Liquidity Analysis — DEX Depth Assessment for Solana Tokens

Liquidity analysis answers three critical questions before every trade: Can I get in at a reasonable price? Can I get out when I need to? and Is this pool safe? Without it, you risk excessive slippage, failed exits, and rug pulls.

Why Liquidity Analysis Matters

Position sizing: Maximum position size is bounded by available liquidity. A $10K position in a pool with $20K TVL will move the price significantly. Rule of thumb: keep trade size under 2% of pool depth to limit slippage below 1%.

Execution cost: Slippage is a direct cost. On a 5 SOL buy, the difference between 0.3% and 3% slippage is real money lost on every entry and exit.

Rug risk detection: Thin liquidity, single pools, unlocked LP tokens, and newly created pools are warning signs. Liquidity analysis catches these before you enter.

Exit planning: Entry liquidity may differ from exit liquidity. If LP is unlocked and owned by one wallet, it can be pulled at any time.

Key Concepts

Total Value Locked (TVL)

Total value of assets deposited in a pool. For a SOL/TOKEN pool with 100 SOL and 1M TOKEN at $0.01 each, TVL = 100 * SOL_price + 1M * $0.01. TVL alone is insufficient — you need depth at the current price range.

Liquidity Depth

How much can be traded before moving the price X%. In constant-product AMMs, depth is uniform. In concentrated liquidity (CLMM), depth varies by price range — thick near the current price, thin or zero outside active ranges.

Concentration Factor (CLMM)

Concentrated liquidity pools focus capital in a narrow price range, providing deeper liquidity within that range but nothing outside it. A pool with $50K TVL concentrated in a +/-5% range provides the same depth as a $500K constant-product pool within that range, but zero depth beyond it.

Slippage Curve

Slippage is not linear. Plotting slippage against trade size produces a curve that's gentle for small trades and steep for large ones. The shape depends on pool type, TVL, and concentration.

Pool Composition

Who provides liquidity matters. Locked LP tokens cannot be withdrawn (safer). Single-sided liquidity means the pool is imbalanced. Pool age indicates stability — pools older than 7 days with consistent TVL are more reliable.

Data Sources

Four complementary data sources, from free to comprehensive:

See

references/data_sources.md

Core Analysis Pipeline

Step 1: Identify Pools

Fetch all pools for a token. Most Solana tokens have multiple pools across Raydium, Orca, and Meteora.

import httpx

def get_pools(mint: str) -> list[dict]:
    """Fetch all DEX pools for a token from DexScreener."""
    resp = httpx.get(f"https://api.dexscreener.com/tokens/v1/solana/{mint}")
    resp.raise_for_status()
    pairs = resp.json()
    return [p for p in pairs if p.get("liquidity", {}).get("usd", 0) > 0]

Step 2: Measure Depth

For each pool, extract liquidity metrics:

def extract_depth(pool: dict) -> dict:
    """Extract liquidity metrics from a DexScreener pool."""
    return {
        "dex": pool.get("dexId", "unknown"),
        "liquidity_usd": pool.get("liquidity", {}).get("usd", 0),
        "volume_24h": pool.get("volume", {}).get("h24", 0),
        "pool_age_hours": _pool_age_hours(pool.get("pairCreatedAt", 0)),
        "pair_address": pool.get("pairAddress", ""),
    }

Step 3: Estimate Slippage

Use Jupiter quotes at multiple sizes to build an empirical slippage curve. This captures real routing across all pools:

import httpx

SOL_MINT = "So11111111111111111111111111111111111111112"
LAMPORTS = 1_000_000_000

async def estimate_slippage(token_mint: str, sol_amounts: list[float]) -> list[dict]:
    """Query Jupiter for slippage at multiple trade sizes.

    Args:
        token_mint: Token mint address to buy.
        sol_amounts: List of SOL amounts to test (e.g., [0.1, 0.5, 1, 5, 10]).

    Returns:
        List of dicts with sol_amount, output_tokens, price_per_token, slippage_bps.
    """
    results = []
    base_price = None
    async with httpx.AsyncClient() as client:
        for sol in sol_amounts:
            lamports = int(sol * LAMPORTS)
            resp = await client.get(
                "https://api.jup.ag/quote/v1",
                params={
                    "inputMint": SOL_MINT,
                    "outputMint": token_mint,
                    "amount": str(lamports),
                    "slippageBps": 5000,
                },
            )
            if resp.status_code != 200:
                continue
            data = resp.json()
            out_amount = int(data["outAmount"])
            price = sol / out_amount if out_amount > 0 else 0
            if base_price is None:
                base_price = price
            slippage_bps = int((price - base_price) / base_price * 10000) if base_price > 0 else 0
            results.append({
                "sol_amount": sol,
                "output_tokens": out_amount,
                "price_per_token": price,
                "slippage_bps": max(0, slippage_bps),
            })
    return results

Step 4: Assess Concentration

For CLMM pools (Orca Whirlpool, Raydium CLMM, Meteora DLMM), liquidity may be concentrated in a narrow range. Check if the current price is within the active range and how deep liquidity extends:

def assess_concentration(pools: list[dict]) -> dict:
    """Assess concentration risk from pool data."""
    clmm_pools = [p for p in pools if p.get("dexId") in ("raydium", "orca") and "clmm" in p.get("labels", [])]
    cpmm_pools = [p for p in pools if p not in clmm_pools]

    total_clmm = sum(p.get("liquidity", {}).get("usd", 0) for p in clmm_pools)
    total_cpmm = sum(p.get("liquidity", {}).get("usd", 0) for p in cpmm_pools)
    total = total_clmm + total_cpmm

    return {
        "clmm_ratio": total_clmm / total if total > 0 else 0,
        "cpmm_liquidity": total_cpmm,
        "clmm_liquidity": total_clmm,
        "concentration_risk": "high" if total_clmm / total > 0.8 and total > 0 else "low",
    }

Step 5: Compute Liquidity Score

Composite score from 0 (dangerous) to 100 (deep, safe liquidity):

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))

Risk Flags

Flag these conditions before entering any position:

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

Position Sizing from Liquidity

Maximum position size should keep slippage under your threshold:

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 Estimation

For detailed slippage mathematics including constant-product formulas, CLMM models, and empirical curve fitting, see

references/slippage_curves.md

Key formula for constant-product AMM:

slippage = Δx / (x + Δx)

Where

Δx

x

Pool Types

Solana DEXes use different AMM designs with different liquidity characteristics. See

references/pool_types.md

• Constant Product (Raydium V4, Orca Legacy): Uniform liquidity, predictable slippage
• Concentrated Liquidity (Raydium CLMM, Orca Whirlpool): Deep at current price, zero outside range
• Dynamic AMM (Meteora DLMM): Adaptive fees, bin-based liquidity

Integration with Other Skills

token-holder-analysis: Check LP token holder distribution before entering. If one wallet holds >50% of LP tokens and they are unlocked, exit risk is high.

position-sizing: Feed

max_position_from_liquidity()

slippage-modeling: Use the empirical slippage curves from this skill as input to execution cost models.

birdeye-api: Fetch detailed pool data including trade history and LP events.

dexscreener-api: Free pool discovery and basic liquidity metrics.

Example Workflow

# 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}")

Files

references/slippage_curves.md

references/pool_types.md

references/data_sources.md

scripts/analyze_liquidity.py

scripts/pool_comparison.py