liquidity-analysis

Name: liquidity-analysis
Author: agiprolabs

191installs53forks

How to install liquidity-analysis?

agiprolabs/claude-trading-skills/liquidity-analysis

$npx -y skills add agiprolabs/claude-trading-skills --skill liquidity-analysis

Installs into the current project.

Summary

DEX liquidity depth assessment, slippage estimation, and pool composition analysis for Solana tokens

Files · 1

View on GitHub

SKILL.md

1	# Liquidity Analysis — DEX Depth Assessment for Solana Tokens
2
3	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.
4
5	## Why Liquidity Analysis Matters
6
7	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%.
8
9	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.
10
11	Rug risk detection: Thin liquidity, single pools, unlocked LP tokens, and newly created pools are warning signs. Liquidity analysis catches these before you enter.
12
13	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.
14
15	## Key Concepts
16
17	### Total Value Locked (TVL)
18
19	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.
20
21	### Liquidity Depth
22
23	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.
24
25	### Concentration Factor (CLMM)
26
27	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.
28
29	### Slippage Curve
30
31	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.
32
33	### Pool Composition
34
35	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.
36
37	## Data Sources
38
39	Four complementary data sources, from free to comprehensive:
40
41	\| Source \| Auth Required \| Best For \| Limitations \|
42	\|--------\|--------------\|----------\|-------------\|
43	\| DexScreener \| None \| Quick pool lookup, liquidity.usd \| No on-chain pool details \|
44	\| Jupiter Quote API \| None \| Empirical slippage at any size \| Aggregate across pools \|
45	\| Birdeye \| API key \| Detailed pool data, trade history \| Rate limited on free tier \|
46	\| On-chain \| RPC only \| LP lock status, exact reserves \| Requires program knowledge \|
47
48	See `references/data_sources.md` for complete endpoint documentation and usage examples.
49
50	## Core Analysis Pipeline
51
52	### Step 1: Identify Pools
53
54	Fetch all pools for a token. Most Solana tokens have multiple pools across Raydium, Orca, and Meteora.
55
56	```python
57	import httpx
58
59	def get_pools(mint: str) -> list[dict]:
60	"""Fetch all DEX pools for a token from DexScreener."""
61	resp = httpx.get(f"https://api.dexscreener.com/tokens/v1/solana/{mint}")
62	resp.raise_for_status()
63	pairs = resp.json()
64	return [p for p in pairs if p.get("liquidity", {}).get("usd", 0) > 0]
65	```
66
67	### Step 2: Measure Depth
68
69	For each pool, extract liquidity metrics:
70
71	```python
72	def extract_depth(pool: dict) -> dict:
73	"""Extract liquidity metrics from a DexScreener pool."""
74	return {
75	"dex": pool.get("dexId", "unknown"),
76	"liquidity_usd": pool.get("liquidity", {}).get("usd", 0),
77	"volume_24h": pool.get("volume", {}).get("h24", 0),
78	"pool_age_hours": _pool_age_hours(pool.get("pairCreatedAt", 0)),
79	"pair_address": pool.get("pairAddress", ""),
80	}
81	```
82
83	### Step 3: Estimate Slippage
84
85	Use Jupiter quotes at multiple sizes to build an empirical slippage curve. This captures real routing across all pools:
86
87	```python
88	import httpx
89
90	SOL_MINT = "So11111111111111111111111111111111111111112"
91	LAMPORTS = 1_000_000_000
92
93	async def estimate_slippage(token_mint: str, sol_amounts: list[float]) -> list[dict]:
94	"""Query Jupiter for slippage at multiple trade sizes.
95
96	Args:
97	token_mint: Token mint address to buy.
98	sol_amounts: List of SOL amounts to test (e.g., [0.1, 0.5, 1, 5, 10]).
99
100	Returns:
101	List of dicts with sol_amount, output_tokens, price_per_token, slippage_bps.
102	"""
103	results = []
104	base_price = None
105	async with httpx.AsyncClient() as client:
106	for sol in sol_amounts:
107	lamports = int(sol * LAMPORTS)
108	resp = await client.get(
109	"https://api.jup.ag/quote/v1",
110	para

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liquidity-analysis

191installs53forks

View on GitHub

How to install liquidity-analysis?

agiprolabs/claude-trading-skills/liquidity-analysis

$npx -y skills add agiprolabs/claude-trading-skills --skill liquidity-analysis

Installs into the current project.

Summary

DEX liquidity depth assessment, slippage estimation, and pool composition analysis for Solana tokens

Files · 1

View on GitHub

SKILL.md

1	# Liquidity Analysis — DEX Depth Assessment for Solana Tokens
2
3	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.
4
5	## Why Liquidity Analysis Matters
6
7	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%.
8
9	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.
10
11	Rug risk detection: Thin liquidity, single pools, unlocked LP tokens, and newly created pools are warning signs. Liquidity analysis catches these before you enter.
12
13	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.
14
15	## Key Concepts
16
17	### Total Value Locked (TVL)
18
19	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.
20
21	### Liquidity Depth
22
23	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.
24
25	### Concentration Factor (CLMM)
26
27	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.
28
29	### Slippage Curve
30
31	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.
32
33	### Pool Composition
34
35	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.
36
37	## Data Sources
38
39	Four complementary data sources, from free to comprehensive:
40
41	\| Source \| Auth Required \| Best For \| Limitations \|
42	\|--------\|--------------\|----------\|-------------\|
43	\| DexScreener \| None \| Quick pool lookup, liquidity.usd \| No on-chain pool details \|
44	\| Jupiter Quote API \| None \| Empirical slippage at any size \| Aggregate across pools \|
45	\| Birdeye \| API key \| Detailed pool data, trade history \| Rate limited on free tier \|
46	\| On-chain \| RPC only \| LP lock status, exact reserves \| Requires program knowledge \|
47
48	See `references/data_sources.md` for complete endpoint documentation and usage examples.
49
50	## Core Analysis Pipeline
51
52	### Step 1: Identify Pools
53
54	Fetch all pools for a token. Most Solana tokens have multiple pools across Raydium, Orca, and Meteora.
55
56	```python
57	import httpx
58
59	def get_pools(mint: str) -> list[dict]:
60	"""Fetch all DEX pools for a token from DexScreener."""
61	resp = httpx.get(f"https://api.dexscreener.com/tokens/v1/solana/{mint}")
62	resp.raise_for_status()
63	pairs = resp.json()
64	return [p for p in pairs if p.get("liquidity", {}).get("usd", 0) > 0]
65	```
66
67	### Step 2: Measure Depth
68
69	For each pool, extract liquidity metrics:
70
71	```python
72	def extract_depth(pool: dict) -> dict:
73	"""Extract liquidity metrics from a DexScreener pool."""
74	return {
75	"dex": pool.get("dexId", "unknown"),
76	"liquidity_usd": pool.get("liquidity", {}).get("usd", 0),
77	"volume_24h": pool.get("volume", {}).get("h24", 0),
78	"pool_age_hours": _pool_age_hours(pool.get("pairCreatedAt", 0)),
79	"pair_address": pool.get("pairAddress", ""),
80	}
81	```
82
83	### Step 3: Estimate Slippage
84
85	Use Jupiter quotes at multiple sizes to build an empirical slippage curve. This captures real routing across all pools:
86
87	```python
88	import httpx
89
90	SOL_MINT = "So11111111111111111111111111111111111111112"
91	LAMPORTS = 1_000_000_000
92
93	async def estimate_slippage(token_mint: str, sol_amounts: list[float]) -> list[dict]:
94	"""Query Jupiter for slippage at multiple trade sizes.
95
96	Args:
97	token_mint: Token mint address to buy.
98	sol_amounts: List of SOL amounts to test (e.g., [0.1, 0.5, 1, 5, 10]).
99
100	Returns:
101	List of dicts with sol_amount, output_tokens, price_per_token, slippage_bps.
102	"""
103	results = []
104	base_price = None
105	async with httpx.AsyncClient() as client:
106	for sol in sol_amounts:
107	lamports = int(sol * LAMPORTS)
108	resp = await client.get(
109	"https://api.jup.ag/quote/v1",
110	para

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