Liquidity Analysis

def compute_liquidity_score(
    total_liquidity_usd: float,
    pool_count: int,
    largest_pool_pct: float,
    oldest_pool_hours: float,
    max_slippage_bps_at_1sol: int,
) -> int:
    """Compute composite liquidity score (0-100).

    Components:
        Depth (40%): log-scaled TVL from $1K (0) to $1M+ (40)
        Diversity (15%): more pools = more resilient
        Concentration (15%): penalty if one pool dominates
        Age (15%): older pools are more reliable
        Slippage (15%): lower slippage = better
    """
    import math
    # Depth: 0-40 points
    depth = min(40, int(40 * math.log10(max(total_liquidity_usd, 1)) / 6))

    # Diversity: 0-15 points
    diversity = min(15, pool_count * 3)

    # Concentration: 0-15 points (penalty for single-pool dominance)
    concentration = int(15 * (1 - largest_pool_pct))

    # Age: 0-15 points (7+ days = full marks)
    age = min(15, int(15 * oldest_pool_hours / 168))

    # Slippage: 0-15 points
    slippage = max(0, 15 - max_slippage_bps_at_1sol // 10)

    return max(0, min(100, depth + diversity + concentration + age + slippage))

def detect_risk_flags(pools: list[dict]) -> list[str]:
    """Detect liquidity risk flags from pool data."""
    flags = []
    if len(pools) < 2:
        flags.append("SINGLE_POOL: Only 1 pool exists — exit may be difficult")

    total_liq = sum(p.get("liquidity", {}).get("usd", 0) for p in pools)
    if total_liq < 10_000:
        flags.append(f"THIN_LIQUIDITY: Total TVL ${total_liq:,.0f} < $10,000")

    for p in pools:
        age_ms = p.get("pairCreatedAt", 0)
        if age_ms > 0:
            import time
            age_hours = (time.time() * 1000 - age_ms) / 3_600_000
            if age_hours < 2:
                flags.append(f"NEW_POOL: {p.get('dexId')} pool is {age_hours:.1f}h old")

    volumes = [p.get("volume", {}).get("h24", 0) for p in pools]
    liqs = [p.get("liquidity", {}).get("usd", 0) for p in pools]
    for v, l, p in zip(volumes, liqs, pools):
        if l > 0 and v / l > 10:
            flags.append(f"VOLUME_MISMATCH: {p.get('dexId')} volume/TVL = {v/l:.1f}x")

    prices = [float(p.get("priceUsd", 0)) for p in pools if float(p.get("priceUsd", 0)) > 0]
    if len(prices) >= 2:
        deviation = (max(prices) - min(prices)) / min(prices)
        if deviation > 0.05:
            flags.append(f"PRICE_DEVIATION: {deviation:.1%} across pools")

    return flags

def max_position_from_liquidity(
    total_liquidity_usd: float,
    max_slippage_pct: float = 1.0,
    trade_type: str = "swing",
) -> float:
    """Estimate maximum position size in USD based on liquidity.

    Uses rule-of-thumb: max_position = tvl_fraction * total_liquidity.
    For constant-product AMM, 1% of TVL produces ~2% slippage.

    Args:
        total_liquidity_usd: Total liquidity across all pools.
        max_slippage_pct: Maximum acceptable slippage percentage.
        trade_type: "scalp", "swing", or "position".

    Returns:
        Maximum position size in USD.
    """
    fractions = {"scalp": 0.01, "swing": 0.03, "position": 0.07}
    base_fraction = fractions.get(trade_type, 0.03)
    adjusted = base_fraction * (max_slippage_pct / 2.0)
    return total_liquidity_usd * adjusted

slippage = Δx / (x + Δx)

# Full liquidity assessment for a token
import httpx

TOKEN = "DezXAZ8z7PnrnRJjz3wXBoRgixCa6xjnB7YaB1pPB263"  # BONK

# 1. Get pools
resp = httpx.get(f"https://api.dexscreener.com/tokens/v1/solana/{TOKEN}")
pools = [p for p in resp.json() if p.get("liquidity", {}).get("usd", 0) > 0]

# 2. Analyze
total_liq = sum(p["liquidity"]["usd"] for p in pools)
largest = max(p["liquidity"]["usd"] for p in pools)
largest_pct = largest / total_liq if total_liq > 0 else 1.0

# 3. Risk flags
flags = detect_risk_flags(pools)

# 4. Score
score = compute_liquidity_score(total_liq, len(pools), largest_pct, 1000, 50)

# 5. Position sizing
max_pos = max_position_from_liquidity(total_liq, max_slippage_pct=1.0, trade_type="swing")

print(f"Total Liquidity: ${total_liq:,.0f}")
print(f"Pools: {len(pools)}")
print(f"Score: {score}/100")
print(f"Max Position (swing, 1% slip): ${max_pos:,.0f}")
for f in flags:
    print(f"  WARNING: {f}")