main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import requests\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import configparser\n",
    "import time\n",
    "from concurrent.futures import ThreadPoolExecutor, as_completed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Nansen API Token\n",
    "Nansen API Token: https://query.nansen.ai/users/me"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = configparser.RawConfigParser()\n",
    "config.read('config.ini')\n",
    "try:\n",
    "    NANSEN_KEY = config['info']['nansen_token']\n",
    "except KeyError:\n",
    "    os.system(\"cls\" if os.name == \"nt\" else \"clear\")\n",
    "    print(\"Run setup.py first\")\n",
    "    sys.exit(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set ERC20 Token name and address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "TOKEN_NAME = \"INJUSDT\" # TOKEN_NAME must be listed on Binance.\n",
    "TOKEN_ADDRESS = \"0xe28b3b32b6c345a34ff64674606124dd5aceca30\" # TOKEN_ADDRESS must always start with \"0x\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get data from Nansen API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "NANSEN_URL = 'https://query.api.nansen.ai/v1/questions/api_tgm_top_transactions_7d'\n",
    "NANSEN_PARAMS = {\"token\": TOKEN_ADDRESS}\n",
    "headers = {'Accept': 'application/json','Content-Type': 'application/json','Authorization': NANSEN_KEY}\n",
    "payload =  json.dumps({\"params\": NANSEN_PARAMS, \"accept_stale\": False})\n",
    "\n",
    "try:\n",
    "    r = requests.post(NANSEN_URL, headers=headers, data=payload)\n",
    "    r.raise_for_status()\n",
    "    nansen_df = pd.DataFrame.from_dict(r.json()['result_data'])\n",
    "except (requests.HTTPError, KeyError):\n",
    "    print('API call failed. Check your token or query.')\n",
    "    sys.exit(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                                  from_address                     time  \\\n",
       "0   0xd55923b9bd1bc209c3470e946cc1105b30a84b31  {'seconds': 1698692567}   \n",
       "1   0xf7dcfe0c18647ff66caa629439bbbe9a643c273b  {'seconds': 1698693071}   \n",
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       "4   0x3e12fae946d773b632db1ed03d295e7fe202a046  {'seconds': 1699000235}   \n",
       "5   0xb01629568c9af7bd6080d08cec27134917452a6c  {'seconds': 1699000571}   \n",
       "6   0x9ff41cba4a131350b3e3056c315dca6e5a63d9e4  {'seconds': 1698505811}   \n",
       "7   0xb514f4a77ce08d8694b242710920e8fa66dae1f5  {'seconds': 1698595751}   \n",
       "8   0xa7665b0ae33b3336db20df4caa42fa377067b4c0  {'seconds': 1698743531}   \n",
       "9   0xd5417e96dd04363c675e41ee6f30bf788412c719  {'seconds': 1698749003}   \n",
       "10  0xa9d1e08c7793af67e9d92fe308d5697fb81d3e43  {'seconds': 1698693131}   \n",
       "11  0x23c49601e5ed926919b9273a13dffa81979d617a  {'seconds': 1698470831}   \n",
       "12  0x956dc986221aca09e8530f3c9a12d981b4a55210  {'seconds': 1698672491}   \n",
       "13  0x3612547276b199cc044ea0a6d0a974af56776c14  {'seconds': 1698833231}   \n",
       "\n",
       "                                    to_address  \\\n",
       "0   0xf7dcfe0c18647ff66caa629439bbbe9a643c273b   \n",
       "1   0xa9d1e08c7793af67e9d92fe308d5697fb81d3e43   \n",
       "2   0x32cb160edf4b7d4d1390d0255d6693efe5381f28   \n",
       "3   0xa9d1e08c7793af67e9d92fe308d5697fb81d3e43   \n",
       "4   0xb01629568c9af7bd6080d08cec27134917452a6c   \n",
       "5   0xa9d1e08c7793af67e9d92fe308d5697fb81d3e43   \n",
       "6   0xb514f4a77ce08d8694b242710920e8fa66dae1f5   \n",
       "7   0xa7665b0ae33b3336db20df4caa42fa377067b4c0   \n",
       "8   0x3612547276b199cc044ea0a6d0a974af56776c14   \n",
       "9   0x66e422eaa2173d3aefd130239b55b2d3a2216f94   \n",
       "10  0xd5417e96dd04363c675e41ee6f30bf788412c719   \n",
       "11  0xb783767aeacd133dcfb07a6d6f6c16f46956c7ff   \n",
       "12  0xa9d1e08c7793af67e9d92fe308d5697fb81d3e43   \n",
       "13  0xd2e780b5287273da52c8f5449e2252c26721e6fb   \n",
       "\n",
       "                                 token_address  \\\n",
       "0   0xe28b3b32b6c345a34ff64674606124dd5aceca30   \n",
       "1   0xe28b3b32b6c345a34ff64674606124dd5aceca30   \n",
       "2   0xe28b3b32b6c345a34ff64674606124dd5aceca30   \n",
       "3   0xe28b3b32b6c345a34ff64674606124dd5aceca30   \n",
       "4   0xe28b3b32b6c345a34ff64674606124dd5aceca30   \n",
       "5   0xe28b3b32b6c345a34ff64674606124dd5aceca30   \n",
       "6   0xe28b3b32b6c345a34ff64674606124dd5aceca30   \n",
       "7   0xe28b3b32b6c345a34ff64674606124dd5aceca30   \n",
       "8   0xe28b3b32b6c345a34ff64674606124dd5aceca30   \n",
       "9   0xe28b3b32b6c345a34ff64674606124dd5aceca30   \n",
       "10  0xe28b3b32b6c345a34ff64674606124dd5aceca30   \n",
       "11  0xe28b3b32b6c345a34ff64674606124dd5aceca30   \n",
       "12  0xe28b3b32b6c345a34ff64674606124dd5aceca30   \n",
       "13  0xe28b3b32b6c345a34ff64674606124dd5aceca30   \n",
       "\n",
       "                                     transaction_hash         value  \n",
       "0   0xa96d6c09eddcb291dcba69407aeb2e8006950c182ccd...  71212.765778  \n",
       "1   0x3ba2dcfb37660f35e5f2bcd0884eafca0e0b14e04fe8...  71212.765778  \n",
       "2   0x6316e09aace0b0959c35f7de24c1b462f7481e48bf3a...  71212.765778  \n",
       "3   0x67afd54ab17b723c28b9acda457e47ffb4e3d96d2591...  71212.765778  \n",
       "4   0x80a8f885f03721ce89b289cef2c9ab35fdc463cf450e...  71179.331686  \n",
       "5   0x997210b8e0940f0362cf4c6bb4b4cd50dc07b0b6d94c...  71179.331686  \n",
       "6   0xcc63d58d784c2b2a14dc90773858f82e8016beb03173...  51458.100000  \n",
       "7   0xab0ac4fa01e8cdacdeeb55f8b4ee3060141ac4d26514...  44458.100000  \n",
       "8   0xd87ef9bd1cb6a064fc11e541f838a335aefcb3fd9d7c...  42158.100000  \n",
       "9   0x63e1d3672b2da1ce061be0dde4148c3c777ec8dc6156...  40958.649199  \n",
       "10  0x32da8f40154d8bed4ee76c76cbfaf88fec98ee02636b...  40861.802808  \n",
       "11  0x143938d2b4895940a2b2d8f624b6511c5aee418b5876...  36000.000000  \n",
       "12  0xd6903342f2250630c6e4c8285b285cb7656b939b31bc...  32303.937935  \n",
       "13  0xd322b89a590f4a28129ad40456ac491e48d209afb955...  30158.100000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nansen_df[:13 + 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fetch address label from Arkham"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_unique_addresses = set(nansen_df[['from_address', 'to_address', 'token_address']].values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_label(UNIQUE_ADDR):\n",
    "    ARKHAM_URL = f\"https://api.arkhamintelligence.com/intelligence/address/{UNIQUE_ADDR}?chain=ethereum\"\n",
    "    \n",
    "    try:\n",
    "        response = requests.get(ARKHAM_URL).json()\n",
    "        arkham_entity_name = response.get('arkhamEntity', {}).get('name')\n",
    "        arkham_label_name = response.get('arkhamLabel', {}).get('name')\n",
    "        populated_tags_list = response.get('populatedTags', [])\n",
    "        populated_tags = populated_tags_list[0].get('label') if populated_tags_list else None\n",
    "        \n",
    "        if arkham_entity_name and arkham_label_name:\n",
    "            TOKEN_LABEL = f\"{arkham_entity_name}: {arkham_label_name}\"\n",
    "        elif arkham_entity_name:\n",
    "            TOKEN_LABEL = arkham_entity_name\n",
    "        elif arkham_label_name:\n",
    "            TOKEN_LABEL = arkham_label_name\n",
    "        elif populated_tags:\n",
    "            TOKEN_LABEL = populated_tags\n",
    "        else:\n",
    "            TOKEN_LABEL = \"Unknown\"\n",
    "    except Exception as e:\n",
    "        TOKEN_LABEL = \"Unknown\"\n",
    "\n",
    "    return f\"{TOKEN_LABEL} [{UNIQUE_ADDR[:8]}]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "address_to_label = {}\n",
    "\n",
    "with ThreadPoolExecutor(max_workers=5) as executor:\n",
    "    futures = {executor.submit(fetch_label, addr): addr for addr in all_unique_addresses}\n",
    "\n",
    "    for future in as_completed(futures):\n",
    "        addr = futures[future]\n",
    "        address_to_label[addr] = future.result()\n",
    "\n",
    "        time.sleep(0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apply label to dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_cols = ['from_address', 'to_address', 'token_address']\n",
    "nansen_df[target_cols] = nansen_df[target_cols].applymap(lambda x: address_to_label.get(x, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "nansen_df['time'] = pd.to_datetime(nansen_df['time'].str['seconds'], unit='s').dt.strftime('%Y-%m-%d %H:%M:%S')\n",
    "nansen_df['value'] = nansen_df['value'].astype(float).map('{:,.0f}'.format)\n",
    "nansen_df = nansen_df[['token_address', 'from_address', 'to_address', 'value', 'time']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>token_address</th>\n",
       "      <th>from_address</th>\n",
       "      <th>to_address</th>\n",
       "      <th>value</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Injective Protocol: Injective Token (INJ) [0xe...</td>\n",
       "      <td>Unknown [0xd55923]</td>\n",
       "      <td>Coinbase Deposit [0xf7dcfe]</td>\n",
       "      <td>71,213</td>\n",
       "      <td>2023-10-30 19:02:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Injective Protocol: Injective Token (INJ) [0xe...</td>\n",
       "      <td>Coinbase Deposit [0xf7dcfe]</td>\n",
       "      <td>Coinbase [0xa9d1e0]</td>\n",
       "      <td>71,213</td>\n",
       "      <td>2023-10-30 19:11:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Injective Protocol: Injective Token (INJ) [0xe...</td>\n",
       "      <td>Unknown [0x85d17b]</td>\n",
       "      <td>Coinbase Deposit [0x32cb16]</td>\n",
       "      <td>71,213</td>\n",
       "      <td>2023-11-02 05:06:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Injective Protocol: Injective Token (INJ) [0xe...</td>\n",
       "      <td>Coinbase Deposit [0x32cb16]</td>\n",
       "      <td>Coinbase [0xa9d1e0]</td>\n",
       "      <td>71,213</td>\n",
       "      <td>2023-11-02 05:11:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Injective Protocol: Injective Token (INJ) [0xe...</td>\n",
       "      <td>Unknown [0x3e12fa]</td>\n",
       "      <td>Unknown [0xb01629]</td>\n",
       "      <td>71,179</td>\n",
       "      <td>2023-11-03 08:30:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Injective Protocol: Injective Token (INJ) [0xe...</td>\n",
       "      <td>Unknown [0xb01629]</td>\n",
       "      <td>Coinbase [0xa9d1e0]</td>\n",
       "      <td>71,179</td>\n",
       "      <td>2023-11-03 08:36:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Injective Protocol: Injective Token (INJ) [0xe...</td>\n",
       "      <td>Coinbase Prime Custody [0x9ff41c]</td>\n",
       "      <td>Coinbase Prime Custody [0xb514f4]</td>\n",
       "      <td>51,458</td>\n",
       "      <td>2023-10-28 15:10:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Injective Protocol: Injective Token (INJ) [0xe...</td>\n",
       "      <td>Coinbase Prime Custody [0xb514f4]</td>\n",
       "      <td>Coinbase Prime Custody [0xa7665b]</td>\n",
       "      <td>44,458</td>\n",
       "      <td>2023-10-29 16:09:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Injective Protocol: Injective Token (INJ) [0xe...</td>\n",
       "      <td>Coinbase Prime Custody [0xa7665b]</td>\n",
       "      <td>Coinbase Prime Custody [0x361254]</td>\n",
       "      <td>42,158</td>\n",
       "      <td>2023-10-31 09:12:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Injective Protocol: Injective Token (INJ) [0xe...</td>\n",
       "      <td>Early EZ Holder [0xd5417e]</td>\n",
       "      <td>Unknown [0x66e422]</td>\n",
       "      <td>40,959</td>\n",
       "      <td>2023-10-31 10:43:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Injective Protocol: Injective Token (INJ) [0xe...</td>\n",
       "      <td>Coinbase [0xa9d1e0]</td>\n",
       "      <td>Early EZ Holder [0xd5417e]</td>\n",
       "      <td>40,862</td>\n",
       "      <td>2023-10-30 19:12:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Injective Protocol: Injective Token (INJ) [0xe...</td>\n",
       "      <td>Bithumb [0x23c496]</td>\n",
       "      <td>Bithumb [0xb78376]</td>\n",
       "      <td>36,000</td>\n",
       "      <td>2023-10-28 05:27:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Injective Protocol: Injective Token (INJ) [0xe...</td>\n",
       "      <td>Coinbase Deposit [0x956dc9]</td>\n",
       "      <td>Coinbase [0xa9d1e0]</td>\n",
       "      <td>32,304</td>\n",
       "      <td>2023-10-30 13:28:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Injective Protocol: Injective Token (INJ) [0xe...</td>\n",
       "      <td>Coinbase Prime Custody [0x361254]</td>\n",
       "      <td>Coinbase Prime Custody [0xd2e780]</td>\n",
       "      <td>30,158</td>\n",
       "      <td>2023-11-01 10:07:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        token_address  \\\n",
       "0   Injective Protocol: Injective Token (INJ) [0xe...   \n",
       "1   Injective Protocol: Injective Token (INJ) [0xe...   \n",
       "2   Injective Protocol: Injective Token (INJ) [0xe...   \n",
       "3   Injective Protocol: Injective Token (INJ) [0xe...   \n",
       "4   Injective Protocol: Injective Token (INJ) [0xe...   \n",
       "5   Injective Protocol: Injective Token (INJ) [0xe...   \n",
       "6   Injective Protocol: Injective Token (INJ) [0xe...   \n",
       "7   Injective Protocol: Injective Token (INJ) [0xe...   \n",
       "8   Injective Protocol: Injective Token (INJ) [0xe...   \n",
       "9   Injective Protocol: Injective Token (INJ) [0xe...   \n",
       "10  Injective Protocol: Injective Token (INJ) [0xe...   \n",
       "11  Injective Protocol: Injective Token (INJ) [0xe...   \n",
       "12  Injective Protocol: Injective Token (INJ) [0xe...   \n",
       "13  Injective Protocol: Injective Token (INJ) [0xe...   \n",
       "\n",
       "                         from_address                         to_address  \\\n",
       "0                  Unknown [0xd55923]        Coinbase Deposit [0xf7dcfe]   \n",
       "1         Coinbase Deposit [0xf7dcfe]                Coinbase [0xa9d1e0]   \n",
       "2                  Unknown [0x85d17b]        Coinbase Deposit [0x32cb16]   \n",
       "3         Coinbase Deposit [0x32cb16]                Coinbase [0xa9d1e0]   \n",
       "4                  Unknown [0x3e12fa]                 Unknown [0xb01629]   \n",
       "5                  Unknown [0xb01629]                Coinbase [0xa9d1e0]   \n",
       "6   Coinbase Prime Custody [0x9ff41c]  Coinbase Prime Custody [0xb514f4]   \n",
       "7   Coinbase Prime Custody [0xb514f4]  Coinbase Prime Custody [0xa7665b]   \n",
       "8   Coinbase Prime Custody [0xa7665b]  Coinbase Prime Custody [0x361254]   \n",
       "9          Early EZ Holder [0xd5417e]                 Unknown [0x66e422]   \n",
       "10                Coinbase [0xa9d1e0]         Early EZ Holder [0xd5417e]   \n",
       "11                 Bithumb [0x23c496]                 Bithumb [0xb78376]   \n",
       "12        Coinbase Deposit [0x956dc9]                Coinbase [0xa9d1e0]   \n",
       "13  Coinbase Prime Custody [0x361254]  Coinbase Prime Custody [0xd2e780]   \n",
       "\n",
       "     value                 time  \n",
       "0   71,213  2023-10-30 19:02:47  \n",
       "1   71,213  2023-10-30 19:11:11  \n",
       "2   71,213  2023-11-02 05:06:47  \n",
       "3   71,213  2023-11-02 05:11:11  \n",
       "4   71,179  2023-11-03 08:30:35  \n",
       "5   71,179  2023-11-03 08:36:11  \n",
       "6   51,458  2023-10-28 15:10:11  \n",
       "7   44,458  2023-10-29 16:09:11  \n",
       "8   42,158  2023-10-31 09:12:11  \n",
       "9   40,959  2023-10-31 10:43:23  \n",
       "10  40,862  2023-10-30 19:12:11  \n",
       "11  36,000  2023-10-28 05:27:11  \n",
       "12  32,304  2023-10-30 13:28:11  \n",
       "13  30,158  2023-11-01 10:07:11  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nansen_df[:13 + 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Binance data for integration with Nansen data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from market_data import get_data\n",
    "PRICE_DATA = get_data(ticker=TOKEN_NAME, days=10, ts=\"5m\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Match the time format for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "PRICE_DATA.index = pd.to_datetime(PRICE_DATA.index)\n",
    "nansen_df['time'] = pd.to_datetime(nansen_df['time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "import numpy as np\n",
    "from scipy.spatial import cKDTree\n",
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting price and transaction value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "nansen_df = nansen_df[:13 + 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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   "source": [
    "fig = go.Figure()\n",
    "\n",
    "# Add price line\n",
    "fig.add_trace(go.Scatter(x=PRICE_DATA.index, y=PRICE_DATA['close'], mode='lines', name='Price'))\n",
    "\n",
    "label_positions = []\n",
    "kdtree = None\n",
    "point_x, point_y, label_x, label_y = [], [], [], []\n",
    "\n",
    "# Calculate y-axis range for label positioning\n",
    "min_price, max_price = min(PRICE_DATA['close']), max(PRICE_DATA['close'])\n",
    "buffer = (max_price - min_price) * 0.1\n",
    "\n",
    "chart_labels = []\n",
    "legend_dict = {}\n",
    "\n",
    "for idx, (i, row) in enumerate(nansen_df.iterrows()):\n",
    "    trade_time = row['time']\n",
    "    trade_value = row['value']\n",
    "    \n",
    "    # Address info\n",
    "    from_address = row['from_address'].split(' ')[0]\n",
    "    to_address = row['to_address'].split(' ')[0]\n",
    "    \n",
    "    # String for legend and chart label\n",
    "    legend_str = f\"{from_address} -> {to_address}\"\n",
    "    chart_label = f\"[{chr(65 + idx)}] {trade_value}\"\n",
    "    \n",
    "    # Update legend dictionary\n",
    "    legend_dict[chr(65 + idx)] = legend_str\n",
    "    \n",
    "    closest_time_index = np.abs(PRICE_DATA.index.to_pydatetime() - pd.Timestamp(trade_time).to_pydatetime()).argmin()\n",
    "    closest_time = PRICE_DATA.index[closest_time_index]\n",
    "    trade_price = PRICE_DATA.loc[closest_time, 'close']\n",
    "\n",
    "    # Store actual points\n",
    "    point_x.append(closest_time)\n",
    "    point_y.append(trade_price)\n",
    "\n",
    "    # Generate candidate label positions\n",
    "    candidate_positions = [(trade_price + i * buffer, 'up') for i in range(1, 6)] + \\\n",
    "                          [(trade_price - i * buffer, 'down') for i in range(1, 6)]\n",
    "\n",
    "    # Use k-d tree to find optimal label position\n",
    "    if kdtree:\n",
    "        distances, _ = kdtree.query(np.array([pos[0] for pos in candidate_positions]).reshape(-1, 1))\n",
    "    else:\n",
    "        distances = [float('inf')] * len(candidate_positions)\n",
    "\n",
    "    optimal_position, _ = candidate_positions[np.argmax(distances)]\n",
    "    label_positions.append([optimal_position])\n",
    "    kdtree = cKDTree(np.array(label_positions).reshape(-1, 1))\n",
    "\n",
    "    label_x.append(closest_time)\n",
    "    label_y.append(optimal_position)\n",
    "    chart_labels.append(chart_label)\n",
    "\n",
    "# Add points and labels\n",
    "fig.add_trace(go.Scatter(x=point_x, y=point_y, mode='markers', marker=dict(color='red', size=5), name='TX Points', legendgroup='group'))\n",
    "fig.add_trace(go.Scatter(x=label_x, y=label_y, mode='text', text=chart_labels, name='TX Values', textfont=dict(color='black', size=12)))\n",
    "\n",
    "\n",
    "# Add lines connecting labels to points\n",
    "for px, py, lx, ly in zip(point_x, point_y, label_x, label_y):\n",
    "    fig.add_shape(type=\"line\", x0=px, y0=py, x1=lx, y1=ly, line=dict(color=\"Gray\", width=0.5))\n",
    "\n",
    "# Update layout with custom legend\n",
    "legend_text = \"<br>\".join([f\"{key}: {value}\" for key, value in legend_dict.items()])\n",
    "one_day_later = max(PRICE_DATA.index) + timedelta(days=1)\n",
    "fig.update_layout(\n",
    "    title=f\"{TOKEN_NAME} 5m with Top Transactions\",\n",
    "    xaxis=dict(\n",
    "        range=[min(PRICE_DATA.index), one_day_later]\n",
    "    ),\n",
    "    xaxis_title='Time',\n",
    "    yaxis_title='Price',\n",
    "    xaxis_rangeslider_visible=False,\n",
    "    showlegend=False,\n",
    "    annotations=[dict(\n",
    "        x=1.05,\n",
    "        y=1,\n",
    "        align=\"left\",\n",
    "        valign=\"top\",\n",
    "        text=legend_text,\n",
    "        showarrow=False,\n",
    "        xref=\"paper\",\n",
    "        yref=\"paper\",\n",
    "        xanchor=\"left\",\n",
    "        yanchor=\"top\",\n",
    "        width=300\n",
    "    )],\n",
    "    height=550,\n",
    "    margin=dict(\n",
    "        r=250,\n",
    "        b=150\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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