This Python library offers a combination of technical analysis tools and fundamental data retrieval functionalities, designed to support investors, researchers, and enthusiasts in the financial markets.

This Python library provides a comprehensive suite of tools for both technical and fundamental analysis, along with more advanced options analysis features. Utilizing the yfinance library, it provides easy access to historical stock data, financial statements, and key financial metrics from Yahoo Finance, alongside a suite of technical indicators for market analysis as well as options analysis tools.

• Technical Analysis (IndicatorCalculator Class): Over 30 technical indicators, including Moving Averages, MACD, Bollinger Bands, RSI, Ichimoku Cloud, and more, to dissect stock market trends and volatility.
• Fundamental Analysis (Fetcher Class): Fetch historical stock data, income statements, balance sheets, cash flows, and key financial ratios (e.g., P/E, ROE, current ratio) for in-depth fundamental analysis.
• Options Analysis (Greeks and OptionPricing Classes): Calculate options greeks and simulate option prices using various models. Supports both European and American options.

pip install stockdatamanager

from stockdatamanager import Fetcher, IndicatorCalculator
from stockdatamanager.options import Greeks, OptionPricing

# Fetching stock data and financial statements
fetcher = Fetcher(ticker='AAPL')
print(fetcher.get_pe_ratio())

# Applying technical analysis
indicators = IndicatorCalculator(dataframe=fetcher.df)
df_with_rsi = indicators.calculate_RSI()

# Calculating options Greeks
greeks = Greeks(ticker = 'AAPL', call = True, identification = 0)
delta = greeks.calculate_delta()

# Pricing an American-style option using the binomial tree method
option_pricing = OptionPricing(ticker='MSFT', call=False, american=True, risk_free_rate='13 weeks', identification=0, use_yfinance_volatility=True)
option_price = option_pricing.calculate_option_price(method='binomial', describe=False)
print(f"Option Price: {option_price}")

fetcher = Fetcher(ticker='AAPL')
income_statement = fetcher.get_income_statement()

transform = Transform(ticker='AAPL')
df_with_macd = transform.calculate_MACD()

Calculate the Delta of an option:

greeks = Greeks(ticker='AAPL', call=True, identification='AAPL220121C00100000')
print(greeks.calculate_delta())

Simulate option pricing using the Crank-Nicholson method:

option_pricing = OptionPricing(ticker='MSFT', call=False, american=True, risk_free_rate='13 weeks', identification='AAPL220121C00100000', use_yfinance_volatility=True)
option_price = option_pricing.calculate_option_price(method='crank-nicolson', describe=False)
print(f"Crank-Nicolson Method Option Price: {option_price}")

Contributions are welcome! Feel free to open an issue or submit a pull request for improvements or new features.

stockdatamanager is made available under the MIT License. See the LICENSE file for more details.

Email: giorgio.micaletto@studbocconi.it