pm-hbtbacktest 1.0.3

A high-frequency trading and market-making backtesting tool accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books.

核心特性

• Polymarket tick 级别回测

• 使用 Numba JIT 和 Rust 实现快速执行

• 面向策略研究和撮合执行模拟

快速开始

安装

pip install pm-hbtbacktest

示例

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


# 扫尾盘策略。
@njit
def endline_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

    # 策略状态：只触发一次、止损一次，避免在同一段尾盘行情里反复开平仓。
    activated = False
    # side=1 表示买 UP；side=-1 表示买 DOWN，在 UP 合约上表现为卖出。
    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 是 UP 多头止损线；DOWN 方向使用对称的 stop_short。
    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))

    # 每 100ms 运行一轮策略逻辑。
    while hbt.elapse(100_000_000) == 0:
        hbt.clear_inactive_orders(asset_no)
        depth = hbt.depth(asset_no)

        # 使用 mid 作为触发价，减少单边盘口噪声影响。
        bid, ask = depth.best_bid, depth.best_ask
        mid = (bid + ask) / 2.0

        # 进入尾盘确定性区域：向上突破买 UP，向下突破买 DOWN。
        if not activated:
            if mid >= up_trigger:
                activated = True
                side = 1
            elif mid <= down_trigger:
                activated = True
                side = -1

        # 触发后只提交一次开仓单。
        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))

            # 单次提交场景里，直接用价格 tick 序号作为 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 是 UP 合约净仓位：正数为持有 UP，负数可理解为持有 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"
df = pd.read_parquet(
    f"https://s.wangshuox.com/poly_l2/{slug}.parquet",
    storage_options={"User-Agent": "Mozilla/5.0"},
)

data = polymarket_to_hbt(df)

asset = BacktestAssetPoly().data(data)
hbt = ROIVectorMarketDepthBacktest([asset])
recorder = Recorder(hbt.num_assets, 5_000_000)

endline_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()