
historical_data['price'].plot(title='QQQ Price History')
plt.show()

# Display top 10 holdings
print("\nTop 10 Holdings:")
print(holdings.head(10))

Error Handling

The library uses custom exceptions for better error handling:

from investgo import get_pair_id, get_historical_prices
from investgo.exceptions import InvalidParameterError, NoDataFoundError, APIError

try:
    pair_id = get_pair_id('INVALID_TICKER')[0]
    data = get_historical_prices(pair_id, "01012023", "31122023")
except NoDataFoundError as e:
    print(f"No data found: {e}")
except InvalidParameterError as e:
    print(f"Invalid parameter: {e}")
except APIError as e:
    print(f"API error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Requirements

• Python 3.6+
• cloudscraper >= 1.2.68
• pandas >= 2.2.1

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Disclaimer

This library is for educational and research purposes.

License

This project is licensed under the MIT License - see the LICENSE file for details.