Building a Polymarket Order Flow Monitor: Tracking Trades, Liquidity, and Market Pressure

Learn how to build a Polymarket order flow monitor using live market data, WebSocket feeds, and Rust to analyze trade activity and liquidity changes.

A sudden increase in bid depth looks bullish. Then the bids disappear, the best bid drops, and the next trade prints lower.

An order book snapshot cannot explain that sequence. You need to know what changed, when it changed, and whether liquidity was actually traded or simply withdrawn.

That is the job of a Polymarket order flow monitor: turn a stream of market events into a reliable picture of trading activity.

By Almach

Polymarket trading bots • Quantitative trading • Rust • Web3 infrastructure

Telegram: https://t.me/almachquant
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Start with the distinction that matters

An order book describes resting interest. Trade flow describes executed activity.

Those are not interchangeable.

Imagine a YES-token market where the best bid contains 4,000 shares. A subsequent update shows only 1,500 shares.

What happened to the missing 2,500?

Perhaps sellers executed against the bid. Perhaps traders canceled their orders. Perhaps the book was replaced with a newer snapshot after a connection interruption.

You cannot determine the cause from those two snapshots alone.

A useful monitor therefore maintains separate observations:

Trade-side classification may require inference. A print near the ask is suggestive of aggressive buying, but timestamp mismatches, changing quotes, and feed semantics can make that inference unreliable.

Build the monitor around event time

The architecture should preserve raw observations before calculating indicators.

Polymarket Market Data | v WebSocket Ingestion | v Event Validation | v Raw Event Journal | v Order Book State | v Trade Flow Aggregation | v Metrics / Alerts / Research 

The raw event journal matters more than it initially appears.

Without it, debugging a suspicious signal becomes guesswork. With it, you can replay a sequence, inspect quote changes, and determine whether your local state was consistent.

Record both the event timestamp, when supplied, and the local receipt timestamp. These represent different clocks.

Also retain market and token identifiers, event type, relevant prices and sizes, and connection status.

Three metrics worth computing

Start with simple measurements that have clear interpretations.

1. Order book imbalance

imbalance = (bid_depth - ask_depth) / (bid_depth + ask_depth) 

A positive value indicates more displayed bid liquidity within the selected price range. It does not establish actual buying pressure.

2. Signed trade flow

signed_flow = aggressive_buy_volume - aggressive_sell_volume 

This metric requires reliable trade-side information or a clearly documented classification method.

If the feed cannot establish trade direction, report unclassified volume rather than manufacturing a signal.

3. Quote response

Measure how the midpoint changes after observed trade activity.

midpoint = (best_bid + best_ask) / 2 midpoint_change = midpoint_after - midpoint_before 

For example, persistent buy-side executions followed by rising quotes may indicate stronger immediate buying pressure than a temporary increase in displayed bid depth.

These relationships should be tested statistically rather than treated as automatic entry rules.

A Rust component for rolling trade flow

Keep aggregation independent of the WebSocket transport.

This simplified Rust component processes already-classified trades and maintains a fixed-size rolling window.

use std::collections::VecDeque; #[derive(Clone, Copy)] enum Side { Buy, Sell, } #[derive(Clone, Copy)] struct Trade { side: Side, size: f64, } struct FlowWindow { trades: VecDeque<Trade>, capacity: usize, signed_volume: f64, } impl FlowWindow { fn new(capacity: usize) -> Self { Self { trades: VecDeque::new(), capacity, signed_volume: 0.0, } } fn push(&mut self, trade: Trade) { if !trade.size.is_finite() || trade.size <= 0.0 || self.capacity == 0 { return; } let signed = match trade.side { Side::Buy => trade.size, Side::Sell => -trade.size, }; self.trades.push_back(trade); self.signed_volume += signed; if self.trades.len() > self.capacity { if let Some(old) = self.trades.pop_front() { self.signed_volume -= match old.side { Side::Buy => old.size, Side::Sell => -old.size, }; } } } fn net_flow(&self) -> f64 { self.signed_volume } } fn main() { let mut flow = FlowWindow::new(100); flow.push(Trade { side: Side::Buy, size: 250.0, }); flow.push(Trade { side: Side::Sell, size: 120.0, }); println!("Net flow: {}", flow.net_flow()); } 

The output is 130, representing net classified buy volume across the supplied events.

This is a trade-count window, not a time-based window. It assumes trade events have already been validated, classified, and deduplicated.

A production monitor also needs bounded memory, reconnect handling, and protection against processing duplicate events.

The failure mode most dashboards hide

A WebSocket disconnect is not merely a temporary absence of information.

It can invalidate the local order book.

After reconnecting, a monitor should rebuild its state from a trustworthy snapshot and apply subsequent updates according to the feed's documented synchronization rules.

Until consistency is restored, order-flow indicators should be marked unavailable.

The same principle applies to trade classification. If quotes and prints cannot be aligned reliably, the correct result is unknown, not a confident buy or sell signal.

Turning observations into research

Once the monitor produces consistent data, compare signed flow, depth changes, and subsequent midpoint returns over defined horizons.

Evaluate different liquidity regimes. Measure missing-event rates. Separate periods with wide spreads from those with tight spreads.

A positive relationship between buy flow and future prices may disappear after accounting for transaction costs, adverse selection, and execution delays.

That does not make the monitor unsuccessful.

Its first responsibility is to measure the market correctly.

A trustworthy Polymarket order flow monitor is valuable because it separates what traders displayed, what actually traded, and what the market did afterward.

Risk disclaimer: Order-flow indicators are research tools, not guaranteed trading signals. Prediction-market trading involves liquidity, execution, model, and resolution risks.