finalytics 0.3.0

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This is a python binding for Finalytics Rust Library designed for retrieving financial data and performing security analysis and portfolio optimization.

Installation

pip install finalytics

Documentation

View Library documentation on readthedocs here

Symbol Search

from finalytics import get_symbols

print(get_symbols(query="Apple", asset_class="Equity"))
print(get_symbols(query="Bitcoin", asset_class="Crypto"))
print(get_symbols(query="S&P 500", asset_class="Index"))
print(get_symbols(query="EURUSD", asset_class="Currency"))
print(get_symbols(query="SPY", asset_class="ETF"))

Security Analysis

from finalytics import Ticker

ticker = Ticker(symbol="AAPL")
print(ticker.get_current_price())
print(ticker.get_summary_stats())
print(ticker.get_price_history(start="2023-01-01", end="2023-10-31", interval="1d"))
print(ticker.get_options_chain())
print(ticker.get_news(compute_sentiment=False))
print(ticker.get_income_statement())
print(ticker.get_balance_sheet())
print(ticker.get_cashflow_statement())
print(ticker.get_financial_ratios())
print(ticker.compute_performance_stats(start="2023-01-01", end="2023-10-31", interval="1d", benchmark="^GSPC", 
                                       confidence_level=0.95, risk_free_rate=0.02))
ticker.display_performance_chart(start="2023-01-01", end="2023-10-31", interval="1d", benchmark="^GSPC", 
                                 confidence_level=0.95, risk_free_rate=0.02, display_format="notebook")
ticker.display_candlestick_chart(start="2023-01-01", end="2023-10-31", interval="1d", display_format="html")
ticker.display_options_chart(risk_free_rate=0.02, chart_type="surface", display_format="png")

Portfolio Optimization

from finalytics import Portfolio

portfolio = Portfolio(ticker_symbols=["AAPL", "GOOG", "MSFT", "BTC-USD"], 
                      benchmark_symbol="^GSPC", start_date="2020-01-01", end_date="2022-01-01", interval="1d", 
                      confidence_level=0.95, risk_free_rate=0.02, max_iterations=1000, 
                      objective_function="max_sharpe")
print(portfolio.get_optimization_results())
portfolio.display_portfolio_charts("performance", "html")