Use Cases

Building a Polymarket Trading Bot with Historical Data

How to build an automated trading bot for Polymarket using historical order book data for strategy development.

Trading Bot Architecture

A prediction market trading bot has two phases: development (using historical data) and execution (live trading). PolyHistorical powers the development phase with historical order book snapshots you can replay and test against.

Development Pipeline

PhaseDescriptionData Source
ResearchIdentify patterns in order book behaviorPolyHistorical API
Strategy CodingImplement entry/exit logicPython / TypeScript
BacktestingReplay historical snapshotsPolyHistorical API
Paper TradingRun strategy on live data without real moneyPolyHistorical + Polymarket
Live ExecutionExecute real trades on PolymarketPolymarket CLOB API

Signal Generation with Order Book Data

The most effective prediction market signals come from order book analysis:

  • Depth imbalance: When bid depth significantly exceeds ask depth (or vice versa), price movement often follows
  • Spread compression: Narrowing spreads indicate growing consensus and potential breakout
  • Liquidity shifts: Sudden removal of depth at key levels can signal informed trading
  • Cross-market signals: Compare BTC Up/Down order books with ETH and SOL markets for correlation trades

Example: Depth Imbalance Signal

def calculate_imbalance(orderbook):
    bid_depth = sum(float(l["size"]) for l in orderbook["bids"][:5])
    ask_depth = sum(float(l["size"]) for l in orderbook["asks"][:5])
    total = bid_depth + ask_depth
    if total == 0:
        return 0
    return (bid_depth - ask_depth) / total  # -1 to +1

Risk Management

  • Never risk more than 2-5% of capital on a single market
  • Set maximum position sizes based on historical liquidity analysis
  • Monitor for regime changes — strategies that worked historically may break
  • Always backtest with PolyHistorical data before deploying with real capital

Related Resources