Quantitative Momentum Strategy for Picking Top FTSE Stocks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c4b3888b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install quandl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc75d032",
   "metadata": {},
   "source": [
    "#### 1. Introduction\n",
    "#### 1.a. Quantitative Momentum Strategy\n",
    "#### 1.b. DataScope Select API\n",
    "#### 2. Importing required packages\n",
    "#### 3. Extracting list of all FTSE stock's\n",
    "#### 4. Pulling Intraday data of all the stocks in the FTSE using DataScope Select API\n",
    "#### 5. Calculating Percentage Change and Momentum of all Stocks\n",
    "#### 6. Finding stocks with greater momentum\n",
    "#### 7. Backtesting the strategy\n",
    "#### 8. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdfb7e3c",
   "metadata": {},
   "source": [
    "### Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3436dd7",
   "metadata": {},
   "source": [
    "With tens of thousands of stocks to choose from, how do we go about selecting a few worth buying? Stock news, uncertainty and emotions add to the bitterness of this process. Whatever some experts suggest, it is impossible to go through all balance sheets to identify companies with low net debt and improving net profit margins.\n",
    "\n",
    "This article uses a simple quantitative momentum strategy using Python and Refinitiv's DataScope Select (DSS) API. It filters and selects the best Intraday stocks.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87c10de3",
   "metadata": {},
   "source": [
    "In Physics, the term Momentum is used to define an object’s quantity and direction of motion. Similarly in financial markets, the momentum of an asset is the direction and speed of price change of the asset in the market."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aa2547f",
   "metadata": {},
   "source": [
    "#### What is Quantitative Momentum Strategy?\n",
    "Quantitative Momentum is an investment strategy which selects stocks for investment whose price increased the most during a period. It is the process of identifying stocks with a great uptrend.\n",
    "\n",
    "Momentum is considered a primary stock factor (a.k.a anomaly, or smart-beta factor) affecting stock returns. Academic research and practitioners’ experience show that Momentum has been outperforming the stock indices all over the world"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "b5a494fd",
   "metadata": {},
   "source": [
    "#### DataScope Select API (DSS API)\n",
    "The DataScope Select APIs offer programmatic access to Refinitiv content in a more flexible way than the browser- and FTP-based data access solutions. They can be used to create an infinite number of client applications written in any desired programming language, for example, C#, C++, Visual Basic, Java, Objective-C and Swift.\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "DSS REST API supports the management of instrument and entity lists, report templates and schedules for the purpose of requesting extractions. It also supports higher level calls that can make data extractions directly, without requiring the creation of instrument lists, report templates or schedules on the DSS server.\n",
    "\n",
    "For more information, please read [here](https://developers.refinitiv.com/en/api-catalog/datascope-select/datascope-select-rest-api).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c51565e1",
   "metadata": {},
   "source": [
    "### Importing required packages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cad72d73",
   "metadata": {},
   "source": [
    "Let's see how this strategy can be implemented in Python using DSS API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "388687f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import gzip\n",
    "import pandas as pd\n",
    "import requests\n",
    "import numpy as np\n",
    "from scipy.stats import percentileofscore as score\n",
    "from math import floor\n",
    "from json import dumps, loads\n",
    "from requests import get, post\n",
    "from time import sleep \n",
    "from getpass import getpass, GetPassWarning\n",
    "from collections import OrderedDict\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "matplotlib.rc_file_defaults()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c41466ab",
   "metadata": {},
   "source": [
    "Here, `Pandas` package is used to deal with data, the `NumPy` package is used to work with arrays, the `Requests` package is used to pull data using DSS API, the `Math` package is used for mathematical functions, the `SciPy` package is used for complex functions, and the `Statistics` package is used for statistical functions, and few other packages are also used for support."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4912e3ac",
   "metadata": {},
   "source": [
    "After importing all the required packages into our python environment, let's define a function to authenticate connection with DSS API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e73bc8ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "def RequestNewToken(username=\"\",password=\"\"):\n",
    "    '''\n",
    "    username: DSS API login id\n",
    "    password: DSS API login password\n",
    "    '''\n",
    "    _AuthenURL = \"https://selectapi.datascope.refinitiv.com/RestApi/v1/Authentication/RequestToken\"\n",
    "    _header= {}\n",
    "    _header['Prefer']='respond-async'\n",
    "    _header['Content-Type']='application/json; odata.metadata=minimal'\n",
    "    _data={'Credentials':{\n",
    "        'Password':password,\n",
    "        'Username':username\n",
    "        }\n",
    "    }\n",
    "\n",
    "    print(\"Send Login request\")\n",
    "    resp=post(_AuthenURL,json=_data,headers=_header)\n",
    "\n",
    "    if resp.status_code!=200:\n",
    "        message=\"Authentication Error Status Code: \"+ str(resp.status_code) +\" Message:\"+dumps(loads(resp.text),indent=4)\n",
    "        raise Exception(str(message))\n",
    "\n",
    "    return loads(resp.text)['value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f2e35b92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Username: 9030588\n",
      "Password: ········\n",
      "Send Login request\n",
      "Token=_H2Ue4WChJVGf31efJPd9jUpNN9r6-x72Iy9gFfCSJAMBCFGcrVhkjESeUMCNeiNmusp6m29KQKS3Fge52Pmt_3bSCVEv2Uxy0DlcPz09A1sJ3biBPF-6qeUNpfp6yudwVjZwSXaL3fn28TIqCnFNJMEnx7ST1o4HymWMze7Qu-z6UfNyZJas39nglkdIHiDrUW4HX67m5H0Wmj4gEzuhhZlXl3GrX_w08QGVxVe7aOkoJaMtlRV1irD5ShQgPTXgiPWkvfrQ_oJsx4KoNIJfijlfB_brGME8lv8hKV5DeJQ\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Taking DSS username and password input from user\n",
    "_DSSUsername = input('Username: ')\n",
    "_DSSPassword = getpass('Password: ')\n",
    "try:\n",
    "    _token = RequestNewToken(_DSSUsername,_DSSPassword)\n",
    "    print(\"Token=\"+_token+\"\\n\")\n",
    "except GetPassWarning as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bed64fa",
   "metadata": {},
   "source": [
    "This authentication token will be valid for 24hrs from the time of generation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8e89967",
   "metadata": {},
   "source": [
    "### Extracting List of all FTSE 100 Stock’s "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e44b1a",
   "metadata": {},
   "source": [
    "Next we are going to extract the list of all FTSE 100 stock’s using `0#.FTSE` ChainRic from DSS API.\n",
    "\n",
    "For this let's define a function to expand this ChainRic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6882cd7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ExpandChain(token,json_payload):\n",
    "    '''\n",
    "    token: Authentication token generated from the above step\n",
    "    json_payload: Sample json object which application parses to get the RIC list by accessing the value of Constituents.\n",
    "    '''\n",
    "    _expandChainURL = \"https://selectapi.datascope.refinitiv.com/RestApi/v1/Search/HistoricalChainResolution\"\n",
    "    _header = {}\n",
    "    _header['Prefer'] = 'respond-async'\n",
    "    _header['Content-Type'] = 'application/json; odata.metadata=minimal'\n",
    "    _header['Accept-Charset'] = 'UTF-8'\n",
    "    _header['Authorization'] = 'Token' + token\n",
    "    resp = post(_expandChainURL, data=None, json=json_payload, headers=_header)\n",
    "    \n",
    "    dataFrame= pd.DataFrame()\n",
    "    if(resp.status_code==200):\n",
    "        json_object=loads(resp.text,object_pairs_hook=OrderedDict)\n",
    "        if len(json_object['value']) > 0:\n",
    "            dataFrame = pd.DataFrame.from_dict(json_object['value'][0]['Constituents'])\n",
    "    else:\n",
    "        print(\"Unable to expand chain response return status code:\",resp.status_code)\n",
    "        \n",
    "    return dataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a028807d",
   "metadata": {},
   "source": [
    "In this code,we have used DSS API to extract the RICs and it's status of the stocks available in the <b>FTSE 100</b> by sending request to DSS's RestAPI using `request` package. This function will post the request to `HistoricalChainResolution` and will return the data in dataframe format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a648773b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining ChainRic, start date and end date\n",
    "_chainRIC = \"0#.FTSE\"\n",
    "_startDate = \"2022-01-13\"\n",
    "_endDate = \"2022-01-13\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76cf9caa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total 103 RICs available in 0#.FTSE\n",
      "    Identifier Status\n",
      "1        .FTSE  Valid\n",
      "2     .AD.FTSE  Valid\n",
      "3        AAL.L  Valid\n",
      "4       ABDN.L  Valid\n",
      "5        ABF.L  Valid\n",
      "6       ADML.L  Valid\n",
      "7        AHT.L  Valid\n",
      "8       ANTO.L  Valid\n",
      "9      AUTOA.L  Valid\n",
      "10        AV.L  Valid\n",
      "11      AVST.L  Valid\n",
      "12       AVV.L  Valid\n",
      "13       AZN.L  Valid\n",
      "14      BAES.L  Valid\n",
      "15      BARC.L  Valid\n",
      "16      BATS.L  Valid\n",
      "17      BDEV.L  Valid\n",
      "18      BHPB.L  Valid\n",
      "19      BKGH.L  Valid\n",
      "20      BLND.L  Valid\n",
      "21      BMEB.L  Valid\n",
      "22      BNZL.L  Valid\n",
      "23        BP.L  Valid\n",
      "24      BRBY.L  Valid\n",
      "25        BT.L  Valid\n",
      "26       CCH.L  Valid\n",
      "27       CPG.L  Valid\n",
      "28      CRDA.L  Valid\n",
      "29       CRH.L  Valid\n",
      "30       DCC.L  Valid\n",
      "31       DGE.L  Valid\n",
      "32       DPH.L  Valid\n",
      "33       ECM.L  Valid\n",
      "34       ENT.L  Valid\n",
      "35      EVRE.L  Valid\n",
      "36      EXPN.L  Valid\n",
      "37      FERG.L  Valid\n",
      "38     FLTRF.L  Valid\n",
      "39      FRES.L  Valid\n",
      "40      GLEN.L  Valid\n",
      "41       GSK.L  Valid\n",
      "42       HIK.L  Valid\n",
      "43      HLMA.L  Valid\n",
      "44      HRGV.L  Valid\n",
      "45      HSBA.L  Valid\n",
      "46      ICAG.L  Valid\n",
      "47       ICP.L  Valid\n",
      "48       IHG.L  Valid\n",
      "49       III.L  Valid\n",
      "50       IMB.L  Valid\n",
      "51       INF.L  Valid\n",
      "52      ITRK.L  Valid\n",
      "53       ITV.L  Valid\n",
      "54        JD.L  Valid\n",
      "55       KGF.L  Valid\n",
      "56      LAND.L  Valid\n",
      "57      LGEN.L  Valid\n",
      "58      LLOY.L  Valid\n",
      "59      LSEG.L  Valid\n",
      "60      MGGT.L  Valid\n",
      "61      MNDI.L  Valid\n",
      "62       MNG.L  Valid\n",
      "63      MRON.L  Valid\n",
      "64        NG.L  Valid\n",
      "65       NWG.L  Valid\n",
      "66       NXT.L  Valid\n",
      "67      OCDO.L  Valid\n",
      "68      PHNX.L  Valid\n",
      "69     POLYP.L  Valid\n",
      "70       PRU.L  Valid\n",
      "71      PSHP.L  Valid\n",
      "72       PSN.L  Valid\n",
      "73      PSON.L  Valid\n",
      "74      RDSa.L  Valid\n",
      "75      RDSb.L  Valid\n",
      "76       REL.L  Valid\n",
      "77       RIO.L  Valid\n",
      "78       RKT.L  Valid\n",
      "79       RMG.L  Valid\n",
      "80       RMV.L  Valid\n",
      "81        RR.L  Valid\n",
      "82       RTO.L  Valid\n",
      "83      SBRY.L  Valid\n",
      "84       SDR.L  Valid\n",
      "85       SGE.L  Valid\n",
      "86      SGRO.L  Valid\n",
      "87       SJP.L  Valid\n",
      "88       SKG.L  Valid\n",
      "89      SMDS.L  Valid\n",
      "90      SMIN.L  Valid\n",
      "91       SMT.L  Valid\n",
      "92        SN.L  Valid\n",
      "93       SPX.L  Valid\n",
      "94       SSE.L  Valid\n",
      "95      STAN.L  Valid\n",
      "96       SVT.L  Valid\n",
      "97      TSCO.L  Valid\n",
      "98        TW.L  Valid\n",
      "99      ULVR.L  Valid\n",
      "100       UU.L  Valid\n",
      "101      VOD.L  Valid\n",
      "102      WPP.L  Valid\n",
      "103      WTB.L  Valid\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    if(_token!=\"\"):\n",
    "        # Sample JSON body \n",
    "        _jsonquery={\n",
    "                    \"Request\": {\n",
    "                        \"ChainRics\": [\n",
    "                                _chainRIC\n",
    "                                ],\n",
    "                        \"Range\": {\n",
    "                            \"Start\": _startDate,\n",
    "                            \"End\": _endDate\n",
    "                        }\n",
    "                     }\n",
    "        }\n",
    "        df=ExpandChain(_token,_jsonquery)\n",
    "        if df.empty:\n",
    "            print(\"Unable to expand chain \"+_chainRIC) \n",
    "        else:\n",
    "            ricCount=len(df['Identifier'])\n",
    "            print(\"Total \"+str(ricCount)+\" RICs available in \"+ _chainRIC)\n",
    "            # Filter and print only RIC name and Status columns\n",
    "            pd.set_option('display.max_rows', ricCount)\n",
    "            ftse_ric = df.filter(items=['Identifier','Status'])\n",
    "            ftse_ric.index = np.arange(1,len(ftse_ric)+1)\n",
    "            print(ftse_ric)\n",
    "            pd.reset_option('display.max_rows')\n",
    "            \n",
    "except Exception as ex:\n",
    "    print(\"Exception occrus:\", ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "89890e66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['.FTSE' '.AD.FTSE' 'AAL.L' 'ABDN.L' 'ABF.L' 'ADML.L' 'AHT.L' 'ANTO.L'\n",
      " 'AUTOA.L' 'AV.L' 'AVST.L' 'AVV.L' 'AZN.L' 'BAES.L' 'BARC.L' 'BATS.L'\n",
      " 'BDEV.L' 'BHPB.L' 'BKGH.L' 'BLND.L' 'BMEB.L' 'BNZL.L' 'BP.L' 'BRBY.L'\n",
      " 'BT.L' 'CCH.L' 'CPG.L' 'CRDA.L' 'CRH.L' 'DCC.L' 'DGE.L' 'DPH.L' 'ECM.L'\n",
      " 'ENT.L' 'EVRE.L' 'EXPN.L' 'FERG.L' 'FLTRF.L' 'FRES.L' 'GLEN.L' 'GSK.L'\n",
      " 'HIK.L' 'HLMA.L' 'HRGV.L' 'HSBA.L' 'ICAG.L' 'ICP.L' 'IHG.L' 'III.L'\n",
      " 'IMB.L' 'INF.L' 'ITRK.L' 'ITV.L' 'JD.L' 'KGF.L' 'LAND.L' 'LGEN.L'\n",
      " 'LLOY.L' 'LSEG.L' 'MGGT.L' 'MNDI.L' 'MNG.L' 'MRON.L' 'NG.L' 'NWG.L'\n",
      " 'NXT.L' 'OCDO.L' 'PHNX.L' 'POLYP.L' 'PRU.L' 'PSHP.L' 'PSN.L' 'PSON.L'\n",
      " 'RDSa.L' 'RDSb.L' 'REL.L' 'RIO.L' 'RKT.L' 'RMG.L' 'RMV.L' 'RR.L' 'RTO.L'\n",
      " 'SBRY.L' 'SDR.L' 'SGE.L' 'SGRO.L' 'SJP.L' 'SKG.L' 'SMDS.L' 'SMIN.L'\n",
      " 'SMT.L' 'SN.L' 'SPX.L' 'SSE.L' 'STAN.L' 'SVT.L' 'TSCO.L' 'TW.L' 'ULVR.L'\n",
      " 'UU.L' 'VOD.L' 'WPP.L' 'WTB.L']\n"
     ]
    }
   ],
   "source": [
    "# List of RICs available in FTSE100\n",
    "ftse_list = ftse_ric.Identifier.values\n",
    "print(ftse_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af6fcf9a",
   "metadata": {},
   "source": [
    "We first extracted the RICs and Status of the stocks in the FTSE 100 using the `ExpandChain` function and stored it into the <i>ftse_ric</i> dataframe. Then we defined a variable ‘ftse_list’ to store the extracted data into a NumPy array. \n",
    "\n",
    "To learn more about \"How to expand Chain RIC using the Tick History REST API in Python\", visit [here](https://developers.refinitiv.com/en/article-catalog/article/how-expand-chain-ric-using-tick-history-rest-api-python)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89d3218d",
   "metadata": {},
   "source": [
    "### Pulling Intraday data of all the stocks in the FTSE using DataScope Select API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94e4b8c6",
   "metadata": {},
   "source": [
    "We have seen list of all the FTSE 100 stock’s RICs, now let’s pull some intraday data using DSS API."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95599e60",
   "metadata": {},
   "source": [
    "For this let's define a function to extract data first for the given ChainRic, i.e., `#0.FTSE`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1d584b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ExtractRaw(token,json_payload):\n",
    "    try:\n",
    "        _extractRawURL=\"https://selectapi.datascope.refinitiv.com/RestApi/v1/Extractions/ExtractRaw\"\n",
    "        # Setup Request Header\n",
    "        _header={}\n",
    "        _header['Prefer']='respond-async'\n",
    "        _header['Content-Type']='application/json; odata.metadata=minimal'\n",
    "        _header['Accept-Charset']='UTF-8'\n",
    "        _header['Authorization']='Token'+token\n",
    "\n",
    "        # Post Http Request to DSS server using extract raw URL\n",
    "        resp=post(_extractRawURL,data=None,json=json_payload,headers=_header)\n",
    "\n",
    "        # Print Status Code return from HTTP Response\n",
    "        print(\"Status Code=\"+str(resp.status_code) )\n",
    "\n",
    "        # Raise exception with error message if the returned status is not 202 (Accepted) or 200 (Ok)\n",
    "        if resp.status_code!=200:\n",
    "            if resp.status_code!=202:\n",
    "                message=\"Error: Status Code:\"+str(resp.status_code)+\" Message:\"+resp.text\n",
    "                raise Exception(message)\n",
    "\n",
    "            # Get location from header, URL must be https so we need to change it using string replace function\n",
    "            _location=str.replace(resp.headers['Location'],\"http://\",\"https://\")\n",
    "\n",
    "            print(\"Get Status from \"+str(_location))\n",
    "            _jobID=\"\"\n",
    "\n",
    "            # Pooling loop to check request status every 30 sec.\n",
    "            while True:\n",
    "                resp=get(_location,headers=_header)\n",
    "                _pollstatus = int(resp.status_code)\n",
    "\n",
    "                if _pollstatus==200:\n",
    "                    break\n",
    "                else:\n",
    "                    print(\"Status:\"+str(resp.headers['Status']))\n",
    "                sleep(retryInterval) # wait for _retyInterval period and re-request the status to check if it already completed\n",
    "\n",
    "        # Get the jobID from HTTP response\n",
    "        json_resp = loads(resp.text)\n",
    "        _jobID = json_resp.get('JobId')\n",
    "        print(\"Status is completed the JobID is \"+ str(_jobID)+ \"\\n\")\n",
    "\n",
    "        # Check if the response contains Notes.If the note exists print it to console.\n",
    "        if len(json_resp.get('Notes')) > 0:\n",
    "            print(\"Notes:\\n======================================\")\n",
    "            for var in json_resp.get('Notes'):\n",
    "                print(var)\n",
    "            print(\"======================================\\n\")\n",
    "\n",
    "        # Request should be completed then Get the result by passing jobID to RAWExtractionResults URL\n",
    "        _getResultURL = str(\"https://selectapi.datascope.refinitiv.com/RestApi/v1/Extractions/RawExtractionResults(\\'\" + _jobID + \"\\')/$value\")\n",
    "        print(\"Retrieve result from \" + _getResultURL)\n",
    "        resp=get(_getResultURL,headers=_header,stream=True)\n",
    "\n",
    "        # Write Output to file\n",
    "        outputfilepath = str(\"FTSE_100_\" + str(os.getpid()) + '.csv.gz')\n",
    "        if resp.status_code==200:\n",
    "            with open(outputfilepath, 'wb') as f:\n",
    "                f.write(resp.raw.read())\n",
    "\n",
    "        # Read data from csv.gz and shows output from dataframe head() and tail() \n",
    "        df=pd.read_csv(outputfilepath,compression='gzip')\n",
    "        return df\n",
    "\n",
    "    except Exception as ex:\n",
    "        print(\"Exception occrus:\", ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d918e94e",
   "metadata": {},
   "source": [
    "In this function as well, we have used DSS API to extract the data for all the RICs of <b>FTSE 100</b> by sending request to DSS's RestAPI using request package. We first setup the request header and post the request to `ExtractRaw` to fetch data.\n",
    "\n",
    "The fetched data will be written to a csv file using `gzip` compression, will be loaded to a dataframe and returned.\n",
    "\n",
    "In case, if there will be any exception that will be raised, for example if the request's returned status is not `Accepted` or `Ok`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "75c0769b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# value in second used by Pooling loop to check request status on the server\n",
    "retryInterval=int(30) \n",
    "\n",
    "# Sample JSON for FTSE ChainRic for pulling data within the given date range\n",
    "ftse_json = {\n",
    "    \"ExtractionRequest\": {\n",
    "        \"@odata.type\": \"#DataScope.Select.Api.Extractions.ExtractionRequests.TickHistoryIntradaySummariesExtractionRequest\",\n",
    "        \"ContentFieldNames\": [\n",
    "            \"High\",\n",
    "            \"Last\",\n",
    "            \"Low\",\n",
    "            \"Open\",\n",
    "            \"Volume\"\n",
    "        ],\n",
    "       \"IdentifierList\": {\n",
    "            \"@odata.type\": \"#DataScope.Select.Api.Extractions.ExtractionRequests.InstrumentIdentifierList\",\n",
    "            \"InstrumentIdentifiers\": [{\n",
    "                \"Identifier\": \"0#.FTSE\",\n",
    "                \"IdentifierType\": \"ChainRIC\"\n",
    "            }\n",
    "            ],\n",
    "            \"UseUserPreferencesForValidationOptions\" : \"False\"\n",
    "        },\n",
    "        \"Condition\": {\n",
    "            \"MessageTimeStampIn\": \"LocalExchangeTime\",\n",
    "            \"ReportDateRangeType\": \"Range\",\n",
    "            \"QueryStartDate\": \"2022-01-13T00:00:00.000000000Z\",\n",
    "            \"QueryEndDate\": \"2022-01-13T23:59:59.999999999Z\",\n",
    "            \"SummaryInterval\": \"OneMinute\",\n",
    "            \"DisplaySourceRIC\": \"True\"\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "089c9006",
   "metadata": {},
   "source": [
    "Here, we have defined `retryInterval` variable for re-requesting the status after it's value (in second) to check if post request is already completed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "35825bd0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status Code=202\n",
      "Get Status from https://selectapi.datascope.refinitiv.com/RestApi/v1/Extractions/ExtractRawResult(ExtractionId='0x07dd8e08527d7c8f')\n",
      "Status:InProgress\n",
      "Status:InProgress\n",
      "Status:InProgress\n",
      "Status is completed the JobID is 0x07dd8e08527d7c8f\n",
      "\n",
      "Notes:\n",
      "======================================\n",
      "Extraction Services Version 15.2.42998 (497fbb60729e), Built Nov 11 2021 19:36:12\n",
      "User ID: 9030588\n",
      "Extraction ID: 2000000353107235\n",
      "Correlation ID: CiD/9030588/0x0000000000000000/REST API/EXT.2000000353107235\n",
      "Schedule: 0x07dd8e08527d7c8f (ID = 0x0000000000000000)\n",
      "Input List (1 items):  (ID = 0x07dd8e08527d7c8f) Created: 2022/01/17 02:11:49 Last Modified: 2022/01/17 02:11:49\n",
      "Report Template (5 fields): _OnD_0x07dd8e08527d7c8f (ID = 0x07dd8e08528d7c8f) Created: 2022/01/17 02:08:04 Last Modified: 2022/01/17 02:08:04\n",
      "Schedule dispatched via message queue (0x07dd8e08527d7c8f), Data source identifier (EC905BA932A744FC8E2F0C9C3BADE040)\n",
      "Schedule Time: 2022/01/17 02:08:04\n",
      "Processing started at 2022/01/17 02:08:04\n",
      "Processing completed successfully at 2022/01/17 02:11:49\n",
      "Extraction finished at 2022/01/17 02:11:49 UTC, with servers: tm02n02, TRTH (182.737 secs)\n",
      "Instrument <CHR,0#.FTSE> expanded to 103 RIC instances: .AD.FTSE to WTB.L.\n",
      "Total instruments after instrument expansion = 103\n",
      "\n",
      "Quota Message: INFO: Tick History Cash Quota Count Before Extraction: 10179; Instruments Approved for Extraction: 88; Tick History Cash Quota Count After Extraction: 10179, 2035.8% of Limit; Tick History Cash Quota Limit: 500\n",
      "Quota Message: ERROR: The RIC 'ABDN.L' in the request would exceed your quota limits. Adjust your input list to continue.\n",
      "Quota Message: ERROR: The RIC 'AVST.L' in the request would exceed your quota limits. Adjust your input list to continue.\n",
      "Quota Message: ERROR: The RIC 'BHPB.L' in the request would exceed your quota limits. Adjust your input list to continue.\n",
      "Quota Message: ERROR: The RIC 'CRH.L' in the request would exceed your quota limits. Adjust your input list to continue.\n",
      "Quota Message: ERROR: The RIC 'ENT.L' in the request would exceed your quota limits. Adjust your input list to continue.\n",
      "Quota Message: ERROR: The RIC 'FERG.L' in the request would exceed your quota limits. Adjust your input list to continue.\n",
      "Quota Message: ERROR: The RIC 'FLTRF.L' in the request would exceed your quota limits. Adjust your input list to continue.\n",
      "Quota Message: ERROR: The RIC 'LSEG.L' in the request would exceed your quota limits. Adjust your input list to continue.\n",
      "Quota Message: ERROR: The RIC 'MNG.L' in the request would exceed your quota limits. Adjust your input list to continue.\n",
      "Quota Message: ERROR: The RIC 'NWG.L' in the request would exceed your quota limits. Adjust your input list to continue.\n",
      "Quota Message: ERROR: The RIC 'PSHP.L' in the request would exceed your quota limits. Adjust your input list to continue.\n",
      "Quota Message: ERROR: The RIC 'RDSa.L' in the request would exceed your quota limits. Adjust your input list to continue.\n",
      "Quota Message: ERROR: The RIC 'RKT.L' in the request would exceed your quota limits. Adjust your input list to continue.\n",
      "Quota Message: ERROR: The RIC 'SKG.L' in the request would exceed your quota limits. Adjust your input list to continue.\n",
      "Quota Message: WARNING: Tick History Cash Quota has been reached or exceeded\n",
      "Quota Message: Note: Quota has exceeded, however, it is not being enforced at this time but you can still make your extractions and instruments are still being counted. Please contact your Account Manager for questions. \n",
      "Manifest: #RIC,Domain,Start,End,Status,Count\n",
      "Manifest: .AD.FTSE,Market Price,,,Inactive,0\n",
      "Manifest: .FTSE,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,511\n",
      "Manifest: AAL.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,506\n",
      "Manifest: ABDN.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,340\n",
      "Manifest: ABF.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,297\n",
      "Manifest: ADML.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,335\n",
      "Manifest: AHT.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:37:00.000000000Z,Active,409\n",
      "Manifest: ANTO.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,395\n",
      "Manifest: AUTOA.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,339\n",
      "Manifest: AV.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,477\n",
      "Manifest: AVST.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,173\n",
      "Manifest: AVV.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,415\n",
      "Manifest: AZN.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:37:00.000000000Z,Active,499\n",
      "Manifest: BAES.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:56:00.000000000Z,Active,362\n",
      "Manifest: BARC.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T17:09:00.000000000Z,Active,508\n",
      "Manifest: BATS.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,485\n",
      "Manifest: BDEV.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,448\n",
      "Manifest: BHPB.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:39:00.000000000Z,Active,501\n",
      "Manifest: BKGH.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,340\n",
      "Manifest: BLND.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,306\n",
      "Manifest: BMEB.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,380\n",
      "Manifest: BNZL.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:55:00.000000000Z,Active,299\n",
      "Manifest: BP.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:49:00.000000000Z,Active,514\n",
      "Manifest: BRBY.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,418\n",
      "Manifest: BT.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:45:00.000000000Z,Active,488\n",
      "Manifest: CCH.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:38:00.000000000Z,Active,274\n",
      "Manifest: CPG.L,Market Price,2022-01-13T07:15:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,432\n",
      "Manifest: CRDA.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,411\n",
      "Manifest: CRH.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,306\n",
      "Manifest: DCC.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:38:00.000000000Z,Active,301\n",
      "Manifest: DGE.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:45:00.000000000Z,Active,508\n",
      "Manifest: DPH.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,299\n",
      "Manifest: ECM.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,217\n",
      "Manifest: ENT.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,404\n",
      "Manifest: EVRE.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,351\n",
      "Manifest: EXPN.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:44:00.000000000Z,Active,457\n",
      "Manifest: FERG.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:45:00.000000000Z,Active,432\n",
      "Manifest: FLTRF.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,439\n",
      "Manifest: FRES.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,270\n",
      "Manifest: GLEN.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T17:14:00.000000000Z,Active,511\n",
      "Manifest: GSK.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T17:08:00.000000000Z,Active,508\n",
      "Manifest: HIK.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:38:00.000000000Z,Active,214\n",
      "Manifest: HLMA.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,364\n",
      "Manifest: HRGV.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,292\n",
      "Manifest: HSBA.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:45:00.000000000Z,Active,504\n",
      "Manifest: ICAG.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:39:00.000000000Z,Active,500\n",
      "Manifest: ICP.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:58:00.000000000Z,Active,264\n",
      "Manifest: IHG.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,373\n",
      "Manifest: III.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T17:10:00.000000000Z,Active,381\n",
      "Manifest: IMB.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:45:00.000000000Z,Active,445\n",
      "Manifest: INF.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,350\n",
      "Manifest: ITRK.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:37:00.000000000Z,Active,299\n",
      "Manifest: ITV.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:36:00.000000000Z,Active,348\n",
      "Manifest: JD.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:38:00.000000000Z,Active,460\n",
      "Manifest: KGF.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:54:00.000000000Z,Active,432\n",
      "Manifest: LAND.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:52:00.000000000Z,Active,301\n",
      "Manifest: LGEN.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:45:00.000000000Z,Active,477\n",
      "Manifest: LLOY.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:38:00.000000000Z,Active,510\n",
      "Manifest: LSEG.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:48:00.000000000Z,Active,461\n",
      "Manifest: MGGT.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:54:00.000000000Z,Active,167\n",
      "Manifest: MNDI.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,377\n",
      "Manifest: MNG.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,336\n",
      "Manifest: MRON.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T17:08:00.000000000Z,Active,363\n",
      "Manifest: NG.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,466\n",
      "Manifest: NWG.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,461\n",
      "Manifest: NXT.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:45:00.000000000Z,Active,445\n",
      "Manifest: OCDO.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:45:00.000000000Z,Active,388\n",
      "Manifest: PHNX.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:45:00.000000000Z,Active,452\n",
      "Manifest: POLYP.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,435\n",
      "Manifest: PRU.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:37:00.000000000Z,Active,393\n",
      "Manifest: PSHP.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,76\n",
      "Manifest: PSN.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,490\n",
      "Manifest: PSON.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,241\n",
      "Manifest: RDSa.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:50:00.000000000Z,Active,512\n",
      "Manifest: RDSb.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:50:00.000000000Z,Active,512\n",
      "Manifest: REL.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:38:00.000000000Z,Active,373\n",
      "Manifest: RIO.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:45:00.000000000Z,Active,504\n",
      "Manifest: RKT.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:48:00.000000000Z,Active,455\n",
      "Manifest: RMG.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:51:00.000000000Z,Active,449\n",
      "Manifest: RMV.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,343\n",
      "Manifest: RR.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,487\n",
      "Manifest: RTO.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,418\n",
      "Manifest: SBRY.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,407\n",
      "Manifest: SDR.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,267\n",
      "Manifest: SGE.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,432\n",
      "Manifest: SGRO.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,352\n",
      "Manifest: SJP.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,311\n",
      "Manifest: SKG.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:36:00.000000000Z,Active,277\n",
      "Manifest: SMDS.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,348\n",
      "Manifest: SMIN.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,330\n",
      "Manifest: SMT.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T17:05:00.000000000Z,Active,497\n",
      "Manifest: SN.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:45:00.000000000Z,Active,459\n",
      "Manifest: SPX.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,345\n",
      "Manifest: SSE.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:52:00.000000000Z,Active,408\n",
      "Manifest: STAN.L,Market Price,2022-01-13T07:15:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,441\n",
      "Manifest: SVT.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T17:13:00.000000000Z,Active,325\n",
      "Manifest: TSCO.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T17:13:00.000000000Z,Active,507\n",
      "Manifest: TW.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T17:11:00.000000000Z,Active,493\n",
      "Manifest: ULVR.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:35:00.000000000Z,Active,504\n",
      "Manifest: UU.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:45:00.000000000Z,Active,292\n",
      "Manifest: VOD.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:45:00.000000000Z,Active,506\n",
      "Manifest: WPP.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:39:00.000000000Z,Active,363\n",
      "Manifest: WTB.L,Market Price,2022-01-13T08:00:00.000000000Z,2022-01-13T16:45:00.000000000Z,Active,416\n",
      "\n",
      "======================================\n",
      "\n",
      "Retrieve result from https://selectapi.datascope.refinitiv.com/RestApi/v1/Extractions/RawExtractionResults('0x07dd8e08527d7c8f')/$value\n"
     ]
    }
   ],
   "source": [
    "# Read the HTTP request body for the FTSE JSON defined above.\n",
    "ftse_df = ExtractRaw(_token,ftse_json)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffb38b96",
   "metadata": {},
   "source": [
    "Here, we can see the Status and progress of the `ExtractRaw` function. Once the data is fetched completely, few metadata for each of the RICs is displayed such as #RIC, Domain, Start, End, Status, and Count. We can also view all the data values fetched using the last url printed at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ef407934",
   "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>#RIC</th>\n",
       "      <th>Alias Underlying RIC</th>\n",
       "      <th>Domain</th>\n",
       "      <th>Date-Time</th>\n",
       "      <th>Type</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>.FTSE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Market Price</td>\n",
       "      <td>2022-01-13T08:00:00.000000000Z</td>\n",
       "      <td>Intraday 1Min</td>\n",
       "      <td>7551.70</td>\n",
       "      <td>7553.41</td>\n",
       "      <td>7534.32</td>\n",
       "      <td>7537.40</td>\n",
       "      <td>11701819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>.FTSE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Market Price</td>\n",
       "      <td>2022-01-13T08:01:00.000000000Z</td>\n",
       "      <td>Intraday 1Min</td>\n",
       "      <td>7537.45</td>\n",
       "      <td>7541.93</td>\n",
       "      <td>7537.45</td>\n",
       "      <td>7540.01</td>\n",
       "      <td>4144926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>.FTSE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Market Price</td>\n",
       "      <td>2022-01-13T08:02:00.000000000Z</td>\n",
       "      <td>Intraday 1Min</td>\n",
       "      <td>7539.95</td>\n",
       "      <td>7540.36</td>\n",
       "      <td>7536.60</td>\n",
       "      <td>7537.22</td>\n",
       "      <td>3154307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>.FTSE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Market Price</td>\n",
       "      <td>2022-01-13T08:03:00.000000000Z</td>\n",
       "      <td>Intraday 1Min</td>\n",
       "      <td>7537.18</td>\n",
       "      <td>7538.59</td>\n",
       "      <td>7536.81</td>\n",
       "      <td>7537.36</td>\n",
       "      <td>4674796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>.FTSE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Market Price</td>\n",
       "      <td>2022-01-13T08:04:00.000000000Z</td>\n",
       "      <td>Intraday 1Min</td>\n",
       "      <td>7537.06</td>\n",
       "      <td>7540.01</td>\n",
       "      <td>7536.89</td>\n",
       "      <td>7539.78</td>\n",
       "      <td>3403918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    #RIC  Alias Underlying RIC        Domain                       Date-Time  \\\n",
       "0  .FTSE                   NaN  Market Price  2022-01-13T08:00:00.000000000Z   \n",
       "1  .FTSE                   NaN  Market Price  2022-01-13T08:01:00.000000000Z   \n",
       "2  .FTSE                   NaN  Market Price  2022-01-13T08:02:00.000000000Z   \n",
       "3  .FTSE                   NaN  Market Price  2022-01-13T08:03:00.000000000Z   \n",
       "4  .FTSE                   NaN  Market Price  2022-01-13T08:04:00.000000000Z   \n",
       "\n",
       "            Type     Open     High      Low     Last    Volume  \n",
       "0  Intraday 1Min  7551.70  7553.41  7534.32  7537.40  11701819  \n",
       "1  Intraday 1Min  7537.45  7541.93  7537.45  7540.01   4144926  \n",
       "2  Intraday 1Min  7539.95  7540.36  7536.60  7537.22   3154307  \n",
       "3  Intraday 1Min  7537.18  7538.59  7536.81  7537.36   4674796  \n",
       "4  Intraday 1Min  7537.06  7540.01  7536.89  7539.78   3403918  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftse_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "628cc1e3",
   "metadata": {},
   "source": [
    "If you don’t know how to pull stock data using DSS API, I suggest to read article on it [here](https://developers.refinitiv.com/en/article-catalog/article/request-tick-history-time-and-sales-data-using-python)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69830bfc",
   "metadata": {},
   "source": [
    "#### Data Pre-Processing \n",
    "At this step, we will pre-process the data to get it ready in the desired format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "00e16b00",
   "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>RIC</th>\n",
       "      <th>Time</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>.FTSE</td>\n",
       "      <td>2022-01-13T08:00</td>\n",
       "      <td>7537.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>.FTSE</td>\n",
       "      <td>2022-01-13T08:01</td>\n",
       "      <td>7540.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>.FTSE</td>\n",
       "      <td>2022-01-13T08:02</td>\n",
       "      <td>7537.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>.FTSE</td>\n",
       "      <td>2022-01-13T08:03</td>\n",
       "      <td>7537.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>.FTSE</td>\n",
       "      <td>2022-01-13T08:04</td>\n",
       "      <td>7539.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     RIC              Time    Close\n",
       "0  .FTSE  2022-01-13T08:00  7537.40\n",
       "1  .FTSE  2022-01-13T08:01  7540.01\n",
       "2  .FTSE  2022-01-13T08:02  7537.22\n",
       "3  .FTSE  2022-01-13T08:03  7537.36\n",
       "4  .FTSE  2022-01-13T08:04  7539.78"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftse_df['Date-Time'] = ftse_df['Date-Time'].apply(lambda x: x.split('.')[0][:-3])\n",
    "ftse_df = ftse_df[['#RIC', 'Date-Time', 'Last']]\n",
    "ftse_df.columns = ['RIC', 'Time', 'Close']\n",
    "ftse_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "07d0e79e",
   "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>.FTSE</th>\n",
       "      <th>AAL.L</th>\n",
       "      <th>ABDN.L</th>\n",
       "      <th>ABF.L</th>\n",
       "      <th>ADML.L</th>\n",
       "      <th>AHT.L</th>\n",
       "      <th>ANTO.L</th>\n",
       "      <th>AUTOA.L</th>\n",
       "      <th>AV.L</th>\n",
       "      <th>AVST.L</th>\n",
       "      <th>...</th>\n",
       "      <th>SSE.L</th>\n",
       "      <th>STAN.L</th>\n",
       "      <th>SVT.L</th>\n",
       "      <th>TSCO.L</th>\n",
       "      <th>TW.L</th>\n",
       "      <th>ULVR.L</th>\n",
       "      <th>UU.L</th>\n",
       "      <th>VOD.L</th>\n",
       "      <th>WPP.L</th>\n",
       "      <th>WTB.L</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-13T07:15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.4713</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13T08:00</th>\n",
       "      <td>7537.40</td>\n",
       "      <td>3360.0</td>\n",
       "      <td>245.000</td>\n",
       "      <td>2087.121</td>\n",
       "      <td>3093.0</td>\n",
       "      <td>5838.00</td>\n",
       "      <td>1437.5</td>\n",
       "      <td>705.6</td>\n",
       "      <td>431.850</td>\n",
       "      <td>604.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>1585.8160</td>\n",
       "      <td>499.0000</td>\n",
       "      <td>2834.0</td>\n",
       "      <td>284.3500</td>\n",
       "      <td>159.75000</td>\n",
       "      <td>3882.0</td>\n",
       "      <td>1047.000</td>\n",
       "      <td>116.5000</td>\n",
       "      <td>1179.0000</td>\n",
       "      <td>3117.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13T08:01</th>\n",
       "      <td>7540.01</td>\n",
       "      <td>3360.5</td>\n",
       "      <td>244.900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5846.76</td>\n",
       "      <td>1431.5</td>\n",
       "      <td>705.2</td>\n",
       "      <td>432.300</td>\n",
       "      <td>604.2513</td>\n",
       "      <td>...</td>\n",
       "      <td>1585.4265</td>\n",
       "      <td>500.6000</td>\n",
       "      <td>2829.6</td>\n",
       "      <td>284.8000</td>\n",
       "      <td>160.30000</td>\n",
       "      <td>3884.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.6200</td>\n",
       "      <td>1181.3500</td>\n",
       "      <td>3124.2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13T08:02</th>\n",
       "      <td>7537.22</td>\n",
       "      <td>3353.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2090.049</td>\n",
       "      <td>3092.0</td>\n",
       "      <td>5836.00</td>\n",
       "      <td>1433.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>430.600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1584.9265</td>\n",
       "      <td>499.9490</td>\n",
       "      <td>NaN</td>\n",
       "      <td>285.4793</td>\n",
       "      <td>160.65000</td>\n",
       "      <td>3884.9</td>\n",
       "      <td>1046.713</td>\n",
       "      <td>116.7400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3128.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13T08:03</th>\n",
       "      <td>7537.36</td>\n",
       "      <td>3355.0</td>\n",
       "      <td>245.201</td>\n",
       "      <td>2089.210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5842.00</td>\n",
       "      <td>1436.0</td>\n",
       "      <td>708.0</td>\n",
       "      <td>430.751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1581.5000</td>\n",
       "      <td>499.4000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>285.8988</td>\n",
       "      <td>160.78529</td>\n",
       "      <td>3881.5</td>\n",
       "      <td>1049.000</td>\n",
       "      <td>116.6798</td>\n",
       "      <td>1181.1585</td>\n",
       "      <td>3123.9820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    .FTSE   AAL.L   ABDN.L     ABF.L  ADML.L    AHT.L  ANTO.L  \\\n",
       "Time                                                                            \n",
       "2022-01-13T07:15      NaN     NaN      NaN       NaN     NaN      NaN     NaN   \n",
       "2022-01-13T08:00  7537.40  3360.0  245.000  2087.121  3093.0  5838.00  1437.5   \n",
       "2022-01-13T08:01  7540.01  3360.5  244.900       NaN     NaN  5846.76  1431.5   \n",
       "2022-01-13T08:02  7537.22  3353.5      NaN  2090.049  3092.0  5836.00  1433.0   \n",
       "2022-01-13T08:03  7537.36  3355.0  245.201  2089.210     NaN  5842.00  1436.0   \n",
       "\n",
       "                  AUTOA.L     AV.L    AVST.L  ...      SSE.L    STAN.L  \\\n",
       "Time                                          ...                        \n",
       "2022-01-13T07:15      NaN      NaN       NaN  ...        NaN  500.4713   \n",
       "2022-01-13T08:00    705.6  431.850  604.0000  ...  1585.8160  499.0000   \n",
       "2022-01-13T08:01    705.2  432.300  604.2513  ...  1585.4265  500.6000   \n",
       "2022-01-13T08:02      NaN  430.600       NaN  ...  1584.9265  499.9490   \n",
       "2022-01-13T08:03    708.0  430.751       NaN  ...  1581.5000  499.4000   \n",
       "\n",
       "                   SVT.L    TSCO.L       TW.L  ULVR.L      UU.L     VOD.L  \\\n",
       "Time                                                                        \n",
       "2022-01-13T07:15     NaN       NaN        NaN     NaN       NaN       NaN   \n",
       "2022-01-13T08:00  2834.0  284.3500  159.75000  3882.0  1047.000  116.5000   \n",
       "2022-01-13T08:01  2829.6  284.8000  160.30000  3884.0       NaN  116.6200   \n",
       "2022-01-13T08:02     NaN  285.4793  160.65000  3884.9  1046.713  116.7400   \n",
       "2022-01-13T08:03     NaN  285.8988  160.78529  3881.5  1049.000  116.6798   \n",
       "\n",
       "                      WPP.L      WTB.L  \n",
       "Time                                    \n",
       "2022-01-13T07:15        NaN        NaN  \n",
       "2022-01-13T08:00  1179.0000  3117.0000  \n",
       "2022-01-13T08:01  1181.3500  3124.2022  \n",
       "2022-01-13T08:02        NaN  3128.0000  \n",
       "2022-01-13T08:03  1181.1585  3123.9820  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftse_df_list = []\n",
    "for ric in ftse_list:\n",
    "    temp_df = ftse_df[ftse_df['RIC']==ric][['Time', 'Close']]\n",
    "    temp_df.columns = ['Time', ric]\n",
    "    temp_df = temp_df.set_index('Time')\n",
    "    ftse_df_list.append(temp_df)\n",
    "    \n",
    "final_ftse_df = pd.concat(ftse_df_list, axis=1)\n",
    "final_ftse_df.sort_index(inplace=True)\n",
    "final_ftse_df.dropna(how='all', axis=1, inplace=True)\n",
    "final_ftse_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08ec464f",
   "metadata": {},
   "source": [
    "Since, now the data is ready in the desired format, now we will save the extracted intraday data of all FTSE 100 stocks. We also dropped column with all null values. It is an optional step but a good practice to save time, so first saving the extracted intraday data in the name of ‘ftse100’ using the ‘to_csv’ function provided by the Pandas package. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6f70eef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541, 102)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_ftse_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bb66f972",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_ftse_df.to_csv('ftse100.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "304af2a8",
   "metadata": {},
   "source": [
    "### Calculating Percentage Change and Momentum of all Stocks¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "686f3834",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   symbol  day_change  momentum\n",
      "0   .FTSE    0.003509  0.558824\n",
      "1   AAL.L   -0.001400  0.372549\n",
      "2  ABDN.L    0.002903  0.549020\n",
      "3   ABF.L    0.010944  0.833333\n",
      "4  ADML.L    0.006481  0.696078\n"
     ]
    }
   ],
   "source": [
    "# CALCULATING DAY CHANGE OF STOCKS\n",
    "\n",
    "dc = []\n",
    "for i in final_ftse_df.columns:\n",
    "    dc.append(final_ftse_df[i].pct_change().sum())\n",
    "    \n",
    "ftse100_momentum = pd.DataFrame(columns = ['symbol', 'day_change'])\n",
    "ftse100_momentum['symbol'] = final_ftse_df.columns\n",
    "ftse100_momentum['day_change'] = dc\n",
    "\n",
    "# CALCULATING MOMENTUM\n",
    "\n",
    "ftse100_momentum['momentum'] = 'N/A'\n",
    "for i in range(len(ftse100_momentum)):\n",
    "    ftse100_momentum.loc[i, 'momentum'] = score(ftse100_momentum.day_change, ftse100_momentum.loc[i, 'day_change'])/100\n",
    "    \n",
    "ftse100_momentum['momentum'] = ftse100_momentum['momentum'].astype(float)    \n",
    "print(ftse100_momentum.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6960e35",
   "metadata": {},
   "source": [
    "First, we created an empty list named `dc` to store the percentage change of each stock. Using for loop, we are iterating over the RICs of all the stocks in the FTSE 100 and appended the calculated percentage change using the ‘pct_change’ function provided by the Pandas package into the ‘dc’ list. Then we created a dataframe named `ftse100_momentum` to store the symbol of the stock, percentage change, and the momentum which we calculated after this.\n",
    "\n",
    "To calculate the momentum, we first created a column named ‘momentum’ in the ‘ftse100_momentum’ dataframe and filled it with null values. Then we passed on a for-loop to fill the null values with the actual momentum values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eed6429",
   "metadata": {},
   "source": [
    "### Finding Stocks with Greater Momentum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e734fee",
   "metadata": {},
   "source": [
    "First, we will create a dataframe named ‘top_picks’ which stores the top 10 stocks having greater momentum than the rest. <br>\n",
    "In order to sort and find the top 10 stocks, we used the ‘nlargest’ function provided by the Pandas package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9871985f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   symbol\n",
      "0  ICAG.L\n",
      "1    BT.L\n",
      "2   PRU.L\n",
      "3  HSBA.L\n",
      "4  STAN.L\n",
      "5   NWG.L\n",
      "6   PSN.L\n",
      "7   IMB.L\n",
      "8   MNG.L\n",
      "9  LLOY.L\n"
     ]
    }
   ],
   "source": [
    "top_picks = ftse100_momentum.nlargest(10, 'momentum')['symbol'].reset_index().drop('index', axis = 1)\n",
    "print(top_picks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "00af3510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ticks in top_picks['symbol']:\n",
    "    final_ftse_df[ticks].plot(label = ticks, figsize = (12,6))\n",
    "plt.title('Stock Prices of Top Picks')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b32a0468",
   "metadata": {},
   "source": [
    "### Backtesting the strategy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf2c59f1",
   "metadata": {},
   "source": [
    "In this step, we are going to backtest Quantitative Momentum Strategy by investing money in the top 10 stocks with the highest momentum, and let’s see what results we get."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "297ebc6f",
   "metadata": {},
   "source": [
    "First, we will define two variables:\n",
    "1. One to store the total investment value\n",
    "2. And other to store the investment value per stock \n",
    "\n",
    "Next, we will use a `for` loop to find the day’s close price of all the top 10 stocks and append those values into the ‘day_close’ variable. \n",
    "\n",
    "In further lines of code, we will create a dataframe named ‘backtest_df’ which will be comprised of the calculated number of stocks to buy in each of them, the returns we got by investing in those top 10 stocks, and finally the return percentage of our investment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a3e0300f",
   "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>.FTSE</th>\n",
       "      <th>AAL.L</th>\n",
       "      <th>ABDN.L</th>\n",
       "      <th>ABF.L</th>\n",
       "      <th>ADML.L</th>\n",
       "      <th>AHT.L</th>\n",
       "      <th>ANTO.L</th>\n",
       "      <th>AUTOA.L</th>\n",
       "      <th>AV.L</th>\n",
       "      <th>AVST.L</th>\n",
       "      <th>...</th>\n",
       "      <th>SSE.L</th>\n",
       "      <th>STAN.L</th>\n",
       "      <th>SVT.L</th>\n",
       "      <th>TSCO.L</th>\n",
       "      <th>TW.L</th>\n",
       "      <th>ULVR.L</th>\n",
       "      <th>UU.L</th>\n",
       "      <th>VOD.L</th>\n",
       "      <th>WPP.L</th>\n",
       "      <th>WTB.L</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-13T07:15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.4713</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13T08:00</th>\n",
       "      <td>7537.40</td>\n",
       "      <td>3360.0</td>\n",
       "      <td>245.000</td>\n",
       "      <td>2087.121</td>\n",
       "      <td>3093.0</td>\n",
       "      <td>5838.00</td>\n",
       "      <td>1437.5</td>\n",
       "      <td>705.6</td>\n",
       "      <td>431.850</td>\n",
       "      <td>604.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>1585.8160</td>\n",
       "      <td>499.0000</td>\n",
       "      <td>2834.0</td>\n",
       "      <td>284.3500</td>\n",
       "      <td>159.75000</td>\n",
       "      <td>3882.0</td>\n",
       "      <td>1047.000</td>\n",
       "      <td>116.5000</td>\n",
       "      <td>1179.0000</td>\n",
       "      <td>3117.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13T08:01</th>\n",
       "      <td>7540.01</td>\n",
       "      <td>3360.5</td>\n",
       "      <td>244.900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5846.76</td>\n",
       "      <td>1431.5</td>\n",
       "      <td>705.2</td>\n",
       "      <td>432.300</td>\n",
       "      <td>604.2513</td>\n",
       "      <td>...</td>\n",
       "      <td>1585.4265</td>\n",
       "      <td>500.6000</td>\n",
       "      <td>2829.6</td>\n",
       "      <td>284.8000</td>\n",
       "      <td>160.30000</td>\n",
       "      <td>3884.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.6200</td>\n",
       "      <td>1181.3500</td>\n",
       "      <td>3124.2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13T08:02</th>\n",
       "      <td>7537.22</td>\n",
       "      <td>3353.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2090.049</td>\n",
       "      <td>3092.0</td>\n",
       "      <td>5836.00</td>\n",
       "      <td>1433.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>430.600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1584.9265</td>\n",
       "      <td>499.9490</td>\n",
       "      <td>NaN</td>\n",
       "      <td>285.4793</td>\n",
       "      <td>160.65000</td>\n",
       "      <td>3884.9</td>\n",
       "      <td>1046.713</td>\n",
       "      <td>116.7400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3128.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13T08:03</th>\n",
       "      <td>7537.36</td>\n",
       "      <td>3355.0</td>\n",
       "      <td>245.201</td>\n",
       "      <td>2089.210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5842.00</td>\n",
       "      <td>1436.0</td>\n",
       "      <td>708.0</td>\n",
       "      <td>430.751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1581.5000</td>\n",
       "      <td>499.4000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>285.8988</td>\n",
       "      <td>160.78529</td>\n",
       "      <td>3881.5</td>\n",
       "      <td>1049.000</td>\n",
       "      <td>116.6798</td>\n",
       "      <td>1181.1585</td>\n",
       "      <td>3123.9820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    .FTSE   AAL.L   ABDN.L     ABF.L  ADML.L    AHT.L  ANTO.L  \\\n",
       "Time                                                                            \n",
       "2022-01-13T07:15      NaN     NaN      NaN       NaN     NaN      NaN     NaN   \n",
       "2022-01-13T08:00  7537.40  3360.0  245.000  2087.121  3093.0  5838.00  1437.5   \n",
       "2022-01-13T08:01  7540.01  3360.5  244.900       NaN     NaN  5846.76  1431.5   \n",
       "2022-01-13T08:02  7537.22  3353.5      NaN  2090.049  3092.0  5836.00  1433.0   \n",
       "2022-01-13T08:03  7537.36  3355.0  245.201  2089.210     NaN  5842.00  1436.0   \n",
       "\n",
       "                  AUTOA.L     AV.L    AVST.L  ...      SSE.L    STAN.L  \\\n",
       "Time                                          ...                        \n",
       "2022-01-13T07:15      NaN      NaN       NaN  ...        NaN  500.4713   \n",
       "2022-01-13T08:00    705.6  431.850  604.0000  ...  1585.8160  499.0000   \n",
       "2022-01-13T08:01    705.2  432.300  604.2513  ...  1585.4265  500.6000   \n",
       "2022-01-13T08:02      NaN  430.600       NaN  ...  1584.9265  499.9490   \n",
       "2022-01-13T08:03    708.0  430.751       NaN  ...  1581.5000  499.4000   \n",
       "\n",
       "                   SVT.L    TSCO.L       TW.L  ULVR.L      UU.L     VOD.L  \\\n",
       "Time                                                                        \n",
       "2022-01-13T07:15     NaN       NaN        NaN     NaN       NaN       NaN   \n",
       "2022-01-13T08:00  2834.0  284.3500  159.75000  3882.0  1047.000  116.5000   \n",
       "2022-01-13T08:01  2829.6  284.8000  160.30000  3884.0       NaN  116.6200   \n",
       "2022-01-13T08:02     NaN  285.4793  160.65000  3884.9  1046.713  116.7400   \n",
       "2022-01-13T08:03     NaN  285.8988  160.78529  3881.5  1049.000  116.6798   \n",
       "\n",
       "                      WPP.L      WTB.L  \n",
       "Time                                    \n",
       "2022-01-13T07:15        NaN        NaN  \n",
       "2022-01-13T08:00  1179.0000  3117.0000  \n",
       "2022-01-13T08:01  1181.3500  3124.2022  \n",
       "2022-01-13T08:02        NaN  3128.0000  \n",
       "2022-01-13T08:03  1181.1585  3123.9820  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_ftse_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d40d0dec",
   "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>selected_symbols</th>\n",
       "      <th>day_close</th>\n",
       "      <th>number_of_stocks</th>\n",
       "      <th>return</th>\n",
       "      <th>return_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ICAG.L</td>\n",
       "      <td>165.52</td>\n",
       "      <td>604</td>\n",
       "      <td>4215.92</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BT.L</td>\n",
       "      <td>178.15</td>\n",
       "      <td>561</td>\n",
       "      <td>2148.63</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PRU.L</td>\n",
       "      <td>1337.00</td>\n",
       "      <td>74</td>\n",
       "      <td>2336.92</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HSBA.L</td>\n",
       "      <td>513.00</td>\n",
       "      <td>194</td>\n",
       "      <td>2539.46</td>\n",
       "      <td>2.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>STAN.L</td>\n",
       "      <td>511.40</td>\n",
       "      <td>195</td>\n",
       "      <td>2905.50</td>\n",
       "      <td>2.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NWG.L</td>\n",
       "      <td>248.20</td>\n",
       "      <td>402</td>\n",
       "      <td>2074.32</td>\n",
       "      <td>2.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PSN.L</td>\n",
       "      <td>2608.00</td>\n",
       "      <td>38</td>\n",
       "      <td>1323.16</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>IMB.L</td>\n",
       "      <td>1695.50</td>\n",
       "      <td>58</td>\n",
       "      <td>1632.70</td>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MNG.L</td>\n",
       "      <td>213.10</td>\n",
       "      <td>469</td>\n",
       "      <td>1017.73</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LLOY.L</td>\n",
       "      <td>53.97</td>\n",
       "      <td>1852</td>\n",
       "      <td>1277.88</td>\n",
       "      <td>1.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  selected_symbols  day_close number_of_stocks   return  return_percentage\n",
       "0           ICAG.L     165.52              604  4215.92               4.22\n",
       "1             BT.L     178.15              561  2148.63               2.15\n",
       "2            PRU.L    1337.00               74  2336.92               2.34\n",
       "3           HSBA.L     513.00              194  2539.46               2.54\n",
       "4           STAN.L     511.40              195  2905.50               2.91\n",
       "5            NWG.L     248.20              402  2074.32               2.07\n",
       "6            PSN.L    2608.00               38  1323.16               1.32\n",
       "7            IMB.L    1695.50               58  1632.70               1.63\n",
       "8            MNG.L     213.10              469  1017.73               1.02\n",
       "9           LLOY.L      53.97             1852  1277.88               1.28"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BACKTEST\n",
    "final_ftse_df_copy = final_ftse_df.copy()\n",
    "final_ftse_df_copy.fillna(-1, inplace=True)\n",
    "portfolio_val = 1000000\n",
    "per_stock_val = portfolio_val/len(top_picks)\n",
    "day_close = []\n",
    "for i in top_picks['symbol']:\n",
    "    data = final_ftse_df_copy[i]\n",
    "    j = -1\n",
    "    while data[j]==-1:\n",
    "        j -= 1\n",
    "    day_close.append(data[j])\n",
    "\n",
    "backtest_df = pd.DataFrame(columns = ['selected_symbols', 'day_close', 'number_of_stocks', 'return', 'return_percentage'])\n",
    "backtest_df['selected_symbols'] = top_picks['symbol']\n",
    "backtest_df['day_close'] = day_close\n",
    "for i in range(len(backtest_df)):\n",
    "    backtest_df.loc[i, 'number_of_stocks'] = floor(per_stock_val/day_close[i])\n",
    "    \n",
    "returns = []\n",
    "for i in top_picks['symbol']:\n",
    "    ret = np.diff(final_ftse_df[i])\n",
    "    ret = ret[~np.isnan(ret)]\n",
    "    returns.append(round(sum(ret), 2))\n",
    "    \n",
    "backtest_returns = []\n",
    "return_percentage = []\n",
    "for i in range(len(backtest_df)):\n",
    "    br = returns[i]*backtest_df.loc[i, 'number_of_stocks']\n",
    "    rp = br/per_stock_val*100\n",
    "    backtest_returns.append(round(br, 2))\n",
    "    return_percentage.append(round(rp, 2))\n",
    "backtest_df['return'] = backtest_returns\n",
    "backtest_df['return_percentage'] = return_percentage\n",
    "backtest_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffed32ec",
   "metadata": {},
   "source": [
    "We got some decent returns above!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "620ca542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABCoAAAH5CAYAAABHx9/sAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAA9hAAAPYQGoP6dpAACmfElEQVR4nOzdd5jU5b3//9f07bO9Lx1WOkhdVFBRigK2RHNMsMSgie0YNJ4fpuFJIokxsX6jRomomINJEAOWFSxAkF6WLh22995mZmfm98fC6Eoflp0tz8d1zXVlPp/789n3kGR35jX3fb8NXq/XKwAAAAAAgHbAGOgCAAAAAAAATiCoAAAAAAAA7QZBBQAAAAAAaDcIKgAAAAAAQLtBUAEAAAAAANoNggoAAAAAANBuEFQAAAAAAIB2wxzoAtpaU1OTtm3bpoSEBBmN5DQAAAAAgIvL4/GoqKhIw4cPl9nc5T6Gn7cu9y+0bds2jR49OtBlAAAAAAC6mI0bN2rUqFGBLqPd63JBRUJCgqTm/4EkJSUFuBoAAAAAQGdXUFCg0aNH+z6P4sy6XFBxYrlHUlKSUlNTA1wNAAAAAKCrYPuBc8O/EgAAAAAAaDcIKgAAAAAAQLtBUAEAAAAAANoNggoAAAAAANBuEFQAAAAAAIB2g6ACAAAAAAC0GwQVAAAAAACg3SCoAAAAAAAA7QZBBQAAAAAAaDcIKgAAAAAAQLtBUAEAAAAAANoNggoAAAAAANBuEFQAAAAAAIB2g6ACAAAAAAC0GwQVHUSDs0nOJo/Kah1yNnlU72wKdEkAAAAAALQ6c6ALwNk5XG69suqw3lh7RNUNTYoINuvucT11/5W9ZbOYAl0eAAAAAACthqCinWtwNumVVYf1/GcHfMeqG5p8z++b0EshVv5rBAAAAAB0Diz9aOdMRqPeWHvklOfeWHtEZiP/FQIAAAAAOg8+5bZzNY0uVTecej+K6oYm1TS62rgiAAAAAAAuHtYMnMGry7MCXYLcHq8ijY1yNHlOOmczG/Xumj0yGQ0BqOxr900aFtCfDwAAAADoPJhR0c55vF4NS4s85blhaZHyeL1tWxAAAAAAABcRMyraOYvJqFE9oiVJWTmVcjR5ZDMbNSwtUqN6RMtsCuxsCgAAAAAAWhNBRQdgNhk0skeURveMVr3TrSCLUdUNTYQUAAAAAIBOh6Cig7CYmlfp5Fc2aOW+EkUEm/Vfo7sFuCoAAAAAAFoXe1R0MKlRIWp0uVVU7VBlPR0/AAAAAACdC0FFBxNqMyktOkSStL+oJsDVAAAAAADQuggqOqB+CWGSCCoAAAAAAJ0PQUUH1Cc+TEaDVFrrVFmtM9DlAAAAAADQaggqOqAgi0ndY0IlMasCAAAAANC5EFR0UN9c/uH1BrgYAAAAAABaCUFFB9UrLkwmo0EV9S6V1DoCXQ4AAAAAAK2CoKKDspmN6snyDwAAAABAJ0NQ0YH1SwyXJO0vZPkHAAAAAKBzIKjowHrGhspiMqi6sUmF1Y2BLgcAAAAAgAtGUNGBWUwG9Yr9elNNAAAAAAA6OoKKDo7uHwAAAACAzoSgooPrHhsqm9moOodbeZUNgS4HAAAAAIALQlDRwZmNBvWOY/kHAAAAAKBzIKjoBE4s/zhYVCuPh/UfAAAAANCZzJs3TwaDQY888ojvmNfr1dy5c5WcnKzg4GBdeeWV2r17d4vrHA6HHnroIcXGxio0NFQzZsxQbm5uizEVFRWaOXOm7Ha77Ha7Zs6cqcrKyjZ4VadHUNEJpEWHKNhiVL3LrdwKln8AAAAAQGexadMm/fWvf9WQIUNaHH/66af15z//WS+99JI2bdqkxMREXXvttaqp+Xqm/SOPPKIlS5Zo0aJFWrNmjWprazVt2jS53W7fmNtvv11ZWVnKzMxUZmamsrKyNHPmzDZ7fadCUNEJmIwG9YkPlyTtY/kHAAAAAHQKtbW1+v73v6/XXntNUVFRvuNer1fPPfecfv7zn+vmm2/WoEGD9Oabb6q+vl5///vfJUlVVVWaP3++/vSnP+maa67R8OHDtXDhQu3cuVOffvqpJGnv3r3KzMzU66+/royMDGVkZOi1117TBx98oH379gXkNUsEFZ2Gb/lHca3cLP8AAAAAgHanpqZG1dXVvofD4Tjj+AceeEDXX3+9rrnmmhbHjxw5osLCQk2aNMl3zGazacKECVq7dq0kacuWLXK5XC3GJCcna9CgQb4x69atk91u15gxY3xjxo4dK7vd7hsTCAQVnURKVIhCrCY5mjzKLq8PdDkAAAAAgG8ZMGCAby8Iu92uefPmnXbsokWLtGXLllOOKSwslCQlJCS0OJ6QkOA7V1hYKKvV2mImxqnGxMfHn3T/+Ph435hAaDdBxak2BzmVVatWacSIEQoKClKvXr30yiuvtE2B7ZzRIPVLOL78o5DlHwAAAADQ3uzZs0dVVVW+x5w5c045LicnR//93/+td955R0FBQae9n8FgaPHc6/WedOzbvj3mVOPP5T4XU7sIKk63Oci3HTlyRNddd52uuOIKbdu2TU888YQefvhhLV68uI0qbd9OLP84XFKrJjfLPwAAAACgPQkPD1dERITvYbPZTjluy5YtKi4u1ogRI2Q2m2U2m7Vq1Sq98MILMpvNvpkU3571UFxc7DuXmJgop9OpioqKM44pKio66eeXlJScNFujLQU8qDjd5iCn8sorr6hbt2567rnn1L9/f/3oRz/SD3/4Qz3zzDNtVG37lmQPVniQWU63V0fL6gJdDgAAAADADxMnTtTOnTuVlZXle4wcOVLf//73lZWVpV69eikxMVErVqzwXeN0OrVq1SqNGzdOkjRixAhZLJYWYwoKCrRr1y7fmIyMDFVVVWnjxo2+MRs2bFBVVZVvTCCYA/aTj/vm5iC//e1vzzh23bp1LTYCkaTJkydr/vz5crlcslgsJ13jcDhabFDyzVYtnY3h+PKPLccqtK+wRn3iwwJdEgAAAADgPIWHh2vQoEEtjoWGhiomJsZ3/JFHHtFTTz2lvn37qm/fvnrqqacUEhKi22+/XZJkt9t1zz336NFHH1VMTIyio6P12GOPafDgwb7NOfv3768pU6Zo1qxZevXVVyVJ9957r6ZNm6b09PQ2fMUtBTSoOLE5yObNm89pfGFh4Sk3C2lqalJpaamSkpJOumbevHl68sknW6XejuBEUHGktE7OJo+s5oBPmgEAAAAAtLLHH39cDQ0Nuv/++1VRUaExY8Zo+fLlCg8P94159tlnZTabdeutt6qhoUETJ07UggULZDKZfGPeeecdPfzww75JATNmzNBLL73U5q/nmwIWVJzYHGT58uVn3Bzk2061Wcipjp8wZ84czZ492/c8Ly9PAwYM8KPijiE+3KbIYIsqG1w6XFqnSxLDz34RAAAAAKBdW7lyZYvnBoNBc+fO1dy5c097TVBQkF588UW9+OKLpx0THR2thQsXtlKVrSNgX7efbXMQt9t90jWJiYmn3CzEbDYrJibmlD/HZrO12Kzkm+lSZ2T4RveP/XT/AAAAAAB0MAGbUXFic5Bvuvvuu3XJJZfof/7nf1pMRTkhIyNDy5Yta3Fs+fLlGjly5Cn3p+iq+iWGa+PRch0rq1ejy6MgC8s/AAAAAAAdQ8A+wZ7YHOSbj29vDjJnzhzdcccdvmt+/OMf69ixY5o9e7b27t2rv/3tb5o/f74ee+yxQL2Mdik2zKqYUKvcXq8OldQGuhwAAAAAAM5Zu/6qvaCgQNnZ2b7nPXv21EcffaSVK1dq2LBh+s1vfqMXXnhBt9xySwCrbJ98yz+KWP4BAAAAAOg4At6e9Ju+vTnIggULThozYcIEbd26tW0K6sD6JYRp3eEy5ZTXq8HpVrD15KU0AAAAAAC0N+16RgX8FxVqVVy4TR6vdKCY5R8AAAAAgI6BoKITS2f5BwAAAACggyGo6MT6JoRJkvIqGlTnaApwNQAAAAAAnB1BRSdmD7Yo0R4kr6T9RSz/AAAAAAC0fwQVnRzLPwAAAAAAHQlBRSfXNyFMBkkFVY2qbnAFuhwAAAAAAM6IoKKTC7OZlRwZLInlHwAAAACA9o+gogtIT2T5BwAAAACgYyCo6AL6xIfJaJCKaxyqqGf5BwAAAACg/SKo6AJCrCalRoVIkvYXMqsCAAAAANB+EVR0ESz/AAAAAAB0BAQVXUTvuDCZDFJZnVOltc5AlwMAAAAAwCkRVHQRQRajuseESmJWBQAAAACg/SKo6EL6fWP5h9cb4GIAAAAAADgFgooupFdsqMxGgyrrXSqpcQS6HAAAAAAATkJQ0YVYzUb1jG1e/rGP5R8AAAAAgHaIoKKL6ZfA8g8AAAAAQPtFUNHF9IwNldVkUE1jkwqqGgNdDgAAAAAALRBUdDFmk0G94sIk0f0DAAAAAND+EFR0QSeWfxwoqpGH5R8AAAAAgHaEoKIL6hYTIpvZqDqnW3kVDYEuBwAAAAAAH4KKLshsNKhPPMs/AAAAAADtD0FFF3Vi+cfB4lp5WP8BAAAAAGgnCCq6qLSoYAVbTGpwuZXD8g8AAAAAQDtBUNFFGY0G9U1oXv6xr5DlHwAAAACA9oGgogs7sfzjUEmtmlj+AQAAAABoBwgqurDkyGCF2kxyNHmUXVYf6HIAAAAAACCo6MqMBqlffPOsin10/wAAAAAAtAMEFV1cv8TmoOJwSa1cbpZ/AAAAAAACi6Cii0uMCFJ4kFkut1dHS+sCXQ4AAAAAoIsjqOjiDIavN9Vk+QcAAAAAINAIKqD040HF0dI6OZs8Aa4GAAAAANCVEVRAceE2RYVY1OTx6nAJyz8AAAAAAIFDUAGWfwAAAAAA2g2CCkj6OqjILqtTo4vlHwAAAACAwCCogCQpJsyq2DCr3F7pUHFtoMsBAAAAAHRRBBXwYfkHAAAAAATeyy+/rCFDhigiIkIRERHKyMjQxx9/7Dt/1113yWAwtHiMHTu2xT0cDoceeughxcbGKjQ0VDNmzFBubm6LMRUVFZo5c6bsdrvsdrtmzpypysrKtniJZ0RQAZ8TQUVuRb3qne4AVwMAAAAAXVNqaqp+//vfa/Pmzdq8ebOuvvpq3XDDDdq9e7dvzJQpU1RQUOB7fPTRRy3u8cgjj2jJkiVatGiR1qxZo9raWk2bNk1u99ef9W6//XZlZWUpMzNTmZmZysrK0syZM9vsdZ6OOdAFoP2IDLEoPtym4hqHDhTXamiqPdAlAQAAAECXM3369BbPf/e73+nll1/W+vXrNXDgQEmSzWZTYmLiKa+vqqrS/Pnz9fbbb+uaa66RJC1cuFBpaWn69NNPNXnyZO3du1eZmZlav369xowZI0l67bXXlJGRoX379ik9Pf0ivsIzY0YFWkhPbJ5Vsb+Q5R8AAAAA0JpqampUXV3tezgcjrNe43a7tWjRItXV1SkjI8N3fOXKlYqPj1e/fv00a9YsFRcX+85t2bJFLpdLkyZN8h1LTk7WoEGDtHbtWknSunXrZLfbfSGFJI0dO1Z2u903JlAIKtBC3+PLP/IrG1Tb2BTgagAAAACg8xgwYIBvPwi73a558+adduzOnTsVFhYmm82mH//4x1qyZIkGDBggSZo6dareeecdff755/rTn/6kTZs26eqrr/YFH4WFhbJarYqKimpxz4SEBBUWFvrGxMfHn/Rz4+PjfWMChaUfaCEiyKwke5AKqhq1v7hWl3aLDHRJAAAAANAp7NmzRykpKb7nNpvttGPT09OVlZWlyspKLV68WHfeeadWrVqlAQMG6LbbbvONGzRokEaOHKnu3bvrww8/1M0333zae3q9XhkMBt/zb/7n040JBGZU4CTpx2dV7Kf7BwAAAAC0mvDwcF8nj4iIiDMGFVarVX369NHIkSM1b948DR06VM8///wpxyYlJal79+46cOCAJCkxMVFOp1MVFRUtxhUXFyshIcE3pqio6KR7lZSU+MYECkEFTtI3IUwGSYVVjapqcAW6HAAAAADo8rxe72n3tCgrK1NOTo6SkpIkSSNGjJDFYtGKFSt8YwoKCrRr1y6NGzdOkpSRkaGqqipt3LjRN2bDhg2qqqryjQkUln7gJKE2s1KigpVb0aD9RbUa1SPq7BcBAAAAAFrFE088oalTpyotLU01NTVatGiRVq5cqczMTNXW1mru3Lm65ZZblJSUpKNHj+qJJ55QbGysbrrpJkmS3W7XPffco0cffVQxMTGKjo7WY489psGDB/u6gPTv319TpkzRrFmz9Oqrr0qS7r33Xk2bNi2gHT8kggqcRnpC+PGgooagAgAAAADaUFFRkWbOnKmCggLZ7XYNGTJEmZmZuvbaa9XQ0KCdO3fqrbfeUmVlpZKSknTVVVfp3XffVXh4uO8ezz77rMxms2699VY1NDRo4sSJWrBggUwmk2/MO++8o4cfftjXHWTGjBl66aWX2vz1fpvB6/V6A11EW8rNzVVaWppycnKUmpp6xrGvLs9qm6LaoQanW6/957A8XumOjO6KDrWedux9k4a1XWEAAAAA0MGcz+dQsEcFTiPYalJadIgkaX9RbYCrAQAAAAB0FQQVOK1vdv/oWvNuAAAAAACBQlCB0+odHyaTwaDyOqfK6k69uywAAAAAAK2JoAKnZTMb1T32+PKPQpZ/AAAAAAAuPoIKnNGJ5R/7WP4BAAAAAGgDBBU4o56xoTIbDapqcKm4pjHQ5QAAAAAAOjmCCpyR1WxUz7hQSXT/AAAAAABcfAQVOCtf949Cln8AAAAAAC6ugAYVL7/8soYMGaKIiAhFREQoIyNDH3/88WnHr1y5UgaD4aTHV1991YZVdz09YkJlNRlV42hSQVVDoMsBAAAAAHRi5kD+8NTUVP3+979Xnz59JElvvvmmbrjhBm3btk0DBw487XX79u1TRESE73lcXNxFr7UrM5sM6h0Xqr2FNdpfVKvkyOBAlwQAAAAA6KQCGlRMnz69xfPf/e53evnll7V+/fozBhXx8fGKjIy8yNXhm/olhh8PKmo0vl+cjIZAVwQAAAAA6IzazR4VbrdbixYtUl1dnTIyMs44dvjw4UpKStLEiRP1xRdfnHGsw+FQdXW171FTU9OaZXcZ3aJDFGQ2qt7pVl5FfaDLAQAAAAB0UgEPKnbu3KmwsDDZbDb9+Mc/1pIlSzRgwIBTjk1KStJf//pXLV68WO+9957S09M1ceJErV69+rT3nzdvnux2u+9xunvjzExGg/rEh0mS9tH9AwAAAABwkRi83sD2cXA6ncrOzlZlZaUWL16s119/XatWrTrnQGH69OkyGAxaunTpKc87HA45HA7f87y8PA0YMEA5OTlKTU09471fXZ51zq+jK8guq9d72/IUZDFq1hW9ZDq+/uO+ScMCWxgAAAAAtGO5ublKS0s7p8+haAczKqxWq/r06aORI0dq3rx5Gjp0qJ5//vlzvn7s2LE6cODAac/bbDZfV5GIiAiFh4e3RtldUmpUsEKsJjW6PMopZ/kHAAAAAKD1BTyo+Dav19tiBsTZbNu2TUlJSRexIpxgNBrUl+UfAAAAAICLKKBdP5544glNnTpVaWlpqqmp0aJFi7Ry5UplZmZKkubMmaO8vDy99dZbkqTnnntOPXr00MCBA+V0OrVw4UItXrxYixcvDuTL6FL6JYRre26VDhfXqumSeJlNtP8AAAAAALSegAYVRUVFmjlzpgoKCmS32zVkyBBlZmbq2muvlSQVFBQoOzvbN97pdOqxxx5TXl6egoODNXDgQH344Ye67rrrAvUSupzkyGCF2cyqdTTpWFmdeh+fYQEAAAAAQGsI+Gaabe18NjFhM81TW72/RFuzK9UvIVzXDU5kM00AAAAAOAM20zw/7W6PCrR//RKaNyQ9UlIrl9sT4GoAAAAAAJ0JQQXOW0JEkCKCzXJ5vDpSWhfocgAAAAAAnQhBBc6bwfD1rIp9hTUBrgYAAAAA0JkQVMAv6ceDiqNl9appdAW4GgAAAABAZ0FQAb/EhtkUFWKR2+PVij1FgS4HAAAAANBJEFTAL99c/rFse36AqwEAAAAAdBYEFfBbemJzUPGfA6WqrHcGuBoAAAAAQGdAUAG/RYdaFRtmU5PHq8xdhYEuBwAAAADQCRBU4IL0SwiTJC3bwfIPAAAAAMCFI6jABTnR/WPdoTKV1DgCXA0AAAAAoKMjqMAFsYdYNDQtUh6v9PGugkCXAwAAAADo4AgqcMGmD0mSRPcPAAAAAMCFI6jABbv+eFCx6WiFCqoaAlwNAAAAAKAjI6jABUuyB2tUjyhJ0oc7WP4BAAAAAPAfQQVaxfShyZKkZQQVAAAAAIALQFCBVjF1UJKMBml7TqWyy+oDXQ4AAAAAoIMiqECriAu3KaN3jCRp2Q421QQAAAAA+IegAq1m+pDm5R8fsPwDAAAAAOAnggq0mimDEmU2GrS3oFoHi2sCXQ4AAAAAoAMiqECriQyx6oq+sZKkZduZVQEAAAAAOH8EFWhVJ7p/fLAjX16vN8DVAAAAAAA6GoIKtKprByTIajbqUEmd9haw/AMAAAAAcH4IKtCqwoMsuio9TlLzrAoAAAAAwPl5+eWXNWTIEEVERCgiIkIZGRn6+OOPfee9Xq/mzp2r5ORkBQcH68orr9Tu3btb3MPhcOihhx5SbGysQkNDNWPGDOXm5rYYU1FRoZkzZ8put8tut2vmzJmqrKxsi5d4RgQVaHUnln8sY/kHAAAAAJy31NRU/f73v9fmzZu1efNmXX311brhhht8YcTTTz+tP//5z3rppZe0adMmJSYm6tprr1VNzdez2h955BEtWbJEixYt0po1a1RbW6tp06bJ7Xb7xtx+++3KyspSZmamMjMzlZWVpZkzZ7b56/02g7eLfZLMzc1VWlqacnJylJqaesaxry7PapuiOrj7Jg1r8bze2aQRv/lUDS633n/gMg1LiwxIXQAAAADQHpzP59DTiY6O1h//+Ef98Ic/VHJysh555BH9z//8j6Tm2RMJCQn6wx/+oPvuu09VVVWKi4vT22+/rdtuu02SlJ+fr7S0NH300UeaPHmy9u7dqwEDBmj9+vUaM2aMJGn9+vXKyMjQV199pfT09NZ58X5gRgVaXYjVrGsGJEiSPtjO8g8AAAAAkKSamhpVV1f7Hg6H46zXuN1uLVq0SHV1dcrIyNCRI0dUWFioSZMm+cbYbDZNmDBBa9eulSRt2bJFLperxZjk5GQNGjTIN2bdunWy2+2+kEKSxo4dK7vd7hsTKAQVuCimD0mSJH2wo0AeT5eatAMAAAAApzRgwADffhB2u13z5s077didO3cqLCxMNptNP/7xj7VkyRINGDBAhYWFkqSEhIQW4xMSEnznCgsLZbVaFRUVdcYx8fHxJ/3c+Ph435hAMQf0p6PTmpAep3CbWYXVjdp8rEKje0YHuiQAAAAACKg9e/YoJSXF99xms512bHp6urKyslRZWanFixfrzjvv1KpVq3znDQZDi/Fer/ekY9/27TGnGn8u97nYmFGBi8JmNmnSwERJdP8AAAAAAEkKDw/3dfKIiIg4Y1BhtVrVp08fjRw5UvPmzdPQoUP1/PPPKzGx+XPWt2c9FBcX+2ZZJCYmyul0qqKi4oxjioqKTvq5JSUlJ83WaGsEFbhopg9tXv7x0c4CNbk9Aa4GAAAAADour9crh8Ohnj17KjExUStWrPCdczqdWrVqlcaNGydJGjFihCwWS4sxBQUF2rVrl29MRkaGqqqqtHHjRt+YDRs2qKqqyjcmUFj6gYvmsj6xigqxqLTWqQ1HynVZn9hAlwQAAAAA7d4TTzyhqVOnKi0tTTU1NVq0aJFWrlypzMxMGQwGPfLII3rqqafUt29f9e3bV0899ZRCQkJ0++23S5LsdrvuuecePfroo4qJiVF0dLQee+wxDR48WNdcc40kqX///poyZYpmzZqlV199VZJ07733atq0aQHt+CERVOAispiMmjIoSf+3MVvLtucTVAAAAADAOSgqKtLMmTNVUFAgu92uIUOGKDMzU9dee60k6fHHH1dDQ4Puv/9+VVRUaMyYMVq+fLnCw8N993j22WdlNpt16623qqGhQRMnTtSCBQtkMpl8Y9555x09/PDDvu4gM2bM0EsvvdS2L/YUDF6vt0u1ZDif/rWvLs9qm6I6uPsmDTvtubUHS3X76xtkD7Zo08+vkdXMaiMAAAAAXcv5fA4Fe1TgIhvTK0axYTZVNbj05cHSQJcDAAAAAGjnCCpwUZmMBk0b0ryp5rLtdP8AAAAAAJwZQQUuuhNBxfI9RWp0uQNcDQAAAACgPWMzTVx0l3aLUrI9SPlVjVq5r0RTBiUGuiQAXcyGT/YFuoQOYczkwO7wDQAAIDGjAm3AaDRo2tBkSdKyHSz/AAAAAACcHkEF2sSJ5R+f7S1SnaMpwNUAAAAAANorggq0icEpdnWPCVGjy6PPvioOdDkAAAAAgHaKoAJtwmAwaPqQ48s/6P4BAAAAADgNggq0mWlDm5d/rNpXoupGV4CrAQAAAAC0RwQVaDPpCeHqGx8mp9uj5buLAl0OAAAAAKAdIqhAmzEYDJrG8g8AAAAAwBkQVKBNnVj+8eXBUpXXOQNcDQAAAACgvSGoQJvqHRemgckRavJ4lbmrMNDlAAAAAADaGYIKtDmWfwAAAAAATueCg4rGxsbWqANdyLQhzcs/1h8pU3E1//sBAAAAAHzNr6DC4/HoN7/5jVJSUhQWFqbDhw9Lkn75y19q/vz5rVogOp+06BAN7xYpr1f6aGdBoMsBAAAAALQjfgUVv/3tb7VgwQI9/fTTslqtvuODBw/W66+/3mrFofM6sfzjgx0EFQAAAACAr/kVVLz11lv661//qu9///symUy+40OGDNFXX33VasWh87p+cJIMBmnzsQrlVTYEuhwAAAAAQDvhV1CRl5enPn36nHTc4/HI5XJdcFHo/BLtQRrVI1qS9OEONtUEAAAAADTzK6gYOHCg/vOf/5x0/J///KeGDx9+wUWha5g+lOUfAAAAAICWzP5c9Otf/1ozZ85UXl6ePB6P3nvvPe3bt09vvfWWPvjgg9auEZ3U1EGJmrt0t3bkVuloaZ16xIYGuiQAAAAAQID5NaNi+vTpevfdd/XRRx/JYDDoV7/6lfbu3atly5bp2muvbe0a0UnFhtk0rneMJOkDln8AAAAAAORnUCFJkydP1qpVq1RbW6v6+nqtWbNGkyZNOq97vPzyyxoyZIgiIiIUERGhjIwMffzxx2e8ZtWqVRoxYoSCgoLUq1cvvfLKK/6+BLQD0+n+AQAAAAD4Br+DitaQmpqq3//+99q8ebM2b96sq6++WjfccIN27959yvFHjhzRddddpyuuuELbtm3TE088oYcffliLFy9u48rRWiYPTJTFZNBXhTXaX1QT6HIAAAAAAAHm1x4VUVFRMhgMJx03GAwKCgpSnz59dNddd+nuu+8+432mT5/e4vnvfvc7vfzyy1q/fr0GDhx40vhXXnlF3bp103PPPSdJ6t+/vzZv3qxnnnlGt9xyyyl/hsPhkMPh8D2vqeHDcHtiD7FofN84ffZVsT7Ynq/Zk9IDXRIAAAAAIID8mlHxq1/9SkajUddff72efPJJzZ07V9dff72MRqMeeOAB9evXTz/5yU/02muvnfM93W63Fi1apLq6OmVkZJxyzLp1605aXjJ58mRt3rz5tG1R582bJ7vd7nsMGDDg3F8o2sQ3u394vd4AVwMAAAAACCS/ZlSsWbNGv/3tb/XjH/+4xfFXX31Vy5cv1+LFizVkyBC98MILmjVr1hnvtXPnTmVkZKixsVFhYWFasmTJacOEwsJCJSQktDiWkJCgpqYmlZaWKikp6aRr5syZo9mzZ/ue5+XlEVa0M9cMSJDNbNTh0jrtzq/WoBR7oEsCAAAAAASIXzMqPvnkE11zzTUnHZ84caI++eQTSdJ1112nw4cPn/Ve6enpysrK0vr16/WTn/xEd955p/bs2XPa8d9ecnLiG/hTLUWRJJvN5tusMyIiQuHh4WetCW0rzGbW1ZfES2JTTQAAAADo6vwKKqKjo7Vs2bKTji9btkzR0dGSpLq6unMKBaxWq/r06aORI0dq3rx5Gjp0qJ5//vlTjk1MTFRhYWGLY8XFxTKbzYqJifHjlaC9OLH8Y9n2fJZ/AAAAAEAX5tfSj1/+8pf6yU9+oi+++EKjR4+WwWDQxo0b9dFHH/naha5YsUITJkw473t7vd4Wm19+U0ZGxkkByfLlyzVy5EhZLJbzfyFoN65Kj1eI1aS8ygZty6nUpd2iAl0SAAAAACAA/AoqZs2apQEDBuill17Se++9J6/Xq0suuUSrVq3SuHHjJEmPPvroWe/zxBNPaOrUqUpLS1NNTY0WLVqklStXKjMzU1Lz/hJ5eXl66623JEk//vGP9dJLL2n27NmaNWuW1q1bp/nz5+v//u///HkZaEeCrSZdOyBB/87K1wfbCwgqAAAAAKCL8iuokKTLLrtMl1122QX98KKiIs2cOVMFBQWy2+0aMmSIMjMzde2110qSCgoKlJ2d7Rvfs2dPffTRR/rpT3+q//f//p+Sk5P1wgsvnLY1KTqW6UOSm4OKHfn6+fX9ZTKeet8RAAAAAEDn5XdQcUJDQ8NJrUEjIiLO6dr58+ef8fyCBQtOOjZhwgRt3br1nOtDx3FFv1iFB5lVXOPQpqPlGtuLfUcAAAAAoKvxazPN+vp6Pfjgg4qPj1dYWJiioqJaPAB/2MwmTRmYKEn6YEd+gKsBgIujye2R2+NVg7NJbo9XLrcn0CUBAAC0K34FFT/72c/0+eef6y9/+YtsNptef/11Pfnkk0pOTvbtJwH440T3j493FqqJN+8AOhm326PNRyv02upD+uvqw3pt9SFtOVohN7/vAAAAfPwKKpYtW6a//OUv+s53viOz2awrrrhCv/jFL/TUU0/pnXfeae0a0YWM6x2j6FCryuqcWne4LNDlAECraXJ7tOlohTYcKZOjqTmYcDR5tOFImTYdrWBmBQAAwHF+BRXl5eXq2bOnpOb9KMrLyyVJl19+uVavXt161aHLMZuMmjqoefnHsu0s/wDQeRgMBmXlVJzyXFZOhYwGNhAGAACQ/AwqevXqpaNHj0qSBgwYoH/84x+SmmdaREZGtlZt6KKmDWle/pG5q1DOJr5hBNA5OFxu30yKk841eeRscrdxRQAAAO2TX0HF3Xffre3bt0uS5syZ49ur4qc//al+9rOftWqB6HpG94xWfLhN1Y1N+s+BkkCXAwAXpLzOqU/3FMliNspmPvWfXZvZKKvZ1MaVAQAAtE9+tSf96U9/6vvPV111lb766itt3rxZvXv31tChQ1utOHRNJqNB1w9J0htfHtWy7fma2D8h0CUBwHmrbnBpw+Ey7S2olldSr7hQDUuL1IYj5SeNHZYWJY/XK5NY/gEAAODXjIq33npLDofD97xbt266+eab1b9/f7p+oFWcWP6xYk+RGl1MhwbQcdQ5mrTyq2K9ufao9hwPKXrHhSkqxKpRPaI1pmeMb2aFzWzU6J7RGpoWKQe/6wAAACT5OaPi7rvv1pQpUxQfH9/ieE1Nje6++27dcccdrVIcuq5Lu0UqJTJYeZUN+uKrYk0dnBTokoBWc/RvbwS6hA6hxw/vDnQJ56XR5daWoxXKyqlQk8crSeoWHaKM3rFKtAf5xo3oEaVRPaPlbHLLajYpr7JB/9ycI6NBunVUGktAAABAl+fXjAqv1yvDKXYnz83Nld1uv+CiAIPBoGlDm8OJZTvo/gGg/XI2ebTxcJneWHNEm4+Vq8njVZI9SDdfmqqbLk1tEVJIksVklMloULDVLJPRoOgQi5xNHpXVOfXxzkJ5joccAACg65o3b55GjRql8PBwxcfH68Ybb9S+fftajLnrrrtkMBhaPMaOHdtijMPh0EMPPaTY2FiFhoZqxowZys3NbTGmoqJCM2fOlN1ul91u18yZM1VZWXmxX+IZndeMiuHDh/v+ASZOnCiz+evL3W63jhw5oilTprR6keiapg9J1qurDuuzvcWqdTQpzObXBCAAuCia3B7tyK3S5qPlaji+bCM2zKZxvWPUMzZUOsd2o2FBFk0flqx/bc7R0bI6/edAiSakx5/9QgAA0GmtWrVKDzzwgEaNGqWmpib9/Oc/16RJk7Rnzx6Fhob6xk2ZMkVvvPH1bF2r1driPo888oiWLVumRYsWKSYmRo8++qimTZumLVu2yGRqnsV5++23Kzc3V5mZmZKke++9VzNnztSyZcva4JWe2nl98rvxxhslSVlZWZo8ebLCwsJ856xWq3r06KFbbrmlVQtE1zUwOUI9Y0N1pLROn+0t0g3DUgJdEgDI4/FqT0G1NhwuU62jSZIUGWJVRq8Y9U0IO+WMw7NJiAjS5IGJ+nBngbJyKhUVYtWQtMhWrhwAAARaTU2Nqqurfc9tNptsNttJ406EBie88cYbio+P15YtWzR+/PgW1ycmJp7yZ1VVVWn+/Pl6++23dc0110iSFi5cqLS0NH366aeaPHmy9u7dq8zMTK1fv15jxoyRJL322mvKyMjQvn37lJ6efsGv2R/nFVT8+te/liT16NFDt912m4KCgs5yBeA/g8Gg6UOS9MLnB7Vsez5BBYCA8nq92l9Uo3WHylTV4JIkhdvMGtMrRv2TImQ0XljHjj4J4RpX79LaQ6Vaub9E9hCLuseEnv1CAADQYQwYMKDF81//+teaO3fuWa+rqqqSJEVHR7c4vnLlSsXHxysyMlITJkzQ7373O99eklu2bJHL5dKkSZN845OTkzVo0CCtXbtWkydP1rp162S3230hhSSNHTtWdrtda9eu7RhBxQl33nmnJMnpdKq4uFgej6fF+W7dul14ZYCkaUOT9cLnB7Vqf4mq6l2yh1gCXRKArsbr1aGSOq07VKayuuaOVyFWk0b1iNbgVLtMRr+2ezqlUT2iVFHv1N6Can20o0C3jUpTdNjJ37IAAICOac+ePUpJ+foL2FPNpvg2r9er2bNn6/LLL9egQYN8x6dOnarvfve76t69u44cOaJf/vKXuvrqq7VlyxbZbDYVFhbKarUqKiqqxf0SEhJUWFgoSSosLDypSYYkxcfH+8YEgl9BxYEDB/TDH/5Qa9eubXH8xCabbjct1tA6+iWEKz0hXPuKavTJnkLdOjIt0CUB6EJyyuv15cFSFVU3SmpuJzqie7SGpUXKYm69gMLHYNDE/vGqanApv7JB/96er++NSlOwlT16AADoDMLDwxUREXFe1zz44IPasWOH1qxZ0+L4bbfd5vvPgwYN0siRI9W9e3d9+OGHuvnmm097v283xzjVstXTNdBoK36987nrrrtkNpv1wQcfKCkpKaAvAJ3ftCFJ2reiRsu25xNUAGgTBZUNWnuoTLkV9ZKaO3UMS4vUiO5RslkubvtQk9GoaUOS9e6mbFU1uPTBjgLdfGlKq87cAAAAHcNDDz2kpUuXavXq1UpNTT3j2KSkJHXv3l0HDhyQJCUmJsrpdKqioqLFrIri4mKNGzfON6aoqOike5WUlCghIaEVX8n58SuoyMrK0pYtW3TJJZe0dj3ASaYNTdafVuzX2kNlKqt1KIZp0AAukpIah9YdKtWR0jpJkslg0OBUu0b1iFZIG3YeCraaNGNYst7dlKP8ygZ9trdYkwYknHMnEQAA0LF5vV499NBDWrJkiVauXKmePXue9ZqysjLl5OQoKSlJkjRixAhZLBatWLFCt956qySpoKBAu3bt0tNPPy1JysjIUFVVlTZu3KjRo0dLkjZs2KCqqipfmBEIfr3rGjBggEpLS1u7FuCUesaGanCKXTvzqvTxrkL9YGz3QJcEoJOpqHNq/eEy7S+qkdQ8BXJgUoRG94pWeFBg9saJDrXpusFJ+ndWvvYWVCsq1KpRPaLPfiEAAOjwHnjgAf3973/Xv//9b4WHh/v2i7Db7QoODlZtba3mzp2rW265RUlJSTp69KieeOIJxcbG6qabbvKNveeee/Too48qJiZG0dHReuyxxzR48GBfF5D+/ftrypQpmjVrll599VVJze1Jp02bFrCNNCXJr3mkf/jDH/T4449r5cqVKisrU3V1dYsH0NqmDWlOBZdtzw9wJQA6k5pGlz7dU6S31x/zhRT9EsI1c2x3TRyQELCQ4oTuMaG6Mj1OkrT2YKkOFtcEtB4AANA2Xn75ZVVVVenKK69UUlKS7/Huu+9Kkkwmk3bu3KkbbrhB/fr105133ql+/fpp3bp1Cg8P993n2Wef1Y033qhbb71Vl112mUJCQrRs2TKZTF8vZX3nnXc0ePBgTZo0SZMmTdKQIUP09ttvt/lr/ia/ZlScSF8mTpzY4jibaeJiuX5IkuZ9/JU2Hi1XUXWjEiJojQvAf/WOJm06Wq6duVVye72SpF6xoRrbO1Zx4e1redmQ1EhV1DmVlVOpT3YVKmKkRfH8DgQAoFPzHn9/cjrBwcH65JNPznqfoKAgvfjii3rxxRdPOyY6OloLFy487xovJr+Cii+++KK16wDOKDUqRCO6R2nLsQp9uKNAP7z87Gu0AODbHC63thyr0LbsSjUdb62dGhWicX1ilGQPDnB1p3dF3zhV1rt0tKxOS7fn63ujuiksiE4gAACgc/LrXc6ECRNauw7grKYNSdKWYxX6YEc+QQWA8+Jq8igrp1JbjpXL0dQcUCREBOmyPrFKiw4JcHVnZzQaNHVwov6xKUdldU4t3Z6n745Mk8VEJxAAAND5+P0O5z//+Y9+8IMfaNy4ccrLy5Mkvf322yf1dgVay/WDk2QwSFuzK5VTXh/ocgB0AI4mt9748ogWrD2itYdK5WjyKCbUpmlDkvW9UWkdIqQ4wWo2acawFAVbTCqpceiTXYVnnRYKAADQEfkVVCxevFiTJ09WcHCwtm7dKofDIUmqqanRU0891aoFAifERwRpbM8YSdKHOwsCXA2A9qzJ7dG7m7J11R9X6slle1TvdCsy2KIpgxL1/bHd1Ds+rEO2+owItmj60GSZDAYdKqnV2oNlgS4JAACg1fkVVPz2t7/VK6+8otdee00Wy9c7oo8bN05bt25tteKAb5s2tLn7xwc76P4B4GQej1dLt+fr2mdX638W71R+VaMSI4I0sX+CZmb0UHpihAwdMKD4pqTIYF0zIEGStPlYuXbnVwW4IgAAgNbl1x4V+/bt0/jx4086HhERocrKygutCTitqYOS9Kt/79auvGodLqlVr7iwQJcEoB3wer36/Kti/fGTffqqsLmFZ3SoVQ9c1UffH9NN2z8/GOAKW9clSRGqrHdqw5Fyfb63WPZgi1KjOs4yFgAA0LlUVlZq48aNKi4uluf4huUn3HHHHed9P7+CiqSkJB08eFA9evRocXzNmjXq1auXP7cEzkl0qFWX9YnV6v0l+mBHgR6e2DfQJQEIsLWHSvXHT/ZpW3alJCncZta943vp7st7KszWeTtjjOkVo/J6lw4U1ejDHQW6bVSaIkOsgS4LAAB0McuWLdP3v/991dXVKTw8vMXsVYPB4FdQ4dfSj/vuu0///d//rQ0bNshgMCg/P1/vvPOOHnvsMd1///3+3BI4Z9OHsPwDgJSVU6kfvL5Bt7+2QduyKxVkMeonV/bWf/7nKj00sW+nDimk5j/8kwYkKCEiSI0ut5Zm5cvhcge6LAAA0MU8+uij+uEPf6iamhpVVlaqoqLC9ygvL/frnn69i3v88cdVVVWlq666So2NjRo/frxsNpsee+wxPfjgg34VApyrSQMT9fMlu7S/qFb7CmuUnhge6JIAtKGvCqv1p+X7tWJPkSTJYjLo+2O66/6reis+PCjA1bUts8mo6UOT9e7GbFXUO/XhzgLdOCxFRmPH3ocDAAB0HHl5eXr44YcVEtJ6y1D9/rrpd7/7nX7+859rz5498ng8GjBggMLC2C8AF5892KLx/eL06d4ifbAjX+mJ6YEuqUObv2F+oEvoEO4Zc0+gS+jyjpbW6dlP92vp9nx5vZLRIN1yaaoenti3Q7UZbW2hNrOmD0vRPzfnKKe8Xl/sK9bES+I7ZFcTAADQ8UyePFmbN29u1W0g/Aoqqqqq5Ha7FR0drZEjR/qOl5eXy2w2KyIiotUKBE5l+tAkfbq3SMu252v2tf06/C7+AE4vv7JBL35+QP/YnCu3xytJun5Ikn56TT/1iScgl6S4cJumDErUsu352pVXpehQq4Z3iwp0WQAAoAu4/vrr9bOf/Ux79uzR4MGDW3QGlaQZM2ac9z39Ciq+973vafr06SftR/GPf/xDS5cu1UcffeTPbYFzdk3/BAVZjDpaVq9dedUanGoPdEkAWllZrUN/WXlIb68/JmdT8+7RV6XH6dFJ6RqUwv/nv61XXJiu6Bun/xwo0er9JYoMtqgnnZEAAMBFNmvWLEnS//7v/550zmAwyO0+/z20/NpMc8OGDbrqqqtOOn7llVdqw4YN/twSOC+hNrMmXpIgiU01gc6mscmtdYdKNf7pLzR/zRE5mzwa3TNa//pxht64ezQhxRlc2i3S9+/z8a5CldY4AlwRAADo7Dwez2kf/oQUkp9BhcPhUFNT00nHXS6XGhoa/CoEOF/Th57o/lEgz/Hp4AA6Lpfbo81Hy7Xgy6PacKRcdU63hqTa9dYPR+vde8dqZI/oQJfY/hkMuio9XmlRIXK5PVq6PV91jpP/XgMAALSGpqYmmc1m7dq1q1Xv61dQMWrUKP31r3896fgrr7yiESNGXHBRwLm4Mj1eoVaT8iobtC2nItDlAPBTk8ej7TmVeuPLo1pzsFSNLrdiQq165Qcj9O8HLtP4fnHsQ3MejEaDrh+SpKgQq2oaXfpge76a3J5AlwUAADohs9ms7t27+z1z4rT39eei3/3ud7rmmmu0fft2TZw4UZL02WefadOmTVq+fHmrFgicTpDFpEkDE7VkW56WbS/QiO582wp0JB6vV3sLqrXhcLmqG12Smrv6jO0Vo/TEcPUalBjgCjsum8WkGcOStWhjjgqrG7ViT5GmDkqkEwgAAGh1v/jFLzRnzhwtXLhQ0dGt85nMr6Disssu0/r16/X000/rH//4h4KDgzVkyBDNnz9fffv2bZXCgHMxfWiSlmzL04c7C/TLaQNkMvImHGjvvF6vDhbXau3hMlXUOSVJYTazRveM1sBkO/8/biWRIVZNG9L8O3J/UY2iQ60a0ysm0GUBAIBO5oUXXtDBgweVnJys7t27KzQ0tMX5rVu3nvc9zzuocLlcuvfee/XLX/5S77zzznn/QKA1Xd4nTvZgi0pqHNpwpEzjescGuiQAp+GVV8dK6/XloVKVHN/kMdhi0qge0RqSZpfZ6NdqRJxBanSIrr4kXp/uLdL6w2WKDLEoPZEW4gAAoPXceOONrX7P8w4qLBaLlixZol/+8petXgxwvqxmo6YMTNS7m3P0wY4CggqgncqtqNfaQ2XKr2zecNlqNurSblEa3i1SNrMpwNV1bgNT7Kqod2rLsQqt2FOkiGCLkuzBgS4LAAB0Er/+9a9b/Z5+fX1100036f3332/lUgD/TB+aLEn6eGeBXGwYB7QrRdWNWrItV//akqv8ygaZjQaN7B6luy/rqbG9Yggp2shlfWLVKzZMbo9XH2zPV3WDK9AlAQAAnJZfe1T06dNHv/nNb7R27VqNGDHipDUoDz/8cKsUB5yLsb2iFRNqVVmdU2sPlWlCv7hAlwR0eWV1Dq07VKaDxbWSJKPBoMEpdo3qGa0wm19/enABDAaDpgxK1D8256i01qGlWfm6dVSqrARFAADgAhmNxjN2aPOnI4hf7xZff/11RUZGasuWLdqyZUuLcwaDgaACbcpsMuq6wUl6e/0xLdueT1ABBFBVg0vrD5fpq8Jqeb3NTSYuSYzQ2F4xsgdbAl1el2YxG32dQMrqHPp4Z6FmDEum9SsAALggS5YsafHc5XJp27ZtevPNN/Xkk0/6dU+/goojR4749cOAi2X60GS9vf6YPtldqN/dNIjp5EAbq3U0aeORMu3Kq5bH65Uk9Y0P09jeMYoJtQW4OpwQHmTRjKHJ+ueWHB0tq9N/9pdqfDrhLgAA8N8NN9xw0rHvfOc7GjhwoN59913dc889533PC9pi3el0at++fWpqarqQ2wAXbGT3KCVGBKmmsUmr95cGuhygy2hwufWfAyVa8OUR7citksfrVfeYUP3X6G66fkgyIUU7lGAP0uSBiZKkbTkV2pFbGdiCAABApzRmzBh9+umnfl3rV1BRX1+ve+65RyEhIRo4cKCys7MlNe9N8fvf/96vQoALYTQadP2QJEnSsu35Aa4G6PwcTW6tP1ymN748oi3HKtTk8SolMljfGZGqm4anKCEiKNAl4gz6JoRrXO8YSdLKfSXKLqsPcEUAAKAzaWho0IsvvqjU1FS/rvcrqJgzZ462b9+ulStXKijo6zej11xzjd59912/CgEu1LTjQcWne4vU4Dz/DVsAnJ3L7dGWY+V648ujWn+4TM4mj+LDbbpxeIq+MzJVqVEhgS4R52hUj2hdkhghr9erD3fmq7zOGeiSAABABxQVFaXo6GjfIyoqSuHh4frb3/6mP/7xj37d0689Kt5//329++67Gjt2bItNuAYMGKBDhw75VQhwoYalRSo1Kli5FQ36/Kti3wwLABfO7fFqd36VNhwpV52jeblfdKhVGb1j1CcujA0ZOyKDQdcMiFd1g0v5VQ1ampWnMZPTFR1qDXRlAACgA3n22WdbvBc0Go2Ki4vTmDFjFBUV5dc9/QoqSkpKFB8ff9Lxuro63qwiYAwGg6YPTdbLKw9p2fZ8ggqgFXi8Xu0rrNG6w2WqbnBJkiKCLBrbO0aXJIbLyO/8Ds1kNGra0CQt2pSjqgaXfvz2Fr39o9FsSAwAAM7Z1VdfrbS0tFNmAdnZ2erWrdt539OvpR+jRo3Shx9+6Ht+oqDXXntNGRkZ/twSaBUnln98sa9YNY2uAFcDdFxeeXWwuEYLj3fTqW5wKdRm1lWXxOuOcd01ICmCkKKTCLaaNWNosqwmozYeLdcT7+2S93jnFgAAgLPp2bOnSkpKTjpeVlamnj17+nVPv2ZUzJs3T1OmTNGePXvU1NSk559/Xrt379a6deu0atUqvwoBJKly5V8u6PoUeTXbfkwV9U7tfn+7LkmMaKXK2pfIK+8PdAnoJFxuj4wGgxxNbtnMJnm8XhXXNGr1/lIVVTdKkoIsJo3sEaWhqZGymC6oWRTaqZgwm64bkqTVOwu0eGuu+sSH6SdX9g50WQAAoAM43RcctbW1Lfa0PB9+BRXjxo3Tl19+qWeeeUa9e/fW8uXLdemll2rdunUaPHiwX4UArcOgfgnh2nCkTPsLazptUAG0hiaPR1uOVWhbToUcLo9sFqOGpUZqaFqkXG6PrCajhneP0qXdIlkK0AV0jwnV3BkD9at/79YfMr9Sz9gQTRnEEjoAAHBqs2fPltS8wuJXv/qVQkK+3lTd7XZrw4YNGjZsmF/39iuokKTBgwfrzTff9Pdy4KJJTwjThiNlOlZer0aXW0EWPmAB39bcvaNC6w+X+Y45XB5tOFIuSZoyKFFhNrNCrH7/mUAHdEdGDx0qrtWb647pkXez9M/IEA1OtQe6LAAA0A5t27ZNUvOMip07d8pq/XpDbqvVqqFDh+qxxx7z695+vwN1u91asmSJ9u7dK4PBoP79++uGG26Q2cybWgRWVKhNsaE2ldY5dKikVgOTeZMNfFODyy2LyaBtORWnPJ+VW6nRPWNkMrIHRVf0y2kDdKSsXqv3l+hHb23Svx+4XIl2/6ZtAgCAzuuLL76QJN199916/vnnFRHRerPZ/VpsvGvXLvXr10933nmnlixZovfee0933nmn+vbtq507d7ZacYC/+iWGS5L2F9YEuBIg8DxerwqqGrT+cJkWbcrW4i25qnO45XB5Tjne4fLI0eRu4yrRXphNRr10+3D1jQ9TUbVDP3prk+qdTYEuCwAAtFNvvPGGIiIidPDgQX3yySdqaGiQdPq9K86FX0HFj370Iw0cOFC5ubnaunWrtm7dqpycHA0ZMkT33nvvOd9n3rx5GjVqlMLDwxUfH68bb7xR+/btO+M1K1eulMFgOOnx1Vdf+fNS0EmlJzQHFTkV9bzBRpdU52zSnoJqfbyrQH9dfVjvbsrR+sNlKqxqVJ2zSSFWk2yWU/8JsFmM7EnRxUUEWfS3u0YpOtSqXXnVmv3udnk8dAIBAAAnKy8v18SJE9WvXz9dd911KigokNScGzz66KN+3dOvoGL79u2aN2+eoqKifMeioqL0u9/9TllZWed8n1WrVumBBx7Q+vXrtWLFCjU1NWnSpEmqq6s767X79u1TQUGB79G3b19/Xgo6qYhgixLCg+SVdLC4NtDlABed2+tVbkW9vjxYqnc2HNNrqw9r+e5C7SusUaPLLZvZqL4JYbp2QIK+P6a7JGl4WtQp7zU8LUoe2lN2eWnRIXp15ghZTUZl7i7UM8vP/EUCAADomh555BFZLBZlZ2e32FDztttuU2Zmpl/39GtDifT0dBUVFWngwIEtjhcXF6tPnz7nfJ9vF/3GG28oPj5eW7Zs0fjx4894bXx8vCIjI8/5Z6Hr6ZcQrqKaRu0vrNGQ1MhAlwO0uupGl7LL6nW0rE7Z5fVyNrVcypEQEaQeMSHqHhOqRHuQjIaWe06M7NEcVHyz68fwtCiN7BEls5E2pJBG9YjW728ZrNn/2K6/rDykXnFh+s6I1ECXBQAA2pHly5frk08+UWpqy/cIffv21bFjx/y6p19BxVNPPaWHH35Yc+fO1dixYyVJ69ev1//+7//qD3/4g6qrq31jz2dDjaqqKklSdHT0WccOHz5cjY2NGjBggH7xi1/oqquuOuU4h8Mhh8Phe15Tw54FXUXfhDD952CJ8qoaVOtwKcxmCXRJwAVp8niUX9moo6V1OlZWp7I6Z4vzwVaTukeHqEdsqLpFh5y1Y4fZaNSI7lEa1SNajia3bGaTPF4vIQVauPnSVB0uqdNLXxzUnPd2qFt0iEb3PPvf6dWL322D6jq+8bfcFugSAAC4IHV1dS1mUpxQWloqm83m1z39CiqmTZsmSbr11ltlOP4N3YmNMqZPn+57bjAY5Haf24ZsXq9Xs2fP1uWXX65BgwaddlxSUpL++te/asSIEXI4HHr77bc1ceJErVy58pSzMObNm6cnn3zyvF4fOofwIIuS7cHKr2rQgaJaDe926mnuQHtW2eDUsdLmWRO5FQ1yub+eNWEwSEn2YHU/PmsiPtx20qyJs7GYmkOJE6GGSXT6wMlmX9tPh0tr9dHOQt339ma9/8Bl6h4TGuiyAABAOzB+/Hi99dZb+s1vfiNJMhgM8ng8+uMf/3jaCQVn41dQcaINSWt68MEHtWPHDq1Zs+aM49LT05Wenu57npGRoZycHD3zzDOnDCrmzJmj2bNn+57n5eVpwIABrVc42rV+CeHKr2rQ/qIaggp0CC63R7kV9Tp2fElHZb2rxflQm9m3nKNbdIiCLGx6iYvPaDToT98dptyKddqRW6UfLtik9+6/TPZgZqoBANDVPfPMM5owYYI2b94sp9Opxx9/XLt371Z5ebm+/PJLv+7pV1AxYcKEcxp3//33a+DAgYqNjT3juIceekhLly7V6tWrT1rXci7Gjh2rhQsXnvKczWZrMd3km8tS0Pn1SQjTqv3FKqxuVHWDSxG8qUY745VX5XVOHSur17Hjsybc3+iuYDQYlBwZpB4xoeoeE6rYcKsMzHpAAARbTXr9jpG64f99qUMldXrw71v1t7tG+WblAACArsflcun+++/X0qVL9fHHH8tkMqmurk4333yzHnjgASUlJfl134v67mLhwoVnDAa8Xq8efPBBvffee/r888/Vs2dPv37Otm3b/P4HQOcWajUrNap5vdT+IvYnQfvgaHLrYHGtPttbpDfWHNXb645p9f4SHSurl9vjVUSQRYNT7Zo+NFn3Teil74xI08ge0YoLtxFSIKDiI4L0+p0jFWI16T8HSjV36e4L6pEOAABObd68eRo1apTCw8MVHx+vG2+8Ufv2tezA5fV6NXfuXCUnJys4OFhXXnmldu/e3WKMw+HQQw89pNjYWIWGhmrGjBnKzc1tMaaiokIzZ86U3W6X3W7XzJkzVVlZeU51WiwW7dq1SzExMXryySf1wQcf6KOPPtJvf/vbC/qMflGDirO9eXnggQe0cOFC/f3vf1d4eLgKCwtVWFiohoYG35g5c+bojjvu8D1/7rnn9P777+vAgQPavXu35syZo8WLF+vBBx+8aK8DHVu/hHBJBBUIHK/Xqz351frLyoO67dV1enXVYX2wI18786pU3eiSyWhQ95gQje8Xpzsyuuvuy3to4iUJ6h0XJpuZpR1oXwYm2/XcbcNkMEjvbMjWgrVHA10SAACdzqpVq/TAAw9o/fr1WrFihZqamjRp0iTV1dX5xjz99NP685//rJdeekmbNm1SYmKirr322hYNJB555BEtWbJEixYt0po1a1RbW6tp06a12Evy9ttvV1ZWljIzM5WZmamsrCzNnDnznGu94447NH/+/NZ54cf5tfSjtbz88suSpCuvvLLF8TfeeEN33XWXJKmgoEDZ2dm+c06nU4899pjy8vIUHBysgQMH6sMPP9R1113XVmWjg+kTH6Yv9hWrpNahijqnokKtgS4JXUBlvVP/OVCqVftLtGp/iUpqvu4+dKPXq6gQq7rHhKhHTKhSooKZPo8OZdLARM2Zeome+ugr/eaDPeoRE6qrLokPdFkAAHQamZmZLZ6/8cYbio+P15YtWzR+/Hh5vV4999xz+vnPf66bb75ZkvTmm28qISFBf//733XfffepqqpK8+fP19tvv61rrrlGUvOqh7S0NH366aeaPHmy9u7dq8zMTK1fv15jxoyRJL322mvKyMjQvn37WuwPeTpOp1Ovv/66VqxYoZEjRyo0tOWG23/+85/P+/UHNKg4l+miCxYsaPH88ccf1+OPP36RKkJnFGQxqVt0iI6W1Wl/UY3G9IoJdEnohDwer3bkVWnVvhKt2l+srJxKfWOrCQVbTBrXO0YT0uM0ZGuuIoMJzNCxzbqilw4V1+ndzTl66P+26V8/ydAliefekhwAgK6opqamxfYI395T8XSqqqokSdHRzS3Cjxw5osLCQk2aNKnFvSZMmKC1a9fqvvvu05YtW+RyuVqMSU5O1qBBg7R27VpNnjxZ69atk91u94UUUvMekHa7XWvXrj2noGLXrl269NJLJUn79+9vcc5wnh3pTghoUAG0lX4J4TpaVqd9RTUa0ytaYp0/WkFJjUP/OdA8Y+I/B0pVXudscb5fQpgm9IvTlenxGtkjyreM4+heQgp0fAaDQb+5cZCOlddp/eFy3bOguW1pXLh//dIBAOgKvt2B8te//rXmzp17xmu8Xq9mz56tyy+/XIMGDZIkFRYWSpISEhJajE1ISNCxY8d8Y6xWq6Kiok4ac+L6wsJCxcefPCsyPj7eN+ZsLkZXUIIKdAm94kJlMhpUUe9Uaa1TsWG8kcb5a3J7tC2n8visiRLtzKtqcT7cZtblfWM1oV+cxveLU3JkcIAqBdqG1WzUKz8YoZv+slZHSut079ub9X+zxtI2FwCA09izZ49SUlJ8z89lNsWDDz6oHTt2aM2aNSed+/aMBa/Xe9ZZDN8ec6rx53Kfi+miBhU/+MEPFBHBNFAEns1sUo/oUB0qrdX+ohqCCpyzmkaXjpXV6/53tug/B0pV09jU4vyglAhN6BenCf3iNbxbJHtNoMuJDLFq/p0jddNf1mpbdqUe/9cOPf+9YYEuCwCAdik8PPy8PiM/9NBDWrp0qVavXq3U1FTf8cTEREnNMyK+2V2juLjYN8siMTFRTqdTFRUVLWZVFBcXa9y4cb4xRUVFJ/3ckpKSk2ZrtCW/g4rKykpt3LhRxcXF8ng8Lc6d6NJxYrNMoD3olxjeHFQU1mhc7xix/AOn0uTxqKCyUUfL6nS0rF5ltc2bYNbUNE99iwyxaHzfOE3oF6cr+sUqPjwokOUC7UKvuDC9/P1LdcffNmrp9nz1jgvT8EAXBQBAB+b1evXQQw9pyZIlWrlypXr27NnifM+ePZWYmKgVK1Zo+PDmv7pOp1OrVq3SH/7wB0nSiBEjZLFYtGLFCt16662SmptV7Nq1S08//bQkKSMjQ1VVVdq4caNGjx4tSdqwYYOqqqp8YUYg+BVULFu2TN///vdVV1en8PDwk6aNfLOdKNBe9IwNldloVFWjS0XVDiVE8AETzaoaXDpaVqdjpXXKqWiQy/11+GowSAkRQbpnTF9N6BenIamRMhkJuYBvG9cnVr+9cZD+v/d26tlP9+vn3WqUfrw9NAAAOD8PPPCA/v73v+vf//63wsPDfftF2O12BQcHy2Aw6JFHHtFTTz2lvn37qm/fvnrqqacUEhKi22+/3Tf2nnvu0aOPPqqYmBhFR0frscce0+DBg31dQPr3768pU6Zo1qxZevXVVyVJ9957r6ZNm3ZOG2leLH4FFY8++qh++MMf+v4hgI7AYjKqV2yo9hfXaH9RDUFFF9bk9ii3oqE5nCirV0V9y00wQ6xmX+vQbtEhCraadM+YfgGqFug4vje6mw6V1Oq1/xzR8t1FigiyKMnO71oAAM7XidUJV155ZYvjb7zxhu666y5JzR0xGxoadP/996uiokJjxozR8uXLFR7+9RcFzz77rMxms2699VY1NDRo4sSJWrBggUymr/eTeuedd/Twww/7uoPMmDFDL7300sV9gWfhV1CRl5enhx9+mJACHU6/xHBfUHF5n9iAbhCDtuP1elVR7/IFE3kV9Wr6Ru9Qo8GgJHuQuseEqkdsiOLCbPxvA/DT/ze1vw6X1Klig1fLtufre6PTFBFkCXRZAAB0KF6v96xjDAaD5s6de8auIUFBQXrxxRf14osvnnZMdHS0Fi5c6E+ZF41fQcXkyZO1efNm9erVq7XrAS6q7jEhspqMqnU0qaCqka4MnZizyaOc8vrmcKK8XtUNrhbnw4PMzcFETKjSooN9rUMBXBiT0aDn/2u4Zu/+QqW1Ti3Nyteto9JkZaNZAABwjvwKKq6//nr97Gc/0549ezR48GBZLC2/KZkxY0arFAe0NrPRqN5xYdpbWK39RTUEFZ2I1+tVaa1Tx45vgllQ1SD3N2ZNmIwGpUQGHw8nQhQdamXWBHCRhNnMmjEsRYs2Zqu01qnMnYWaNjRZbO8CAADOhV9BxaxZsyRJ//u//3vSOYPBILfbfWFVARdRv8Rw7S2s1oGiGo3vFycjH1Y7rEaXW9nl9Tp2fElHraNl61B7sEU9ji/nSI0KoXUo0IYigsyaPjRZi7fk6nBpndYcKNH4fnGBLgsAAHQAfgUV325HCnQk3aJDFGQxqd7lVl5Fg9Ki2WulvXC5PTIaDHI0uWUzm+TxeluEC16vV8U1Dh0tbQ4mCqsb5fnG+j2zyaDUyBD1iG2eNREZYg3EywBwXJI9SJMGJuijnYXaml2pqFCrBqfYA10WAABo5847qGhqalJQUJCysrI0aNCgi1ETcFEZDQb1iQvTrvwq7S+qIahoJ5rcHm0+WqGs3Ao5XB7ZLEYNS43SqB5Ryi6v1/6iWmWX16ne2XLGVnSoVT1iQtU9JkQpkcEyM2sCaFf6JYSrot6ldYfK9MVXxbIHW9SN37sAAOAMzjuoMJvN6t69O8s70KGlJ4RrV36VDpbU6qpL4ln+EWCu4yHFhiNlvmMOl0cbjpTJK68SwoP0VWG1JMlqNiotKsQXTkQE000AaO9G94xWRZ1TXxXW6MMdBbptdJqimfEEAABOw6+vHn/xi19ozpw5Ki8vb+16gDaREhWsEIvZt8cBAsfr9cpoMCgrt+KU57fnVqpbTIgyesXoOyNSdd/43po+NFmDU+2EFEAHYZB0zYAEJdmD5GjyaGlWvhpcfOEBAABOza89Kl544QUdPHhQycnJ6t69u0JDQ1uc37p1a6sUB1wsBoNBfRPCtD23UvsLa9QjJvTsF6HVNDjdyqmoV3ZZvaobXZrYP0EO16n3vnG4PHK5PRrTK6aNqwTQmsxGg6YPTdaiTdmqrHfpwx0Fuml4iky0AgEAAN/iV1Bx4403tnIZQNtLTwjX9txKHSqpVZPHI7ORvQ0ulia3R/mVjcour1d2RZ1Kahw6sQdmsNWkEKtJNovxlGGFzWKUzWxq44oBXAwhVpNmDE3RPzbnKLeiQZ9/VaxrBiSIqAIAAHyTX0HFr3/969auA2hzifYghdnMqnU06VhZvXrHhQW6pE7D6/WqpMbRHEyU1yu/skFNHm+LMbFhNqVFh6hbdIi8Xq+GpUa12KPihGGpUfJ4vTLxUQboFGLDrJo6OFFLs/K1O79aUaFWjeweFeiyAABAO+JXUAF0BgaDQf0SwrU1u0L7C2sIKi5QdYPLF0zkVNSr4VvdOcJsZqVFh6h7dIjSokMUamv562dUj+YPKqfq+kEnD6Bz6RkTqgn94rRyX4m+PFiqyBCL+vA7GAAAHOdXUGE0GmU4Q5cEOoKgozgRVBwurZPL7ZGFD8TnrNHlVm5Fg7LL65Rd3qDKemeL81aTUSlRweoWHaLuMSGKCrGe8feG2WTUyB5RGt0zWo4mt2xmkzxeLyEF0EkNTYtURZ1T23Or9MmuQkWMTFN8uC3QZQEAgHbAr6BiyZIlLZ67XC5t27ZNb775pp588slWKQxoCwkRNtmDLKpqdOlIaZ36JYQHuqR2y+3xqqCqwTdroqi60bfPhCQZDQYlRASpW3SIusWEKDEi6Lw3yTsRFIVYm381sdwD6LwMkiakx6mywaVjZfVampWv741OU5iNyZ4AAHR1fr0buOGGG0469p3vfEcDBw7Uu+++q3vuueeCCwPahkH9EsO16Wi59hfWEFR8g9frVVmdszmYKKtXXmWDXO6Wm11GhVrVLap5n4nU6GA2vQRwXowGg6YOTtI/NuWovM6pZdvz9Z0RabKYCCkBAOjKWvVrizFjxmjWrFmteUvgouuX0BxUHC2v8y056KpqHU3KLqtXTnm9sivqVedoanE+xGpSWlTzjIm06BBFBFkCVCmAziLIbNQNw5K1aGO2iqodWr67UNcNTtIZVooBAIBOrtWCioaGBr344otKTU1trVsCbSI2zKroEKvK6506XFKn/kkRgS6pzdQ5mnSktFbHypo3wCyrbbnPhNloUHJksLrHhKhbdKhiw868zwQA+MMebNG0ocl6b2ueDhTXau3hUl3WOzbQZQEAgADxK6iIiopq8WHF6/WqpqZGISEhWrhwYasVB7SN5u4f64+UaX9RTacOKprcHm3PrdKXB0u15kCptmZXKDg033feYJDiw4N83TmS7EFsZgmgTaREBmti/3gt312kTUcqFBVi1YBO/PsYAACcnl9BxbPPPtsiqDAajYqLi9OYMWMUFUUvdHQ8/RKbg4rs8no1utwKsnSO5R9er1dHSuu05ngwse5wmWoaWy7niAi2NG+AGR2itKgQBVs7x2sH0PEMSIpQRZ1Tm45W6LO9RbIHW5QSGRzosgAAQBvzK6i4+uqrlZaWdsop4NnZ2erWrdsFFwa0pagQq+LCbCqpdehgca0GpdgDXZLfymod+vJQmdYcKNGXB8uUV9nQ4rw92KJxvWN0ed9YXdEnTisOFgaoUgA42bjesaqod+lgca0+2J6v20Z3U2Qw++EAANCV+BVU9OzZUwUFBYqPj29xvKysTD179pTb7W6V4oC21C8hXCW1Du0vqulQQUWjy61NR8u15kCp/nOgVHsKqluct5qMGtE9Spf3jdXlfWI1KMXesm3owTYuGADOwGCQJg9MUE2jS0XVDi3Nytdto9JkM7MMDQCArsKvoMLr9Z7yeG1trYKCgi6oICBQ+iWE68tDpcqtqFeds0mh1lZtitNqvF6vduZWNS/nOFiiTUcr5Gxq2Tb0ksRwXdE3Vpf1idXontEKaaevBQBOxWIyavrQZC3a2Ny29KOdBbphWLKMbOYLAECXcF6fXmbPni1JMhgM+tWvfqWQkBDfObfbrQ0bNmjYsGGtWiDQViKCLUqMCFJhdaMOFtVqaFpkoEvyqW5wKbu8Xtnlzd05Xv5kTYvziRFBzUs5+sZqXO9YxYXbAlQpALSOMJtZM4Yl65+bc3SsrF6r9pfoqvT4s18IAAA6vPMKKrZt2ybp+De6O3fKarX6zlmtVg0dOlSPPfZY61YItKF+CeEqrG7U/qKagAYVDpdbuRX1OnY8nKhqcLU4H2Yza2yvGF3eJ0aX941T77hQ2oYC6HTiw22aPChRH+4o0PacKkWFWDWsHYXIAADg4jivoOKLL76QJN199916/vnnFRFB2zB0Ln0TwrX6QInyqxpU0+hSeFDbbODm9nhUUNWonPLmcKK4ulHfXGBlMBiUFBHk687xxPRrZaFtKIAuoE9cmC7rE6s1B0q1an+JIkMs6hETGuiyAADAReTXwvU33nhDknTw4EEdOnRI48ePV3BwsLxeL9/qokMLs5mVEhmsvMoGHSiq1aXdL1a7Xa/Kap2+5Ry5FQ1q8rTcZyIqxKrux4OJlKhgWc1ftw0lpADQlYzoHqWKOqd251fro52FunVkmmLDrGe/EAAAdEh+BRXl5eX67ne/qy+++EIGg0EHDhxQr1699KMf/UiRkZH605/+1Np1Am2mX0K48iobtL+oplWDilpHk3LK65VdVqfs8gbVu5panA+xmJR2PJjoFhOiMBvt+ABAkgySrr4kXpUNLuVVNGjp9jx9b1Q3hVhNZ70WAAB0PH4FFY888ogsFouys7PVv39/3/HbbrtNP/3pTwkq0KH1iQ/Tyn3FKqppVFWDU/Zg/761c7o9yjs+YyK7vF7l9c4W581Gg1Iivw4mmr8dZEYSAJyKyWjQtCFJendTjirrXfpgR75uvjRVZiO/NwEA6Gz8CiqWL1+uTz75RKmpqS2O9+3bV8eOHWuVwoBACbGalRYdouzyeh0uqdPwbucWVHi8XhVVNyq7rDmYKKxulOdbrXwTwoPULaY5nEiyB8lkZAkHAJyrYItJM4Ym691NOcqvbNSne4o0eVAiES8AAJ2MX0FFXV1di9akJ5SWlspmoy0iOr7BKXYNTY1Ut+gQNTibZDWb5PF6v7U3hFcV9S5fMJFbUS+nu+U+E/Zgi28DzNSoEAVZmKYMABciOtSq64ck6f1tefqqsEZRoVaN6Rkd6LIAAEAr8iuoGD9+vN566y395je/kdTckcDj8eiPf/yjrrrqqlYtEAiEHrGh2nSkXMv3FMrR5JHNbNSwtCiN7BGlnPJ6HSyuVU55vWocLfeZCLKYlBYV4gsnIoLZZwIAWlu36BBddUm8PttbrHWHyhQVYlG/hPBAlwUAAFqJX0HFM888owkTJmjz5s1yOp16/PHHtXv3bpWXl+vLL79s7RqBNtXk9mjzsQptPFruO+Zo8mjDkTJ5vV4lRARpT0G1JMlkMCg5MtgXTMSF2+h8AwBtYHCKXRV1Tm3NrtTy3UWKCLYoMSIo0GUBAIBWcN5Bhcvl0v3336+lS5fq448/lslkUl1dnW6++WY98MADSkpKuhh1Am3GYDAoK6filOe251bqR5f30tieMUqyByk5MlhmWoUCQEBc3jdOFfUuHSmt09KsfH1vdDdFBPn1HQzgly+++CLQJXQIzLgGcL7O+6+5xWLRrl27FBMToyeffPJi1AQElLPJLUeT55TnHE0eudwejekV08ZVAQC+zWiQpg5O1D825ai01qmlWXm6dVSarATIAAB0aH79Jb/jjjs0f/781q4FaBesZpNs5lP/X8NmNspqZkNMAGgvrCajZgxLVojVpNJapzJ3FcrjPft1AACg/fJrfqTT6dTrr7+uFStWaOTIkQoNDW1x/s9//nOrFAcEgtfr1bC0KG04UnbSuWFpUfJ4vTLRDA8A2o2IIIumD03Wv7bk6nBJnb48WKIr+sYFuiwAAOAnv4KKXbt26dJLL5Uk7d+/v8U5NhJER2c2GTWqR5QkKSunokXXj1E9omQyMqUYANqbJHuQJg1M0Mc7C7XlWKUiQ6wanGIPdFkAAMAPfgUVbByEzs5kNGpE9yiN6hEtZ5NbVrOpeSYFIQUAtFvpCeGqqHNq/eFyffFVsSKDLUqLDgl0WQAA4DzxqQs4DYvJKJPRoGCrWSajQRY2ZwOAdm9MrxilJ4bL45U+2FGginpnoEsCAADniU9eAACg0zBIunZAgpLsQXI0ebRqX4kcTR65PV7VO91ye7xyuU/d2QkAALQPNBsHAACditlo0LShycrcVaBJAxO15ViFtudWyuHyyGYxalhapEb1iJbZyL5aAAC0RwQVAACg0wm1mjR1UJKyciq18Ui577jD5dGGw83PR3aPYlkfAADtEH+dAQBAp2QzG7U9t/KU57JyKmWkUxkAoB1bvXq1pk+fruTkZBkMBr3//vstzt91110yGAwtHmPHjm0xxuFw6KGHHlJsbKxCQ0M1Y8YM5ebmthhTUVGhmTNnym63y263a+bMmaqsrLzIr+7MCCoAAECn5GjyyOE69X4UDpdHjib2qgAAtF91dXUaOnSoXnrppdOOmTJligoKCnyPjz76qMX5Rx55REuWLNGiRYu0Zs0a1dbWatq0aXK73b4xt99+u7KyspSZmanMzExlZWVp5syZF+11nQuWfgAAgE7JZjbKZjGeMqywWYyymfm+BgDQfk2dOlVTp0494xibzabExMRTnquqqtL8+fP19ttv65prrpEkLVy4UGlpafr00081efJk7d27V5mZmVq/fr3GjBkjSXrttdeUkZGhffv2KT09vXVf1DniLzQAAOiUPF6vhqVFnvLcsLRIebzeti0IANDl1dTUqLq62vdwOBwXdL+VK1cqPj5e/fr106xZs1RcXOw7t2XLFrlcLk2aNMl3LDk5WYMGDdLatWslSevWrZPdbveFFJI0duxY2e1235hAIKgAAACdksVk1Kge0RrTK1o2S/NbHpvFqNE9ozUsLVJldc4AVwgA6GoGDBjg2wvCbrdr3rx5ft9r6tSpeuedd/T555/rT3/6kzZt2qSrr77aF34UFhbKarUqKiqqxXUJCQkqLCz0jYmPjz/p3vHx8b4xgcDSDwAA0GmZjQaN7B6l0T2i5WjyyGY2qrC6Uf/cnKtaR5NuuTRFCRFBgS4TANBF7NmzRykpKb7nNpvN73vddtttvv88aNAgjRw5Ut27d9eHH36om2+++bTXeb1eGb6xobThFJtLf3tMW2NGBQAA6NQsJqNMRoNCrCaZjAYlRAQpxGqSs8mjJdvyVFrLzAoAQNsIDw9XRESE73EhQcW3JSUlqXv37jpw4IAkKTExUU6nUxUVFS3GFRcXKyEhwTemqKjopHuVlJT4xgRCQIOKefPmadSoUQoPD1d8fLxuvPFG7du376zXrVq1SiNGjFBQUJB69eqlV155pQ2qBQAAnYHZaNCMYclKtAep0eXRkm25qqx3BbosAAAuSFlZmXJycpSUlCRJGjFihCwWi1asWOEbU1BQoF27dmncuHGSpIyMDFVVVWnjxo2+MRs2bFBVVZVvTCAENKhYtWqVHnjgAa1fv14rVqxQU1OTJk2apLq6utNec+TIEV133XW64oortG3bNj3xxBN6+OGHtXjx4jasHAAAdGRWk1E3DktWbJhVdQ633tuaqxpHU6DLAgDAp7a2VllZWcrKypLU/Fk4KytL2dnZqq2t1WOPPaZ169bp6NGjWrlypaZPn67Y2FjddNNNkiS73a577rlHjz76qD777DNt27ZNP/jBDzR48GBfF5D+/ftrypQpmjVrltavX6/169dr1qxZmjZtWsA6fkgB3qMiMzOzxfM33nhD8fHx2rJli8aPH3/Ka1555RV169ZNzz33nKTmf9jNmzfrmWee0S233HKxSwYAAJ1EkMWkmy5N0b8256qi3qX3tubquyPSFGI1Bbo0AAC0efNmXXXVVb7ns2fPliTdeeedevnll7Vz50699dZbqqysVFJSkq666iq9++67Cg8P913z7LPPymw269Zbb1VDQ4MmTpyoBQsWyGT6+m/dO++8o4cfftjXHWTGjBl66aWX2uhVnlq72kyzqqpKkhQdHX3aMevWrWvRXkWSJk+erPnz58vlcslisbQ453A4WrR8qampacWKAQBARxZqNeumS1P1z805qqhzacm2PN1yaaqCLGzjBQAIrCuvvFLeM7TS/uSTT856j6CgIL344ot68cUXTzsmOjpaCxcu9KvGi6Xd/BX2er2aPXu2Lr/8cg0aNOi04woLC0/a1CMhIUFNTU0qLS09afy8efNatH8ZMGBAq9cOAAA6roggs26+NEUhVpNKahz6d1aenG5PoMsCAKDLajdBxYMPPqgdO3bo//7v/8469tttUk6kTKdqnzJnzhxVVVX5Hnv27GmdggEAQKcRFWLVTZemyGY2qqCqUR9sz1eT5/TfYgEAgIunXQQVDz30kJYuXaovvvhCqampZxybmJiowsLCFseKi4tlNpsVExNz0nibzdai/cs31+sAAACcEBdm043DU2QxGZRd3qCPdxbIfYYptwAA4OIIaFDh9Xr14IMP6r333tPnn3+unj17nvWajIyMFu1VJGn58uUaOXLkSftTAAAAnI8ke5BmDEuWyWjQoZI6rdhdJLIKAADaVkCDigceeEALFy7U3//+d4WHh6uwsFCFhYVqaGjwjZkzZ47uuOMO3/Mf//jHOnbsmGbPnq29e/fqb3/7m+bPn6/HHnssEC8BAAB0MmlRIbp+SJKMBumrwhp9vq9YZBUAALSdgAYVL7/8sqqqqnTllVcqKSnJ93j33Xd9YwoKCpSdne173rNnT3300UdauXKlhg0bpt/85jd64YUXaE0KAABaTa/YUE0elCiDQdqZW6U1B0oJKwAAaCMBbU96plYrJyxYsOCkYxMmTNDWrVsvQkUAAADN0hPC5XJ79OmeYm05ViGr2agxPU/fQh0AALSOdrGZJgAAQHs0KNmu8f1iJUnrDpVpW3ZlYAsCAKALIKgAAAA4g0u7RWlsr+aZFKv2l2h3fnWAKwIAoHMjqAAAADiLMb1idGm3SEnSp3uLtL+oNrAFAQDQiRFUAAAAnIVB0hX94jQoJUJer/TJ7gIdKa0LdFkAAHRKAd1MEwAAoKMwSLr6kgS53F7tK6zRhzsKdOPwFKVGBQe6NElSyerDgS6hQ4gb3yvQJQAAzoIZFQAAAOfIaJAmDUxQr7hQNXm8WpqVp8LqxkCXBQBAp0JQAQAAcB5MBoOuG5yktOhgOd1evb8tT6W1jkCXBQBAp0FQAQAAcJ7MRoOmD01Woj1IjS6P3tuap4p6V6DLAgCgUyCoAAAA8IPVZNSNw5IVG2ZVvdOtJVtzVdPYFOiyAADo8NhMEwAAwE9BFpNuujRV/9qco4p6l97bmqvvjkxTiNUU6NIAoFW8vPtgoEvoEH4ysE+gS+hUmFEBAABwAUKtzWFFRJBZFfUuLdmWp0aXJ9BlAQDQYRFUAAAAXKCIILNuujRVIVaTSmoc+ndWnpxuwgoAAPxBUAEAANAKokIsuvnSFAVZjCqoatSy7flq8ngDXRYAAB0OQQUAAEAriQ2z6cbhKbKaDMopb9BHOwvk9hJWAABwPggqAAAAWlFiRJCmD0uR2WjQ4ZI6Ld9dJCZWAABw7ggqAAAAWllaVLCuH5Iko0HaV1ijL74qFlkFAADnhqACAADgIugZG6opgxJlMEg786q05kAJYQUAAOeAoAIAAOAi6ZcQrmv6J0iSthyr1MYj5QGuCACA9o+gAgAA4CIamByh8f3iJEnrDpVpa3ZlYAsCAKCdI6gAAAC4yC7tFqmM3jGSpNX7S7QrvyrAFQEA0H4RVAAAALSB0T2jNaJ7lCTps73F2ldUE+CKAABonwgqAAAA2oBB0uV9YzU4xS6vV/pkV6E+/6oo0GUBANDuEFQAAAC0EYOkqy6J1yWJ4fJ4pZ8s3Kp1h8oCXRYAAO0KQQUAAEAbMhqkawcmqFdcqBxNHv3ozU3KyqkMdFkAALQbBBUAAABtzGQw6LrBSRrXO0Z1Trfu/NtGfVVYHeiyAABoFwgqAAAAAsBsNOi1O0ZqeLdIVTW49IPXN+pIaV2gywIAIOAIKgAAAAIk1GbWgrtGq39ShEprHfrB6xuUV9kQ6LIAAAgoggoAAIAAsodY9PY9o9UrLlR5lQ36wesbVFLjCHRZAAAEDEEFAABAgMWG2bTwnjFKiQzWkdI6zZy/QVX1rkCXBQBAQBBUAAAAtAPJkcF650djFBdu01eFNbrzjY2qdTQFuiwAANocQQUAAEA70SM2VAvvGaPIEIuycio1683NanS5A10WAABtiqACAACgHUlPDNebd49WqNWkdYfL9MA7W+VyewJdFgAAbYagAgAAoJ0Zmhap+XeNks1s1GdfFWv2P7bL7fEGuiwAANoEQQUAAEA7NLZXjF6ZOUIWk0HLtufr50t2yuslrAAAdH4EFQAAAO3UVenxeu624TIapEWbcvS7D/cSVgBAF7F69WpNnz5dycnJMhgMev/991uc93q9mjt3rpKTkxUcHKwrr7xSu3fvbjHG4XDooYceUmxsrEJDQzVjxgzl5ua2GFNRUaGZM2fKbrfLbrdr5syZqqysvMiv7swIKgAAANqx64ck6fe3DJEkvb7miF747GCAKwIAtIW6ujoNHTpUL7300inPP/300/rzn/+sl156SZs2bVJiYqKuvfZa1dTU+MY88sgjWrJkiRYtWqQ1a9aotrZW06ZNk9v99UbNt99+u7KyspSZmanMzExlZWVp5syZF/31nYk5oD8dAAAAZ3XryDTVOZr05LI9evbT/Qq1mfSjK3oFuiygze3a/ddAl9AhDBp4b6BLQCuYOnWqpk6despzXq9Xzz33nH7+85/r5ptvliS9+eabSkhI0N///nfdd999qqqq0vz58/X222/rmmuukSQtXLhQaWlp+vTTTzV58mTt3btXmZmZWr9+vcaMGSNJeu2115SRkaF9+/YpPT29bV7stzCjAgAAoAO4+7KeevTafpKk3364V4s2Zge4IrQnTW6P3B6vGpxuuT1eOsUA7VRNTY2qq6t9D4fD4dd9jhw5osLCQk2aNMl3zGazacKECVq7dq0kacuWLXK5XC3GJCcna9CgQb4x69atk91u94UUkjR27FjZ7XbfmEBgRgUAAEAH8eDVfVTraNKrqw9rzpKdCrWZNX1ocqDLQoC5PV5tPlahrJxKOZo8spmNGpYWqVE9omUyGgJdHoBvGDBgQIvnv/71rzV37tzzvk9hYaEkKSEhocXxhIQEHTt2zDfGarUqKirqpDEnri8sLFR8fPxJ94+Pj/eNCQSCCgAAgA7CYDDo/5t6iWodTXpnQ7Z++m6WQqwmTeyfcPaL0Sk1uT3afKxCG46U+445mjy+5yO6R8liYhI10F7s2bNHKSkpvuc2m+2C7mcwtAwjvV7vSce+7dtjTjX+XO5zMfFbCwAAoAMxGAz6zQ2DdOOwZDV5vPrJO1u19lBpoMtCgBgMBmXlVJ7yXFZOpYwB/KAB4GTh4eGKiIjwPfwNKhITEyXppFkPxcXFvlkWiYmJcjqdqqioOOOYoqKik+5fUlJy0myNtkRQAQAA0MEYjQb98btDde2ABDmbPPrRm5tVWNUY6LLQxlxujxpcbjmaTr0fhaPJI+dpzgHo2Hr27KnExEStWLHCd8zpdGrVqlUaN26cJGnEiBGyWCwtxhQUFGjXrl2+MRkZGaqqqtLGjRt9YzZs2KCqqirfmEAgqAAAAOiALCajXvyv4bq8T6zqnW69n5Wv0hr/NmVDx1LV4NKq/SVauP6YbGajbOZTv6W3mY2ynuYcgPavtrZWWVlZysrKktS8gWZWVpays7NlMBj0yCOP6KmnntKSJUu0a9cu3XXXXQoJCdHtt98uSbLb7brnnnv06KOP6rPPPtO2bdv0gx/8QIMHD/Z1Aenfv7+mTJmiWbNmaf369Vq/fr1mzZqladOmBazjh8QeFQAAAB1WkMWkv94xQj94fYMaC2v13rZ8fXdEiqJCrYEuDRdBbkWDtmVX6HBpne9YQVWjhqZFauM39qg4YWhqpBpcboXZeMsPdESbN2/WVVdd5Xs+e/ZsSdKdd96pBQsW6PHHH1dDQ4Puv/9+VVRUaMyYMVq+fLnCw8N91zz77LMym8269dZb1dDQoIkTJ2rBggUymUy+Me+8844efvhhX3eQGTNm6KWXXmqjV3lq/NYCAADowEKsZr1x92i98eKXKql16L2tefruqDRFBPE2rzNwe7zaV1SjrOxKldR+PWOme3SIhneLUlpUiFIig2WQWnT9GJoaqWFpkXpva65G9ohWemL46X8IgHbpyiuvlNfrPe15g8GguXPnnrFrSFBQkF588UW9+OKLpx0THR2thQsXXkiprY6/YAAAAB2cPdiimy5N0T8356ii3qX3tuTquyNTFco36R1WvdOtHbmV2plbpXqXW5JkNhrUPylCw9IiFf2NWTMmg0EjukdpVI9oOZs8spqNcnu8WnuoVKV1TmXuLlRVg0uje0YH6uUAwHnhrxcAAEAnEGI16eZLU/XPzbmqbHBpybY8fWdEqoIsprNfjHajpMahrJxK7Suskfv4N6lhNrOGpkZqUErEaf/7PNGCNNjafN5kNGhCv3gZDQZty6nUusNlqmpw6epL4mUy0gkEQPtGUAEAANBJhAeZdfOlKfrnllyV1jr1/rZ83XxpChsqtnNer3S4tFZZ2ZXKrWzwHU+MCNLwbpHqEx/mV5tRg0Ea3y9OkSEWrdxXoj0F1appdOn6Icmn3YATANoDggoAAIBOJDLEopuHN4cVhdWNWro9XzcOS5HZxLfo7Y2zyaPd+dXanlOpqkaXpOZwoW98uIalRSrJHtQqP2dIaqQigiz6aFeBcioa9I9NObphWLIigi2tcn8AaG1EqQAAAJ1MTJhVNw1PltVsVG5Fgz7cWSC35/QbsqFtnWgvOn/NEa0+UKKqRpdsZqNGdo/SDy/rqamDElstpDihR2yovjsiTWFWs8rrnXp3U44Kqxpb9WcAQGshqAAAAOiEEiKCdMPQZJlNBh0prdMnuwtFVhFYuRUNWrY9XwvWHlVWTqWcbo+iQiy6+pJ4/ejyXrqsT+xFbSUaF27TbaPTFBdmU73LrX9tzdXB4tqL9vMAwF8s/QAAAOikUqKCNW1wkpbtKND+olpZTUWa2D9Bfmx3AD+drb1ot+iQNv3vI8xm1ndGpurjnQU6WlavD3cW6PI+sRrRPartigCAsyCoAAAA6MR6xIZqysBEfbSrQLvyq2UxGTW+XxxhxUV2uvaiA5IiNPRb7UXbmtVk1PShyVq9v0Tbc6u05mCpqhpcujI9zq9NOwGgtRFUAAAAdHJ9E8J0rTtBy/cUaVtOpWxmo8b2jgl0WZ2Sv+1F25rRYNCV6fGyB1u1+kCJduZVqbrBpesGJ9ElBkDAEVQAAAB0AQOSI+Rs8mjl/hKtP1Iuq9moS5nu3youVnvRtjC8W6Qigs3K3FWoY+X1+ufmHM0YlqLwID4mAAicgMalq1ev1vTp05WcnCyDwaD333//jONXrlwpg8Fw0uOrr75qm4IBAAA6sGHdInXZ8ZkUqw+UaldeVYAr6ticTR5ty67Um2uP6oMdBcqtbJDBIPVLCNetI9N026g09UsIb7chxQm948L0nRGpCrGaVFrn1LubslVc4zj7hQBwkQQ0Kq2rq9PQoUN1991365Zbbjnn6/bt26eIiAjf87i4uItRHgAAQKczske0HE0ebT5Woc++KpbFZFR6Ynigy+pQssvqtWDtUe3afEROt0eSZDMbNTjFrqFpkRe1c8fFkhARpNtGpWlpVr7K6pz65+YcTR2UpF5xoYEuDUAXFNDfolOnTtXUqVPP+7r4+HhFRkae01iHwyGH4+tEuKam5rx/HgAAQGdhMEiX9YmV0+3RjtwqfbK7SBaTkQ+kZ+H1erX+cLn+9uURfbq3SF6vNMzsUXSIVcO6Rap/YoTMpvY9c+JsIoIs+u7INH20s0DZ5fX6YEe+xveL07C0yECXBqCL6ZA75QwfPlxJSUmaOHGivvjiizOOnTdvnux2u+8xYMCANqoSAACgfTIYpKvS49U/MVwer1cf7ixQTnl9oMtqlxxNbv1rS66uf2GN/uu19VqxpzmkuDI9TjcOS9HMjO4anGLv8CHFCTazUTcMS9ag5Ah5Ja3aX6JV+0p0fF9QAGgTHSqoSEpK0l//+lctXrxY7733ntLT0zVx4kStXr36tNfMmTNHVVVVvseePXvasGIAAID2yWCQrh2QoN5xoXJ7vFq6vUAFVY2BLqvdKKlx6LlP9+uy33+ux/65XXsKqhVkMeoHY7vp09kTtODu0eoeExLoMi8Ko8Ggif0TdFnvWElSVm6lPtiRL9fxZS4AcLF1qAV06enpSk9P9z3PyMhQTk6OnnnmGY0fP/6U19hsNtlsNt/z6urqi14nAABAR2A0GjR1cJKWZeXrWHm93t+Wp++MSFVcuO3sF3dSu/Or9MaXR7U0K9+3/0SSPUh3ZPTQf41OU2SINcAVtp2RPaJkD7bokz2FOlxap39tydWMockK7YB7cADoWDr8b5mxY8dq4cKFgS4DAACgQzIbDZo2NElLtuYpv6pRS7bl6bsjUhUV2nU+kLs9Xn22t0h/+/KI1h8u9x0f3i1SP7ysp6YMSpTF1KEmIreavglhCgtK1bLt+SqucejdTTmaMSxZsWFdN8wCcPF1+KBi27ZtSkpKCnQZAAAAHZbFZNSMYSl6b2uuimscem9rnr47MlURwZZAl3ZR1TS69M/NuVqw9qiyj+/RYTYadN3gJN19WQ8N7xYV4ArbhyR7c0eQf2flqaK++d/susFJnXbpC4DAC2hQUVtbq4MHD/qeHzlyRFlZWYqOjla3bt00Z84c5eXl6a233pIkPffcc+rRo4cGDhwop9OphQsXavHixVq8eHGgXgIAAECnEGQx6sbhKfrX5hyV17t8YUVnnOZ/or3oPzbnqNbRJEmKDLHo9tHdNDOju5LswQGusP2xB1t028hu+mBHvnIrG/Tv7Xm6Kj1eg1PsgS4NQCcU0L88mzdv1lVXXeV7Pnv2bEnSnXfeqQULFqigoEDZ2dm+806nU4899pjy8vIUHBysgQMH6sMPP9R1113X5rUDAAB0NiFWk26+NFX/3JKrygaXlmzN03dGpirIYgp0aRfM65XyKhv087c2a8Xx9qKS1Cc+TD+8rKduGp6iYGvHf50Xk+14mPXZ3iLtLazR518Vq7Lepcv7xMrQOZqeAGgnAhpUXHnllfKeodfRggULWjx//PHH9fjjj1/kqgAAALqusCCzbh6eon9syVVpnVPvb8vTzZemymrumHs0NHm82l9Yo23ZlSqpdWi5s3kGxZXpcfrhZT11Rd9YGfiUfc5MRoMmDUyUPcSq9YfLtDW7QtUNLk0elCizkX9HAK2j883lAwAAwAWxh1h08/AU/WtLrgqrHVqala8bh6fIbOo4H0TrHG7tzKvUjtwq1TvdkiSzyaAfjO2mu8b1VJ/4sABX2LGN6Rkte7BFn+4p0sGSWtVuydX0ockKYVYKgFZAUAEAAICTxIRZddPwFP1ra65yKxv04Y58TRuaLFM7/9a8pMahbdmV2ldUI7eneeZuuM2soWmRGpQSobSJfQNcYedxSWK4wm1mfbAjX4XVjXp3U7ZuGJai6C7UMQbAxdEx5/ABAADgoouPsOmGockymww6UlavT3YVynP6VbsB4/FKh4pr9a8tuXpnQ7b2FFTL7fEqyR6k6wYl6q7Lemhkj6hOsddGe5MSFaxbR6UpMtii6sYmvbs5RznHO6gAgL+YUQEAAIDTSokK1vQhyVq6PV/7i2tl2Vuka/ontIvNEx1NHu3Jr1ZWTqWqGlySJKPBoL7xYRreLVKJ9qAAV9g1RIVYdeuoNH2wPV/5VY16PytPV1+SoIHJEYEuDUAHRVABAACAM+oeE6KpgxL14c4C7c6vltVk1Ph+cQELK6rqXcrKqdTugmo5mzySpCCLSYNTIjQkNVLhQbzFbWvBFpNuujRVK/YUaX9RjT7dW6SqBpfG9Y4JdGkAOiB+iwMAAOCs+sSHaVL/BH2yp0jbciplNRuV0YYfQk+0F92WXaHDpXW+9qLRoVYNT4vUJUkRsnSgzT47I7PRoCkDE2UPtmjT0XJtOlqu6gaXMi53s+wGwHkhqAAAAMA56Z8cIafboy/2lWjDkXJZzUaN6B51UX/mt9uLntAjJkTDu0WpW3RIu1iGgmYGgzSud4zswRZ9/lWR9hXVaOb8DXp15kg22QRwzggqAAAAcM6GpkXK2eTRl4fK9J8DpbKajBqcam/1n3O69qIDkiI0LC2SD73t3MDkCEUEmfXBzgJtPFqhm//ypd64e7R6xoYGujQAHQBBBQAAAM7LqJ7Rcro92nS0Qp/vK5bVbFR6Ynir3Pts7UVZQtBxpEWH6LaRacrfbdHRsnrd9Jcv9deZIzW6Z3SgSwPQzhFUAAAA4LyN6x0rZ5NH23Or9MnuIllMRvWK8+/bco9XOlJSq205lcr9/9u787CmrrwP4N9sJCwBZJGwL+ICuCEoonWru9VWa6tYbXWqbR2dVkcd+6qdSluX6ttpfavVzrS4va2K1qU67WtRR+xUrUoVRcQdRTAIsm+GQPL+QbljGhC0QBLy/TxPnsece8713MPNyb2/nHtOQYWQ7umkQLifM9q5O0Ai5vMdlsjF3gZ7Z/XBjK1JOH+nEFO+PIXVL3TF2HBvU1eNiMwYAxVERERE9NhEImBgx7aorNIhLbsE36WoMba7F3xd7Bq9Dy4vah3clXLseK03/hyfjIOp2Zgbn4yM/HK8+XQwRJxghIjqwEAFERERET0RkQgYGuoBbbUO13PLsP+8Gs+He8PT+dEBBi4van1sbSRYP7kHPjx4Gf/48SY+PnQVt/PKsfL5LrCRik1dPSIyM/wWICIiIqInJhaLMKKLJw4k38Xt/HIcu5qDMd28oJBJoKnSQS4VQ6fXQyoWc3lRKycWi7B4VAj8XOywdH8qdp/NRFZhOf4+JRJOdjJTV4+IzAgDFURERET0u0jFIozu5okjaTno38Ed5+8UIjmzEBqtDnKZGN19nNHdzxn/SstBfnklAC4vas2m9PaHTxtb/GnbOfx8Mx/PbziOTdN6wc+18Y8NEVHrxnFWRERERPS7ySRiDOrUFufvFOJUej402ppHOjRaHU6l5yM5oxBPtXdDVx8nvBLtj7Hh3vB3ZZDCWg3s2Ba7ZkbD00mBG7llGLf+OM5mFJi6WkRkJhioICIiIqImIRWLkJxZWOe25MxC+Lva4elObeFib9OyFSOzFOLpiH2z+yLMyxF5ZZWY9I+f8d0FtamrRURmgIEKIiIiImoSmiqdMJLCaJtWB01V3dvIenk4KrDzjWgMCWkLTZUOs7edxefHbkBfO4kJEVklBiqIiIiIqEnIpWLIZXVfXsplYsi5ugPVwV4uxd9fjsS0PgEAgA//7zIW770IbTUDW2RIW61DtU6P8soqVOv0PEdaMU6mSURERERNQqfXI9zHGT+n5xttC/dxhk6vhwSclIKMScQixD4bBn9XO3zwz0vYfjoDmQXl+GxyDzgquCIIAVU6HZJuFyD5TqGwolB3X2f0DGgDqZhB0NaGf1EiIiIiahIyiRiRAS7oHegijKyQy8ToHeiCyAAXyCS89KRH+0PfQPzj5UjYyiT497X7eHHDSWQVVpi6WmRi2modztwqqJmo99dHyDRVNRP1nrlVwJEVrRC/LYiIiIioyUglIkQEtMHr/YLwev8gvN4vCBEBbSCVcCQFNc6QUA/sfCMabZVyXLlXgrGfHUdKZpGpq0UmJBaJkHynsM5tyXcKIW6FywfFxsZCJBIZvFQqlbBdr9cjNjYWXl5esLW1xcCBA5GammqwD41GgzfffBNubm6wt7fHs88+i8zMzJY+lCfCQAURERERNSmZRAyJWAQ7GwkkYhFHUtBj6+LjhH2z+6KTSoncEg0m/P0kElKzTV0tamFVOh3S88pQXllV72S8miodNFXVLVyzlhEWFga1Wi28UlJShG2rV6/Gxx9/jHXr1uHMmTNQqVQYOnQoSkpKhDxz587F3r17sWPHDvz0008oLS3F6NGjUV1t/u3Fbw0iIiIiIjI7Xs622DUzGv07uKNCW403vvoFyRmFABcEadX0ej0yC8pxKO0evvgxHQmp2VDIJPVOxiuXiiGXSlq4li1DKpVCpVIJL3d3dwA1bbRmzRosWbIEzz//PDp37owtW7agvLwc27ZtAwAUFRUhLi4Of/vb3zBkyBCEh4fjq6++QkpKCg4fPmzKw2oUBiqIiIiIiMgsKRUyxE2NxEtRftDrgWNXc5F4JQc6HaMVrU1BeSVO3riPTSdu4ZuzWUi9WwxNtQ5SiRhFFVp093Wus1x335qJei1FSUkJiouLhZdGo6k377Vr1+Dl5YXAwEDExMTg5s2bAID09HRkZ2dj2LBhQl65XI4BAwbgxIkTAIBffvkFWq3WII+Xlxc6d+4s5DFnXPWDiIiIiIjMlkwixvKxnRHgaocz5xJxPrMIxQ+qMLKzCjIueWvRHmircfVeCdKyi6Eu+s8Nu41EjPYeDghRKeHtbAuRSARnu5rVXyx91Y/Q0FCD90uXLkVsbKxRvqioKGzduhUdOnTAvXv3sGzZMvTp0wepqanIzq55DMrDw8OgjIeHB27fvg0AyM7Oho2NDdq0aWOUp7a8OWOggoiIiIiIzJpIJMLr/duhbZUnDqZmI/1+GXb9kolnu3nBQcFbGktSrdPjVl4Z0tTFSL9fjupfR0OIAPi72iHE0xFBbvZGc9tIxWJE+rdBrwAXaKqqIZdKoNPrLSpIAQCXLl2Ct7e38F4ul9eZb+TIkcK/u3TpgujoaLRr1w5btmxB7969AdR8Lh6m1+uN0n6rMXnMAT/VRERERERkEYI9HPCCwgcHzt9FbokG8Wfu4NnuXnBX1n2zR+ZBDz3uFWuQpi7GlXsleKD9z8SYbg42CPV0REcPJezlj749rQ1e2NnU5JPA/G+4f0upVMLR0fGxy9nb26NLly64du0axo4dC6Bm1ISnp6eQJycnRxhloVKpUFlZiYKCAoNRFTk5OejTp8/vO4gWYFnhJyIiIiIismoqJwUm9vSFi70NSjVV2JV0B7ful5m6WlSHkgdanL6Vj60nb2PHmTs4n1mEB1od7Gwk6OHnjMm9/DAlyh89/No0GKSwdhqNBmlpafD09ERgYCBUKhUOHTokbK+srMSxY8eEIERERARkMplBHrVajYsXL1pEoIJnAxERERERWRRHWxkmRPriu5S7uJNfgf3n72Jgx7bo6uNk6qpZvcpqHa7nlCJNXYzMggphkRapWIR2be0RonKEn4sdxBbw+IEpLViwAGPGjIGfnx9ycnKwbNkyFBcXY+rUqRCJRJg7dy5WrFiB9u3bo3379lixYgXs7Ozw0ksvAQCcnJwwffp0zJ8/H66urnBxccGCBQvQpUsXDBkyxMRH1zAGKoiIiIiIyOLIZWI8190b/7qcg0t3i3H0cg6KyivxVHt38B64Zen0etwpKEeaugQ3ckqhfWhVFp82CoSoHBHc1qHVLiPaHDIzMzFp0iTcv38f7u7u6N27N37++Wf4+/sDABYuXIiKigrMmjULBQUFiIqKQkJCApRKpbCPTz75BFKpFBMmTEBFRQUGDx6MzZs3QyIx/78DAxVERERERGSRJGIRhoZ4wNlWhhM38nA2oxBFD7QYEeYJqYTRiuZ2v1SDtOxiXM4uQZmmWkhvYydDiEqJjipHONnKTFhDy7Vjx45HbheJRIiNja1zxZBaCoUCa9euxdq1a5u4ds2PgQoiIiIiIrJcIqBnoAscbWU4dOkebuSU4ZsHdzCmmxfnPWgGZZVVuJJds6RobkmlkK6QidGhrRIhnkqonBQQWeBEl2Q++MklIiIiIiKL11GlhFIhxYHzd3GvuGZFkOe6e8PVwcbUVbN4VTodbuaWIS27GLfzylH7ZIdEBAS41cw7EeBmZ3FLhZL5YqCCiIiIiIhaBS9nW0zs6Ydvk7NQWK7FzqQ7eKaLJ/xc7UxdNYuj1+uRdLsAh9Pu4VpOKTRV/1lSVOUoR4inIzp4KGErM//5DsjyMFBBRERERESthrOdDBMjfXHgwl3cLXyAb5OzMLiTB0K9HU1dNYtwO68Me85mYe+5LGTkl0PuVTNKQqmQIkSlRCdPR7jYcZQKNS8GKoiIiIiIqFVR2EjwfA8fHLp0D1eyS3Ao7R4KKyoR3c6NK4LUoahci3+m3MXes1lIul0gpNvbSNDB0wEhno7wcbaFiI1HLYSBCiIiIiIianUkYhFGhKngbCvDqfR8nLlVgKIKLYaGqrgiCABttQ7HruRiz7lMHL6Ug8rqmkc7xCLgqfbuGN/DG8NCVdh8Ld3ENSVrxEAFERERERG1TiKgdztXONrKcOTyPVy9V4oSTSbGdPWCrY31za2g1+txMasYu89m4sD5u8gr+8+qHR09lBgf4Y3nunvDw1FhwloSMVBBREREREStXKiXI5QKKb67oIa68AHik+7guW5eaGNvHXMtqIsqsO/cXew5m4lrOaVCupuDHGO7e2FcD2+Eejry0Q4yGwxUEBERERFRq+frYocJPX3xbXIWisq1iE+6gzFdveDdxtbUVWsWZZoq/JCajT1ns3D8xn3of11SVC4VY1iYCs/38Ea/YDdIJVxSlMwPAxVERERERGQVXOxtMLGnHw6cv4vsogfYcy4TQ0M80MmzdawIotcDmQXl2LgzGQcvZqO8slrY1ivQBeN7eGNkF084KmQmrCVRwxioICIiIiIiq2FnI8H4Hj5IuJSNa/dK8UPqPRRVaBEV6ApY6JMP+aWVSFMX4/K9EpQ+qMIhdRYAIMDVDs/38MG4cG/4utiZuJZEjcdABRERERERWRWpRISRnT3hpLiPpNsF+PlmPooqtBgc4gGJ2DKiFRWV1biSXYK07GLkFGuEdLlUjMlRfni+hw96+Dlz3gmySAxUEBERERGR1RGJgL7t3eBkJ8O/LucgTV2C4gdVGN3VEwqZea4IUlWtR/r9MlzOLsat+2XQ/TrvhFgEBLjZI8TTEYFu9ujWpYtpK0r0OzFQQUREREREVquztxOUChm+T1Ejq6ACO5Pu4Llu3nCyM5N5HPQ1q3akqUtw9V4JNFU6YZOHoxwhno7o4KG0yuVWqfVioIKIiIiIiKyav6sdJkTWrAhSUFa7IognPJ1NtyJIUbkWl399tKOoXCukOyikCFEp0cnTES5WsrwqWR8GKoiIiIiIyOq5OthgYk9f7D9/FznFGuw+m4VhYR7o4KFssTpoqnS4fq8EaeoSZBVWCOkyiQjBbZUI8VTCp40dOO0EtXYMVBAREREREQGwl0vxQoQPDl7Mxs3cMvxfSjaKK7SI9HdpthVBdDo9bueXI01djJu5ZaiunXhCBPi52CFEpUS7tg6QScTNUwEiM8RABRERERER0a9kEjFGd/XCv6/m4tydQhy/nofCci2e7tQW4qZaEUQP5JRocFldjCv3SlBeWS1scnWwQYjKER1VSjgoeLtG1olnPhERERER0UNEIqB/R3c42clw7GouUu8Wo+RBFUZ19YRc+uQjG8o0Vbisrpl3Iq+0Uki3tZGgk0fNvBNtlfJmG71BZCkYqCAiIiIiIqpDN19nOCpk+L+LamTkl2NX0h08280LjraNXxFEW63DjZwypGUX405+OfS/PtkhEYsQ5F6zpKi/i13TjdYgagUYqCAiIiIiIqpHoLs9Xojwxf7zWcgrrUT8mTsYF+4NZzsZRCIRKqt0sJGKodPrhXkk9Hogs6Acl7NLcO1eCbTVemF/Xs4KhHg6on1bJeQyzjtBVBcGKoiIiIiIiB6hraMcMT398O35u6jW6WEvlyLpVgGSMwuh0eogl4nR3ccZkQEuSMkqxLmMQpQ8qBLKO9nKEOKpRCeVI5zsGj8ag8haMVBBRERERETUAAeFFC9G+OB+qQbJdwpxOj1f2KbR6nAqPR96AG2VcpQ8qIJcKkYHDyU6eSrh5WTLeSeIHoNJxxr9+OOPGDNmDLy8vCASibBv374Gyxw7dgwRERFQKBQICgrC559/3vwVJSIiIiIiq2cjFcPDUYHzmYV1bj+fWQh/V3uM6eaFGf2C8HRIW3g5M0hB9LhMGqgoKytDt27dsG7dukblT09Px6hRo9CvXz+cO3cOixcvxltvvYXdu3c3c02JiIiIiIiAyiodNFpdnds0Wh201ToEudtDKmF0guhJmfTRj5EjR2LkyJGNzv/555/Dz88Pa9asAQCEhIQgKSkJH330EcaPH99MtSQiIiIiIqphIxVDLhPXGayQy8Sw+R3LlxJRDYv6FJ08eRLDhg0zSBs+fDiSkpKg1WrrLKPRaFBcXCy8SkpKWqKqRERERETUCun1enT3ca5zW3cfZ+j0+jq3EVHjWVSgIjs7Gx4eHgZpHh4eqKqqwv379+sss3LlSjg5OQmv0NDQlqgqERERERG1QlKJGD0DXBAV6CIsLyqXiREV6IKeAS7CEqVE9OQsbtUPkcjwWS/9rxHL36bXWrRoEebNmye8z8rKYrCCiIiIiIiemEQiQkRAG/QMdEFllQ42UjF0ej0knJeCqElYVKBCpVIhOzvbIC0nJwdSqRSurq51lpHL5ZDL5cL74uLiZq0jERERERG1frUjJ2xtJAAACZf2IGoyFjUuKTo6GocOHTJIS0hIQGRkJGQymYlqRURERERERERNxaSBitLSUiQnJyM5ORlAzfKjycnJyMjIAFDz2MYrr7wi5J85cyZu376NefPmIS0tDRs3bkRcXBwWLFhgiuoTERERERERURMz6aMfSUlJGDRokPC+di6JqVOnYvPmzVCr1ULQAgACAwPx/fff489//jM+++wzeHl54dNPP+XSpERERERERESthEkDFQMHDhQmw6zL5s2bjdIGDBiAs2fPNmOtiIiIiIiIiMhULGqOCiIiIiIiIiJq3RioICIiIiIiIiKzwUAFEREREREREZkNBiqIiIiIiIiIyGwwUEFEREREREREZoOBCiIiIiIiIiIyGwxUEBEREREREZHZYKCCiIiIiIiIiMwGAxVEREREREREZmj9+vUIDAyEQqFAREQE/v3vf5u6Si2CgQoiIiIiIiIiMxMfH4+5c+diyZIlOHfuHPr164eRI0ciIyPD1FVrdlJTV6Cl6XQ6AIBarW4wb1FBfnNXp1XIzMxssn09yC9qsn21ZqVN2OaF+YVNtq/WrCnP87zCwibbV2smbcI2zy/Ia7J9tWZNeZ7nFxQ22b5as6Zs85J8nueNocm0abJ95efzWrExmvQ7NK+wyfbVmjVlmxflsW9pjMxMxSO3195/FhUVwdHRUUiXy+WQy+V1lvn4448xffp0zJgxAwCwZs0a/PDDD9iwYQNWrlzZRDU3TyK9Xq83dSVa0pkzZ9CrVy9TV4OIiIiIiIis3NKlSxEbG2uUXllZCTs7O+zatQvjxo0T0ufMmYPk5GQcO3asBWvZ8qxuREV4eDhOnz4NDw8PiMWW9eRLSUkJQkNDcenSJSiVSlNXxyqwzVse27zlsc1bHtu85bHNWx7bvOWxzVse27zlWWqb63Q6ZGRkIDQ0FFLpf27D6xtNcf/+fVRXV8PDw8Mg3cPDA9nZ2c1aV3NgdYEKqVSKnj17mroaT6S4uBgA4O3tbTBciJoP27zlsc1bHtu85bHNWx7bvOWxzVse27zlsc1bniW3uZ+f32OXEYlEBu/1er1RWmtkWUMKiIiIiIiIiFo5Nzc3SCQSo9ETOTk5RqMsWiMGKoiIiIiIiIjMiI2NDSIiInDo0CGD9EOHDqFPnz4mqlXLsbpHPyyZXC7H0qVL632OiZoe27zlsc1bHtu85bHNWx7bvOWxzVse27zlsc1bnjW1+bx58/Dyyy8jMjIS0dHR+Mc//oGMjAzMnDnT1FVrdla36gcRERERERGRJVi/fj1Wr14NtVqNzp0745NPPkH//v1NXa1mx0AFEREREREREZkNzlFBRERERERERGaDgQoiIiIiIiIiMhsMVBARERERERGR2WCggoiIiIiIiIjMBgMVzWTatGkYO3as8D47OxtvvvkmgoKCIJfL4evrizFjxuDIkSNGZVesWAGJRIIPP/ywzn1nZ2djzpw5CA4OhkKhgIeHB5566il8/vnnKC8vr7dOsbGx6N69++89NIs2bdo0iEQi4eXq6ooRI0bgwoUL2Lx5s8G2ul6JiYlG+3v472zNHm5bmUyGoKAgLFiwAGVlZbh165ZBOzo5OaF37944cOCAwT7qO0cLCwvrbP+HDRw4EHPnzm3agzIj9Z1riYmJEIlEKCwsBAD8/e9/R7du3WBvbw9nZ2eEh4dj1apVQv7Y2Fijv0W/fv1w7NixOv/fhvqjR9WltcjJycEbb7wBPz8/yOVyqFQqDB8+HCtXrmywz9i8eTMAoKKiAm3atIGLiwsqKiqM/o+AgACIRCL8/PPPBulz587FwIED661b7WcrOTm5CY/Y9Gr7k9+ed/v27YNIJEJpaSlkMhni4+MNtk+cOBEikQg3btwwSG/Xrh0WL14svC8uLsZf//pXhIWFwdbWFq6urujZsydWr16NgoKCeuu1efNmODs7//4DNHOP6s8BYPfu3YiKioKTkxOUSiXCwsIwf/58oXzt9+mIESMM9tuYvtyav1cfPvbav0FdSxDOmjULIpEI06ZNMyhb3/VNY/9Pa/E4bdtQX/QwvV6PL774AtHR0XB0dISDgwPCwsIwZ84cXL9+vd76tNZ+/Lceda4FBARgzZo19Za9c+cOpk+fDi8vL9jY2MDf3x9z5sxBXl4eAGD69Ono0qULKisrDcp9//33kMlkSEpKalQ9yLwwUNECbt26hYiICPzrX//C6tWrkZKSgoMHD2LQoEGYPXu2Uf5NmzZh4cKF2Lhxo9G2mzdvIjw8HAkJCVixYgXOnTuHw4cP489//jMOHDiAw4cPt8QhWbQRI0ZArVZDrVbjyJEjkEqlGD16NCZOnCikq9VqREdH47XXXjNI69Onj6mrb9Zq2/bmzZtYtmwZ1q9fjwULFgjbDx8+DLVajVOnTqFXr14YP348Ll68aMIaty5xcXGYN28e3nrrLZw/fx7Hjx/HwoULUVpaapAvLCxMOKdPnjyJ9u3bY/To0SgqKjLa56P6I2sxfvx4nD9/Hlu2bMHVq1exf/9+DBw4EKGhoQb9w4QJEwz6F7VajYkTJwKoubHr3LkzQkNDsWfPnjr/H4VCgbfffrslD82sKRQKrFq1qs7AgYODAyIjI3H06FGD9GPHjsHX19cgPTMzEzdv3sSgQYMAAPn5+ejduzc2bdqEBQsW4NSpUzh+/DiWLl2K5ORkbNu2rXkPzELU158fPnwYMTExeOGFF3D69Gn88ssvWL58udENglQqxZEjR4z+RtR4vr6+2LFjh0Fw88GDB9i+fTv8/PyM8td3fUPGHqdtH9UX1dLr9XjppZfw1ltvYdSoUUhISMCFCxfw6aefwtbWFsuWLWu2Y2ntbt68icjISFy9ehXbt2/H9evX8fnnn+PIkSOIjo5Gfn4+1qxZg5KSEixdulQoV1hYiNdffx1LlixBZGSkCY+AnpTU1BWwBrXR2dOnT8Pe3l5IDwsLw6uvvmqQ99ixY6ioqMD777+PrVu34scffzRYJ3fWrFmQSqVISkoy2FeXLl0wfvx4cLXZhtX+IgoAKpUKb7/9Nvr374/S0lIhHQBsbGxgZ2dnkEaP9nDbvvTSSzh69Cj27dsn3Hy5urpCpVJBpVJh+fLlWLt2LY4ePYrOnTubstqtxoEDBzBhwgRMnz5dSAsLCzPKJ5VKDT4D7733HjZt2oSrV6+iZ8+eQr6G+iNrUFhYiJ9++gmJiYkYMGAAAMDf3x+9evUyymtrawuNRlNnnxEXF4cpU6ZAr9cjLi4OkydPNsrzxhtvYMOGDfj+++8xatSopj8YCzNkyBBcv34dK1euxOrVq422Dxo0yCDok5aWhoqKCsydOxeJiYmYMWMGAODo0aOQyWTo27cvAGDx4sXIyMjAlStX4O3tLZTv1KkTRo8eze/RX9XXn8vlcjz11FP4y1/+IuTt0KGD0S+U9vb2mDBhAv7rv/4Lp06dasmqtxo9evTAzZs3sWfPHqHP2LNnD3x9fREUFGSUv77rm9zcXLi7u7do3c3d47RtQ30RAMTHx2PHjh349ttv8eyzzwrpQUFBGDx4MPuV32H27NmwsbFBQkICbG1tAQB+fn4IDw9Hu3btsGTJEmzYsAGbN2/GsGHDMHbsWERFRWHu3Lnw9PTEO++8Y+IjoCfFERXNLD8/HwcPHsTs2bMNAgu1fjuENC4uDpMmTYJMJsOkSZMQFxcnbMvLy0NCQkK9+wJgNAyNHq20tBRff/01goOD4erqaurqtDq2trbQarVG6VqtFl988QUAQCaTtXS1Wi2VSoWff/4Zt2/fbnQZjUYjDGfv2LGjwbZH9UfWwsHBAQ4ODti3bx80Gs0T7ePGjRs4efIkJkyYgAkTJuDEiRO4efOmUb6AgADMnDkTixYtgk6n+71Vt3gSiQQrVqzA2rVrkZmZabR90KBBuHLlCtRqNYCagES/fv3w9NNPGzxacPToUURFRcHOzg46nQ7x8fGYMmWKQZDiYfwerVttf65SqZCamtqo0XCxsbFISUnBN9980wI1bJ3+8Ic/YNOmTcL7jRs3Gv3IVRde3zSssW3bUF8EANu3b0fHjh0NghQPY7/yZPLz8/HDDz9g1qxZQpCilkqlwuTJkxEfHw+9Xo+BAwdi1qxZmDp1Knbt2oWdO3di69atkEr5u7ylYqCimV2/fh16vR6dOnVqMG9xcTF2796NKVOmAACmTJmCb775BsXFxQb7+u3NhJubm3AxzWHDDfvnP/8ptJdSqcT+/fsRHx8PsZgfh6Z0+vRpbNu2DYMHDxbS+vTpAwcHBygUCsyfPx8BAQGYMGGCCWtpWR4+d2tfI0eOFLYvXboUzs7OCAgIQMeOHTFt2jTs3LnT6KY3JSVFKG9ra4uPPvoI27dvh6Ojo5Cnof7IWkilUmzevBlbtmyBs7Mz+vbti8WLFzf43PfDNm7ciJEjRwpzVIwYMaLeR2neeecdpKen4+uvv26qQ7Bo48aNQ/fu3Q2G89bq27cvZDKZEJSoHfXSo0cPFBUV4dq1a0J67WMfubm5KCwsNPoejYiIED4TkyZNat6DskAP9+dvvvkmevbsiS5duiAgIAAxMTHYuHFjnYE8Ly8vzJkzB0uWLEFVVZUJam75Xn75Zfz000+4desWbt++jePHjwv98m/x+ubxPE7bPqovAoCrV68a9Stz584V/h4+Pj5NXn9rcO3aNej1eoSEhNS5PSQkBAUFBcjNzQUAYe6omJgYrFixot5yZBnYczWz2qFejYmkbtu2DUFBQejWrRsAoHv37ggKCsKOHTsM8v12X6dPn0ZycjLCwsKe+Bc/azJo0CAkJycjOTkZp06dwrBhwzBy5MjH+hWa6lZ7kaRQKBAdHY3+/ftj7dq1wvb4+HicO3cO+/fvR3BwML788ku4uLiYsMaW5eFzt/b15ZdfCts9PT1x8uRJpKSk4K233oJWq8XUqVMxYsQIg2BFx44dhfK//PIL/vjHP+LFF180mGyqsf2RNRg/fjzu3r2L/fv3Y/jw4UhMTESPHj2EiTIfpbq6Glu2bDG4+J0yZQq2bNmC6upqo/zu7u5YsGAB3n33XaNn/q3VqlWrsGXLFly6dMkg3c7ODr169RICFceOHcPAgQMhlUrRt29fJCYmIiMjA+np6Xj66acNyv72e3Tv3r1ITk7G8OHD65zs1BrV15/b29vju+++w/Xr1/HOO+/AwcEB8+fPR69eveqc0Pvtt99Gbm6uVc9z83u4ubnhmWeewZYtW7Bp0yY888wzcHNzqzMvr28ez+O0LVB/X1Trt/3KkiVLkJycjHfffddorihqGr+9z7K1tcX8+fNhZ2eHOXPmmLJq1AQYqGhm7du3h0gkQlpaWoN5N27ciNTUVEilUuGVmpoqDLcODg6GSCTC5cuXDcoFBQUhODjYaEgU1c3e3h7BwcEIDg5Gr169EBcXh7KyMuFRBHpytRdJV65cwYMHD7Bnzx60bdtW2O7r64v27dvjmWeewZdffomJEyciJydH2O7o6FjnhI61q0g4OTk1+zGYs4fP3dpXXcPXO3fujNmzZ+Prr7/GoUOHcOjQIYNVPWxsbITy4eHh+PDDD+Ht7W0w43ZD/ZG1USgUGDp0KN59912cOHEC06ZNq/eXtYf98MMPyMrKwsSJE4V2jImJQWZmJhISEuosM2/ePJSXl2P9+vVNfRgWqX///hg+fLjBqh21Bg0ahKNHjyI1NRUVFRXo0aMHAGDAgAE4evQojh49CoVCgd69ewOoCQQ5OzsbfY/6+fkhODgYSqWy+Q/IQjTUn7dr1w4zZszAl19+ibNnz+LSpUtGq7AANY+4Llq0CO+9994jVyaj+r366qvCyK5HPfbB65vH19i2BR7dF7Vv396oX3F3d0dwcLDB54YeT+29T33BocuXL6NNmzYGASapVAqJRMLHbVoBBiqamYuLC4YPH47PPvtMWNbrYbU3YCkpKUhKSkJiYqLBr6U//vgjzpw5g4sXL8LV1RVDhw7FunXr6twXPRmRSASxWMxf0ZpA7UWSv79/g3NPDBgwAJ07d8by5cuFtE6dOiEzMxPZ2dkGec+cOQOxWIzg4OBmqXdrFhoaCgAN9hkSiUT4DDSmP7J2oaGhjeqH4+LiEBMTYzQSZvLkyfUGfRwcHPDXv/4Vy5cvt7pHbeqzcuVKHDhwACdOnDBIHzRoEK5du4Zt27bhqaeegkQiAVDTvyQmJiIxMRHR0dFQKBQAALFYjAkTJuCrr75CVlZWix+HJXmc/jwgIAB2dnb1fibefPNNiMVi/M///E9zVLXVGzFiBCorK1FZWYnhw4c3uhyvbxr2uG1bX180adIkXLlyBd9++21zVdUq1d77rF+/3ug8zs7Oxtdffy0sS02tD2cXaQHr169Hnz590KtXL7z//vvo2rUrqqqqcOjQIWzYsAFpaWmIi4tDr1696pxRPzo6GnFxcfjkk0+wfv169O3bF5GRkYiNjUXXrl0hFotx5swZXL58GREREUK5V155Bd7e3li5cqWQVlFRYbROs4ODg1XdAGo0GuFGuKCgAOvWrUNpaSnGjBnTYNnBgwdj3Lhx+NOf/iSkFRUVGbWpi4tLnUuHkaH58+fjxRdfxMKFC+Ht7Y1hw4YhJCQEMTExWL58Oby8vHDhwgUsWLAAM2fONPi1s1OnTli5ciXGjRsnpOXm5hr9LWpXGbEGf/zjH+Hl5YWnn34aPj4+UKvVWLZsGdzd3REdHS3kq6qqEj4DJSUliI+Px6VLl4Q5bhrbH61btw579+7FkSNHDPKkpKQY/TLdvXv3Jj7alpGXl4cXX3wRr776Krp27QqlUomkpCSsXr0azz333CPL5ubm4sCBA9i/f7/RyjZTp07FM888U+9s/G+88QbWrFmD7du3IyoqSkjfu3cvFi1aZPTL3ZUrV4z2ERoaChsbm8c5XLPVtWtXTJ482eBRMqBm3hu5XI61a9diyZIlQnrPnj1RVFSE3bt3G6xOAQArVqxAYmIioqKi8P777yMyMhL29va4cOECTp48afC3WrRoEbKysrB161Yhrbq62qifsbGxEYKCrV1sbCzKy8sxatQo+Pv7o7CwEJ9++im0Wi2GDh1aZxmFQoH33nuvziXZ+b3aMIlEIozMrQ3G1aUx1zdsb0ONbdta9fVFMTEx2LNnD2JiYrBo0SIMHz4cHh4euH37NuLj4w32ba39OFD/uQYAWVlZRtv8/Pywbt069OnTB8OHD8eyZcsQGBiI1NRU/OUvf4G3t7fBD1514TlvuRioaAGBgYE4e/Ysli9fjvnz50OtVsPd3R0RERHYsGEDKisr8dVXX9U7Eeb48eOxcuVKrFq1Cu3atcO5c+ewYsUKLFq0CJmZmZDL5QgNDcWCBQswa9YsoVxGRobRBEpXr15FeHi4QVrtL0/W4uDBg/D09AQAKJVKdOrUCbt27cLAgQMbLHvjxg3cv3/fIC0xMdGoTadOndqo59et3ejRoxEQEIDly5dj/fr1kEqlSEhIwOLFizF58mTk5OTA398fM2bMwMKFCw3KXrlyxegxkW3btmHbtm0GaUuXLkVsbGxzH4pZGDJkCDZu3IgNGzYgLy8Pbm5uiI6OxpEjRwxmfU9NTRU+A3Z2dmjXrh02bNiAV1555bH6o/v37+PGjRtGeeoKcFjq0mwODg6IiorCJ598ghs3bkCr1cLX1xevvfZancN/H7Z161bY29sbTChba9CgQVAqlfjf//1fzJs3z2i7TCbDBx98gJdeeskgvaioqM6L2ZiYGKO09PR0BAQENHCEluODDz7Azp07DdJqH+uonZ+ilkwmE8792ok0a7m6uuL06dNYtWoV/vu//xvp6ekQi8Vo3749Jk6ciLlz5wp51Wo1MjIyDMqXlpYa9fn+/v64detWkxynuRswYAA+++wzvPLKK7h37x7atGmD8PBwJCQkGE0m+LCpU6fib3/7m9EQbn6vNs7Dkx3XpzHXN2xvY41p24fV1ReJRCLEx8fjiy++wKZNm7B69WpotVr4+Phg8ODB+Pjjj4W81tyP13euAcBHH32Ejz76yGDbpk2bMG3aNCQlJSE2NhYTJ05EXl4eVCoVxo4di6VLlzY41xnPecsl0lvq1SMRERERERERtTqco4KIiIiIiIiIzAYDFURERERERERkNhioICIiIiIiIiKzwUAFEREREREREZkNBiqIiIiIiIiIyGwwUEFEREREREREZoOBCiIiIiIiIiIyGwxUEBEREREREZHZYKCCiIiIiIiIiMwGAxVEREREREREZDYYqCAiIiIiIiIis/H/AXWGVfsAqywAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = sns.set_style(style=None, rc=None )\n",
    "fig, ax1 = plt.subplots(figsize=(12,6))\n",
    "sns.lineplot(data = backtest_df['return_percentage'], marker='o', sort = False, ax=ax1)\n",
    "ax2 = ax1.twinx()\n",
    "sns.barplot(data = backtest_df, x='selected_symbols', y='return', alpha=0.5, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6c18f95",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "In this article, we learned about Quantitative Momentum Strategy and it's implementation in Python using DSS API to pick tradable stocks.\n",
    "\n",
    "We defined a function to authenticate with DSS Rest API and get a token, and further expanded FTSE ChainRic to get the list of all stocks of FTSE 100. We can consider stocks listed on other exchanges to experiment around as well. \n",
    "\n",
    "We defined another function to pull intraday data for FTSE ChainRic using DSS Rest API. By performing research given the amount of data being pulled, we can learn different ways of data extraction using DSS API along with extensive data processing.\n",
    "\n",
    "\n",
    "Further try tuning this strategy accordingly by using several other metrics."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}