> ## 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.

# Pricing

> Black-Scholes reference and spread selection.

b1nary provides market data. You bring your own pricing model.

## Market data

`GET /mm/market` returns:

```json theme={null}
{
  "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

```python theme={null}
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)
```

You can use any pricing model. Black-Scholes is a starting point. Advanced models account for skew, kurtosis, and realized vs implied vol.

## 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     |

Start with 300 bps (3%) and adjust based on your fill rate and P\&L.