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

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

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

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

Token Velocity

Token velocity measures how frequently tokens change hands:

velocity = daily_volume / circulating_supply

High velocity combined with low unique trader count is a wash trading red flag.

Wash Trading Detection

Wash trading inflates volume to make a token appear more active than it truly is. Key detection signals:

1. Low unique trader ratio —

   unique_wallets / trade_count < 0.3

2. Volume/TVL anomaly —

   daily_volume / tvl > 10

   (volume vastly exceeds liquidity)
3. Uniform trade sizes — low entropy in trade size distribution
4. Self-trading — same wallet on both sides within short windows
5. Funded-together clusters — multiple wallets funded from the same source

See

references/wash_trading.md

Data Sources

Birdeye API

Primary source for trade history on Solana tokens:

• GET /defi/txs/token

  — recent trades for a token

• GET /defi/ohlcv

  — candle data with volume

• GET /defi/price/volume

  — aggregated volume data

Requires API key. See the

birdeye-api

DexScreener API

Free, no-auth alternative for pair-level data:

• GET /latest/dex/tokens/{address}

  — token pairs with volume

• GET /latest/dex/pairs/solana/{pairAddress}

  — pair details

Helius API

For wallet-level trade analysis and parsed transactions:

• Parse swap transactions to extract trade details
• Attribute trades to specific wallets
• See the

  helius-api

  skill for transaction parsing.

Composite Momentum Score

Combine multiple flow signals into a single score (range: -100 to +100):

def compute_momentum_score(
    buy_ratio: float,
    volume_accel: float,
    whale_buy_pct: float,
    unique_trader_trend: float,
) -> float:
    """Compute composite momentum score from flow signals.

    Args:
        buy_ratio: Buy volume / total volume (0 to 1).
        volume_accel: Current vol / previous vol.
        whale_buy_pct: Whale buy volume / total whale volume (0 to 1).
        unique_trader_trend: Change in unique traders vs previous period.

    Returns:
        Score from -100 (strong sell pressure) to +100 (strong buy pressure).
    """
    # Buy ratio component: 0.5 = neutral, maps to [-40, +40]
    buy_component = (buy_ratio - 0.5) * 80

    # Volume acceleration: >1 = growing, maps to [-20, +20]
    vol_component = min(max((volume_accel - 1.0) * 20, -20), 20)

    # Whale direction: 0.5 = neutral, maps to [-25, +25]
    whale_component = (whale_buy_pct - 0.5) * 50

    # Unique trader growth: positive = healthy, maps to [-15, +15]
    trader_component = min(max(unique_trader_trend * 15, -15), 15)

    score = buy_component + vol_component + whale_component + trader_component
    return max(-100, min(100, score))

Integration with Other Skills

birdeye-api

helius-api

liquidity-analysis

whale-tracking

token-holder-analysis

position-sizing

regime-detection

Files

References

• references/trade_classification.md

  — Buy/sell classification logic, size buckets, aggregation

• references/flow_signals.md

  — Complete signal catalog with formulas and interpretation

• references/wash_trading.md

  — Detection methods, metrics, and risk scoring

Scripts

• scripts/trade_flow_analysis.py

  — Fetch trades, classify, compute flow signals and momentum

• scripts/volume_profile.py

  — Hourly volume profiles, trend detection, anomaly identification