mbrl_lob.ipynb

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  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "756b83909f734caeaf88fe1420a70f53": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [
              "widget-interact"
            ],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "VBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "VBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_319ec994e69f45acb01b5e57fca578ae",
              "IPY_MODEL_3ab36d08e1c4446196d333823e315f8f"
            ],
            "layout": "IPY_MODEL_b2cef3636e1f4e18bc0e84da7ebb85ea"
          }
        },
        "319ec994e69f45acb01b5e57fca578ae": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntSliderModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "IntSliderModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "IntSliderView",
            "continuous_update": true,
            "description": "idx",
            "description_tooltip": null,
            "disabled": false,
            "layout": "IPY_MODEL_766ccfcc10ef412d820fb13bfd5bd6eb",
            "max": 22706,
            "min": 0,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": "d",
            "step": 1,
            "style": "IPY_MODEL_bdb15f8720154f05a0fe67792efeb5df",
            "value": 11353
          }
        },
        "3ab36d08e1c4446196d333823e315f8f": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_777165287249444b99f54b676cd2db53",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 1200x400 with 3 Axes>",
                  "image/png": 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\n"
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\n"
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\n"
                },
                "metadata": {}
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Implementation of the paper <Model-based Reinforcement Learning for Predictions and Control for Limit Order Books (Wei et al., 2019)>."
      ],
      "metadata": {
        "id": "tOa_xlSfyDY_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0vOzrZVIAIZJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3d1d71c-596e-4b11-b4bf-419f846106c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "% GPU info\n",
            "Wed Jun 14 12:53:51 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   40C    P8     9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n",
            "\n",
            "% CPU info\n",
            "processor\t: 0\n",
            "vendor_id\t: GenuineIntel\n",
            "cpu family\t: 6\n",
            "model\t\t: 63\n",
            "model name\t: Intel(R) Xeon(R) CPU @ 2.30GHz\n",
            "stepping\t: 0\n",
            "microcode\t: 0xffffffff\n",
            "cpu MHz\t\t: 2299.998\n",
            "cache size\t: 46080 KB\n",
            "physical id\t: 0\n",
            "\n",
            "% Memory info\n",
            "MemTotal:       13294264 kB\n",
            "MemFree:        10200320 kB\n",
            "MemAvailable:   12254784 kB\n"
          ]
        }
      ],
      "source": [
        "print(\"% GPU info\")\n",
        "!nvidia-smi\n",
        "print(\"\\n% CPU info\")\n",
        "!head /proc/cpuinfo\n",
        "print(\"\\n% Memory info\")\n",
        "!head -n 3 /proc/meminfo"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import glob\n",
        "import csv\n",
        "import torch\n",
        "import random\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.gridspec as gridspec\n",
        "import ipywidgets as widgets\n",
        "\n",
        "from datetime import datetime\n",
        "from multiprocessing import Pool\n",
        "from tqdm import tqdm\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "dataset_path = '/content/drive/MyDrive/Dev/MBRL_LOB/LOB_data'\n",
        "model_path = '/content/drive/MyDrive/Dev/MBRL_LOB/Models'"
      ],
      "metadata": {
        "id": "dD1CqzkdkxQG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset: Limit Order Book(LOB) and Tradeprint Data Collected in Korea Exchange"
      ],
      "metadata": {
        "id": "pVd4GZZ4jjdc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class DataConfig:\n",
        "  TICKER = \"KQ150\"\n",
        "  OPEN = \"090000\"\n",
        "  CLOSE = \"154500\"\n",
        "\n",
        "  ORDERBOOK_LEVEL = 3\n",
        "  TIME_WINDOW = 40\n",
        "  MAX_TRADEPRINT_LEN =10\n",
        "  MAX_TRADE_QUANTITY = 10\n",
        "  SEQ_LENGTH = 10\n",
        "  TRANSLATION_SLIDE = 40\n",
        "\n",
        "  LATENT_SIZE = 16\n",
        "\n",
        "  TRAIN_RATIO = 0.7\n",
        "  VAL_RATIO = 0.1\n",
        "  TEST_RATIO = 0.2\n",
        "\n",
        "def get_multiply_factor(Ticker):\n",
        "  match Ticker:\n",
        "    case \"KS200\": mul_fac = 100\n",
        "    case \"KQ150\": mul_fac = 10\n",
        "  return mul_fac\n",
        "\n",
        "DataConfig.MULTIPLY = get_multiply_factor(DataConfig.TICKER)"
      ],
      "metadata": {
        "id": "ZEaYWL-8C89-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# helper function related to handle time\n",
        "\n",
        "def time_to_idx(timestring):\n",
        "  hour = int(timestring[:2])\n",
        "  minute = int(timestring[2:4])\n",
        "  second = int(timestring[4:])\n",
        "  return (hour - 9)*60*60 + minute*60 + second\n",
        "\n",
        "def idx_to_time(idx):\n",
        "  second = idx % 60\n",
        "  minute = int((idx - second)/60) % 60\n",
        "  hour = (idx - second - minute*60)//(60*60)\n",
        "  return '%02d:%02d:%02d' %(hour+9, minute, second)\n"
      ],
      "metadata": {
        "id": "pHcvBU8oPiFg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Functions to handle dataset file\n",
        "\n",
        "def get_file_path(data_type, ticker, date=\"*\"):\n",
        "  match data_type:\n",
        "    case \"tradeprint\": filename1 = \"H0IFCNT0\"\n",
        "    case \"orderbook\": filename1 = \"H0IFASP0\"\n",
        "\n",
        "  match ticker:\n",
        "    case \"KS200\": filename2 = \"101T06\"\n",
        "    case \"KQ150\": filename2 = \"106T06\"\n",
        "\n",
        "  return os.path.join(dataset_path, f\"{filename1}_{filename2}_{date}.csv\")\n",
        "\n",
        "def get_file_list(data_type, ticker):\n",
        "  return glob.glob(get_file_path(data_type, ticker))\n",
        "\n",
        "def get_days_list(ticker):\n",
        "  def extract_days(file_list):\n",
        "    day_list = []\n",
        "    for filename in file_list:\n",
        "      day_list.append(filename.split('.')[0].split('_')[-1])\n",
        "    return day_list\n",
        "\n",
        "  # return days which has coupled tradeprint and orderbook data\n",
        "  day_list_1 = extract_days(get_file_list(\"tradeprint\", ticker))\n",
        "  day_list_2 = extract_days(get_file_list(\"orderbook\", ticker))\n",
        "  intersect_day = list(set(day_list_1) & set(day_list_2))\n",
        "  intersect_day.sort()\n",
        "  return intersect_day\n",
        "\n",
        "def load_orderbook_data(filename):\n",
        "  time_data = []\n",
        "  orderbook_data = []\n",
        "  mul_fac = DataConfig.MULTIPLY\n",
        "\n",
        "  with open(filename, 'r', encoding='utf-8') as csvfile:\n",
        "    reader = csv.reader(csvfile)\n",
        "    for idx, row in enumerate(reader):\n",
        "      temp_timestamp = time_to_idx(row[1])\n",
        "      update = len(time_data) == 0 or temp_timestamp != time_data[-1]\n",
        "      if (not row[2] == '0.00') and update:\n",
        "        # Process orderbook data in single timestamp\n",
        "        temp_orderbook_data = []\n",
        "        for i in range(1, 6):\n",
        "          # Process orderbook data in single level\n",
        "          if i <= DataConfig.ORDERBOOK_LEVEL:\n",
        "            single_level = [\n",
        "              ###################################################\n",
        "              int(float(row[1+i])*mul_fac), # price  | ask side\n",
        "              int(row[21+i]),               # volume |\n",
        "              ###################################################\n",
        "              int(float(row[6+i])*mul_fac), # price  | bid side\n",
        "              int(row[26+i])                # volume |\n",
        "              ###################################################\n",
        "            ]\n",
        "            temp_orderbook_data.append(single_level)\n",
        "        time_data.append(temp_timestamp)\n",
        "        orderbook_data.append(temp_orderbook_data)\n",
        "  return time_data, np.array(orderbook_data)\n",
        "\n",
        "def load_tradeprint_data(filename):\n",
        "  time_data = []\n",
        "  tradeprint_data = []\n",
        "  mul_fac = DataConfig.MULTIPLY\n",
        "\n",
        "  with open(filename, 'r', encoding='utf-8') as csvfile:\n",
        "    reader = csv.reader(csvfile)\n",
        "    file_length = sum(1 for row in reader)\n",
        "    csvfile.seek(0)\n",
        "    prev_sell_volume, prev_buy_volume = 0, 0\n",
        "\n",
        "    for idx, row in enumerate(reader):\n",
        "      for i in range(int(len(row)/50)):\n",
        "        # Process tradeprint data in single timestamp\n",
        "        if (idx==0 and i==0) or (idx==file_length-1 and i==int(len(row)/50)-1):\n",
        "          # Single-price auction call on market opening and closing\n",
        "          trade_type = 0 # auction\n",
        "        else:\n",
        "          # Regular market\n",
        "          sell_volume = int(row[41+50*i])-prev_sell_volume\n",
        "          buy_volume = int(row[42+50*i])-prev_buy_volume\n",
        "          if sell_volume>0: trade_type = -1 # sell\n",
        "          elif buy_volume>0: trade_type = 1 # buy\n",
        "\n",
        "          single_print = [\n",
        "              ###################################################\n",
        "              trade_type,                       # buy or sell\n",
        "              int(float(row[5+50*i])*mul_fac),  # price\n",
        "              int(row[9+50*i]),                 # volume\n",
        "              ###################################################\n",
        "          ]\n",
        "          time_data.append(time_to_idx(row[1+50*i]))\n",
        "          tradeprint_data.append(single_print)\n",
        "\n",
        "        prev_sell_volume = int(row[41+50*i])\n",
        "        prev_buy_volume = int(row[42+50*i])\n",
        "\n",
        "  return time_data, np.array(tradeprint_data)\n"
      ],
      "metadata": {
        "id": "Dl2Hy7oXBO0P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split days for train and test\n",
        "\n",
        "days = get_days_list(DataConfig.TICKER)\n",
        "\n",
        "val_length = max(round(len(days) * DataConfig.VAL_RATIO), 1)\n",
        "test_length = max(round(len(days) * DataConfig.TEST_RATIO), 1)\n",
        "train_length = len(days) - (val_length + test_length)\n",
        "\n",
        "train_days = days[:train_length]\n",
        "val_days = days[train_length:train_length+val_length]\n",
        "test_days = days[train_length+val_length:]\n",
        "\n",
        "print(f\"% {len(train_days)} days for train ({train_days[0]} ~ {train_days[-1]})\")\n",
        "print(f\"% {len(val_days)} days for validation ({val_days[0]} ~ {val_days[-1]})\")\n",
        "print(f\"% {len(test_days)} days for test ({test_days[0]} ~ {test_days[-1]})\")"
      ],
      "metadata": {
        "id": "odFRKJy8X0jv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75e52f51-3b63-4e58-d3f5-5585a83a5b10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "% 7 days for train (2023-04-05 ~ 2023-04-13)\n",
            "% 1 days for validation (2023-04-14 ~ 2023-04-14)\n",
            "% 2 days for test (2023-04-17 ~ 2023-04-18)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Orderbook:\n",
        "  def __init__(self, day):\n",
        "    self.day = day\n",
        "    self.ticker = DataConfig.TICKER\n",
        "    self.level = DataConfig.ORDERBOOK_LEVEL\n",
        "    self.T = DataConfig.TIME_WINDOW\n",
        "\n",
        "    file_path = get_file_path(\"orderbook\", self.ticker, day)\n",
        "    self.orderbook_time, self.orderbook_data = load_orderbook_data(file_path)\n",
        "    self.orderbook_freq = (6*60+35)*60/len(self.orderbook_time)\n",
        "\n",
        "    self.dataset_mode = \"\"\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.orderbook_time) - self.T + 1\n",
        "\n",
        "  def __getitem__(self, index):\n",
        "    # Orderbook data: feature-level min-max normalization\n",
        "    data = np.reshape(\n",
        "        self.orderbook_data[index : index+self.T], (self.T, self.level*4)).T\n",
        "    price = data[list(range(0, self.level*4, 2)), :]\n",
        "    vol = data[list(range(1, self.level*4, 2)), :]\n",
        "\n",
        "    norm_data = np.zeros(data.shape)\n",
        "    norm_data[list(range(0, self.level*4, 2)), :] = \\\n",
        "      (price - np.min(price))/(np.max(price)-np.min(price))\n",
        "    norm_data[list(range(1, self.level*4, 2)), :] = \\\n",
        "      vol/np.max(vol)\n",
        "    return np.expand_dims(norm_data, axis=0)\n",
        "\n",
        "  def __gettime__(self, index, only_last = True):\n",
        "    # Time data\n",
        "    time = self.orderbook_time[index:index+self.T]\n",
        "    time = [time[-1]]\n",
        "    return time\n",
        "\n",
        "  def __getmidprice__(self, index):\n",
        "    ask1 = self.orderbook_data[index+self.T-1,0][0]\n",
        "    bid1 = self.orderbook_data[index+self.T-1,0][2]\n",
        "    return (ask1+bid1)/2\n"
      ],
      "metadata": {
        "id": "axdsylu0GhlZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def even_sampling(data_length, sampling_len):\n",
        "  sampling_min_dist = data_length // sampling_len\n",
        "  remains = data_length - sampling_min_dist * sampling_len\n",
        "  remains_dist = random.sample(list(range(remains))+[0]*(sampling_len-remains), sampling_len)\n",
        "  return [sampling_min_dist*i + x for i, x in enumerate(sorted(remains_dist))]\n",
        "\n",
        "class Tradeprint:\n",
        "  def __init__(self, day):\n",
        "    self.day = day\n",
        "    self.ticker = DataConfig.TICKER\n",
        "    self.trans_slide = DataConfig.TRANSLATION_SLIDE\n",
        "\n",
        "    file_path = get_file_path(\"tradeprint\", self.ticker, day)\n",
        "    self.tradeprint_time, self.tradeprint_data = load_tradeprint_data(file_path)\n",
        "    self.tradeprint_freq = (6*60+35)*60/len(self.tradeprint_time)\n",
        "    self.time_map = self.__generatemap__()\n",
        "\n",
        "  def __generatemap__(self):\n",
        "    time_map = []\n",
        "    cursor = 0\n",
        "    for timeidx in range(time_to_idx(DataConfig.CLOSE)):\n",
        "      temp_map = []\n",
        "\n",
        "      while cursor <= len(self.tradeprint_time)-1 and \\\n",
        "            self.tradeprint_time[cursor] == timeidx:\n",
        "        temp_map.append(cursor)\n",
        "        cursor = cursor + 1\n",
        "        if cursor > len(self.tradeprint_time)-1:\n",
        "          break\n",
        "\n",
        "      time_map.append(temp_map)\n",
        "\n",
        "    return time_map\n",
        "\n",
        "  def __getitem__(self, time_idx, pad = False):\n",
        "    end = time_idx[-1]\n",
        "    tp_data = []\n",
        "    for i in range(self.trans_slide):\n",
        "      tp_data = tp_data + list(self.tradeprint_data[self.time_map[end-self.trans_slide+i+1]])\n",
        "\n",
        "    # filtering abnormal trade with large quantity\n",
        "    tp_data = np.reshape(np.array(tp_data), (-1, 3))\n",
        "    filter_idx = np.where(tp_data[:,2] <= DataConfig.MAX_TRADE_QUANTITY)\n",
        "\n",
        "    # zero-padding or sampling data to fit length\n",
        "    if pad:\n",
        "      if tp_data.shape[0] < DataConfig.MAX_TRADEPRINT_LEN:\n",
        "        zero_padding = np.zeros((DataConfig.MAX_TRADEPRINT_LEN-len(tp_data), 3))\n",
        "        tp_data = np.concatenate((tp_data, zero_padding), axis = 0)\n",
        "      elif tp_data.shape[0] > DataConfig.MAX_TRADEPRINT_LEN:\n",
        "        tp_data = tp_data[even_sampling(len(tp_data), DataConfig.MAX_TRADEPRINT_LEN),:]\n",
        "\n",
        "    return tp_data\n"
      ],
      "metadata": {
        "id": "xPk4HgF70_K8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class TaskDataset:\n",
        "  def __init__(self, day):\n",
        "    self.day = day\n",
        "    self.level = DataConfig.ORDERBOOK_LEVEL\n",
        "    self.T = DataConfig.TIME_WINDOW\n",
        "    self.seq_length = DataConfig.SEQ_LENGTH\n",
        "    self.action_length = DataConfig.MAX_TRADEPRINT_LEN\n",
        "    self.latent_size = DataConfig.LATENT_SIZE\n",
        "    self.trans_slide = DataConfig.TRANSLATION_SLIDE\n",
        "\n",
        "    # dataset\n",
        "    self.orderbook = Orderbook(day)\n",
        "    self.tradeprint = Tradeprint(day)\n",
        "\n",
        "    # secondary data\n",
        "    self.z = []\n",
        "    self.action = []\n",
        "    self.price_change = []\n",
        "\n",
        "    self.mode = \"VAE\"\n",
        "\n",
        "  def __fillz__(self, z):\n",
        "    self.z = z\n",
        "\n",
        "  def __fillaction__(self, action):\n",
        "    self.action = action\n",
        "\n",
        "  def __fillpricechange__(self, price_change):\n",
        "    self.price_change = price_change\n",
        "\n",
        "  def __len__(self):\n",
        "    match self.mode:\n",
        "      case \"VAE\":\n",
        "        return self.orderbook.__len__()\n",
        "\n",
        "      case \"MDNRNN\":\n",
        "        return self.orderbook.__len__() // self.trans_slide - 1\n",
        "\n",
        "      case \"REWARD\":\n",
        "        return self.orderbook.__len__() // self.trans_slide - 1\n",
        "\n",
        "  def __getitem__(self, index):\n",
        "    match self.mode:\n",
        "      case \"VAE\":\n",
        "        return self.orderbook.__getitem__(index)\n",
        "\n",
        "      case \"MDNRNN\":\n",
        "        if index < self.seq_length:\n",
        "          obs_z = np.zeros((self.seq_length, self.latent_size))\n",
        "          obs_z_next = np.zeros((self.seq_length, self.latent_size))\n",
        "          action = np.zeros((self.seq_length, self.action_length))\n",
        "          for i in range(self.seq_length):\n",
        "            if index-i >= 0:\n",
        "              obs_z[-i-1,:] = self.z[(index-i),:]\n",
        "              obs_z_next[-i-1,:] = self.z[(index-i+1),:]\n",
        "              action[-i-1,:] = self.action[(index-i),:]\n",
        "        else:\n",
        "          obs_z = self.z[index-self.seq_length:index,:]\n",
        "          obs_z_next = self.z[(index+1)-self.seq_length:(index+1),:]\n",
        "          action = self.action[index-self.seq_length:index,:]\n",
        "        return obs_z, action.astype(float), obs_z_next\n",
        "\n",
        "      case \"REWARD\":\n",
        "        obs_z = self.z[index]\n",
        "        obs_z_next = self.z[(index+1)]\n",
        "        price_change = self.price_change[index]\n",
        "        return obs_z, obs_z_next, price_change\n"
      ],
      "metadata": {
        "id": "7FXUMyowQJHl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Dataset:\n",
        "  def __init__(self, days):\n",
        "    self.days = days\n",
        "    self.day_datasets = []\n",
        "    self.mode = \"None\"\n",
        "\n",
        "    with Pool() as pool:\n",
        "      self.day_datasets = pool.map(TaskDataset, self.days)\n",
        "\n",
        "  def get_dataloader(self, mode, batch_size, shuffle, numworkers):\n",
        "    # set dataset mode\n",
        "    self.mode = mode\n",
        "    for dataset in self.day_datasets:\n",
        "      dataset.mode = self.mode\n",
        "\n",
        "    # make dataloaders\n",
        "    day_dataloaders = []\n",
        "    for dataset in self.day_datasets:\n",
        "      day_dataloaders.append(torch.utils.data.DataLoader(\n",
        "          dataset,\n",
        "          batch_size=batch_size,\n",
        "          shuffle=shuffle,\n",
        "          num_workers=numworkers\n",
        "      ))\n",
        "    return day_dataloaders\n",
        "\n",
        "train_dataset = Dataset(train_days)\n",
        "val_dataset = Dataset(val_days)\n",
        "test_dataset = Dataset(test_days)\n"
      ],
      "metadata": {
        "id": "lQ2wuYszLeEM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# [Interactive demo 1] LOB and tradeprint data visualization\n",
        "\n",
        "day_to_show = -1\n",
        "example_orderbook = val_dataset.day_datasets[day_to_show].orderbook\n",
        "example_tradeprint = val_dataset.day_datasets[day_to_show].tradeprint\n",
        "\n",
        "def visualize_data(idx):\n",
        "  plt.rcParams['axes.formatter.useoffset'] = False\n",
        "\n",
        "  # Get orderbook and tradeprint data\n",
        "  lob_shot = example_orderbook.__getitem__(idx)\n",
        "  lob_shot = np.squeeze(lob_shot)\n",
        "  lob_time = example_orderbook.__gettime__(idx, only_last = False)\n",
        "  td_print = example_tradeprint.__getitem__(lob_time)\n",
        "  fig = plt.figure(figsize=(12, 4))\n",
        "  gs = gridspec.GridSpec(nrows=2, ncols=2,\n",
        "                         width_ratios=[1.5, 1], height_ratios=[3, 1])\n",
        "\n",
        "  ax0 = fig.add_subplot(gs[:, 0])\n",
        "  ax0.imshow(lob_shot, cmap='gray')\n",
        "  ax0.set_xlabel(\"Time (t-ΔT, t)\")\n",
        "  ax0.set_ylabel(r\"$\\{a_p, a_s, b_p, b_s\\}_i$\")\n",
        "  ax0.set_title(\"a. Shot of LOB(Limit order book)\")\n",
        "\n",
        "  ax1 = fig.add_subplot(gs[0, 1])\n",
        "  if len(td_print)>0:\n",
        "    mul_fac = DataConfig.MULTIPLY\n",
        "    ax1.plot(td_print[:,1]/mul_fac, 'o-', color='darkgrey')\n",
        "    ax1.set_xlim(-0.5, len(td_print)-0.5)\n",
        "  ax1.set_xticks([])\n",
        "  ax1.set_ylabel(\"Trade price\")\n",
        "  ax1.set_title(\"b. Tradeprint within timewindow\")\n",
        "\n",
        "  ax2 = fig.add_subplot(gs[1, 1])\n",
        "  if len(td_print)>0:\n",
        "    for trade_type in [-1, 1]:\n",
        "      x = np.nonzero(td_print[:,0] == trade_type)[0]\n",
        "      y = td_print[:,2][x]\n",
        "      match trade_type:\n",
        "        case 1: color = '#DD1717'\n",
        "        case -1: color = '#0F4392'\n",
        "      ax2.bar(x, y, color=color)\n",
        "    ax2.set_xlim(-0.5, len(td_print)-0.5)\n",
        "  ax2.set_ylabel(\"Trade volume\")\n",
        "\n",
        "  plt.subplots_adjust(left=0.15, bottom=0.1, right=0.85, top=0.78,\n",
        "                      wspace=0.4, hspace=0.1)\n",
        "  plt.suptitle(f\"[Interactive Demo 1] LOB and tradeprint data visualization\\n\\\n",
        "  {DataConfig.TICKER}, {train_days[day_to_show]}\\\n",
        "  {idx_to_time(lob_time[day_to_show])}\", fontsize=14)\n",
        "  plt.show()\n",
        "\n",
        "widgets.interact(\n",
        "    visualize_data,\n",
        "    idx=(0, example_orderbook.__len__()-1, 1))\n"
      ],
      "metadata": {
        "id": "Rtcu86QhpIH4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461,
          "referenced_widgets": [
            "756b83909f734caeaf88fe1420a70f53",
            "319ec994e69f45acb01b5e57fca578ae",
            "3ab36d08e1c4446196d333823e315f8f",
            "b2cef3636e1f4e18bc0e84da7ebb85ea",
            "766ccfcc10ef412d820fb13bfd5bd6eb",
            "bdb15f8720154f05a0fe67792efeb5df",
            "777165287249444b99f54b676cd2db53"
          ]
        },
        "outputId": "e38fe493-80ef-47da-e58b-df95099f6d57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(IntSlider(value=11353, description='idx', max=22706), Output()), _dom_classes=('widget-i…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "756b83909f734caeaf88fe1420a70f53"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.visualize_data(idx)>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Components of Financial World Model"
      ],
      "metadata": {
        "id": "FkI2LEImLbsm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Convolutional Variational Autoencoder for Limit Order Book\n",
        "Projection of the LOB into Low-Dimensional Latent Space"
      ],
      "metadata": {
        "id": "siqeKpSdjovZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "class VAEConfig:\n",
        "  EPOCHS = 20\n",
        "  LEARNING_RATE = 0.0001"
      ],
      "metadata": {
        "id": "04VI6QzcDJM_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "\n",
        "class Encoder(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(Encoder, self).__init__()\n",
        "    self.latent_size = DataConfig.LATENT_SIZE\n",
        "\n",
        "    self.conv1 = nn.Conv2d(1, 32, (2, 2), stride=(1, 1))\n",
        "    self.conv2 = nn.Conv2d(32, 16, (2, 2), stride=(1, 1))\n",
        "    self.conv3 = nn.Conv2d(16, 16, (5, 3) , stride=(1, 1))\n",
        "\n",
        "    self.fc_mu = nn.Linear(16*36*6, self.latent_size)\n",
        "    self.fc_logsigma = nn.Linear(16*36*6, self.latent_size)\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = F.relu(self.conv1(x))\n",
        "    x = F.relu(self.conv2(x))\n",
        "    x = F.relu(self.conv3(x))\n",
        "    x = x.view(x.size(0), -1)\n",
        "    mu = self.fc_mu(x)\n",
        "    logsigma = self.fc_logsigma(x)\n",
        "\n",
        "    return mu, logsigma\n",
        "\n",
        "class Decoder(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(Decoder, self).__init__()\n",
        "    self.latent_size = DataConfig.LATENT_SIZE\n",
        "\n",
        "    self.fc1 = nn.Linear(DataConfig.LATENT_SIZE, 16*36*6)\n",
        "    self.deconv1 = nn.ConvTranspose2d(16*36*6, 16, (3, 10), stride=(1, 1))\n",
        "    self.deconv2 = nn.ConvTranspose2d(16, 16, (4, 11), stride=(1, 1))\n",
        "    self.deconv3 = nn.ConvTranspose2d(16, 1, (7, 21), stride=(1, 1))\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = x.unsqueeze(-1).unsqueeze(-1)\n",
        "    x = F.relu(self.deconv1(x))\n",
        "    x = F.relu(self.deconv2(x))\n",
        "    reconstruction = F.sigmoid(self.deconv3(x))\n",
        "    return reconstruction\n",
        "\n",
        "class VAE(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(VAE, self).__init__()\n",
        "    self.encoder = Encoder()\n",
        "    self.decoder = Decoder()\n",
        "\n",
        "  def forward(self, x):\n",
        "    mu, logsigma = self.encoder(x)\n",
        "    sigma = logsigma.exp()\n",
        "    eps = torch.randn_like(sigma)\n",
        "    z = eps.mul(sigma).add_(mu)\n",
        "\n",
        "    recon_x = self.decoder(z)\n",
        "    return recon_x, mu, logsigma\n"
      ],
      "metadata": {
        "id": "T--fLRD2sx-R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vae = VAE().to(device)\n",
        "vae_optimizer = torch.optim.Adam(vae.parameters(), lr=VAEConfig.LEARNING_RATE)\n",
        "\n",
        "vae_train_loader = train_dataset.get_dataloader(\"VAE\", 512, True, 2)\n",
        "vae_val_loader = val_dataset.get_dataloader(\"VAE\", 512, True, 2)"
      ],
      "metadata": {
        "id": "tZnyV7G2jkWn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def loss_vae(x, x_recon, mu, logsigma):\n",
        "  bce = F.mse_loss(x, x_recon, reduction='sum')\n",
        "  kld = -0.5 * torch.sum(1 + 2 * logsigma - mu.pow(2) - (2 * logsigma).exp())\n",
        "  return bce + kld\n",
        "\n",
        "def train_one_epoch():\n",
        "  vae.train()\n",
        "  train_loss = []\n",
        "\n",
        "  for day_loader in vae_train_loader:\n",
        "    for input in day_loader:\n",
        "      input = input.to(device, dtype=torch.float)\n",
        "      vae_optimizer.zero_grad()\n",
        "      recon_x, mu, logsigma = vae(input)\n",
        "      loss = loss_vae(input, recon_x, mu, logsigma)\n",
        "      loss.backward()\n",
        "      vae_optimizer.step()\n",
        "      train_loss.append(loss.item())\n",
        "  train_loss = np.mean(train_loss)\n",
        "\n",
        "  return train_loss\n",
        "\n",
        "def validation_one_epoch():\n",
        "  vae.eval()\n",
        "  val_loss = []\n",
        "\n",
        "  for day_loader in vae_val_loader:\n",
        "    for input in day_loader:\n",
        "      input = input.to(device, dtype=torch.float)\n",
        "      recon_x, mu, logsigma = vae(input)\n",
        "      loss = loss_vae(input, recon_x, mu, logsigma)\n",
        "      val_loss.append(loss.item())\n",
        "  val_loss = np.mean(val_loss)\n",
        "\n",
        "  return val_loss\n",
        "\n",
        "def batch_gd():\n",
        "  training_info = {\n",
        "    'train_loss_hist': [],\n",
        "    'val_loss_hist': [],\n",
        "  }\n",
        "\n",
        "  best_test_loss = np.inf\n",
        "  best_test_epoch = 0\n",
        "\n",
        "  for iter in range(VAEConfig.EPOCHS):\n",
        "    t0 = datetime.now()\n",
        "    train_loss = train_one_epoch()\n",
        "    val_loss = validation_one_epoch()\n",
        "\n",
        "    # Save losses\n",
        "    training_info['train_loss_hist'].append(train_loss)\n",
        "    training_info['val_loss_hist'].append(val_loss)\n",
        "\n",
        "    if val_loss < best_test_loss:\n",
        "      torch.save(vae.state_dict(), os.path.join(model_path, \"vae_best_val.pt\"))\n",
        "      best_test_loss = val_loss\n",
        "      best_test_epoch = iter\n",
        "      print('model saved')\n",
        "\n",
        "    dt = datetime.now() - t0\n",
        "    print(f'Epoch {iter + 1}/{VAEConfig.EPOCHS}, '\n",
        "          f'Train Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}, '\n",
        "          f'Duration: {dt}, Best Val Epoch: {best_test_epoch}')\n",
        "\n",
        "  return training_info\n",
        "\n",
        "training_info = batch_gd()"
      ],
      "metadata": {
        "id": "SsLXyNtmkp9Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e941da7-c777-4e60-89fc-57a5d8ac94d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model saved\n",
            "Epoch 1/20, Train Loss: 10774.4622, Validation Loss: 7865.4672, Duration: 0:00:32.340843, Best Val Epoch: 0\n",
            "model saved\n",
            "Epoch 2/20, Train Loss: 7728.9560, Validation Loss: 7232.9868, Duration: 0:00:25.109037, Best Val Epoch: 1\n",
            "model saved\n",
            "Epoch 3/20, Train Loss: 6915.6669, Validation Loss: 6702.2359, Duration: 0:00:24.763410, Best Val Epoch: 2\n",
            "model saved\n",
            "Epoch 4/20, Train Loss: 6596.4149, Validation Loss: 6566.3794, Duration: 0:00:24.996461, Best Val Epoch: 3\n",
            "model saved\n",
            "Epoch 5/20, Train Loss: 6510.3916, Validation Loss: 6529.0896, Duration: 0:00:24.639267, Best Val Epoch: 4\n",
            "model saved\n",
            "Epoch 6/20, Train Loss: 6464.9195, Validation Loss: 6489.3091, Duration: 0:00:24.812510, Best Val Epoch: 5\n",
            "model saved\n",
            "Epoch 7/20, Train Loss: 6435.0654, Validation Loss: 6471.9928, Duration: 0:00:25.261895, Best Val Epoch: 6\n",
            "model saved\n",
            "Epoch 8/20, Train Loss: 6408.1303, Validation Loss: 6442.3610, Duration: 0:00:25.005082, Best Val Epoch: 7\n",
            "model saved\n",
            "Epoch 9/20, Train Loss: 6388.2894, Validation Loss: 6428.2732, Duration: 0:00:25.220651, Best Val Epoch: 8\n",
            "model saved\n",
            "Epoch 10/20, Train Loss: 6369.5711, Validation Loss: 6403.4759, Duration: 0:00:25.112864, Best Val Epoch: 9\n",
            "Epoch 11/20, Train Loss: 6352.2756, Validation Loss: 6416.9228, Duration: 0:00:25.423198, Best Val Epoch: 9\n",
            "model saved\n",
            "Epoch 12/20, Train Loss: 6327.6869, Validation Loss: 6377.4778, Duration: 0:00:28.992421, Best Val Epoch: 11\n",
            "model saved\n",
            "Epoch 13/20, Train Loss: 6293.3766, Validation Loss: 6361.0337, Duration: 0:00:27.596375, Best Val Epoch: 12\n",
            "model saved\n",
            "Epoch 14/20, Train Loss: 6258.6890, Validation Loss: 6330.4634, Duration: 0:00:25.763379, Best Val Epoch: 13\n",
            "model saved\n",
            "Epoch 15/20, Train Loss: 6236.5250, Validation Loss: 6323.1461, Duration: 0:00:25.796949, Best Val Epoch: 14\n",
            "model saved\n",
            "Epoch 16/20, Train Loss: 6220.5667, Validation Loss: 6307.2034, Duration: 0:00:26.958376, Best Val Epoch: 15\n",
            "model saved\n",
            "Epoch 17/20, Train Loss: 6208.2193, Validation Loss: 6306.2133, Duration: 0:00:25.680865, Best Val Epoch: 16\n",
            "Epoch 18/20, Train Loss: 6197.1739, Validation Loss: 6329.9037, Duration: 0:00:25.513610, Best Val Epoch: 16\n",
            "Epoch 19/20, Train Loss: 6185.8227, Validation Loss: 6334.1629, Duration: 0:00:25.492382, Best Val Epoch: 16\n",
            "model saved\n",
            "Epoch 20/20, Train Loss: 6181.9792, Validation Loss: 6303.7737, Duration: 0:00:25.436039, Best Val Epoch: 19\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# [Figure 1] Visualize training process for variational autoencoder\n",
        "\n",
        "plt.figure(figsize=(10, 4))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(training_info['train_loss_hist'])\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.title(\"Training loss\")\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(training_info['val_loss_hist'])\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.title(\"Validation loss\")\n",
        "plt.suptitle(\"[Figure 1]: Training process of convolutional VAE\", fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "xURRq7SaJumr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "bb05ea44-0d47-4d67-e8a8-8a61386241c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the VAE model which shows best validation loss\n",
        "vae = VAE().to(device)\n",
        "vae.load_state_dict(torch.load(os.path.join(model_path, \"vae_best_val.pt\")))"
      ],
      "metadata": {
        "id": "9P4ZvtwfSfzM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2abdb43-3d4a-49d0-ea2a-802769c40022"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# [Interactive Demo 2] Latent representation and reconstruction of LOB using VAE\n",
        "\n",
        "def visualize_vae(idx):\n",
        "  lob_shot = example_orderbook.__getitem__(idx)\n",
        "  lob_time = example_orderbook.__gettime__(idx)\n",
        "  with torch.no_grad():\n",
        "    input = torch.from_numpy(np.expand_dims(lob_shot, axis=0))\n",
        "    input = input.to(device, dtype=torch.float)\n",
        "    recon_x, mu, logsigma = vae(input)\n",
        "    z = (mu + logsigma.exp() * torch.randn_like(mu))\n",
        "\n",
        "  fig = plt.figure(figsize=(12, 4))\n",
        "  gs = gridspec.GridSpec(nrows=1, ncols=3, width_ratios=[5, 1, 5])\n",
        "\n",
        "  ax0 = fig.add_subplot(gs[0])\n",
        "  ax0.imshow(lob_shot.squeeze(), cmap='gray')\n",
        "  ax0.set_xlabel(\"Time (t-ΔT, t)\")\n",
        "  ax0.set_ylabel(r\"$\\{a_p, a_s, b_p, b_s\\}_i$\")\n",
        "  ax0.set_title(\"a. Original input\")\n",
        "  ax1 = fig.add_subplot(gs[1])\n",
        "  ax1.imshow(z.cpu().T, cmap='gray')\n",
        "  ax1.set_xticks([])\n",
        "  ax1.set_title(\"b. Latent representation\")\n",
        "  ax1.set_ylabel(\"Latent vector (z)\")\n",
        "  ax2 = fig.add_subplot(gs[2])\n",
        "  ax2.imshow(recon_x.cpu().squeeze(), cmap='gray')\n",
        "  ax2.set_xlabel(\"Time (t-ΔT, t)\")\n",
        "  ax2.set_ylabel(r\"$\\{a_p, a_s, b_p, b_s\\}_i$\")\n",
        "  ax2.set_title(\"c. Reconstructed\")\n",
        "\n",
        "  plt.subplots_adjust(left=0.15, bottom=0.1, right=0.85, top=0.78,\n",
        "                      wspace=0.4, hspace=0.1)\n",
        "  plt.suptitle(f\"[Interactive Demo 2:\\\n",
        "  Latent representation and reconstruction of LOB using VAE]\\n\\\n",
        "  {DataConfig.TICKER}, {train_days[day_to_show]} {idx_to_time(lob_time[day_to_show])}\",\n",
        "  fontsize=14)\n",
        "  plt.show()\n",
        "\n",
        "widgets.interact(\n",
        "    visualize_vae,\n",
        "    idx=(0, example_orderbook.__len__()-1, 1))"
      ],
      "metadata": {
        "id": "csjEz4oCy-ow",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438,
          "referenced_widgets": [
            "64c376b0c0614f158e3b250a578731ee",
            "fc8975cc90b24364be6d045684a67574",
            "2c3114c2b6c549a6bddbb83b9531d0f5",
            "209d24f812204168ad85700c059d7fe5",
            "d69fadbbc4784423aadd1d1ff16f70aa",
            "4abf2db3a3fa44c4bf0494eb0fa8abc9",
            "aea9b855232041519927dead1b4dccf9"
          ]
        },
        "outputId": "d0008e1e-1797-4a6b-9732-1db0e5784d45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(IntSlider(value=11353, description='idx', max=22706), Output()), _dom_classes=('widget-i…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "64c376b0c0614f158e3b250a578731ee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.visualize_vae(idx)>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# [Interactive Demo 3] Generated LOB from random latent vector\n",
        "\n",
        "def generate_from_latent(\n",
        "    z1=random.random()*5-2.5, z2=random.random()*5-2.5, z3=random.random()*5-2.5,\n",
        "    z4=random.random()*5-2.5, z5=random.random()*5-2.5, z6=random.random()*5-2.5,\n",
        "    z7=random.random()*5-2.5, z8=random.random()*5-2.5, z9=random.random()*5-2.5,\n",
        "    z10=random.random()*5-2.5, z11=random.random()*5-2.5, z12=random.random()*5-2.5,\n",
        "    z13=random.random()*5-2.5, z14=random.random()*5-2.5, z15=random.random()*5-2.5,\n",
        "    z16=random.random()*5-2.5):\n",
        "\n",
        "  with torch.no_grad():\n",
        "    z = np.array([z1, z2, z3, z4, z5, z6, z7, z8, z9, z10, z11, z12, z13, z14, z15, z16])\n",
        "    z = np.expand_dims(z, axis=0)\n",
        "    input = torch.from_numpy(z)\n",
        "    input = input.to(device, dtype=torch.float)\n",
        "\n",
        "    # lob_shot = np.squeeze(lob_shot)\n",
        "    recon_x = vae.decoder(input)\n",
        "\n",
        "  fig = plt.figure(figsize=(12, 4))\n",
        "  gs = gridspec.GridSpec(nrows=1, ncols=2, width_ratios=[1, 5])\n",
        "\n",
        "  ax1 = fig.add_subplot(gs[0])\n",
        "  ax1.imshow(z.T, cmap='gray')\n",
        "  ax1.set_xticks([])\n",
        "  ax1.set_title(\"a. Random latent vector\")\n",
        "  ax1.set_ylabel(\"Latent vector (z)\")\n",
        "  ax2 = fig.add_subplot(gs[1])\n",
        "  ax2.imshow(recon_x.cpu().squeeze(), cmap='gray')\n",
        "  ax2.set_xlabel(\"Time (t-ΔT, t)\")\n",
        "  ax2.set_ylabel(r\"$\\{a_p, a_s, b_p, b_s\\}_i$\")\n",
        "  ax2.set_title(\"b. Reconstructed\")\n",
        "\n",
        "  plt.subplots_adjust(left=0.2, bottom=0.1, right=0.8, top=0.78,\n",
        "                      wspace=0.4, hspace=0.1)\n",
        "  plt.suptitle(f\"[Interactive Demo 3: Generated LOB from random latent vector]\",\n",
        "  fontsize=14)\n",
        "  plt.show()\n",
        "\n",
        "z_dist = (-2.5, 2.5, 0.1)\n",
        "widgets.interact(\n",
        "    generate_from_latent,\n",
        "    z1 = z_dist, z2 = z_dist, z3 = z_dist, z4 = z_dist,\n",
        "    z5 = z_dist, z6 = z_dist, z7 = z_dist, z8 = z_dist,\n",
        "    z9 = z_dist, z10 = z_dist, z11 = z_dist, z12 = z_dist,\n",
        "    z13 = z_dist, z14 = z_dist, z15 = z_dist, z16 = z_dist)"
      ],
      "metadata": {
        "id": "ayDR0CdaBJ08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 970,
          "referenced_widgets": [
            "3043b1ca5c7f441c95f5d1c40b803d4d",
            "24472dd7540b4b37ae55e6c87751c6d0",
            "01686b2b55fd4f778267708476b77f30",
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          ]
        },
        "outputId": "b76998bb-2711-4e6d-8066-eb9814765c81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=-1.0887078996917783, description='z1', max=2.5, min=-2.5), FloatSlider…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3043b1ca5c7f441c95f5d1c40b803d4d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.generate_from_latent(z1=-1.0887078996917783, z2=0.07778727686588649, z3=2.1650367845083114, z4=-1.1584250512281458, z5=2.4710643114587647, z6=0.14873959425662875, z7=-1.2474020912804313, z8=0.6870698491364324, z9=-1.7903099280742123, z10=0.9838324443029478, z11=-2.402622414252905, z12=2.428949733510845, z13=0.6201073142931603, z14=0.039314111904340976, z15=0.8973194186522382, z16=-1.3562424809959894)>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def fill_z(dataset):\n",
        "  vae_loader = dataset.get_dataloader(\"VAE\", 256, False, 2)\n",
        "\n",
        "  for day_idx, day_loader in enumerate(vae_loader):\n",
        "    day_dataset = dataset.day_datasets[day_idx]\n",
        "    day_z = []\n",
        "    for input in day_loader:\n",
        "      with torch.no_grad():\n",
        "        input = input.to(device, dtype=torch.float)\n",
        "        mu, logsigma = vae.encoder(input)\n",
        "        z = (mu + logsigma.exp() * torch.randn_like(mu)).cpu().detach().numpy()\n",
        "        day_z.append(z)\n",
        "    day_z = np.concatenate(day_z, 0)\n",
        "    day_z = day_z[range(0, len(day_z), DataConfig.TRANSLATION_SLIDE),:]\n",
        "    day_dataset.__fillz__(day_z)\n",
        "\n",
        "def fill_action(dataset):\n",
        "  for day_idx in range(len(dataset.day_datasets)):\n",
        "    day_dataset = dataset.day_datasets[day_idx]\n",
        "    day_action = []\n",
        "    for index in range(0, day_dataset.__len__() - 1, DataConfig.TRANSLATION_SLIDE):\n",
        "      tmp_time = day_dataset.orderbook.__gettime__(index)\n",
        "      tmp_action = day_dataset.tradeprint.__getitem__(tmp_time, pad = True)\n",
        "      day_action.append(np.multiply(tmp_action[:, 0], tmp_action[:, 2]))\n",
        "    day_dataset.__fillaction__(np.array(day_action)/DataConfig.MAX_TRADE_QUANTITY)\n",
        "\n",
        "def fill_midprice_change(dataset):\n",
        "  for day_idx in range(len(dataset.day_datasets)):\n",
        "    day_dataset = dataset.day_datasets[day_idx]\n",
        "    day_midprice_change = []\n",
        "    for index in range(0, day_dataset.__len__() - DataConfig.TRANSLATION_SLIDE, DataConfig.TRANSLATION_SLIDE):\n",
        "      tmp_midprice = day_dataset.orderbook.__getmidprice__(index)\n",
        "      tmp_midprice_next = day_dataset.orderbook.__getmidprice__(index+DataConfig.TRANSLATION_SLIDE)\n",
        "      tmp_price_change = (tmp_midprice_next - tmp_midprice) / tmp_midprice\n",
        "      day_midprice_change.append(tmp_price_change)\n",
        "\n",
        "    day_dataset.__fillpricechange__(np.array(day_midprice_change))\n",
        "\n",
        "def fill_secondary_data(dataset):\n",
        "  fill_z(dataset)\n",
        "  fill_action(dataset)\n",
        "  fill_midprice_change(dataset)\n",
        "\n",
        "fill_secondary_data(train_dataset)\n",
        "fill_secondary_data(val_dataset)"
      ],
      "metadata": {
        "id": "GLj3TdiocAEL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Transition Model: Recurrent Neural Network + Mixture Density Network\n",
        "Learning the probabilistic density between current and next state in deep latant space"
      ],
      "metadata": {
        "id": "kAuNY5Lo9vdd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Functions for z normalization and undo\n",
        "Z_MAX = 1.8\n",
        "\n",
        "def z_norm(z):\n",
        "  z[np.where(z>Z_MAX)]=Z_MAX\n",
        "  z[np.where(z<-Z_MAX)]=-Z_MAX\n",
        "  min_max_norm = (z+Z_MAX)/(2*Z_MAX)\n",
        "  return min_max_norm\n",
        "\n",
        "def z_undo_norm(norm_z):\n",
        "  norm_z[np.where(norm_z>1)]=1\n",
        "  norm_z[np.where(norm_z<0)]=0\n",
        "  undo_min_max_norm = norm_z*(2*Z_MAX)-Z_MAX\n",
        "  return undo_min_max_norm"
      ],
      "metadata": {
        "id": "KW-n1wO3NbIp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# [Figure 2] Normalizing latent representation data\n",
        "\n",
        "plt.figure(figsize=(10, 4))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.hist(train_dataset.day_datasets[-1].z.flatten())\n",
        "plt.title(\"Raw data\")\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.hist(z_norm(train_dataset.day_datasets[-1].z.flatten()))\n",
        "plt.title(\"After truncated min-max normalization\")\n",
        "plt.suptitle(\"[Figure 2] Normalizing latent representation data\", fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "KXsybiKclTme",
        "outputId": "710eda27-2a29-4030-a018-583e6f3a6cae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class MDNRNNConfig:\n",
        "  RNN_NEURONS = 128\n",
        "  HIDDENS = 10\n",
        "  N_GAUSSIAN = 5\n",
        "\n",
        "  EPOCHS = 50\n",
        "  LEARNING_RATE = 0.001"
      ],
      "metadata": {
        "id": "eOr9RULvsZnf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MDNRNN(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.latents = DataConfig.LATENT_SIZE\n",
        "    self.actions = DataConfig.MAX_TRADEPRINT_LEN\n",
        "    self.hiddens = MDNRNNConfig.HIDDENS\n",
        "    self.gaussians = MDNRNNConfig.N_GAUSSIAN\n",
        "\n",
        "    self.rnn = nn.LSTM(self.latents + self.actions, self.hiddens)\n",
        "    gmm_out = (2 * self.latents + 1) * self.gaussians\n",
        "    self.gmm_linear = nn.Linear(self.hiddens, gmm_out)\n",
        "\n",
        "  def forward(self, latents, actions):\n",
        "    seq_len, bs = actions.size(0), actions.size(1)\n",
        "\n",
        "    ins = torch.cat([latents, actions], dim=-1)\n",
        "    outs, _ = self.rnn(ins)\n",
        "    gmm_outs = self.gmm_linear(outs)\n",
        "\n",
        "    stride = self.gaussians * self.latents\n",
        "\n",
        "    mus = gmm_outs[:, :, :stride]\n",
        "    mus = mus.view(seq_len, bs, self.gaussians, self.latents)\n",
        "\n",
        "    sigmas = gmm_outs[:, :, stride:2 * stride]\n",
        "    sigmas = sigmas.view(seq_len, bs, self.gaussians, self.latents)\n",
        "    sigmas = torch.exp(sigmas)\n",
        "\n",
        "    pi = gmm_outs[:, :, 2 * stride: 2 * stride + self.gaussians]\n",
        "    pi = pi.view(seq_len, bs, self.gaussians)\n",
        "    logpi = F.log_softmax(pi, dim=-1)\n",
        "\n",
        "    return mus, sigmas, logpi"
      ],
      "metadata": {
        "id": "6CYnYnNUY7wk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mdnrnn = MDNRNN().to(device)\n",
        "mdnrnn_optimizer = torch.optim.Adam(mdnrnn.parameters(), lr=VAEConfig.LEARNING_RATE)\n",
        "\n",
        "mdnrnn_train_loader = train_dataset.get_dataloader(\"MDNRNN\", 256, True, 2)\n",
        "mdnrnn_val_loader = val_dataset.get_dataloader(\"MDNRNN\", 256, True, 2)"
      ],
      "metadata": {
        "id": "rnMIRmfrJt2l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def gmm_loss(batch, mus, sigmas, logpi, reduce=True):\n",
        "  batch = batch.unsqueeze(-2)\n",
        "  normal_dist = torch.distributions.normal.Normal(mus, sigmas)\n",
        "  g_log_probs = normal_dist.log_prob(batch)\n",
        "  g_log_probs = logpi + torch.sum(g_log_probs, dim=-1)\n",
        "  max_log_probs = torch.max(g_log_probs, dim=-1, keepdim=True)[0]\n",
        "  g_log_probs = g_log_probs - max_log_probs\n",
        "\n",
        "  g_probs = torch.exp(g_log_probs)\n",
        "  probs = torch.sum(g_probs, dim=-1)\n",
        "\n",
        "  log_prob = max_log_probs.squeeze() + torch.log(probs)\n",
        "  if reduce:\n",
        "    return - torch.mean(log_prob)\n",
        "  return - log_prob\n",
        "\n",
        "def train_one_epoch():\n",
        "  mdnrnn.train()\n",
        "  train_loss = []\n",
        "\n",
        "  for day_loader in mdnrnn_train_loader:\n",
        "    for obs_z, action, obs_z_next in day_loader:\n",
        "      obs_z = z_norm(obs_z)\n",
        "      obs_z_next = z_norm(obs_z_next)\n",
        "      obs_z = obs_z.to(device, dtype=torch.float)\n",
        "      obs_z_next = obs_z_next.to(device, dtype=torch.float)\n",
        "      action = action.to(device, dtype=torch.float)\n",
        "      action = action / 10\n",
        "      mdnrnn_optimizer.zero_grad()\n",
        "      mus, sigmas, logpi = mdnrnn(obs_z, action)\n",
        "      loss = gmm_loss(obs_z_next, mus, sigmas, logpi)\n",
        "      loss.backward()\n",
        "      mdnrnn_optimizer.step()\n",
        "      train_loss.append(loss.item())\n",
        "  train_loss = np.mean(train_loss)\n",
        "\n",
        "  return train_loss\n",
        "\n",
        "def validation_one_epoch():\n",
        "  mdnrnn.eval()\n",
        "  val_loss = []\n",
        "\n",
        "  for day_loader in mdnrnn_val_loader:\n",
        "    for obs_z, action, obs_z_next in day_loader:\n",
        "      obs_z = z_norm(obs_z)\n",
        "      obs_z_next = z_norm(obs_z_next)\n",
        "      obs_z = obs_z.to(device, dtype=torch.float)\n",
        "      obs_z_next = obs_z_next.to(device, dtype=torch.float)\n",
        "      action = action.to(device, dtype=torch.float)\n",
        "      action = action / 10\n",
        "\n",
        "      mus, sigmas, logpi = mdnrnn(obs_z, action)\n",
        "      loss = gmm_loss(obs_z_next, mus, sigmas, logpi)\n",
        "      val_loss.append(loss.item())\n",
        "  val_loss = np.mean(val_loss)\n",
        "\n",
        "  return val_loss\n",
        "\n",
        "def batch_gd():\n",
        "  training_info = {\n",
        "    'train_loss_hist': [],\n",
        "    'val_loss_hist': [],\n",
        "  }\n",
        "\n",
        "  best_test_loss = np.inf\n",
        "  best_test_epoch = 0\n",
        "\n",
        "  for iter in range(MDNRNNConfig.EPOCHS):\n",
        "    t0 = datetime.now()\n",
        "    train_loss = train_one_epoch()\n",
        "    val_loss = validation_one_epoch()\n",
        "\n",
        "    # Save losses\n",
        "    training_info['train_loss_hist'].append(train_loss)\n",
        "    training_info['val_loss_hist'].append(val_loss)\n",
        "\n",
        "    if val_loss < best_test_loss:\n",
        "      torch.save(mdnrnn.state_dict(), os.path.join(model_path, \"mdnrnn_best_val.pt\"))\n",
        "      best_test_loss = val_loss\n",
        "      best_test_epoch = iter\n",
        "      print('model saved')\n",
        "\n",
        "    dt = datetime.now() - t0\n",
        "    print(f'Epoch {iter + 1}/{MDNRNNConfig.EPOCHS}, '\n",
        "          f'Train Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}, '\n",
        "          f'Duration: {dt}, Best Val Epoch: {best_test_epoch}')\n",
        "\n",
        "  return training_info\n",
        "\n",
        "training_info = batch_gd()\n"
      ],
      "metadata": {
        "id": "HEcIPOavZ7sD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ceb4132a-781c-4a51-c96a-244c02c4d9c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model saved\n",
            "Epoch 1/50, Train Loss: 17.5099, Validation Loss: 17.3970, Duration: 0:00:02.568444, Best Val Epoch: 0\n",
            "model saved\n",
            "Epoch 2/50, Train Loss: 17.3598, Validation Loss: 17.2516, Duration: 0:00:01.502783, Best Val Epoch: 1\n",
            "model saved\n",
            "Epoch 3/50, Train Loss: 17.2151, Validation Loss: 17.1006, Duration: 0:00:01.473502, Best Val Epoch: 2\n",
            "model saved\n",
            "Epoch 4/50, Train Loss: 17.0582, Validation Loss: 16.9471, Duration: 0:00:01.492306, Best Val Epoch: 3\n",
            "model saved\n",
            "Epoch 5/50, Train Loss: 16.9001, Validation Loss: 16.7737, Duration: 0:00:01.527678, Best Val Epoch: 4\n",
            "model saved\n",
            "Epoch 6/50, Train Loss: 16.7196, Validation Loss: 16.5915, Duration: 0:00:01.467062, Best Val Epoch: 5\n",
            "model saved\n",
            "Epoch 7/50, Train Loss: 16.5170, Validation Loss: 16.3857, Duration: 0:00:01.797466, Best Val Epoch: 6\n",
            "model saved\n",
            "Epoch 8/50, Train Loss: 16.3047, Validation Loss: 16.1422, Duration: 0:00:02.168856, Best Val Epoch: 7\n",
            "model saved\n",
            "Epoch 9/50, Train Loss: 16.0429, Validation Loss: 15.8717, Duration: 0:00:01.486752, Best Val Epoch: 8\n",
            "model saved\n",
            "Epoch 10/50, Train Loss: 15.7679, Validation Loss: 15.5877, Duration: 0:00:01.490514, Best Val Epoch: 9\n",
            "model saved\n",
            "Epoch 11/50, Train Loss: 15.4616, Validation Loss: 15.2646, Duration: 0:00:01.475133, Best Val Epoch: 10\n",
            "model saved\n",
            "Epoch 12/50, Train Loss: 15.1345, Validation Loss: 14.9231, Duration: 0:00:01.474052, Best Val Epoch: 11\n",
            "model saved\n",
            "Epoch 13/50, Train Loss: 14.7803, Validation Loss: 14.5597, Duration: 0:00:01.446861, Best Val Epoch: 12\n",
            "model saved\n",
            "Epoch 14/50, Train Loss: 14.4159, Validation Loss: 14.1720, Duration: 0:00:01.506505, Best Val Epoch: 13\n",
            "model saved\n",
            "Epoch 15/50, Train Loss: 14.0307, Validation Loss: 13.7863, Duration: 0:00:01.946951, Best Val Epoch: 14\n",
            "model saved\n",
            "Epoch 16/50, Train Loss: 13.6423, Validation Loss: 13.4057, Duration: 0:00:01.907498, Best Val Epoch: 15\n",
            "model saved\n",
            "Epoch 17/50, Train Loss: 13.2527, Validation Loss: 12.9794, Duration: 0:00:01.468214, Best Val Epoch: 16\n",
            "model saved\n",
            "Epoch 18/50, Train Loss: 12.8559, Validation Loss: 12.5915, Duration: 0:00:01.684444, Best Val Epoch: 17\n",
            "model saved\n",
            "Epoch 19/50, Train Loss: 12.4688, Validation Loss: 12.1948, Duration: 0:00:02.038039, Best Val Epoch: 18\n",
            "model saved\n",
            "Epoch 20/50, Train Loss: 12.0659, Validation Loss: 11.7959, Duration: 0:00:01.457923, Best Val Epoch: 19\n",
            "model saved\n",
            "Epoch 21/50, Train Loss: 11.6760, Validation Loss: 11.3891, Duration: 0:00:01.457092, Best Val Epoch: 20\n",
            "model saved\n",
            "Epoch 22/50, Train Loss: 11.2881, Validation Loss: 11.0034, Duration: 0:00:01.579779, Best Val Epoch: 21\n",
            "model saved\n",
            "Epoch 23/50, Train Loss: 10.8926, Validation Loss: 10.6072, Duration: 0:00:02.290611, Best Val Epoch: 22\n",
            "model saved\n",
            "Epoch 24/50, Train Loss: 10.4937, Validation Loss: 10.2068, Duration: 0:00:01.474139, Best Val Epoch: 23\n",
            "model saved\n",
            "Epoch 25/50, Train Loss: 10.0688, Validation Loss: 9.7531, Duration: 0:00:01.437561, Best Val Epoch: 24\n",
            "model saved\n",
            "Epoch 26/50, Train Loss: 9.6180, Validation Loss: 9.2674, Duration: 0:00:01.474606, Best Val Epoch: 25\n",
            "model saved\n",
            "Epoch 27/50, Train Loss: 9.1022, Validation Loss: 8.7149, Duration: 0:00:01.492709, Best Val Epoch: 26\n",
            "model saved\n",
            "Epoch 28/50, Train Loss: 8.5098, Validation Loss: 8.0490, Duration: 0:00:01.449847, Best Val Epoch: 27\n",
            "model saved\n",
            "Epoch 29/50, Train Loss: 7.8882, Validation Loss: 7.3788, Duration: 0:00:01.527383, Best Val Epoch: 28\n",
            "model saved\n",
            "Epoch 30/50, Train Loss: 7.2208, Validation Loss: 6.6755, Duration: 0:00:01.762405, Best Val Epoch: 29\n",
            "model saved\n",
            "Epoch 31/50, Train Loss: 6.5788, Validation Loss: 6.0196, Duration: 0:00:02.221562, Best Val Epoch: 30\n",
            "model saved\n",
            "Epoch 32/50, Train Loss: 5.9745, Validation Loss: 5.4965, Duration: 0:00:01.481957, Best Val Epoch: 31\n",
            "model saved\n",
            "Epoch 33/50, Train Loss: 5.4212, Validation Loss: 4.9661, Duration: 0:00:01.499086, Best Val Epoch: 32\n",
            "model saved\n",
            "Epoch 34/50, Train Loss: 4.9765, Validation Loss: 4.5014, Duration: 0:00:01.491257, Best Val Epoch: 33\n",
            "model saved\n",
            "Epoch 35/50, Train Loss: 4.5860, Validation Loss: 4.1855, Duration: 0:00:01.470184, Best Val Epoch: 34\n",
            "model saved\n",
            "Epoch 36/50, Train Loss: 4.2355, Validation Loss: 3.8373, Duration: 0:00:01.466332, Best Val Epoch: 35\n",
            "model saved\n",
            "Epoch 37/50, Train Loss: 3.9347, Validation Loss: 3.5283, Duration: 0:00:01.460227, Best Val Epoch: 36\n",
            "model saved\n",
            "Epoch 38/50, Train Loss: 3.6751, Validation Loss: 3.2610, Duration: 0:00:01.823492, Best Val Epoch: 37\n",
            "model saved\n",
            "Epoch 39/50, Train Loss: 3.4417, Validation Loss: 3.0699, Duration: 0:00:02.066764, Best Val Epoch: 38\n",
            "model saved\n",
            "Epoch 40/50, Train Loss: 3.2229, Validation Loss: 2.8381, Duration: 0:00:01.478122, Best Val Epoch: 39\n",
            "model saved\n",
            "Epoch 41/50, Train Loss: 3.0211, Validation Loss: 2.6995, Duration: 0:00:01.540300, Best Val Epoch: 40\n",
            "model saved\n",
            "Epoch 42/50, Train Loss: 2.8664, Validation Loss: 2.4851, Duration: 0:00:01.586566, Best Val Epoch: 41\n",
            "model saved\n",
            "Epoch 43/50, Train Loss: 2.7123, Validation Loss: 2.3194, Duration: 0:00:01.478846, Best Val Epoch: 42\n",
            "model saved\n",
            "Epoch 44/50, Train Loss: 2.5963, Validation Loss: 2.2425, Duration: 0:00:01.521943, Best Val Epoch: 43\n",
            "model saved\n",
            "Epoch 45/50, Train Loss: 2.4650, Validation Loss: 2.0899, Duration: 0:00:01.507733, Best Val Epoch: 44\n",
            "model saved\n",
            "Epoch 46/50, Train Loss: 2.3666, Validation Loss: 2.0053, Duration: 0:00:02.292465, Best Val Epoch: 45\n",
            "model saved\n",
            "Epoch 47/50, Train Loss: 2.2508, Validation Loss: 1.8637, Duration: 0:00:01.666407, Best Val Epoch: 46\n",
            "model saved\n",
            "Epoch 48/50, Train Loss: 2.1732, Validation Loss: 1.7533, Duration: 0:00:01.482746, Best Val Epoch: 47\n",
            "model saved\n",
            "Epoch 49/50, Train Loss: 2.0899, Validation Loss: 1.7275, Duration: 0:00:01.471997, Best Val Epoch: 48\n",
            "model saved\n",
            "Epoch 50/50, Train Loss: 2.0058, Validation Loss: 1.6409, Duration: 0:00:01.487632, Best Val Epoch: 49\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# [Figure 3] Training process of MDN-RNN\n",
        "\n",
        "plt.figure(figsize=(10, 4))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(training_info['train_loss_hist'])\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.title(\"Training loss\")\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(training_info['val_loss_hist'])\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.title(\"Validation loss\")\n",
        "plt.suptitle(\"[Figure 3] Training process of MDN-RNN\", fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Bc6jVgVojtRv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "478b4f37-5698-4129-d42e-3d703d16a243"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the MDNRNN model which shows best validation loss\n",
        "mdnrnn = MDNRNN().to(device)\n",
        "mdnrnn.load_state_dict(torch.load(os.path.join(model_path, \"mdnrnn_best_val.pt\")))"
      ],
      "metadata": {
        "id": "20OWxGUtFewX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed44f515-a9cb-4dcb-9888-8227a1ebc057"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# [Interactive Demo 4] Predict the future state as mixture density\n",
        "\n",
        "from torch.distributions.categorical import Categorical\n",
        "\n",
        "example_dataset = train_dataset.day_datasets[-1]\n",
        "example_dataset.mode = \"MDNRNN\"\n",
        "\n",
        "def visualize_mdnrnn(idx):\n",
        "  fig, axs = plt.subplots(nrows=4, ncols=1, height_ratios=[1.5, 1.5, 2, 1.3],\n",
        "                          constrained_layout=True, figsize=(8, 7))\n",
        "  fig.suptitle(f\"[Interactive Demo 4] Predict the future state as mixture density\", fontsize=14)\n",
        "  for ax in axs:\n",
        "    ax.remove()\n",
        "  gridspec = axs[0].get_subplotspec().get_gridspec()\n",
        "  subfigs = [fig.add_subfigure(gs) for gs in gridspec]\n",
        "\n",
        "  obs = example_dataset.orderbook[idx*DataConfig.TRANSLATION_SLIDE]\n",
        "  obs_next = example_dataset.orderbook[(idx+1)*DataConfig.TRANSLATION_SLIDE]\n",
        "  obs_z, action, obs_z_next = example_dataset.__getitem__(idx)\n",
        "\n",
        "  for i in range(2):\n",
        "    match i:\n",
        "      case 0:\n",
        "        title = 'Current state S(t)'\n",
        "        fig1 = obs.squeeze()\n",
        "        fig2 = np.expand_dims(obs_z[-1,:], 0).T\n",
        "        fig3_input = torch.from_numpy(obs_z[-1,:]).to(device, dtype=torch.float)\n",
        "      case 1:\n",
        "        title = 'Real future state S(t+1) (after transition)'\n",
        "        fig1 = obs_next.squeeze()\n",
        "        fig2 = np.expand_dims(obs_z_next[-1,:], 0).T\n",
        "        fig3_input = torch.from_numpy(obs_z_next[-1,:]).to(device, dtype=torch.float)\n",
        "\n",
        "    fig3 =  vae.decoder(fig3_input).squeeze().cpu().detach().numpy()\n",
        "    subfigs[i].suptitle(title)\n",
        "    axs = subfigs[i].subplots(nrows=1, ncols=3)\n",
        "    axs[0].imshow(fig1, cmap='gray')\n",
        "    axs[0].set_ylabel(\"\")\n",
        "    axs[0].set_title(\"a. Original input\")\n",
        "    axs[1].imshow(fig2, cmap='gray')\n",
        "    axs[1].set_xticks([])\n",
        "    axs[1].set_ylabel(\"z\")\n",
        "    axs[2].imshow(fig3, cmap='gray')\n",
        "    axs[2].set_ylabel(\"\")\n",
        "    axs[2].set_title(\"c. Reconstructed\")\n",
        "\n",
        "  subfigs[2].suptitle(\"Predicted mixture of future state S(t+1) by MDN-RNN\")\n",
        "  axs = subfigs[2].subplots(nrows=1, ncols=6, width_ratios=[3, 1, 1, 1, 1, 1])\n",
        "\n",
        "  with torch.no_grad():\n",
        "    obs_z = z_norm(obs_z)\n",
        "    obs_z = torch.from_numpy(np.expand_dims(obs_z, 0)).to(device, dtype=torch.float)\n",
        "    action = torch.from_numpy(np.expand_dims(action, 0)).to(device, dtype=torch.float)\n",
        "    mus, sigmas, logpi = mdnrnn(obs_z, action)\n",
        "\n",
        "    axs[0].bar(range(1, 6), torch.exp(logpi[:,-1,:]).squeeze().cpu().detach().numpy())\n",
        "    axs[0].set_xticks(range(1, 6),\n",
        "                      ['Density 1', 'Density 2', 'Density 3', 'Density 4', 'Density 5'],\n",
        "                      rotation=45)\n",
        "    axs[0].set_ylabel(\"Gaussian mixture\\ncoefficient\")\n",
        "\n",
        "    for idx in range(MDNRNNConfig.N_GAUSSIAN):\n",
        "      mu = mus[:, -1, idx, :].squeeze()\n",
        "      sigma = sigmas[:, -1, idx, :].squeeze()\n",
        "      lstate = (mu + sigma * torch.rand_like(mu))\n",
        "\n",
        "      axs[idx+1].imshow(lstate.unsqueeze(1).cpu().detach().numpy(), cmap='gray')\n",
        "      axs[idx+1].set_xticks([0], [f'Density {idx+1}'], rotation=45)\n",
        "\n",
        "    mixt = Categorical(torch.exp(logpi[:,-1,:])).sample().item()\n",
        "    mu = mus[:, -1, mixt, :].squeeze()\n",
        "    sigma = sigmas[:, -1, mixt, :].squeeze()\n",
        "    lstate = (mu + sigma * torch.rand_like(mu)).cpu().detach().numpy()\n",
        "    lstate = torch.from_numpy(z_undo_norm(lstate)).to(device, dtype=torch.float)\n",
        "\n",
        "    recon_x = vae.decoder(lstate).squeeze().cpu().detach().numpy()\n",
        "    subfigs[3].suptitle(\"Predicted future state S(t+1) sampled from Gaussian mixture\")\n",
        "    axs = subfigs[3].subplots(nrows=1, ncols=1)\n",
        "    axs.imshow(recon_x, cmap='gray')\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "widgets.interact(\n",
        "    visualize_mdnrnn,\n",
        "    idx=(0, example_dataset.__len__(), 1))"
      ],
      "metadata": {
        "id": "NZvaMreNjtcK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 777,
          "referenced_widgets": [
            "97973504c2b14f7e9f7b93aef529d7b4",
            "27d4905f5a7c43ba9c39271624a7b47e",
            "bbbb20eb5e3f441584b649c9378d58eb",
            "d0707ef9bc9e433fa7caa6e2a982948e",
            "e8a0eded0f044f7e943aadd23fa65f1e",
            "dd95bb18e4534ceba1dfec5365e33b58",
            "8d0f5577698c4d91bc4691d121afe147"
          ]
        },
        "outputId": "5ae424e8-5d76-4b31-93bc-737a09934de3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(IntSlider(value=285, description='idx', max=570), Output()), _dom_classes=('widget-inter…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "97973504c2b14f7e9f7b93aef529d7b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.visualize_mdnrnn(idx)>"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Reward Model: Recurrent Neural Network + Fully Connected"
      ],
      "metadata": {
        "id": "8O0yvs-D4jkc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Functions for reward(midprice change) normalization and undo\n",
        "\n",
        "def reward_norm(price_change):\n",
        "  min_max_norm = (price_change)/0.002\n",
        "  sigmoid_trans = 1/(1 + np.exp(-min_max_norm))\n",
        "  return sigmoid_trans\n",
        "\n",
        "def reward_undo_norm(norm_price_change):\n",
        "  undo_sigmoid_trans = -np.log(1/(norm_price_change+1e-8)-1)\n",
        "  undo_min_max_norm = undo_sigmoid_trans*0.002\n",
        "  return undo_min_max_norm"
      ],
      "metadata": {
        "id": "m0lGpoj3grwF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 4))\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.hist(train_dataset.day_datasets[-1].price_change)\n",
        "plt.title(\"Raw data\")\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.hist(train_dataset.day_datasets[-1].price_change/0.002)\n",
        "plt.title(\"After min-max normalization\")\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.hist(reward_norm(train_dataset.day_datasets[-1].price_change))\n",
        "plt.title(\"After sigmoid transformation\")\n",
        "plt.suptitle(\"[Figure 3]: Normalizing reward(midprice change) data\", fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "e6L89-tKe-oD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "32f67664-0907-46e8-de42-fbee51ee8ee4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class RewardConfig:\n",
        "  EPOCHS = 10\n",
        "  LEARNING_RATE = 0.005\n",
        "\n",
        "  RNN_NEURONS = 128\n",
        "  HIDDENS = 40"
      ],
      "metadata": {
        "id": "tu9UVYVbiell"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class RewardModel(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.latents = DataConfig.LATENT_SIZE\n",
        "    self.neurons = RewardConfig.RNN_NEURONS\n",
        "    self.hiddens = RewardConfig.HIDDENS\n",
        "\n",
        "    self.rnn = nn.LSTM(2 * self.latents, self.neurons, batch_first=False)\n",
        "    self.fc1 = nn.Linear(self.neurons, self.hiddens)\n",
        "    self.fc2 = nn.Linear(self.hiddens, 1)\n",
        "\n",
        "  def forward(self, z1, z2):\n",
        "    x = torch.cat([z1, z2], dim=-1)\n",
        "    x, _ = self.rnn(x)\n",
        "\n",
        "    x = x.view(-1, self.neurons)\n",
        "    x = F.relu(x)\n",
        "    x = F.relu(self.fc1(x))\n",
        "    forecast_y = self.fc2(x)\n",
        "    return forecast_y"
      ],
      "metadata": {
        "id": "n76OFRaLbgQ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reward_model = RewardModel().to(device)\n",
        "reward_optimizer = torch.optim.Adam(reward_model.parameters(), lr=RewardConfig.LEARNING_RATE)\n",
        "\n",
        "reward_train_loader = train_dataset.get_dataloader(\"REWARD\", 256, True, 2)\n",
        "reward_val_loader = val_dataset.get_dataloader(\"REWARD\", 256, True, 2)"
      ],
      "metadata": {
        "id": "U4c5O-NPogLG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def reward_loss(true, pred):\n",
        "  mse_loss = (true - pred) ** 2\n",
        "  weight = abs(true - reward_norm(0))\n",
        "  # return torch.sum(mse_loss)\n",
        "  return torch.sum(torch.multiply(mse_loss, weight))\n",
        "\n",
        "def train_one_epoch():\n",
        "  reward_model.train()\n",
        "  train_loss = []\n",
        "\n",
        "  for day_loader in reward_train_loader:\n",
        "    for obs_z, obs_z_next, price_change in day_loader:\n",
        "      obs_z = z_norm(obs_z)\n",
        "      obs_z_next = z_norm(obs_z_next)\n",
        "      obs_z = obs_z.to(device, dtype=torch.float)\n",
        "      obs_z_next = obs_z_next.to(device, dtype=torch.float)\n",
        "      price_change = reward_norm(price_change)\n",
        "      price_change = price_change.to(device, dtype=torch.float)\n",
        "      price_change = torch.unsqueeze(price_change, 1)\n",
        "\n",
        "      reward_optimizer.zero_grad()\n",
        "      pred = reward_model(obs_z, obs_z_next)\n",
        "      loss = reward_loss(price_change, pred)\n",
        "      loss.backward()\n",
        "      reward_optimizer.step()\n",
        "      train_loss.append(loss.item())\n",
        "  train_loss = np.mean(train_loss)\n",
        "\n",
        "  return train_loss\n",
        "\n",
        "def validation_one_epoch():\n",
        "  reward_model.eval()\n",
        "  val_loss = []\n",
        "\n",
        "  for day_loader in reward_val_loader:\n",
        "    for obs_z, obs_z_next, price_change in day_loader:\n",
        "      obs_z = z_norm(obs_z)\n",
        "      obs_z_next = z_norm(obs_z_next)\n",
        "      obs_z = obs_z.to(device, dtype=torch.float)\n",
        "      obs_z_next = obs_z_next.to(device, dtype=torch.float)\n",
        "      price_change = reward_norm(price_change)\n",
        "      price_change = price_change.to(device, dtype=torch.float)\n",
        "      price_change = torch.unsqueeze(price_change, 1)\n",
        "\n",
        "      pred = reward_model(obs_z, obs_z_next)\n",
        "      loss = reward_loss(price_change, pred)\n",
        "      val_loss.append(loss.item())\n",
        "  val_loss = np.mean(val_loss)\n",
        "\n",
        "  return val_loss\n",
        "\n",
        "def batch_gd():\n",
        "  training_info = {\n",
        "    'train_loss_hist': [],\n",
        "    'val_loss_hist': [],\n",
        "  }\n",
        "\n",
        "  best_test_loss = np.inf\n",
        "  best_test_epoch = 0\n",
        "\n",
        "  for iter in range(RewardConfig.EPOCHS):\n",
        "    t0 = datetime.now()\n",
        "    train_loss = train_one_epoch()\n",
        "    val_loss = validation_one_epoch()\n",
        "\n",
        "    # Save losses\n",
        "    training_info['train_loss_hist'].append(train_loss)\n",
        "    training_info['val_loss_hist'].append(val_loss)\n",
        "\n",
        "    if val_loss < best_test_loss:\n",
        "      torch.save(reward_model.state_dict(), os.path.join(model_path, \"reward_best_val.pt\"))\n",
        "      best_test_loss = val_loss\n",
        "      best_test_epoch = iter\n",
        "      print('model saved')\n",
        "\n",
        "    dt = datetime.now() - t0\n",
        "    print(f'Epoch {iter + 1}/{RewardConfig.EPOCHS}, '\n",
        "          f'Train Loss: {train_loss:.8f}, Validation Loss: {val_loss:.8f}, '\n",
        "          f'Duration: {dt}, Best Val Epoch: {best_test_epoch}')\n",
        "\n",
        "  return training_info\n",
        "\n",
        "training_info = batch_gd()"
      ],
      "metadata": {
        "id": "6rYfAz2q-bqf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb186fcb-d2ca-4dfc-d399-f30940d234f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model saved\n",
            "Epoch 1/10, Train Loss: 0.47705281, Validation Loss: 0.07174594, Duration: 0:00:01.510890, Best Val Epoch: 0\n",
            "model saved\n",
            "Epoch 2/10, Train Loss: 0.11978133, Validation Loss: 0.04503648, Duration: 0:00:01.482695, Best Val Epoch: 1\n",
            "model saved\n",
            "Epoch 3/10, Train Loss: 0.10561722, Validation Loss: 0.03845433, Duration: 0:00:01.503666, Best Val Epoch: 2\n",
            "model saved\n",
            "Epoch 4/10, Train Loss: 0.09753676, Validation Loss: 0.03453969, Duration: 0:00:01.484688, Best Val Epoch: 3\n",
            "model saved\n",
            "Epoch 5/10, Train Loss: 0.08351980, Validation Loss: 0.02898294, Duration: 0:00:01.553564, Best Val Epoch: 4\n",
            "model saved\n",
            "Epoch 6/10, Train Loss: 0.07012265, Validation Loss: 0.02690920, Duration: 0:00:01.568983, Best Val Epoch: 5\n",
            "model saved\n",
            "Epoch 7/10, Train Loss: 0.04672753, Validation Loss: 0.01834606, Duration: 0:00:02.290280, Best Val Epoch: 6\n",
            "Epoch 8/10, Train Loss: 0.04228343, Validation Loss: 0.02170652, Duration: 0:00:01.462842, Best Val Epoch: 6\n",
            "model saved\n",
            "Epoch 9/10, Train Loss: 0.04210339, Validation Loss: 0.01391385, Duration: 0:00:01.469296, Best Val Epoch: 8\n",
            "Epoch 10/10, Train Loss: 0.03190493, Validation Loss: 0.01502842, Duration: 0:00:01.482158, Best Val Epoch: 8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# [Figure 4] Training process of Reward model\n",
        "\n",
        "plt.figure(figsize=(10, 4))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(training_info['train_loss_hist'])\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.title(\"Training loss\")\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(training_info['val_loss_hist'])\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.title(\"Validation loss\")\n",
        "plt.suptitle(\"[Figure 4] Training process of Reward model\", fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Q-E2RwosAzHr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "73c82abc-f1cc-4a53-e63a-97425e5f1e25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the reward model which shows best validation loss\n",
        "reward_model = RewardModel().to(device)\n",
        "reward_model.load_state_dict(torch.load(os.path.join(model_path, \"reward_best_val.pt\")))"
      ],
      "metadata": {
        "id": "ANq6XmcRGVGH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "060f6e67-f70f-4276-fb3b-a454d2178d60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# [Figure 5] Regression performance of Reward Model\n",
        "\n",
        "price_change_true = []\n",
        "price_change_pred = []\n",
        "\n",
        "for day_loader in reward_val_loader:\n",
        "  for obs_z, obs_z_next, price_change in day_loader:\n",
        "    with torch.no_grad():\n",
        "      price_change_true.append(price_change)\n",
        "      obs_z = z_norm(obs_z)\n",
        "      obs_z_next = z_norm(obs_z_next)\n",
        "      obs_z = obs_z.to(device, dtype=torch.float)\n",
        "      obs_z_next = obs_z_next.to(device, dtype=torch.float)\n",
        "      price_change = reward_norm(price_change)\n",
        "      price_change = price_change.to(device, dtype=torch.float)\n",
        "      price_change = torch.unsqueeze(price_change, 1)\n",
        "\n",
        "      pred = reward_model(obs_z, obs_z_next).cpu().detach().numpy()\n",
        "      pred = reward_undo_norm(pred)\n",
        "      price_change_pred.append(pred)\n",
        "\n",
        "price_change_true = np.squeeze(np.concatenate(price_change_true, 0))\n",
        "price_change_pred = np.squeeze(np.concatenate(price_change_pred, 0))\n",
        "\n",
        "idx = np.where(np.abs(price_change_true)<0.002)\n",
        "poly1d_fn = np.poly1d(np.polyfit(price_change_true[idx], price_change_pred[idx], 1))\n",
        "\n",
        "plt.figure(figsize=(10, 4))\n",
        "plt.axis('square')\n",
        "plt.plot(price_change_true, price_change_pred, 'o',\n",
        "         price_change_true, poly1d_fn(price_change_true), '-k')\n",
        "plt.xlim(-0.002, 0.002)\n",
        "plt.ylim(-0.002, 0.002)\n",
        "plt.axhline(y=0, linestyle = '--', color = 'grey')\n",
        "plt.axvline(x=0, linestyle = '--', color = 'grey')\n",
        "plt.xlabel(\"Real midprice change\")\n",
        "plt.ylabel(\"Predicted midprice change\")\n",
        "plt.title(\"[Figure 5] Regression performance of Reward Model\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "lEoCqWk7zuit",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "e8f4db42-129b-4c89-b14d-1a4640aa617c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model-Based Reinforcement Learning"
      ],
      "metadata": {
        "id": "20wu3DDnsyfd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/openai/gym.git@9180d12e1b66e7e2a1a622614f787a6ec147ac40 --quiet\n",
        "!pip install stable-baselines3 --quiet"
      ],
      "metadata": {
        "id": "26kZxc1FXpXH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f5853d9-dd9a-4bd7-f786-635ecf2f5034"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for gym (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m174.5/174.5 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gym\n",
        "from gym import spaces\n",
        "\n",
        "from stable_baselines3.common.vec_env import DummyVecEnv\n",
        "from stable_baselines3 import PPO, A2C"
      ],
      "metadata": {
        "id": "mefoSh6HXuKb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Markov Decision Process Formatting and Custom Gym Environment"
      ],
      "metadata": {
        "id": "wmiURKNJLw0V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class RLConfig:\n",
        "  EPISODES = 10\n",
        "  TIME_HORIZON = 500\n",
        "\n",
        "  N_ACTION = 21\n",
        "  MAX_PROFIT = 100000000000\n",
        "  INITIAL_BALANCE = 100000000\n",
        "  INITIAL_ASSET_PRICE = 10000000\n",
        "\n",
        "  TRADING_FEE = 0.003/100\n",
        "  SLIPPAGE = 0.03/100*10"
      ],
      "metadata": {
        "id": "XAKiECvpiILn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Helper function for reinforcement learning\n",
        "\n",
        "def get_transition(obs_z, action):\n",
        "  with torch.no_grad():\n",
        "    obs_z = torch.from_numpy(np.expand_dims(obs_z, 0)).to(device, dtype=torch.float)\n",
        "    action = torch.from_numpy(np.expand_dims(action, 0)).to(device, dtype=torch.float)\n",
        "    mus, sigmas, logpi = mdnrnn(obs_z, action)\n",
        "\n",
        "  mixt = Categorical(torch.exp(logpi[:,-1,:])).sample().item()\n",
        "  mu = mus[:, -1, mixt, :].squeeze()\n",
        "  sigma = sigmas[:, -1, mixt, :].squeeze()\n",
        "  lstate = (mu + sigma * torch.rand_like(mu))\n",
        "  return lstate.cpu().detach().numpy()\n",
        "\n",
        "def get_midprice_change(obs_z, obs_z_next):\n",
        "  with torch.no_grad():\n",
        "    obs_z = torch.from_numpy(np.expand_dims(obs_z, 0)).to(device, dtype=torch.float)\n",
        "    obs_z_next = torch.from_numpy(np.expand_dims(obs_z_next, 0)).to(device, dtype=torch.float)\n",
        "    pred_reward = reward_model(obs_z, obs_z_next).squeeze().cpu().detach().numpy()\n",
        "  return reward_undo_norm(pred_reward)\n",
        "\n",
        "def get_initial_state():\n",
        "  tmp_day_dataset = random.choice(train_dataset.day_datasets)\n",
        "  return np.expand_dims(z_norm(tmp_day_dataset.z[0]), 1)\n",
        "\n",
        "class DataQueue:\n",
        "  def __init__(self):\n",
        "    self.latent_queue = []\n",
        "    self.action_queue = []\n",
        "    for i in range(DataConfig.SEQ_LENGTH):\n",
        "      self.latent_queue.append(np.zeros((DataConfig.LATENT_SIZE,)))\n",
        "      self.action_queue.append(np.zeros((DataConfig.MAX_TRADEPRINT_LEN,)))\n",
        "\n",
        "  def enqueue_latent(self, latent):\n",
        "    self.latent_queue.pop(0)\n",
        "    self.latent_queue.append(np.reshape(latent, (DataConfig.LATENT_SIZE,)))\n",
        "\n",
        "  def enqueue_action(self, action, random_multi_agent = True):\n",
        "    num_other_action = random.randint(0, int(DataConfig.MAX_TRADEPRINT_LEN)-1)\n",
        "    temp_action = np.zeros((DataConfig.MAX_TRADEPRINT_LEN,))\n",
        "\n",
        "    rand_agent_action = []\n",
        "    if random_multi_agent == True:\n",
        "      for i in range(num_other_action):\n",
        "        tmp_action = random.randint(-10, 11)/10\n",
        "        if tmp_action != 0:\n",
        "          rand_agent_action.append(tmp_action)\n",
        "      rand_agent_action.append(action/DataConfig.MAX_TRADE_QUANTITY)\n",
        "      random.shuffle(rand_agent_action)\n",
        "\n",
        "      for i in range(len(rand_agent_action)):\n",
        "        temp_action[i] = rand_agent_action[i]\n",
        "    else:\n",
        "      if action != 0:\n",
        "        temp_action[0] = action/DataConfig.MAX_TRADE_QUANTITY\n",
        "\n",
        "    self.action_queue.pop(0)\n",
        "    self.action_queue.append(temp_action)\n",
        "\n",
        "  def get_all_state(self):\n",
        "    return np.array(self.latent_queue), np.array(self.action_queue)\n",
        "\n",
        "  def get_state(self, idx):\n",
        "    return self.latent_queue[idx].squeeze(), self.action_queue[idx].squeeze()"
      ],
      "metadata": {
        "id": "B7wTSuz5XrlV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# OpenAI gym based RL environment using trained neural networks\n",
        "\n",
        "class MarketEnv(gym.Env):\n",
        "  metadata = {'render.modes': ['human']}\n",
        "\n",
        "  def __init__(self):\n",
        "    super(MarketEnv, self).__init__()\n",
        "    self.reward_range = (-RLConfig.MAX_PROFIT, RLConfig.MAX_PROFIT)\n",
        "    self.action_space = spaces.Discrete(RLConfig.N_ACTION)\n",
        "    self.observation_space = spaces.Dict({\n",
        "        'latent_vector': spaces.Box(low=0, high=1, shape=(DataConfig.LATENT_SIZE,)),\n",
        "        'current_position': spaces.Box(low=-DataConfig.MAX_TRADE_QUANTITY,\n",
        "                                       high=DataConfig.MAX_TRADE_QUANTITY,\n",
        "                                       shape=(1,))\n",
        "    })\n",
        "\n",
        "  def _take_action(self, action):\n",
        "    max_action = (RLConfig.N_ACTION-1)/2\n",
        "    position_change = int((action-max_action)/max_action * DataConfig.MAX_TRADE_QUANTITY)\n",
        "\n",
        "    if position_change > 0:\n",
        "      available_amount = DataConfig.MAX_TRADE_QUANTITY - self.position\n",
        "      position_change = min(position_change, available_amount)\n",
        "    elif position_change < 0:\n",
        "      available_amount =  - DataConfig.MAX_TRADE_QUANTITY - self.position\n",
        "      position_change = max(position_change, available_amount)\n",
        "\n",
        "    fee = abs(position_change) * self.asset_price * (RLConfig.TRADING_FEE + RLConfig.SLIPPAGE)\n",
        "    return position_change, fee\n",
        "\n",
        "  def _next_observation(self, position_change):\n",
        "    self.data_queue.enqueue_action(position_change / DataConfig.MAX_TRADE_QUANTITY)\n",
        "    obs_z_seq, action_seq = self.data_queue.get_all_state()\n",
        "    obs_z_next = get_transition(obs_z_seq, action_seq)\n",
        "    obs_z, __ = self.data_queue.get_state(-1)\n",
        "    return obs_z, obs_z_next\n",
        "\n",
        "  def step(self, action):\n",
        "    position_change, fee = self._take_action(action)\n",
        "    obs_z, obs_z_next = self._next_observation(position_change)\n",
        "    midprice_change = get_midprice_change(obs_z, obs_z_next)\n",
        "\n",
        "    reward = midprice_change * self.position * self.asset_price - fee\n",
        "    self.current_step += 1\n",
        "    self.balance += reward\n",
        "    self.asset_price *= (1+midprice_change)\n",
        "    self.position += position_change\n",
        "    self.data_queue.enqueue_latent(obs_z_next)\n",
        "    self.data_queue.enqueue_action(position_change)\n",
        "\n",
        "    cum_reward = (self.balance/RLConfig.INITIAL_BALANCE-1)*100\n",
        "\n",
        "    episode_over = (self.current_step == RLConfig.TIME_HORIZON)\n",
        "    obs_z, __ = self.data_queue.get_state(-1)\n",
        "    obs = {\n",
        "      'latent_vector': obs_z,\n",
        "      'current_position': self.position\n",
        "    }\n",
        "\n",
        "    info = {\n",
        "        'midprice_change': midprice_change,\n",
        "        'position_change': position_change,\n",
        "        'fee': fee,\n",
        "        'cum_reward': cum_reward\n",
        "    }\n",
        "\n",
        "    return obs, reward, episode_over, info\n",
        "\n",
        "  def reset(self):\n",
        "    self.position = 0\n",
        "    self.current_step = 0\n",
        "\n",
        "    self.balance = RLConfig.INITIAL_BALANCE\n",
        "    self.asset_price = RLConfig.INITIAL_ASSET_PRICE\n",
        "\n",
        "    initial_state = get_initial_state()\n",
        "    self.data_queue = DataQueue()\n",
        "    self.data_queue.enqueue_latent(initial_state)\n",
        "\n",
        "    obs_z, __ = self.data_queue.get_state(-1)\n",
        "    obs = {\n",
        "      'latent_vector': obs_z,\n",
        "      'current_position': self.position\n",
        "    }\n",
        "    return obs\n",
        "\n",
        "  def render(self, mode='human', close=False):\n",
        "    rate_of_return = (self.balance/RLConfig.INITIAL_BALANCE-1)*100\n",
        "    benchmark_return = (self.asset_price/RLConfig.INITIAL_ASSET_PRICE-1)*100\n",
        "\n",
        "    print(f'Step: {self.current_step}')\n",
        "    print(f'Reward: {rate_of_return}')\n"
      ],
      "metadata": {
        "id": "KOfn5AMcAi8w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Training Agent in Financial World Model\n",
        "Model-based Simulated Market"
      ],
      "metadata": {
        "id": "Xc8ds9t8L2qE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train agent inside financial world model\n",
        "\n",
        "env = DummyVecEnv([lambda: MarketEnv()])\n",
        "model = A2C(\"MultiInputPolicy\", env, verbose=0)\n",
        "model.learn(total_timesteps=20000)"
      ],
      "metadata": {
        "id": "TnyJxSRiMMiN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "717f8179-b853-473a-a948-1f7c890ae1c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<stable_baselines3.a2c.a2c.A2C at 0x7fd5e44d5e10>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test agent in simulated market\n",
        "\n",
        "cum_reward_history = []\n",
        "\n",
        "for i_episode in range(RLConfig.EPISODES):\n",
        "  observation = env.reset()\n",
        "  cum_reward_episode = []\n",
        "  for t in range(RLConfig.TIME_HORIZON):\n",
        "    action, _states = model.predict(observation)\n",
        "    observation, reward, done, info = env.step(action)\n",
        "    cum_reward_episode.append(info[0]['cum_reward'])\n",
        "  print(f\"Episode {i_episode} finished: Final reward {info[0]['cum_reward']}\")\n",
        "  cum_reward_history.append(cum_reward_episode)\n",
        "\n",
        "env.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vShJiHfZTOPW",
        "outputId": "31217faa-cfb7-42d0-8595-970a7b7d05ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Episode 0 finished: Final reward 21.928387089584845\n",
            "Episode 1 finished: Final reward 18.20701696349669\n",
            "Episode 2 finished: Final reward 18.769878015900975\n",
            "Episode 3 finished: Final reward 17.284546394112276\n",
            "Episode 4 finished: Final reward 21.441443556111793\n",
            "Episode 5 finished: Final reward 18.084759307723218\n",
            "Episode 6 finished: Final reward 19.398124173618836\n",
            "Episode 7 finished: Final reward 17.40930292828704\n",
            "Episode 8 finished: Final reward 16.874674583837002\n",
            "Episode 9 finished: Final reward 20.49796327378013\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# [Figure 6] Cumultative reward of trained agent in Financial world models\n",
        "\n",
        "for i in range(RLConfig.EPISODES):\n",
        "  plt.plot(cum_reward_history[i])\n",
        "plt.xlabel(\"Timestep\")\n",
        "plt.ylabel(\"Reward\")\n",
        "plt.title(\"[Figure 6] Cumulative reward of trained agent in Financial world models\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "XgCNXwYbEcpq",
        "outputId": "caa4537b-5fce-4009-d8bd-53d06bae34fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}