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8f1601b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | from __future__ import annotations
from market_data.schemas import OptionChain, OptionContract
from .payoff import estimate_breakevens
from .schemas import OptionLeg, OptionStrategy
def usable_contracts(contracts: list[OptionContract]) -> list[OptionContract]:
return [
contract
for contract in contracts
if contract.mid is not None
and contract.mid > 0
and not {"missing_or_zero_bid_ask", "zero_open_interest"}.intersection(contract.liquidity_warnings)
]
def nearest_contract(contracts: list[OptionContract], target_strike: float) -> OptionContract | None:
valid = usable_contracts(contracts)
if not valid:
return None
return min(valid, key=lambda contract: abs(contract.strike - target_strike))
def contract_to_leg(contract: OptionContract, action: str, quantity: int = 1) -> OptionLeg:
return OptionLeg(
action=action,
option_type=contract.option_type,
strike=contract.strike,
expiration=contract.expiration,
quantity=quantity,
premium=contract.mid or contract.last_price or 0.0,
implied_volatility=contract.implied_volatility,
liquidity_warnings=contract.liquidity_warnings,
)
def make_strategy(
name: str,
volatility_view: str,
directional_view: str,
legs: list[OptionLeg],
rationale: str,
risks: list[str],
score: float,
) -> OptionStrategy:
net_cash_flow = sum(leg.cash_flow() for leg in legs)
net_debit_or_credit = -net_cash_flow
breakevens = estimate_breakevens(legs)
max_profit: float | str | None = None
max_loss: float | str | None = None
if name in {"long_straddle", "long_strangle"}:
max_loss = round(max(net_debit_or_credit, 0.0), 2)
max_profit = "unlimited"
elif name == "short_straddle":
max_profit = round(abs(min(net_debit_or_credit, 0.0)), 2)
max_loss = "unlimited"
elif name == "iron_condor":
call_strikes = sorted(leg.strike for leg in legs if leg.option_type == "call")
put_strikes = sorted(leg.strike for leg in legs if leg.option_type == "put")
width = max(call_strikes[-1] - call_strikes[0], put_strikes[-1] - put_strikes[0])
credit = abs(min(net_debit_or_credit, 0.0))
max_profit = round(credit, 2)
max_loss = round(width * 100 - credit, 2)
elif name == "calendar_spread":
max_loss = round(max(net_debit_or_credit, 0.0), 2)
max_profit = "path_dependent"
return OptionStrategy(
name=name,
volatility_view=volatility_view,
directional_view=directional_view,
legs=legs,
rationale=rationale,
risks=risks,
max_profit=max_profit,
max_loss=max_loss,
breakevens=breakevens,
net_debit_or_credit=round(net_debit_or_credit, 2),
score=score,
)
def generate_volatility_strategies(
near_chain: OptionChain,
volatility_view: str = "neutral",
directional_view: str = "neutral",
far_chain: OptionChain | None = None,
) -> list[OptionStrategy]:
if near_chain.underlying_price is None:
return []
spot = near_chain.underlying_price
atm_call = nearest_contract(near_chain.calls, spot)
atm_put = nearest_contract(near_chain.puts, spot)
otm_call = nearest_contract(near_chain.calls, spot * 1.05)
otm_put = nearest_contract(near_chain.puts, spot * 0.95)
strategies: list[OptionStrategy] = []
if atm_call and atm_put:
if volatility_view in {"long_vol", "neutral", "vol_expansion"}:
strategies.append(
make_strategy(
name="long_straddle",
volatility_view="long_vol",
directional_view="neutral",
legs=[contract_to_leg(atm_call, "buy"), contract_to_leg(atm_put, "buy")],
rationale="Benefits from a large realized move or IV expansion; risk is premium paid.",
risks=["theta_decay", "iv_crush", "requires_large_move"],
score=0.75,
)
)
if volatility_view in {"short_vol", "neutral", "vol_compression"}:
strategies.append(
make_strategy(
name="short_straddle",
volatility_view="short_vol",
directional_view="neutral",
legs=[contract_to_leg(atm_call, "sell"), contract_to_leg(atm_put, "sell")],
rationale="Benefits from realized volatility staying below implied volatility.",
risks=["unlimited_tail_risk", "gap_risk", "margin_requirement"],
score=0.45,
)
)
if otm_call and otm_put and volatility_view in {"long_vol", "neutral", "vol_expansion"}:
strategies.append(
make_strategy(
name="long_strangle",
volatility_view="long_vol",
directional_view="neutral",
legs=[contract_to_leg(otm_call, "buy"), contract_to_leg(otm_put, "buy")],
rationale="Lower-cost long volatility expression than a straddle, but needs a larger move.",
risks=["theta_decay", "wide_breakevens", "iv_crush"],
score=0.65,
)
)
if far_chain and atm_call and volatility_view in {"long_vol", "neutral", "term_structure"}:
far_call = nearest_contract(far_chain.calls, atm_call.strike)
if far_call:
strategies.append(
make_strategy(
name="calendar_spread",
volatility_view="term_structure",
directional_view="neutral",
legs=[contract_to_leg(atm_call, "sell"), contract_to_leg(far_call, "buy")],
rationale="Expresses a term-structure view and benefits if longer-dated IV holds up.",
risks=["path_dependency", "front_expiry_gamma", "term_structure_shift"],
score=0.60,
)
)
if otm_call and otm_put and volatility_view in {"short_vol", "neutral", "vol_compression"}:
long_call = nearest_contract(near_chain.calls, otm_call.strike * 1.03)
long_put = nearest_contract(near_chain.puts, otm_put.strike * 0.97)
if long_call and long_put:
strategies.append(
make_strategy(
name="iron_condor",
volatility_view="short_vol",
directional_view="neutral",
legs=[
contract_to_leg(otm_put, "sell"),
contract_to_leg(long_put, "buy"),
contract_to_leg(otm_call, "sell"),
contract_to_leg(long_call, "buy"),
],
rationale="Defined-risk short volatility strategy for range-bound markets.",
risks=["short_gamma", "tail_loss_to_width", "assignment_risk"],
score=0.70,
)
)
return sorted(strategies, key=lambda strategy: strategy.score, reverse=True)
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