/market-microstructure

name: market-microstructure
description: DEX orderflow analysis, trade classification, buyer/seller pressure, and microstructure signals for Solana tokens

Market Microstructure — DEX Orderflow Analysis

Overview

Market microstructure on Solana DEXes differs fundamentally from traditional finance. There are no orderbooks on AMMs — every trade is a swap against a liquidity pool. Yet trade flow analysis remains powerful: the sequence, size, and direction of swaps reveal accumulation, distribution, whale activity, and wash trading patterns.

This skill covers:

• **Trade classification** — identifying buys vs sells from swap direction
• **Volume profiles** — time-based and size-based breakdowns
• **Buyer/seller pressure** — ratio metrics, net flow, trade count asymmetry
• **Trade size distribution** — whale detection, retail vs institutional flow
• **Flow momentum signals** — acceleration, volume spikes, composite scores
• **Token velocity** — turnover rate as a sentiment proxy
• **Wash trading detection** — spotting fake volume and bot patterns

Why Microstructure Matters on DEXes

On CEXes, microstructure means orderbook depth, bid-ask spread, and queue position. On AMMs, liquidity sits in pool curves — there is no spread or queue. But the **trade tape** (the chronological list of swaps) contains rich signal:

1. **Who is trading?** — Whale wallets vs retail, smart money vs bots 2. **How are they trading?** — Large single swaps vs DCA-style splits 3. **When are they trading?** — Volume clustering around events or time zones 4. **What direction?** — Net buy vs sell pressure over sliding windows

These signals feed into entry/exit timing, position sizing, and token quality scoring.

Trade Classification

Buy vs Sell Identification

On Solana DEXes, every swap has an input token and output token:

| Swap Direction | Classification | Meaning | |----------------|---------------|---------| | SOL → Token | **Buy** | Trader spending SOL to acquire token | | USDC → Token | **Buy** | Trader spending stables to acquire token | | Token → SOL | **Sell** | Trader converting token back to SOL | | Token → USDC | **Sell** | Trader converting token to stables | | Token A → Token B | Context-dependent | Classify based on which token you're analyzing |

From API Data Sources

**Birdeye Trade History** (`/defi/txs/token`):

• Returns `side` field: `"buy"` or `"sell"`
• Includes `from` (input token) and `to` (output token) amounts

**DexScreener Pair Trades:**

• Returns `type` field indicating swap direction relative to the pair

**Helius Parsed Transactions:**

• Parse swap instructions to extract input/output mints and amounts
• Classify based on which mint matches your target token

See `references/trade_classification.md` for detailed classification logic and size buckets.

Volume Profiles

Time-Based Profiles

Aggregate trade volume into fixed time buckets to identify patterns:

# Hourly volume profile
hourly_volume = {}
for trade in trades:
    hour = trade["timestamp"] // 3600 * 3600
    hourly_volume.setdefault(hour, {"buy_vol": 0, "sell_vol": 0})
    if trade["side"] == "buy":
        hourly_volume[hour]["buy_vol"] += trade["volume_usd"]
    else:
        hourly_volume[hour]["sell_vol"] += trade["volume_usd"]

Key metrics from time profiles:

• **Peak hours** — when is the token most actively traded?
• **Volume trend** — is volume increasing, decreasing, or stable?
• **Volume anomalies** — spikes exceeding 3x the rolling average

Size-Based Profiles

Classify trades into size buckets to separate whale activity from retail:

| Bucket | SOL Range | Typical Actor | |--------|-----------|---------------| | Micro | < 0.1 SOL | Dust / test trades | | Small | 0.1 – 1 SOL | Retail traders | | Medium | 1 – 10 SOL | Active traders | | Large | 10 – 50 SOL | Serious positions | | Whale | 50+ SOL | Whales / institutions |

Buyer/Seller Pressure Metrics

Core Ratios

def compute_pressure(trades: list[dict], period_seconds: int = 3600) -> dict:
    """Compute buy/sell pressure metrics over a time period."""
    buy_vol = sum(t["volume_usd"] for t in trades if t["side"] == "buy")
    sell_vol = sum(t["volume_usd"] for t in trades if t["side"] == "sell")
    total_vol = buy_vol + sell_vol

    buy_trades = sum(1 for t in trades if t["side"] == "buy")
    sell_trades = sum(1 for t in trades if t["side"] == "sell")
    total_trades = buy_trades + sell_trades

    return {
        "buy_sell_ratio": buy_vol / sell_vol if sell_vol > 0 else float("inf"),
        "buy_volume_pct": buy_vol / total_vol if total_vol > 0 else 0.5,
        "net_flow_usd": buy_vol - sell_vol,
        "trade_count_ratio": buy_trades / total_trades if total_trades > 0 else 0.5,
    }

Signal Interpretation

| Metric | Bullish | Neutral | Bearish | |--------|---------|---------|---------| | Buy Volume % | > 60% | 40–60% | < 40% | | Net Flow | Positive, increasing | Near zero | Negative, increasing | | Trade Count Ratio | > 0.55 | 0.45–0.55 | < 0.45 | | Large Trade Ratio | High buy-side | Balanced | High sell-side |

See `references/flow_signals.md` for the full signal catalog and composite scoring.

Trade Size Distribution

Analyzing the distribution of trade sizes reveals market structure:

import statistics

def analyze_trade_sizes(trades: list[dict]) -> dict:
    """Analyze trade size distribution."""
    sizes = [t["volume_usd"] for t in trades]
    if not sizes:
        return {}

    return {
        "mean": statistics.mean(sizes),
        "median": statistics.median(sizes),
        "stdev": statistics.stdev(sizes) if len(sizes) > 1 else 0,
        "skew_indicator": statistics.mean(sizes) / statistics.median(sizes),
        "max_trade": max(sizes),
        "whale_pct": sum(s for s in sizes if s > 5000) / sum(sizes),
    }

**Interpreting skew:** A `skew_indicator` (mean/median) well above 1.0 indicates a fat-tailed distribution — a few large trades domina