runner.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a8fa9b22-44bb-43fd-a0c9-f1d2a9cd6f74",
   "metadata": {
    "jupyter": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import time\n",
    "import sys\n",
    "sys.path.append(os.getcwd())\n",
    "from src.backup.hawkes import dataLoader, inference, simulate, simulate_optimized #not importing fit because some error with tick installation\n",
    "from src.fit import ConditionalLeastSquaresLogLin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7758498b-1483-4118-ad59-43002ebcef0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "%matplotlib inline\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62315369-8a35-4e74-af4a-0d543b359144",
   "metadata": {},
   "source": [
    "# Generate Fake Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f15fc84-7f24-41c6-989b-76b3844152d8",
   "metadata": {},
   "source": [
    "## Create Fake Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "350e6ec4-3415-4067-8bbb-50d36ef15fb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isdir('tmp') : os.mkdir('tmp')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0d5ae78-8038-4102-b3b2-87d8ebf25ed0",
   "metadata": {},
   "source": [
    "### Make Norms Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7f042fa-f6f6-472a-9ec5-ac2a8637fe59",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = np.zeros((12,12))\n",
    "for i in range(12):\n",
    "    mat[i][i] = .5\n",
    "for i in range(12):\n",
    "    for j in range(12):\n",
    "        if i == j: continue\n",
    "        mat[i][j] = np.random.choice([1,-1])*mat[i][i]*np.exp(-.75*np.abs(j-i))\n",
    "# print(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9c9ec7e3-3326-48e8-b420-90e182b8f7b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [\"lo_deep_Ask\", \"co_deep_Ask\", \"lo_top_Ask\",\"co_top_Ask\", \"mo_Ask\", \"lo_inspread_Ask\" ,\n",
    "            \"lo_inspread_Bid\" , \"mo_Bid\", \"co_top_Bid\", \"lo_top_Bid\", \"co_deep_Bid\",\"lo_deep_Bid\" ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "60b6d602-7e98-4cae-84d3-75e3f60e23a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots()\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes('right', size='5%', pad=0.05)\n",
    "im = ax.imshow(mat.transpose(), cmap='coolwarm')\n",
    "fig.colorbar(im, cax=cax, orientation='vertical')\n",
    "# Show all ticks and label them with the respective list entries\n",
    "ax.set_xticks(np.arange(len(cols)), labels=[c +\"->\" for c in cols])\n",
    "ax.set_yticks(np.arange(len(cols)), labels=cols)\n",
    "\n",
    "# Rotate the tick labels and set their alignment.\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "         rotation_mode=\"anchor\")\n",
    "\n",
    "# Loop over data dimensions and create text annotations.\n",
    "for i in range(len(cols)):\n",
    "    for j in range(len(cols)):\n",
    "        text = ax.text(i,j, np.round(mat[i][j], decimals=2),\n",
    "                               ha=\"center\", va=\"center\")\n",
    "# fig.savefig(\"heatmap.png\")\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77f32a65-7cf7-4640-b44e-986fe8c84431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.complex128(0.9452125130879911+0j)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# norm should be less than 1\n",
    "np.max(np.linalg.eig(mat)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40b585af-262b-42a7-8677-7817d4772e67",
   "metadata": {},
   "source": [
    "### Make fake TOD params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aec73c69-6714-415b-9e13-32c2fb84abd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create fake TOD params - 1.0 for now but can be changed to whatever you wish\n",
    "faketod = {}\n",
    "for k in cols:\n",
    "    faketod[k] = {}\n",
    "    for k1 in np.arange(13):\n",
    "        faketod[k][k1] = 1.0\n",
    "with open(\"tmp//fakeData_Params_sod_eod_dictTOD_constt\", \"wb\") as f:\n",
    "    pickle.dump(faketod, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8490d7e7-20cf-4be4-bfa6-fc4677a22356",
   "metadata": {},
   "source": [
    "### Make Fake Power Law Kernel Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8b57ad1a-bbae-461e-b2f4-b49135072cac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create fake power law kernel params from the above norm matrix\n",
    "paramsFake = {}\n",
    "for i in range(12):\n",
    "    paramsFake[cols[i]] = 0.1*np.random.choice([0.3,0.4,0.5,0.6,0.7])\n",
    "    for j in range(12):\n",
    "        maxTOD = np.max(list(faketod[cols[j]].values()))\n",
    "        beta = np.random.choice([1.5,1.6,1.7,1.8,1.9])\n",
    "        gamma = (1+np.random.rand())*5e3\n",
    "        alpha = np.abs(mat[i][j])*gamma*(beta-1)/maxTOD\n",
    "        paramsFake[cols[i]+\"->\"+cols[j]] = (np.sign(mat[i][j]), np.array([alpha, beta, gamma]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "42e33a99-a3de-4af5-8713-6fbecbe2b3e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = np.zeros((12,12))\n",
    "for i in range(len(cols)):\n",
    "    for j in range(len(cols)):\n",
    "        kernelParams = paramsFake.get(cols[j]+\"->\"+cols[i], None)\n",
    "        if kernelParams is None: continue\n",
    "        mat[i][j] = kernelParams[0]*kernelParams[1][0]/((-1 + kernelParams[1][1])*kernelParams[1][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a3c03451-d617-404a-a196-2ebbe9b3c2a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# norm matrix from this fake power law kernels - does it match the above figure? - YES.\n",
    "fig, ax = plt.subplots()\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes('right', size='5%', pad=0.05)\n",
    "im = ax.imshow(mat.transpose(), cmap='coolwarm')\n",
    "fig.colorbar(im, cax=cax, orientation='vertical')\n",
    "# Show all ticks and label them with the respective list entries\n",
    "ax.set_xticks(np.arange(len(cols)), labels=[c +\"->\" for c in cols])\n",
    "ax.set_yticks(np.arange(len(cols)), labels=cols)\n",
    "\n",
    "# Rotate the tick labels and set their alignment.\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "         rotation_mode=\"anchor\")\n",
    "\n",
    "# Loop over data dimensions and create text annotations.\n",
    "for i in range(len(cols)):\n",
    "    for j in range(len(cols)):\n",
    "        text = ax.text(i,j, np.round(mat[i][j], decimals=2),\n",
    "                               ha=\"center\", va=\"center\")\n",
    "fig.savefig(\"tmp//heatmap.png\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3064fe86-2b93-4a5e-98fd-6f6a5fa1c0a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.complex128(0.9452125130879943+0j)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # norm should be less than 1\n",
    "np.max(np.linalg.eig(mat)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26c526b0-8b41-479b-bb25-7055c1d16bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"tmp//fake_ParamsInferredWCutoff_sod_eod_true\", \"wb\") as f:\n",
    "    pickle.dump(paramsFake, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c940d7ae-d111-4139-8fb2-2878035885af",
   "metadata": {},
   "source": [
    "## Simulate Fake LOB Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fdb68348",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The initial LOB: lob0 [{'Ask_touch': (np.float64(45.02), np.int64(322)), 'Bid_touch': (np.float64(44.97), np.int64(500)), 'Ask_deep': (np.float64(45.03), np.int64(256)), 'Bid_deep': (np.float64(44.96), np.int64(866))}] lob0_l3 [{'Ask_deep': [np.float64(52.0), np.float64(51.0), np.float64(51.0), np.float64(51.0), np.float64(51.0)], 'Ask_touch': [np.float64(66.0), np.float64(64.0), np.float64(64.0), np.float64(64.0), np.float64(64.0)], 'Bid_touch': [np.float64(100.0), np.float64(100.0), np.float64(100.0), np.float64(100.0), np.float64(100.0)], 'Bid_deep': [np.float64(174.0), np.float64(173.0), np.float64(173.0), np.float64(173.0), np.float64(173.0)]}]\n",
      "initial spread:  0.05000000000000426 \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(100)), 'Bid_touch': (np.float64(44.97), np.int64(500)), 'Ask_deep': (np.float64(45.02), np.int64(322)), 'Bid_deep': (np.float64(44.96), np.int64(866))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.int64(100)), 'Ask_deep': (np.float64(45.02), np.int64(322)), 'Bid_deep': (np.float64(44.97), np.int64(500))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(100)), 'Bid_touch': (np.float64(44.99), np.int64(100)), 'Ask_deep': (np.float64(45.02), np.int64(322)), 'Bid_deep': (np.float64(44.98), np.int64(100))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(100)), 'Bid_touch': (np.float64(45.0), np.int64(1067)), 'Ask_deep': (np.float64(45.02), np.int64(322)), 'Bid_deep': (np.float64(44.99), np.int64(100))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(100)), 'Bid_touch': (np.float64(45.0), np.int64(1067)), 'Ask_deep': (np.float64(45.02), np.int64(322)), 'Bid_deep': (np.float64(44.99), np.int64(233))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(100)), 'Bid_touch': (np.float64(45.0), np.int64(1067)), 'Ask_deep': (np.float64(45.02), np.float64(258.0)), 'Bid_deep': (np.float64(44.99), np.int64(233))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(100)), 'Bid_touch': (np.float64(45.0), np.int64(809)), 'Ask_deep': (np.float64(45.02), np.float64(258.0)), 'Bid_deep': (np.float64(44.99), np.int64(233))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.float64(258.0)), 'Bid_touch': (np.float64(45.0), np.int64(809)), 'Ask_deep': (np.float64(45.03), np.int64(1540)), 'Bid_deep': (np.float64(44.99), np.int64(233))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.float64(258.0)), 'Bid_touch': (np.float64(45.01), np.int64(100)), 'Ask_deep': (np.float64(45.03), np.int64(1540)), 'Bid_deep': (np.float64(45.0), np.int64(809))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.float64(258.0)), 'Bid_touch': (np.float64(45.01), np.int64(100)), 'Ask_deep': (np.float64(45.03), np.int64(1540)), 'Bid_deep': (np.float64(44.99), np.int64(1185))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.float64(258.0)), 'Bid_touch': (np.float64(45.01), np.int64(100)), 'Ask_deep': (np.float64(45.03), np.int64(1540)), 'Bid_deep': (np.float64(44.99), np.float64(1126.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.float64(258.0)), 'Bid_touch': (np.float64(45.01), np.int64(100)), 'Ask_deep': (np.float64(45.03), np.int64(1540)), 'Bid_deep': (np.float64(44.99), np.float64(1067.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.float64(258.0)), 'Bid_touch': (np.float64(44.99), np.float64(1067.0)), 'Ask_deep': (np.float64(45.03), np.int64(1540)), 'Bid_deep': (np.float64(44.98), np.int64(1480))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.float64(258.0)), 'Bid_touch': (np.float64(44.99), np.float64(1067.0)), 'Ask_deep': (np.float64(45.03), np.int64(1540)), 'Bid_deep': (np.float64(44.98), np.int64(1915))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.float64(258.0)), 'Bid_touch': (np.float64(44.99), np.float64(1008.0)), 'Ask_deep': (np.float64(45.03), np.int64(1540)), 'Bid_deep': (np.float64(44.98), np.int64(1915))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.float64(258.0)), 'Bid_touch': (np.float64(44.99), np.float64(949.0)), 'Ask_deep': (np.float64(45.03), np.int64(1540)), 'Bid_deep': (np.float64(44.98), np.int64(1915))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.float64(258.0)), 'Bid_touch': (np.float64(44.99), np.float64(890.0)), 'Ask_deep': (np.float64(45.03), np.int64(1540)), 'Bid_deep': (np.float64(44.98), np.int64(1915))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.float64(258.0)), 'Bid_touch': (np.float64(44.99), np.float64(831.0)), 'Ask_deep': (np.float64(45.03), np.int64(1540)), 'Bid_deep': (np.float64(44.98), np.int64(1915))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.float64(258.0)), 'Bid_touch': (np.float64(44.99), np.float64(731.0)), 'Ask_deep': (np.float64(45.03), np.int64(1540)), 'Bid_deep': (np.float64(44.98), np.int64(1915))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.float64(258.0)), 'Bid_touch': (np.float64(44.99), np.float64(731.0)), 'Ask_deep': (np.float64(45.03), np.int64(1540)), 'Bid_deep': (np.float64(44.98), np.float64(1767.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.float64(258.0)), 'Bid_touch': (np.float64(44.99), np.float64(912.0)), 'Ask_deep': (np.float64(45.03), np.int64(1540)), 'Bid_deep': (np.float64(44.98), np.float64(1767.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(100)), 'Bid_touch': (np.float64(44.99), np.float64(912.0)), 'Ask_deep': (np.float64(45.02), np.float64(258.0)), 'Bid_deep': (np.float64(44.98), np.float64(1767.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(243)), 'Bid_touch': (np.float64(44.99), np.float64(912.0)), 'Ask_deep': (np.float64(45.01), np.int64(100)), 'Bid_deep': (np.float64(44.98), np.float64(1767.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(243)), 'Bid_touch': (np.float64(44.99), np.float64(1012.0)), 'Ask_deep': (np.float64(45.01), np.int64(100)), 'Bid_deep': (np.float64(44.98), np.float64(1767.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(243)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(100)), 'Bid_deep': (np.float64(44.98), np.float64(1767.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(243)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.02), np.int64(500)), 'Bid_deep': (np.float64(44.98), np.float64(1767.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.int64(500)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.03), np.int64(112)), 'Bid_deep': (np.float64(44.98), np.float64(1767.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.int64(500)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.03), np.float64(101.0)), 'Bid_deep': (np.float64(44.98), np.float64(1767.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.int64(500)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.03), np.float64(101.0)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(100)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.02), np.int64(500)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(100)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(100)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.int64(360)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.03), np.int64(1000)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.int64(460)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.03), np.int64(1000)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.int64(460)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.03), np.float64(900.0)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.int64(560)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.03), np.float64(900.0)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.int64(1411)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.03), np.float64(900.0)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.02), np.int64(1411)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.03), np.float64(800.0)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(100)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.02), np.int64(1411)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(100)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.02), np.float64(560.0)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(100)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.02), np.float64(460.0)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(200)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.02), np.float64(460.0)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(200)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.02), np.float64(410.0)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(300)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.02), np.float64(410.0)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(400)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.02), np.float64(410.0)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(500)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.02), np.float64(410.0)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(600)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.02), np.float64(410.0)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.01), np.int64(600)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.02), np.float64(360.0)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(713)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1109)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1209)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1309)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1848)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1980)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(2386)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(2286)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(2186)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(2172)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(2072)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1906)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1868)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1971)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1946)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1846)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1753)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1653)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1753)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1853)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1953)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1853)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1953)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(2053)), 'Bid_touch': (np.float64(44.99), np.float64(953.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(2053)), 'Bid_touch': (np.float64(44.99), np.float64(951.0)), 'Ask_deep': (np.float64(45.01), np.int64(600)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(2053)), 'Bid_touch': (np.float64(44.99), np.float64(951.0)), 'Ask_deep': (np.float64(45.01), np.int64(1351)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(2053)), 'Bid_touch': (np.float64(44.99), np.float64(951.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1953)), 'Bid_touch': (np.float64(44.99), np.float64(951.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1753)), 'Bid_touch': (np.float64(44.99), np.float64(951.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1608)), 'Bid_touch': (np.float64(44.99), np.float64(951.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(951.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(746.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(736.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(836.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(936.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(855.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(855.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2092.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(855.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2102.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(855.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2202.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(855.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2054.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(855.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2044.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(855.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(1609.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(855.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(1713.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(855.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(1813.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(855.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(1875.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(855.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2462.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(855.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2314.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(855.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2166.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(855.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2066.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(796.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2066.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(796.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2827.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(737.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2827.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(717.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2827.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(617.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2827.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(517.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2827.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(317.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2827.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(317.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(417.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(212.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(169.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(56.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.99), np.float64(156.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.98), np.float64(2240.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.98), np.float64(2196.0)), 'Ask_deep': (np.float64(45.01), np.int64(1451)), 'Bid_deep': (np.float64(44.97), np.int64(2017))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.98), np.float64(2196.0)), 'Ask_deep': (np.float64(45.01), np.int64(1461)), 'Bid_deep': (np.float64(44.97), np.int64(2017))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(1604)), 'Bid_touch': (np.float64(44.98), np.float64(2196.0)), 'Ask_deep': (np.float64(45.01), np.int64(1602)), 'Bid_deep': (np.float64(44.97), np.int64(2017))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(2196.0)), 'Ask_deep': (np.float64(45.0), np.int64(1604)), 'Bid_deep': (np.float64(44.97), np.int64(2017))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(2196.0)), 'Ask_deep': (np.float64(45.0), np.int64(1604)), 'Bid_deep': (np.float64(44.97), np.int64(2492))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(2196.0)), 'Ask_deep': (np.float64(45.0), np.int64(1604)), 'Bid_deep': (np.float64(44.97), np.float64(2284.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(1435.0)), 'Ask_deep': (np.float64(45.0), np.int64(1604)), 'Bid_deep': (np.float64(44.97), np.float64(2284.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(1435.0)), 'Ask_deep': (np.float64(45.0), np.int64(1604)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(1435.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(1425.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(1525.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(1625.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(1725.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(1825.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(1835.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(2799.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(2699.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(2599.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(2483.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(2483.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(1519.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(1419.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(1315.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(1253.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(1105.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(1095.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(947.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(590.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(300.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2083.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(300.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2287.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(200.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2287.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(100.0)), 'Ask_deep': (np.float64(45.0), np.int64(1831)), 'Bid_deep': (np.float64(44.97), np.float64(2287.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(100.0)), 'Ask_deep': (np.float64(45.0), np.int64(1731)), 'Bid_deep': (np.float64(44.97), np.float64(2287.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(100.0)), 'Ask_deep': (np.float64(45.0), np.int64(2208)), 'Bid_deep': (np.float64(44.97), np.float64(2287.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(200)), 'Bid_touch': (np.float64(44.98), np.float64(100.0)), 'Ask_deep': (np.float64(45.0), np.int64(3014)), 'Bid_deep': (np.float64(44.97), np.float64(2287.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(100.0)), 'Ask_deep': (np.float64(45.0), np.int64(3014)), 'Bid_deep': (np.float64(44.97), np.float64(2287.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(3014)), 'Bid_touch': (np.float64(44.98), np.float64(100.0)), 'Ask_deep': (np.float64(45.01), np.int64(3260)), 'Bid_deep': (np.float64(44.97), np.float64(2287.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(100.0)), 'Ask_deep': (np.float64(45.0), np.int64(3014)), 'Bid_deep': (np.float64(44.97), np.float64(2287.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(100.0)), 'Ask_deep': (np.float64(45.0), np.int64(3015)), 'Bid_deep': (np.float64(44.97), np.float64(2287.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(100.0)), 'Ask_deep': (np.float64(45.0), np.int64(3079)), 'Bid_deep': (np.float64(44.97), np.float64(2287.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.float64(100.0)), 'Ask_deep': (np.float64(45.0), np.int64(3079)), 'Bid_deep': (np.float64(44.97), np.float64(2287.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.97), np.float64(2287.0)), 'Ask_deep': (np.float64(45.0), np.int64(3079)), 'Bid_deep': (np.float64(44.96), np.int64(161))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.97), np.float64(2086.0)), 'Ask_deep': (np.float64(45.0), np.int64(3079)), 'Bid_deep': (np.float64(44.96), np.int64(161))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.97), np.float64(1885.0)), 'Ask_deep': (np.float64(45.0), np.int64(3079)), 'Bid_deep': (np.float64(44.96), np.int64(161))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.98), np.int64(100)), 'Bid_touch': (np.float64(44.97), np.float64(1885.0)), 'Ask_deep': (np.float64(44.99), np.int64(100)), 'Bid_deep': (np.float64(44.96), np.int64(161))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.98), np.int64(100)), 'Bid_touch': (np.float64(44.97), np.float64(1825.0)), 'Ask_deep': (np.float64(44.99), np.int64(100)), 'Bid_deep': (np.float64(44.96), np.int64(161))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(51)), 'Bid_touch': (np.float64(44.97), np.float64(1825.0)), 'Ask_deep': (np.float64(45.0), np.int64(566)), 'Bid_deep': (np.float64(44.96), np.int64(161))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(51)), 'Bid_touch': (np.float64(44.98), np.int64(100)), 'Ask_deep': (np.float64(45.0), np.int64(566)), 'Bid_deep': (np.float64(44.97), np.float64(1825.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(51)), 'Bid_touch': (np.float64(44.97), np.float64(1825.0)), 'Ask_deep': (np.float64(45.0), np.int64(566)), 'Bid_deep': (np.float64(44.96), np.int64(998))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(51)), 'Bid_touch': (np.float64(44.97), np.float64(1925.0)), 'Ask_deep': (np.float64(45.0), np.int64(566)), 'Bid_deep': (np.float64(44.96), np.int64(998))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(51)), 'Bid_touch': (np.float64(44.97), np.float64(1825.0)), 'Ask_deep': (np.float64(45.0), np.int64(566)), 'Bid_deep': (np.float64(44.96), np.int64(998))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(51)), 'Bid_touch': (np.float64(44.97), np.float64(1621.0)), 'Ask_deep': (np.float64(45.0), np.int64(566)), 'Bid_deep': (np.float64(44.96), np.int64(998))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(51)), 'Bid_touch': (np.float64(44.97), np.float64(1420.0)), 'Ask_deep': (np.float64(45.0), np.int64(566)), 'Bid_deep': (np.float64(44.96), np.int64(998))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(51)), 'Bid_touch': (np.float64(44.98), np.int64(100)), 'Ask_deep': (np.float64(45.0), np.int64(566)), 'Bid_deep': (np.float64(44.97), np.float64(1420.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(51)), 'Bid_touch': (np.float64(44.98), np.int64(100)), 'Ask_deep': (np.float64(45.0), np.int64(650)), 'Bid_deep': (np.float64(44.97), np.float64(1420.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(45.0), np.int64(650)), 'Bid_touch': (np.float64(44.98), np.int64(100)), 'Ask_deep': (np.float64(45.01), np.int64(4)), 'Bid_deep': (np.float64(44.97), np.float64(1420.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.int64(100)), 'Ask_deep': (np.float64(45.0), np.int64(650)), 'Bid_deep': (np.float64(44.97), np.float64(1420.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.int64(200)), 'Ask_deep': (np.float64(45.0), np.int64(650)), 'Bid_deep': (np.float64(44.97), np.float64(1420.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.int64(200)), 'Ask_deep': (np.float64(45.0), np.int64(750)), 'Bid_deep': (np.float64(44.97), np.float64(1420.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.int64(100)), 'Ask_deep': (np.float64(45.0), np.int64(750)), 'Bid_deep': (np.float64(44.97), np.float64(1420.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.int64(100)), 'Ask_deep': (np.float64(45.0), np.int64(850)), 'Bid_deep': (np.float64(44.97), np.float64(1420.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.98), np.int64(100)), 'Ask_deep': (np.float64(45.0), np.int64(1619)), 'Bid_deep': (np.float64(44.97), np.float64(1420.0))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.99), np.int64(100)), 'Bid_touch': (np.float64(44.97), np.float64(1420.0)), 'Ask_deep': (np.float64(45.0), np.int64(1619)), 'Bid_deep': (np.float64(44.96), np.int64(50))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.98), np.int64(100)), 'Bid_touch': (np.float64(44.97), np.float64(1420.0)), 'Ask_deep': (np.float64(44.99), np.int64(100)), 'Bid_deep': (np.float64(44.96), np.int64(50))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.98), np.int64(100)), 'Bid_touch': (np.float64(44.97), np.float64(1420.0)), 'Ask_deep': (np.float64(44.99), np.int64(100)), 'Bid_deep': (np.float64(44.96), np.int64(150))} \n",
      "\n",
      "Snapshot LOB0:  {'Ask_touch': (np.float64(44.98), np.int64(100)), 'Bid_touch': (np.float64(44.97), np.float64(1520.0)), 'Ask_deep': (np.float64(44.99), np.int64(100)), 'Bid_deep': (np.float64(44.96), np.int64(150))} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "N = 1\n",
    "for i in range(N):\n",
    "    T, lob, lobL3, timefraction=simulate_optimized.simulate_optimized(100, \"tmp//fake_ParamsInferredWCutoff_sod_eod_true\", \"tmp//fakeData_Params_sod_eod_dictTOD_constt\", beta = 1., avgSpread = .01, spread0 = 5, price0 = 45, verbose=True)\n",
    "    with open(\"tmp//fake_simulated_sod_eod_\"+ str(i) , \"wb\") as f:\n",
    "        pickle.dump((T, lob, lobL3), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "07b057b2-9b3d-492e-b5a9-e34e43022236",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save as 12D \n",
    "paths = [\"tmp//fake_simulated_sod_eod_\"+ str(i) for i in range(N)]\n",
    "for p in paths:\n",
    "    resPath = p\n",
    "    with open(resPath, 'rb') as f:\n",
    "        results = pickle.load(f)\n",
    "    \n",
    "    ask_t = []\n",
    "    bid_t = []\n",
    "    ask_d = []\n",
    "    bid_d= []\n",
    "    event = []\n",
    "    time = []\n",
    "    for r, j in zip(results[1][1:], results[0][1:]):\n",
    "        ask_t.append(r['Ask_touch'][0])\n",
    "        bid_t.append(r['Bid_touch'][0])\n",
    "        ask_d.append(r['Ask_deep'][0])\n",
    "        bid_d.append(r['Bid_deep'][0])\n",
    "        event.append(j[0])\n",
    "        time.append(j[1])\n",
    "    df = pd.DataFrame({\"Time\" : time, \"event\" : event, \"Ask Price 1\" : ask_t, \"Bid Price 1\": bid_t, \"Ask Price 2\": ask_d, \"Bid Price 2\" : bid_d})\n",
    "    df['BidDiff'] = df['Bid Price 1'].diff()\n",
    "    df['AskDiff'] = df['Ask Price 1'].diff()\n",
    "    df['BidDiff2']= df['Bid Price 2'].diff()\n",
    "    df['AskDiff2']= df['Ask Price 2'].diff()\n",
    "    id = (resPath.split(\"/\")[-1]).split(\"_\")[-1]\n",
    "    df[\"Date\"] = id\n",
    "    df.to_csv(\"tmp//fake_\" + id +\"_12D.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c5483c08-77b5-4ecb-ad30-2bd1318f27f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>event</th>\n",
       "      <th>Ask Price 1</th>\n",
       "      <th>Bid Price 1</th>\n",
       "      <th>Ask Price 2</th>\n",
       "      <th>Bid Price 2</th>\n",
       "      <th>BidDiff</th>\n",
       "      <th>AskDiff</th>\n",
       "      <th>BidDiff2</th>\n",
       "      <th>AskDiff2</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.926209</td>\n",
       "      <td>lo_inspread_Ask</td>\n",
       "      <td>45.01</td>\n",
       "      <td>44.97</td>\n",
       "      <td>45.02</td>\n",
       "      <td>44.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.931718</td>\n",
       "      <td>lo_inspread_Bid</td>\n",
       "      <td>45.01</td>\n",
       "      <td>44.98</td>\n",
       "      <td>45.02</td>\n",
       "      <td>44.97</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.932691</td>\n",
       "      <td>lo_inspread_Bid</td>\n",
       "      <td>45.01</td>\n",
       "      <td>44.99</td>\n",
       "      <td>45.02</td>\n",
       "      <td>44.98</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.934051</td>\n",
       "      <td>lo_inspread_Bid</td>\n",
       "      <td>45.01</td>\n",
       "      <td>45.00</td>\n",
       "      <td>45.02</td>\n",
       "      <td>44.99</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.113047</td>\n",
       "      <td>lo_deep_Bid</td>\n",
       "      <td>45.01</td>\n",
       "      <td>45.00</td>\n",
       "      <td>45.02</td>\n",
       "      <td>44.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time            event  Ask Price 1  Bid Price 1  Ask Price 2  \\\n",
       "0  0.926209  lo_inspread_Ask        45.01        44.97        45.02   \n",
       "1  0.931718  lo_inspread_Bid        45.01        44.98        45.02   \n",
       "2  0.932691  lo_inspread_Bid        45.01        44.99        45.02   \n",
       "3  0.934051  lo_inspread_Bid        45.01        45.00        45.02   \n",
       "4  2.113047      lo_deep_Bid        45.01        45.00        45.02   \n",
       "\n",
       "   Bid Price 2  BidDiff  AskDiff  BidDiff2  AskDiff2 Date  \n",
       "0        44.96      NaN      NaN       NaN       NaN    0  \n",
       "1        44.97     0.01      0.0      0.01       0.0    0  \n",
       "2        44.98     0.01      0.0      0.01       0.0    0  \n",
       "3        44.99     0.01      0.0      0.01       0.0    0  \n",
       "4        44.99     0.00      0.0      0.00       0.0    0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "069951cf-eea6-4640-b43e-a1f7e5a46e7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22aff3245c0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['Time'] , df['Ask Price 1'], c = 'r' , label = 'Ask Price 1')\n",
    "plt.plot(df['Time'] , df['Bid Price 1'], c = 'g', label = 'Bid Price 1')\n",
    "plt.plot(df['Time'] , df['Ask Price 2'], c = 'r', alpha = 0.4, label = 'Ask Price 2')\n",
    "plt.plot(df['Time'] , df['Bid Price 2'], c = 'g', alpha = 0.4, label = 'Bid Price 2')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf60a2fd-a5c1-4b23-96f9-eb8b839685ae",
   "metadata": {},
   "source": [
    "# Fit Fake Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "373e5e04-bc70-4e31-ba34-56b03940a4ca",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Bin to custom bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d3cae40d-c22a-44f8-af2e-370040e840a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "lo_deep_Ask\n",
      "co_deep_Ask\n",
      "lo_top_Ask\n",
      "co_top_Ask\n",
      "mo_Ask\n",
      "lo_inspread_Ask\n",
      "lo_inspread_Bid\n",
      "mo_Bid\n",
      "co_top_Bid\n",
      "lo_top_Bid\n",
      "co_deep_Bid\n",
      "lo_deep_Bid\n",
      "done with binning\n"
     ]
    }
   ],
   "source": [
    "#### WARNING: THIS PIECE OF CODE TAKES A LONG TIME ####\n",
    "ric = \"fake\"\n",
    "for d in range(N):\n",
    "    l =  dataLoader.Loader(ric, d, d, dataPath = \"tmp//\")\n",
    "    df = pd.read_csv(l.dataPath+\"fake_\"+str(d)+\"_12D.csv\")\n",
    "    data = {str(d) : list(df.groupby('event')['Time'].apply(np.array)[cols].values)}\n",
    "    cls = ConditionalLeastSquaresLogLin.ConditionalLeastSquaresLogLin(data, loader = l) \n",
    "    cls.runTransformDate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80527ff9-c4e9-4a0f-a49e-585556aa687c",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Hawkes Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "38289b40-a9b8-4ef6-9dd4-147145ab3321",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning into 'nphc2'...\n"
     ]
    }
   ],
   "source": [
    "!pip install numba\n",
    "!git clone https://github.com/achab/nphc.git nphc2\n",
    "# in line 1 of nphc2/nphc/cumulants.py, remove the autojit import and line 235 remove decorator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "189377b2-4bbc-4b3d-8a70-b033c9a819fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: pip in c:\\program files\\windowsapps\\pythonsoftwarefoundation.python.3.12_3.12.2032.0_x64__qbz5n2kfra8p0\\lib\\site-packages (24.2)\n",
      "Collecting pip\n",
      "  Downloading pip-24.3.1-py3-none-any.whl.metadata (3.7 kB)\n",
      "Downloading pip-24.3.1-py3-none-any.whl (1.8 MB)\n",
      "   ---------------------------------------- 0.0/1.8 MB ? eta -:--:--\n",
      "   ----------------- ---------------------- 0.8/1.8 MB 5.6 MB/s eta 0:00:01\n",
      "   ---------------------------------------- 1.8/1.8 MB 5.3 MB/s eta 0:00:00\n",
      "Installing collected packages: pip\n",
      "Successfully installed pip-24.3.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  WARNING: The scripts pip.exe, pip3.12.exe and pip3.exe are installed in 'C:\\Users\\konar\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\Scripts' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n",
      "WARNING: Skipping tensorflow as it is not installed.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting tensorflow\n",
      "  Using cached tensorflow-2.18.0-cp312-cp312-win_amd64.whl.metadata (3.3 kB)\n",
      "Collecting tensorflow-intel==2.18.0 (from tensorflow)\n",
      "  Using cached tensorflow_intel-2.18.0-cp312-cp312-win_amd64.whl.metadata (4.9 kB)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (2.1.0)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (1.6.3)\n",
      "Requirement already satisfied: flatbuffers>=24.3.25 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (24.3.25)\n",
      "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (0.6.0)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (0.2.0)\n",
      "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (18.1.1)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (3.4.0)\n",
      "Requirement already satisfied: packaging in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (24.1)\n",
      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.3 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (5.28.3)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (2.32.3)\n",
      "Requirement already satisfied: setuptools in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (70.1.0)\n",
      "Requirement already satisfied: six>=1.12.0 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (1.16.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (2.5.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (4.12.2)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (1.17.0)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (1.68.0)\n",
      "Requirement already satisfied: tensorboard<2.19,>=2.18 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (2.18.0)\n",
      "Requirement already satisfied: keras>=3.5.0 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (3.6.0)\n",
      "Requirement already satisfied: numpy<2.1.0,>=1.26.0 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (2.0.0)\n",
      "Requirement already satisfied: h5py>=3.11.0 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (3.12.1)\n",
      "Requirement already satisfied: ml-dtypes<0.5.0,>=0.4.0 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.18.0->tensorflow) (0.4.1)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.18.0->tensorflow) (0.45.0)\n",
      "Requirement already satisfied: rich in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from keras>=3.5.0->tensorflow-intel==2.18.0->tensorflow) (13.9.4)\n",
      "Requirement already satisfied: namex in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from keras>=3.5.0->tensorflow-intel==2.18.0->tensorflow) (0.0.8)\n",
      "Requirement already satisfied: optree in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from keras>=3.5.0->tensorflow-intel==2.18.0->tensorflow) (0.13.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.18.0->tensorflow) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.18.0->tensorflow) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.18.0->tensorflow) (2.2.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.18.0->tensorflow) (2024.6.2)\n",
      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorboard<2.19,>=2.18->tensorflow-intel==2.18.0->tensorflow) (3.7)\n",
      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorboard<2.19,>=2.18->tensorflow-intel==2.18.0->tensorflow) (0.7.2)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorboard<2.19,>=2.18->tensorflow-intel==2.18.0->tensorflow) (3.1.3)\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from werkzeug>=1.0.1->tensorboard<2.19,>=2.18->tensorflow-intel==2.18.0->tensorflow) (2.1.5)\n",
      "Requirement already satisfied: markdown-it-py>=2.2.0 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from rich->keras>=3.5.0->tensorflow-intel==2.18.0->tensorflow) (3.0.0)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from rich->keras>=3.5.0->tensorflow-intel==2.18.0->tensorflow) (2.18.0)\n",
      "Requirement already satisfied: mdurl~=0.1 in c:\\users\\konar\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.5.0->tensorflow-intel==2.18.0->tensorflow) (0.1.2)\n",
      "Using cached tensorflow-2.18.0-cp312-cp312-win_amd64.whl (7.5 kB)\n",
      "Using cached tensorflow_intel-2.18.0-cp312-cp312-win_amd64.whl (390.3 MB)\n",
      "Installing collected packages: tensorflow-intel, tensorflow\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Could not install packages due to an OSError: [Errno 2] No such file or directory: 'C:\\\\Users\\\\konar\\\\AppData\\\\Local\\\\Packages\\\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\\\LocalCache\\\\local-packages\\\\Python312\\\\site-packages\\\\tensorflow\\\\include\\\\external\\\\com_github_grpc_grpc\\\\src\\\\core\\\\ext\\\\filters\\\\client_channel\\\\lb_policy\\\\grpclb\\\\client_load_reporting_filter.h'\n",
      "HINT: This error might have occurred since this system does not have Windows Long Path support enabled. You can find information on how to enable this at https://pip.pypa.io/warnings/enable-long-paths\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade pip\n",
    "!pip uninstall tensorflow\n",
    "!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "25b983da-0fe5-4472-b828-897c1a815690",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorflow.python'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[8], line 5\u001B[0m\n\u001B[0;32m      3\u001B[0m sys\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mappend(os\u001B[38;5;241m.\u001B[39mgetcwd()\u001B[38;5;241m+\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\\\\u001B[39;00m\u001B[38;5;124mnphc2\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m      4\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mmatplotlib\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mpyplot\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mplt\u001B[39;00m\n\u001B[1;32m----> 5\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mnphc\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mmain\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m NPHC\n\u001B[0;32m      6\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mpandas\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mpd\u001B[39;00m\n\u001B[0;32m      7\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mdatetime\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mdt\u001B[39;00m\n",
      "File \u001B[1;32m~\\IdeaProjects\\lobSimulations\\nphc2\\nphc\\main.py:5\u001B[0m\n\u001B[0;32m      3\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mscipy\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mlinalg\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m inv, qr, sqrtm, norm\n\u001B[0;32m      4\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mitertools\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m product\n\u001B[1;32m----> 5\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mtensorflow\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mtf\u001B[39;00m\n\u001B[0;32m      6\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mnumpy\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mnp\u001B[39;00m\n\u001B[0;32m      9\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mstarting_point\u001B[39m(cumulants_list,random\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m):\n",
      "File \u001B[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\tensorflow\\__init__.py:40\u001B[0m\n\u001B[0;32m     37\u001B[0m _os\u001B[38;5;241m.\u001B[39menviron\u001B[38;5;241m.\u001B[39msetdefault(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mENABLE_RUNTIME_UPTIME_TELEMETRY\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m1\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m     39\u001B[0m \u001B[38;5;66;03m# Do not remove this line; See https://github.com/tensorflow/tensorflow/issues/42596\u001B[39;00m\n\u001B[1;32m---> 40\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mtensorflow\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mpython\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m pywrap_tensorflow \u001B[38;5;28;01mas\u001B[39;00m _pywrap_tensorflow  \u001B[38;5;66;03m# pylint: disable=unused-import\u001B[39;00m\n\u001B[0;32m     41\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mtensorflow\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mpython\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mtools\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m module_util \u001B[38;5;28;01mas\u001B[39;00m _module_util\n\u001B[0;32m     42\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mtensorflow\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mpython\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mutil\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mlazy_loader\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m KerasLazyLoader \u001B[38;5;28;01mas\u001B[39;00m _KerasLazyLoader\n",
      "\u001B[1;31mModuleNotFoundError\u001B[0m: No module named 'tensorflow.python'"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "sys.path.append(os.getcwd()+\"\\\\nphc2\")\n",
    "import matplotlib.pyplot as plt\n",
    "from nphc.main import NPHC\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53acdd08-52ed-4ceb-a3a8-69a87a054561",
   "metadata": {},
   "outputs": [],
   "source": [
    "big_data = []\n",
    "cols = [\"lo_deep_Ask\", \"co_deep_Ask\", \"lo_top_Ask\",\"co_top_Ask\", \"mo_Ask\", \"lo_inspread_Ask\" ,\n",
    "            \"lo_inspread_Bid\" , \"mo_Bid\", \"co_top_Bid\", \"lo_top_Bid\", \"co_deep_Bid\",\"lo_deep_Bid\" ]\n",
    "for d in range(10): #pd.date_range(dt.date(2019,1,2), dt.date(2019,1,4)):\n",
    "    \n",
    "    df = pd.read_csv(\"./simulated_data/fake_\"+str(d)+\"_12D.csv\")\n",
    "    \n",
    "    timestamps = list(df.groupby('event')['Time'].apply(np.array)[cols].values)\n",
    "    big_data.append(timestamps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bb57b22-26c4-469e-88c8-7a1773162034",
   "metadata": {},
   "outputs": [],
   "source": [
    "nphc = NPHC()\n",
    "nphc.fit(big_data,half_width=1.,filtr=\"rectangular\",method=\"parallel_by_day\")\n",
    "cumulants_list = [nphc.L, nphc.C, nphc.K_c]\n",
    "from nphc.main import starting_point\n",
    "start_point = starting_point(cumulants_list, random=True)\n",
    "R_pred = nphc.solve(training_epochs=50000,display_step=500,learning_rate=1e-2,optimizer='adam')\n",
    "d = len(nphc.L[0])\n",
    "from scipy.linalg import inv\n",
    "G_pred = np.eye(d) - inv(R_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "529b88fc-2e33-45cb-82c9-e56d004aad9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (12,10)\n",
    "plt.rcParams['xtick.labelsize'] = 26\n",
    "plt.rcParams['ytick.labelsize'] = 26\n",
    "dimensions = cols \n",
    "sns_plot = sns.heatmap(G_pred.T,cmap=\"RdBu_r\",xticklabels=dimensions,yticklabels=dimensions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4afd169-dea0-4e7d-9aa4-bb47ded5a987",
   "metadata": {},
   "outputs": [],
   "source": [
    "boundsDict = {}\n",
    "for i in range(12):\n",
    "    for j in range(12):\n",
    "        boundsDict[cols[i]+\"->\"+cols[j]] = np.sign(G_pred[i][j])\n",
    "import pickle\n",
    "with open(os.getcwd()+\"/simulated_data/fake_2019-01-02_2019-03-31_graphDict\", \"wb\") as f:\n",
    "    pickle.dump(boundsDict, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d45714c-45dc-42da-a5c5-e200a80c2920",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## 12D Hawkes Non-Parametric Fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab3f9023-ef6f-4e1f-bbd3-73f424925b3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ric = \"fake\"\n",
    "dictIp = {}\n",
    "d = 0\n",
    "l =  dataLoader.Loader(ric, d, d, dataPath = \"./simulated_data/\")\n",
    "for d in range(10):\n",
    "    dictIp[str(d)] = []\n",
    "cls = fit.ConditionalLeastSquaresLogLin(dictIp, loader = l, solver=\"osqp\", path_dictTOD = \"./fakeData_Params_sod_eod_dictTOD_constt\")\n",
    "thetas = cls.fitConditionalInSpread(spreadBeta = 1., avgSpread = 1.)\n",
    "with open(l.dataPath + ric + \"_Params_2019-01-02_2019-03-31_CLSLogLin_19\" , \"wb\") as f:\n",
    "    pickle.dump(thetas, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e2b7749-4b38-4368-9b74-e00501f68169",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Infer power law params:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "595bc4d1-abc7-4998-838d-9fc208e78872",
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b = inference.run(dt.date(2019,1,2), dt.date(2019,3,31), ric = \"fake\" , avgSpread = 0.01, spreadBeta=1. , suffix = \"_CLSLogLin_19\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0714c74-6401-42ce-862b-7b573bb9ef41",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Simulate LOB from Fitted Fake Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "056bc6ee-7916-4cd2-8924-d557ff611d1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### WARNING: THIS PIECE OF CODE TAKES A LONG TIME ####\n",
    "for i in range(1):\n",
    "    T, lob, lobL3=simulate.simulate(100, \"fake_ParamsInferredWCutoff_2019-01-02_2019-01-31_CLSLogLin_19\", \"fakeData_Params_sod_eod_dictTOD_constt\", beta = 1., avgSpread = 0.01, spread0 = 5, price0 = 45)\n",
    "    with open(\"./simulated_data/fake_simulated_CLSLogLinFit_sod_eod_\"+ str(i) , \"wb\") as f:\n",
    "        pickle.dump((T, lob, lobL3), f)"
   ]
  }
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