market-microstructure

Name: market-microstructure
Author: agiprolabs

237installs53forks

How to install market-microstructure?

agiprolabs/claude-trading-skills/market-microstructure

$npx -y skills add agiprolabs/claude-trading-skills --skill market-microstructure

Installs into the current project.

Summary

DEX orderflow analysis, trade classification, buyer/seller pressure, and microstructure signals for Solana tokens

Files · 1

View on GitHub

SKILL.md

1	# Market Microstructure — DEX Orderflow Analysis
2
3	## Overview
4
5	Market microstructure on Solana DEXes differs fundamentally from traditional finance.
6	There are no orderbooks on AMMs — every trade is a swap against a liquidity pool. Yet
7	trade flow analysis remains powerful: the sequence, size, and direction of swaps reveal
8	accumulation, distribution, whale activity, and wash trading patterns.
9
10	This skill covers:
11	- Trade classification — identifying buys vs sells from swap direction
12	- Volume profiles — time-based and size-based breakdowns
13	- Buyer/seller pressure — ratio metrics, net flow, trade count asymmetry
14	- Trade size distribution — whale detection, retail vs institutional flow
15	- Flow momentum signals — acceleration, volume spikes, composite scores
16	- Token velocity — turnover rate as a sentiment proxy
17	- Wash trading detection — spotting fake volume and bot patterns
18
19	## Why Microstructure Matters on DEXes
20
21	On CEXes, microstructure means orderbook depth, bid-ask spread, and queue position.
22	On AMMs, liquidity sits in pool curves — there is no spread or queue. But the **trade
23	tape** (the chronological list of swaps) contains rich signal:
24
25	1. Who is trading? — Whale wallets vs retail, smart money vs bots
26	2. How are they trading? — Large single swaps vs DCA-style splits
27	3. When are they trading? — Volume clustering around events or time zones
28	4. What direction? — Net buy vs sell pressure over sliding windows
29
30	These signals feed into entry/exit timing, position sizing, and token quality scoring.
31
32	## Trade Classification
33
34	### Buy vs Sell Identification
35
36	On Solana DEXes, every swap has an input token and output token:
37
38	\| Swap Direction \| Classification \| Meaning \|
39	\|----------------\|---------------\|---------\|
40	\| SOL → Token \| Buy \| Trader spending SOL to acquire token \|
41	\| USDC → Token \| Buy \| Trader spending stables to acquire token \|
42	\| Token → SOL \| Sell \| Trader converting token back to SOL \|
43	\| Token → USDC \| Sell \| Trader converting token to stables \|
44	\| Token A → Token B \| Context-dependent \| Classify based on which token you're analyzing \|
45
46	### From API Data Sources
47
48	Birdeye Trade History (`/defi/txs/token`):
49	- Returns `side` field: `"buy"` or `"sell"`
50	- Includes `from` (input token) and `to` (output token) amounts
51
52	DexScreener Pair Trades:
53	- Returns `type` field indicating swap direction relative to the pair
54
55	Helius Parsed Transactions:
56	- Parse swap instructions to extract input/output mints and amounts
57	- Classify based on which mint matches your target token
58
59	See `references/trade_classification.md` for detailed classification logic and size buckets.
60
61	## Volume Profiles
62
63	### Time-Based Profiles
64
65	Aggregate trade volume into fixed time buckets to identify patterns:
66
67	```python
68	# Hourly volume profile
69	hourly_volume = {}
70	for trade in trades:
71	hour = trade["timestamp"] // 3600 * 3600
72	hourly_volume.setdefault(hour, {"buy_vol": 0, "sell_vol": 0})
73	if trade["side"] == "buy":
74	hourly_volume[hour]["buy_vol"] += trade["volume_usd"]
75	else:
76	hourly_volume[hour]["sell_vol"] += trade["volume_usd"]
77	```
78
79	Key metrics from time profiles:
80	- Peak hours — when is the token most actively traded?
81	- Volume trend — is volume increasing, decreasing, or stable?
82	- Volume anomalies — spikes exceeding 3x the rolling average
83
84	### Size-Based Profiles
85
86	Classify trades into size buckets to separate whale activity from retail:
87
88	\| Bucket \| SOL Range \| Typical Actor \|
89	\|--------\|-----------\|---------------\|
90	\| Micro \| < 0.1 SOL \| Dust / test trades \|
91	\| Small \| 0.1 – 1 SOL \| Retail traders \|
92	\| Medium \| 1 – 10 SOL \| Active traders \|
93	\| Large \| 10 – 50 SOL \| Serious positions \|
94	\| Whale \| 50+ SOL \| Whales / institutions \|
95
96	## Buyer/Seller Pressure Metrics
97
98	### Core Ratios
99
100	```python
101	def compute_pressure(trades: list[dict], period_seconds: int = 3600) -> dict:
102	"""Compute buy/sell pressure metrics over a time period."""
103	buy_vol = sum(t["volume_usd"] for t in trades if t["side"] == "buy")
104	sell_vol = sum(t["volume_usd"] for t in trades if t["side"] == "sell")
105	total_vol = buy_vol + sell_vol
106
107	buy_trades = sum(1 for t in trades if t["side"] == "buy")
108	sell_trades = sum(1 for t in trades if t["side"] == "sell")
109	total_trades = buy_trades + sell_trades
110
111	return {
112	"buy_sell_ratio": buy_vol / sell_vol if sell_vol > 0 else float("inf"),
113	"buy_volume_pct": buy_vol / total_vol if total_vol > 0 else 0.5,
114	"net_flow_usd": buy_vol - sell_vol,
115	"trade_count_ratio": buy_trades / total_trades if total_trades > 0 else 0.5,
116	}
117	```
118
119	### Signal Interpretation
120
121	\| Metric \| Bullish \| Neutral \| Bearish \|
122	\|--------\|---------\|---------\|---------\|
123	\| Buy Volume % \| > 60% \| 40–60% \| < 40% \|
124	\| Net Flow \| Positive, increasing \| Near zero \| Negative, increasing

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market-microstructure

237installs53forks

View on GitHub

How to install market-microstructure?

agiprolabs/claude-trading-skills/market-microstructure

$npx -y skills add agiprolabs/claude-trading-skills --skill market-microstructure

Installs into the current project.

Summary

DEX orderflow analysis, trade classification, buyer/seller pressure, and microstructure signals for Solana tokens

Files · 1

View on GitHub

SKILL.md

1	# Market Microstructure — DEX Orderflow Analysis
2
3	## Overview
4
5	Market microstructure on Solana DEXes differs fundamentally from traditional finance.
6	There are no orderbooks on AMMs — every trade is a swap against a liquidity pool. Yet
7	trade flow analysis remains powerful: the sequence, size, and direction of swaps reveal
8	accumulation, distribution, whale activity, and wash trading patterns.
9
10	This skill covers:
11	- Trade classification — identifying buys vs sells from swap direction
12	- Volume profiles — time-based and size-based breakdowns
13	- Buyer/seller pressure — ratio metrics, net flow, trade count asymmetry
14	- Trade size distribution — whale detection, retail vs institutional flow
15	- Flow momentum signals — acceleration, volume spikes, composite scores
16	- Token velocity — turnover rate as a sentiment proxy
17	- Wash trading detection — spotting fake volume and bot patterns
18
19	## Why Microstructure Matters on DEXes
20
21	On CEXes, microstructure means orderbook depth, bid-ask spread, and queue position.
22	On AMMs, liquidity sits in pool curves — there is no spread or queue. But the **trade
23	tape** (the chronological list of swaps) contains rich signal:
24
25	1. Who is trading? — Whale wallets vs retail, smart money vs bots
26	2. How are they trading? — Large single swaps vs DCA-style splits
27	3. When are they trading? — Volume clustering around events or time zones
28	4. What direction? — Net buy vs sell pressure over sliding windows
29
30	These signals feed into entry/exit timing, position sizing, and token quality scoring.
31
32	## Trade Classification
33
34	### Buy vs Sell Identification
35
36	On Solana DEXes, every swap has an input token and output token:
37
38	\| Swap Direction \| Classification \| Meaning \|
39	\|----------------\|---------------\|---------\|
40	\| SOL → Token \| Buy \| Trader spending SOL to acquire token \|
41	\| USDC → Token \| Buy \| Trader spending stables to acquire token \|
42	\| Token → SOL \| Sell \| Trader converting token back to SOL \|
43	\| Token → USDC \| Sell \| Trader converting token to stables \|
44	\| Token A → Token B \| Context-dependent \| Classify based on which token you're analyzing \|
45
46	### From API Data Sources
47
48	Birdeye Trade History (`/defi/txs/token`):
49	- Returns `side` field: `"buy"` or `"sell"`
50	- Includes `from` (input token) and `to` (output token) amounts
51
52	DexScreener Pair Trades:
53	- Returns `type` field indicating swap direction relative to the pair
54
55	Helius Parsed Transactions:
56	- Parse swap instructions to extract input/output mints and amounts
57	- Classify based on which mint matches your target token
58
59	See `references/trade_classification.md` for detailed classification logic and size buckets.
60
61	## Volume Profiles
62
63	### Time-Based Profiles
64
65	Aggregate trade volume into fixed time buckets to identify patterns:
66
67	```python
68	# Hourly volume profile
69	hourly_volume = {}
70	for trade in trades:
71	hour = trade["timestamp"] // 3600 * 3600
72	hourly_volume.setdefault(hour, {"buy_vol": 0, "sell_vol": 0})
73	if trade["side"] == "buy":
74	hourly_volume[hour]["buy_vol"] += trade["volume_usd"]
75	else:
76	hourly_volume[hour]["sell_vol"] += trade["volume_usd"]
77	```
78
79	Key metrics from time profiles:
80	- Peak hours — when is the token most actively traded?
81	- Volume trend — is volume increasing, decreasing, or stable?
82	- Volume anomalies — spikes exceeding 3x the rolling average
83
84	### Size-Based Profiles
85
86	Classify trades into size buckets to separate whale activity from retail:
87
88	\| Bucket \| SOL Range \| Typical Actor \|
89	\|--------\|-----------\|---------------\|
90	\| Micro \| < 0.1 SOL \| Dust / test trades \|
91	\| Small \| 0.1 – 1 SOL \| Retail traders \|
92	\| Medium \| 1 – 10 SOL \| Active traders \|
93	\| Large \| 10 – 50 SOL \| Serious positions \|
94	\| Whale \| 50+ SOL \| Whales / institutions \|
95
96	## Buyer/Seller Pressure Metrics
97
98	### Core Ratios
99
100	```python
101	def compute_pressure(trades: list[dict], period_seconds: int = 3600) -> dict:
102	"""Compute buy/sell pressure metrics over a time period."""
103	buy_vol = sum(t["volume_usd"] for t in trades if t["side"] == "buy")
104	sell_vol = sum(t["volume_usd"] for t in trades if t["side"] == "sell")
105	total_vol = buy_vol + sell_vol
106
107	buy_trades = sum(1 for t in trades if t["side"] == "buy")
108	sell_trades = sum(1 for t in trades if t["side"] == "sell")
109	total_trades = buy_trades + sell_trades
110
111	return {
112	"buy_sell_ratio": buy_vol / sell_vol if sell_vol > 0 else float("inf"),
113	"buy_volume_pct": buy_vol / total_vol if total_vol > 0 else 0.5,
114	"net_flow_usd": buy_vol - sell_vol,
115	"trade_count_ratio": buy_trades / total_trades if total_trades > 0 else 0.5,
116	}
117	```
118
119	### Signal Interpretation
120
121	\| Metric \| Bullish \| Neutral \| Bearish \|
122	\|--------\|---------\|---------\|---------\|
123	\| Buy Volume % \| > 60% \| 40–60% \| < 40% \|
124	\| Net Flow \| Positive, increasing \| Near zero \| Negative, increasing

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