Program ID: 675kPX9MHTjS2zt1qfr1NYHuzeLXfQM9H24wFSUt1Mp8
Fee: 0.25% per swap (0.22% to LPs, 0.03% to RAY buyback)

Program ID: CAMMCzo5YL8w4VFF8KVHrK22GGUsp5VTaW7grrKgrWqK
Fee tiers: 0.01%, 0.05%, 0.25%, 1%, 2%
Tick spacing: 1, 10, 60, 120, 240 (corresponding to fee tiers)

Program ID: whirLbMiicVdio4qvUfM5KAg6Ct8VwpYzGff3uctyCc
Fee tiers: 0.01%, 0.02%, 0.04%, 0.05%, 0.16%, 0.30%, 0.65%, 1%, 2%
Tick spacing: 1, 2, 4, 8, 16, 64, 128, 256, 512 (varies by fee)

Program ID: LBUZKhRxPF3XUpBCjp4YzTKgLccjZhTSDM9YuVaPwxo
Fee: Dynamic (base fee + variable fee based on volatility)
Bin step: 1-100 basis points per bin

Program ID: Eo7WjKq67rjJQSZxS6z3YkapzY3eMj6Xy8X5EQVn5UaB
Fee: Volatility-based dynamic fee

Program ID: PSwapMdSai8tjrEXcxFeQth87xC4rRsa4VA5mhGhXkP
Fee: 0.25% per swap (0.20% to LPs, 0.05% protocol)
Migration fee: 0 SOL (post-March 2025)

PumpFun Bonding Curve → ~$69K market cap → Migration → Raydium V4 or PumpSwap

volume_efficiency = volume_24h / tvl

fee_apr = volume_efficiency * fee_rate * 365
# Example: V/TVL of 2.0 at 0.25% fee = 2.0 * 0.0025 * 365 = 182.5% APR

pool_health = {
    "tvl_usd": 150_000,          # Total value locked
    "volume_24h_usd": 300_000,   # 24-hour trading volume
    "volume_tvl_ratio": 2.0,     # Volume efficiency
    "fee_apr_estimate": 182.5,   # Annualized fee rate (%)
    "pool_age_hours": 720,       # Time since creation
    "lp_count_estimate": 45,     # Number of LP positions
    "tvl_trend_24h": -0.05,      # TVL change (-5%)
    "price_change_24h": 0.12,    # Price change (+12%)
}

def compute_health_score(
    tvl_usd: float,
    volume_24h: float,
    pool_age_hours: float,
    lp_count: int,
    tvl_change_24h: float,
) -> float:
    """Score from 0-100 indicating pool health.

    Components (each 0-20):
    - TVL adequacy: Is there enough liquidity?
    - Volume efficiency: Is the pool actively traded?
    - Maturity: How long has the pool existed?
    - LP diversity: How many independent LPs?
    - TVL stability: Is liquidity growing or shrinking?
    """
    # TVL score (0-20): logarithmic scale, peaks at $1M+
    tvl_score = min(20, max(0, 5 * math.log10(max(tvl_usd, 1)) - 10))

    # Volume score (0-20): V/TVL ratio, sweet spot 0.5-3.0
    v_tvl = volume_24h / max(tvl_usd, 1)
    volume_score = min(20, max(0, v_tvl * 10)) if v_tvl < 5 else max(0, 20 - (v_tvl - 5) * 4)

    # Age score (0-20): older = more trusted
    age_score = min(20, pool_age_hours / 72 * 20)  # Max at 72h

    # LP diversity score (0-20)
    lp_score = min(20, lp_count * 2)  # Max at 10 LPs

    # Stability score (0-20): penalize large negative TVL changes
    stability_score = max(0, 20 + tvl_change_24h * 40)  # -50% → 0, 0% → 20

    return tvl_score + volume_score + age_score + lp_score + stability_score

def rank_pools_for_execution(pools: list[dict], trade_size_usd: float) -> list[dict]:
    """Rank pools by execution quality for a given trade size.

    Factors:
    1. Sufficient TVL (trade size < 2% of TVL for acceptable slippage)
    2. Active volume (recent trades confirm the pool is live)
    3. Lowest fee tier (when liquidity is sufficient)
    4. Pool type efficiency (concentrated > constant product for same TVL)
    5. Pool health score (age, LP count, stability)
    """
    for pool in pools:
        size_ratio = trade_size_usd / max(pool["tvl_usd"], 1)
        pool["estimated_slippage"] = size_ratio * 100  # Rough % estimate

        # Prefer pools where trade is < 2% of TVL
        pool["size_ok"] = size_ratio < 0.02

        # Concentrated liquidity is more efficient
        efficiency_mult = 1.0
        if pool["pool_type"] in ("clmm", "whirlpool", "dlmm"):
            efficiency_mult = 0.3  # ~3x less slippage per TVL dollar

        pool["adjusted_slippage"] = pool["estimated_slippage"] * efficiency_mult
        pool["execution_score"] = (
            (1.0 / max(pool["adjusted_slippage"], 0.001)) * 0.5
            + pool.get("health_score", 50) * 0.3
            + (1.0 / max(pool["fee_rate"], 0.0001)) * 0.2
        )

    return sorted(pools, key=lambda p: p["execution_score"], reverse=True)

PROGRAM_IDS = {
    "raydium_v4": "675kPX9MHTjS2zt1qfr1NYHuzeLXfQM9H24wFSUt1Mp8",
    "raydium_clmm": "CAMMCzo5YL8w4VFF8KVHrK22GGUsp5VTaW7grrKgrWqK",
    "orca_whirlpool": "whirLbMiicVdio4qvUfM5KAg6Ct8VwpYzGff3uctyCc",
    "meteora_dlmm": "LBUZKhRxPF3XUpBCjp4YzTKgLccjZhTSDM9YuVaPwxo",
    "meteora_dynamic": "Eo7WjKq67rjJQSZxS6z3YkapzY3eMj6Xy8X5EQVn5UaB",
    "pumpswap": "PSwapMdSai8tjrEXcxFeQth87xC4rRsa4VA5mhGhXkP",
    "pumpfun_bonding": "6EF8rrecthR5Dkzon8Nwu78hRvfCKubJ14M5uBEwF6P",
}

import httpx

# Step 1: Fetch all pools for a token from DexScreener
async def analyze_token_pools(token_mint: str) -> dict:
    url = f"https://api.dexscreener.com/tokens/v1/solana/{token_mint}"
    async with httpx.AsyncClient() as client:
        resp = await client.get(url)
        pools = resp.json()

    results = []
    for pool in pools:
        dex = pool.get("dexId", "unknown")
        pool_info = {
            "dex": dex,
            "pool_address": pool.get("pairAddress"),
            "tvl_usd": pool.get("liquidity", {}).get("usd", 0),
            "volume_24h": pool.get("volume", {}).get("h24", 0),
            "age_hours": pool.get("pairCreatedAt", 0),
            "fee_rate": estimate_fee_rate(dex),
            "pool_type": classify_pool_type(dex),
        }
        pool_info["volume_efficiency"] = (
            pool_info["volume_24h"] / max(pool_info["tvl_usd"], 1)
        )
        results.append(pool_info)

    return {"token": token_mint, "pool_count": len(results), "pools": results}