perp-funding-basis

# Perpetual Funding Rate & Basis Trading

## Overview

Analyze perpetual futures funding rates and spot-futures basis to identify carry trade opportunities, market positioning extremes, and directional sentiment signals. Funding rates are the single most important microstructure indicator in crypto derivatives — they reveal real-time leverage positioning and crowd sentiment.

## Core Concepts

### 1. Funding Rate Mechanics

Perpetual futures have no expiry date. Instead, a **funding rate** is exchanged between longs and shorts every 8 hours (on most exchanges) to keep the perpetual price anchored to the spot price.

```
If perp price > spot price → funding rate positive → longs pay shorts
If perp price < spot price → funding rate negative → shorts pay longs
```

**OKX funding rate schedule**: payments at 00:00, 08:00, 16:00 UTC

**Annualized funding rate:**
```python
# OKX funding rate is per 8-hour period
# Annualized = rate × 3 (per day) × 365
funding_rate_8h = 0.01  # 0.01% per 8h
annualized = funding_rate_8h * 3 * 365  # = 10.95% annualized
```

### 2. Funding Rate Signal Framework

| Funding Rate (8h) | Annualized | Market State | Signal |
|--------------------|------------|-------------|--------|
| > +0.05% | > +54.75% | Extreme long crowding | Contrarian short / reduce longs |
| +0.02% to +0.05% | +21.9% to +54.75% | Elevated long bias | Cautious, carry trade viable |
| +0.005% to +0.02% | +5.5% to +21.9% | Mild long bias | Neutral to mild bullish |
| -0.005% to +0.005% | -5.5% to +5.5% | Balanced | Neutral |
| -0.02% to -0.005% | -21.9% to -5.5% | Mild short bias | Neutral to mild bearish |
| < -0.02% | < -21.9% | Short squeeze territory | Contrarian long / reduce shorts |

**Funding rate regime detection:**
```python
def funding_regime(rates_7d):
    """Classify funding rate regime from 7-day history."""
    avg = sum(rates_7d) / len(rates_7d)
    consecutive_positive = all(r > 0 for r in rates_7d[-3:])
    consecutive_negative = all(r < 0 for r in rates_7d[-3:])

    if avg > 0.03 and consecutive_positive:
        return "overheated_long"       # High risk of long squeeze
    elif avg > 0.01 and consecutive_positive:
        return "bullish_carry"          # Good carry trade environment
    elif avg < -0.02 and consecutive_negative:
        return "overheated_short"       # High risk of short squeeze
    elif avg < -0.005 and consecutive_negative:
        return "bearish_carry"          # Inverse carry trade
    else:
        return "neutral"
```

### 3. Spot-Futures Basis Analysis

**Basis = Futures price - Spot price**

For dated futures (quarterly), basis reflects cost-of-carry expectations:

```python
# Annualized basis
def annualized_basis(futures_price, spot_price, days_to_expiry):
    basis_pct = (futures_price - spot_price) / spot_price
    annualized = basis_pct * (365 / days_to_expiry)
    return annualized

# Example: BTC spot $65,000, quarterly future $66,500, 45 days to expiry
# Basis: 2.31%, Annualized: 18.7%
```

**Basis signal interpretation:**

| Annualized Basis | Market State | Signal |
|-----------------|-------------|--------|
| > 30% | Extreme contango, euphoric leverage | Sell basis (cash-carry), top warning |
| 15-30% | Elevated contango, bullish leverage | Carry trade attractive |
| 5-15% | Normal contango | Neutral, mild bullish |
| 0-5% | Flat basis | Low conviction, wait for direction |
| < 0% (backwardation) | Bearish, forced selling | Contrarian long, extreme pessimism |

### 4. Cash-Carry Arbitrage (Delta-Neutral)

**Strategy: buy spot + sell perpetual futures → collect funding rate**

```python
# Cash-carry trade P&L
def carry_trade_pnl(spot_entry, funding_rates, position_size):
    """
    Delta-neutral carry: long spot + short perp
    P&L comes purely from funding rate collection.
    """
    total_funding_collected = 0
    for rate in funding_rates:
        if rate > 0:  # Longs pay shorts → we collect as short
            total_funding_collected += rate * position_size
        else:  # Shorts pay longs → we pay as short
            total_funding_collected += rate * position_size  # This is negative

    return total_funding_collected

# Example: $100,000 position, avg funding +0.015% per 8h, 30 days
# Revenue: 0.015% × 3 × 30 × $100,000 = $1,350 (16.2% annualized)
```

**Carry trade execution on OKX:**
1. Buy spot BTC-USDT on OKX spot market
2. Open equal-sized short BTC-USDT-SWAP on OKX perpetual
3. Net delta = 0 (spot long cancels perp short)
4. Collect positive funding rate every 8 hours
5. Close both legs when funding rate turns negative or basis compresses

**Risk factors:**
- Funding rate can flip negative → carry becomes a cost
- Liquidation risk on short perp if insufficient margin (use 3-5x max leverage)
- Exchange counterparty risk (keep position across 2-3 exchanges)
- Basis can widen further before mean-reverting → mark-to-market loss on short leg

### 5. Cross-Exchange Funding Arbitrage

Different exchanges have different funding rates for the same asset. Arbitrage the spread:

```python
# Example: BTC-USDT perpetual funding rates
exchange_rates = {
    "OKX": 0.015,       # +0.015% per 8h
    "Binance": 0.020,   # +0.020% per 8h
    "Bybit": 0.025,     # +0.025% per 8h
}

# Strategy: short on highest funding (Bybit) + long on lowest funding (OKX)
# Net carry = 0.025% - 0.015% = 0.010% per 8h
# Annualized: 0.010% × 3 × 365 = 10.95%
# Risk: execution cost + potential for rates to converge/flip
```

### 6. Funding Rate as Directional Indicator

**Divergence signals (most powerful):**

| Price Action | Funding Rate | Interpretation | Signal |
|-------------|-------------|----------------|--------|
| Price making new highs | Funding declining | Longs not chasing → distribution | Bearish divergence |
| Price making new lows | Funding rising (less negative) | Shorts not pressing → accumulation | Bullish divergence |
| Price consolidating | Funding spiking positive | Leverage building without breakout | Squeeze risk |
| Price consolida