docs/tutorials/Getting Started.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "10108f95",
   "metadata": {},
   "source": [
    "# Getting Started"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb70e585",
   "metadata": {},
   "source": [
    "## Printing the best bid and the best ask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0f0e1b5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numba import njit\n",
    "\n",
    "# numba.njit is strongly recommended for fast backtesting.\n",
    "@njit\n",
    "def print_bbo(hbt):\n",
    "    # Iterating until hftbacktest reaches the end of data.\n",
    "    while hbt.run:\n",
    "        # Elapses 60-sec every iteration.\n",
    "        # Time unit is the same as data's timestamp's unit.\n",
    "        # timestamp of the sample data is in microseconds.\n",
    "        if not hbt.elapse(60 * 1e6):\n",
    "            # hftbacktest encounters the end of data while elapsing.\n",
    "            return False\n",
    "        \n",
    "        # Prints the best bid and the best offer.\n",
    "        print(\n",
    "            'current_timestamp:', hbt.current_timestamp,\n",
    "            ', best_bid:', round(hbt.best_bid, 3),\n",
    "            ', best_ask:', round(hbt.best_ask, 3)\n",
    "        )\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d9e717ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load btcusdt_20230405.npz\n"
     ]
    }
   ],
   "source": [
    "from hftbacktest import HftBacktest, FeedLatency, Linear\n",
    "\n",
    "hbt = HftBacktest(\n",
    "    'btcusdt_20230405.npz',\n",
    "    tick_size=0.1,   # Tick size of a target trading asset\n",
    "    lot_size=0.001,    # Lot size of a target trading asset, minimum trading unit.\n",
    "    maker_fee=0.0002, # 0.02%, Maker fee, rebates if it is negative.\n",
    "    taker_fee=0.0007, # 0.07%, Taker fee. \n",
    "    order_latency=FeedLatency(), # Latency model: ConstantLatency, FeedLatency.\n",
    "    asset_type=Linear, # Asset type: Linear, Inverse.\n",
    "    snapshot='btcusdt_20230404_eod.npz'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d4ecb19",
   "metadata": {},
   "source": [
    "You can see the best bid and the best ask every 60-sec."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "128fb48a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current_timestamp: 1680652860032116 , best_bid: 28150.7 , best_ask: 28150.8\n",
      "current_timestamp: 1680652920032116 , best_bid: 28144.1 , best_ask: 28144.2\n",
      "current_timestamp: 1680652980032116 , best_bid: 28149.9 , best_ask: 28150.0\n",
      "current_timestamp: 1680653040032116 , best_bid: 28145.7 , best_ask: 28145.8\n",
      "current_timestamp: 1680653100032116 , best_bid: 28140.5 , best_ask: 28140.6\n",
      "current_timestamp: 1680653160032116 , best_bid: 28143.8 , best_ask: 28143.9\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print_bbo(hbt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7754e06a",
   "metadata": {},
   "source": [
    "## Feeding the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0003e60",
   "metadata": {},
   "source": [
    "When you possess adequate memory, preloading the data into memory and providing it as input will be more efficient than lazy-loading during repeated backtesting. HftBacktest is compatible with either `numpy` arrays or `pandas` DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1222569e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "btcusdt_20230405 = np.load('btcusdt_20230405.npz')['data']\n",
    "btcusdt_20230404_eod = np.load('btcusdt_20230404_eod.npz')['data']\n",
    "\n",
    "hbt = HftBacktest(\n",
    "    btcusdt_20230405,\n",
    "    tick_size=0.1,\n",
    "    lot_size=0.001,\n",
    "    maker_fee=0.0002,\n",
    "    taker_fee=0.0007,\n",
    "    order_latency=FeedLatency(),\n",
    "    asset_type=Linear,\n",
    "    snapshot=btcusdt_20230404_eod\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c1d92d6",
   "metadata": {},
   "source": [
    "You can also provide the list of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "41eed45e",
   "metadata": {},
   "outputs": [],
   "source": [
    "hbt = HftBacktest(\n",
    "    [\n",
    "        btcusdt_20230405\n",
    "    ],\n",
    "    tick_size=0.1,\n",
    "    lot_size=0.001,\n",
    "    maker_fee=0.0002,\n",
    "    taker_fee=0.0007,\n",
    "    order_latency=FeedLatency(),\n",
    "    asset_type=Linear,\n",
    "    snapshot=btcusdt_20230404_eod\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a39e7cf0",
   "metadata": {},
   "source": [
    "Due to the vast size of tick-by-tick market depth and trade data, loading the entire dataset into memory can be challenging, particularly when backtesting across multiple days. HftBacktest offers lazy loading support and is compatible with `npy`, `npz` (data should be stored under the `data` key), and pickled `pandas` DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b58d8f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load btcusdt_20230405.npz\n"
     ]
    }
   ],
   "source": [
    "hbt = HftBacktest(\n",
    "    [\n",
    "        'btcusdt_20230405.npz'\n",
    "    ],\n",
    "    tick_size=0.1,\n",
    "    lot_size=0.001,\n",
    "    maker_fee=0.0002,\n",
    "    taker_fee=0.0007,\n",
    "    order_latency=FeedLatency(),\n",
    "    asset_type=Linear,\n",
    "    snapshot='btcusdt_20230404_eod.npz'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ec3131e",
   "metadata": {},
   "source": [
    "## Getting the market depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "467b649d",
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def print_3depth(hbt):\n",
    "    while hbt.run:\n",
    "        if not hbt.elapse(60 * 1e6):\n",
    "            return False\n",
    "        \n",
    "        # a key of bid_depth or ask_depth is price in tick format.\n",
    "        # (integer) price_tick = price / tick_size\n",
    "        print('current_timestamp:', hbt.current_timestamp)\n",
    "        i = 0\n",
    "        for tick_price in range(hbt.best_ask_tick, hbt.high_ask_tick + 1):\n",
    "            if tick_price in hbt.ask_depth:\n",
    "                print(\n",
    "                    'ask: ',\n",
    "                    hbt.ask_depth[tick_price],\n",
    "                    '@',\n",
    "                    round(tick_price * hbt.tick_size, 3)\n",
    "                )\n",
    "                i += 1\n",
    "                if i == 3:\n",
    "                    break\n",
    "        i = 0\n",
    "        for tick_price in range(hbt.best_bid_tick, hbt.low_bid_tick - 1, -1):\n",
    "            if tick_price in hbt.bid_depth:\n",
    "                print(\n",
    "                    'bid: ',\n",
    "                    hbt.bid_depth[tick_price],\n",
    "                    '@',\n",
    "                    round(tick_price * hbt.tick_size, 3)\n",
    "                )\n",
    "                i += 1\n",
    "                if i == 3:\n",
    "                    break\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b819fe6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d26e299a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current_timestamp: 1680652860032116\n",
      "ask:  9.228 @ 28150.8\n",
      "ask:  0.387 @ 28150.9\n",
      "ask:  3.996 @ 28151.0\n",
      "bid:  3.135 @ 28150.7\n",
      "bid:  0.002 @ 28150.6\n",
      "bid:  0.813 @ 28150.5\n",
      "current_timestamp: 1680652920032116\n",
      "ask:  1.224 @ 28144.2\n",
      "ask:  0.223 @ 28144.3\n",
      "ask:  0.001 @ 28144.5\n",
      "bid:  10.529 @ 28144.1\n",
      "bid:  0.168 @ 28144.0\n",
      "bid:  0.29 @ 28143.9\n",
      "current_timestamp: 1680652980032116\n",
      "ask:  3.397 @ 28150.0\n",
      "ask:  1.282 @ 28150.1\n",
      "ask:  0.003 @ 28150.4\n",
      "bid:  7.951 @ 28149.9\n",
      "bid:  0.02 @ 28149.8\n",
      "bid:  0.02 @ 28149.7\n",
      "current_timestamp: 1680653040032116\n",
      "ask:  3.905 @ 28145.8\n",
      "ask:  1.695 @ 28145.9\n",
      "ask:  0.003 @ 28146.0\n",
      "bid:  5.793 @ 28145.7\n",
      "bid:  0.059 @ 28145.6\n",
      "bid:  0.044 @ 28145.5\n",
      "current_timestamp: 1680653100032116\n",
      "ask:  6.8 @ 28140.6\n",
      "ask:  0.001 @ 28140.7\n",
      "ask:  0.004 @ 28141.1\n",
      "bid:  2.416 @ 28140.5\n",
      "bid:  0.004 @ 28140.4\n",
      "bid:  0.012 @ 28140.3\n",
      "current_timestamp: 1680653160032116\n",
      "ask:  3.666 @ 28143.9\n",
      "ask:  1.422 @ 28144.0\n",
      "ask:  1.455 @ 28144.1\n",
      "bid:  3.189 @ 28143.8\n",
      "bid:  5.136 @ 28143.7\n",
      "bid:  0.012 @ 28143.5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hbt = HftBacktest(\n",
    "    btcusdt_20230405,\n",
    "    tick_size=0.1,\n",
    "    lot_size=0.001,\n",
    "    maker_fee=0.0002,\n",
    "    taker_fee=0.0007,\n",
    "    order_latency=FeedLatency(),\n",
    "    asset_type=Linear,\n",
    "    snapshot=btcusdt_20230404_eod\n",
    ")\n",
    "\n",
    "print_3depth(hbt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b27f7386",
   "metadata": {},
   "source": [
    "## Submitting an order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d5e29cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hftbacktest import GTC, NONE, NEW, FILLED, CANCELED, EXPIRED\n",
    "\n",
    "@njit\n",
    "def print_orders(hbt):\n",
    "    # You can access open orders and also closed orders via hbt.orders.\n",
    "    # hbt.orders is a Numba dictionary and its key is order_id(int).\n",
    "    for order_id, order in hbt.orders.items():\n",
    "        order_status = ''\n",
    "        if order.status == NONE:\n",
    "            order_status = 'NONE' # Exchange hasn't received an order yet.\n",
    "        elif order.status == NEW:\n",
    "            order_status = 'NEW'\n",
    "        elif order.status == FILLED:\n",
    "            order_status = 'FILLED'\n",
    "        elif order.status == CANCELED:\n",
    "            order_status = 'CANCELED'\n",
    "        elif order.status == EXPIRED:\n",
    "            order_status = 'EXPIRED' \n",
    "            \n",
    "        order_req = ''\n",
    "        if order.req == NONE:\n",
    "            order_req = 'NONE'\n",
    "        elif order.req == NEW:\n",
    "            order_req = 'NEW'\n",
    "        elif order.req == CANCELED:\n",
    "            order_req = 'CANCEL'\n",
    "            \n",
    "        print(\n",
    "            'current_timestamp:', hbt.current_timestamp, \n",
    "             ', order_id:', order_id,\n",
    "             ', order_price:', order.price,\n",
    "             ', order_qty:', order.qty,\n",
    "             ', order_status:', order_status,\n",
    "             ', order_req:', order_req\n",
    "        )\n",
    "\n",
    "@njit\n",
    "def submit_order(hbt):\n",
    "    is_order_submitted = False\n",
    "    while hbt.run:\n",
    "        if not hbt.elapse(30 * 1e6):\n",
    "            return False\n",
    "        \n",
    "        # Prints open orders.\n",
    "        print_orders(hbt)\n",
    "        \n",
    "        if not is_order_submitted:\n",
    "            # Submits a buy order at 100 tick below the best bid.\n",
    "            order_id = 1\n",
    "            order_price = hbt.best_bid - 100 * hbt.tick_size\n",
    "            order_qty = 1\n",
    "            time_in_force = GTC # Good 'till cancel\n",
    "            hbt.submit_buy_order(order_id, order_price, order_qty, time_in_force)\n",
    "            is_order_submitted = True\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ade32153",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current_timestamp: 1680652860032116 , order_id: 1 , order_price: 28146.300000000003 , order_qty: 1.0 , order_status: NEW , order_req: NONE\n",
      "current_timestamp: 1680652890032116 , order_id: 1 , order_price: 28146.300000000003 , order_qty: 1.0 , order_status: FILLED , order_req: NONE\n",
      "current_timestamp: 1680652920032116 , order_id: 1 , order_price: 28146.300000000003 , order_qty: 1.0 , order_status: FILLED , order_req: NONE\n",
      "current_timestamp: 1680652950032116 , order_id: 1 , order_price: 28146.300000000003 , order_qty: 1.0 , order_status: FILLED , order_req: NONE\n",
      "current_timestamp: 1680652980032116 , order_id: 1 , order_price: 28146.300000000003 , order_qty: 1.0 , order_status: FILLED , order_req: NONE\n",
      "current_timestamp: 1680653010032116 , order_id: 1 , order_price: 28146.300000000003 , order_qty: 1.0 , order_status: FILLED , order_req: NONE\n",
      "current_timestamp: 1680653040032116 , order_id: 1 , order_price: 28146.300000000003 , order_qty: 1.0 , order_status: FILLED , order_req: NONE\n",
      "current_timestamp: 1680653070032116 , order_id: 1 , order_price: 28146.300000000003 , order_qty: 1.0 , order_status: FILLED , order_req: NONE\n",
      "current_timestamp: 1680653100032116 , order_id: 1 , order_price: 28146.300000000003 , order_qty: 1.0 , order_status: FILLED , order_req: NONE\n",
      "current_timestamp: 1680653130032116 , order_id: 1 , order_price: 28146.300000000003 , order_qty: 1.0 , order_status: FILLED , order_req: NONE\n",
      "current_timestamp: 1680653160032116 , order_id: 1 , order_price: 28146.300000000003 , order_qty: 1.0 , order_status: FILLED , order_req: NONE\n",
      "current_timestamp: 1680653190032116 , order_id: 1 , order_price: 28146.300000000003 , order_qty: 1.0 , order_status: FILLED , order_req: NONE\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hbt = HftBacktest(\n",
    "    btcusdt_20230405,\n",
    "    tick_size=0.1,\n",
    "    lot_size=0.001,\n",
    "    maker_fee=0.0002,\n",
    "    taker_fee=0.0007,\n",
    "    order_latency=FeedLatency(),\n",
    "    asset_type=Linear,\n",
    "    snapshot=btcusdt_20230404_eod\n",
    ")\n",
    "\n",
    "submit_order(hbt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edcdf5c0",
   "metadata": {},
   "source": [
    "## Clearing inactive orders (FILLED, CANCELED, EXPIRED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f163eef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hftbacktest import GTC\n",
    "\n",
    "@njit\n",
    "def clear_inactive_orders(hbt):\n",
    "    is_order_submitted = False\n",
    "    while hbt.run:\n",
    "        if not hbt.elapse(30 * 1e6):\n",
    "            return False\n",
    "        \n",
    "        print_orders(hbt)\n",
    "        \n",
    "        # Removes inactive(FILLED, CANCELED, EXPIRED) orders from hbt.orders.\n",
    "        hbt.clear_inactive_orders()\n",
    "        \n",
    "        if not is_order_submitted:\n",
    "            order_id = 1\n",
    "            order_price = hbt.best_bid - 100 * hbt.tick_size\n",
    "            order_qty = 1\n",
    "            time_in_force = GTC\n",
    "            hbt.submit_buy_order(order_id, order_price, order_qty, time_in_force)\n",
    "            is_order_submitted = True\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3748a4b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current_timestamp: 1680652860032116 , order_id: 1 , order_price: 28146.300000000003 , order_qty: 1.0 , order_status: NEW , order_req: NONE\n",
      "current_timestamp: 1680652890032116 , order_id: 1 , order_price: 28146.300000000003 , order_qty: 1.0 , order_status: FILLED , order_req: NONE\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hbt = HftBacktest(\n",
    "    btcusdt_20230405,\n",
    "    tick_size=0.1,\n",
    "    lot_size=0.001,\n",
    "    maker_fee=0.0002,\n",
    "    taker_fee=0.0007,\n",
    "    order_latency=FeedLatency(),\n",
    "    asset_type=Linear,\n",
    "    snapshot=btcusdt_20230404_eod\n",
    ")\n",
    "\n",
    "clear_inactive_orders(hbt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6d85dd",
   "metadata": {},
   "source": [
    "## Watching a order status - pending due to order latency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "859bca22",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hftbacktest import GTC\n",
    "\n",
    "@njit\n",
    "def watch_pending(hbt):\n",
    "    is_order_submitted = False\n",
    "    while hbt.run:\n",
    "        # Elapses 0.01-sec every iteration.\n",
    "        if not hbt.elapse(0.01 * 1e6):\n",
    "            return False\n",
    "        \n",
    "        print_orders(hbt)\n",
    "        \n",
    "        hbt.clear_inactive_orders()\n",
    "        \n",
    "        if not is_order_submitted:\n",
    "            order_id = 1\n",
    "            order_price = hbt.best_bid - 100 * hbt.tick_size\n",
    "            order_qty = 1\n",
    "            time_in_force = GTC\n",
    "            hbt.submit_buy_order(order_id, order_price, order_qty, time_in_force)\n",
    "            is_order_submitted = True\n",
    "            \n",
    "        # Prevents too many prints\n",
    "        if hbt.orders[order_id].status == NEW:\n",
    "            return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a0c2e47e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current_timestamp: 1680652800052116 , order_id: 1 , order_price: 28145.100000000002 , order_qty: 1.0 , order_status: NONE , order_req: NEW\n",
      "current_timestamp: 1680652800062116 , order_id: 1 , order_price: 28145.100000000002 , order_qty: 1.0 , order_status: NONE , order_req: NEW\n",
      "current_timestamp: 1680652800072116 , order_id: 1 , order_price: 28145.100000000002 , order_qty: 1.0 , order_status: NEW , order_req: NONE\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hbt = HftBacktest(\n",
    "    btcusdt_20230405,\n",
    "    tick_size=0.1,\n",
    "    lot_size=0.001,\n",
    "    maker_fee=0.0002,\n",
    "    taker_fee=0.0007,\n",
    "    order_latency=FeedLatency(),\n",
    "    asset_type=Linear,\n",
    "    snapshot=btcusdt_20230404_eod\n",
    ")\n",
    "\n",
    "watch_pending(hbt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00c1f5ed",
   "metadata": {},
   "source": [
    "## Waiting for an order response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b2e43564",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hftbacktest import GTC\n",
    "\n",
    "@njit\n",
    "def wait_for_order_response(hbt):\n",
    "    order_id = 0\n",
    "    is_order_submitted = False\n",
    "    while hbt.run:\n",
    "        if not hbt.elapse(0.01 * 1e6):\n",
    "            return False\n",
    "        \n",
    "        print_orders(hbt)\n",
    "        \n",
    "        hbt.clear_inactive_orders()\n",
    "        \n",
    "        # Prevent too many prints\n",
    "        if order_id in hbt.orders:\n",
    "            if hbt.orders[order_id].status == NEW:\n",
    "                return False\n",
    "        \n",
    "        if not is_order_submitted:\n",
    "            order_id = 1\n",
    "            order_price = hbt.best_bid\n",
    "            order_qty = 1\n",
    "            time_in_force = GTC\n",
    "            hbt.submit_buy_order(order_id, order_price, order_qty, time_in_force)\n",
    "            # Wait for the order response for a given order id.\n",
    "            print('an order is submitted at', hbt.current_timestamp)\n",
    "            hbt.wait_order_response(order_id)\n",
    "            print('an order response is received at', hbt.current_timestamp)\n",
    "            is_order_submitted = True\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fcfb211b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "an order is submitted at 1680652800042116\n",
      "an order response is received at 1680652800070493\n",
      "current_timestamp: 1680652800080493 , order_id: 1 , order_price: 28155.100000000002 , order_qty: 1.0 , order_status: NEW , order_req: NONE\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hbt = HftBacktest(\n",
    "    btcusdt_20230405,\n",
    "    tick_size=0.1,\n",
    "    lot_size=0.001,\n",
    "    maker_fee=0.0002,\n",
    "    taker_fee=0.0007,\n",
    "    order_latency=FeedLatency(),\n",
    "    asset_type=Linear,\n",
    "    snapshot=btcusdt_20230404_eod\n",
    ")\n",
    "\n",
    "wait_for_order_response(hbt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c27280",
   "metadata": {},
   "source": [
    "## Printing position, balance, fee, and equity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c38365d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def position(hbt):\n",
    "    is_order_submitted = False\n",
    "    while hbt.run:\n",
    "        if not hbt.elapse(60 * 1e6):\n",
    "            return False\n",
    "        \n",
    "        print_orders(hbt)\n",
    "        \n",
    "        hbt.clear_inactive_orders()\n",
    "        \n",
    "        # Prints position, balance, fee, and equity\n",
    "        print(\n",
    "            'current_timestamp:', hbt.current_timestamp,\n",
    "            ', position:', hbt.position,\n",
    "            ', balance:', hbt.balance,\n",
    "            ', fee:', hbt.fee,\n",
    "            ', equity:', hbt.equity\n",
    "        )\n",
    "        \n",
    "        if not is_order_submitted:\n",
    "            order_id = 1\n",
    "            order_price = hbt.best_bid\n",
    "            order_qty = 1\n",
    "            time_in_force = GTC\n",
    "            hbt.submit_buy_order(order_id, order_price, order_qty, time_in_force)\n",
    "            hbt.wait_order_response(order_id)\n",
    "            is_order_submitted = True\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a9cf70b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current_timestamp: 1680652860032116 , position: 0.0 , balance: 0.0 , fee: 0.0 , equity: 0.0\n",
      "current_timestamp: 1680652920095398 , order_id: 1 , order_price: 28150.7 , order_qty: 1.0 , order_status: FILLED , order_req: NONE\n",
      "current_timestamp: 1680652920095398 , position: 1.0 , balance: -28150.7 , fee: 5.630140000000001 , equity: -12.180139999999273\n",
      "current_timestamp: 1680652980095398 , position: 1.0 , balance: -28150.7 , fee: 5.630140000000001 , equity: -6.380140000000001\n",
      "current_timestamp: 1680653040095398 , position: 1.0 , balance: -28150.7 , fee: 5.630140000000001 , equity: -10.580140000000728\n",
      "current_timestamp: 1680653100095398 , position: 1.0 , balance: -28150.7 , fee: 5.630140000000001 , equity: -15.780139999997818\n",
      "current_timestamp: 1680653160095398 , position: 1.0 , balance: -28150.7 , fee: 5.630140000000001 , equity: -12.480139999998546\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hbt = HftBacktest(\n",
    "    btcusdt_20230405,\n",
    "    tick_size=0.1,\n",
    "    lot_size=0.001,\n",
    "    maker_fee=0.0002,\n",
    "    taker_fee=0.0007,\n",
    "    order_latency=FeedLatency(),\n",
    "    asset_type=Linear,\n",
    "    snapshot=btcusdt_20230404_eod\n",
    ")\n",
    "\n",
    "position(hbt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d6c739c",
   "metadata": {},
   "source": [
    "## Canceling an open order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "502bf0f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def submit_and_cancel_order(hbt):\n",
    "    is_order_submitted = False\n",
    "    while hbt.run:\n",
    "        if not hbt.elapse(0.1 * 1e6):\n",
    "            return False\n",
    "        \n",
    "        print_orders(hbt)\n",
    "        \n",
    "        hbt.clear_inactive_orders()\n",
    "        \n",
    "        # Cancels if there is an open order\n",
    "        for order_id, order in hbt.orders.items():\n",
    "            # an order is only cancellable if order status is NEW.\n",
    "            # cancel request is negated if the order is already filled or filled before cancel request is processed.\n",
    "            if order.cancellable:\n",
    "                hbt.cancel(order_id)\n",
    "                # You can see status still NEW and see req CANCEL.\n",
    "                print_orders(hbt)\n",
    "                # cancels request also has order entry/response latencies the same as submitting.\n",
    "                hbt.wait_order_response(order_id)\n",
    "       \n",
    "        if not is_order_submitted:\n",
    "            order_id = 1\n",
    "            order_price = hbt.best_bid - 100 * hbt.tick_size\n",
    "            order_qty = 1\n",
    "            time_in_force = GTC\n",
    "            hbt.submit_buy_order(order_id, order_price, order_qty, time_in_force)\n",
    "            hbt.wait_order_response(order_id)\n",
    "            is_order_submitted = True\n",
    "        else:\n",
    "            if len(hbt.orders) == 0:\n",
    "                return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "638c3012",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current_timestamp: 1680652800254816 , order_id: 1 , order_price: 28145.100000000002 , order_qty: 1.0 , order_status: NEW , order_req: NONE\n",
      "current_timestamp: 1680652800254816 , order_id: 1 , order_price: 28145.100000000002 , order_qty: 1.0 , order_status: NEW , order_req: CANCEL\n",
      "current_timestamp: 1680652800973764 , order_id: 1 , order_price: 28145.100000000002 , order_qty: 1.0 , order_status: CANCELED , order_req: NONE\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hbt = HftBacktest(\n",
    "    btcusdt_20230405,\n",
    "    tick_size=0.1,\n",
    "    lot_size=0.001,\n",
    "    maker_fee=0.0002,\n",
    "    taker_fee=0.0007,\n",
    "    order_latency=FeedLatency(),\n",
    "    asset_type=Linear,\n",
    "    snapshot=btcusdt_20230404_eod\n",
    ")\n",
    "\n",
    "submit_and_cancel_order(hbt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a60476c",
   "metadata": {},
   "source": [
    "## Market order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9e87515b",
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def print_orders_exec_price(hbt):\n",
    "    for order_id, order in hbt.orders.items():\n",
    "        order_status = ''\n",
    "        if order.status == NONE:\n",
    "            order_status = 'NONE'\n",
    "        elif order.status == NEW:\n",
    "            order_status = 'NEW'\n",
    "        elif order.status == FILLED:\n",
    "            order_status = 'FILLED'\n",
    "        elif order.status == CANCELED:\n",
    "            order_status = 'CANCELED'\n",
    "        elif order.status == EXPIRED:\n",
    "            order_status = 'EXPIRED' \n",
    "            \n",
    "        order_req = ''\n",
    "        if order.req == NONE:\n",
    "            order_req = 'NONE'\n",
    "        elif order.req == NEW:\n",
    "            order_req = 'NEW'\n",
    "        elif order.req == CANCELED:\n",
    "            order_req = 'CANCEL'\n",
    "            \n",
    "        print(\n",
    "            'current_timestamp:', hbt.current_timestamp, \n",
    "             ', order_id:', order_id,\n",
    "             ', order_price:', order.price,\n",
    "             ', order_qty:', order.qty,\n",
    "             ', order_status:', order_status,\n",
    "             ', exec_price:', order.exec_price\n",
    "        )\n",
    "        \n",
    "@njit\n",
    "def market_order(hbt):\n",
    "    is_order_submitted = False\n",
    "    while hbt.run:\n",
    "        if not hbt.elapse(60 * 1e6):\n",
    "            return False\n",
    "        \n",
    "        print_orders(hbt)\n",
    "        \n",
    "        hbt.clear_inactive_orders()\n",
    "        \n",
    "        print(\n",
    "            'current_timestamp:', hbt.current_timestamp,\n",
    "             ', position:', hbt.position,\n",
    "             ', balance:', hbt.balance,\n",
    "             ', fee:', hbt.fee,\n",
    "             ', equity:', hbt.equity\n",
    "        )\n",
    "        \n",
    "        if not is_order_submitted:\n",
    "            order_id = 1\n",
    "            # Sets a deep price in the opposite side to take liquidity.\n",
    "            order_price = hbt.best_bid - 50 * hbt.tick_size\n",
    "            order_qty = 1\n",
    "            time_in_force = GTC\n",
    "            hbt.submit_sell_order(order_id, order_price, order_qty, time_in_force)\n",
    "            hbt.wait_order_response(order_id)\n",
    "            # You can see the order immediately filled.\n",
    "            # Also you can see the order executed at the best bid which is different from what it was submitted at.\n",
    "            print('best_bid:', hbt.best_bid)\n",
    "            print_orders_exec_price(hbt)            \n",
    "            is_order_submitted = True\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0b6003d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current_timestamp: 1680652860032116 , position: 0.0 , balance: 0.0 , fee: 0.0 , equity: 0.0\n",
      "best_bid: 28150.7\n",
      "current_timestamp: 1680652860095398 , order_id: 1 , order_price: 28145.7 , order_qty: 1.0 , order_status: FILLED , exec_price: 28150.7\n",
      "current_timestamp: 1680652920095398 , order_id: 1 , order_price: 28145.7 , order_qty: 1.0 , order_status: FILLED , order_req: NONE\n",
      "current_timestamp: 1680652920095398 , position: -1.0 , balance: 28150.7 , fee: 19.70549 , equity: -13.155490000000729\n",
      "current_timestamp: 1680652980095398 , position: -1.0 , balance: 28150.7 , fee: 19.70549 , equity: -18.95549\n",
      "current_timestamp: 1680653040095398 , position: -1.0 , balance: 28150.7 , fee: 19.70549 , equity: -14.755489999999273\n",
      "current_timestamp: 1680653100095398 , position: -1.0 , balance: 28150.7 , fee: 19.70549 , equity: -9.555490000002184\n",
      "current_timestamp: 1680653160095398 , position: -1.0 , balance: 28150.7 , fee: 19.70549 , equity: -12.855490000001456\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hbt = HftBacktest(\n",
    "    btcusdt_20230405,\n",
    "    tick_size=0.1,\n",
    "    lot_size=0.001,\n",
    "    maker_fee=0.0002,\n",
    "    taker_fee=0.0007,\n",
    "    order_latency=FeedLatency(),\n",
    "    asset_type=Linear,\n",
    "    snapshot=btcusdt_20230404_eod\n",
    ")\n",
    "\n",
    "market_order(hbt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "980ebc4d",
   "metadata": {},
   "source": [
    "## GTX, Post-Only order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1b0255bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hftbacktest import GTX\n",
    "\n",
    "@njit\n",
    "def submit_gtx(hbt):\n",
    "    is_order_submitted = False\n",
    "    while hbt.run:\n",
    "        if not hbt.elapse(60 * 1e6):\n",
    "            return False\n",
    "        \n",
    "        print_orders(hbt)\n",
    "        \n",
    "        hbt.clear_inactive_orders()\n",
    "        \n",
    "        print(\n",
    "            'current_timestamp:', hbt.current_timestamp,\n",
    "             ', position:', hbt.position,\n",
    "             ', balance:', hbt.balance,\n",
    "             ', fee:', hbt.fee,\n",
    "             ', equity:', hbt.equity\n",
    "        )\n",
    "        \n",
    "        if not is_order_submitted:\n",
    "            order_id = 1\n",
    "            # Sets a deep price in the opposite side and it will be rejected by GTX.\n",
    "            order_price = hbt.best_bid - 100 * hbt.tick_size\n",
    "            order_qty = 1\n",
    "            time_in_force = GTX\n",
    "            hbt.submit_sell_order(order_id, order_price, order_qty, time_in_force)\n",
    "            hbt.wait_order_response(order_id)\n",
    "            is_order_submitted = True\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f1030532",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current_timestamp: 1680652860032116 , position: 0.0 , balance: 0.0 , fee: 0.0 , equity: 0.0\n",
      "current_timestamp: 1680652920095398 , order_id: 1 , order_price: 28140.7 , order_qty: 1.0 , order_status: EXPIRED , order_req: NONE\n",
      "current_timestamp: 1680652920095398 , position: 0.0 , balance: 0.0 , fee: 0.0 , equity: 0.0\n",
      "current_timestamp: 1680652980095398 , position: 0.0 , balance: 0.0 , fee: 0.0 , equity: 0.0\n",
      "current_timestamp: 1680653040095398 , position: 0.0 , balance: 0.0 , fee: 0.0 , equity: 0.0\n",
      "current_timestamp: 1680653100095398 , position: 0.0 , balance: 0.0 , fee: 0.0 , equity: 0.0\n",
      "current_timestamp: 1680653160095398 , position: 0.0 , balance: 0.0 , fee: 0.0 , equity: 0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hbt = HftBacktest(\n",
    "    btcusdt_20230405,\n",
    "    tick_size=0.1,\n",
    "    lot_size=0.001,\n",
    "    maker_fee=0.0002,\n",
    "    taker_fee=0.0007,\n",
    "    order_latency=FeedLatency(),\n",
    "    asset_type=Linear,\n",
    "    snapshot=btcusdt_20230404_eod\n",
    ")\n",
    "\n",
    "submit_gtx(hbt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff830d3",
   "metadata": {},
   "source": [
    "## Plotting BBO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "641721c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def plot_bbo(hbt, local_timestamp, best_bid, best_ask):\n",
    "    while hbt.run:\n",
    "        if not hbt.elapse(1 * 1e6):\n",
    "            return False\n",
    "\n",
    "        # Records data points\n",
    "        local_timestamp.append(hbt.current_timestamp)\n",
    "        best_bid.append(hbt.best_bid)\n",
    "        best_ask.append(hbt.best_ask)\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9ecfecc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Uses Numba list for njit.\n",
    "from numba.typed import List\n",
    "from numba import float64\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "hbt = HftBacktest(\n",
    "    btcusdt_20230405,\n",
    "    tick_size=0.1,\n",
    "    lot_size=0.001,\n",
    "    maker_fee=0.0002,\n",
    "    taker_fee=0.0007,\n",
    "    order_latency=FeedLatency(),\n",
    "    asset_type=Linear,\n",
    "    snapshot=btcusdt_20230404_eod\n",
    ")\n",
    "\n",
    "local_timestamp = List.empty_list(float64, allocated=10000)\n",
    "best_bid = List.empty_list(float64, allocated=10000)\n",
    "best_ask = List.empty_list(float64, allocated=10000)\n",
    "\n",
    "plot_bbo(hbt, local_timestamp, best_bid, best_ask)\n",
    "\n",
    "local_timestamp = pd.to_datetime(local_timestamp, unit='us', utc=True)\n",
    "best_bid = pd.Series(best_bid, index=local_timestamp)\n",
    "best_ask = pd.Series(best_ask, index=local_timestamp)\n",
    "\n",
    "best_bid.plot()\n",
    "best_ask.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1069fc95",
   "metadata": {},
   "source": [
    "## Printing stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e001470d",
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def submit_order_stats(hbt, recorder):\n",
    "    buy_order_id = 1\n",
    "    sell_order_id = 2\n",
    "    half_spread = 1 * hbt.tick_size\n",
    "    \n",
    "    while hbt.run:\n",
    "        if not hbt.elapse(1 * 1e6):\n",
    "            return False\n",
    "        \n",
    "        hbt.clear_inactive_orders()\n",
    "        \n",
    "        mid = (hbt.best_bid + hbt.best_ask) / 2.0\n",
    "        \n",
    "        if buy_order_id not in hbt.orders:\n",
    "            order_price = round((mid - half_spread) / hbt.tick_size) * hbt.tick_size\n",
    "            order_qty = 1\n",
    "            time_in_force = GTC\n",
    "            hbt.submit_buy_order(buy_order_id, order_price, order_qty, time_in_force)\n",
    "            \n",
    "        if sell_order_id not in hbt.orders:\n",
    "            order_price = round((mid + half_spread) / hbt.tick_size) * hbt.tick_size\n",
    "            order_qty = 1\n",
    "            time_in_force = GTC\n",
    "            hbt.submit_sell_order(sell_order_id, order_price, order_qty, time_in_force)\n",
    "            \n",
    "        recorder.record(hbt)\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a3795e40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========== Summary ===========\n",
      "Sharpe ratio: -355.7\n",
      "Sortino ratio: -210.0\n",
      "Risk return ratio: -52717.6\n",
      "Annualised return: -3141.57 %\n",
      "Max. draw down: 0.06 %\n",
      "The number of trades per day: 9\n",
      "Avg. daily trading volume: 9\n",
      "Avg. daily trading amount: 255269\n",
      "Max leverage: 1.27\n",
      "Median leverage: 0.14\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from hftbacktest import Stat\n",
    "\n",
    "hbt = HftBacktest(\n",
    "    btcusdt_20230405,\n",
    "    tick_size=0.1,\n",
    "    lot_size=0.001,\n",
    "    maker_fee=0.0002,\n",
    "    taker_fee=0.0007,\n",
    "    order_latency=FeedLatency(),\n",
    "    asset_type=Linear,\n",
    "    snapshot=btcusdt_20230404_eod\n",
    ")\n",
    "\n",
    "stat = Stat(hbt)\n",
    "submit_order_stats(hbt, stat.recorder)\n",
    "\n",
    "# Default resample is 5min.\n",
    "stat.summary(capital=200000, resample='1s')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}