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: 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 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: 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 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: Positive buy momentum with increasing volume is a strong accumulation signal. Token Velocity Token velocity measures how frequently tokens change hands: Velocity Interpretation < 0.01 Low activity, illiquid, or strong holders 0.01–0.05 Normal trading activity 0.05–0.20 Active trading, possible speculation 0.20 Very high turnover, potential wash trading 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 for detailed detection methods and scoring. 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 skill for endpoint details. 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): Score Range Interpretation +60 to +100 Strong accumulation — heavy buy pressure +20 to +60 Moderate buying — cautious accumulation 20 to +20 Neutral / balanced flow 60 to 20 Moderate selling — distribution underway 100 to 60 Strong distribution — heavy sell pressure Integration with Other Skills Skill How It Connects birdeye api Primary data source for trade history and volume helius api Wallet attributed trade data from parsed transactions liquidity analysis Volume/TVL ratios, liquidity context for flow signals whale tracking Identify whale wallets for large trade attribution token holder analysis Supply distribution context for velocity metrics position sizing Use flow signals to adjust entry sizing regime detection Combine flow momentum with regime classification 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

market-microstructure