script/DolphinDB_Python_Parser_Intro_for_Quantitative_Finance/因子实现_Python版本/委托量加权平均委托价格.ipynb · 浙江智臾科技有限公司/Tutorials_CN

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委托量加权平均委托价格.ipynb 4.58 KB

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da1c1a7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "from functools import reduce\n",
    "import os\n",
    "import multiprocessing\n",
    "import time\n",
    "import warnings\n",
    "from tqdm import tqdm\n",
    "import datetime\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "pd.options.display.width = 1200\n",
    "pd.options.display.max_colwidth = 100\n",
    "pd.options.display.max_columns = 10\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dc3ee62a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def volumeWeightedAvgPrice(df, lag):\n",
    "    res = df[[\"TradeTime\", \"SecurityID\"]]\n",
    "    totalAmount = (df[\"OrderQty\"]*df[\"Price\"]).rolling(lag).sum()\n",
    "    totalVolume = df[\"OrderQty\"].rolling(lag).sum()\n",
    "    res[\"volumeWeightedAvgPrice\"] = totalAmount / totalVolume\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90fa9a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/ssd/ssd3/data/oneStock_oneFile_TL/20230201/entrust/000001.csv\")\n",
    "t0 = time.time()\n",
    "res = volumeWeightedAvgPrice(df, 60)\n",
    "print(\"cal time: \", time.time() - t0, \"s\")\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5585c733",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pool_func(tick_obj, trade_path_obj):\n",
    "    single_tick_res = []\n",
    "    tmp_date = trade_path_obj.split('/')[-2]\n",
    "    # print(tmp_date)\n",
    "    tmp_date = tmp_date[0:4] + \"-\" + tmp_date[4:6] + \"-\" + tmp_date[6:8]\n",
    "    # print(tmp_date)\n",
    "    for tick in tqdm(tick_obj):\n",
    "        try:\n",
    "            df = pd.read_csv(os.path.join(trade_path_obj, tick))\n",
    "            Indicator = volumeWeightedAvgPrice(df, 60)\n",
    "            single_tick_res.append(Indicator)\n",
    "            # print(Indicator)\n",
    "            # print(\"开盘后大单净买入占比:\", Indicator)\n",
    "        except Exception as error:\n",
    "            continue\n",
    "\n",
    "    return pd.concat(single_tick_res)\n",
    "\n",
    "\n",
    "class multi_task_split:\n",
    "\n",
    "    def __init__(self, data, processes_to_use):\n",
    "        self.data = data\n",
    "        self.processes_to_use = processes_to_use\n",
    "\n",
    "    def num_of_jobs(self):\n",
    "        return min(len(self.data), self.processes_to_use, multiprocessing.cpu_count())\n",
    "\n",
    "    def split_args(self):\n",
    "        q, r = divmod(len(self.data), self.num_of_jobs())\n",
    "        return (self.data[i * q + min(i, r): (i + 1) * q + min(i + 1, r)] for i in range(self.num_of_jobs()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46a9f806",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_use = 24\n",
    "# 路径修改为存放数据路径\n",
    "trade_path = r\"/ssd/ssd3/data/oneStock_oneFile_TL/20230201/entrust\"\n",
    "stock_pool = os.listdir(trade_path)\n",
    "processes_decided = multi_task_split(stock_pool, n_use).num_of_jobs()\n",
    "print(\"进程数：\", processes_decided)\n",
    "split_args_to_process = list(multi_task_split(stock_pool, n_use).split_args())\n",
    "args = [(split_args_to_process[i], trade_path) for i in range(len(split_args_to_process))]\n",
    "print(\"#\" * 50 + \"Multiprocessing Start\" + \"#\" * 50)\n",
    "t0 = time.time()\n",
    "with multiprocessing.Pool(processes=processes_decided) as pool:\n",
    "    res = tqdm(pool.starmap(pool_func, args))\n",
    "    print(\"cal time: \", time.time() - t0, \"s\")\n",
    "    res_combined = pd.concat(res, axis=0)\n",
    "    pool.close()\n",
    "    print(\"cal time: \", time.time() - t0, \"s\")\n",
    "print(res_combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "227d6d41",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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提交于 2023-09-12 11:57 . add DolphinDB_Python_Parser_Intro_for_Quantitative_Finance.md

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