local-fx-market-inefficiency.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Existence of Local Market Inefficiencies in local currency exchange desks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gather and load FX rates from the several local brick-and-mortar FX desks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import prerequisites\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "\n",
    "\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "shop_a_rates = pd.read_csv(\"fx_data/shop_a.csv\")    # Kowloon region\n",
    "shop_b_rates = pd.read_csv(\"fx_data/shop_b.csv\")    # Kowloon region\n",
    "shop_c_rates = pd.read_csv(\"fx_data/shop_c.csv\")    # Kowloon region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename columns \n",
    "shop_a_rates.rename(columns={\"buy_price\": \"shop_a_buy\", \"sell_price\": \"shop_a_sell\"}, inplace=True) # Shop A\n",
    "\n",
    "shop_b_rates.rename(columns={\"buy_price\": \"shop_b_buy\", \"sell_price\": \"shop_b_sell\"}, inplace=True) # Shop B\n",
    "\n",
    "shop_c_rates.rename(columns={\"buy_price\": \"shop_c_buy\", \"sell_price\": \"shop_c_sell\"}, inplace=True) # Shop C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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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>date</th>\n",
       "      <th>pair</th>\n",
       "      <th>shop_a_buy</th>\n",
       "      <th>shop_a_sell</th>\n",
       "      <th>shop_b_buy</th>\n",
       "      <th>shop_b_sell</th>\n",
       "      <th>shop_c_buy</th>\n",
       "      <th>shop_c_sell</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-AED</td>\n",
       "      <td>1.9200</td>\n",
       "      <td>2.19000</td>\n",
       "      <td>2.10000</td>\n",
       "      <td>2.16000</td>\n",
       "      <td>2.1000</td>\n",
       "      <td>2.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-AUD</td>\n",
       "      <td>5.0300</td>\n",
       "      <td>5.29000</td>\n",
       "      <td>5.14000</td>\n",
       "      <td>5.21000</td>\n",
       "      <td>5.1100</td>\n",
       "      <td>5.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-CAD</td>\n",
       "      <td>5.5600</td>\n",
       "      <td>5.81000</td>\n",
       "      <td>5.67000</td>\n",
       "      <td>5.75000</td>\n",
       "      <td>5.6300</td>\n",
       "      <td>5.7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-CHF</td>\n",
       "      <td>8.3100</td>\n",
       "      <td>8.83000</td>\n",
       "      <td>8.62000</td>\n",
       "      <td>8.72000</td>\n",
       "      <td>8.6200</td>\n",
       "      <td>8.7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-CNY</td>\n",
       "      <td>0.9470</td>\n",
       "      <td>0.93500</td>\n",
       "      <td>1.05932</td>\n",
       "      <td>1.07066</td>\n",
       "      <td>1.0630</td>\n",
       "      <td>1.0680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-EUR</td>\n",
       "      <td>8.1800</td>\n",
       "      <td>8.45000</td>\n",
       "      <td>8.31000</td>\n",
       "      <td>8.39000</td>\n",
       "      <td>8.2900</td>\n",
       "      <td>8.3700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-GBP</td>\n",
       "      <td>9.6800</td>\n",
       "      <td>9.99000</td>\n",
       "      <td>9.79000</td>\n",
       "      <td>9.93000</td>\n",
       "      <td>9.8000</td>\n",
       "      <td>9.9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-JPY</td>\n",
       "      <td>0.0475</td>\n",
       "      <td>0.04929</td>\n",
       "      <td>0.04830</td>\n",
       "      <td>0.04890</td>\n",
       "      <td>0.0482</td>\n",
       "      <td>0.0490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-KRW</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.00568</td>\n",
       "      <td>0.00555</td>\n",
       "      <td>0.00575</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-MYR</td>\n",
       "      <td>1.6000</td>\n",
       "      <td>1.70500</td>\n",
       "      <td>1.64000</td>\n",
       "      <td>1.70000</td>\n",
       "      <td>1.6400</td>\n",
       "      <td>1.6850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-NZD</td>\n",
       "      <td>4.5800</td>\n",
       "      <td>4.85000</td>\n",
       "      <td>4.69000</td>\n",
       "      <td>4.79000</td>\n",
       "      <td>4.6800</td>\n",
       "      <td>4.7700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-PHP</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>0.13800</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>0.13600</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.1345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-SAR</td>\n",
       "      <td>1.9500</td>\n",
       "      <td>2.14800</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.10000</td>\n",
       "      <td>2.0500</td>\n",
       "      <td>2.0900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-SGD</td>\n",
       "      <td>5.5800</td>\n",
       "      <td>5.86000</td>\n",
       "      <td>5.71000</td>\n",
       "      <td>5.79000</td>\n",
       "      <td>5.6900</td>\n",
       "      <td>5.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-THB</td>\n",
       "      <td>0.2070</td>\n",
       "      <td>0.21900</td>\n",
       "      <td>0.21000</td>\n",
       "      <td>0.21800</td>\n",
       "      <td>0.2120</td>\n",
       "      <td>0.2150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-USD</td>\n",
       "      <td>7.7000</td>\n",
       "      <td>7.81800</td>\n",
       "      <td>7.77000</td>\n",
       "      <td>7.81000</td>\n",
       "      <td>7.7850</td>\n",
       "      <td>7.8000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date     pair  shop_a_buy  shop_a_sell  shop_b_buy  shop_b_sell  \\\n",
       "0   2024-06-28  HKD-AED      1.9200      2.19000     2.10000      2.16000   \n",
       "1   2024-06-28  HKD-AUD      5.0300      5.29000     5.14000      5.21000   \n",
       "2   2024-06-28  HKD-CAD      5.5600      5.81000     5.67000      5.75000   \n",
       "3   2024-06-28  HKD-CHF      8.3100      8.83000     8.62000      8.72000   \n",
       "4   2024-06-28  HKD-CNY      0.9470      0.93500     1.05932      1.07066   \n",
       "5   2024-06-28  HKD-EUR      8.1800      8.45000     8.31000      8.39000   \n",
       "6   2024-06-28  HKD-GBP      9.6800      9.99000     9.79000      9.93000   \n",
       "7   2024-06-28  HKD-JPY      0.0475      0.04929     0.04830      0.04890   \n",
       "8   2024-06-28  HKD-KRW      0.0054      0.00568     0.00555      0.00575   \n",
       "9   2024-06-28  HKD-MYR      1.6000      1.70500     1.64000      1.70000   \n",
       "10  2024-06-28  HKD-NZD      4.5800      4.85000     4.69000      4.79000   \n",
       "11  2024-06-28  HKD-PHP      0.1230      0.13800     0.13000      0.13600   \n",
       "12  2024-06-28  HKD-SAR      1.9500      2.14800     2.00000      2.10000   \n",
       "13  2024-06-28  HKD-SGD      5.5800      5.86000     5.71000      5.79000   \n",
       "14  2024-06-28  HKD-THB      0.2070      0.21900     0.21000      0.21800   \n",
       "15  2024-06-28  HKD-USD      7.7000      7.81800     7.77000      7.81000   \n",
       "\n",
       "    shop_c_buy  shop_c_sell  \n",
       "0       2.1000       2.1300  \n",
       "1       5.1100       5.2000  \n",
       "2       5.6300       5.7300  \n",
       "3       8.6200       8.7300  \n",
       "4       1.0630       1.0680  \n",
       "5       8.2900       8.3700  \n",
       "6       9.8000       9.9200  \n",
       "7       0.0482       0.0490  \n",
       "8       0.0055       0.0057  \n",
       "9       1.6400       1.6850  \n",
       "10      4.6800       4.7700  \n",
       "11      0.1300       0.1345  \n",
       "12      2.0500       2.0900  \n",
       "13      5.6900       5.7800  \n",
       "14      0.2120       0.2150  \n",
       "15      7.7850       7.8000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge rates\n",
    "merged_rates = pd.merge(shop_a_rates, shop_b_rates, on=[\"pair_label\", \"date_label\"])\n",
    "merged_rates = pd.merge(merged_rates, shop_c_rates, on=[\"pair_label\", \"date_label\"])\n",
    "\n",
    "merged_rates.rename(columns={\"date_label\": \"date\", \"pair_label\": \"pair\"}, inplace=True)\n",
    "\n",
    "merged_rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A total of 16 FX pairs were found to be in all 4 datasets. The datasets were filtered to ensure they all had the same 16 FX pairs to ensure consistency. \n",
    "\n",
    "As shown above in the table, the buy and sell prices of each shop is represented by a column with each row representing a FX pair. A single day screenshot was taken and 3 shops were included. They have been anonymized for this project. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HKD-AED',\n",
       " 'HKD-AUD',\n",
       " 'HKD-CAD',\n",
       " 'HKD-CHF',\n",
       " 'HKD-CNY',\n",
       " 'HKD-EUR',\n",
       " 'HKD-GBP',\n",
       " 'HKD-JPY',\n",
       " 'HKD-KRW',\n",
       " 'HKD-MYR',\n",
       " 'HKD-NZD',\n",
       " 'HKD-PHP',\n",
       " 'HKD-SAR',\n",
       " 'HKD-SGD',\n",
       " 'HKD-THB',\n",
       " 'HKD-USD']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get available matching pairs\n",
    "available_pairs = list(merged_rates['pair'].unique())\n",
    "available_pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We extract all pairs available in the price table for later use for calculating arbitrage opportunities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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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>date</th>\n",
       "      <th>pair</th>\n",
       "      <th>shop_a_buy</th>\n",
       "      <th>shop_a_sell</th>\n",
       "      <th>shop_b_buy</th>\n",
       "      <th>shop_b_sell</th>\n",
       "      <th>shop_c_buy</th>\n",
       "      <th>shop_c_sell</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-JPY</td>\n",
       "      <td>0.0475</td>\n",
       "      <td>0.04929</td>\n",
       "      <td>0.0483</td>\n",
       "      <td>0.0489</td>\n",
       "      <td>0.0482</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date     pair  shop_a_buy  shop_a_sell  shop_b_buy  shop_b_sell  \\\n",
       "7  2024-06-28  HKD-JPY      0.0475      0.04929      0.0483       0.0489   \n",
       "\n",
       "   shop_c_buy  shop_c_sell  \n",
       "7      0.0482        0.049  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter for an FX pair\n",
    "merged_rates.loc[merged_rates['pair'] == \"HKD-JPY\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above line is used to filter out a row in the price table based on the selected FX trading pair. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate arbitrage opportunities within the localized market"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get best arbitrage opportunity for a pair\n",
    "def get_best_rates(pair):\n",
    "\n",
    "    # Note: buy and sell are reversed because SHOPS SELL and you BUY. You're goal is to BUY at LOWEST price and SELL at HIGHEST\n",
    "\n",
    "    # Filter merged rates to be buy or sell\n",
    "    shop_sell_rates = merged_rates[['shop_a_sell', 'shop_b_sell', 'shop_c_sell']]\n",
    "    shop_buy_rates = merged_rates[['shop_a_buy', 'shop_b_buy', 'shop_c_buy']]\n",
    "\n",
    "    # Rename shop column names to be less confusing\n",
    "    with warnings.catch_warnings():\n",
    "        warnings.simplefilter(\"ignore\")\n",
    "        shop_sell_rates.rename(columns={\"shop_a_sell\": \"shop_a\", \"shop_b_sell\": \"shop_b\", \"shop_c_sell\": \"shop_c\"}, inplace=True)\n",
    "        shop_buy_rates.rename(columns={\"shop_a_buy\": \"shop_a\", \"shop_b_buy\": \"shop_b\", \"shop_c_buy\": \"shop_c\"}, inplace=True)\n",
    "\n",
    "\n",
    "    # Calculate\n",
    "    lowest_buy_shop = shop_sell_rates.loc[merged_rates['pair'] == pair].min().idxmin()\n",
    "    lowest_buy = shop_sell_rates.loc[merged_rates['pair'] == pair].min().min()\n",
    "\n",
    "    highest_sell_shop = shop_buy_rates.loc[merged_rates['pair'] == pair].max().idxmax()\n",
    "    highest_sell = shop_buy_rates.loc[merged_rates['pair'] == pair].max().max()\n",
    "\n",
    "    difference = highest_sell - lowest_buy\n",
    "\n",
    "    return lowest_buy_shop, lowest_buy, highest_sell_shop, highest_sell, difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HKD-AED ('shop_c', 2.13, 'shop_b', 2.1, -0.029999999999999805)\n",
      "HKD-AUD ('shop_c', 5.2, 'shop_b', 5.14, -0.0600000000000005)\n",
      "HKD-CAD ('shop_c', 5.73, 'shop_b', 5.67, -0.0600000000000005)\n",
      "HKD-CHF ('shop_b', 8.72, 'shop_b', 8.62, -0.10000000000000142)\n",
      "HKD-CNY ('shop_a', 0.935, 'shop_c', 1.063, 0.1279999999999999)\n",
      "HKD-EUR ('shop_c', 8.37, 'shop_b', 8.31, -0.05999999999999872)\n",
      "HKD-GBP ('shop_c', 9.92, 'shop_c', 9.8, -0.11999999999999922)\n",
      "HKD-JPY ('shop_b', 0.0489, 'shop_b', 0.0483, -0.0005999999999999964)\n",
      "HKD-KRW ('shop_a', 0.00568, 'shop_b', 0.00555, -0.0001299999999999999)\n",
      "HKD-MYR ('shop_c', 1.685, 'shop_b', 1.64, -0.04500000000000015)\n",
      "HKD-NZD ('shop_c', 4.77, 'shop_b', 4.69, -0.07999999999999918)\n",
      "HKD-PHP ('shop_c', 0.1345, 'shop_b', 0.13, -0.004500000000000004)\n",
      "HKD-SAR ('shop_c', 2.09, 'shop_c', 2.05, -0.040000000000000036)\n",
      "HKD-SGD ('shop_c', 5.78, 'shop_b', 5.71, -0.07000000000000028)\n",
      "HKD-THB ('shop_c', 0.215, 'shop_c', 0.212, -0.0030000000000000027)\n",
      "HKD-USD ('shop_c', 7.8, 'shop_c', 7.785, -0.01499999999999968)\n"
     ]
    }
   ],
   "source": [
    "# Get most profitable opportunities for each pair\n",
    "for pair in available_pairs:\n",
    "    print(pair, get_best_rates(pair))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After creating a function to calculate the best prices and the maximum profit for a target FX pair, we print the results by iterating through each currency pair in the price table.\n",
    "\n",
    "It seems for the date 2024-06-28, there are no currency pairs that allow a profitable arbitrage opportunity. This does hint that there could be an existent inefficient local market. There is a slight variation in the exchange rate as well for both buy and sell, which is different than the midmarket rate shown in later sections of the notebook. However, this does end up indicating whether the market is inefficient or efficient as a spread is established for profit margins and risk."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare market rates with local FX shop rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>date_label</th>\n",
       "      <th>pair_label</th>\n",
       "      <th>market_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-AED</td>\n",
       "      <td>2.12635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-AUD</td>\n",
       "      <td>5.19130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-CAD</td>\n",
       "      <td>5.70260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-CHF</td>\n",
       "      <td>8.69519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-CNY</td>\n",
       "      <td>1.07431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-EUR</td>\n",
       "      <td>8.36157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-GBP</td>\n",
       "      <td>9.87990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-JPY</td>\n",
       "      <td>0.04858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-KRW</td>\n",
       "      <td>0.00563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-MYR</td>\n",
       "      <td>1.65464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-NZD</td>\n",
       "      <td>4.75408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-PHP</td>\n",
       "      <td>0.13310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-SAR</td>\n",
       "      <td>2.08240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-SGD</td>\n",
       "      <td>5.75076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-THB</td>\n",
       "      <td>0.21211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-USD</td>\n",
       "      <td>7.80901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    date_label pair_label  market_price\n",
       "0   2024-06-28    HKD-AED       2.12635\n",
       "1   2024-06-28    HKD-AUD       5.19130\n",
       "2   2024-06-28    HKD-CAD       5.70260\n",
       "3   2024-06-28    HKD-CHF       8.69519\n",
       "4   2024-06-28    HKD-CNY       1.07431\n",
       "5   2024-06-28    HKD-EUR       8.36157\n",
       "6   2024-06-28    HKD-GBP       9.87990\n",
       "7   2024-06-28    HKD-JPY       0.04858\n",
       "8   2024-06-28    HKD-KRW       0.00563\n",
       "9   2024-06-28    HKD-MYR       1.65464\n",
       "10  2024-06-28    HKD-NZD       4.75408\n",
       "11  2024-06-28    HKD-PHP       0.13310\n",
       "12  2024-06-28    HKD-SAR       2.08240\n",
       "13  2024-06-28    HKD-SGD       5.75076\n",
       "14  2024-06-28    HKD-THB       0.21211\n",
       "15  2024-06-28    HKD-USD       7.80901"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import FX price data\n",
    "market_rates = pd.read_csv(\"fx_data/market_rates.csv\")\n",
    "\n",
    "market_rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to pull the data for the midmarket rates to compare with the local FX shop rates. \n",
    "\n",
    "#### Midmarket Rates?\n",
    "Midmarket rates are the rates that are calculated by merging buy and sell prices together. No FX platform or trading desk can give the midmarket rate as it is mostly only an indicator of the fair value of the traded currency pair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>pair</th>\n",
       "      <th>shop_a_buy</th>\n",
       "      <th>shop_a_sell</th>\n",
       "      <th>shop_b_buy</th>\n",
       "      <th>shop_b_sell</th>\n",
       "      <th>shop_c_buy</th>\n",
       "      <th>shop_c_sell</th>\n",
       "      <th>market_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-AED</td>\n",
       "      <td>1.9200</td>\n",
       "      <td>2.19000</td>\n",
       "      <td>2.10000</td>\n",
       "      <td>2.16000</td>\n",
       "      <td>2.1000</td>\n",
       "      <td>2.1300</td>\n",
       "      <td>2.12635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-AUD</td>\n",
       "      <td>5.0300</td>\n",
       "      <td>5.29000</td>\n",
       "      <td>5.14000</td>\n",
       "      <td>5.21000</td>\n",
       "      <td>5.1100</td>\n",
       "      <td>5.2000</td>\n",
       "      <td>5.19130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-CAD</td>\n",
       "      <td>5.5600</td>\n",
       "      <td>5.81000</td>\n",
       "      <td>5.67000</td>\n",
       "      <td>5.75000</td>\n",
       "      <td>5.6300</td>\n",
       "      <td>5.7300</td>\n",
       "      <td>5.70260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-CHF</td>\n",
       "      <td>8.3100</td>\n",
       "      <td>8.83000</td>\n",
       "      <td>8.62000</td>\n",
       "      <td>8.72000</td>\n",
       "      <td>8.6200</td>\n",
       "      <td>8.7300</td>\n",
       "      <td>8.69519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-CNY</td>\n",
       "      <td>0.9470</td>\n",
       "      <td>0.93500</td>\n",
       "      <td>1.05932</td>\n",
       "      <td>1.07066</td>\n",
       "      <td>1.0630</td>\n",
       "      <td>1.0680</td>\n",
       "      <td>1.07431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-EUR</td>\n",
       "      <td>8.1800</td>\n",
       "      <td>8.45000</td>\n",
       "      <td>8.31000</td>\n",
       "      <td>8.39000</td>\n",
       "      <td>8.2900</td>\n",
       "      <td>8.3700</td>\n",
       "      <td>8.36157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-GBP</td>\n",
       "      <td>9.6800</td>\n",
       "      <td>9.99000</td>\n",
       "      <td>9.79000</td>\n",
       "      <td>9.93000</td>\n",
       "      <td>9.8000</td>\n",
       "      <td>9.9200</td>\n",
       "      <td>9.87990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-JPY</td>\n",
       "      <td>0.0475</td>\n",
       "      <td>0.04929</td>\n",
       "      <td>0.04830</td>\n",
       "      <td>0.04890</td>\n",
       "      <td>0.0482</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.04858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-KRW</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.00568</td>\n",
       "      <td>0.00555</td>\n",
       "      <td>0.00575</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>0.00563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-MYR</td>\n",
       "      <td>1.6000</td>\n",
       "      <td>1.70500</td>\n",
       "      <td>1.64000</td>\n",
       "      <td>1.70000</td>\n",
       "      <td>1.6400</td>\n",
       "      <td>1.6850</td>\n",
       "      <td>1.65464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-NZD</td>\n",
       "      <td>4.5800</td>\n",
       "      <td>4.85000</td>\n",
       "      <td>4.69000</td>\n",
       "      <td>4.79000</td>\n",
       "      <td>4.6800</td>\n",
       "      <td>4.7700</td>\n",
       "      <td>4.75408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-PHP</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>0.13800</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>0.13600</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.1345</td>\n",
       "      <td>0.13310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-SAR</td>\n",
       "      <td>1.9500</td>\n",
       "      <td>2.14800</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.10000</td>\n",
       "      <td>2.0500</td>\n",
       "      <td>2.0900</td>\n",
       "      <td>2.08240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-SGD</td>\n",
       "      <td>5.5800</td>\n",
       "      <td>5.86000</td>\n",
       "      <td>5.71000</td>\n",
       "      <td>5.79000</td>\n",
       "      <td>5.6900</td>\n",
       "      <td>5.7800</td>\n",
       "      <td>5.75076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-THB</td>\n",
       "      <td>0.2070</td>\n",
       "      <td>0.21900</td>\n",
       "      <td>0.21000</td>\n",
       "      <td>0.21800</td>\n",
       "      <td>0.2120</td>\n",
       "      <td>0.2150</td>\n",
       "      <td>0.21211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2024-06-28</td>\n",
       "      <td>HKD-USD</td>\n",
       "      <td>7.7000</td>\n",
       "      <td>7.81800</td>\n",
       "      <td>7.77000</td>\n",
       "      <td>7.81000</td>\n",
       "      <td>7.7850</td>\n",
       "      <td>7.8000</td>\n",
       "      <td>7.80901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date     pair  shop_a_buy  shop_a_sell  shop_b_buy  shop_b_sell  \\\n",
       "0   2024-06-28  HKD-AED      1.9200      2.19000     2.10000      2.16000   \n",
       "1   2024-06-28  HKD-AUD      5.0300      5.29000     5.14000      5.21000   \n",
       "2   2024-06-28  HKD-CAD      5.5600      5.81000     5.67000      5.75000   \n",
       "3   2024-06-28  HKD-CHF      8.3100      8.83000     8.62000      8.72000   \n",
       "4   2024-06-28  HKD-CNY      0.9470      0.93500     1.05932      1.07066   \n",
       "5   2024-06-28  HKD-EUR      8.1800      8.45000     8.31000      8.39000   \n",
       "6   2024-06-28  HKD-GBP      9.6800      9.99000     9.79000      9.93000   \n",
       "7   2024-06-28  HKD-JPY      0.0475      0.04929     0.04830      0.04890   \n",
       "8   2024-06-28  HKD-KRW      0.0054      0.00568     0.00555      0.00575   \n",
       "9   2024-06-28  HKD-MYR      1.6000      1.70500     1.64000      1.70000   \n",
       "10  2024-06-28  HKD-NZD      4.5800      4.85000     4.69000      4.79000   \n",
       "11  2024-06-28  HKD-PHP      0.1230      0.13800     0.13000      0.13600   \n",
       "12  2024-06-28  HKD-SAR      1.9500      2.14800     2.00000      2.10000   \n",
       "13  2024-06-28  HKD-SGD      5.5800      5.86000     5.71000      5.79000   \n",
       "14  2024-06-28  HKD-THB      0.2070      0.21900     0.21000      0.21800   \n",
       "15  2024-06-28  HKD-USD      7.7000      7.81800     7.77000      7.81000   \n",
       "\n",
       "    shop_c_buy  shop_c_sell  market_price  \n",
       "0       2.1000       2.1300       2.12635  \n",
       "1       5.1100       5.2000       5.19130  \n",
       "2       5.6300       5.7300       5.70260  \n",
       "3       8.6200       8.7300       8.69519  \n",
       "4       1.0630       1.0680       1.07431  \n",
       "5       8.2900       8.3700       8.36157  \n",
       "6       9.8000       9.9200       9.87990  \n",
       "7       0.0482       0.0490       0.04858  \n",
       "8       0.0055       0.0057       0.00563  \n",
       "9       1.6400       1.6850       1.65464  \n",
       "10      4.6800       4.7700       4.75408  \n",
       "11      0.1300       0.1345       0.13310  \n",
       "12      2.0500       2.0900       2.08240  \n",
       "13      5.6900       5.7800       5.75076  \n",
       "14      0.2120       0.2150       0.21211  \n",
       "15      7.7850       7.8000       7.80901  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge market rates with local rates\n",
    "\n",
    "market_rates.rename(columns={\"pair_label\": \"pair\", \"date_label\": \"date\"}, inplace=True)   # Rename label to match merged rates df\n",
    "\n",
    "merged_rates = pd.merge(merged_rates, market_rates, on=[\"date\", \"pair\"])\n",
    "merged_rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After merging the midmarket rate (\"market_price\") into the price table, we notice at a glance that the rate is quite favourable for one party in some cases. Let's dive deeper and see how much opportunity there is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "#T_ca31f_row1_col5 {\n",
       "  background-color: #96b6d7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row1_col6, #T_ca31f_row3_col2, #T_ca31f_row3_col3, #T_ca31f_row6_col4, #T_ca31f_row6_col5, #T_ca31f_row6_col7 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row1_col7, #T_ca31f_row2_col4 {\n",
       "  background-color: #abbfdc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row2_col2 {\n",
       "  background-color: #a8bedc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row2_col3 {\n",
       "  background-color: #045382;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row2_col5 {\n",
       "  background-color: #03456c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row2_col6, #T_ca31f_row3_col7 {\n",
       "  background-color: #045585;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row3_col4 {\n",
       "  background-color: #046097;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row3_col5 {\n",
       "  background-color: #65a3cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row3_col6 {\n",
       "  background-color: #034c78;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row4_col2 {\n",
       "  background-color: #b5c4df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row4_col3, #T_ca31f_row4_col5, #T_ca31f_row7_col2, #T_ca31f_row7_col4, #T_ca31f_row7_col6, #T_ca31f_row8_col2, #T_ca31f_row8_col4, #T_ca31f_row8_col6, #T_ca31f_row14_col6, #T_ca31f_row15_col7 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row4_col4 {\n",
       "  background-color: #e3e0ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row4_col6 {\n",
       "  background-color: #e9e5f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row4_col7 {\n",
       "  background-color: #f7f0f7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row5_col2 {\n",
       "  background-color: #80aed2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row5_col3 {\n",
       "  background-color: #046299;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row5_col4 {\n",
       "  background-color: #509ac6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row5_col5 {\n",
       "  background-color: #4496c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row5_col6 {\n",
       "  background-color: #04588a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row5_col7 {\n",
       "  background-color: #adc1dd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row6_col2 {\n",
       "  background-color: #6ba5cd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row6_col3, #T_ca31f_row13_col3 {\n",
       "  background-color: #03517e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row6_col6 {\n",
       "  background-color: #023c5f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row7_col3, #T_ca31f_row8_col3 {\n",
       "  background-color: #6fa7ce;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row7_col5 {\n",
       "  background-color: #f4eef6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row7_col7 {\n",
       "  background-color: #dddbec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row8_col5 {\n",
       "  background-color: #f5eef6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row8_col7 {\n",
       "  background-color: #dfddec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row9_col2 {\n",
       "  background-color: #e8e4f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row9_col4 {\n",
       "  background-color: #e4e1ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row9_col5, #T_ca31f_row10_col6 {\n",
       "  background-color: #034f7d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row9_col6, #T_ca31f_row14_col5 {\n",
       "  background-color: #e0deed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row9_col7 {\n",
       "  background-color: #0567a1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row10_col2, #T_ca31f_row13_col4 {\n",
       "  background-color: #88b1d4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row10_col3 {\n",
       "  background-color: #045d92;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row10_col4 {\n",
       "  background-color: #1379b5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row10_col5 {\n",
       "  background-color: #0a73b2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row10_col7 {\n",
       "  background-color: #71a8ce;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row11_col2 {\n",
       "  background-color: #fbf4f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row11_col3 {\n",
       "  background-color: #67a4cc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row11_col4 {\n",
       "  background-color: #faf3f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row11_col5 {\n",
       "  background-color: #ede7f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row11_col6 {\n",
       "  background-color: #faf2f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row11_col7 {\n",
       "  background-color: #d9d8ea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row12_col2 {\n",
       "  background-color: #b1c2de;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row12_col3, #T_ca31f_row13_col6 {\n",
       "  background-color: #0570b0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row12_col4 {\n",
       "  background-color: #034e7b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row12_col5, #T_ca31f_row12_col6 {\n",
       "  background-color: #9ebad9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row12_col7 {\n",
       "  background-color: #b3c3de;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row13_col2 {\n",
       "  background-color: #8bb2d4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row13_col5 {\n",
       "  background-color: #0567a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row13_col7 {\n",
       "  background-color: #056ba7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row14_col2 {\n",
       "  background-color: #fdf5fa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row14_col3 {\n",
       "  background-color: #63a2cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row14_col4 {\n",
       "  background-color: #fcf4fa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row14_col7 {\n",
       "  background-color: #d2d2e7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row15_col2 {\n",
       "  background-color: #c5cce3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row15_col3 {\n",
       "  background-color: #60a1ca;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ca31f_row15_col4 {\n",
       "  background-color: #8fb4d6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row15_col5 {\n",
       "  background-color: #f2ecf5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ca31f_row15_col6 {\n",
       "  background-color: #c1cae2;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_ca31f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ca31f_level0_col0\" class=\"col_heading level0 col0\" >date</th>\n",
       "      <th id=\"T_ca31f_level0_col1\" class=\"col_heading level0 col1\" >pair</th>\n",
       "      <th id=\"T_ca31f_level0_col2\" class=\"col_heading level0 col2\" >shop_a_buy</th>\n",
       "      <th id=\"T_ca31f_level0_col3\" class=\"col_heading level0 col3\" >shop_a_sell</th>\n",
       "      <th id=\"T_ca31f_level0_col4\" class=\"col_heading level0 col4\" >shop_b_buy</th>\n",
       "      <th id=\"T_ca31f_level0_col5\" class=\"col_heading level0 col5\" >shop_b_sell</th>\n",
       "      <th id=\"T_ca31f_level0_col6\" class=\"col_heading level0 col6\" >shop_c_buy</th>\n",
       "      <th id=\"T_ca31f_level0_col7\" class=\"col_heading level0 col7\" >shop_c_sell</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_ca31f_row0_col0\" class=\"data row0 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row0_col1\" class=\"data row0 col1\" >HKD-AED</td>\n",
       "      <td id=\"T_ca31f_row0_col2\" class=\"data row0 col2\" >0.206350</td>\n",
       "      <td id=\"T_ca31f_row0_col3\" class=\"data row0 col3\" >0.063650</td>\n",
       "      <td id=\"T_ca31f_row0_col4\" class=\"data row0 col4\" >0.026350</td>\n",
       "      <td id=\"T_ca31f_row0_col5\" class=\"data row0 col5\" >0.033650</td>\n",
       "      <td id=\"T_ca31f_row0_col6\" class=\"data row0 col6\" >0.026350</td>\n",
       "      <td id=\"T_ca31f_row0_col7\" class=\"data row0 col7\" >0.003650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_ca31f_row1_col0\" class=\"data row1 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row1_col1\" class=\"data row1 col1\" >HKD-AUD</td>\n",
       "      <td id=\"T_ca31f_row1_col2\" class=\"data row1 col2\" >0.161300</td>\n",
       "      <td id=\"T_ca31f_row1_col3\" class=\"data row1 col3\" >0.098700</td>\n",
       "      <td id=\"T_ca31f_row1_col4\" class=\"data row1 col4\" >0.051300</td>\n",
       "      <td id=\"T_ca31f_row1_col5\" class=\"data row1 col5\" >0.018700</td>\n",
       "      <td id=\"T_ca31f_row1_col6\" class=\"data row1 col6\" >0.081300</td>\n",
       "      <td id=\"T_ca31f_row1_col7\" class=\"data row1 col7\" >0.008700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_ca31f_row2_col0\" class=\"data row2 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row2_col1\" class=\"data row2 col1\" >HKD-CAD</td>\n",
       "      <td id=\"T_ca31f_row2_col2\" class=\"data row2 col2\" >0.142600</td>\n",
       "      <td id=\"T_ca31f_row2_col3\" class=\"data row2 col3\" >0.107400</td>\n",
       "      <td id=\"T_ca31f_row2_col4\" class=\"data row2 col4\" >0.032600</td>\n",
       "      <td id=\"T_ca31f_row2_col5\" class=\"data row2 col5\" >0.047400</td>\n",
       "      <td id=\"T_ca31f_row2_col6\" class=\"data row2 col6\" >0.072600</td>\n",
       "      <td id=\"T_ca31f_row2_col7\" class=\"data row2 col7\" >0.027400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_ca31f_row3_col0\" class=\"data row3 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row3_col1\" class=\"data row3 col1\" >HKD-CHF</td>\n",
       "      <td id=\"T_ca31f_row3_col2\" class=\"data row3 col2\" >0.385190</td>\n",
       "      <td id=\"T_ca31f_row3_col3\" class=\"data row3 col3\" >0.134810</td>\n",
       "      <td id=\"T_ca31f_row3_col4\" class=\"data row3 col4\" >0.075190</td>\n",
       "      <td id=\"T_ca31f_row3_col5\" class=\"data row3 col5\" >0.024810</td>\n",
       "      <td id=\"T_ca31f_row3_col6\" class=\"data row3 col6\" >0.075190</td>\n",
       "      <td id=\"T_ca31f_row3_col7\" class=\"data row3 col7\" >0.034810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_ca31f_row4_col0\" class=\"data row4 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row4_col1\" class=\"data row4 col1\" >HKD-CNY</td>\n",
       "      <td id=\"T_ca31f_row4_col2\" class=\"data row4 col2\" >0.127310</td>\n",
       "      <td id=\"T_ca31f_row4_col3\" class=\"data row4 col3\" >-0.139310</td>\n",
       "      <td id=\"T_ca31f_row4_col4\" class=\"data row4 col4\" >0.014990</td>\n",
       "      <td id=\"T_ca31f_row4_col5\" class=\"data row4 col5\" >-0.003650</td>\n",
       "      <td id=\"T_ca31f_row4_col6\" class=\"data row4 col6\" >0.011310</td>\n",
       "      <td id=\"T_ca31f_row4_col7\" class=\"data row4 col7\" >-0.006310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_ca31f_row5_col0\" class=\"data row5 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row5_col1\" class=\"data row5 col1\" >HKD-EUR</td>\n",
       "      <td id=\"T_ca31f_row5_col2\" class=\"data row5 col2\" >0.181570</td>\n",
       "      <td id=\"T_ca31f_row5_col3\" class=\"data row5 col3\" >0.088430</td>\n",
       "      <td id=\"T_ca31f_row5_col4\" class=\"data row5 col4\" >0.051570</td>\n",
       "      <td id=\"T_ca31f_row5_col5\" class=\"data row5 col5\" >0.028430</td>\n",
       "      <td id=\"T_ca31f_row5_col6\" class=\"data row5 col6\" >0.071570</td>\n",
       "      <td id=\"T_ca31f_row5_col7\" class=\"data row5 col7\" >0.008430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_ca31f_row6_col0\" class=\"data row6 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row6_col1\" class=\"data row6 col1\" >HKD-GBP</td>\n",
       "      <td id=\"T_ca31f_row6_col2\" class=\"data row6 col2\" >0.199900</td>\n",
       "      <td id=\"T_ca31f_row6_col3\" class=\"data row6 col3\" >0.110100</td>\n",
       "      <td id=\"T_ca31f_row6_col4\" class=\"data row6 col4\" >0.089900</td>\n",
       "      <td id=\"T_ca31f_row6_col5\" class=\"data row6 col5\" >0.050100</td>\n",
       "      <td id=\"T_ca31f_row6_col6\" class=\"data row6 col6\" >0.079900</td>\n",
       "      <td id=\"T_ca31f_row6_col7\" class=\"data row6 col7\" >0.040100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_ca31f_row7_col0\" class=\"data row7 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row7_col1\" class=\"data row7 col1\" >HKD-JPY</td>\n",
       "      <td id=\"T_ca31f_row7_col2\" class=\"data row7 col2\" >0.001080</td>\n",
       "      <td id=\"T_ca31f_row7_col3\" class=\"data row7 col3\" >0.000710</td>\n",
       "      <td id=\"T_ca31f_row7_col4\" class=\"data row7 col4\" >0.000280</td>\n",
       "      <td id=\"T_ca31f_row7_col5\" class=\"data row7 col5\" >0.000320</td>\n",
       "      <td id=\"T_ca31f_row7_col6\" class=\"data row7 col6\" >0.000380</td>\n",
       "      <td id=\"T_ca31f_row7_col7\" class=\"data row7 col7\" >0.000420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_ca31f_row8_col0\" class=\"data row8 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row8_col1\" class=\"data row8 col1\" >HKD-KRW</td>\n",
       "      <td id=\"T_ca31f_row8_col2\" class=\"data row8 col2\" >0.000230</td>\n",
       "      <td id=\"T_ca31f_row8_col3\" class=\"data row8 col3\" >0.000050</td>\n",
       "      <td id=\"T_ca31f_row8_col4\" class=\"data row8 col4\" >0.000080</td>\n",
       "      <td id=\"T_ca31f_row8_col5\" class=\"data row8 col5\" >0.000120</td>\n",
       "      <td id=\"T_ca31f_row8_col6\" class=\"data row8 col6\" >0.000130</td>\n",
       "      <td id=\"T_ca31f_row8_col7\" class=\"data row8 col7\" >0.000070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_ca31f_row9_col0\" class=\"data row9 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row9_col1\" class=\"data row9 col1\" >HKD-MYR</td>\n",
       "      <td id=\"T_ca31f_row9_col2\" class=\"data row9 col2\" >0.054640</td>\n",
       "      <td id=\"T_ca31f_row9_col3\" class=\"data row9 col3\" >0.050360</td>\n",
       "      <td id=\"T_ca31f_row9_col4\" class=\"data row9 col4\" >0.014640</td>\n",
       "      <td id=\"T_ca31f_row9_col5\" class=\"data row9 col5\" >0.045360</td>\n",
       "      <td id=\"T_ca31f_row9_col6\" class=\"data row9 col6\" >0.014640</td>\n",
       "      <td id=\"T_ca31f_row9_col7\" class=\"data row9 col7\" >0.030360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_ca31f_row10_col0\" class=\"data row10 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row10_col1\" class=\"data row10 col1\" >HKD-NZD</td>\n",
       "      <td id=\"T_ca31f_row10_col2\" class=\"data row10 col2\" >0.174080</td>\n",
       "      <td id=\"T_ca31f_row10_col3\" class=\"data row10 col3\" >0.095920</td>\n",
       "      <td id=\"T_ca31f_row10_col4\" class=\"data row10 col4\" >0.064080</td>\n",
       "      <td id=\"T_ca31f_row10_col5\" class=\"data row10 col5\" >0.035920</td>\n",
       "      <td id=\"T_ca31f_row10_col6\" class=\"data row10 col6\" >0.074080</td>\n",
       "      <td id=\"T_ca31f_row10_col7\" class=\"data row10 col7\" >0.015920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_ca31f_row11_col0\" class=\"data row11 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row11_col1\" class=\"data row11 col1\" >HKD-PHP</td>\n",
       "      <td id=\"T_ca31f_row11_col2\" class=\"data row11 col2\" >0.010100</td>\n",
       "      <td id=\"T_ca31f_row11_col3\" class=\"data row11 col3\" >0.004900</td>\n",
       "      <td id=\"T_ca31f_row11_col4\" class=\"data row11 col4\" >0.003100</td>\n",
       "      <td id=\"T_ca31f_row11_col5\" class=\"data row11 col5\" >0.002900</td>\n",
       "      <td id=\"T_ca31f_row11_col6\" class=\"data row11 col6\" >0.003100</td>\n",
       "      <td id=\"T_ca31f_row11_col7\" class=\"data row11 col7\" >0.001400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_ca31f_row12_col0\" class=\"data row12 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row12_col1\" class=\"data row12 col1\" >HKD-SAR</td>\n",
       "      <td id=\"T_ca31f_row12_col2\" class=\"data row12 col2\" >0.132400</td>\n",
       "      <td id=\"T_ca31f_row12_col3\" class=\"data row12 col3\" >0.065600</td>\n",
       "      <td id=\"T_ca31f_row12_col4\" class=\"data row12 col4\" >0.082400</td>\n",
       "      <td id=\"T_ca31f_row12_col5\" class=\"data row12 col5\" >0.017600</td>\n",
       "      <td id=\"T_ca31f_row12_col6\" class=\"data row12 col6\" >0.032400</td>\n",
       "      <td id=\"T_ca31f_row12_col7\" class=\"data row12 col7\" >0.007600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_ca31f_row13_col0\" class=\"data row13 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row13_col1\" class=\"data row13 col1\" >HKD-SGD</td>\n",
       "      <td id=\"T_ca31f_row13_col2\" class=\"data row13 col2\" >0.170760</td>\n",
       "      <td id=\"T_ca31f_row13_col3\" class=\"data row13 col3\" >0.109240</td>\n",
       "      <td id=\"T_ca31f_row13_col4\" class=\"data row13 col4\" >0.040760</td>\n",
       "      <td id=\"T_ca31f_row13_col5\" class=\"data row13 col5\" >0.039240</td>\n",
       "      <td id=\"T_ca31f_row13_col6\" class=\"data row13 col6\" >0.060760</td>\n",
       "      <td id=\"T_ca31f_row13_col7\" class=\"data row13 col7\" >0.029240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_ca31f_row14_col0\" class=\"data row14 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row14_col1\" class=\"data row14 col1\" >HKD-THB</td>\n",
       "      <td id=\"T_ca31f_row14_col2\" class=\"data row14 col2\" >0.005110</td>\n",
       "      <td id=\"T_ca31f_row14_col3\" class=\"data row14 col3\" >0.006890</td>\n",
       "      <td id=\"T_ca31f_row14_col4\" class=\"data row14 col4\" >0.002110</td>\n",
       "      <td id=\"T_ca31f_row14_col5\" class=\"data row14 col5\" >0.005890</td>\n",
       "      <td id=\"T_ca31f_row14_col6\" class=\"data row14 col6\" >0.000110</td>\n",
       "      <td id=\"T_ca31f_row14_col7\" class=\"data row14 col7\" >0.002890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca31f_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_ca31f_row15_col0\" class=\"data row15 col0\" >2024-06-28</td>\n",
       "      <td id=\"T_ca31f_row15_col1\" class=\"data row15 col1\" >HKD-USD</td>\n",
       "      <td id=\"T_ca31f_row15_col2\" class=\"data row15 col2\" >0.109010</td>\n",
       "      <td id=\"T_ca31f_row15_col3\" class=\"data row15 col3\" >0.008990</td>\n",
       "      <td id=\"T_ca31f_row15_col4\" class=\"data row15 col4\" >0.039010</td>\n",
       "      <td id=\"T_ca31f_row15_col5\" class=\"data row15 col5\" >0.000990</td>\n",
       "      <td id=\"T_ca31f_row15_col6\" class=\"data row15 col6\" >0.024010</td>\n",
       "      <td id=\"T_ca31f_row15_col7\" class=\"data row15 col7\" >-0.009010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1cd7e227bb0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing discrepancies in pair pricing to show market efficiency\n",
    "\n",
    "# Calculate differences in prices\n",
    "shops = [\"shop_a_buy\", \"shop_a_sell\", \"shop_b_buy\", \"shop_b_sell\", \"shop_c_buy\", \"shop_c_sell\"]\n",
    "\n",
    "shop_pl = pd.DataFrame({'date': merged_rates['date'], 'pair': merged_rates['pair']})\n",
    "\n",
    "for shop in shops:\n",
    "\n",
    "    if \"buy\" in shop:   # Determine which formula to profit profitability for local shop\n",
    "        shop_pl[shop] = merged_rates['market_price'] - merged_rates[shop]\n",
    "    else:\n",
    "        shop_pl[shop] = merged_rates[shop] - merged_rates['market_price'] \n",
    "\n",
    "shop_pl.style.background_gradient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After adding a background gradient based on row values in each column, we notice that profitability is dependent on the rate set for the day and the amount exchanged. There are some pairs which do not end up being profitable if the midmarket price fluctuates and ends up worse than when a shop does its latest price update. \n",
    "\n",
    "We notice that several SELL pairs end up losing money, while some are not as profitable as others. It also seems that some strong currencies such as the GBP and CHF have a better margin set for the shop compared to others such as the Yen (JPY) or Won (KRW). Without transactional data, we cannot conclude whether the lower margin is due to the change in midmarket price from the initial set price by the shop, the volume of money that is exchanged for currencies such as the Yen (JPY) or Won (KRW), or another factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shop_pl.set_index(['date', 'pair'], inplace=True)\n",
    "\n",
    "plt.figure(figsize=(14, 10))\n",
    "sns.heatmap(shop_pl, annot=True, cmap=sns.color_palette(\"Blues\", as_cmap=True), cbar=True)\n",
    "\n",
    "plt.title(\"Shop P/L by Pair\")\n",
    "plt.xlabel(\"Shop\")\n",
    "plt.ylabel(\"FX Pair\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The heatmap shows us the overall opportunities and seems that Swiss Franc (CHF) at Shop A BUY prices would give the best rate for the shop if it were to buy compared to the midmarket rate. Despite having a good rate for Swiss Franc (CHF), it still seems to lose on selling CNY, with Shop A itself taking the most loss compared to Shop B and C. However, all three shops are taking hits and losing on the HKD-CNY pair when selling based on the \"snapshot\" for the prices compared to the midmarket rate at the \"snapshot\" time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thoughts\n",
    "\n",
    "More data would be needed to determine market inefficiencies. A challenge is gathering data from more shops, as there is no central API or dataset for all FX rates for each shop in the Hong Kong SAR. This also does not have data on volumes traded as opposed to FX exchange APIs for large FX brokerages. Without the trading volumes, we cannot understand the extent of the local shops' profitability and volumes to compare with online FX brokers to determine and confirm market inefficiency.  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}