btc_pct_change_per_session.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "Visualization on % change of price of BTC the last 30 days per trading session (US, London, Asia).\n",
    "Other algorithms could be used to see differences between sessions, I implemented here the simplest one, % price change calculated as:\n",
    "((SessionClosePrice - SessionOpenPrice) / SessionOpenPrice) * 100\n",
    "\n",
    "# References:\n",
    "- I used OpenAI to boostrap the skeleton, but the computation logic was totally wrong, the fetch data code missed casting columns to float, the visualization code was correct."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "start_time": "2023-04-29T19:01:17.009490Z",
     "end_time": "2023-04-29T19:01:17.053295Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "outputs": [
    {
     "data": {
      "text/plain": "                               open            high             low  \\\ntimestamp                                                             \n2023-04-08 21:30:00  27925.22000000  27929.81000000  27916.63000000   \n2023-04-08 22:00:00  27929.80000000  27939.29000000  27888.70000000   \n2023-04-08 22:30:00  27888.70000000  27951.00000000  27885.86000000   \n2023-04-08 23:00:00  27937.77000000  27987.41000000  27933.06000000   \n2023-04-08 23:30:00  27960.69000000  27962.97000000  27927.71000000   \n...                             ...             ...             ...   \n2023-04-29 15:00:00  29336.05000000  29345.72000000  29312.44000000   \n2023-04-29 15:30:00  29335.43000000  29344.40000000  29210.24000000   \n2023-04-29 16:00:00  29271.21000000  29300.00000000  29244.88000000   \n2023-04-29 16:30:00  29265.24000000  29286.88000000  29256.45000000   \n2023-04-29 17:00:00  29281.78000000  29281.78000000  29281.77000000   \n\n                              close        volume     close_time  \\\ntimestamp                                                          \n2023-04-08 21:30:00  27929.80000000  258.08281000  1680991199999   \n2023-04-08 22:00:00  27888.71000000  360.78110000  1680992999999   \n2023-04-08 22:30:00  27937.77000000  304.05155000  1680994799999   \n2023-04-08 23:00:00  27960.70000000  423.62125000  1680996599999   \n2023-04-08 23:30:00  27938.38000000  302.14366000  1680998399999   \n...                             ...           ...            ...   \n2023-04-29 15:00:00  29335.43000000  317.70661000  1682782199999   \n2023-04-29 15:30:00  29271.21000000  532.43199000  1682783999999   \n2023-04-29 16:00:00  29265.23000000  306.04660000  1682785799999   \n2023-04-29 16:30:00  29281.78000000  300.50679000  1682787599999   \n2023-04-29 17:00:00  29281.77000000   20.02593000  1682789399999   \n\n                    quote_asset_volume  number_of_trades  \\\ntimestamp                                                  \n2023-04-08 21:30:00   7206332.23833520             14816   \n2023-04-08 22:00:00  10072737.74696720             17143   \n2023-04-08 22:30:00   8487206.95077240             14365   \n2023-04-08 23:00:00  11845940.92881490             14653   \n2023-04-08 23:30:00   8441787.95908160             12459   \n...                                ...               ...   \n2023-04-29 15:00:00   9317599.65982480             11894   \n2023-04-29 15:30:00  15594576.97080470             20534   \n2023-04-29 16:00:00   8958537.97247510             11350   \n2023-04-29 16:30:00   8796620.83308730             10043   \n2023-04-29 17:00:00    586394.76872390               534   \n\n                    taker_buy_base_asset_volume taker_buy_quote_asset_volume  \\\ntimestamp                                                                      \n2023-04-08 21:30:00                142.67173000             3983772.88775660   \n2023-04-08 22:00:00                150.07316000             4189957.17093470   \n2023-04-08 22:30:00                183.81454000             5130684.89131130   \n2023-04-08 23:00:00                205.98336000             5759431.39565220   \n2023-04-08 23:30:00                149.08881000             4165254.55670730   \n...                                         ...                          ...   \n2023-04-29 15:00:00                178.97382000             5248923.99830330   \n2023-04-29 15:30:00                241.09552000             7061379.18399410   \n2023-04-29 16:00:00                147.34618000             4313057.07374820   \n2023-04-29 16:30:00                154.10409000             4511088.40162100   \n2023-04-29 17:00:00                  9.24278000              270645.05054840   \n\n                    ignore  \ntimestamp                   \n2023-04-08 21:30:00      0  \n2023-04-08 22:00:00      0  \n2023-04-08 22:30:00      0  \n2023-04-08 23:00:00      0  \n2023-04-08 23:30:00      0  \n...                    ...  \n2023-04-29 15:00:00      0  \n2023-04-29 15:30:00      0  \n2023-04-29 16:00:00      0  \n2023-04-29 16:30:00      0  \n2023-04-29 17:00:00      0  \n\n[1000 rows x 11 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>open</th>\n      <th>high</th>\n      <th>low</th>\n      <th>close</th>\n      <th>volume</th>\n      <th>close_time</th>\n      <th>quote_asset_volume</th>\n      <th>number_of_trades</th>\n      <th>taker_buy_base_asset_volume</th>\n      <th>taker_buy_quote_asset_volume</th>\n      <th>ignore</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      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2023-04-08 21:30:00</th>\n      <td>27925.22000000</td>\n      <td>27929.81000000</td>\n      <td>27916.63000000</td>\n      <td>27929.80000000</td>\n      <td>258.08281000</td>\n      <td>1680991199999</td>\n      <td>7206332.23833520</td>\n      <td>14816</td>\n      <td>142.67173000</td>\n      <td>3983772.88775660</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-08 22:00:00</th>\n      <td>27929.80000000</td>\n      <td>27939.29000000</td>\n      <td>27888.70000000</td>\n      <td>27888.71000000</td>\n      <td>360.78110000</td>\n      <td>1680992999999</td>\n      <td>10072737.74696720</td>\n      <td>17143</td>\n      <td>150.07316000</td>\n      <td>4189957.17093470</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-08 22:30:00</th>\n      <td>27888.70000000</td>\n      <td>27951.00000000</td>\n      <td>27885.86000000</td>\n      <td>27937.77000000</td>\n      <td>304.05155000</td>\n      <td>1680994799999</td>\n      <td>8487206.95077240</td>\n      <td>14365</td>\n      <td>183.81454000</td>\n      <td>5130684.89131130</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-08 23:00:00</th>\n      <td>27937.77000000</td>\n      <td>27987.41000000</td>\n      <td>27933.06000000</td>\n      <td>27960.70000000</td>\n      <td>423.62125000</td>\n      <td>1680996599999</td>\n      <td>11845940.92881490</td>\n      <td>14653</td>\n      <td>205.98336000</td>\n      <td>5759431.39565220</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-08 23:30:00</th>\n      <td>27960.69000000</td>\n      <td>27962.97000000</td>\n      <td>27927.71000000</td>\n      <td>27938.38000000</td>\n      <td>302.14366000</td>\n      <td>1680998399999</td>\n      <td>8441787.95908160</td>\n      <td>12459</td>\n      <td>149.08881000</td>\n      <td>4165254.55670730</td>\n      <td>0</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 15:00:00</th>\n      <td>29336.05000000</td>\n      <td>29345.72000000</td>\n      <td>29312.44000000</td>\n      <td>29335.43000000</td>\n      <td>317.70661000</td>\n      <td>1682782199999</td>\n      <td>9317599.65982480</td>\n      <td>11894</td>\n      <td>178.97382000</td>\n      <td>5248923.99830330</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 15:30:00</th>\n      <td>29335.43000000</td>\n      <td>29344.40000000</td>\n      <td>29210.24000000</td>\n      <td>29271.21000000</td>\n      <td>532.43199000</td>\n      <td>1682783999999</td>\n      <td>15594576.97080470</td>\n      <td>20534</td>\n      <td>241.09552000</td>\n      <td>7061379.18399410</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 16:00:00</th>\n      <td>29271.21000000</td>\n      <td>29300.00000000</td>\n      <td>29244.88000000</td>\n      <td>29265.23000000</td>\n      <td>306.04660000</td>\n      <td>1682785799999</td>\n      <td>8958537.97247510</td>\n      <td>11350</td>\n      <td>147.34618000</td>\n      <td>4313057.07374820</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 16:30:00</th>\n      <td>29265.24000000</td>\n      <td>29286.88000000</td>\n      <td>29256.45000000</td>\n      <td>29281.78000000</td>\n      <td>300.50679000</td>\n      <td>1682787599999</td>\n      <td>8796620.83308730</td>\n      <td>10043</td>\n      <td>154.10409000</td>\n      <td>4511088.40162100</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 17:00:00</th>\n      <td>29281.78000000</td>\n      <td>29281.78000000</td>\n      <td>29281.77000000</td>\n      <td>29281.77000000</td>\n      <td>20.02593000</td>\n      <td>1682789399999</td>\n      <td>586394.76872390</td>\n      <td>534</td>\n      <td>9.24278000</td>\n      <td>270645.05054840</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1000 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Define the API endpoint and parameters\n",
    "endpoint = 'https://api.binance.com/api/v3/klines'\n",
    "params = {\n",
    "    'symbol': 'BTCUSDT',\n",
    "    'interval': '30m',  # 30 minutes\n",
    "    'limit': 2 * 24 * 30  # 2 30 minutes for 1h, 24 of these for one day, 30 of these for 1 month\n",
    "}\n",
    "\n",
    "# Fetch the OHLCV data from the Binance API\n",
    "response = requests.get(endpoint, params=params)\n",
    "\n",
    "data = response.json()\n",
    "df = pd.DataFrame(data, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time',\n",
    "                                 'quote_asset_volume', 'number_of_trades', 'taker_buy_base_asset_volume',\n",
    "                                 'taker_buy_quote_asset_volume', 'ignore'])\n",
    "\n",
    "# Convert the timestamp to a datetime object and set it as the index\n",
    "df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')\n",
    "df.set_index('timestamp', inplace=True)\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T19:01:17.053289Z",
     "end_time": "2023-04-29T19:01:17.452408Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [
    {
     "data": {
      "text/plain": "open                            object\nhigh                            object\nlow                             object\nclose                           object\nvolume                          object\nclose_time                       int64\nquote_asset_volume              object\nnumber_of_trades                 int64\ntaker_buy_base_asset_volume     object\ntaker_buy_quote_asset_volume    object\nignore                          object\ndtype: object"
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T19:01:17.432408Z",
     "end_time": "2023-04-29T19:01:17.452636Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "outputs": [
    {
     "data": {
      "text/plain": "open                            float64\nhigh                            float64\nlow                             float64\nclose                           float64\nvolume                          float64\nclose_time                        int64\nquote_asset_volume              float64\nnumber_of_trades                  int64\ntaker_buy_base_asset_volume      object\ntaker_buy_quote_asset_volume     object\nignore                           object\ndtype: object"
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.astype({\n",
    "    'open': 'float64', 'high': 'float64',\n",
    "    'close': 'float64', 'low': 'float64',\n",
    "    'quote_asset_volume': 'float64',\n",
    "    'volume': 'float64'})\n",
    "df.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T19:01:17.435695Z",
     "end_time": "2023-04-29T19:01:17.452816Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "outputs": [
    {
     "data": {
      "text/plain": "                         open      high       low     close     volume  \\\ntimestamp                                                                \n2023-04-09 09:30:00  27897.11  27929.67  27882.05  27898.58  373.63709   \n2023-04-09 10:00:00  27898.59  27914.29  27876.15  27907.68  346.93858   \n2023-04-09 10:30:00  27907.68  27907.69  27861.55  27893.22  261.59023   \n2023-04-09 11:00:00  27893.23  27902.21  27866.87  27866.89  187.78858   \n2023-04-09 11:30:00  27866.89  27949.99  27866.88  27930.20  464.24282   \n...                       ...       ...       ...       ...        ...   \n2023-04-29 14:00:00  29384.10  29393.39  29337.60  29363.13  597.05658   \n2023-04-29 14:30:00  29363.13  29372.48  29325.34  29336.06  572.94926   \n2023-04-29 15:00:00  29336.05  29345.72  29312.44  29335.43  317.70661   \n2023-04-29 15:30:00  29335.43  29344.40  29210.24  29271.21  532.43199   \n2023-04-29 16:00:00  29271.21  29300.00  29244.88  29265.23  306.04660   \n\n                        close_time  quote_asset_volume  number_of_trades  \\\ntimestamp                                                                  \n2023-04-09 09:30:00  1681034399999        1.042720e+07             10629   \n2023-04-09 10:00:00  1681036199999        9.679294e+06             10174   \n2023-04-09 10:30:00  1681037999999        7.295428e+06              8759   \n2023-04-09 11:00:00  1681039799999        5.236426e+06              8798   \n2023-04-09 11:30:00  1681041599999        1.295898e+07             14183   \n...                            ...                 ...               ...   \n2023-04-29 14:00:00  1682778599999        1.753431e+07             13806   \n2023-04-29 14:30:00  1682780399999        1.681543e+07             13882   \n2023-04-29 15:00:00  1682782199999        9.317600e+06             11894   \n2023-04-29 15:30:00  1682783999999        1.559458e+07             20534   \n2023-04-29 16:00:00  1682785799999        8.958538e+06             11350   \n\n                    taker_buy_base_asset_volume taker_buy_quote_asset_volume  \\\ntimestamp                                                                      \n2023-04-09 09:30:00                196.54831000             5485274.78116910   \n2023-04-09 10:00:00                179.06468000             4995780.51008880   \n2023-04-09 10:30:00                117.88651000             3287446.41575290   \n2023-04-09 11:00:00                 73.78587000             2057499.85173790   \n2023-04-09 11:30:00                284.55337000             7942976.80601700   \n...                                         ...                          ...   \n2023-04-29 14:00:00                231.90077000             6809174.51332350   \n2023-04-29 14:30:00                258.18100000             7576818.97723580   \n2023-04-29 15:00:00                178.97382000             5248923.99830330   \n2023-04-29 15:30:00                241.09552000             7061379.18399410   \n2023-04-29 16:00:00                147.34618000             4313057.07374820   \n\n                    ignore  \ntimestamp                   \n2023-04-09 09:30:00      0  \n2023-04-09 10:00:00      0  \n2023-04-09 10:30:00      0  \n2023-04-09 11:00:00      0  \n2023-04-09 11:30:00      0  \n...                    ...  \n2023-04-29 14:00:00      0  \n2023-04-29 14:30:00      0  \n2023-04-29 15:00:00      0  \n2023-04-29 15:30:00      0  \n2023-04-29 16:00:00      0  \n\n[294 rows x 11 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>open</th>\n      <th>high</th>\n      <th>low</th>\n      <th>close</th>\n      <th>volume</th>\n      <th>close_time</th>\n      <th>quote_asset_volume</th>\n      <th>number_of_trades</th>\n      <th>taker_buy_base_asset_volume</th>\n      <th>taker_buy_quote_asset_volume</th>\n      <th>ignore</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      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2023-04-09 09:30:00</th>\n      <td>27897.11</td>\n      <td>27929.67</td>\n      <td>27882.05</td>\n      <td>27898.58</td>\n      <td>373.63709</td>\n      <td>1681034399999</td>\n      <td>1.042720e+07</td>\n      <td>10629</td>\n      <td>196.54831000</td>\n      <td>5485274.78116910</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-09 10:00:00</th>\n      <td>27898.59</td>\n      <td>27914.29</td>\n      <td>27876.15</td>\n      <td>27907.68</td>\n      <td>346.93858</td>\n      <td>1681036199999</td>\n      <td>9.679294e+06</td>\n      <td>10174</td>\n      <td>179.06468000</td>\n      <td>4995780.51008880</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-09 10:30:00</th>\n      <td>27907.68</td>\n      <td>27907.69</td>\n      <td>27861.55</td>\n      <td>27893.22</td>\n      <td>261.59023</td>\n      <td>1681037999999</td>\n      <td>7.295428e+06</td>\n      <td>8759</td>\n      <td>117.88651000</td>\n      <td>3287446.41575290</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-09 11:00:00</th>\n      <td>27893.23</td>\n      <td>27902.21</td>\n      <td>27866.87</td>\n      <td>27866.89</td>\n      <td>187.78858</td>\n      <td>1681039799999</td>\n      <td>5.236426e+06</td>\n      <td>8798</td>\n      <td>73.78587000</td>\n      <td>2057499.85173790</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-09 11:30:00</th>\n      <td>27866.89</td>\n      <td>27949.99</td>\n      <td>27866.88</td>\n      <td>27930.20</td>\n      <td>464.24282</td>\n      <td>1681041599999</td>\n      <td>1.295898e+07</td>\n      <td>14183</td>\n      <td>284.55337000</td>\n      <td>7942976.80601700</td>\n      <td>0</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 14:00:00</th>\n      <td>29384.10</td>\n      <td>29393.39</td>\n      <td>29337.60</td>\n      <td>29363.13</td>\n      <td>597.05658</td>\n      <td>1682778599999</td>\n      <td>1.753431e+07</td>\n      <td>13806</td>\n      <td>231.90077000</td>\n      <td>6809174.51332350</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 14:30:00</th>\n      <td>29363.13</td>\n      <td>29372.48</td>\n      <td>29325.34</td>\n      <td>29336.06</td>\n      <td>572.94926</td>\n      <td>1682780399999</td>\n      <td>1.681543e+07</td>\n      <td>13882</td>\n      <td>258.18100000</td>\n      <td>7576818.97723580</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 15:00:00</th>\n      <td>29336.05</td>\n      <td>29345.72</td>\n      <td>29312.44</td>\n      <td>29335.43</td>\n      <td>317.70661</td>\n      <td>1682782199999</td>\n      <td>9.317600e+06</td>\n      <td>11894</td>\n      <td>178.97382000</td>\n      <td>5248923.99830330</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 15:30:00</th>\n      <td>29335.43</td>\n      <td>29344.40</td>\n      <td>29210.24</td>\n      <td>29271.21</td>\n      <td>532.43199</td>\n      <td>1682783999999</td>\n      <td>1.559458e+07</td>\n      <td>20534</td>\n      <td>241.09552000</td>\n      <td>7061379.18399410</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 16:00:00</th>\n      <td>29271.21</td>\n      <td>29300.00</td>\n      <td>29244.88</td>\n      <td>29265.23</td>\n      <td>306.04660</td>\n      <td>1682785799999</td>\n      <td>8.958538e+06</td>\n      <td>11350</td>\n      <td>147.34618000</td>\n      <td>4313057.07374820</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>294 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage delta in the US, London, and Asia sessions\n",
    "us_df = df.between_time('09:30', '16:00')\n",
    "london_df = df.between_time('03:00', '11:00')\n",
    "asia_df = pd.concat([df.between_time('19:00', '23:59'), df.between_time('00:00', '04:00')])\n",
    "\n",
    "us_df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T19:01:17.451718Z",
     "end_time": "2023-04-29T19:01:17.470045Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "outputs": [
    {
     "data": {
      "text/plain": "                         open      high       low     close     volume  \\\ntimestamp                                                                \n2023-04-09 03:00:00  28036.31  28046.65  28018.63  28046.64  321.83306   \n2023-04-09 03:30:00  28046.64  28054.01  28027.70  28027.71  264.04898   \n2023-04-09 04:00:00  28027.71  28034.49  28010.00  28033.73  337.21687   \n2023-04-09 04:30:00  28033.72  28041.06  28017.05  28037.39  212.48153   \n2023-04-09 05:00:00  28037.38  28067.46  28037.38  28063.03  249.53440   \n...                       ...       ...       ...       ...        ...   \n2023-04-29 09:00:00  29313.86  29316.59  29250.96  29295.04  575.63694   \n2023-04-29 09:30:00  29295.03  29310.00  29269.98  29298.84  345.93353   \n2023-04-29 10:00:00  29298.85  29309.40  29245.45  29264.94  273.15436   \n2023-04-29 10:30:00  29264.93  29287.22  29241.55  29267.56  338.93320   \n2023-04-29 11:00:00  29267.56  29275.76  29245.99  29256.88  227.87772   \n\n                        close_time  quote_asset_volume  number_of_trades  \\\ntimestamp                                                                  \n2023-04-09 03:00:00  1681010999999        9.020700e+06              8783   \n2023-04-09 03:30:00  1681012799999        7.405399e+06              7930   \n2023-04-09 04:00:00  1681014599999        9.451234e+06              7907   \n2023-04-09 04:30:00  1681016399999        5.956367e+06              7349   \n2023-04-09 05:00:00  1681018199999        6.999979e+06              7942   \n...                            ...                 ...               ...   \n2023-04-29 09:00:00  1682760599999        1.685508e+07             15037   \n2023-04-29 09:30:00  1682762399999        1.013201e+07             14669   \n2023-04-29 10:00:00  1682764199999        7.998346e+06             12224   \n2023-04-29 10:30:00  1682765999999        9.919855e+06             12651   \n2023-04-29 11:00:00  1682767799999        6.668243e+06              8794   \n\n                    taker_buy_base_asset_volume taker_buy_quote_asset_volume  \\\ntimestamp                                                                      \n2023-04-09 03:00:00                176.25914000             4940496.67911640   \n2023-04-09 03:30:00                135.91265000             3811700.32621310   \n2023-04-09 04:00:00                164.82296000             4619626.88520950   \n2023-04-09 04:30:00                121.64797000             3409928.01939480   \n2023-04-09 05:00:00                147.07855000             4125738.18034270   \n...                                         ...                          ...   \n2023-04-29 09:00:00                268.86672000             7872200.07680300   \n2023-04-29 09:30:00                161.61119000             4733331.91448250   \n2023-04-29 10:00:00                103.78381000             3039165.82852970   \n2023-04-29 10:30:00                165.18398000             4834621.28181140   \n2023-04-29 11:00:00                103.92586000             3041007.27774640   \n\n                    ignore  \ntimestamp                   \n2023-04-09 03:00:00      0  \n2023-04-09 03:30:00      0  \n2023-04-09 04:00:00      0  \n2023-04-09 04:30:00      0  \n2023-04-09 05:00:00      0  \n...                    ...  \n2023-04-29 09:00:00      0  \n2023-04-29 09:30:00      0  \n2023-04-29 10:00:00      0  \n2023-04-29 10:30:00      0  \n2023-04-29 11:00:00      0  \n\n[357 rows x 11 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>open</th>\n      <th>high</th>\n      <th>low</th>\n      <th>close</th>\n      <th>volume</th>\n      <th>close_time</th>\n      <th>quote_asset_volume</th>\n      <th>number_of_trades</th>\n      <th>taker_buy_base_asset_volume</th>\n      <th>taker_buy_quote_asset_volume</th>\n      <th>ignore</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      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2023-04-09 03:00:00</th>\n      <td>28036.31</td>\n      <td>28046.65</td>\n      <td>28018.63</td>\n      <td>28046.64</td>\n      <td>321.83306</td>\n      <td>1681010999999</td>\n      <td>9.020700e+06</td>\n      <td>8783</td>\n      <td>176.25914000</td>\n      <td>4940496.67911640</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-09 03:30:00</th>\n      <td>28046.64</td>\n      <td>28054.01</td>\n      <td>28027.70</td>\n      <td>28027.71</td>\n      <td>264.04898</td>\n      <td>1681012799999</td>\n      <td>7.405399e+06</td>\n      <td>7930</td>\n      <td>135.91265000</td>\n      <td>3811700.32621310</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-09 04:00:00</th>\n      <td>28027.71</td>\n      <td>28034.49</td>\n      <td>28010.00</td>\n      <td>28033.73</td>\n      <td>337.21687</td>\n      <td>1681014599999</td>\n      <td>9.451234e+06</td>\n      <td>7907</td>\n      <td>164.82296000</td>\n      <td>4619626.88520950</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-09 04:30:00</th>\n      <td>28033.72</td>\n      <td>28041.06</td>\n      <td>28017.05</td>\n      <td>28037.39</td>\n      <td>212.48153</td>\n      <td>1681016399999</td>\n      <td>5.956367e+06</td>\n      <td>7349</td>\n      <td>121.64797000</td>\n      <td>3409928.01939480</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-09 05:00:00</th>\n      <td>28037.38</td>\n      <td>28067.46</td>\n      <td>28037.38</td>\n      <td>28063.03</td>\n      <td>249.53440</td>\n      <td>1681018199999</td>\n      <td>6.999979e+06</td>\n      <td>7942</td>\n      <td>147.07855000</td>\n      <td>4125738.18034270</td>\n      <td>0</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 09:00:00</th>\n      <td>29313.86</td>\n      <td>29316.59</td>\n      <td>29250.96</td>\n      <td>29295.04</td>\n      <td>575.63694</td>\n      <td>1682760599999</td>\n      <td>1.685508e+07</td>\n      <td>15037</td>\n      <td>268.86672000</td>\n      <td>7872200.07680300</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 09:30:00</th>\n      <td>29295.03</td>\n      <td>29310.00</td>\n      <td>29269.98</td>\n      <td>29298.84</td>\n      <td>345.93353</td>\n      <td>1682762399999</td>\n      <td>1.013201e+07</td>\n      <td>14669</td>\n      <td>161.61119000</td>\n      <td>4733331.91448250</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 10:00:00</th>\n      <td>29298.85</td>\n      <td>29309.40</td>\n      <td>29245.45</td>\n      <td>29264.94</td>\n      <td>273.15436</td>\n      <td>1682764199999</td>\n      <td>7.998346e+06</td>\n      <td>12224</td>\n      <td>103.78381000</td>\n      <td>3039165.82852970</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 10:30:00</th>\n      <td>29264.93</td>\n      <td>29287.22</td>\n      <td>29241.55</td>\n      <td>29267.56</td>\n      <td>338.93320</td>\n      <td>1682765999999</td>\n      <td>9.919855e+06</td>\n      <td>12651</td>\n      <td>165.18398000</td>\n      <td>4834621.28181140</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 11:00:00</th>\n      <td>29267.56</td>\n      <td>29275.76</td>\n      <td>29245.99</td>\n      <td>29256.88</td>\n      <td>227.87772</td>\n      <td>1682767799999</td>\n      <td>6.668243e+06</td>\n      <td>8794</td>\n      <td>103.92586000</td>\n      <td>3041007.27774640</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>357 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "london_df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T19:01:17.479204Z",
     "end_time": "2023-04-29T19:01:17.515746Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "outputs": [
    {
     "data": {
      "text/plain": "                         open      high       low     close     volume  \\\ntimestamp                                                                \n2023-04-09 03:00:00  28036.31  28046.65  28018.63  28046.64  321.83306   \n2023-04-09 03:30:00  28046.64  28054.01  28027.70  28027.71  264.04898   \n2023-04-09 04:00:00  28027.71  28034.49  28010.00  28033.73  337.21687   \n2023-04-09 04:30:00  28033.72  28041.06  28017.05  28037.39  212.48153   \n2023-04-09 05:00:00  28037.38  28067.46  28037.38  28063.03  249.53440   \n...                       ...       ...       ...       ...        ...   \n2023-04-29 09:00:00  29313.86  29316.59  29250.96  29295.04  575.63694   \n2023-04-29 09:30:00  29295.03  29310.00  29269.98  29298.84  345.93353   \n2023-04-29 10:00:00  29298.85  29309.40  29245.45  29264.94  273.15436   \n2023-04-29 10:30:00  29264.93  29287.22  29241.55  29267.56  338.93320   \n2023-04-29 11:00:00  29267.56  29275.76  29245.99  29256.88  227.87772   \n\n                        close_time  quote_asset_volume  number_of_trades  \\\ntimestamp                                                                  \n2023-04-09 03:00:00  1681010999999        9.020700e+06              8783   \n2023-04-09 03:30:00  1681012799999        7.405399e+06              7930   \n2023-04-09 04:00:00  1681014599999        9.451234e+06              7907   \n2023-04-09 04:30:00  1681016399999        5.956367e+06              7349   \n2023-04-09 05:00:00  1681018199999        6.999979e+06              7942   \n...                            ...                 ...               ...   \n2023-04-29 09:00:00  1682760599999        1.685508e+07             15037   \n2023-04-29 09:30:00  1682762399999        1.013201e+07             14669   \n2023-04-29 10:00:00  1682764199999        7.998346e+06             12224   \n2023-04-29 10:30:00  1682765999999        9.919855e+06             12651   \n2023-04-29 11:00:00  1682767799999        6.668243e+06              8794   \n\n                    taker_buy_base_asset_volume taker_buy_quote_asset_volume  \\\ntimestamp                                                                      \n2023-04-09 03:00:00                176.25914000             4940496.67911640   \n2023-04-09 03:30:00                135.91265000             3811700.32621310   \n2023-04-09 04:00:00                164.82296000             4619626.88520950   \n2023-04-09 04:30:00                121.64797000             3409928.01939480   \n2023-04-09 05:00:00                147.07855000             4125738.18034270   \n...                                         ...                          ...   \n2023-04-29 09:00:00                268.86672000             7872200.07680300   \n2023-04-29 09:30:00                161.61119000             4733331.91448250   \n2023-04-29 10:00:00                103.78381000             3039165.82852970   \n2023-04-29 10:30:00                165.18398000             4834621.28181140   \n2023-04-29 11:00:00                103.92586000             3041007.27774640   \n\n                    ignore  \ntimestamp                   \n2023-04-09 03:00:00      0  \n2023-04-09 03:30:00      0  \n2023-04-09 04:00:00      0  \n2023-04-09 04:30:00      0  \n2023-04-09 05:00:00      0  \n...                    ...  \n2023-04-29 09:00:00      0  \n2023-04-29 09:30:00      0  \n2023-04-29 10:00:00      0  \n2023-04-29 10:30:00      0  \n2023-04-29 11:00:00      0  \n\n[357 rows x 11 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>open</th>\n      <th>high</th>\n      <th>low</th>\n      <th>close</th>\n      <th>volume</th>\n      <th>close_time</th>\n      <th>quote_asset_volume</th>\n      <th>number_of_trades</th>\n      <th>taker_buy_base_asset_volume</th>\n      <th>taker_buy_quote_asset_volume</th>\n      <th>ignore</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      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2023-04-09 03:00:00</th>\n      <td>28036.31</td>\n      <td>28046.65</td>\n      <td>28018.63</td>\n      <td>28046.64</td>\n      <td>321.83306</td>\n      <td>1681010999999</td>\n      <td>9.020700e+06</td>\n      <td>8783</td>\n      <td>176.25914000</td>\n      <td>4940496.67911640</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-09 03:30:00</th>\n      <td>28046.64</td>\n      <td>28054.01</td>\n      <td>28027.70</td>\n      <td>28027.71</td>\n      <td>264.04898</td>\n      <td>1681012799999</td>\n      <td>7.405399e+06</td>\n      <td>7930</td>\n      <td>135.91265000</td>\n      <td>3811700.32621310</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-09 04:00:00</th>\n      <td>28027.71</td>\n      <td>28034.49</td>\n      <td>28010.00</td>\n      <td>28033.73</td>\n      <td>337.21687</td>\n      <td>1681014599999</td>\n      <td>9.451234e+06</td>\n      <td>7907</td>\n      <td>164.82296000</td>\n      <td>4619626.88520950</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-09 04:30:00</th>\n      <td>28033.72</td>\n      <td>28041.06</td>\n      <td>28017.05</td>\n      <td>28037.39</td>\n      <td>212.48153</td>\n      <td>1681016399999</td>\n      <td>5.956367e+06</td>\n      <td>7349</td>\n      <td>121.64797000</td>\n      <td>3409928.01939480</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-09 05:00:00</th>\n      <td>28037.38</td>\n      <td>28067.46</td>\n      <td>28037.38</td>\n      <td>28063.03</td>\n      <td>249.53440</td>\n      <td>1681018199999</td>\n      <td>6.999979e+06</td>\n      <td>7942</td>\n      <td>147.07855000</td>\n      <td>4125738.18034270</td>\n      <td>0</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 09:00:00</th>\n      <td>29313.86</td>\n      <td>29316.59</td>\n      <td>29250.96</td>\n      <td>29295.04</td>\n      <td>575.63694</td>\n      <td>1682760599999</td>\n      <td>1.685508e+07</td>\n      <td>15037</td>\n      <td>268.86672000</td>\n      <td>7872200.07680300</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 09:30:00</th>\n      <td>29295.03</td>\n      <td>29310.00</td>\n      <td>29269.98</td>\n      <td>29298.84</td>\n      <td>345.93353</td>\n      <td>1682762399999</td>\n      <td>1.013201e+07</td>\n      <td>14669</td>\n      <td>161.61119000</td>\n      <td>4733331.91448250</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 10:00:00</th>\n      <td>29298.85</td>\n      <td>29309.40</td>\n      <td>29245.45</td>\n      <td>29264.94</td>\n      <td>273.15436</td>\n      <td>1682764199999</td>\n      <td>7.998346e+06</td>\n      <td>12224</td>\n      <td>103.78381000</td>\n      <td>3039165.82852970</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 10:30:00</th>\n      <td>29264.93</td>\n      <td>29287.22</td>\n      <td>29241.55</td>\n      <td>29267.56</td>\n      <td>338.93320</td>\n      <td>1682765999999</td>\n      <td>9.919855e+06</td>\n      <td>12651</td>\n      <td>165.18398000</td>\n      <td>4834621.28181140</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2023-04-29 11:00:00</th>\n      <td>29267.56</td>\n      <td>29275.76</td>\n      <td>29245.99</td>\n      <td>29256.88</td>\n      <td>227.87772</td>\n      <td>1682767799999</td>\n      <td>6.668243e+06</td>\n      <td>8794</td>\n      <td>103.92586000</td>\n      <td>3041007.27774640</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>357 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "london_df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T19:01:17.489802Z",
     "end_time": "2023-04-29T19:01:17.567876Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "outputs": [
    {
     "data": {
      "text/plain": "{Timestamp('2023-04-08 00:00:00', freq='D'): {'asia': 0.04712585970674485},\n Timestamp('2023-04-09 00:00:00', freq='D'): {'us': 0.05007687176198956,\n  'london': -0.6042877967892418,\n  'asia': 1.379392792280717},\n Timestamp('2023-04-10 00:00:00', freq='D'): {'us': 0.43492200923586305,\n  'london': 0.14729991967065426,\n  'asia': 4.637731713586056},\n Timestamp('2023-04-11 00:00:00', freq='D'): {'us': 0.48657939179073195,\n  'london': 0.07060463696951069,\n  'asia': 1.899866216109064},\n Timestamp('2023-04-12 00:00:00', freq='D'): {'us': 0.0193756892793121,\n  'london': 0.22165277896628882,\n  'asia': -1.03428990911719},\n Timestamp('2023-04-13 00:00:00', freq='D'): {'us': 0.3681108974579392,\n  'london': 0.2855244515383808,\n  'asia': 1.6252973176253953},\n Timestamp('2023-04-14 00:00:00', freq='D'): {'us': -1.7684100962789495,\n  'london': 0.5578543460213503,\n  'asia': 0.30648084009134224},\n Timestamp('2023-04-15 00:00:00', freq='D'): {'us': -0.3287325249854449,\n  'london': 0.08294930875576277,\n  'asia': -0.5640213831849817},\n Timestamp('2023-04-16 00:00:00', freq='D'): {'us': -0.06244057502093803,\n  'london': 0.015430772992506834,\n  'asia': 0.03152324963443894},\n Timestamp('2023-04-17 00:00:00', freq='D'): {'us': -1.637639901805803,\n  'london': -0.849433710859427,\n  'asia': -2.8853318268543497},\n Timestamp('2023-04-18 00:00:00', freq='D'): {'us': 1.2710467880000527,\n  'london': 1.7834556162171518,\n  'asia': 3.2270855822933258},\n Timestamp('2023-04-19 00:00:00', freq='D'): {'us': 0.330307595739913,\n  'london': -3.0834124983485536,\n  'asia': -5.210366948529641},\n Timestamp('2023-04-20 00:00:00', freq='D'): {'us': -0.9885965553675368,\n  'london': 0.044533232576892096,\n  'asia': -1.9218949130294272},\n Timestamp('2023-04-21 00:00:00', freq='D'): {'us': -0.0922752015038292,\n  'london': -0.8457388603376846,\n  'asia': -3.472674388756415},\n Timestamp('2023-04-22 00:00:00', freq='D'): {'us': 1.3847362835498078,\n  'london': 0.04944656192218706,\n  'asia': 2.0321067064179608},\n Timestamp('2023-04-23 00:00:00', freq='D'): {'us': -0.4698738268966246,\n  'london': -0.1641054188856011,\n  'asia': -0.8133559335438773},\n Timestamp('2023-04-24 00:00:00', freq='D'): {'us': 0.08706943760176424,\n  'london': -1.2370441413766704,\n  'asia': -0.2887216257426893},\n Timestamp('2023-04-25 00:00:00', freq='D'): {'us': 0.14411297931004166,\n  'london': -0.25511972855348414,\n  'asia': 2.8710770591906583},\n Timestamp('2023-04-26 00:00:00', freq='D'): {'us': 3.6247179106407708,\n  'london': 1.9014571697485858,\n  'asia': 0.4045468679330676},\n Timestamp('2023-04-27 00:00:00', freq='D'): {'us': 0.7596151218818845,\n  'london': 0.06361434894484401,\n  'asia': 3.721517535101699},\n Timestamp('2023-04-28 00:00:00', freq='D'): {'us': -0.2550057411371799,\n  'london': -0.3381765273988978,\n  'asia': -0.5465044513970003},\n Timestamp('2023-04-29 00:00:00', freq='D'): {'us': -0.10172373948754883,\n  'london': -0.4017037686622039,\n  'asia': 0.33532696340607065}}"
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "deltas = {}\n",
    "for session_name, session_df in [('us', us_df), ('london', london_df), ('asia', asia_df)]:\n",
    "    groups = session_df.groupby(pd.Grouper(freq='D'))\n",
    "\n",
    "    for i, (name, group) in enumerate(groups):\n",
    "\n",
    "        if name not in deltas:\n",
    "            deltas[name] = dict()\n",
    "        delta = (group['close'].iloc[-1] - group['open'].iloc[0]) / group['open'].iloc[0] * 100\n",
    "        deltas[name][session_name] = delta\n",
    "\n",
    "# We must sort them because it may happen that the first day we have for London, US, Asia may not be exactly same, we may have\n",
    "# London and Asia starting from 20, and US from 21, in that case if we iterate first from US DataFrame, we will populate the deltas\n",
    "# dictionary with 21th day first, then the 20th day will be added at the end when iterating through Asia/London dataframe, that would break\n",
    "# the chart\n",
    "deltas = {k: v for k, v in sorted(deltas.items())}\n",
    "deltas"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T19:01:17.506817Z",
     "end_time": "2023-04-29T19:01:17.569545Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot the percentage deltas of the three sessions\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "keys = deltas.keys()\n",
    "us_values = [v.get('us', 0) for v in deltas.values()]\n",
    "london_values = [v.get('london', 0) for v in deltas.values()]\n",
    "asia_values = [v.get('asia', 0) for v in deltas.values()]\n",
    "\n",
    "ax.plot(keys, us_values, label='US')\n",
    "ax.plot(keys, london_values, label='London')\n",
    "ax.plot(keys, asia_values, label='Asia')\n",
    "\n",
    "ax.axhline(y=0, color='black', linestyle='--', linewidth=1)\n",
    "\n",
    "ax.set_title('% change BTC price per session')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('% price change')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T18:57:51.281973Z",
     "end_time": "2023-04-29T18:57:51.413394Z"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}