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Financial Data Generation

Generate realistic market data including stock prices, OHLCV bars, and options chains.

Overview

The finance generators create data suitable for:

  • Quantitative finance research
  • Trading strategy backtesting
  • Options pricing demos
  • Risk management analysis
  • Portfolio analytics

Stock Prices

The stock_prices() function generates realistic price series using Geometric Brownian Motion (GBM).

Basic Usage

from superstore import stock_prices

# Generate 252 days of stock prices (1 trading year)
df = stock_prices(nper=252, initial_price=150.0)

# Multiple tickers with correlation
df = stock_prices(nper=252, tickers=["AAPL", "GOOGL", "MSFT"])

Output Schema

Column Type Description
date date Trading date
ticker str Stock ticker symbol
close float Closing price

Options Chain

The options_chain() function generates options chain data with Black-Scholes pricing and Greeks.

Basic Usage

from superstore import options_chain

# Generate options chain for a $150 stock
df = options_chain(underlying_price=150.0, n_strikes=10)

Output Schema

Column Type Description
strike float Strike price
expiration date Expiration date
type str Option type (call/put)
premium float Option premium
delta float Delta
gamma float Gamma
theta float Theta
vega float Vega
iv float Implied volatility

Comprehensive Finance Data

The finance() function generates complete financial datasets including OHLCV bars, multi-asset returns, and options.

Basic Usage

from superstore import finance

# Generate full finance dataset
data = finance(tickers=["AAPL", "GOOGL"], nper=252)

Configuration

Use FinanceConfig for detailed control:

from superstore import finance, FinanceConfig

config = FinanceConfig(
    ndays=500,
    n_assets=3,
    seed=42,
)
data = finance(config=config)

Basic Settings

config = FinanceConfig(
    ndays=252,         # Trading days (252 = 1 year)
    n_assets=3,        # Number of assets
    tickers=["AAPL", "GOOGL", "MSFT"],
    start_date="2024-01-02",
    seed=42,
)
Parameter Default Description
ndays 252 Number of trading days
n_assets 1 Number of assets
tickers ["AAPL"] Ticker symbols
start_date "2024-01-02" Start date (ISO format)
asset_correlation 0.5 Correlation between assets

Stock Configuration

Configure the underlying price process:

config = FinanceConfig(
    ndays=252,
    stock={
        "annual_drift": 0.10,        # 10% expected annual return
        "annual_volatility": 0.25,   # 25% annual volatility
        "initial_price": 175.0,      # Starting price

        # Jump diffusion
        "enable_jumps": True,
        "jump_probability": 0.03,    # 3% daily jump probability
        "jump_mean": -0.01,          # Slight downward bias (crashes)
        "jump_stddev": 0.08,         # Jump size variability
    }
)
Parameter Default Description
annual_drift 0.08 Annual expected return (mu)
annual_volatility 0.20 Annual volatility (sigma)
initial_price 100.0 Initial stock price
enable_jumps False Enable jump diffusion
jump_probability 0.02 Daily probability of jump
jump_mean 0.0 Mean jump size (log-normal)
jump_stddev 0.05 Jump size standard deviation

OHLCV Configuration

Configure OHLCV (Open-High-Low-Close-Volume) bars:

config = FinanceConfig(
    ndays=252,
    ohlcv={
        "avg_volume": 2_000_000,       # Average daily volume
        "volume_volatility": 0.6,      # Volume variability
        "intraday_volatility": 0.03,   # Intraday price range
        "volume_price_correlation": 0.4, # Volume-return correlation
    }
)
Parameter Default Description
avg_volume 1,000,000 Average daily trading volume
volume_volatility 0.5 Volatility of volume (log-normal)
intraday_volatility 0.02 Intraday price range volatility
volume_price_correlation 0.3 Volume-absolute return correlation

Options Configuration

Configure options chain generation with Black-Scholes pricing:

config = FinanceConfig(
    ndays=252,
    options={
        "risk_free_rate": 0.045,       # Risk-free rate
        "dividend_yield": 0.015,        # Dividend yield
        "expirations": [7, 14, 30, 45, 60, 90],  # Days to expiration
        "strike_offsets": [0.85, 0.90, 0.95, 1.0, 1.05, 1.10, 1.15],
    }
)
Parameter Default Description
risk_free_rate 0.05 Annual risk-free rate
dividend_yield 0.02 Annual dividend yield
expirations [7, 14, 30, 60, 90] Days to expiration
strike_offsets [0.90, 0.95, ...] Strike as multiplier of spot

Complete Example

from superstore import finance, FinanceConfig

config = FinanceConfig(
    ndays=500,
    n_assets=4,
    tickers=["AAPL", "GOOGL", "MSFT", "AMZN"],
    start_date="2023-01-03",
    asset_correlation=0.65,
    seed=42,

    # Stock process
    stock={
        "annual_drift": 0.12,
        "annual_volatility": 0.22,
        "initial_price": 150.0,
        "enable_jumps": True,
        "jump_probability": 0.02,
        "jump_mean": -0.005,
        "jump_stddev": 0.06,
    },

    # OHLCV bars
    ohlcv={
        "avg_volume": 5_000_000,
        "volume_volatility": 0.5,
        "intraday_volatility": 0.025,
        "volume_price_correlation": 0.35,
    },

    # Options chain
    options={
        "risk_free_rate": 0.05,
        "dividend_yield": 0.01,
        "expirations": [7, 14, 21, 30, 45, 60, 90, 120],
        "strike_offsets": [0.85, 0.90, 0.95, 0.97, 1.0, 1.03, 1.05, 1.10, 1.15],
    },
)

data = finance(config=config)

API Reference

See the full API documentation: