Finalytics is a high-performance Python binding for the Finalytics Rust library, designed for retrieving financial data, security analysis, and portfolio optimization. It provides a fast, modular interface for advanced analytics, and powers dashboards and applications across platforms.
pip install finalyticsFinalytics Python exposes five core modules for financial analytics:
Efficiently filter and rank securities using advanced metrics and custom filters.
Usage Example:
from finalytics import Screener
screener = Screener(
quote_type="EQUITY",
filters=[
'{"operator": "eq", "operands": ["exchange", "NMS"]}',
'{"operator": "eq", "operands": ["sector", "Technology"]}',
'{"operator": "gte", "operands": ["intradaymarketcap", 10000000000]}',
'{"operator": "gte", "operands": ["returnonequity.lasttwelvemonths", 0.15]}'
],
sort_field="intradaymarketcap",
sort_descending=True,
offset=0,
size=10
)
print(screener.overview())
print(screener.metrics())
screener.display()Analyze a single security in depth: performance, financials, options, news, and more.
Usage Example:
from finalytics import Ticker
ticker = Ticker(
symbol="AAPL",
start_date="2023-01-01",
end_date="2024-12-31",
interval="1d",
benchmark_symbol="^GSPC",
confidence_level=0.95,
risk_free_rate=0.02
)
ticker.report("performance")
ticker.report("financials")
ticker.report("options")
ticker.report("news")Work with multiple securities at once—aggregate reports, batch analytics, and portfolio construction.
Usage Example:
from finalytics import Tickers
tickers = Tickers(
symbols=["NVDA", "GOOG", "AAPL", "MSFT", "BTC-USD"],
start_date="2023-01-01",
end_date="2024-12-31",
interval="1d",
benchmark_symbol="^GSPC",
confidence_level=0.95,
risk_free_rate=0.02
)
tickers.report("performance")Optimize and analyze portfolios using advanced objective functions and constraints. Supports rebalancing strategies, scheduled cash flows (DCA), ad-hoc transactions, and out-of-sample evaluation.
Objective Functions:
max_sharpe, max_sortino, max_return, min_vol, min_var, min_cvar, min_drawdown,
risk_parity, max_diversification, hierarchical_risk_parity
Usage Example: Optimization with Out-of-Sample Evaluation
from finalytics import Portfolio
# Optimize on 2023 - 2024 data (in-sample)
portfolio = Portfolio(
ticker_symbols=["NVDA", "GOOG", "AAPL", "MSFT", "BTC-USD"],
benchmark_symbol="^GSPC",
start_date="2023-01-01",
end_date="2024-12-31",
interval="1d",
confidence_level=0.95,
risk_free_rate=0.02,
objective_function="max_sharpe"
)
portfolio.report("optimization")
# Update to 2025 data for out-of-sample evaluation
portfolio.update_dates("2025-01-01", "2026-01-01")
portfolio.performance_stats()
portfolio.report("performance")Usage Example: Explicit Allocation with Rebalancing and DCA
from finalytics import Portfolio
portfolio = Portfolio(
ticker_symbols=["AAPL", "MSFT", "NVDA", "BTC-USD"],
benchmark_symbol="^GSPC",
start_date="2023-01-01",
end_date="2024-12-31",
interval="1d",
confidence_level=0.95,
risk_free_rate=0.02,
weights=[25000.0, 25000.0, 25000.0, 25000.0],
rebalance_strategy={"type": "calendar", "frequency": "quarterly"},
scheduled_cash_flows=[
{
"amount": 2000.0,
"frequency": "monthly",
"start_date": None,
"end_date": None,
"allocation": "pro_rata"
}
]
)
portfolio.report("performance")Usage Example: Optimization with Weight & Categorical Constraints
from finalytics import Portfolio
portfolio = Portfolio(
ticker_symbols=["AAPL", "MSFT", "NVDA", "JPM", "XOM", "BTC-USD"],
benchmark_symbol="^GSPC",
start_date="2023-01-01",
end_date="2024-12-31",
interval="1d",
confidence_level=0.95,
risk_free_rate=0.02,
objective_function="max_sharpe",
# Per-asset bounds: (lower, upper) in the same order as ticker_symbols
asset_constraints=[
(0.05, 0.40), # AAPL
(0.05, 0.40), # MSFT
(0.05, 0.40), # NVDA
(0.05, 0.30), # JPM
(0.05, 0.20), # XOM
(0.05, 0.25), # BTC-USD
],
# Categorical constraints: (name, category_per_symbol, weight_per_category)
categorical_constraints=[
(
"Sector",
["Tech", "Tech", "Tech", "Finance", "Energy", "Crypto"],
[
("Tech", 0.30, 0.60),
("Finance", 0.05, 0.30),
("Energy", 0.05, 0.20),
("Crypto", 0.05, 0.25),
],
),
(
"Asset Class",
["Equity", "Equity", "Equity", "Equity", "Equity", "Crypto"],
[
("Equity", 0.70, 0.95),
("Crypto", 0.05, 0.30),
],
),
],
)
portfolio.report("optimization")Load your own price data from CSV files as Polars DataFrames and use it with any Finalytics module.
DataFrames must have columns: timestamp (unix epoch i64), open, high, low, close, volume, adjclose.
Usage Example:
import polars as pl
from finalytics import Ticker, Tickers, Portfolio
# Load data from CSV files
aapl = pl.read_csv("examples/datasets/aapl.csv")
msft = pl.read_csv("examples/datasets/msft.csv")
nvda = pl.read_csv("examples/datasets/nvda.csv")
goog = pl.read_csv("examples/datasets/goog.csv")
btcusd = pl.read_csv("examples/datasets/btcusd.csv")
gspc = pl.read_csv("examples/datasets/gspc.csv")
# Single Ticker from custom data
ticker = Ticker(
symbol="AAPL",
benchmark_symbol="^GSPC",
confidence_level=0.95,
risk_free_rate=0.02,
ticker_data=aapl,
benchmark_data=gspc
)
ticker.report("performance")
# Multiple Tickers from custom data
tickers = Tickers(
symbols=["NVDA", "GOOG", "AAPL", "MSFT", "BTC-USD"],
benchmark_symbol="^GSPC",
confidence_level=0.95,
risk_free_rate=0.02,
tickers_data=[nvda, goog, aapl, msft, btcusd],
benchmark_data=gspc
)
tickers.report("performance")
# Portfolio optimization from custom data
portfolio = Portfolio(
ticker_symbols=["NVDA", "GOOG", "AAPL", "MSFT", "BTC-USD"],
benchmark_symbol="^GSPC",
confidence_level=0.95,
risk_free_rate=0.02,
objective_function="max_sharpe",
tickers_data=[nvda, goog, aapl, msft, btcusd],
benchmark_data=gspc
)
portfolio.report("optimization")- See the Quarto documentation for full details.
Finalytics is also available in:
Finalytics — Modular, high-performance financial analytics for Python.