Market Microstructure

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:

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:

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

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 dominate. This is normal for tokens with whale interest but can also signal manipulation.

Momentum Signals from Trade Flow

Volume Acceleration

Compare current period volume to the previous period:

acceleration = current_volume / previous_volume if previous_volume > 0 else 0

• acceleration > 2.0 — volume surge, potential breakout or dump
• acceleration 0.8–1.2 — stable activity
• acceleration < 0.5 — dying interest

Buy Pressure Acceleration

Track how the buy ratio changes over time:

current_buy_ratio = current_buy_vol / current_total_vol
previous_buy_ratio = prev_buy_vol / prev_total_vol
buy_momentum = current_buy_ratio - previous_buy_ratio

Positive buy_momentum with increasing volume is a strong accumulation signal.