Composable Option Strategy Builder

OptionStrategy lets you assemble multi-leg option strategies leg by leg (buy_call, sell_call, buy_put, sell_put, buy_underlying, sell_underlying), or reach for predefined constructors like spreads, straddles, strangles, condors, and butterflies.

Scope note: this is a static expiration-analysis tool. It does not model early exercise, margin, assignment, funding, slippage, taxes, or dynamic hedging.

Sections:

1. Build a strategy leg by leg

2. Compare predefined strategy constructors

3. Payoff profile table

4. Visualizations

Setup

import abaquant
print(f"AbaQuant version: {abaquant.__version__}")

AbaQuant version: 1.0.0rc1

from abaquant.derivatives import OptionStrategy
from abaquant.visualization import VisualizationError

1. Build a strategy leg by leg

A debit call spread: buy the 110 call, sell the 120 call.

spread = OptionStrategy().buy_call(strike=110.0, premium=4.2).sell_call(strike=120.0, premium=1.8)

diagnostics = {
    "profit_at_125": spread.payoff(spot_price=125.0),
    "gross_payoff_at_125": spread.payoff(spot_price=125.0, include_premium=False),
    "net_inception_cost": spread.net_inception_cost(),
    "max_profit": spread.max_profit(),
    "max_loss": spread.max_loss(),
    "break_even_points": spread.break_even_points(),
}
for key, value in diagnostics.items():
    print(f"{key:22s}: {value}")

profit_at_125         : 7.6
gross_payoff_at_125   : 10.0
net_inception_cost    : 2.4000000000000004
max_profit            : 7.6
max_loss              : -2.4000000000000004
break_even_points     : [112.4]

2. Compare predefined strategy constructors

Bull call spread, protective put, straddle, strangle, iron condor, and butterfly — each built from one class-method call.

strategies = {
    "bull_call_spread": OptionStrategy.bull_call_spread(
        lower_strike=110.0, upper_strike=120.0, lower_premium=4.2, upper_premium=1.8
    ),
    "protective_put": OptionStrategy.protective_put(
        underlying_entry_price=100.0, put_strike=95.0, put_premium=3.0
    ),
    "straddle": OptionStrategy.straddle(strike=100.0, call_premium=5.0, put_premium=4.5),
    "strangle": OptionStrategy.strangle(
        put_strike=95.0, call_strike=105.0, put_premium=3.0, call_premium=3.4
    ),
    "iron_condor": OptionStrategy.iron_condor(
        lower_put_strike=85.0, short_put_strike=95.0, short_call_strike=105.0,
        upper_call_strike=115.0, lower_put_premium=1.0, short_put_premium=3.2,
        short_call_premium=3.0, upper_call_premium=1.1,
    ),
    "butterfly": OptionStrategy.butterfly(
        lower_strike=90.0, middle_strike=100.0, upper_strike=110.0,
        lower_premium=12.0, middle_premium=6.0, upper_premium=2.0,
    ),
}

comparison = {
    name: {
        "legs": len(strategy.legs),
        "max_profit": strategy.max_profit(),
        "max_loss": strategy.max_loss(),
        "break_even_points": strategy.break_even_points(),
    }
    for name, strategy in strategies.items()
}
import pandas as pd
pd.DataFrame(comparison).T

	legs	max_profit	max_loss	break_even_points
bull_call_spread	2	7.6	-2.4	[112.4]
protective_put	2	inf	-8.0	[103.0]
straddle	2	inf	-9.5	[90.5, 109.5]
strangle	2	inf	-6.4	[88.6, 111.4]
iron_condor	4	4.1	-5.9	[90.9, 109.1]
butterfly	3	8.0	-2.0	[92.0, 108.0]

3. Payoff profile table

Evaluate the bull call spread across a handful of terminal spot prices.

profile = spread.profile(spot_prices=[90.0, 110.0, 112.4, 120.0, 130.0])
profile[["spot_price", "gross_payoff", "net_profit"]]

	spot_price	gross_payoff	net_profit
0	90.0	0.0	-2.400000e+00
1	110.0	0.0	-2.400000e+00
2	112.4	2.4	5.329071e-15
3	120.0	10.0	7.600000e+00
4	130.0	10.0	7.600000e+00

4. Visualizations

Aggregate payoff, per-leg components, and the iron condor payoff.

try:
    figures = {
        "payoff": spread.visualize(chart="payoff"),
        "components": spread.visualize(chart="components"),
        "iron_condor": strategies["iron_condor"].visualize(chart="payoff"),
    }
    print(f"Created {len(figures)} figures: {list(figures)}")
except VisualizationError as exc:
    print(f"Visualization skipped (optional dependency missing): {exc}")

Created 3 figures: ['payoff', 'components', 'iron_condor']

Takeaway

Predefined constructors cover the common named strategies; add_leg()-style chaining handles anything custom. Combine .profile() with .visualize(chart="components") to see how each leg contributes to the overall payoff shape.