crypto_arbitrage.ipynb

{
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  {
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   "source": [
    "## Crypto Arbitrage\n",
    "\n",
    "In this Challenge, you'll take on the role of an analyst at a high-tech investment firm. The vice president (VP) of your department is considering arbitrage opportunities in Bitcoin and other cryptocurrencies. As Bitcoin trades on markets across the globe, can you capitalize on simultaneous price dislocations in those markets by using the powers of Pandas?\n",
    "\n",
    "For this assignment, you’ll sort through historical trade data for Bitcoin on two exchanges: Bitstamp and Coinbase. Your task is to apply the three phases of financial analysis to determine if any arbitrage opportunities exist for Bitcoin.\n",
    "\n",
    "This aspect of the Challenge will consist of 3 phases.\n",
    "\n",
    "1. Collect the data.\n",
    "\n",
    "2. Prepare the data.\n",
    "\n",
    "3. Analyze the data. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Import the required libraries and dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collect the Data\n",
    "\n",
    "To collect the data that you’ll need, complete the following steps:\n",
    "\n",
    "Instructions. \n",
    "\n",
    "1. Using the Pandas `read_csv` function and the `Path` module, import the data from `bitstamp.csv` file, and create a DataFrame called `bitstamp`. Set the DatetimeIndex as the Timestamp column, and be sure to parse and format the dates.\n",
    "\n",
    "2. Use the `head` (and/or the `tail`) function to confirm that Pandas properly imported the data.\n",
    "\n",
    "3. Repeat Steps 1 and 2 for `coinbase.csv` file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Step 1: Using the Pandas `read_csv` function and the `Path` module, import the data from `bitstamp.csv` file, and create a DataFrame called `bitstamp`. Set the DatetimeIndex as the Timestamp column, and be sure to parse and format the dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the CSV file called \"bitstamp.csv\" using the Path module. \n",
    "# The CSV file is located in the Resources folder.\n",
    "# Set the index to the column \"Date\"\n",
    "# Set the parse_dates and infer_datetime_format parameters\n",
    "bitstamp_path = Path(\"./Resources/bitstamp.csv\")\n",
    "bitstamp = pd.read_csv(\n",
    "    bitstamp_path,\n",
    "    index_col = \"Timestamp\",\n",
    "    parse_dates = True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Use the `head` (and/or the `tail`) function to confirm that Pandas properly imported the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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       "                         Open      High       Low      Close  BTC Volume  \\\n",
       "Timestamp                                                                  \n",
       "2018-01-01 00:00:00  13681.04  13681.04  13637.93  $13646.48    3.334553   \n",
       "2018-01-01 00:01:00  13646.48  13658.75  13610.18  $13658.75    2.663188   \n",
       "2018-01-01 00:02:00  13616.93  13616.93  13610.06  $13610.22    0.084653   \n",
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       "2018-01-01 00:04:00  13635.35  13636.35  13620.00   $13620.0    1.069665   \n",
       "2018-01-01 00:05:00  13620.00  13634.15  13610.00   $13610.0    4.716162   \n",
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       "2018-01-01 00:09:00  13571.28  13571.28  13550.00   $13565.0    0.120942   \n",
       "\n",
       "                        USD Volume  Weighted Price  \n",
       "Timestamp                                           \n",
       "2018-01-01 00:00:00   45482.128785    13639.647479  \n",
       "2018-01-01 00:01:00   36361.390888    13653.332816  \n",
       "2018-01-01 00:02:00    1152.144036    13610.136247  \n",
       "2018-01-01 00:03:00   97856.416478    13623.361128  \n",
       "2018-01-01 00:04:00   14582.660932    13632.923329  \n",
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       "2018-01-01 00:09:00    1641.166577    13569.829917  "
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the head (and/or tail) function to confirm that the data was imported properly.\n",
    "bitstamp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "                        Open     High      Low     Close  BTC Volume  \\\n",
       "Timestamp                                                              \n",
       "2018-03-31 23:55:00  6935.01  6939.07  6922.56  $6922.56    1.044354   \n",
       "2018-03-31 23:56:00  6922.02  6922.02  6918.00  $6920.32    3.069539   \n",
       "2018-03-31 23:57:00  6920.33  6936.42  6920.33  $6934.72   28.239049   \n",
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       "2018-03-31 23:59:00  6929.98  6929.98  6928.00  $6928.01    0.209363   \n",
       "\n",
       "                        USD Volume  Weighted Price  \n",
       "Timestamp                                           \n",
       "2018-03-31 23:55:00    7240.034602     6932.550078  \n",
       "2018-03-31 23:56:00   21245.076275     6921.260233  \n",
       "2018-03-31 23:57:00  195789.408220     6933.286106  \n",
       "2018-03-31 23:58:00    5817.007705     6929.080007  \n",
       "2018-03-31 23:59:00    1450.735763     6929.289993  "
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bitstamp.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Repeat Steps 1 and 2 for `coinbase.csv` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the CSV file called \"coinbase.csv\" using the Path module. \n",
    "# The CSV file is located in the Resources folder.\n",
    "# Set the index to the column \"Timestamp\"\n",
    "# Set the parse_dates and infer_datetime_format parameters\n",
    "coinbase_path = Path(\"./Resources/coinbase.csv\")\n",
    "coinbase = pd.read_csv(\n",
    "    coinbase_path,\n",
    "    index_col = \"Timestamp\",\n",
    "    parse_dates = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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       "                         Open      High       Low      Close  BTC Volume  \\\n",
       "Timestamp                                                                  \n",
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       "\n",
       "                       USD Volume  Weighted Price  \n",
       "Timestamp                                          \n",
       "2018-01-01 00:00:00  283451.08537    13619.105106  \n",
       "2018-01-01 00:01:00  183283.97801    13602.426919  \n",
       "2018-01-01 00:02:00  156789.19686    13590.872506  \n",
       "2018-01-01 00:03:00  221413.64182    13560.332806  \n",
       "2018-01-01 00:04:00  135141.26944    13574.719401  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the head (and/or tail) function to confirm that the data was imported properly.\n",
    "coinbase.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the Data\n",
    "\n",
    "To prepare and clean your data for analysis, complete the following steps:\n",
    "\n",
    "1. For the bitstamp DataFrame, replace or drop all `NaN`, or missing, values in the DataFrame.\n",
    "\n",
    "2. Use the `str.replace` function to remove the dollar signs ($) from the values in the Close column.\n",
    "\n",
    "3. Convert the data type of the Close column to a `float`.\n",
    "\n",
    "4. Review the data for duplicated values, and drop them if necessary.\n",
    "\n",
    "5. Repeat Steps 1–4 for the coinbase DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: For the bitstamp DataFrame, replace or drop all `NaN`, or missing, values in the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open              473\n",
       "High              473\n",
       "Low               473\n",
       "Close               0\n",
       "BTC Volume        473\n",
       "USD Volume        473\n",
       "Weighted Price    473\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the bitstamp DataFrame, replace or drop all NaNs or missing values in the DataFrame\n",
    "bitstamp.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 129540 entries, 2018-01-01 00:00:00 to 2018-03-31 23:59:00\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   Open            129067 non-null  float64\n",
      " 1   High            129067 non-null  float64\n",
      " 2   Low             129067 non-null  float64\n",
      " 3   Close           129540 non-null  object \n",
      " 4   BTC Volume      129067 non-null  float64\n",
      " 5   USD Volume      129067 non-null  float64\n",
      " 6   Weighted Price  129067 non-null  float64\n",
      "dtypes: float64(6), object(1)\n",
      "memory usage: 7.9+ MB\n"
     ]
    }
   ],
   "source": [
    "bitstamp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open              float64\n",
       "High              float64\n",
       "Low               float64\n",
       "Close              object\n",
       "BTC Volume        float64\n",
       "USD Volume        float64\n",
       "Weighted Price    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bitstamp.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "bitstamp=bitstamp.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open              0\n",
       "High              0\n",
       "Low               0\n",
       "Close             0\n",
       "BTC Volume        0\n",
       "USD Volume        0\n",
       "Weighted Price    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bitstamp.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Use the `str.replace` function to remove the dollar signs ($) from the values in the Close column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Saidee\\AppData\\Local\\Temp\\ipykernel_14700\\1350999282.py:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
      "  bitstamp.loc[:,\"Close\"]=bitstamp.loc[:,\"Close\"].str.replace(\"$\",\"\")\n"
     ]
    },
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       "                         Open      High       Low     Close  BTC Volume  \\\n",
       "Timestamp                                                                 \n",
       "2018-01-01 00:00:00  13681.04  13681.04  13637.93  13646.48    3.334553   \n",
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       "2018-03-31 23:59:00   6929.98   6929.98   6928.00   6928.01    0.209363   \n",
       "\n",
       "                        USD Volume  Weighted Price  \n",
       "Timestamp                                           \n",
       "2018-01-01 00:00:00   45482.128785    13639.647479  \n",
       "2018-01-01 00:01:00   36361.390888    13653.332816  \n",
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       "2018-01-01 00:04:00   14582.660932    13632.923329  \n",
       "...                            ...             ...  \n",
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       "2018-03-31 23:59:00    1450.735763     6929.289993  \n",
       "\n",
       "[129067 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the str.replace function to remove the dollar sign, \n",
    "bitstamp.loc[:,\"Close\"]=bitstamp.loc[:,\"Close\"].str.replace(\"$\",\"\")\n",
    "bitstamp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Convert the data type of the Close column to a `float`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "                         Open      High       Low     Close  BTC Volume  \\\n",
       "Timestamp                                                                 \n",
       "2018-01-01 00:00:00  13681.04  13681.04  13637.93  13646.48    3.334553   \n",
       "2018-01-01 00:01:00  13646.48  13658.75  13610.18  13658.75    2.663188   \n",
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       "...                       ...       ...       ...       ...         ...   \n",
       "2018-03-31 23:55:00   6935.01   6939.07   6922.56   6922.56    1.044354   \n",
       "2018-03-31 23:56:00   6922.02   6922.02   6918.00   6920.32    3.069539   \n",
       "2018-03-31 23:57:00   6920.33   6936.42   6920.33   6934.72   28.239049   \n",
       "2018-03-31 23:58:00   6927.65   6929.42   6927.65   6927.65    0.839507   \n",
       "2018-03-31 23:59:00   6929.98   6929.98   6928.00   6928.01    0.209363   \n",
       "\n",
       "                        USD Volume  Weighted Price  \n",
       "Timestamp                                           \n",
       "2018-01-01 00:00:00   45482.128785    13639.647479  \n",
       "2018-01-01 00:01:00   36361.390888    13653.332816  \n",
       "2018-01-01 00:02:00    1152.144036    13610.136247  \n",
       "2018-01-01 00:03:00   97856.416478    13623.361128  \n",
       "2018-01-01 00:04:00   14582.660932    13632.923329  \n",
       "...                            ...             ...  \n",
       "2018-03-31 23:55:00    7240.034602     6932.550078  \n",
       "2018-03-31 23:56:00   21245.076275     6921.260233  \n",
       "2018-03-31 23:57:00  195789.408220     6933.286106  \n",
       "2018-03-31 23:58:00    5817.007705     6929.080007  \n",
       "2018-03-31 23:59:00    1450.735763     6929.289993  \n",
       "\n",
       "[129067 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the Close data type to a float\n",
    "bitstamp.loc[:, \"Close\"] = bitstamp.loc[:, \"Close\"].astype(\"float\")\n",
    "bitstamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open              float64\n",
       "High              float64\n",
       "Low               float64\n",
       "Close             float64\n",
       "BTC Volume        float64\n",
       "USD Volume        float64\n",
       "Weighted Price    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bitstamp.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Review the data for duplicated values, and drop them if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the data for duplicate values, and drop them if necessary\n",
    "bitstamp.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Repeat Steps 1–4 for the coinbase DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "                         Open      High       Low      Close  BTC Volume  \\\n",
       "Timestamp                                                                  \n",
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       "\n",
       "                        USD Volume  Weighted Price  \n",
       "Timestamp                                           \n",
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       "\n",
       "[129322 rows x 7 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Repeat Steps 1–4 for the coinbase DataFrame\n",
    "coinbase=coinbase.dropna()\n",
    "coinbase\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Saidee\\AppData\\Local\\Temp\\ipykernel_14700\\503903540.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
      "  coinbase.loc[:,\"Close\"]=coinbase.loc[:,\"Close\"].str.replace(\"$\",\"\")\n"
     ]
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       "                         Open      High       Low     Close  BTC Volume  \\\n",
       "Timestamp                                                                 \n",
       "2018-01-01 00:00:00  13620.00  13620.00  13608.49  13608.49   20.812754   \n",
       "2018-01-01 00:01:00  13607.14  13607.14  13601.66  13601.66   13.474359   \n",
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       "...                       ...       ...       ...       ...         ...   \n",
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       "2018-03-31 23:59:00   6937.30   6937.30   6931.09    6934.0    1.012720   \n",
       "\n",
       "                        USD Volume  Weighted Price  \n",
       "Timestamp                                           \n",
       "2018-01-01 00:00:00  283451.085370    13619.105106  \n",
       "2018-01-01 00:01:00  183283.978010    13602.426919  \n",
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       "...                            ...             ...  \n",
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       "2018-03-31 23:59:00    7022.275088     6934.070316  \n",
       "\n",
       "[129322 rows x 7 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.loc[:,\"Close\"]=coinbase.loc[:,\"Close\"].str.replace(\"$\",\"\")\n",
    "coinbase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "        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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>BTC Volume</th>\n",
       "      <th>USD Volume</th>\n",
       "      <th>Weighted Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</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>2018-01-01 00:00:00</th>\n",
       "      <td>13620.00</td>\n",
       "      <td>13620.00</td>\n",
       "      <td>13608.49</td>\n",
       "      <td>13608.49</td>\n",
       "      <td>20.812754</td>\n",
       "      <td>283451.085370</td>\n",
       "      <td>13619.105106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:01:00</th>\n",
       "      <td>13607.14</td>\n",
       "      <td>13607.14</td>\n",
       "      <td>13601.66</td>\n",
       "      <td>13601.66</td>\n",
       "      <td>13.474359</td>\n",
       "      <td>183283.978010</td>\n",
       "      <td>13602.426919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:02:00</th>\n",
       "      <td>13601.44</td>\n",
       "      <td>13601.44</td>\n",
       "      <td>13580.00</td>\n",
       "      <td>13580.00</td>\n",
       "      <td>11.536360</td>\n",
       "      <td>156789.196860</td>\n",
       "      <td>13590.872506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:03:00</th>\n",
       "      <td>13587.31</td>\n",
       "      <td>13587.31</td>\n",
       "      <td>13542.70</td>\n",
       "      <td>13550.34</td>\n",
       "      <td>16.328039</td>\n",
       "      <td>221413.641820</td>\n",
       "      <td>13560.332806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:04:00</th>\n",
       "      <td>13550.34</td>\n",
       "      <td>13585.95</td>\n",
       "      <td>13550.34</td>\n",
       "      <td>13583.44</td>\n",
       "      <td>9.955364</td>\n",
       "      <td>135141.269440</td>\n",
       "      <td>13574.719401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31 23:55:00</th>\n",
       "      <td>6945.20</td>\n",
       "      <td>6948.06</td>\n",
       "      <td>6930.00</td>\n",
       "      <td>6930.00</td>\n",
       "      <td>5.802288</td>\n",
       "      <td>40243.918480</td>\n",
       "      <td>6935.869979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31 23:56:00</th>\n",
       "      <td>6930.00</td>\n",
       "      <td>6930.01</td>\n",
       "      <td>6930.00</td>\n",
       "      <td>6930.01</td>\n",
       "      <td>0.625053</td>\n",
       "      <td>4331.620701</td>\n",
       "      <td>6930.005567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31 23:57:00</th>\n",
       "      <td>6930.01</td>\n",
       "      <td>6933.91</td>\n",
       "      <td>6930.01</td>\n",
       "      <td>6933.91</td>\n",
       "      <td>0.776431</td>\n",
       "      <td>5382.532162</td>\n",
       "      <td>6932.405747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31 23:58:00</th>\n",
       "      <td>6933.91</td>\n",
       "      <td>6938.00</td>\n",
       "      <td>6933.90</td>\n",
       "      <td>6937.31</td>\n",
       "      <td>0.133413</td>\n",
       "      <td>925.356547</td>\n",
       "      <td>6936.048538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31 23:59:00</th>\n",
       "      <td>6937.30</td>\n",
       "      <td>6937.30</td>\n",
       "      <td>6931.09</td>\n",
       "      <td>6934.00</td>\n",
       "      <td>1.012720</td>\n",
       "      <td>7022.275088</td>\n",
       "      <td>6934.070316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129322 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Open      High       Low     Close  BTC Volume  \\\n",
       "Timestamp                                                                 \n",
       "2018-01-01 00:00:00  13620.00  13620.00  13608.49  13608.49   20.812754   \n",
       "2018-01-01 00:01:00  13607.14  13607.14  13601.66  13601.66   13.474359   \n",
       "2018-01-01 00:02:00  13601.44  13601.44  13580.00  13580.00   11.536360   \n",
       "2018-01-01 00:03:00  13587.31  13587.31  13542.70  13550.34   16.328039   \n",
       "2018-01-01 00:04:00  13550.34  13585.95  13550.34  13583.44    9.955364   \n",
       "...                       ...       ...       ...       ...         ...   \n",
       "2018-03-31 23:55:00   6945.20   6948.06   6930.00   6930.00    5.802288   \n",
       "2018-03-31 23:56:00   6930.00   6930.01   6930.00   6930.01    0.625053   \n",
       "2018-03-31 23:57:00   6930.01   6933.91   6930.01   6933.91    0.776431   \n",
       "2018-03-31 23:58:00   6933.91   6938.00   6933.90   6937.31    0.133413   \n",
       "2018-03-31 23:59:00   6937.30   6937.30   6931.09   6934.00    1.012720   \n",
       "\n",
       "                        USD Volume  Weighted Price  \n",
       "Timestamp                                           \n",
       "2018-01-01 00:00:00  283451.085370    13619.105106  \n",
       "2018-01-01 00:01:00  183283.978010    13602.426919  \n",
       "2018-01-01 00:02:00  156789.196860    13590.872506  \n",
       "2018-01-01 00:03:00  221413.641820    13560.332806  \n",
       "2018-01-01 00:04:00  135141.269440    13574.719401  \n",
       "...                            ...             ...  \n",
       "2018-03-31 23:55:00   40243.918480     6935.869979  \n",
       "2018-03-31 23:56:00    4331.620701     6930.005567  \n",
       "2018-03-31 23:57:00    5382.532162     6932.405747  \n",
       "2018-03-31 23:58:00     925.356547     6936.048538  \n",
       "2018-03-31 23:59:00    7022.275088     6934.070316  \n",
       "\n",
       "[129322 rows x 7 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.loc[:, \"Close\"] =coinbase.loc[:, \"Close\"].astype(\"float\")\n",
    "coinbase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open              float64\n",
       "High              float64\n",
       "Low               float64\n",
       "Close             float64\n",
       "BTC Volume        float64\n",
       "USD Volume        float64\n",
       "Weighted Price    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze the Data\n",
    "\n",
    "Your analysis consists of the following tasks: \n",
    "\n",
    "1. Choose the columns of data on which to focus your analysis.\n",
    "\n",
    "2. Get the summary statistics and plot the data.\n",
    "\n",
    "3. Focus your analysis on specific dates.\n",
    "\n",
    "4. Calculate the arbitrage profits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Choose columns of data on which to focus your analysis.\n",
    "\n",
    "Select the data you want to analyze. Use `loc` or `iloc` to select the following columns of data for both the bitstamp and coinbase DataFrames:\n",
    "\n",
    "* Timestamp (index)\n",
    "\n",
    "* Close\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00</th>\n",
       "      <td>13646.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:01:00</th>\n",
       "      <td>13658.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:02:00</th>\n",
       "      <td>13610.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:03:00</th>\n",
       "      <td>13639.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:04:00</th>\n",
       "      <td>13620.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Close\n",
       "Timestamp                    \n",
       "2018-01-01 00:00:00  13646.48\n",
       "2018-01-01 00:01:00  13658.75\n",
       "2018-01-01 00:02:00  13610.22\n",
       "2018-01-01 00:03:00  13639.09\n",
       "2018-01-01 00:04:00  13620.00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use loc or iloc to select `Timestamp (the index)` and `Close` from bitstamp DataFrame\n",
    "bitstamp_sliced = bitstamp.loc[:,[\"Close\"]]\n",
    "print()\n",
    "bitstamp_sliced.head()\n",
    "\n",
    "# Review the first five rows of the DataFrame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00</th>\n",
       "      <td>13608.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:01:00</th>\n",
       "      <td>13601.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:02:00</th>\n",
       "      <td>13580.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:03:00</th>\n",
       "      <td>13550.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:04:00</th>\n",
       "      <td>13583.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Close\n",
       "Timestamp                    \n",
       "2018-01-01 00:00:00  13608.49\n",
       "2018-01-01 00:01:00  13601.66\n",
       "2018-01-01 00:02:00  13580.00\n",
       "2018-01-01 00:03:00  13550.34\n",
       "2018-01-01 00:04:00  13583.44"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use loc or iloc to select `Timestamp (the index)` and `Close` from coinbase DataFrame\n",
    "coinbase_sliced = coinbase.iloc[:,[3]]\n",
    "print()\n",
    "coinbase_sliced.head()\n",
    "\n",
    "# Review the first five rows of the DataFrame\n",
    "# YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Get summary statistics and plot the data.\n",
    "\n",
    "Sort through the time series data associated with the bitstamp and coinbase DataFrames to identify potential arbitrage opportunities. To do so, complete the following steps:\n",
    "\n",
    "1. Generate the summary statistics for each DataFrame by using the `describe` function.\n",
    "\n",
    "2. For each DataFrame, create a line plot for the full period of time in the dataset. Be sure to tailor the figure size, title, and color to each visualization.\n",
    "\n",
    "3. In one plot, overlay the visualizations that you created in Step 2 for bitstamp and coinbase. Be sure to adjust the legend and title for this new visualization.\n",
    "\n",
    "4. Using the `loc` and `plot` functions, plot the price action of the assets on each exchange for different dates and times. Your goal is to evaluate how the spread between the two exchanges changed across the time period that the datasets define. Did the degree of spread change as time progressed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>129067.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10459.842453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2315.976088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5944.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8613.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10145.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11444.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17234.980000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Close\n",
       "count  129067.000000\n",
       "mean    10459.842453\n",
       "std      2315.976088\n",
       "min      5944.000000\n",
       "25%      8613.370000\n",
       "50%     10145.950000\n",
       "75%     11444.810000\n",
       "max     17234.980000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate the summary statistics for the bitstamp DataFrame\n",
    "bitstamp_sliced.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>129322.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10449.140958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2317.197419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5882.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8609.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10137.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11397.237500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17177.990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Close\n",
       "count  129322.000000\n",
       "mean    10449.140958\n",
       "std      2317.197419\n",
       "min      5882.310000\n",
       "25%      8609.230000\n",
       "50%     10137.440000\n",
       "75%     11397.237500\n",
       "max     17177.990000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate the summary statistics for the coinbase DataFrame\n",
    "coinbase_sliced.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'BITSTAMP PRICES'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a line plot for the bitstamp DataFrame for the full length of time in the dataset \n",
    "# Be sure that the figure size, title, and color are tailored to each visualization\n",
    "bitstamp_sliced.plot(figsize=(10,7),\n",
    "                     title=\"BITSTAMP PRICES\",\n",
    "                     color=\"pink\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'COINBASE PRICES'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a line plot for the coinbase DataFrame for the full length of time in the dataset \n",
    "# Be sure that the figure size, title, and color are tailored to each visualization\n",
    "coinbase_sliced.plot(figsize=(10,7),\n",
    "                     title=\"COINBASE PRICES\",\n",
    "                     color=\"green\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'BITSTAMP vs COINBASE PRICES'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e7zJJEyZM4MorrwRg3LhxVK9enc2bN3vPmZWV5d12uVxkZ2dz0EEHcfDBB/PTTz8FXP+JJ54gMzOTXbt2edP27dvH2WefTffu3enWrRuHHXYYOTk5Aecr+njwwQcT0g66HFI59GaXgoD9vxobWn87FDNQ5xtTSilVORhjGD58OKNHj/bOhD9v3jw2bdrEeeedxwsvvMAxxxzDvn37OPXUU3n++ee54oorgs7ToEEDHnvsMR566KGgvKK1LwE+//xzxo4dy3fffefNnzRpEn369GHatGmcd955ADz11FM0btyY+fPnA9bs/WlpaUHnSyTtGatIbr7Z6RoopZRSCfHNN9+QlpbGpZde6k3Lzs7mn3/+YcCAARxzzDEAVK9enWeffTZsL9QFF1zAu+++y/bt2yNeb/fu3dStW9e7v2zZMnJycrj33nsDZuLfsGEDzZs39+536tSJjIyMuL7GWGnPWAXy8E+PcDMPO10NpZRSlcy1M65l3sZ5CT1ndpNsnjzuybD5f//9N7169QpKX7BgQVB6+/btycnJYffu3UHls7KyuOCCC3jqqacYP358QF7Rcku5ubls2LCBr7/+2ps3adIkzjzzTA4//HCWLFnC5s2badSoERdccAHHHHMMU6dOZfDgwYwePdq7mHjR+YqMHTuWM844I5bmiEh7xsqZgvF3hs275egkVkQppZRygDEGEQmZFy796quvZuLEiUHBWtFjxcWLFzNjxgxGjRrlHXs2efJkRo4cSUpKCiNGjOC9994DrN655cuXc9NNN7F9+3b69OnDokWLAs5X9JGIQAy0Z6zcGfvTPdDf6VoopZSqSiL1YJWVrl27MnXq1JDp33//fUDa8uXLycrKombNmiHPVadOHc466yyef/75sNc79NBD2bp1K1u2bGHjxo38+++/HH201cuRn59Pu3btvGPSsrKyGDFiBCNGjCAlJYVPP/20TNfx1J6xcuYxv0BsTOVYB10ppZQKMmjQIPLy8nj55Ze9ab/99hsdOnRg1qxZfPnll4D1aPDqq6/m5ijjpq+//npefPFFCgsLQ+YvXrwYt9tN/fr1mTRpEuPGjWPlypWsXLmS9evXs27dOlatWsWPP/7Ijh07ACtIW7hwIa1bt07QVx2aBmPlQJo7dPrZ85NbD6WUUipZRIRp06Yxc+ZM2rdvT9euXRk3bhzNmjXjo48+4t5776VTp050796dPn36eKezCKdBgwYMHz6cvLw8b1rRGK/s7GzOOOMMJk6ciMvlYvLkyQwfPjzg+OHDhzN58mSWLVvGwIED6d69Oz179qR3796ceuqpQefLzs7m1ltvJRGk6NlpRdO7d28zZ07F7zo64OkDWLZjWci8OR0epfe/NwakzUg9j2Nvfz0ZVVNKKVWJLVq0qEwfvVVlodpWROYaY3qHKq89Yw4LF4gBdD58WFDacYUTyq4ySimllEo6DcbKseot2zOi9fHBGX5dsEoppZSq2DQYK+eGtT8xYD+jEEaek4nnt18dqpFSSqnKoqIOVSrP4mlTDcbKuV5Negbs56XCu93gzjcvcKhGSimlKoPMzEy2bdumAVkCGWPYtm0bmZmZJTpO5xkrp1we63NGZo2Q+X820zhaKaVU/Fq0aMHatWvZsmWL01WpVDIzM2nRokWJjtFgrBxZ+Cx0sd/cdduxVs16TQHI3gDzmvrKNnCFnvhOKaWUikVaWhpt27Z1uhoKfUzpqCVblwTsH5jZPKhMoxqNmHnuTL6d2ZwDtvnSa6SUrAtUKaWUUuWTBmMOcrsLvNsftL8N+fOvkLPuD2k3hNrjHyTV40tbO+fr4IJKKaWUqnA0GHNQbU+6d7tFvTZQrx7Xb+sIWG9NBjjnHBY39O1+dGDZ108ppZRSZU+DMacYg2eH77ljq5bdAej07CSe+RSWv6ZjwpRSSqmqQIMxpzz2GIUDrFXBD1sFjbv3s9IPPpgrn5pNs2WbHaycUkoppZJF36Z0yvvve9+YHDO3WF6/fkmvjlJKKaWcoT1jDtmUls/gUda2S+fbU0oppaqsqMGYiLwmIptF5O9i6VeJyBIRWSAiD/uljxWRpXbesX7pvURkvp33tIiInZ4hIu/a6b+ISJsEfn3l1nkd/mZ1HXtn0KC4zlG4f2/C6qOUUkopZ8TSMzYBOM4/QUSOAoYCPYwxXYFH7fQuwEigq33M8yLisg97ARgDdLA/is55IbDDGHMA8ATwUCm+ngpjRqt873ZO84YRSoaXt293oqqjlFJKKYdEDcaMMd8D24slXwY8aIzJs8sUjTYfCkw2xuQZY1YAS4G+ItIUqGWMmW2sRbDeAIb5HTPR3p4KDC7qNasqls58N67j3M2bwU8/Jbg2SimllEqmeMeMdQQOtx8rficifez05sAav3Jr7bTm9nbx9IBjjDGFwC6gfqiLisgYEZkjInMq01paP7SK77g3DgIzYEBiK6OUUkqppIo3GEsF6gL9gJuAKXZvVqgeLRMhnSh5gYnGvGSM6W2M6d2wYXyP9sqjp2bEd9xVJ8Co4Ymti1JKKaWSK95gbC3wgbH8CniABnZ6S79yLYD1dnqLEOn4HyMiqUBtgh+LVmp9n5oa97FvHZTAiiillFIq6eINxj4EBgGISEcgHdgKfAyMtN+QbIs1UP9XY8wGYI+I9LN70EYBH9nn+hgYbW+fBnxtjyurOk49Ne5DqxVEL6OUUkqp8ivqpK8iMgk4EmggImuBu4DXgNfs6S7ygdF2ALVARKYAC4FC4ApjjNs+1WVYb2ZWAz6zPwBeBd4UkaVYPWIjE/OlVQ3nzXO6BkoppZQqjajBmDHmzDBZ54Qpfx9wX4j0OUC3EOm5wOnR6qFC67HJ6RoopZRSqjR0Bn6HHLomepniVj0B934VmJbvCl1WKaWUUhWDBmMOaZBeB4Cr25wR8zGtuvbn9h9gkt94/6X1ElwxpZRSSiWVBmMOKRRD75zaPDV6cuwHzZoFS5bguf46b1K9/WVQOaWUUkoljQZjDnFjSA05xVoEItCxIx/v+8N3niq1VoFSSilV+Wgw5pBCMaSa+Jp/ryfXu+3W76BSSilVoemfcocUigdXSXvGbH1rd/FuP3A47MrdlahqKaWUUirJNBhziPWYMr7mv+7k+wP2b//69kRUSSmllFIO0GDMIdZjyvh6xrLqNsbc5Vuk4LnfnktUtZRSSimVZBqMJYrbDc88A3l5MRUvFINLm18ppZSq8jQaSJB1rz3FtOevhvt8iw+88+7/sWP7upDl3RL/Y0qllFJKVR4aDSRIi/U3MGIkmO3bAPh3/necvfg+6j3TImT5Qoljaoso3p73BjJeWLvp34SeVymllFJlR4OxBPOsXAnA7l2RF40sFBL+mPKcj0YDMOqxwxJ6XqWUUkqVHQ3GEuyb1d8BUC29RsRyBSmGtDJq/oVmc5mcVymllFKJp8FYKXy+5H/IeGH+xr+8ab83yOer5V+RkV4t9EHGwObN5KcY0knMKt8Xz4VCT6F3f1MWuH/7JSHnVkoppVTZ0mCsFKZNvguAH6c86k27ZWABQ94cwo8LPw990AUXQOPGFHgKSYtzBv7i3AJf/DE1IO3zRZ8k5NxKKaWUKlsajJVCyq49ALwz782gvC1ffhTymO3vTmB5XShwQdre3JBlSspzYEcy/1oYkLZ846KEnFsppZRSZUuDsVLYt2ktAD+0DpG5eEnIY+rfAu2vgW3VIS2/MGSZWM161fo8ofo/DF59T0Deiq/eL9W5lVJKKZUcGoyVwj8Nwzffjcf6tl/47QVrY8GCgDJp6zaW6voDzhlL++2h8x7vX6pTK6WUUipJNBgrhUx3bPOEXf7p5QB8fGq3gPQ0TykrcP/9pLhSS3kSpZRSSjlJg7FSyMh3l6j80DMD99NT00tdB5cJnX7Q/trMWDqD/QX7S30NpZRSSpUd7VYphXRXBrAvarlaYcbpuw/pW+o6uMIsNt6mThuOf/t4gIBFxZVSSilVvmjPWClsyoxtAP7uzNDpv2yZV+o6FIT5Dn6U92fYY9weN9e+fS6rty0v9fWVUkopVToajJVCkw17Yi67/4N3g9JmNs4pdR32SkGJj5n96/s8tfQtRj05sNTXV0oppVTpaDCWJA88OzIo7cYfS39eVxwvAZi9VhDo3rql9BVQSimlVKloMFYKJRmJtTctxPGNGpa6DqvrlPwYk5EBQK47r9TXV0oppVTpaDBWCtGCqUftFZHq7YO9IV6cPKXd8QmvU6/10cu4xQoj5zRP+OWVUkopVUIajJXCZlfk5Yyun20t4p3uhkZ7fel3r2jLux+4OGLsfxNepzm/HBSUtua76QH7KcQ2P5pSSimlyp4GY6XwS23fAP7e64LzZepUXB7wCLiGHA3As66TuWPCcv7zZyFUq5b4Ss2bF5T018KvA/bTRWc0UUoppcoLDcYSZNy3vu0T/gEzDhgxghQDm7NgXMFMAA4aeEbS69a6ht/zSLebtP35Sa+DUkoppULTLpIEOW6pbzvVA6xYASLsb9EY2OTNa7em9NNZlFThAe2825u6teXzhmtgcNKroZRSSqkQtGcsAX54LXBZotSWraFNGwBysgJH7jfrckgSa2Z55t+3vNtNRq7h/zQQU0oppcoNDcYSoGYeMGKEd3/Ermbe7fdqrQksnJ2dnEr5eW3ltKRfUymllFKx0ceUCZBigEceIb/DB+SmQs0fn056Hervg2mTgbvK+ELGWB8pGscrpZRSiaDBWALI++9Du3aktTuAtKZNoXfvpNdh9Dw4fHUSLlQUhBldfFwppZRKBO3eSABx2THtv//C9987UoeDt6XBLbcAcMNPYQolIIB6/FAYcEGpT6OUUkopmwZjCZDV+oCYyr0fvFZ4wgwaPR4efBCA7I2hy+Tu3l7q69xwLPzUCownjkUxlVJKKRVEg7FSuMiTDUDrZl1iKj9iUdnVJeXEk7zb8SweHhPxzdxfmB959QGllFJKxUaDsVKoYdKoHSUmqbfPb+e228qsLikul3c7NUQwduJTfUvVm2Vyc5Fxvv2811+G3FzIi7LY+IoVcV9TKaWUqgo0GCsFt3Fbb1JGUNSXNGYOcN99ZVYX2bHTu70rMzj/052/4SH+YCx/356A/Zqbr2VH3Wrk1ApxsSKTJ0O7djBzZtzXVUoppSo7DcZKwW08AZO9hiJ2/r1fRy5XWtKkqXe77nVjQ5Z5e2GIQWsxDupftmFBUFq9W6FmpM6+116zPk+YENM1lFJKqapIg7FScOOJOj5L7KkgjEQuV1omxXeBjNr1Q5a55MtrghPd7qjnnrP6Z7pOPSp8ge1hXgwo6hF7772o11BKKaWqKg3GSsHqGYscZaXbsU5Zz8pl/K+wbl3Mx3nchVHL9Hn90Ij5BYeGXuLpu9Yg4+CZ/jqdnVJKKRWOBmOl4MFEfUw5c3odbv8eGv27vmwr4/emY88a1lQbbXaELz7OZS1Q6S7IZ/Nfs/ntlbvjvvR9nTeHTL/UfsHz6qP2x31upZRSqrLTYKwU3MYTdQB/59Mv596vQWrXLtO61K1Wz7vdvGE7zDhY8VT48ukpaQC43QUc8eph9F13V9yTwrYcOipk+uKGcZ1OKaWUqlI0GCsFN9EfU3L33ZCfD9Wrl2ldUlP8HgW2bWt9Hht6ID+AS6ypMDyFBSypZw18y3n3TV+B00+HRo1iunb9tBgCTV0+SSmllApJg7FSiOVtSkQgLa3M6xJQjU6d4O+/4Z57wpZPSbGCMbe7wJt265wHvdtrv5jKlr1bQgZRL6QND9h3e6KPO2PfvuhllFJKqSooajAmIq+JyGYR+TtE3o0iYkSkgV/aWBFZKiJLRORYv/ReIjLfzntaxBrkJCIZIvKunf6LiLRJ0NdW5mLqGStjRVNneIq/ItC1K/hNBFsjPzDbJda33l3oC8aeq+lbIqDl9dDoZmD2bLpv8h23+OQvKMgLDKwK/QK6sLZujV5GKaWUqoJi6RmbABxXPFFEWgJHA6v90roAI4Gu9jHPi0hRRPACMAboYH8UnfNCYIcx5gDgCeCheL6QpNuyBc/OHY53LRZNreE2kaeoKD4rv8t+rOmOEkitufcmGu717Xc6+Gj22h19x/1rfS6MpWfsAl1dXCmllAolaixhjPkeCDWR1BPAzQQ+IRsKTDbG5BljVgBLgb4i0hSoZYyZbYwxwBvAML9jJtrbU4HBRb1m5dm04QfyfpPtjveMHbI+8vXfmwIPfxE4K78guFavAcC9a2fwQX6PJv+b9xP7iz1lbVajiXXtBj0AKMyL/rak+5synvVWKaWUqqDi6tgRkVOAdcaYP4tlNQfW+O2vtdOa29vF0wOOMcYUAruAkLOWisgYEZkjInO2bNkST9VL5vnn4eOPvbv57nzyCq21GEccbcWn0d6mLGv/ey+Nn16BdFd6yPzTFsJNPwWmHZzZFn7+BQD3r78EH7TfF1x93xpmt7S275chAJx71St83ew2zh90g3WOiROi1nPYSEo0/5lSSilVVZQ4GBOR6sDtwJ2hskOkmQjpkY4JTjTmJWNMb2NM74YNkzBvwhVXwNCh3t1WDzcl877AtRgX1o3hEV0Zqp0nHLqWEr2tOLbm8Tx4mLX9v91zgvJzC3zB2KzWfsfdac2oL6mpHHXxfaQVWM8+Yxkz9kkn2PD+hJjrqJRSSlUV8fSMtQfaAn+KyEqgBfC7iDTB6vFq6Ve2BbDeTm8RIh3/Y0QkFahN6MeiSdfuGhjsN4XWpvzgahW4gpKSa8wY63NJ3tj851/W2rNRXLzrzaDs/fl7g9JCcWXVBOCSk8NUrVic96j8HHMVlVJKqaqixMGYMWa+MaaRMaaNMaYNVjB1sDFmI/AxMNJ+Q7It1kD9X40xG4A9ItLPHg82CvjIPuXHwGh7+zTga3tcmeNW1IWv2zldiyiefNJ6rJgaZsmhsWNheOBUFHtXL414Somx9VMzIsydtnYtnmJ9npmpGbGdWCmllKpCYpnaYhIwG+gkImtF5MJwZY0xC4ApwEJgBnCFMd7X/C4DXsEa1L8M+MxOfxWoLyJLgeuBW+P8WpLG7Ym+uHbSpKRAZmb4/Pvvhw8+CEj6qsYmUsN8CfsK9vH43GdjunRqXd/Qvte/fdKXsX8/tGzJjmqBgV2fSd/HdF6llFKqKonlbcozjTFNjTFpxpgWxphXi+W3McZs9du/zxjT3hjTyRjzmV/6HGNMNzvvyqLeL2NMrjHmdGPMAcaYvsaY5Yn8AhPFPwDL37rRwZqUXqG7MGiqiyL/9/X/cc+vj8R0ntTOXb3bF3x3HQD73nqdxa2qc+iF8H4X8H/ZdHpLnfhVKaWUKs7pabIqjBu/uNG7ffYnFXvOrNo9+jB4Vehv/fa9oSdnbbY7OM2VEjxgrsayC+h8JfzcMrh890bdS1RPpZRSqirQYCxGb8z1dQhOW/OFgzUpvdnulVz2V+ipMN6c/1bI9FAvKgSshwn8NPfDkMf2aZgNQPWW5X0AnlJKKZV8GoyFM326d9MYw/bCPQ5WJrHGVDuMj0cdEjIvaFkl25YawWkuCYzQBnwyPLgQMP1EK8Db5Y7tLU2llFKqKtFgLIw1D4z1bj9757Fhy60dMSsZ1Umo9F59WZiyrdTniXWhBFeq1Qt3876PopRUSimlqh4NxsKY29y3fZt7ZthyzbsPSEJtEufqvldzzoDLWZyzMigvI8L8tacvCJ1+wj/Rr5maGvqRqFJKKaU0GAurxsoN3u2cMNNjnfdHkiqTQE8d/xQZqRmM2ntAQHrftZAXZqoygCnNrgmZ/tCXka/XJLMBrjQNxpRSSqlwNBgLY7cr+hI/r1Wgp27T34HHPvftpxI43uvXFkT26KMhk2vkRz5s3c2bvI8plVJKKRVMg7EwVg89MmoZee+9sq9Igpz0D1w/27fvkhJ+68PM8N/4f9+GPWTfbftIkRTSIs3Ur5RSSlVxGoyFMej8e6IXOu20sq9IGSn+JmQk13QcFTaveuMW9F8dOq9aWjUA0jKsz6fvbRPzNZVSSqmqQoOxMDo16OR0FcpUSXrGHv7Py+Ez9+8nPcTSSp32BfeGvVdjZczXVEoppaoKDcbCyEzNxNwVOOdWzTyHKpMIf/wBX33l3XUtWhzzoemuCGO+UlO54rfApA/W9OfP+7aXtIZKKaVUlRTh/TlV3B6/tyrrVbRlFrOzA3YlL/LI+80Pw/4nHmbHKcdEPfVpCwP3h1/6FKSGeQVVKaWUUgE0GItTg4oWjBWXEr5T9JEvoGH/ITDyElrVqhX5PKEmfu3Vq5SVU0oppaoOfUwZp5enRy9TrkUIxjwCzJwJ0QKxEO76ltABmm3NrjUlPqdSSilVmWkwFqfGL7zhdBVKxZMSPmC64+gSdJgWC7zO2dAwYvFWT7bi2x/fjv38SimlVCWnwVictlWPbV3G8srt95034+DYlb4ALJ8I6yIVVywYSxk6LOohq7+v6N2KSimlVOJoMBanzIUxLMpYjgX0jKWnc3vqoPhOVCwYa3rOpVEP+XHDr/FdSymllKqENBgrgQ7bfNs9Gx3kXEUSwNO+nW8nL4/Dn5rm3e2xKzP2ExULxqrVqBP1kL17tkUto5RSSlUVGoyVwJy5vrcEZfBgB2tSep56dQMTqvsmaTWUQIsW0Lw5778LZ/8FNIw8Zgygx+aK/YhXKaWUSiQNxqLoucG3XWv0GN9OnTpJr0sieSLEQydsqhn7iTIyYO1aRizw8NZ7bqgZ/dgerfrEfv54vPMOrA6zRpNSSilVzmgwFsWV/sObLr6YNjvgkLWOVSdhPBH6v47Mb17yE4pEnC4j4NrZZfiINyeHrRefTUHb1mV3DaWUUiqBNBiLIv2yK307Iqx4Cn5+xbn6JIonUuawYWV7bRPx6qU79xWX0/BmOH8YMHVqmV1HKaWUShQNxqI4Gmug+1W/OFyRBPNI+J4x4yrb26Isg7G/vngTgLd7wNoLTy+z6yillFKJosshRdE4oz6F4yGlKHb55RfILMHbhuVU29TwA+0zpGxvi7IMxnr6zaxx7nD4psyupJRSSiWGBmPR1KuHywBHHmnt9+3rZG0Spn3tNrArMG3HR515LXMRR/Up22k7yjIY8/dt26RcRimllCoVfUwZTatW1ucTTnC2Hgl2wum3c/mvsK7nW960Om07c/1skNQK2DPmdsOnnyb+vEoppVQZ056xaHr0gKVLoV276GUrEMnK4rn/FRs39t//QteuMGRImV7bmBLNZBabJ56Am26CcYk/tVJKKVWWtGcsFu3bB800Xyk1bAh33x3zFBUlMfYH33aZ9IwtX574cyqllFJJoMGYSop7vobP7CeiZRGMmRde4N96ITK2bEn4tZRSSqlE0mBMJYVr/t+0P/8GADyRZzmLy6jh0PHqwLRWO4FGjRJ+LaWUUiqRNBhTydG1KynHHQ+A56OPEntuY3grxAugq+vAmz0SeymllFIq0TQYU0mTsmcPAJ7t2xJ6XuN2h80bNSKhl1JKKaUSToMxlTQpqWkAnDccJv7yYsLOm5+3L2HnUkoppZJNgzGVNGIHYwD3fnh9ws6bl6/BmFJKqYpLgzGVNClp6d7t9H35CTtv3lefJ+xcSimlVLJpMKaSxj8YW1ivMGFTXOQ8+kBCzqOUUko5QYMxlTQpfo8pAfq/2j8h593WrE7geZv1S8h5lVJKqWTQYEwljX/PGMAv635JyHn/6FI3YP/TkdMTcl6llFIqGTQYU0mTkp4RlCbjhV/Wli4oc2VUC9hPy6hGJxqU6pxKKaVUsmgwppImVDAG8Om/n5bqvNK0acB+emoGs25aVKpzKqWUUsmiwZhKmhRXapmcNy2rdsC+S1w0qO7rGcvdubVMrquUUkolggZjKmkkLS16oTh0qdYKgHqptUiRFEQkIH9e94Ysal871KFKKaWU48qmq0KpEFJcZROMGXuKjNe63MqRx18elH/oRQC7MWVydaWUUqp0tGdMJU1KiqtMzuvxWGtTpqakUTszfA9YouY1U0oppRJJgzGVNOHGjBV/rBjA7ebFGway9e9fwxYpCsYkJfLt7PaEX1BcKaWUckrUYExEXhORzSLyt1/aIyKyWET+EpFpIlLHL2+siCwVkSUicqxfei8RmW/nPS32X2ARyRCRd+30X0SkTWK/RFVehAvGjAn/APHv76dyaa3vOfuhQ8KWMR6rxytaz5t7987olVRKKaWSLJaesQnAccXSZgLdjDE9gH+AsQAi0gUYCXS1j3leRIr+Qr4AjAE62B9F57wQ2GGMOQB4Ango3i9GlW/xPKbMM4UAfHGAX6Ix8NRTsHMn4OsZK37+ahI4yWzh/D9LfH2llFKqrEUNxowx3wPbi6V9YYz9VxJ+BlrY20OBycaYPGPMCmAp0FdEmgK1jDGzjdUN8gYwzO+Yifb2VGCwRHxupSqqFInjqfjnM4LTvv0Wrr0WunYFwj+m/HXoJwH77r05Jb++UkopVcYSMWbsAuAze7s5sMYvb62d1tzeLp4ecIwd4O0C6iegXqqciScYkzff8m2Pt2P0/ft5tD+s2LceCP+YMjM1M2C/sCC3xNcvU88/D7fc4nQtlFJKOaxUwZiI3A4UAm8XJYUoZiKkRzom1PXGiMgcEZmzZcuWklZXOSxch2ekjtCnQwwVm7fyZ246Btpda+17H1NKYDC2rmBbwL57/77YK5sMV1wBDz/sdC2UUko5LO5gTERGAycBZxvfCOy1QEu/Yi2A9XZ6ixDpAceISCpQm2KPRYsYY14yxvQ2xvRu2LBhvFVXFcjE7OC0Za8+ErDvMaEfU6ZUqx6wX/jHXN/Oiy/CP/8kpI6lNnu20zVQSinloLiCMRE5DrgFOMUY49/d8DEw0n5Dsi3WQP1fjTEbgD0i0s8eDzYK+MjvmNH29mnA1ybS63WqyttO4ONG86s17UXxx5RZtQID9uN2P29tfPUVXHopdOpUdpWMJi/Pt92/P+zf71xdlFJKOSqWqS0mAbOBTiKyVkQuBJ4FagIzRWSeiPwXwBizAJgCLARmAFcYY4omd7oMeAVrUP8yfOPMXgXqi8hS4Hrg1kR9caoCW7IENm0KmTXmlMB9zwzrVioejHVu2Dlgf35j6/POpX8j4+CJfgmpaXy2Flsv0+WCZcusN0WVUkpVKVGXQzLGnBki+dUI5e8D7guRPgfoFiI9Fzg9Wj1U1TJq7IGcvgA4q1hGiGDlTzvIkigD+Itcu+tdAK4/Dq4rbUXjtWhR4P4rr1hjyEADsqrMGNizB2rVcromSqkk0hn4lfOKBR/57nzePAhOKR6IhShLbi432lMLu/fsin6tnTv5amM5GKO1q1hdiwIxgB07klsXVW5ccXc/+t5YGzZscLoqSqkk0mBMOe+PPwJ2F//5ZdiinlUrAxOmTPFtbvo66qV2/fg1a/2XrywoiKWGgX74AURg7droZcNp2zZ8Xm45m4JDJcX8JT/wPL/yW3NYuuw3p6ujlEoiDcaU83btDNjN3R2+Z8izLjAA+vTe0d7tonnHIqkz59SAffeWzTFUMNC2Y4/g6uMh/5vwQWNUqak8dQjcdHSIvPXRvw5V+fSYfIR3Oy9dfzWHdddd8O67TtdCqYTSn3jluPvkx4D9zTmhB+4DuGd8GrDvH8z0rn1gUHkJOY2dz57NayLmh3Lz0fDMITDl1etLfKxXfj7XHg+PDgiRV6NG/OdVFc6uH79m/+K/A9LS06o5VJvyb9qUu/nxppFOV0OphNJgTDmuUDze7U05mzh57g1hy949O3DpUuMXa9VMCf4DtuWmLUzqGfQ+ideeLSV/1FhgvydQuKtYD17DhnDjjZEPNgZE2DOgT/gyGRklrpOquOp8OZg6b3cPSPN4CsOUrnpMbi67vp/p3R8xEg67EJgRYqk0pSooDcZUubJq16qI+fcfEbi/yG8qsV69i815AdSvXp+j+p4R9ny7t64rUf0A3jzI+hzwKsH+/fyRupUNLz4W+WB7jFu+34ufhSmQ54KGN8H4gYDbHfpYVWnlF3uv3RhP6IJV0OhrWlHnm2NY16MtnOV7q+e5O493sFZKJZYGY6pcKSjMj/vY9o1CT+LasFGbsMfs3hH+kWg0G7OAHHvx8f37OfhS6HB1lINGWo9X3H4/edccByvqwtYaMO4oYN68uOukKpbcPaHHRxatt6rgzWbW0nctTl1Jn6xJ3vQrT3SqRkolngZjqnywlybK/2dRlIKhNXPVoVXtViHzJMIC5bt3xRiMbd8OH38ckHTbEKzZ/K2LALA3PcI5li2zTlUtsGfs+b6wv3E936VG6bR7VUJuLj8eVC9kVtF6q1XBvrUrWHz/dbB3b1Ce+fPPgP05zZNVK6WSS4MxVT7YSxP9s2dlXIfPOnNm2LxIC5Hv3rM1bF6A+vVh6NCgmfN3/f6TteHfk7F6dZhKzkLGQf1boMNVgVmbp73t3c5JBz75JLZ6qQprwqB6DBkdOq+q9Iz98/qj1Hi1HZ0LnmTCYVmYPXt8mbm5XPp/2WGPPXxn7bB5SlU0GoypcuGEs63PBSa+gcsZYWbbj2bX3pBr0odXbELW5Wv+sjb8x3llZwceYwxs2MDmzSu8SblpgUWOe8c3/sUtwMknl6xeqsK5/dDw65FWlTFjnVbf5N0+fxiceJlv5YF1j4/jpd6hj+uyJ5NCqkYbqapBgzFVLnzWwfqcHyIYe77aaVGPb5rZMGqZUHbvi222+0tOAhkHuW9NCEj/Zb71Rpf7w2netN8zi53zySehWTOmr/8mpmsVuLB64gr1jbrKbH2EFY/yZlSBntHp04OSin4PAFy8+JGQhy0bOZvWhVkUaDCmKhENxlS5UuAJnhF/ydbFUY+TmjXjul6oYCz/xef5u7HA7t3etKL/0L/+/f2g8oU/fMd3D17q3e91SWC++4vP+a0Z/OOKYbkmrLcr2bbNmtxSVSmj5lmf79gyJWK5iu6XFoL8Hvz2s7/P2vuCrSWtHuHZnv/HtkuW065TPwpTYG1mXllXU6mk0WBMlSsFIeZXeqrG30Fp/XcVGy9SvXpc13umRfBs9xkbr6D75fD2+b2C8r7ODFwz8LKTYPy4IxkcZuwPwLi8z+k7Bqbl/xm+kJ/comkO7r8/cCyaqlRcIdaD39emGQCrq8exTFcFYTwe+l0cIT8/+I3qjuffyBWn3EO9JtYyYjNrb2VjZiEdrhY8c+eUVVWVShoNxlS5EuuYsQ59j0vI9bZHiOE+Y2lQWt7e3UFp9x4RlBTgrR72sa7I5a5KORQo1rPminKQKldeuH84Ml5YtiZ84O32uFm/Zz3n7zkgODPF+pW8qlr8U7yUd+59ORHzP7kjcF7A99vfFrbs0vrg+iTCBMpKVRAajKmkWvwMfPt66DxjDPkxzjz+7IhXSlWPjib0lAL+ltcNcd1DYjv/399OYfnv1rQXK+3zrK4TXG7cwHHe7YWtfZFhQOC2YEFsF1WOu7zgQwC++CD0eCeAO988n+aPN2ftpqW02Ov7Rr/PGXjsgfsRp0ip4Ar3RH5cP3P+h8xtJpy+vi4Hbk9hxDnhV9Ao4imovMGrqho0GFNJ1WnMWAY2OzRk3q7Nq2PuGctKzypVPdqkRR/wP7sl1puQ9vxg/rJ3hH97M9Pjovt3Z9B++hCYNi1sOYAb+ltLP/14wY+c1PEkb/oDh8PW6nDLEDDdukG3blHrq8qP1MLwj5enzH0TgBkdoNBvPrGeI65gr6fyj4MqeHNCwP6sZv8XsP/MIdD7Eniv2Q7SXbFFpbl7YxuPqVR5pcGYSq7774effgqZ5fG4yQ8xgD+aV/cdHbXMnEa+X/h/H/MRj/zH6lnr6mkQ+cC33oIDgh8n3To7/OPD3BS/aS5GjAjKb5XRmOv7XY/7TjdZ6VmYuwz9W/anbZ223jKHroGGN8PDh0HKOLR3rIJJzwj//Htpfd/2xprQxGMtDN+qa39GHep7EeTdc3sy+aQ2ZVXF5PjjD1i5MiDpu1fv9G7/c+p3DLj4HrbctIW3a18QdHiGCf1zdt2WwJ/J/RqMqQpOgzFVbng8bvI8wY8bHm93GSfsaRL2uGFjJ0Y9d6/L7vFudz30FNo3OhCAUemRx5t88ud7Icd6LT/nhJDlD14Px/3r278wxAtjB2a14bFjHyOl2MoAJ3Y8kSHthgDwWP/AY2RcxGqqciDf7bt3m1cPf78Wt+rO7Wy5aQuuFBd92x3uTR95wDzO7LMKvoltSpTyqN67B3Pi7W2tFSrsCZNzRlg9wM82GEWHbtaAywbVGzD77+CFv3+rEzwrP8DjTy2mW04NTttlTcm/f+/OMqi9UsmjwZgqN9wFeeS6gx/TXDbiAU4zncMeV69O0xJfK8VlvbIYbXLNd1ZOJ/OO4PTzho0LWT7DHbju5GsHB5e5rdNFIY9NTUnlqeOeAmBm+4jVUuXQsgU/eLfd1WKfhDjdlU6D6lYPbWaN4Fnlz3puUOkr55Ad1eDTjtY/E3uuuQyAlOnWHGpHHnZuQNm6PUMPXwjJ5WL+Izmc0s76pyh3X/CLNUpVJBqMqXLjnX/eJ88d3DPmSk1n9Cl3hjiiZH447DUWDrWWTSoKxjxRgrFJ3UOn16kXepE8l8eeJyyCBlmNwua5JMLbk2ecAb/9FvnkyjF/LJ3l3d6YF3qZrcIoL6hkZtUJSgt3DzoqJyfytCuFhbzdI3AZsvMKp2Luu48z7KVXa7mqBeTfdFHwmz2Tpkauxh+FawD4c5s+xlcVmwZjyhEvHPFwUNqGRb+F7BlzpaWTMvBIJhzyIAA39rwSgNu+h2ODZ58I67DB59M523oMGCkY67wl8nkm555MtRq16ZvaJriuderizqoR8fjMauFfPnClRAjGpkyBvn0jV045Jm+3bwLh81Y9FbJM2j1pIdOLZNSIMC1/ObFn+wbksZq8f0G/sGVycrZzzqmBaR90gZu/843dbNz+oID8mhk1WXXUx2TlwbR/e7Fg9cmM/CvyP0v9s6we82ZrdkFB5Z2bTVV+GowpRwxtEzxPmOfXn8ndHDwJa4odoIw69mZmnT+Lh09+GoD7sk5hxmf1g8rHIigYW7ECvvgCiB6Mte1vPRqZ2uH2gPQpp03BJS7cKSFm8/STmRl+tYCa6eHzZBw8cBiQmxu5gsoRrTwlf8N3WbGYLatG9ClXnLb8DavS46uH76Ut2LwxZPqjA3zb6SH+KWl1xMnsGZfHsLfm0OXVj62xZhFkpVn/+PRfdQePDazE84GoSk+DMeWIJjWDx3l9k7WNz0NMtFpERBjQagBS9Av6o4+8g4JLqijA83g8mIIC+jzQjrpfHwsEjvkKpUYfa3R9y9MvDEg/vevpuCQFN1GCsZohJjCzNc5qzBldzwibf9sQKBx9bth85Zy6O0oeJLebFfh4TTIyElWdMnPL3IcA8ESIk/K++jz+C6SXIKjatMm7eeOx8V9SKadpMKYcIQ2Cp5T4vVkSr28HdJ7vv+ODpy9jTnPYaQ9h8f8j03Vz8LFdm/QoOklQXipCoQQHY2em+B7J1G3WLmLdJp822bu99Krg4PTS3CgDaZQj8msUG7S/bRsMHx55bFWXLoH7UXqCEIGjo0/lUpY+t2eVWBB+6CNNN98c8RyHr0pMXd5b8kFgwt69sEunuVAVjwZjqspK8ViB14qFPwaku/3+Hkb6gxPKltR8/qodPO6t6w7fWKHiU1pE0r5e8GuVf5ewTqrs/L5+LjJekPHCxqzAIPyzfg2Q7A/ZWNsFP/4IbndA/qh/I48tLHLqLt/LIo1ughH1vyx9xR32/X3BwxHisScz8Gdp0BVZXDC6TkLOrVQyaTCmqixPCnzWAaax2JtmjMGdnhrhqNAWnWVNZDun2g7yQ6wA7a5rDcwesLrk9Wy+N3BQ/+Io89Sq5On1cm/v9vBl9wfknXCO9bnpjfDvKYcx6+phAfnPvhBb95DH2EFcYSFbasC08LO8lJmZz17Pih8+Dki75ufYjv297YPBiU1LPh1NKGc1HhKw/01beL1nQk6tVFJpMKaqtLnN4KdWvv1Fv/4Pd53aHLA3eOzOA4MfYOKwwAlmPz/nc07tfCoHdgg/R1LftXBZzzH0XgdvHfFkzHUb1NaaX+qaOoGDYXbFPoWVKic6Xg2HN/rEu597025q1gz98slhzXxvKfbckkrhXmth7a8+eLRsKxnBMdue4MAvhkJODodttwbeb6sWpvAbbwTs9hx1S8B+t81RHsWWwLA73g6Z/vfU5xN2DaWSQYMxpfysvPdGPHhokh88iPjWw25l1EGjAtKOaX8MU/8TeQxXrw3Q8KQz+O3GJbQZfU1M9Si4o4CZ51pzoh3R9UQA0u0pqjpsi+kUqhzLqB7+rdn/O+Qm7/YfDQuZ3twKxoYsGlvm9Qpl36MPAJCfCmeeV5NZ9az6vHVQ6PKyYnRQ2oomvt6xN28KvRxaXESYuLpXUHL3BVck7hpKJYEGY6pcyjLOvKbe6PcluHfuIIXE/fd+1j32YuEdO8Z8TGpKqnds2SFDL2ffef+S980AWu2EAenBa2WqysOVUvLH5GVm7Vpq7L3Nuzs52gS0a9cG7M4dMxeANpfcwtk1B9A5rRkHtT4koVUc9UICgzulHKLBmCqXrjXOTG7aZwz8Ww9cxX40bu92eVznM3cZDus5rNT1qtb6AJg1ixSXK+KUAip59ubsiF4ohOF7W0XM79c8scFKaSwZcUT0QsuXw1dfAfDRqMC6H9zUtx7YW9fPYuFt63xT0yRKejrHFrQOSp4752Oefm1MYq+lVBnRYEyVS4MOOdOxa6+vBZsLdgaktU8r+SuMp+1IzCBlfylG8BB5VnKVHBvWLAxKe+qz8OXT7HH4Ux9aEfG8aTXrBKWtWhjY+2MiTZeRQL2OjlxXgNbPtEdmWQPphw1MzFuSJdUqP3gAW+//DeWaNS9TuPQfB2qkVMloMKbKpT69TmbNdWvYdOOm6IXLQPEpLQY1KnlvxbC8yPOJxUMAj4k8qaxKjlUr5wXs99wAV/8SvnyBC5rmSNSpTYqWxBo3cJw3rc17AwLKbN1QgnXASqFjlPGJj99zAqvrWNv5WwN/VqefMjn4gDLyZ8GasHkfffPfpNVDqXhpMKbKJUlNpUWtFjSqUT4m1WraNPbxXkVScvYmvB4pBYV4VkbvrVBl78WPAhevb16tEeyI/OgyxRM9kE5NScXcZbjryLvCllm3OjkLY49vHH41CIAbPL6uwH3rA6fqOKln5GMTqZUr/LJoqdqRrCoADcZUuSSpkRdUTrb01iXv5SrYtyfh9fi3fgyDqFVSvNd0e8B+zdTqUKdOxGPWJWgd8A++fzExJ4rC3bRJzGV3LJjj3f7i7FIshxSHc0Y/FjavVbvs5FVEqThpMKbKpbSs2k5XgTndny3V8TWPPCZBNQmhoKDszq1KrM5+uGFH8mZjfWvr15EL7NtnLZ10552Ry0Xh9hTGXHb1Oy94t48+oAzv/RBO7jaCG5qeyqbTfw3KKyjB16CUUzQYU44zdwU/uklzOdszNrTwAHqNKPlcRY1yfNvDryq7iSf35+ZEL6SS4ralzdjxEPTaVydp13SHWP/U364ZHyLj4OJ595TqOoWewCWccq7byuYbQyzYCnzYZGeprlUaKZLCo2Om0qhLn6A8T7GvQanySIMxpUL4xLUMgG9dF/Fny/ujlPYpzEjOHFGLv5mSlOuoMPxeorj3hv9BjRpw3XVJu/zqGpF7e+56+WwAXgmeDzUmf11wIouvOhO3vRRT/0LrzeAaterTsEZD3k07K+iYJ1usDUorDzQYUxWBBmNKhVDU8zDw/16mxwWxz3yen5qcH6mD/7g0KddRoRm/yU0lOxtycqCP1SvzQp2zvXm/9p9QqutkFIu5audanx/NPCW4sMcDc6xxW0/1C84uiYNaf0rnBpNxr7EG5U88c0pAD/Z/xr4V9tiDC5196WbyX4Ev22gwpioCDcaUSqQkzf+knLV26e9h8y65yrc2Y/cD+gfk9dnoKl48oqcOClzXsZodnNXICF5O6dObhiH/68PKz98t0TUC5OYyr6lvUtZHcr4AwFV82ECEiVvPzgx+VJhMZ0z+m5151/FZzcsADcZUxaDBmFIJdPH6xgDc+lcSX0AwBjY5Mx9bVbXGFX7MnqT4fq2mS+Bj65+fzy/Rdbp2Ojxg/+56pwIwY803QWXH754OwC2vjCzRNfzt+utXevp1us63bmdSC4Ifi+40t/Dd6zCv34SA9OOHONxrm5ZG7fsfJ7Od1UPmcesAflX+aTCmqqxBmV0Sfs5rDzyfTlvhyiFlv6jzut3rAPj7sI5cfmET+OuvMr9mVTThw3G8+sEdAWmNMq15rW6vNyzisSmt23BnN+tFkMtqHBl1wtfiDut+YsD+IZ0GA/BRreCZ7n9tYX2e0q1Elwjw4F2DQ6an5uwLSqs97kGOWGnIbxg4x1fNdsl7qzSSFHvyXO0ZUxWBBmOqynI3bpjwc7a69i4WH/wazcfcmPBzF7djmzVuqfsxS3mhD8z++Lkyv2ZVYozBGMP5f47novn3BuTl51oT+nbPijL/nAj/Odx6XHb5qGfiqsekpld5t9sPHOarXxk8En+wX+hepIZ9jwp7TGGDet7tQRkH0qJh+4TXKx4pLqtXsiTTcyjlFA3GVJXlMb4/Zk0TNT9raiqcfz64SjY2KFZH+k2+f8WLQ2Gpb1mc88y0MrlmVXXt2+eScrffr0i/NyjzzjgNgIz1oad58Ne1UVfMXYZujeLrsmrXY6B3Oy3dtwaj2528ueZSU8K/Jdyv5aHe7a9uXZSM6sTE1zOm4zhV+afBmKqyJg6byLHVrOns3cXGI9dy1XCgRtEdmerrdfi+2ibuubCDd7/u5j1WwFCoPQGJ8PSytwP2c/71LUF0sD0sat+Wsl8Yu2+/U/ljzO+473QHBGMFBXne7UiP4sye0v2n0SbyCk+IPZi/Y2rjUl0n0Vx2z5g+plQVgQZjqspqW7ctrze/HAB3sZ+Ez48L/+q+k+4Y/VrA/p2DfNu16zTh004pvNkrzXrbbc4cVOK8/PRozNdf808DX+Q+u0/o5YJSE/z3P7tpT1IkBUnzvdVYkL/fmlIDeOq+EFNd2B48oXRrMP37aF7UMiuuWcGvNywp1XUSrahnbHfhXnILcx2ujVKRaTCmqrS05q0A8Ai8fPLL3vSWHZ19PT+clMOPCJt3kRzMiWfDqBGwphbeea9UYlzf8HdSfhhMJ98QLq475JqQZZef9Quz+72S+Er4val52G1NkMdqkrtrG9d7Pg17yG1DSnfJ1NT0qGXa1GlD7UznlzDzl2I/Wh254mG6PtcVNmyA/v1h40aHa6ZUsKjBmIi8JiKbReRvv7R6IjJTRP61P9f1yxsrIktFZImIHOuX3ktE5tt5T4vdty0iGSLyrp3+i4i0SfDXqFRYqemZgPWY8qKDL/KmN6/V3KkqxW3SPx94t1td72BFqpD69VqETG95YF/6HXthmV67aNqJbXu3lMn594zdw/abt0cvWE4V9YwBLN+5nDWjh9FkwGwWnBH+ZQSlnBJLz9gE4LhiabcCXxljOgBf2fuISBdgJNDVPuZ5ESn6iXgBGAN0sD+KznkhsMMYcwDwBPBQvF+MUiXl6mqPGasEfcTTyseMAlVKrbpNk37N62YH7qfsStTbJ9BxK2Tlwb7b9pGVnkXdanWjH1ROFb1NWeTT7b+yKQsu6rDYoRopFV7UP0HGmO+B4v8eDQUm2tsTgWF+6ZONMXnGmBXAUqCviDQFahljZhtjDPBGsWOKzjUVGFzUa6ZUWUurYc1k3r5RJ4drkniLGzhdg8rt8FW+wevJ1O3I/wTs73vztTAlS65JDvReD9XSqkUvXM6lFHuj+dKTrc8/t3SgMkpFEW9/QGNjzAYA+3PRYmTNgTV+5dbaac3t7eLpAccYYwqBXUDgLII2ERkjInNEZM6WLWXTNa+cMWv10Y5cNzM1k49HfswXF37nyPXL0qs9na5BxbRxzrcs/ij6eK+LO5yRhNoEK1z4d8D+BRv+690esqx05/YIpJjo5SoC/8eUSpV3iX44E+rfRBMhPdIxwYnGvGSM6W2M6d2wYeIn7FTJdXqX02lcwxr4MuDF8AOQy9rJnU6mcVb5ei0/EVbWcboGFVOL6UfRed7FUcudc92Esq9MKAsXBux+38a3XScty7v94IC7AOiwL/ZeLo+E/oVcEaVEmBvt3aP074cqX+INxjbZjx6xPxfNfLgW8O8EbgGst9NbhEgPOEZEUoHaBD8WVZXQlNOnsPFG+82m1PC/OJPpt+On8c1RE6MXrAAKtGMgLrGOH5TMzLKtSBjbIsRWd9/+pXf75sF30T2nBrtdheS7Y1sT01SinjGXK/zvlJFHbk3sxYxh9fxZiT2nqlLiDcY+Bkbb26OBj/zSR9pvSLbFGqj/q/0oc4+I9LPHg40qdkzRuU4DvrbHlSmVdL37DuPII0Y5XY249fKbg/SjA52rR2W3eHX4eb3K2hsDw08h0blNH6oVCldKP0SEPS43mzIKyLg3w1tmV4Oa5F50XsjjK9VjyjJaBSOUl09rR+sPDmfWW/cn7ZqqcollaotJwGygk4isFZELgQeBo0XkX+Boex9jzAJgCrAQmAFcYYwpmv7wMuAVrEH9y4DP7PRXgfoishS4HvvNTKVUyc25aj519jtdiwosxqVzOr36UfRCZSQjI8LqECkp7LvHwzN3Wq9crqzmm+x0Z+5OAOpclUO1lqF7fytVMBbhMWWijemxEoDDl92etGuqyiXq3WqMOTNM1uAw5e8D7guRPgcIWpzNGJMLnB6tHkqpGHTrxo4r1yCv6itj8fDsDF77Z+u1G2jwpG8Ki0P21Qsqk0y90lvzZ27Jl2E6+qFufNT70eCMDRvYcfn51J04pXIFYxEeU5Ypjydgcl6lYqF3jCp3huo0QCX2widw+/f2TovQE5Gq6Mbc1j1gf9GzUL92E0b4jZm/7bCxSa5VoFtGvxwy/db9vSIeN4d1NJ/j+99698aVANx7TU/qZX/OG7edyNxmsKziTi0WoMyDMWOY/cjV5G9aT/ZW37X++fydsr2uqpQ0GFPlzhtnT3W6CuXavB+6cXCxjpFL317CvVd+EFTW7dZFw2O1O2cbrzbdEJDWabP12PK/V37mTTv5hOuSWq/iOjbpyh0pwbPIp6RnhCgd3un39ADgjq6bABjd0BqAvriSvGhYplNbGMObF/el/75nuG78oZxbz/f9SFm7ruyuqyotDcZUuZN2zPFOV6FcO+jL+Vx9kG/qhWa7gY4dYfhwb9r99kt1BQW6QHKstq0PnqSraFLXhocfx9I36jJxGkgSB4aHM2rMc9Qi8G3O9u17l+gcXzTaA9sr74vrZdkztn7+T4xqOQeA5xuv5tOtP3nzPPv2ltl1VeWlwZgqd6qlV3e6CuVeaqqvF2TQvuA50tLscegFufqHIVarls2NmN9+6TZG/e6OWCZZDmjcmV13Bb6pcf6oJ4PKPf9J5PP888eXkQtUYKF6xnIO+Ygbf4SMUnYY7/v5h4D9rxr5fs7ce3NKd3JVJWkwplQF5PH7yX3hkLuD8osGYb87f3KSalTxZRVG+XUoUm4HZr858KmQSzNd9vr8iMeN+CB4FYELtrdOWL2cFKo9qh8+mNS+/fCUcmbb1MLwb90a7RlTcSifv1mUUhH1r229mNxxK2SdNyYof5fdcXbx19cmsVYVmyuz4q7HOLznWaEzunUj757wxy1oFJw2vFvleLldxPrz1tAvNpIaNUiRlNBLvJSAad48bJ5n/75Snl1VReVj2nOlVIm0r94C93hIOS70+Lo8/ckusYLC2GapL0+WXLmEd/9+lxq1w68Kn17CJ6tduxxZukqVNyL4r7CXIlLqnrHC+nXC5nn2a8+YKjntGVOqImrSxHoU2bVryOw3DkpudSqDfZcHrkf5y7Hl/63ejvU7csfAOyIXqlmTcd9ELnLOFt88aq2yByagZs4zxvcocVyzs2jqtsaipkhKwGP+UC564XhGv3AMS//+nhXL5gTlFxTkhT3Ws1eDMVVyGowpVRH16gXffw/3h15+ZUPNkp+y83OdkfGCjK8sS0WXzFHnWZ/PnA8DN2bSs8exjtYnYXbvZpzfTBjTOt4ZVKRZw3bebVeNrKD8CiktHQBJTeWui99m/d1WkJRiP7588fGzwx766uYZvLF5Jh3eH0i7t/oE5RdGWOuz6DHl05f25KJT0+KuvqpaNBhTqqI6/HBIi/7Lfnfe7phOt3hr1Z1td+fcH73b8zvX49sX9pNWvZIEJcUMO3N8UNqGBs4sel6W6jZpA8D9xweuOiAF1quUl+55hxW/zQw6buPKv4PSPHsCf4YKCwuCyvz3T2vVi6Jg7Jqm83i1h87zp2KjwZhSldDg5b7tTxZ/HLX8vvyq/Whl6VzfH+Urul3gYE3KRsPcwGkevvmzZ8D+QyNeSGZ1kiLdlY65y3DhYVcHpK/Zs9a73e7TY4KOazqxe1Ca6/HAxdlD9Yz91cTqUX6p4eqA9Jy8PbFXWlVZGowpVQnNeMu3vfzH6VHL//zJfwP2t29YzkuvXwGmkixUGMGFtxxInw2+3qLjD698wdjrDS8C4JcODwFw5JRfvXlZedC0aQdumQXvTXGkekn1U+aW8Jnu2N52KOoZ+6L7w960xVnWBMsLqu/F4zde7dXXrrQ28vLYNP4m2B84P5xSoMGYUpVS6oQ3vNt3bIn+FzZ/wV8B+/Vfas8lq5/n4xlPJbxu5YoxvFZ9SUBS62adHapM2TnxuhfYd/jn9D3rZish1fe6betd1ucH39/Fab9X/hUbFtYoNvWE3z8cH53Zk1AeyzvSu/3XLx8zYN5VAKSmprPr1l3sv30/x++w3mjdafaTt2Ort/y1m9+ABQt477nLacKjPD5YJ7VWwTQYU6oyOvfcEhWvt3FXyPRn/gq9KHVl8dajowL279p0oEM1KWMiVBsU+Eju2f/Bhb/D7BGfWgm1akFGyda3rAzy8309Vdc0Dz1Jbs2W7b3b3375inc7de9+amXUIjM1E1eu9Yblwkawf/umgONnDOvGvx++BsANleS9EJVYOhuRUlXBpk3QOHjZpCJ/yMaQ6V/mLiyrGpUL5+57K2C/W27tMCUrnyu6ngf9+sHgqr0WbH7+ftIzrN6qVXVCl/G4fQPxf/19OlhrrLP9X1+Pcm7H9uCx1jft+HIP8OsAO/6c4if0lNvVHJQz9G5Qqgq4/MImEfMX1gw/b1JVcur/veN0FZLn9dfhkkucroXj3BHmDCviyfGtN/l2D1/6HpdvjFm7Bgd4t7dFeRJZOOPT2CuoqgQNxpSqAl4IniopwPFb6ySlHuWdtGsXvZCq0LLsFyGHb6wLgLswn7wdW5n29OVhj/nvqvdDpg8edZd3+9QLH4u5DmOfPDnmsk7YOH82d17VTRc9TyINxpRSmFYtna6C4zZcs8bpKqgkaJZrTQbbPMtadcBdUEDm0w0ZscM3vUf77TBr0aEsaWm9LfmXX8dyo0LfnGxNW3bxbqemxz5X26MDgLzy1xt9w6Tz+HD269w4rj/3NFjAtFducLpKVYYGY0pVEbkbffMr8dtvUOgbB1OYU07nQjIGduwos9N3KqgFwIbr19OkTosyu44qP4rWl3CJNfdaYWFwULT0f+0Z8M4sOl5wkzfN2FNSXJExICH1WH5074ScJ5Ee/2ciw7+4wPsoNtcUwJ49sGqVsxWrAjQYU6qSmv12tYD9IU/1AmDuHRchn/blzauO8OadUv3DZFYtZnueeZQJg+rBsmVlcv7+uQ1psddFk5pNoxdWlULXup0A+C7F6gl95cO7ggstXRo0wH7rJmsmZXeMc5FF89VpByfkPGWqsBB5vBYyoU3Mc7Cp+GgwplQl1W/abwH7P2ZuBqA/rwIwqsnspNeppC777mbOHwa//FU2A57dxoPLVM21OKuqiTf9yMyzP2detZ0A3Lnzg5iOy91uTRZbaKwe5TdOfLVU9ejQtFupjk8kGS90uKtuUPouj2/aD7k3FU9B+DU5VeloMKbKjfXXr2fxFVV3fcSE69o1ZHJ+8Qlt/MaufNY+uJfA7XHoP+I6dbyPS5Y9fkfJjy8ogCFDIDf8RKYajFU9WRk1GXJA8DJI0bj3WHPxFXgKySyAc3uXfKWGxztfx5d9ngUgt6B8zcS/NGVnUFrBru0B+4/fMSTySXbu9A5/eOuwWpw/TMrl2LjySIMxVW40rdmUTg06OV2NKmfebb4/KtXqNgrKH//tuCTWxmf0Ub6JaM8eEnpS2kg8A/ozYdtX5Hc6IGwZt/HgQoMxFV5/e6nJgt3W2MVC4ybNE7rs5N3hg7zPD3mW6/7zOPWqWT1QuXnlfz3Y69K/Dtifs+yHiOUb31eX485P4+NxZ3Lu0XuY0BPkwUx2vFO6XsSqQIMxpaq4G7b65tbqc+CgoPxZP05KZnW83sj2bR+8r+STsb6TN4fzh0H/49aFLvDPP7i3bdGesSrqtOWxvf14lb2MZ8EuOxjzFJIaJhjrcc6N3u0X/24bkHfMcVcA4Npn9dR++dlzJalumTGeMF9MCMccNipsnnvrFjZnwecHwFCZHJDX69eL4q5fVaHBmFJVyN9LfwxMaNWKr/2m1qre/kBWXzCfG2r6/sP/xlM2g+dLYsiGatELFTNquPV5brPQ+eMu6cR77XOp4XGVomaqouqfZU1LkVIsFjm62O2eZj+l/3K91StUYNykhgng8/bv9m6PeW+5d7tpjq/8pt0bAHiua/noGVu1bG7MZT0RVg0oWL0ibF6/3AYlqlNVpMGYUlXIhCm3B+zfdGDw3FotW3bj0es/D0hz+y0H44T2a2L8w7V5M382Edwb1hOpwyvng8mMP9LanltnX/iCqtK6dtwMADzF/gquqxm4/43dwXXN/mnIeOG/GX+FfUzZpF7rgP3fG1pjHUfm+h6V96vREYCeG+KseIK1fadvwP4P645lembgeLhPDrgTALcJP360YPfOsHkDu1TtJbdiocGYUlXIjH1/Bew/GuOUSfv3lXzMViJdfsQeTjo7+uPEe05vTPZlMPrK5hHL1Zx/ZqKqpiooadgwZPrCYsMmn+sbXCbco+0mBwbOHdbz8rvZeuxXPPLg7960mp40UjwwYLWDL8eE8eXhL3PYSzPwNAlcx7Z3DyuY2r53W9hj8/fsDJvn3hR67Vvlo8GYUlXIfomvh2vPX3MSXJMI9u8PmlfMnQL/6xj90DvtIW/+6wcC/LPq9+DCSsXg6l0HBqWtqRl5nFVtvxd46/cbhKtGli9h8GA8KfDsIXDNI8FjNJ3ydtpIBg+yxnb1GHK2N/24fyElLQ2A2/KtKWZWvvkMu/4InBqnIMf6h+0Av3jt8pVWwFvw1cwyq3dlocGYUpXYZ2nnBeznxBmMFVZLT0BtYnTuuXBA+Dcg49FpQi/c+Xnk7d3N9BGBU350313y8Wiq6hh3e8kCiY8mwZ/vRRgjVdP3HPS53O/jrVaJpd+TzpjpY4LSD7I7rc66zfeiTpvm1s9IeiF8dtks3Pt8wwRWzJxC2+VXU+fj/gHnyd9pRWFFLzwAfJllz812WGBZFUyDMaUqsfxdgUsJbXbFN7fRnzuXJKI6MVk7831knLV95S+JO+/cqU/z7Mi2nHLQQm/aZ2f+j18f2h7hKFXVNC0IfMuyVo36JTr+lKc/p/V38xJYo8Qo8BTw8u8vB6W3MDXptbN6UPq8E6ezcswCGDCAOhm+t5lvePWMoLK/Pn4DrTePBeCHVr50d4bVo3ZjzZ9KW/1KT4MxpSqxQ48K/yp6ca82DHz9fP2jvu0ZX7+UqCpF1fpa3/azhyTuvM++fwvTGgUGXsd1PIHM1NgXeFaV34PtLwnYd2WU8P445hhoHnnMotO++3YC8363HjnmpQkZKcE93wf1Psm7EHpm14O86dM6+8q4PW7Mxo0csudxb9rw1sfyzAFXAzC4yaEhr//kocLUkd1L/XVUJhqMKVWZ9ewZc9HTRj8UsF8v2/eLdI8n/Cz20Rz+7MGc+mrgZJhm3F382URg4cKg8sXfbgs80ES8VvfdgX84u2z2bb/Zw/BjK5QKqUVeBoflNuKsUY8EZkg5mIfOGGtFiUT4+WeO/O58ek4/EYA88ZBu4gsFdu/Zyp+z3gtIK8jdy8VnPMz1nc7noSs+9KYbjweMYfY1I7juODi98998ec/5cX8ZlY0GY0pVYrVq1Iu5bPVqtQL2Xe/71uzbUyf+cVWztv3BB2sDx908+NXdZF8GH097MKi8RIi3brv3KLrfEL4unmLB2u8HPh6mpFKBbh8ynh8e2ESqKy1q2Ytjn5orIT688z+k35PO2+NPjet4j99bm7fc4fsna+fXn/JD/Rx25+8OdViAJ+qMDEob/vDB/JEfOD3OSbe8RkZqBo+NfI061XzrXX53UG3y35pI/3rTvGnffDuhJF9GpabBmFKVWEpq9D8sRVJTAhetdNX1jZWZtj+xbyPeZi9xN7TwzaA8/1kD7m4d+J/zA57v+LtWLmZ7sXFeHg8fHN6ATam+dfCqFUD6FdeErcO0lLNKXnFVaY3Iju1+cDd4hhfv/bNU19pyxSrvtonS2wswPHUqBS44hw/Y8fdvJb5e/r493u2HD/Ol1/3B6h37vWH0F3tOOGd8UFrf/fX4Ym9gW9Rv0SHk8UedlkPG8sCf51oHHhSybFWkwZhSlVhJgrHixOXMzPSX5frGkhzX7AiW7DgnqMzqj94I2N+6ciGnDtnG1hq+tDHrmyApKVTPD32dxs1imCtDVWo9t/nGStXJrBO2XN0C3z8qKVdcifToEbZsLBo08D0vjzSRaij13g8x8VkUngRM2tyxfvDPy+87FzF5/RcAHLA7lY2XLw8qE0lGq7bRC1URGowpVYmlpMQWUB27I8QbY8kaK1Po94di61ay5s737rpy9tLxyTcZmh/4Sztnz1YAdsz+hr//ezd56wMflYxaksndj88D4LOBoV8+6Hf2LQmovKrIPAW+SD0tPfzj78//soKvCXUTP8YpvzAveqFS8v86E2Fq93sA+Kq1L5CccPjjNG5YsuDqutwPGXH7ATH1DlZ2GowpVYlJhLXk/H1eN8TM2iL0C14tKW578vaETJ+S7eu92zf5TR7xWxWgSc/DAfgoPXDduw051uRIgyYOovumuyh8f0pA/sR39lOrjjWLeCO3b1D/oOVww09w9l8gmfoWZVXncfl+PiQ1NWy5PoWNMeNgdNezw5aJ177cYj8XxQITj4l9IW9/hZ5Cxr91EXv278JdmJjB/+8c+xIzT/2ITk2D34ScuebbkMf8WPPaiOeclr6MNy/oVfrKVXAajCmlwnL5/V2Q8UJeKf6Lr/VgrZDpZ5yOt3ds4ofjvOnfnPYJzdqGfhw094uJAMxrau2/t/yTsNc9sL1vfowpZ0zl0c8Nb72v/4krMOkxPsZ/6SW48koYODDhdZjy97ve7W03XMbPrQL/LPuvC3vM8hTqxPhi85vv3Mq4Za9y4yNDuP+l4Ef9/rZeG9tCmWf2u5gh3U4hpSD4sWe7jqHnoel/3ePMOf3LgLRtw38O2B/d5o+Yrl+ZaTCmVBVwVd+rgtKWXb0sRMlAtdKyAvY3r1lc+spsC+6F273Q+mX8T6rvra4ju54Y9hQvHxT4x+Cmg7eGv15H31iX+ifE9zaaqpyyCqw/gQuei1KwRQt45hmI0HsWr7mzfL26x+35L4deBKvefp5f77wQgAK/f4C6N+lOfoxDOX/705pH7CUzh4fNrLDlZp4wmfq1m5Sozjlb1gWlDWp7VOjCIvTqMpjbG/p+9mp1Kt2Yu8pIgzGlKjlzl+Hp458OSLs57Uja1W0X9dh+LQMnbWz1Rjb3v39tzNfePvn1oLRfrw+ewXvtkL6wZUvY9Sd7rQ/cX1YPcl77b8iyJ1QLfkNr60Ow+eHo9VVVy3ujpvPA9oPpvD5Bc3jF4bX9vtnp59kxUZulV3CI6zX2/PI9BQW+YCxd0shPgb+mPsdlJwnuY44Oe96NKfvC5l2/yfez36NRySdfzT4leFmlzIysECV97r18Ku/0vJfH2owhNaMaz6QP45C1vnzjLl+LpiebBmNKVUGNUmpGLwQMrtY1KO32v5+K+Trd5l4YlHZc06+C0i4+GVZefBr/hll55qnPgtNqrrksZNn/3TwvKK3+tn003Br+j5Oqmlr0GcytT82NOF4sGdas+BO2bKGwWK9XrRkD+efAht79LdvXUOiCs765kv/2gWYHfUk406qvCpv32LP/erez6jYucX3T0zJ55qCxnJTp6+HKaNQs6nFnnnI7149+EYArx07j55d9wwVmPnJpietRmWgwplQVse6AF7zbQmxvSvYdGjrgidXm6oFjs+b89hE7Qry09lMraNsz/KLJdW65K6brfdbv2dAZ1apZH0qVQ63eyGbuQY1C5vX164R6pfkmABbYRTdH7owKLyWFlVcu448LfqV6rZKtvVnkymH3M/0W3xxjNavVjlA6ur/W/Y7xeFjw00elOk9FpcGYUlVEs1PP824XD8Z23rIz5DGuRiUbS1Kcu9hvmD6fDovpuN9H/Riw3/n8m+gZwxjjQT2GxlgzpZx1QbF5lHtfErqcv/oJ6Ny92ljzlLWu347sln1Kfb68/8tj323xV+yEpdYvieyBZzDwzpZ0mzmMdybfXup6VTQajClVVfhP5VCsY6xGeg1CSalWvQwrFF7Ptv0D61G9Bi8Mjb5YeaS5opQqTx6/6L3ohYr5pM8Tpbrmw60u4qFbg4cJlEa6K51qafH/3N1ZdxgA+bfdwg9p1uDQs5fcn4iqVSgajClVBRXvGSu+FJIvI46xNKtXw7vvRi9XQjW6R5+LSLLifW6jVHLVPvk0Gu4t2TGpNQMfBXaK8BJxKDed/zKZmeXrZyRzqTWH4E2h3kXYvBn2lrCRKigNxpSqwt4Z8gKXtj09Ypnz1jWMmB+kf38YOTJo8spoTltgff7kqFdC5ndrcTBHtQl+fd6M89vJyCjRNZVy0pbQHdIh/eA5j9xtmwLSYp3mojwzI60FyBeGGDInLzRGHs0i/485vsTVq0v8u6UiKFUwJiLXicgCEflbRCaJSKaI1BORmSLyr/25rl/5sSKyVESWiMixfum9RGS+nfe0SLLWYVGqaipaguXMAZfywqgpEcu2SQ89sDisdfYcRMbQZkf4Yt/3DZyaYqr94uaJRwS/gVnk69FfBydWwl/MqmoIt25qKIeNf52C6r6hBmf9BWtqYd3/s2eDJ/RM/fcedS9/HvcR60f9VcralpHGwb9fsrenB+wPecYa27bru8+R11tz43GVrx8p7q9IRJoDVwO9jTHdABcwErgV+MoY0wH4yt5HRLrY+V2B44DnRaQorn8BGAN0sD+Oi7deSqnwhtjzvO5fsyJyQT9yQIegtNzCGKYB93g4f1747AN6Do65DkpVRsNjnEP5yswjABg49GruzTierefM550eUOiC645PQb7oz+p7bgh57O1H3E6PQ06haduSzyeWDK2yjwxKW5tZwHtXD/Hum3QrOJv+wQMAPNYf2BN6ebWKqrThZSpQTURSgerAemAoMNHOnwgMs7eHApONMXnGmBXAUqCviDQFahljZhtrtdA3/I5RSiXQl+2tz/9sWxLzMRvN7qC0avfFMGDX42FPevjsBg1bkyaJm9+pUU7CTqVUUqQ19s3NFW782CRO5ZlbvgMgRVK4/dZPqd++mzf/SXte5iN3PFlW1SxT9Rq3CUrbWt3wn/q+Fw1mNc3n/d41MDm+H/IZVx7HN1+/VmkWGY87GDPGrAMeBVYDG4BdxpgvgMbGmA12mQ1AUR9kc8B/2eG1dlpze7t4ehARGSMic0RkzpYtW+KtulJV1ln2kwpJjXFNPqBrm77xXczj4dEB4bPTXGnk31nymc+/nBW8csC+qzez+s4Iz0SVKodqVPOt1xpu/NjIm9+M6Vwr6kYvU179NeCtqGVOO3kfH+bM9e4/Vnshg364kGuuDu65r4hK85iyLlZvV1ugGVBDRCKtRhpqHJiJkB6caMxLxpjexpjeDRuWcFCxUop37Amzp7WPPQjq0qF/9EJ+dlSD35oRdgxLaQ1+9IOgtGp1G5JRs06ZXE+pspLSOPQ8ftdm+r2oEmay4mPyWwbs14pxAfHyqPuQs2Mqt9kvYP2y/k4AnmkQfY3diqA0jymHACuMMVuMMQXAB0B/YJP96BH782a7/FrA/+5pgfVYc629XTxdKVVG3BJ7136vptGnlPB39Ln2rOGffupN++3i3zjxnxKdJryDfGtPprv1XR9VcZ1/zmNBaTljc3j85uhzgU2/NXBA/u7MMAUrkVmtQ6dvmfF+citSBkoTjK0G+olIdfvtx8HAIuBjYLRdZjRQtLbBx8BIEckQkbZYA/V/tR9l7hGRfvZ5Rvkdo5RKoPNdVmDVZ3sJJmks4cvNc+1hMKsu8k2Z0btZ74TMHh6kkowXUVVTz2YHMzS/bUBajfQaiAgrL17I8ovmhz02XXuCvRr9cprTVSi10owZ+wWYCvwOzLfP9RLwIHC0iPwLHG3vY4xZAEwBFgIzgCuMMUXLtF8GvII1qH8ZEGJZYKVUaR3/hzVKuPHW/TEfUyOjJu23wzOfBqZ7TOTHkH3GQBO/F57S3b7tvy7+PfgAoN322OpUzw7s/nivXmwHKFVOuTJDr3LRulln2jbvFjKvyPU/FUvweDjlvAxmvlbxlhO6q9F/gtKO2F7TgZo4o1RvUxpj7jLGHGiM6WaMOdd+U3KbMWawMaaD/Xm7X/n7jDHtjTGdjDGf+aXPsc/R3hhzpaksr0coVc646lrBS0me7rlqZLG010SufGcpaX4B1SOzHop43JYasD8NDtpo7X/cyZfXvVnPkMc8mn1TTHXatusSzDjoMm1WTOWVKq+Obj0o7mPHPvBDwL7c42J623yOWVPxlhMad9m7mMs28Wrd87xp3zy+nWUnfxlQbla9G3nuoNuY0u1uVraylocavLdxMqtaJirfzGlKqbBcWdZ/msUX8I5q1Cho357vJvseb/75zaSoh+3KhD/tMcqbw6zC8u4pvrfFDux0WGz1+e9/rUeUnTvHVl6pciq7uW9M5pjNrUp0rNSqHb1QRdKoEWef+4h3N8WVSruDA+cjHHDVI1w+7D5OP/UOWp9/La33uGhOreJnqnA0GFOqCnF1sR57uDPjWzao18ufeLe/2R1+PEtJ/KfnOXx07AQakUXbvsck5JxKVRRdDxvh3c5JLdkbyPXbdQ0YClAZpNeux/BF8L8aY7xpj663fm992fvp4PIeId+UfIqc8iZxMy4qpco9V81asAHcteMbi5HergN8a21vTOBwjlP6jeaUfqOjF1SqkqmZUZNR21rwRv21nNb73JIdnJLChkcNMr7yvFUsKSl8MDlwpNINz8/jhh07oEGDoPJpHsgvjH0MbHmlPWNKVSGudOv9d7enML4TtGwZvUwY2RviPlSpSu2Z67/kiZzDGHbGuLiOb1rJeseCuFwhAzGAhXUL+aD+ppB5FYkGY0pVIa6u9mPKDgck/dqeyvPPu1IJVatNJ6595AckPcL6YRFsqDovHYb1w/Rnna5CqWgwplQVUvQavTsr9Ov0ZWFcjRMB+Cv0ZONKKVVqR/x+Fflfz8TsDl5LtyLQYEypKqRGmrWeSLorvv/A49HYHTjBbAupZG+AKeWwon94qrqMH45h7PnxD6VwkgZjSlUhBzc9mOv6XccrJ78S9zly7ylZedeHHwOw7fJVjEnpw7JbdfCYUol05w3TeePQh/nnxBlOVyXp5rouD9h/qIf2jCmlyjlXiovHj32c1nXCLPIWg4ytO7zbOTs3B2YuXhxUfmKXfADqNWzFi3f8Snp6CZZiUkpFJSKce8xNdOh9rNNVSbrsC24LSjObKt6Afg3GlFIlU6eOd/P3D18IyHr57OBJWH8s2TyWSikVs5RmzcnKC0yb92fF6yHUYEwpFbeBq8axYuYU7/6YU4LLHLw1eePTlFI+7/V73OkqJMWOWwIXtfXsr3jzjmkwppQqlTnzPouY37L7gCTVRCnl77ijLna6CkmRWrtuwP5Xj1zmUE3ip8GYUqpUXJu3BKWJ3wTaj/W7M4m1UUoVyUoPsyBsJTch2+kalJwGY0qpUnlx4/+sjZwcb5rxm+BVmjVPco2UqrrWXPYPADOHvu9wTZJr04WLuXdlewAWNYS8jWv57MpjwVOy9T6dosGYUqpUvm1jff7m4ctD5rep3z55lVGqimvRqAPmLsOQ7BHRC1cijVp04uwjr/Luj7msJSc0/ILv/jvWwVrFToMxpVSp5Kdan2e4l4TMTxH9NaOUKnuZTVp4t9/Itj5/uO6roHIrxl7KD4eVr8lh9bekUiohcgh8v/yajqP4/dwfHaqNUqqqyWwWPI/OxiVzg9LaZb7IEUevZXoXVzKqFRMNxpRSCfF8+p8B+0+eOZGe7fo7VBulVFXj3zNWZEkD63Ohp5Dbv7qdVesWePNOOcPDpl3rk1W9iDQYU0qV2P2Hj4uYXz0/OfVQSqkiGXUbBqX90dT6POP3Kdw/637avNItIP/TyXcno2pRaTCmlCqxWwf+X8T8QfsbJ6kmSillkdTUsHnmxRdDpj/094sUugvKqkox02BMKVVi4goca7F6+4qA/WqkJbM6SikV1rZnH+aO1O9D5i1pAGn3Or9KiAZjSqlSW//aUwH7qRVjah+lVBXQYNst/NkkcpkN/wQP9E8mDcaUUqXm/vWXgP0zuo10qCZKKVVy0/73qKPX12BMKVVqBb/9HLDfvV0/h2qilFLRzT10QsD+FbsnO1MRmwZjSqlSc0ux/f17namIUkrFYOuOdQH7T3O8QzWxaDCmlCq1nNaBAzLc+zQYU0ol358nfhJTuUGDL/Ju771oBVfd9WlZVSkm4d8DVUqpGD3bNTD48jRr6lBNlFJVWVqdejGVS23QiLUD3idn0xqqN29TtpWKpT5OV0ApVfHtSC0M2G/UsadDNVFKVWWdWveKuWzzIeVnMXUNxpRSpfZ7nf3e7e9Gf0uDhq0drI1SqqpKSYs8Z1je/+XhkvKzJmURHTOmlCo14zeAv1ez3s5VRCmlbAduCdzfe9te0l3puFI0GFNKVXI10ms4XQWllGJxsaUqq6dVd6YiMdBgTCmllFKVxpMZQwEYs6ji/GOowZhSSimlKo2r65/I9Hfg+eYXe9NS3Q5WKAYajCmllFKq0pDRoznptNtwjbubvJv3cNmmVuSPK9/RmL5NqZRSSqnKIz0d7rvP2gSef36Vs/WJgfaMKaWUUko5SIMxpVRcUkR/fSilVCLob1OlVFzmXzafZwc/7nQ1lFKqwtNgTCkVly4Nu3D5IVc6XQ2llKrwNBhTSsVNUvUdIKWUKi0NxpRS8ROJXkYppVREGowppZRSSjlIgzGllFJKKQdpMKaUUkop5SANxpRSCTOizQlOV0EppSocDcaUUgkz8ZTXnK6CUkpVOKUKxkSkjohMFZHFIrJIRA4VkXoiMlNE/rU/1/UrP1ZElorIEhE51i+9l4jMt/OeFtFXtJSqiFLTMpyuglJKVTil7Rl7CphhjDkQOAhYBNwKfGWM6QB8Ze8jIl2AkUBX4DjgeRFx2ed5ARgDdLA/jitlvZRSDtBgTCmlSi7uYExEagFHAK8CGGPyjTE7gaHARLvYRGCYvT0UmGyMyTPGrACWAn1FpClQyxgz2xhjgDf8jlFKVSCu1HSnq6CUUhVOaXrG2gFbgNdF5A8ReUVEagCNjTEbAOzPjezyzYE1fsevtdOa29vF04OIyBgRmSMic7Zs2VKKqiulyoK4XNELKaWUClCaYCwVOBh4wRjTE9iL/UgyjFDjwEyE9OBEY14yxvQ2xvRu2LBhSeurlFJKKVXulCYYWwusNcb8Yu9PxQrONtmPHrE/b/Yr39Lv+BbAeju9RYh0pZRSSqlKL+5gzBizEVgjIp3spMHAQuBjYLSdNhr4yN7+GBgpIhki0hZroP6v9qPMPSLSz36LcpTfMUqpci5nbI7TVVBKqQottZTHXwW8LSLpwHLgfKwAb4qIXAisBk4HMMYsEJEpWAFbIXCFMcZtn+cyYAJQDfjM/lBKVQA10muw//b9eIzH6aoopVSFJNYLjBVP7969zZw5c5yuhlJKKaVUVCIy1xjTO1SezsCvlFJKKeUgDcaUUkoppRykwZhSSimllIM0GFNKKaWUcpAGY0oppZRSDtJgTCmllFLKQRqMKaWUUko5SIMxpZRSSikHaTCmlFJKKeUgDcaUUkoppRykwZhSSimllIM0GFNKKaWUcpAGY0oppZRSDhJjjNN1iIuIbAFWlfFlGgBby/galZW2nbO0/eOnbRc/bbv4ads5Kxnt39oY0zBURoUNxpJBROYYY3o7XY+KSNvOWdr+8dO2i5+2Xfy07ZzldPvrY0qllFJKKQdpMKaUUkop5SANxiJ7yekKVGDads7S9o+ftl38tO3ip23nLEfbX8eMKaWUUko5SHvGlFJKKaUcpMGYUkoppZSDNBhTpSYi4nQdlCopvW+VE/S+U6FU+WBMRFz2Z/0BKQGxXCciLYwOPEw6vW/jo/dt6eh9Fx+975xX3u/dKhuMich5IvIHcI3TdaloRGQU8A3QE9hdXm/uykjv2/jpfRs/ve/ip/edsyrKvVsl36YUkQOBN4DPge7A9caY5SKSYozxOFu78k1EBgA/AH2NMXOK5Yn+11d29L6Nn9638dP7Ln563zmrIt27VaZnTERqFm0bYxYDo4AngIXAlXZ6ufrmlBdF3bsAxpgfgV+AznberSJysohk6S+WxNP7Nn5638ZP77v46X3nrIp671aJYExEbgX+EJGHROQ8O3mJMWY7MA1oLyJH2GWrRJvESkTuBu4UEf/FTS8FJorIPKAOcBXwiP1fiEoQvW/jp/dt/PS+i5/ed86qyPduuapMWRCRQcAJwNHADOABEenh91/JIqzn+ZeAFTH7/2dTVYlIhoiMBUYDPbDGOwBgjPkT6z+MC40xtwJnA02B1k7UtTLS+zY+et+Wjt538dH7znkV/d6t9MEYkAb8YYxZYYz5BngKeMAvfy8wFcgRkXtE5GGgTfKrWe4UAJ8AXYCfgaNEpG1RpjHmeWPMXHt7C7AdqOdERSspvW/jo/dt6eh9Fx+975xXoe/dqhCMVQfqi0gmgDHmQaCpiJxu7xsgF2tw32XAFmPMMqcqW17Yz9T/McbsBd4FWgB9RSQDfK8Hi0g9EXkM67/B35yqbyWk920c9L4tNb3v4qD3XblQoe/dShOMFd3sxRljpgHtgZP8kh8GrvfbfwBYALQyxjxSZpUspyK0XZ79eSUwCxgIHGinGfs/v3ex/iMZaIxZmpQKVyL+g039xzDofRtdhLbT+zYKEannt633XQlEaDu975JARDqHSq/o926Fn9pCRIYCI4AnjDHz/NIFSDfG5InISOByYJQxZqWItAJuAW41xuwRkUxjTK4T9XdSlLaTomfqxhi3iNQC7gNmAy5gpzFmuojUN8Zsc6L+FZmIHA/cDKzBGmB6n53uAlL1vg0vStsZvW/DE5HjgNuAlVg9AzfY6SlAmt534UVpO/S+K3si8gxWsHWUHfRWmns31ekKxEPEmp9FRI4C7sF6Xn+oiKwyxuwoygfyRKQd1n8jXYD/E2vyt5OBlcaYPQDl9ZtTFkrQdsZuu+1Yv0h2i8i/wHPANuBqAP3FEjs7yE0BLgYuBO7Cass7ROQCY8xrxhg34Nb7NlAJ2g69bwP5td2FwAVYvQN/AG+IyPHGmM/sx2z6+7KYErSd3ndlwO/vUZF6wA5giIi8aYzJqzT3rjGmQn1g9+bZ222w3koZBEzA6votyksBbgW2AIcDtYEBwETgJqe/jgrSdhuA4wHB6m5fDtzm9NdRET+Ktf0JQAe//WuxJiME679ovW9L13Z634Zuuy5Yva4AjYApQCvAZafdrvddqdpO77uya/+idr4K6x+yb4Bufvm3AFsr8r1boXrGRORKYLCIfA9MMnY3JbBBRI4FBorIUmPMOqAJsAvoaIzZYZf7UUR+NvZ/0FVJnG3XpajtRGQl0N1YA1RVCfi1/Q/AG8aYT0XEJSKpxphCrAkhF9vFG6H3rVecbaf3LUE/85ONMQvt9J5YPTapWH/E9gM3ovedV5xtp/ddgvi1/3fAFGPMehFJB47Dmj6kKTBSRH4FVgG7sf5Jq7j3rtPRYAmi5OFYb58cBbwOPAsc5Jd/EPAWMDzEsS78ouyq9lHKtkt1uv4V+SNM22f7ty1Wz2T/EMfqfRt/21Xp+zbSzzzQDmsAM0AWViDR0+9Yve/ib7sqfd+VYfsfbOeNtz+fiRWALQLq+x1bYe/divQ25SHAC8aaP2QcsAK/hT+NNbHeb0B3ERkk1ky8Rc+c3cb+TlVRpWm7QgfqW5mEavui8SOFYr363hKYKyItRORi0PvWVpq2q+r3bai2uxbAGLPcGLPa3s7BGmdTD/S+s5Wm7ar6fZcIodr/cjvvRLuX/BbgI6w53fZCxb93y30wZg+gBOv5+1kAxphVwP+AGiJyil/xScBFWD8g9ZNZz/JI2845MbT9UDv/QKz2vgb4GJ0IUtuuFKK0XfViP/OIyP8BXbFe96ei/iFLBG07Z0Vp/zoicijWRK4/GWOyjTHnYg2p6WyXrdDtX+6CMRGp7bft/ybFVGCf3y/iDcC3QBexZGF9o+YDPYwxN0HF/waVhLadc+Jo+872L592WL9M2gInGmMegqrV9tp28YvnZ94ue7yIzAI6AqcZYzYmr9blg7ads0rY/l8DRwBvG2Nu8TvNcGPMH0mpcBkrN8GYiBwiIh8BL4vIBSKSYYwx4ls7agfWQp+X2d+4XVjP7DPtb2IucI0x5kRjzAZnvgpnaNs5pxRtX81u+6XAYcaYy6pa22vbxa80bWfnLwIuNcaM0rbTtkumONu/BtbfK49YL/AUze1WfqeqKKFyEYyJSA+sN1Sm2h+DgAMAjO9tiGrA51hR8ksi0gxrMdYCu1yhMWZzkqvuOG075ySo7ecbY6rcsijadvErZdvl2+VWGmP+TnLVHadt56xStn9hUTljz+1WmZSLYAzoBSw1xrwJzAQygdVFz5BF5B6sSLkxcAOwCXgH2Ak86ESFyxFtO+eUpu0fCHXCKkTbLn76Mx8/bTtnafuH4chySCIyEMg1xvxi7zfCWtrkUaw5RNYCC+2PZ4HXgDuN31peIlLdGLMv2XV3mradc7Tt46dtFz9tu/hp2zlL278ETHLnD6kJfIC1ZMRrQF2/vAOBh7DWlAJrkdVPsecXsdNSklnf8vShbadtXxE/tO207bTtqt6Htn/JP5L9mDIf662Ic4D1wOlFGcaYxfY3aa2dNNcuU9R9mWIq4XPiEtC2c462ffy07eKnbRc/bTtnafuXUJkHYyIySkQGikgdY0we8ArwJfAP0FtEOvoV/wK4y35+PBLohrXeFFXxm6Nt5xxt+/hp28VP2y5+2nbO0vYvnTIZM2Y3cBOsgXceYBnWq6nXGGO22mU6YD0zzjPG3GOnVQNewlpjzgVcbew1waoKbTvnaNvHT9suftp28dO2c5a2fwIl+rknvtXVOwJv2dupwDPA+8XKDgeeBzoA1f3KNnH6+a0TH9p22vYV8UPbTttO267qfWj7J/YjlQQRkVTgbsAlIp8CtQA3eNeRuxpYLyIDjTHf2enTRKQz8BmQJSJHGWMWAVVqRmNtO+do28dP2y5+2nbx07ZzlrZ/2UjImDH79dW5QF2sWbHvwZqY8SgR6QveJUruxlr4s+i404HbgW+wluFZlIj6VCTads7Rto+ftl38tO3ip23nLG3/spOQMWMicjjQxlgTuSEiz2Otc7gfuMoY00us5QsaAU8DtxhjVtjHYYz5odSVqKC07ZyjbR8/bbv4advFT9vOWdr+ZSdRb1POBaaIb22pH4FWxpgJWF2ZVxnrDYkWgNsYswKsb4x+c7TtHKRtHz9tu/hp28VP285Z2v5lJCHBmDFmnzEmz/jWljoa2GJvnw90FpFPgEnA74m4ZmWhbeccbfv4advFT9suftp2ztL2LzsJG8APYEfLBmtdqY/t5D3AbVjziKwwxqxL5DUrC20752jbx0/bLn7advHTtnOWtn/iJXrSVw+QhjV5Ww87Qr4D8BhjZuk3JyJtO+do28dP2y5+2nbx07ZzlrZ/giV80lcR6Qf8ZH+8box5NaEXqMS07ZyjbR8/bbv4advFT9vOWdr+iVUWwVgL4FzgcWMtiaBipG3nHG37+GnbxU/bLn7ads7S9k+sMlkOSSmllFJKxabMFwpXSimllFLhaTCmlFJKKeUgDcaUUkoppRykwZhSSimllIM0GFNKKaWUcpAGY0qpck1E6ovIPPtjo4iss7dz7IWKy+q6R4pI/7I6v1JKFUnockhKKZVoxphtQDaAiIwDcowxjybh0kcCOViTWiqlVJnRnjGlVIVk91x9Ym+PE5GJIvKFiKwUkREi8rCIzBeRGSKSZpfrJSLfichcEflcRJra6VeLyEIR+UtEJotIG+BS4Dq7F+5wETlZRH4RkT9E5EsRaVzCa68UkYdE5Ff74wBHGk4pVe5oMKaUqizaAycCQ4G3gG+MMd2B/cCJdlD0DHCaMaYX8Bpwn33srUBPY0wP4FJjzErgv8ATxphsY8wPwCygnzGmJzAZuDnWa/uV222M6Qs8CzyZ4K9fKVVB6WNKpVRl8ZkxpkBE5gMuYIadPh9oA3QCugEzRQS7zAa7zF/A2yLyIfBhmPO3AN61e9PSgRUluHaRSX6fnyjxV6iUqpS0Z0wpVVnkARhjPECB8a315sH6x1OABXZPV7Yxprsx5hi7zInAc0AvYK6IhPpH9RngWbvH6xIgswTXLmLCbCulqjANxpRSVcUSoKGIHAogImki0lVEUoCWxphvsB491gGygD1ATb/jawPr7O3RcdbhDL/Ps+M8h1KqktHHlEqpKsEYky8ipwFPi0htrN9/TwL/AG/ZaYI1TmyniEwHporIUOAqYBzwnoisA34G2sZRjQwR+QXrH+EzS/s1KaUqB/H1piullCorIrIS6G2M2ep0XZRS5Ys+plRKKaWUcpD2jCmllFJKOUh7xpRSSimlHKTBmFJKKaWUgzQYU0oppZRykAZjSimllFIO0mBMKaWUUspB/w+dYMTZfKxbCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Overlay the visualizations for the bitstamp and coinbase DataFrames in one plot\n",
    "# The plot should visualize the prices over the full lenth of the dataset\n",
    "# Be sure to include the parameters: legend, figure size, title, and color and label\n",
    "\n",
    "bitstamp_sliced[\"Close\"].plot(legend=True,\n",
    "                     figsize=(10,7),\n",
    "                     title=\"BITSTAMP vs COINBASE PRICES\",\n",
    "                     color=\"RED\",\n",
    "                     label=\"BITSTAMP\")\n",
    "coinbase_sliced[\"Close\"].plot(legend=True,\n",
    "                              figsize=(10,7),\n",
    "                              color=\"green\",\n",
    "                              label=\"COINBASE\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'BITSTAMP vs COINBASE PRICES'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the loc and plot functions, create an overlay plot that visualizes \n",
    "# the price action of both DataFrames for a one month period early in the dataset\n",
    "# Be sure to include the parameters: legend, figure size, title, and color and label\n",
    "\n",
    "bitstamp_sliced.loc[\"2018-01-01\":\"2018-01-31\",\"Close\"].plot(legend=True,\n",
    "                     figsize=(10,7),\n",
    "                     title=\"BITSTAMP vs COINBASE PRICES\",\n",
    "                     color=\"RED\",\n",
    "                     label=\"BITSTAMP\")\n",
    "coinbase_sliced.loc[\"2018-01-01\":\"2018-01-31\",\"Close\"].plot(legend=True,\n",
    "                              figsize=(10,7),\n",
    "                              color=\"green\",\n",
    "                              label=\"COINBASE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'BITSTAMP vs COINBASE PRICES'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the loc and plot functions, create an overlay plot that visualizes \n",
    "# the price action of both DataFrames for a one month period later in the dataset\n",
    "# Be sure to include the parameters: legend, figure size, title, and color and label\n",
    "bitstamp_sliced.loc[\"2018-02-01\":\"2018-02-28\",\"Close\"].plot(legend=True,\n",
    "                     figsize=(10,7),\n",
    "                     title=\"BITSTAMP vs COINBASE PRICES\",\n",
    "                     color=\"RED\",\n",
    "                     label=\"BITSTAMP\")\n",
    "coinbase_sliced.loc[\"2018-02-01\":\"2018-02-28\",\"Close\"].plot(legend=True,\n",
    "                              figsize=(10,7),\n",
    "                              color=\"green\",\n",
    "                              label=\"COINBASE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question** Based on the visualizations of the different time periods, has the degree of spread change as time progressed?\n",
    "\n",
    "**Answer** In the begining of the month of febuarary the lowest point was on the sixth and the lowest point for january was towards the middle of the month. Also During January, the price at both exchanges is higher than in February. There is more spread during February with the lowest price close to $6,000 and the highest price close to $12,000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Focus Your Analysis on Specific Dates\n",
    "\n",
    "Focus your analysis on specific dates by completing the following steps:\n",
    "\n",
    "1. Select three dates to evaluate for arbitrage profitability. Choose one date that’s early in the dataset, one from the middle of the dataset, and one from the later part of the time period.\n",
    "\n",
    "2. For each of the three dates, generate the summary statistics and then create a box plot. This big-picture view is meant to help you gain a better understanding of the data before you perform your arbitrage calculations. As you compare the data, what conclusions can you draw?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'BITSTAMP vs COINBASE PRICES'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an overlay plot that visualizes the two dataframes over a period of one day early in the dataset. \n",
    "# Be sure that the plots include the parameters `legend`, `figsize`, `title`, `color` and `label` \n",
    "bitstamp_sliced.loc[\"2018-01-27\":\"2018-01-29\",\"Close\"].plot(legend=True,\n",
    "                     figsize=(10,7),\n",
    "                     title=\"BITSTAMP vs COINBASE PRICES\",\n",
    "                     color=\"RED\",\n",
    "                     label=\"BITSTAMP\")\n",
    "coinbase_sliced.loc[\"2018-01-27\":\"2018-01-29\",\"Close\"].plot(legend=True,\n",
    "                              figsize=(10,7),\n",
    "                              color=\"green\",\n",
    "                              label=\"COINBASE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1436.000000\n",
       "mean      247.552326\n",
       "std        68.343472\n",
       "min        55.030000\n",
       "25%       210.022500\n",
       "50%       251.180000\n",
       "75%       290.925000\n",
       "max       439.010000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-01-28 00:00:00    275.38\n",
       "2018-01-28 00:01:00    275.00\n",
       "2018-01-28 00:02:00    268.77\n",
       "2018-01-28 00:03:00    275.03\n",
       "2018-01-28 00:04:00    294.26\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the early date that you have selected, calculate the arbitrage spread \n",
    "# by subtracting the bitstamp lower closing prices from the coinbase higher closing prices\n",
    "arbitrage_spread_early = bitstamp_sliced.loc[\"2018-01-28\":\"2018-01-28\",\"Close\"]-coinbase_sliced.loc[\"2018-01-28\":\"2018-01-28\",\"Close\"]\n",
    "\n",
    "# Generate summary statistics for the early DataFrame\n",
    "display(arbitrage_spread_early.describe())\n",
    "arbitrage_spread_early.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the arbitrage spread from early in the dataset in a box plot\n",
    "arbitrage_spread_early.plot(figsize=(7,12),\n",
    "                            kind=\"box\",\n",
    "                            title=\"Early Date Arbitrage Spread\")\n",
    "plt.show()\n",
    "                            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'BITSTAMP vs COINBASE PRICES Feb 26'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an overlay plot that visualizes the two dataframes over a period of one day from the middle of the dataset. \n",
    "# Be sure that the plots include the parameters `legend`, `figsize`, `title`, `color` and `label` \n",
    "bitstamp_sliced.loc[\"2018-02-26\":\"2018-02-26\",\"Close\"].plot(legend=True,\n",
    "                     figsize=(10,7),\n",
    "                     title=\"BITSTAMP vs COINBASE PRICES Feb 26\",\n",
    "                     color=\"RED\",\n",
    "                     label=\"BITSTAMP\")\n",
    "coinbase_sliced.loc[\"2018-02-26\":\"2018-02-26\",\"Close\"].plot(legend=True,\n",
    "                              figsize=(10,7),\n",
    "                              color=\"green\",\n",
    "                              label=\"COINBASE\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1438.000000\n",
       "mean       14.721537\n",
       "std        13.454594\n",
       "min       -25.010000\n",
       "25%         5.412500\n",
       "50%        15.585000\n",
       "75%        23.925000\n",
       "max        75.490000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-02-26 00:00:00     7.00\n",
       "2018-02-26 00:01:00    19.47\n",
       "2018-02-26 00:02:00    -1.72\n",
       "2018-02-26 00:03:00    16.66\n",
       "2018-02-26 00:04:00     4.97\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the date in the middle that you have selected, calculate the arbitrage spread \n",
    "# by subtracting the bitstamp lower closing prices from the coinbase higher closing prices\n",
    "arbitrage_spread_middle = bitstamp_sliced.loc[\"2018-02-26\":\"2018-02-26\",\"Close\"]-coinbase_sliced.loc[\"2018-02-26\":\"2018-02-26\",\"Close\"]\n",
    "\n",
    "display(arbitrage_spread_middle.describe())\n",
    "arbitrage_spread_middle.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the arbitrage spread from the middle of the dataset in a box plot\n",
    "arbitrage_spread_middle.plot(figsize=(7,12),\n",
    "                            kind=\"box\",\n",
    "                            title=\"Middle Date Arbitrage Spread\")\n",
    "plt.show()\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'BITSTAMP vs COINBASE PRICES March 10'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an overlay plot that visualizes the two dataframes over a period of one day from late in the dataset. \n",
    "# Be sure that the plots include the parameters `legend`, `figsize`, `title`, `color` and `label` \n",
    "bitstamp_sliced.loc[\"2018-03-10\":\"2018-03-10\",\"Close\"].plot(legend=True,\n",
    "                     figsize=(10,7),\n",
    "                     title=\"BITSTAMP vs COINBASE PRICES March 10\",\n",
    "                     color=\"RED\",\n",
    "                     label=\"BITSTAMP\")\n",
    "coinbase_sliced.loc[\"2018-03-10\":\"2018-03-10\",\"Close\"].plot(legend=True,\n",
    "                              figsize=(10,7),\n",
    "                              color=\"green\",\n",
    "                              label=\"COINBASE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1433.000000\n",
       "mean       -6.541417\n",
       "std        15.953638\n",
       "min       -93.180000\n",
       "25%       -16.040000\n",
       "50%        -7.170000\n",
       "75%         4.240000\n",
       "max        36.860000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-03-10 00:00:00    -5.88\n",
       "2018-03-10 00:01:00     6.88\n",
       "2018-03-10 00:02:00    15.98\n",
       "2018-03-10 00:03:00    11.25\n",
       "2018-03-10 00:04:00     3.72\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the date from the late that you have selected, calculate the arbitrage spread \n",
    "# by subtracting the bitstamp lower closing prices from the coinbase higher closing prices\n",
    "arbitrage_spread_late = bitstamp_sliced.loc[\"2018-03-10\":\"2018-03-10\",\"Close\"]-coinbase_sliced.loc[\"2018-03-10\":\"2018-03-10\",\"Close\"]\n",
    "\n",
    "display(arbitrage_spread_late.describe())\n",
    "arbitrage_spread_late.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the arbitrage spread from late in the dataset in a box plot\n",
    "arbitrage_spread_late.plot(figsize=(7,12),\n",
    "                            kind=\"box\",\n",
    "                            title=\"Late Date Arbitrage Spread\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Calculate the Arbitrage Profits\n",
    "\n",
    "Calculate the potential profits for each date that you selected in the previous section. Your goal is to determine whether arbitrage opportunities still exist in the Bitcoin market. Complete the following steps:\n",
    "\n",
    "1. For each of the three dates, measure the arbitrage spread between the two exchanges by subtracting the lower-priced exchange from the higher-priced one. Then use a conditional statement to generate the summary statistics for each arbitrage_spread DataFrame, where the spread is greater than zero.\n",
    "\n",
    "2. For each of the three dates, calculate the spread returns. To do so, divide the instances that have a positive arbitrage spread (that is, a spread greater than zero) by the price of Bitcoin from the exchange you’re buying on (that is, the lower-priced exchange). Review the resulting DataFrame.\n",
    "\n",
    "3. For each of the three dates, narrow down your trading opportunities even further. To do so, determine the number of times your trades with positive returns exceed the 1% minimum threshold that you need to cover your costs.\n",
    "\n",
    "4. Generate the summary statistics of your spread returns that are greater than 1%. How do the average returns compare among the three dates?\n",
    "\n",
    "5. For each of the three dates, calculate the potential profit, in dollars, per trade. To do so, multiply the spread returns that were greater than 1% by the cost of what was purchased. Make sure to drop any missing values from the resulting DataFrame.\n",
    "\n",
    "6. Generate the summary statistics, and plot the results for each of the three DataFrames.\n",
    "\n",
    "7. Calculate the potential arbitrage profits that you can make on each day. To do so, sum the elements in the profit_per_trade DataFrame.\n",
    "\n",
    "8. Using the `cumsum` function, plot the cumulative sum of each of the three DataFrames. Can you identify any patterns or trends in the profits across the three time periods?\n",
    "\n",
    "(NOTE: The starter code displays only one date. You'll want to do this analysis for two additional dates)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. For each of the three dates, measure the arbitrage spread between the two exchanges by subtracting the lower-priced exchange from the higher-priced one. Then use a conditional statement to generate the summary statistics for each arbitrage_spread DataFrame, where the spread is greater than zero.\n",
    "\n",
    "*NOTE*: For illustration, only one of the three dates is shown in the starter code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arbitrage Spread\n",
      "Early Date: Jan 28th 2018\n",
      "\n",
      "Timestamp\n",
      "2018-01-28 00:00:00    275.38\n",
      "2018-01-28 00:01:00    275.00\n",
      "2018-01-28 00:02:00    268.77\n",
      "2018-01-28 00:03:00    275.03\n",
      "2018-01-28 00:04:00    294.26\n",
      "                        ...  \n",
      "2018-01-28 23:55:00    220.10\n",
      "2018-01-28 23:56:00    195.11\n",
      "2018-01-28 23:57:00    208.74\n",
      "2018-01-28 23:58:00    195.12\n",
      "2018-01-28 23:59:00    192.63\n",
      "Name: Close, Length: 1440, dtype: float64\n",
      "\n",
      "Early Date Statistics: Jan 28th 2018\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1436.000000\n",
       "mean      247.552326\n",
       "std        68.343472\n",
       "min        55.030000\n",
       "25%       210.022500\n",
       "50%       251.180000\n",
       "75%       290.925000\n",
       "max       439.010000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date early in the dataset, measure the arbitrage spread between the two exchanges\n",
    "# by subtracting the lower-priced exchange from the higher-priced one\n",
    "\n",
    "print(\"Arbitrage Spread\")\n",
    "print(\"Early Date: Jan 28th 2018\")\n",
    "print()\n",
    "arbitrage_spread_early = bitstamp.loc[\"2018-01-28\":\"2018-01-28\", \"Close\"]-coinbase.loc[\"2018-01-28\":\"2018-01-28\", \"Close\"]\n",
    "print(arbitrage_spread_early)\n",
    "\n",
    "# Use a conditional statement to generate the summary statistics for each arbitrage_spread DataFrame\n",
    "print()\n",
    "print(\"Early Date Statistics: Jan 28th 2018\")\n",
    "early_boolean_filter = arbitrage_spread_early > 0\n",
    "arbitrage_spread_early.loc[early_boolean_filter].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arbitrage Spread\n",
      "middle Date: Feb 26th 2018\n",
      "\n",
      "Timestamp\n",
      "2018-02-26 00:00:00     7.00\n",
      "2018-02-26 00:01:00    19.47\n",
      "2018-02-26 00:02:00    -1.72\n",
      "2018-02-26 00:03:00    16.66\n",
      "2018-02-26 00:04:00     4.97\n",
      "                       ...  \n",
      "2018-02-26 23:55:00    21.96\n",
      "2018-02-26 23:56:00    11.54\n",
      "2018-02-26 23:57:00    16.99\n",
      "2018-02-26 23:58:00     9.86\n",
      "2018-02-26 23:59:00     3.03\n",
      "Name: Close, Length: 1440, dtype: float64\n",
      "\n",
      "Middle Date Statistics: Feb 26th 2018\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1238.000000\n",
       "mean       18.256801\n",
       "std        10.751899\n",
       "min         0.090000\n",
       "25%         9.972500\n",
       "50%        17.695000\n",
       "75%        24.960000\n",
       "max        75.490000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Arbitrage Spread\")\n",
    "print(\"middle Date: Feb 26th 2018\")\n",
    "print()\n",
    "arbitrage_spread_middle = bitstamp.loc[\"2018-02-26\":\"2018-02-26\", \"Close\"]-coinbase.loc[\"2018-02-26\":\"2018-02-26\", \"Close\"]\n",
    "print(arbitrage_spread_middle)\n",
    "\n",
    "# Use a conditional statement to generate the summary statistics for each arbitrage_spread DataFrame\n",
    "print()\n",
    "print(\"Middle Date Statistics: Feb 26th 2018\")\n",
    "middle_boolean_filter = arbitrage_spread_middle > 0\n",
    "arbitrage_spread_middle.loc[middle_boolean_filter].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arbitrage Spread\n",
      "Late Date: March 10th 2018\n",
      "\n",
      "Timestamp\n",
      "2018-03-10 00:00:00    -5.88\n",
      "2018-03-10 00:01:00     6.88\n",
      "2018-03-10 00:02:00    15.98\n",
      "2018-03-10 00:03:00    11.25\n",
      "2018-03-10 00:04:00     3.72\n",
      "                       ...  \n",
      "2018-03-10 23:55:00   -26.50\n",
      "2018-03-10 23:56:00   -29.68\n",
      "2018-03-10 23:57:00   -16.79\n",
      "2018-03-10 23:58:00   -19.39\n",
      "2018-03-10 23:59:00   -17.62\n",
      "Name: Close, Length: 1440, dtype: float64\n",
      "\n",
      "Late Date Statistics: Mar 10th 2018\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    466.000000\n",
       "mean      10.886202\n",
       "std        8.020605\n",
       "min        0.010000\n",
       "25%        4.707500\n",
       "50%        8.870000\n",
       "75%       15.547500\n",
       "max       36.860000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Arbitrage Spread\")\n",
    "print(\"Late Date: March 10th 2018\")\n",
    "print()\n",
    "arbitrage_spread_late = bitstamp.loc[\"2018-03-10\":\"2018-03-10\", \"Close\"]-coinbase.loc[\"2018-03-10\":\"2018-03-10\", \"Close\"]\n",
    "print(arbitrage_spread_late)\n",
    "\n",
    "# Use a conditional statement to generate the summary statistics for each arbitrage_spread DataFrame\n",
    "print()\n",
    "print(\"Late Date Statistics: Mar 10th 2018\")\n",
    "late_boolean_filter = arbitrage_spread_late > 0\n",
    "arbitrage_spread_late.loc[late_boolean_filter].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. For each of the three dates, calculate the spread returns. To do so, divide the instances that have a positive arbitrage spread (that is, a spread greater than zero) by the price of Bitcoin from the exchange you’re buying on (that is, the lower-priced exchange). Review the resulting DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Spread return for EARLY DATE - Jan 28 2018\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-01-28 00:00:00    0.023999\n",
       "2018-01-28 00:01:00    0.023965\n",
       "2018-01-28 00:02:00    0.023392\n",
       "2018-01-28 00:03:00    0.023936\n",
       "2018-01-28 00:04:00    0.025610\n",
       "                         ...   \n",
       "2018-01-28 23:55:00    0.019518\n",
       "2018-01-28 23:56:00    0.017302\n",
       "2018-01-28 23:57:00    0.018510\n",
       "2018-01-28 23:58:00    0.017303\n",
       "2018-01-28 23:59:00    0.017094\n",
       "Name: Close, Length: 1440, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# For the date early in the dataset, calculate the spread returns by dividing the instances when the arbitrage spread is positive (> 0) \n",
    "# by the price of Bitcoin from the exchange you are buying on (the lower-priced exchange).\n",
    "\n",
    "spread_return_early= arbitrage_spread_early[arbitrage_spread_early > 0] / coinbase_sliced[\"Close\"].loc[\"2018-01-28\"]\n",
    "\n",
    "# Review the spread return DataFrame\n",
    "print()\n",
    "print(\"Spread return for EARLY DATE - Jan 28 2018\")\n",
    "spread_return_early\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Spread return for Middle DATE - Feb 26 2018\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-02-26 00:00:00    0.000723\n",
       "2018-02-26 00:01:00    0.002015\n",
       "2018-02-26 00:02:00         NaN\n",
       "2018-02-26 00:03:00    0.001724\n",
       "2018-02-26 00:04:00    0.000514\n",
       "                         ...   \n",
       "2018-02-26 23:55:00    0.002132\n",
       "2018-02-26 23:56:00    0.001120\n",
       "2018-02-26 23:57:00    0.001649\n",
       "2018-02-26 23:58:00    0.000957\n",
       "2018-02-26 23:59:00    0.000294\n",
       "Name: Close, Length: 1440, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spread_return_middle= arbitrage_spread_middle[arbitrage_spread_middle > 0] / coinbase_sliced[\"Close\"].loc[\"2018-02-26\"]\n",
    "\n",
    "# Review the spread return DataFrame\n",
    "print()\n",
    "print(\"Spread return for Middle DATE - Feb 26 2018\")\n",
    "spread_return_middle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Spread return for Late DATE - Mar 10 2018\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-03-10 00:00:00         NaN\n",
       "2018-03-10 00:01:00    0.000739\n",
       "2018-03-10 00:02:00    0.001715\n",
       "2018-03-10 00:03:00    0.001206\n",
       "2018-03-10 00:04:00    0.000399\n",
       "                         ...   \n",
       "2018-03-10 23:55:00         NaN\n",
       "2018-03-10 23:56:00         NaN\n",
       "2018-03-10 23:57:00         NaN\n",
       "2018-03-10 23:58:00         NaN\n",
       "2018-03-10 23:59:00         NaN\n",
       "Name: Close, Length: 1440, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spread_return_late= arbitrage_spread_late[arbitrage_spread_late > 0] / coinbase_sliced[\"Close\"].loc[\"2018-03-10\"]\n",
    "\n",
    "# Review the spread return DataFrame\n",
    "print()\n",
    "print(\"Spread return for Late DATE - Mar 10 2018\")\n",
    "spread_return_late"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-26.5"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arbitrage_spread_late.loc[\"2018-03-10 23:55:00\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-03-10 00:01:00     6.88\n",
       "2018-03-10 00:02:00    15.98\n",
       "2018-03-10 00:03:00    11.25\n",
       "2018-03-10 00:04:00     3.72\n",
       "2018-03-10 00:05:00     3.74\n",
       "                       ...  \n",
       "2018-03-10 22:40:00     6.79\n",
       "2018-03-10 23:04:00    12.68\n",
       "2018-03-10 23:05:00     6.27\n",
       "2018-03-10 23:15:00    13.77\n",
       "2018-03-10 23:17:00     1.37\n",
       "Name: Close, Length: 466, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arbitrage_spread_late[arbitrage_spread_late>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-03-10 00:00:00    -5.88\n",
       "2018-03-10 00:01:00     6.88\n",
       "2018-03-10 00:02:00    15.98\n",
       "2018-03-10 00:03:00    11.25\n",
       "2018-03-10 00:04:00     3.72\n",
       "                       ...  \n",
       "2018-03-10 23:55:00   -26.50\n",
       "2018-03-10 23:56:00   -29.68\n",
       "2018-03-10 23:57:00   -16.79\n",
       "2018-03-10 23:58:00   -19.39\n",
       "2018-03-10 23:59:00   -17.62\n",
       "Name: Close, Length: 1433, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arbitrage_spread_late=arbitrage_spread_late.dropna()\n",
    "arbitrage_spread_late"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Spread return for Late DATE - Mar 10 2018\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-03-10 00:01:00    0.000739\n",
       "2018-03-10 00:02:00    0.001715\n",
       "2018-03-10 00:03:00    0.001206\n",
       "2018-03-10 00:04:00    0.000399\n",
       "2018-03-10 00:05:00    0.000401\n",
       "                         ...   \n",
       "2018-03-10 22:40:00    0.000794\n",
       "2018-03-10 23:04:00    0.001477\n",
       "2018-03-10 23:05:00    0.000729\n",
       "2018-03-10 23:15:00    0.001592\n",
       "2018-03-10 23:17:00    0.000158\n",
       "Name: Close, Length: 466, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spread_return_late= arbitrage_spread_late[arbitrage_spread_late > 0] / coinbase_sliced[\"Close\"].loc[\"2018-03-10\"]\n",
    "\n",
    "# Review the spread return DataFrame\n",
    "print()\n",
    "print(\"Spread return for Late DATE - Mar 10 2018\")\n",
    "spread_return_late=spread_return_late.dropna()\n",
    "spread_return_late"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. For each of the three dates, narrow down your trading opportunities even further. To do so, determine the number of times your trades with positive returns exceed the 1% minimum threshold that you need to cover your costs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Profitable trades EARLY DATE\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-01-28 00:00:00    0.023999\n",
       "2018-01-28 00:01:00    0.023965\n",
       "2018-01-28 00:02:00    0.023392\n",
       "2018-01-28 00:03:00    0.023936\n",
       "2018-01-28 00:04:00    0.025610\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date early in the dataset, determine the number of times your trades with positive returns \n",
    "# exceed the 1% minimum threshold (.01) that you need to cover your costs\n",
    "profitable_trades_early = spread_return_early > .01\n",
    "\n",
    "# Review the first five profitable trades\n",
    "print()\n",
    "print(\"Profitable trades EARLY DATE\")\n",
    "profitable_trades_early = spread_return_early.loc[profitable_trades_early]\n",
    "profitable_trades_early.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Profitable trades MIDDLE DATE\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Series([], Name: Close, dtype: float64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profitable_trades_middle = spread_return_middle > .01\n",
    "\n",
    "# Review the first five profitable trades\n",
    "print()\n",
    "print(\"Profitable trades MIDDLE DATE\")\n",
    "profitable_trades_middle = spread_return_middle.loc[profitable_trades_middle]\n",
    "profitable_trades_middle.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Profitable trades late DATE\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Series([], Name: Close, dtype: float64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profitable_trades_late = spread_return_late > .01\n",
    "\n",
    "# Review the first five profitable trades\n",
    "print()\n",
    "print(\"Profitable trades late DATE\")\n",
    "profitable_trades_late = spread_return_late.loc[profitable_trades_late]\n",
    "profitable_trades_late.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of all Profitable Trades for all 3 dates\n",
      "\n",
      "Profitable trades EARLY DATE:  1378\n",
      "Profitable trades MIDDLE DATE:  0\n",
      "Profitable trades LATER DATE:  0\n"
     ]
    }
   ],
   "source": [
    "# Analysis of Profitable Trades all 3 dates\n",
    "print(\"Number of all Profitable Trades for all 3 dates\")\n",
    "print()\n",
    "print(\"Profitable trades EARLY DATE: \", len(profitable_trades_early))\n",
    "print(\"Profitable trades MIDDLE DATE: \", len(profitable_trades_middle))\n",
    "print(\"Profitable trades LATER DATE: \", len(profitable_trades_late))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Generate the summary statistics of your spread returns that are greater than 1%. How do the average returns compare among the three dates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EARLY DATE Profitable Trades\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1378.000000\n",
       "mean        0.022201\n",
       "std         0.005421\n",
       "min         0.010087\n",
       "25%         0.019016\n",
       "50%         0.022191\n",
       "75%         0.025413\n",
       "max         0.038510\n",
       "Name: Close, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MIDDLE DATE Profitable Trades\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    0.0\n",
       "mean     NaN\n",
       "std      NaN\n",
       "min      NaN\n",
       "25%      NaN\n",
       "50%      NaN\n",
       "75%      NaN\n",
       "max      NaN\n",
       "Name: Close, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "LATER DATE Profitable Trades\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    0.0\n",
       "mean     NaN\n",
       "std      NaN\n",
       "min      NaN\n",
       "25%      NaN\n",
       "50%      NaN\n",
       "75%      NaN\n",
       "max      NaN\n",
       "Name: Close, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For the date early in the dataset, generate the summary statistics for the profitable trades\n",
    "# or you trades where the spread returns are are greater than 1%\n",
    "\n",
    "print(\"EARLY DATE Profitable Trades\")\n",
    "print()\n",
    "display(profitable_trades_early.describe())\n",
    "print()\n",
    "print(\"MIDDLE DATE Profitable Trades\")\n",
    "print()\n",
    "display(profitable_trades_middle.describe())\n",
    "print()\n",
    "print(\"LATER DATE Profitable Trades\")\n",
    "print()\n",
    "display(profitable_trades_late.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There were only profitable returns on the early date."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. For each of the three dates, calculate the potential profit, in dollars, per trade. To do so, multiply the spread returns that were greater than 1% by the cost of what was purchased. Make sure to drop any missing values from the resulting DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profits per trade Early\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-01-28 00:00:00    275.38\n",
       "2018-01-28 00:01:00    275.00\n",
       "2018-01-28 00:02:00    268.77\n",
       "2018-01-28 00:03:00    275.03\n",
       "2018-01-28 00:04:00    294.26\n",
       "                        ...  \n",
       "2018-01-28 23:55:00    220.10\n",
       "2018-01-28 23:56:00    195.11\n",
       "2018-01-28 23:57:00    208.74\n",
       "2018-01-28 23:58:00    195.12\n",
       "2018-01-28 23:59:00    192.63\n",
       "Name: Close, Length: 1378, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1378"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Profits per trade Middle\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Series([], Name: Close, dtype: float64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Profits per trade Late\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Series([], Name: Close, dtype: float64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# For the date early in the dataset, calculate the potential profit per trade in dollars \n",
    "# Multiply the profitable trades by the cost of the Bitcoin that was purchased\n",
    "\n",
    "profit_per_trade_early = profitable_trades_early * coinbase_sliced[\"Close\"].loc[\"2018-01-28\"]\n",
    "profit_per_trade_middle = profitable_trades_middle * bitstamp_sliced[\"Close\"].loc[\"2018-02-26\"]\n",
    "profit_per_trade_late = profitable_trades_late * coinbase_sliced[\"Close\"].loc[\"2018-01-10\"]\n",
    "\n",
    "# Drop any missing values from the profit DataFrame\n",
    "\n",
    "profit_per_trade_early = profit_per_trade_early.dropna()\n",
    "profit_per_trade_middle = profit_per_trade_middle.dropna()\n",
    "profit_per_trade_late = profit_per_trade_late.dropna()\n",
    "\n",
    "# View the early profit DataFrame\n",
    "print(\"Profits per trade Early\")\n",
    "display(profit_per_trade_early,len(profit_per_trade_early))\n",
    "print()\n",
    "print(\"Profits per trade Middle\")\n",
    "display(profit_per_trade_middle,len(profit_per_trade_middle))\n",
    "print()\n",
    "print(\"Profits per trade Late\")\n",
    "display(profit_per_trade_late,len(profit_per_trade_late))\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Generate the summary statistics, and plot the results for each of the three DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early date profits per trade\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1378.000000\n",
       "mean      253.931996\n",
       "std        62.057953\n",
       "min       113.970000\n",
       "25%       217.272500\n",
       "50%       254.150000\n",
       "75%       292.987500\n",
       "max       439.010000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Middle date profits per trade\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    0.0\n",
       "mean     NaN\n",
       "std      NaN\n",
       "min      NaN\n",
       "25%      NaN\n",
       "50%      NaN\n",
       "75%      NaN\n",
       "max      NaN\n",
       "Name: Close, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Late date profits per trade\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    0.0\n",
       "mean     NaN\n",
       "std      NaN\n",
       "min      NaN\n",
       "25%      NaN\n",
       "50%      NaN\n",
       "75%      NaN\n",
       "max      NaN\n",
       "Name: Close, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate the summary statistics for the early profit per trade DataFr\n",
    "print(\"Early date profits per trade\")\n",
    "display(profit_per_trade_early.describe())\n",
    "print()\n",
    "print(\"Middle date profits per trade\")\n",
    "display(profit_per_trade_middle.describe())\n",
    "print()\n",
    "print(\"Late date profits per trade\")\n",
    "display(profit_per_trade_late.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Early date profit per trade'}, xlabel='hour'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results for the early profit per trade DataFrame\n",
    "profit_per_trade_early.plot(figsize=(15,7),\n",
    "                           title=\"Early date profit per trade\",\n",
    "                            color=\"orange\",\n",
    "                            label=\"$ profit\",\n",
    "                           xlabel=\"hour\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Middle date profit per trade'}, xlabel='hour'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "profit_per_trade_middle.plot(figsize=(15,7),\n",
    "                             title=\"Middle date profit per trade\",\n",
    "                            color=\"Red\",\n",
    "                            label=\"$ profit\",\n",
    "                           xlabel=\"hour\")\n",
    "\n",
    "                             \n",
    "                                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'late date profit per trade'}, xlabel='hour'>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "profit_per_trade_late.plot(figsize=(15,7),\n",
    "                             title=\"late date profit per trade\",\n",
    "                            color=\"Red\",\n",
    "                            label=\"$ profit\",\n",
    "                           xlabel=\"hour\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. Calculate the potential arbitrage profits that you can make on each day. To do so, sum the elements in the profit_per_trade DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Potential arbitrate profits: EARLY Date\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "349918.2900000001"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Potential arbitrate profits: MIDDLE Date\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Potential arbitrate profits: LATER Date\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Calculate the sum of the potential profits for the early profit per trade DataFrame\n",
    "print(\"Potential arbitrate profits: EARLY Date\")\n",
    "display(profit_per_trade_early.sum())\n",
    "print()\n",
    "print(\"Potential arbitrate profits: MIDDLE Date\")\n",
    "display(profit_per_trade_middle.sum())\n",
    "print()\n",
    "print(\"Potential arbitrate profits: LATER Date\")\n",
    "display(profit_per_trade_late.sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8. Using the `cumsum` function, plot the cumulative sum of each of the three DataFrames. Can you identify any patterns or trends in the profits across the three time periods?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-01-28 00:00:00       275.38\n",
       "2018-01-28 00:01:00       550.38\n",
       "2018-01-28 00:02:00       819.15\n",
       "2018-01-28 00:03:00      1094.18\n",
       "2018-01-28 00:04:00      1388.44\n",
       "                         ...    \n",
       "2018-01-28 23:55:00    349126.69\n",
       "2018-01-28 23:56:00    349321.80\n",
       "2018-01-28 23:57:00    349530.54\n",
       "2018-01-28 23:58:00    349725.66\n",
       "2018-01-28 23:59:00    349918.29\n",
       "Name: Close, Length: 1378, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the cumsum function to calculate the cumulative profits over time for the early profit per trade DataFrame\n",
    "cumulative_profit_early = profit_per_trade_early.cumsum()\n",
    "cumulative_profit_early"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Cumulative early date profit per trade'}, xlabel='hour'>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the cumulative sum of profits for the early profit per trade DataFrame\n",
    "cumulative_profit_early.plot(figsize=(15,7),\n",
    "                           title=\"Cumulative early date profit per trade\",\n",
    "                            color=\"orange\",\n",
    "                            label=\"$ profit\",\n",
    "                           xlabel=\"hour\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Close, dtype: float64)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_profit_middle = profit_per_trade_middle.cumsum()\n",
    "cumulative_profit_middle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Cumulative Middle date profit per trade'}, xlabel='hour'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VQUlemuSkJMcneVRVHb+k20lJjpv/nZLkZfP6G5L8bHffJcm9kjxlmW0BAABYpfWYyTshyYXdfVF3X5/kNUlOXtLn5CSn9+Q9SW5ZVYd39xXdfU6SdPfnkpyf5Mh1qAkAAGBTWo+Qd2SSSxaWL83XBrVd9qmqY5PcPcl716EmAACATWk9Ql4ts653p09V3SzJXyR5and/dtmDVJ1SVduqattVV121x8UCAACMbD1C3qVJjl5YPirJ5avtU1UHZwp4r+7uN+7sIN19Wndv7e6thx122DqUDQAAMJ71CHnvT3JcVd2xqg5J8sgkZyzpc0aSx8132bxXkmu7+4qqqiR/lOT87v7tdagFAABgU9uy1h109w1VdWqStyc5KMkruvu8qnry3P7yJGcmeVCSC5P8e5Inzpt/d5LHJvlwVX1wXvfM7j5zrXUBAABsRtW99PK5/d/WrVt727ZtG10GAADAhqiqs7t763Jt6/Jl6AAAAOwfhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYyLqEvKp6YFVdUFUXVtUzlmmvqnrR3H5uVd1jtdsCAACwemsOeVV1UJKXJjkpyfFJHlVVxy/pdlKS4+Z/pyR52W5sCwAAwCqtx0zeCUku7O6Luvv6JK9JcvKSPicnOb0n70lyy6o6fJXbAgAAsErrEfKOTHLJwvKl87rV9FnNtkmSqjqlqrZV1barrrpqzUUDAACMaD1CXi2zrlfZZzXbTiu7T+vurd299bDDDtvNEgEAADaHLeuwj0uTHL2wfFSSy1fZ55BVbAsAAMAqrcdM3vuTHFdVd6yqQ5I8MskZS/qckeRx810275Xk2u6+YpXbAgAAsEprnsnr7huq6tQkb09yUJJXdPd5VfXkuf3lSc5M8qAkFyb59yRPXGnbtdYEAACwWVX3spfA7de2bt3a27Zt2+gyAAAANkRVnd3dW5drW5cvQwcAAGD/IOQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIGsKeVV166o6q6o+Ov9/q530e2BVXVBVF1bVMxbW/2ZV/UtVnVtVb6qqW66lHgAAgM1urTN5z0jyzu4+Lsk75+UdVNVBSV6a5KQkxyd5VFUdPzefleSu3f1tST6S5BfWWA8AAMCmttaQd3KSV84/vzLJw5bpc0KSC7v7ou6+Pslr5u3S3e/o7hvmfu9JctQa6wEAANjU1hrybtfdVyTJ/P9tl+lzZJJLFpYvndct9aQkb1tjPQAAAJvall11qKq/SXL7ZZqetcpj1DLreskxnpXkhiSvXqGOU5KckiTHHHPMKg8NAACwuewy5HX3/XfWVlVXVtXh3X1FVR2e5JPLdLs0ydELy0cluXxhH49P8oNJ7tfdnZ3o7tOSnJYkW7du3Wk/AACAzWytp2uekeTx88+PT/KWZfq8P8lxVXXHqjokySPn7VJVD0zy9CQP7e5/X2MtAAAAm95aQ97zk5xYVR9NcuK8nKo6oqrOTJL5xiqnJnl7kvOTvK67z5u3f0mSmyc5q6o+WFUvX2M9AAAAm9ouT9dcSXdfk+R+y6y/PMmDFpbPTHLmMv2+aS3HBwAAYEdrnckDAABgPyLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBrCnlVdeuqOquqPjr/f6ud9HtgVV1QVRdW1TOWaf+5quqqOnQt9QAAAGx2a53Je0aSd3b3cUneOS/voKoOSvLSJCclOT7Jo6rq+IX2o5OcmORf11gLAADAprfWkHdyklfOP78yycOW6XNCkgu7+6Luvj7Ja+bttvudJE9L0musBQAAYNNba8i7XXdfkSTz/7ddps+RSS5ZWL50XpeqemiSy7r7Q2usAwAAgCRbdtWhqv4mye2XaXrWKo9Ry6zrqrrpvI8fWNVOqk5JckqSHHPMMas8NAAAwOayy5DX3fffWVtVXVlVh3f3FVV1eJJPLtPt0iRHLywfleTyJHdKcsckH6qq7evPqaoTuvsTy9RxWpLTkmTr1q1O7QQAAFjGWk/XPCPJ4+efH5/kLcv0eX+S46rqjlV1SJJHJjmjuz/c3bft7mO7+9hMYfAeywU8AAAAVmetIe/5SU6sqo9mukPm85Okqo6oqjOTpLtvSHJqkrcnOT/J67r7vDUeFwAAgGXs8nTNlXT3NUnut8z6y5M8aGH5zCRn7mJfx66lFgAAANY+kwcAAMB+RMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABhIdfdG17DbquqqJB/f6DpYs0OTXL3RRTAs44u9yfhibzPG2JuMrzHcobsPW67hgAx5jKGqtnX31o2ugzEZX+xNxhd7mzHG3mR8jc/pmgAAAAMR8gAAAAYi5LGRTtvoAhia8cXeZHyxtxlj7E3G1+BckwcAADAQM3kAAAADEfIAAGBAVVUbXQMbQ8hjr6mqZb+3A9ZDVX39RtfAuKrqllV1z6rastG1MJ6qukVVfXtVHbzRtTCeeXz916o6uLtb0NuchDz2iqr65iRXVtX/mJf9gmHdVNWzk5xZVc+oqu+b1/l9xrqoqlOSXJDkOUlOq6rjN7YiRlJVP59kW5JfTfKKqjpig0tiPM9L8ptJnrDBdbCBvClib7lJksuTPKqqbtXu8MM6qKpjq+rMJMcmeXqSSvLkqrp5d395Q4tjCFV1kyT3TnKf7n5wksuSnFJVd53bfWDFHquqJyS5b6bx9ZAkX5/kPnObscWaVNVB848XJnlhkh+sqm+cZ/MO2vmWjEjIY11sP+VkYTblRkl+Jsn/S/Irc5s/YOyRhVOaPpfkTd39pO5+T5J3JPlipjdKsEeq6vbbf3d19+eTfFeS283Npyf5VJIfmdt9YMVuWRxfSc5K8t+6+xPz8ruTnJwYW+yZJb+//mNe/S1J/jXTjPFPLGljkxDyWJOquk1VvSzJ86rqbguzKd+S5DuTPCXJA+ZT6r5to+rkwLRkfN2ju69J8sqFN0xXJrlLkn/bsCI5YFXVwVX1kiT/O8nvV9WPzU2vSPKwJOnujyZ5X5JbVdXdNqJODkxLxtdpVfUj3X1Zd//rwoeet07yjxtXJQeqZX5/PWKh+aok70zy6iR3r6pXVNX37/sq2UhCHnusqm6c5I+TXJfplKZfqqofn5v/Ncm27v5sptM2/y7J/TakUA5Iy4yvX6yqJ3X39QsfJtw5ycXdLeSxJx6S5JjuPi7Jm5M8s6q+JVOou1lVnTj3+0iSI5J8YUOq5EC1OL7elOSX5/GVfPX911FJPrHcxrALS39//VJV3WVuu0WS2yc5KckJ8z8fJmwyQh5rcXiS23X3z3X3CzOd1nTPqrp3ptPnfr2q3pvk2kw3MThnwyrlQLSz8XXiQp87ZHoDnqo60UwLq7Ewi3JDkquTpLv/KslfJXlSpg8VPpTkx6tqS3dflOSmmcYkrGiF8fXWJD85X6e+/dS5w5L8XVV9b1X9clUZY6xoFePriEzvu96S6YyEH0/yz0l+dJ8Xy4YS8liV5a6n6+6PJbm2qh42r3p3kn9J8vBMF/3+Q5LndfdDM93p6b+78Jfl7Ob4esA8y5ckd0/yDVX1iiRPS+KaA5a1+FUIC9c+fV2Sa6rqyHn5N5JsTXLbJH+Y6W/kn1fV2zJ9cPWRfVcxB5JVjq/fTPKfMl+6UFXfnunaz1cm+a1MZ79csc+K5oCxG+PrrkmOznQK5+O6+8TufnOS12a6RwKbiJDHLlXVIYsXhG9/Q15VhyR5Y5L7VdXXdfcnM336vSXJl7v7cd39liTp7j/t7h914S9L7eb4OjfT+Lr9HPS+N8kPJHn//Mfsw/v+EbA/q6otVfVbSV5QVfdf0vyuTNcP320eY1cn+dskT+vu65M8JsmLk7yxu7+vuy/bl7Wz/9uD8fXOJD83tx88t5/V3Sd091v3Vd0cGPZgfP1dpt9fb+/ud8z7qO5+U3e/d58Wz4YT8lhRTd9z976qem5VPXSxbX4T9IFM4+gx8+r3Jvn+zDMq22fu3FmT5ezh+Pq+qbm/kOkW0Vu7+2X7rmoOFPPvnRdlujblfUmeXlVPqaqvS5LuvirJmUn+S5K7zZu9JslVVXXj+frPf+juP9j31bO/28Px9dpM42tLpjMTbtfdL97XtbP/28Px9edJPlNVh2x/3+WurZvXll13YbOa74j5mEzXqNw5ybOr6prufndVHdzdX8o0c/e2TBeUfzjTG/KrM31C+ZVb9volw1JrHF/b/8j9yYYUz4Hi5pne/Dyguz9XVVcneVCmr0N4VZJ098uq6llJnlZV/5TkUUneMn+IACvZ0/F1RnffEHcFZmVr+f11/QbVzH7ETB4rOTTJX3f3Od39mkzXqPxeknT3l+Y34l/o7r9M8vtJfjLTtQUv627nfrMraxlfro1il+a7+16c5Anzqndnmh2+93xzgu1ekOR3Ml3L8rvd/dx9WCYHqDWMr1/eh2VygPL7i7US8ljJjTJd85Qk6e6XJ0lVnTIvf2n7xcDdfVqSn+zub+7uN25EsRxwjC/2hTdlumbl8PmrNs5N8sVMHzKkqr47SXX3P3b3T3f36RtYKwce44u9yfhijwl5fI3t19F19+sz3eDixxaan5np/O/t54v//nzaXZzexGoYX+xj/5jkmsyfhnf3OZm+M+rGVfWdSY5L0q4bZg8ZX+xNxhd7zDV5fMX8S6K2X0dXVbfJdArdc6vqjd39+SRXJDm/pu+OuqGqfqa7r93AsjlAGF9shO6+oqrenOT5VXVhkvcnuT7Jl7r7A5lu5gN7xPhibzK+WItyPwySaXZl4c337ZP8epLvSHL/JM9P8qUkf5Ppgt/PdvdPbFStHHiMLzZaVZ2UaXx9V5KXdPdLNrgkBmJ8sTcZX+wJIY8dzHdpekSSN2T6IvMbquoWSb4nyROTnN/dv7iRNXLgMr7YSFV1cKab/d6w0bUwHuOLvcn4YncJeSRJqurIJH+Z5MNJntndl87rt9/KfoefYXcYXwAA+46QR5KvXC91j+4+e16+UaZPjAwQ1sz4AgDYd4Q8drBwc4wvb3QtjMf4AgDY+4Q8AACAgfiePAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AZlV1bFX980bXAQBrIeQBwF5UVVs2ugYANhchDwB2dFBV/UFVnVdV76iqm1TV3arqPVV1blW9qapulSRV9a6q2jr/fGhVXTz//ISqen1VvTXJOzbuoQCwGQl5ALCj45K8tLu/NclnkvxQktOTPL27vy3Jh5P80ir2c+8kj+/u++6tQgFgOUIeAOzoY939wfnns5PcKcktu/vv53WvTPK9q9jPWd39qb1QHwCsSMgDgB19ceHn/0hyyxX63pCv/i298ZK269axJgBYNSEPAFZ2bZJPV9V95uXHJtk+q3dxku+Yf/7hfVwXACzLHb8AYNcen+TlVXXTJBcleeK8/reSvK6qHpvkbzeqOABYVN290TUAAACwTpyuCQAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABjI/wdtPirm6sbfXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumulative_profit_middle.plot(figsize=(15,7),\n",
    "                           title=\"Cumulative Middle date profit per trade\",\n",
    "                            color=\"red\",\n",
    "                            label=\"$ profit\",\n",
    "                           xlabel=\"hour\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Close, dtype: float64)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_profit_late = profit_per_trade_late.cumsum()\n",
    "cumulative_profit_late"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Cumulative late date profit per trade'}, xlabel='hour'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumulative_profit_late.plot(figsize=(15,7),\n",
    "                           title=\"Cumulative late date profit per trade\",\n",
    "                            color=\"red\",\n",
    "                            label=\"$ profit\",\n",
    "                           xlabel=\"hour\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** After reviewing the profit information across each date from the different time periods, can you identify any patterns or trends?\n",
    "    \n",
    "**Answer:** After review of the 3 different dates. We can only find opportunities for profit on the Early Date, closer to the end of January."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}