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: