Documentation Index
Fetch the complete documentation index at: https://docs.b1nary.app/llms.txt
Use this file to discover all available pages before exploring further.
b1nary provides market data. You bring your own pricing model.
Market data
GET /mm/market returns:
{
"eth_spot": 2450.50,
"eth_iv": 0.65,
"protocol_fee_bps": 400,
"gas_price_gwei": 0.01,
"available_otokens": [
{
"address": "0x...",
"strike_price": 2400.0,
"expiry": 1741200000,
"is_put": true
}
]
}
| Field | Description |
|---|
eth_spot | Current ETH/USD price from Chainlink |
eth_iv | Implied volatility from Deribit (annualized, decimal: 0.65 = 65%) |
protocol_fee_bps | Protocol fee: 400 = 4%. Deducted from gross premium |
available_otokens | Options available for quoting, created by the platform each week |
Black-Scholes reference
from math import log, sqrt, exp
from scipy.stats import norm
def bs_price(is_put, spot, strike, T, r, sigma):
"""T = time to expiry in years, r = risk-free rate, sigma = IV."""
if T <= 0:
return max(strike - spot, 0) if is_put else max(spot - strike, 0)
d1 = (log(spot / strike) + (r + 0.5 * sigma**2) * T) / (sigma * sqrt(T))
d2 = d1 - sigma * sqrt(T)
if is_put:
return strike * exp(-r * T) * norm.cdf(-d2) - spot * norm.cdf(-d1)
return spot * norm.cdf(d1) - strike * exp(-r * T) * norm.cdf(d2)
def bs_delta(is_put, spot, strike, T, r, sigma):
"""Returns delta: [-1, 0] for puts, [0, 1] for calls."""
if T <= 0:
if is_put:
return -1.0 if spot < strike else 0.0
return 1.0 if spot > strike else 0.0
d1 = (log(spot / strike) + (r + 0.5 * sigma**2) * T) / (sigma * sqrt(T))
return norm.cdf(d1) - 1.0 if is_put else norm.cdf(d1)
def bid_price(is_put, spot, strike, T, r, sigma, spread_bps=300):
theo = bs_price(is_put, spot, strike, T, r, sigma)
return max(theo * (1 - spread_bps / 10000), 1e-6)
Spread selection
The spread (in basis points) controls your edge vs fill rate:
| Spread | Edge | Fills |
|---|
| Wide (500 bps = 5%) | More per trade | Fewer fills |
| Tight (100 bps = 1%) | Less per trade | More fills |
| Adaptive | Widen when volatility is high, tighten when confident | Dynamic |
