Key Features

• Tick-level Polymarket backtesting

• Fast execution with Numba JIT and Rust

• Built-in Polymarket fee model

• Configurable queue position and latency models

• Free Polymarket tick data from pmdata.dev

• Built for strategy research and exchange execution simulation

• Compatible with the hftbacktest ecosystem

Quick Start

pip install pm-hftbacktest

Example

from hftbacktest import (
    init_orderbook,
    polymarket_to_hbt,
    BacktestAssetPoly,
    ROIVectorMarketDepthBacktest,
    Recorder,
    GTC,
    LIMIT,
)
from hftbacktest.stats import PolyAssetRecord
import pandas as pd
from numba import njit
import numpy as np


# Endgame trading strategy.
@njit
def endgame_trading(
    hbt,
    recorder,
    up_trigger: float,
    stop_long: float,
    order_qty: float,
):
    asset_no = 0

    if not init_orderbook(hbt, asset_no):
        return

    hbt_tick_size = hbt.depth(asset_no).tick_size
    price_tick_size = 0.01

    # Strategy state: enter once and stop once to avoid repeated
    # open/close cycles in the same endgame move.
    activated = False
    # side=1 means buying UP; side=-1 means buying DOWN, represented
    # as selling the UP contract.
    side = 0
    submitted_once = False
    stop_submitted = False

    up_trigger = min(max(up_trigger, price_tick_size), 1.0 - price_tick_size)
    down_trigger = 1.0 - up_trigger

    # stop_long is the UP long stop level. The DOWN side uses the
    # symmetric stop_short level.
    stop_long = min(max(stop_long, price_tick_size), 1.0 - price_tick_size)
    stop_short = 1.0 - stop_long
    order_qty = np.float64(max(order_qty, 0.0))

    # Run strategy logic every 100ms.
    while hbt.elapse(100_000_000) == 0:
        hbt.clear_inactive_orders(asset_no)
        depth = hbt.depth(asset_no)

        # Use the mid price as the trigger price to reduce one-sided
        # order book noise.
        bid, ask = depth.best_bid, depth.best_ask
        mid = (bid + ask) / 2.0

        # Enter the endgame certainty zone: buy UP on an upside break
        # and buy DOWN on a downside break.
        if not activated:
            if mid >= up_trigger:
                activated = True
                side = 1
            elif mid <= down_trigger:
                activated = True
                side = -1

        # Submit the entry order only once after the trigger.
        if activated and (not submitted_once):
            if side > 0:
                p = up_trigger
            else:
                p = down_trigger

            p = round(p / price_tick_size) * price_tick_size
            p = max(price_tick_size, min(1.0 - price_tick_size, p))

            # For a single-submit example, use the price tick index
            # directly as the order id.
            oid = np.uint64(round(p / hbt_tick_size))

            if side > 0:
                hbt.submit_buy_order(asset_no, oid, p, order_qty, GTC, LIMIT, False)
            else:
                hbt.submit_sell_order(asset_no, oid, p, order_qty, GTC, LIMIT, False)
            submitted_once = True

        # position is the net position in the UP contract: positive means
        # holding UP, while negative can be interpreted as holding DOWN.
        pos = hbt.position(asset_no)
        if (not stop_submitted) and (pos != 0):
            need_stop = (pos > 0 and mid <= stop_long) or (
                pos < 0 and mid >= stop_short
            )
            if need_stop:
                close_qty = np.float64(np.abs(pos))
                if pos > 0:
                    px = (
                        round((bid - price_tick_size) / price_tick_size)
                        * price_tick_size
                    )
                    px = max(price_tick_size, min(1.0 - price_tick_size, px))
                    oid = np.uint64(round(px / hbt_tick_size))
                    hbt.submit_sell_order(
                        asset_no, oid, px, close_qty, GTC, LIMIT, False
                    )
                else:
                    px = (
                        round((ask + price_tick_size) / price_tick_size)
                        * price_tick_size
                    )
                    px = max(price_tick_size, min(1.0 - price_tick_size, px))
                    oid = np.uint64(round(px / hbt_tick_size))
                    hbt.submit_buy_order(
                        asset_no, oid, px, close_qty, GTC, LIMIT, False
                    )
                stop_submitted = True

        recorder.record(hbt)

    recorder.record(hbt)


slug = "btc-updown-15m-1778263200"
api_key = "<YOUR_API_KEY>"
storage_options = {"api_key": api_key, "User-Agent": "Mozilla/5.0"}

l2_df = pd.read_parquet(
    f"https://api.pmdata.dev/download/poly_l2/{slug}.parquet",
    storage_options=storage_options,
)
trade_df = pd.read_parquet(
    f"https://api.pmdata.dev/download/poly_trade/{slug}.parquet",
    storage_options=storage_options,
)

data = polymarket_to_hbt(l2_df, trade_df=trade_df)

asset = (
    BacktestAssetPoly()
    .data(data)
    .binary_fee_model(
        maker_fee_rate=-0.07 * 0.2,
        taker_fee_rate=0.07,
    )
)
hbt = ROIVectorMarketDepthBacktest([asset])
recorder = Recorder(hbt.num_assets, 5_000_000)

endgame_trading(
    hbt,
    recorder.recorder,
    up_trigger=0.84,
    stop_long=0.4,
    order_qty=5,
)
_ = hbt.close()

BOOK_SIZE = 100
stats = PolyAssetRecord(recorder.get(0)).resample("1s").stats(book_size=BOOK_SIZE)
print(f"earn: {stats.earn}")
stats.plot()