alpaca_plays.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import datetime as dt\n",
    "import os\n",
    "import io\n",
    "from github import Github\n",
    "import logging\n",
    "\n",
    "import Universes\n",
    "import importlib # for not reloading the utils every time after adjustment\n",
    "import utils_for_alpaca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'CorporateActionsRequest' from 'alpaca.trading.requests' (c:\\Users\\kalmar\\.conda\\envs\\alpaca_env\\lib\\site-packages\\alpaca\\trading\\requests.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01malpaca\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01menums\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Sort\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01malpaca\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrading\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01menums\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OrderSide, TimeInForce, AssetClass, AssetStatus, AssetExchange, OrderStatus, OrderType, OrderClass, QueryOrderStatus, CorporateActionType, CorporateActionSubType\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01malpaca\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrading\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrequests\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GetCalendarRequest, GetAssetsRequest, GetOrdersRequest, MarketOrderRequest, LimitOrderRequest, StopLossRequest, TakeProfitRequest, TrailingStopOrderRequest, ReplaceOrderRequest, GetPortfolioHistoryRequest, GetCorporateAnnouncementsRequest, CorporateActionsRequest\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01malpaca\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrequests\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m StockLatestQuoteRequest, StockTradesRequest, StockLatestTradeRequest, StockQuotesRequest, StockBarsRequest, StockSnapshotRequest, StockLatestBarRequest\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01malpaca\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtimeframe\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TimeFrame, TimeFrameUnit\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'CorporateActionsRequest' from 'alpaca.trading.requests' (c:\\Users\\kalmar\\.conda\\envs\\alpaca_env\\lib\\site-packages\\alpaca\\trading\\requests.py)"
     ]
    }
   ],
   "source": [
    "\n",
    "import alpaca\n",
    "from alpaca.common.enums import Sort\n",
    "from alpaca.trading.enums import OrderSide, TimeInForce, AssetClass, AssetStatus, AssetExchange, OrderStatus, OrderType, OrderClass, QueryOrderStatus, CorporateActionType, CorporateActionSubType\n",
    "from alpaca.trading.requests import GetCalendarRequest, GetAssetsRequest, GetOrdersRequest, MarketOrderRequest, LimitOrderRequest, StopLossRequest, TakeProfitRequest, TrailingStopOrderRequest, ReplaceOrderRequest, GetPortfolioHistoryRequest, GetCorporateAnnouncementsRequest\n",
    "from alpaca.data.requests import StockLatestQuoteRequest, StockTradesRequest, StockLatestTradeRequest, StockQuotesRequest, StockBarsRequest, StockSnapshotRequest, StockLatestBarRequest, CorporateActionsRequest\n",
    "from alpaca.data.timeframe import TimeFrame, TimeFrameUnit\n",
    "from alpaca.data.enums import Adjustment, DataFeed, Exchange\n",
    "from alpaca.trading.client import TradingClient\n",
    "from alpaca.data import StockHistoricalDataClient\n",
    "from alpaca.broker.client import BrokerClient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-01-20 23:02:48,699 - root - INFO - Successfully read config for PaperTesting strategy - [utils_for_alpaca.py:109]\n",
      "2025-01-20 23:02:48,928 - root - INFO - Strategy PaperTesting has been initialized - [utils_for_alpaca.py:241]\n"
     ]
    }
   ],
   "source": [
    "alpaca = utils_for_alpaca.MyAlpaca(os.environ['ALPACA_API_KEY_PAPER'],os.environ['ALPACA_API_SECRET_PAPER'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "API_KEY_PAPER = os.environ['ALPACA_API_KEY_PAPER']\n",
    "API_SECRET_PAPER = os.environ['ALPACA_API_SECRET_PAPER']\n",
    "stock_client = StockHistoricalDataClient(API_KEY_PAPER,API_SECRET_PAPER)\n",
    "broker_client = BrokerClient(API_KEY_PAPER,API_SECRET_PAPER,sandbox=False,api_version=\"v2\")\n",
    "trading_client = TradingClient(API_KEY_PAPER, API_SECRET_PAPER) # dir(trading_client)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Account information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data from account object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Representation.__pretty__ of {   'account_blocked': False,\n",
       "    'account_number': 'PA3BFSDVG64I',\n",
       "    'accrued_fees': '0',\n",
       "    'buying_power': '92497.76',\n",
       "    'cash': '-114186.74',\n",
       "    'created_at': datetime.datetime(2024, 5, 3, 20, 1, 7, 609557, tzinfo=TzInfo(UTC)),\n",
       "    'crypto_status': <AccountStatus.ACTIVE: 'ACTIVE'>,\n",
       "    'currency': 'USD',\n",
       "    'daytrade_count': 0,\n",
       "    'daytrading_buying_power': '92497.76',\n",
       "    'equity': '106136.28',\n",
       "    'id': UUID('dadd0de5-8668-43d1-8e56-1c7bde71b3bc'),\n",
       "    'initial_margin': '121974.41',\n",
       "    'last_equity': '104719.74',\n",
       "    'last_maintenance_margin': '81595.3',\n",
       "    'long_market_value': '220323.02',\n",
       "    'maintenance_margin': '82634.97',\n",
       "    'multiplier': '4',\n",
       "    'non_marginable_buying_power': '0',\n",
       "    'options_approved_level': 2,\n",
       "    'options_buying_power': '23501.3',\n",
       "    'options_trading_level': 2,\n",
       "    'pattern_day_trader': True,\n",
       "    'pending_transfer_in': None,\n",
       "    'pending_transfer_out': None,\n",
       "    'portfolio_value': '106136.28',\n",
       "    'regt_buying_power': '0',\n",
       "    'short_market_value': '0',\n",
       "    'shorting_enabled': True,\n",
       "    'sma': '103439.03',\n",
       "    'status': <AccountStatus.ACTIVE: 'ACTIVE'>,\n",
       "    'trade_suspended_by_user': False,\n",
       "    'trading_blocked': False,\n",
       "    'transfers_blocked': False}>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account = alpaca.trading_client.get_account()\n",
    "#dir(account)\n",
    "# Account properties description: https://docs.alpaca.markets/docs/account-plans#account-properties\n",
    "account.__pretty__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1416.54"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check our current balance vs. our balance at the last market close\n",
    "balance_change = round(float(account.equity) - float(account.last_equity),2) \n",
    "balance_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account.buying_power # buying power is 2x equity if 2000 < equity < 25000, and 4x equity if equity > 25000\n",
    "account.regt_buying_power # regulation T buying power = (2 x equity) - (long_position_value - short_position_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Account activity types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>activity_type</th>\n",
       "      <th>transaction_time</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>qty</th>\n",
       "      <th>side</th>\n",
       "      <th>symbol</th>\n",
       "      <th>leaves_qty</th>\n",
       "      <th>order_id</th>\n",
       "      <th>cum_qty</th>\n",
       "      <th>order_status</th>\n",
       "      <th>activity_sub_type</th>\n",
       "      <th>date</th>\n",
       "      <th>net_amount</th>\n",
       "      <th>description</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20241220105727192::63ab8f03-0deb-4836-bd62-333...</td>\n",
       "      <td>FILL</td>\n",
       "      <td>2024-12-20T15:57:27.19223Z</td>\n",
       "      <td>fill</td>\n",
       "      <td>79.07</td>\n",
       "      <td>63</td>\n",
       "      <td>buy</td>\n",
       "      <td>PBH</td>\n",
       "      <td>0</td>\n",
       "      <td>13b56759-4723-4735-9fb8-effc19317ad3</td>\n",
       "      <td>74</td>\n",
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       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20241220105726781::63e587d5-05f5-49cc-9d9d-d35...</td>\n",
       "      <td>FILL</td>\n",
       "      <td>2024-12-20T15:57:26.781224Z</td>\n",
       "      <td>partial_fill</td>\n",
       "      <td>79.07</td>\n",
       "      <td>7</td>\n",
       "      <td>buy</td>\n",
       "      <td>PBH</td>\n",
       "      <td>63</td>\n",
       "      <td>13b56759-4723-4735-9fb8-effc19317ad3</td>\n",
       "      <td>11</td>\n",
       "      <td>partially_filled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20241220105726597::f23f0da9-0021-448e-94fc-891...</td>\n",
       "      <td>FILL</td>\n",
       "      <td>2024-12-20T15:57:26.597409Z</td>\n",
       "      <td>fill</td>\n",
       "      <td>79.07</td>\n",
       "      <td>79</td>\n",
       "      <td>buy</td>\n",
       "      <td>PBH</td>\n",
       "      <td>0</td>\n",
       "      <td>8686851d-4e61-478c-a66c-8d047f2d6b7f</td>\n",
       "      <td>79</td>\n",
       "      <td>filled</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20241220105726539::172a9899-2093-4033-85b7-aa2...</td>\n",
       "      <td>FILL</td>\n",
       "      <td>2024-12-20T15:57:26.539478Z</td>\n",
       "      <td>fill</td>\n",
       "      <td>79.07</td>\n",
       "      <td>85</td>\n",
       "      <td>buy</td>\n",
       "      <td>PBH</td>\n",
       "      <td>0</td>\n",
       "      <td>c001d541-5a5c-43d8-846b-1da43da187d3</td>\n",
       "      <td>85</td>\n",
       "      <td>filled</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20241220105726463::531307bf-bfa5-4249-916c-a81...</td>\n",
       "      <td>FILL</td>\n",
       "      <td>2024-12-20T15:57:26.463031Z</td>\n",
       "      <td>partial_fill</td>\n",
       "      <td>79.07</td>\n",
       "      <td>4</td>\n",
       "      <td>buy</td>\n",
       "      <td>PBH</td>\n",
       "      <td>70</td>\n",
       "      <td>13b56759-4723-4735-9fb8-effc19317ad3</td>\n",
       "      <td>4</td>\n",
       "      <td>partially_filled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>20241217093029509::23f15f91-ec68-4383-a2ae-fc6...</td>\n",
       "      <td>FILL</td>\n",
       "      <td>2024-12-17T14:30:29.509455Z</td>\n",
       "      <td>partial_fill</td>\n",
       "      <td>31.86</td>\n",
       "      <td>10</td>\n",
       "      <td>buy</td>\n",
       "      <td>CNP</td>\n",
       "      <td>6</td>\n",
       "      <td>c4b3ebad-84a0-42c8-990f-cbbe74b51757</td>\n",
       "      <td>10</td>\n",
       "      <td>partially_filled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>20241217093023348::09dce7ab-e953-498b-894c-c50...</td>\n",
       "      <td>FILL</td>\n",
       "      <td>2024-12-17T14:30:23.348259Z</td>\n",
       "      <td>fill</td>\n",
       "      <td>137.87</td>\n",
       "      <td>1</td>\n",
       "      <td>buy</td>\n",
       "      <td>LYV</td>\n",
       "      <td>0</td>\n",
       "      <td>1fe28d40-2048-48b0-9656-7977701875d6</td>\n",
       "      <td>3</td>\n",
       "      <td>filled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>20241217093023095::9c6cdcc1-45c1-45b1-9352-c33...</td>\n",
       "      <td>FILL</td>\n",
       "      <td>2024-12-17T14:30:23.095575Z</td>\n",
       "      <td>partial_fill</td>\n",
       "      <td>137.87</td>\n",
       "      <td>2</td>\n",
       "      <td>buy</td>\n",
       "      <td>LYV</td>\n",
       "      <td>1</td>\n",
       "      <td>1fe28d40-2048-48b0-9656-7977701875d6</td>\n",
       "      <td>2</td>\n",
       "      <td>partially_filled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>20241217000000000::d5049a34-99e5-4bc7-8623-92e...</td>\n",
       "      <td>FEE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>REG</td>\n",
       "      <td>2024-12-17</td>\n",
       "      <td>-3.55</td>\n",
       "      <td>REG fee for proceed of $127652.86 on 2024-12-1...</td>\n",
       "      <td>executed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>20241217000000000::31b1304e-b947-4935-8a2f-bd9...</td>\n",
       "      <td>FEE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TAF</td>\n",
       "      <td>2024-12-17</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>TAF fee for proceed of 1535 shares (5 trades) ...</td>\n",
       "      <td>executed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   id activity_type  \\\n",
       "0   20241220105727192::63ab8f03-0deb-4836-bd62-333...          FILL   \n",
       "1   20241220105726781::63e587d5-05f5-49cc-9d9d-d35...          FILL   \n",
       "2   20241220105726597::f23f0da9-0021-448e-94fc-891...          FILL   \n",
       "3   20241220105726539::172a9899-2093-4033-85b7-aa2...          FILL   \n",
       "4   20241220105726463::531307bf-bfa5-4249-916c-a81...          FILL   \n",
       "..                                                ...           ...   \n",
       "95  20241217093029509::23f15f91-ec68-4383-a2ae-fc6...          FILL   \n",
       "96  20241217093023348::09dce7ab-e953-498b-894c-c50...          FILL   \n",
       "97  20241217093023095::9c6cdcc1-45c1-45b1-9352-c33...          FILL   \n",
       "98  20241217000000000::d5049a34-99e5-4bc7-8623-92e...           FEE   \n",
       "99  20241217000000000::31b1304e-b947-4935-8a2f-bd9...           FEE   \n",
       "\n",
       "               transaction_time          type   price  qty side symbol  \\\n",
       "0    2024-12-20T15:57:27.19223Z          fill   79.07   63  buy    PBH   \n",
       "1   2024-12-20T15:57:26.781224Z  partial_fill   79.07    7  buy    PBH   \n",
       "2   2024-12-20T15:57:26.597409Z          fill   79.07   79  buy    PBH   \n",
       "3   2024-12-20T15:57:26.539478Z          fill   79.07   85  buy    PBH   \n",
       "4   2024-12-20T15:57:26.463031Z  partial_fill   79.07    4  buy    PBH   \n",
       "..                          ...           ...     ...  ...  ...    ...   \n",
       "95  2024-12-17T14:30:29.509455Z  partial_fill   31.86   10  buy    CNP   \n",
       "96  2024-12-17T14:30:23.348259Z          fill  137.87    1  buy    LYV   \n",
       "97  2024-12-17T14:30:23.095575Z  partial_fill  137.87    2  buy    LYV   \n",
       "98                          NaN           NaN     NaN  NaN  NaN    NaN   \n",
       "99                          NaN           NaN     NaN  NaN  NaN    NaN   \n",
       "\n",
       "   leaves_qty                              order_id cum_qty      order_status  \\\n",
       "0           0  13b56759-4723-4735-9fb8-effc19317ad3      74            filled   \n",
       "1          63  13b56759-4723-4735-9fb8-effc19317ad3      11  partially_filled   \n",
       "2           0  8686851d-4e61-478c-a66c-8d047f2d6b7f      79            filled   \n",
       "3           0  c001d541-5a5c-43d8-846b-1da43da187d3      85            filled   \n",
       "4          70  13b56759-4723-4735-9fb8-effc19317ad3       4  partially_filled   \n",
       "..        ...                                   ...     ...               ...   \n",
       "95          6  c4b3ebad-84a0-42c8-990f-cbbe74b51757      10  partially_filled   \n",
       "96          0  1fe28d40-2048-48b0-9656-7977701875d6       3            filled   \n",
       "97          1  1fe28d40-2048-48b0-9656-7977701875d6       2  partially_filled   \n",
       "98        NaN                                   NaN     NaN               NaN   \n",
       "99        NaN                                   NaN     NaN               NaN   \n",
       "\n",
       "   activity_sub_type        date net_amount  \\\n",
       "0                NaN         NaN        NaN   \n",
       "1                NaN         NaN        NaN   \n",
       "2                NaN         NaN        NaN   \n",
       "3                NaN         NaN        NaN   \n",
       "4                NaN         NaN        NaN   \n",
       "..               ...         ...        ...   \n",
       "95               NaN         NaN        NaN   \n",
       "96               NaN         NaN        NaN   \n",
       "97               NaN         NaN        NaN   \n",
       "98               REG  2024-12-17      -3.55   \n",
       "99               TAF  2024-12-17      -0.26   \n",
       "\n",
       "                                          description    status  \n",
       "0                                                 NaN       NaN  \n",
       "1                                                 NaN       NaN  \n",
       "2                                                 NaN       NaN  \n",
       "3                                                 NaN       NaN  \n",
       "4                                                 NaN       NaN  \n",
       "..                                                ...       ...  \n",
       "95                                                NaN       NaN  \n",
       "96                                                NaN       NaN  \n",
       "97                                                NaN       NaN  \n",
       "98  REG fee for proceed of $127652.86 on 2024-12-1...  executed  \n",
       "99  TAF fee for proceed of 1535 shares (5 trades) ...  executed  \n",
       "\n",
       "[100 rows x 17 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here is smth wrong with limits\n",
    "endpoint = '/account/activities'\n",
    "params = {'after':'2023-11-30'}\n",
    "pd.DataFrame(alpaca.trading_client.get(path=endpoint, data=params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>activity_type</th>\n",
       "      <th>activity_sub_type</th>\n",
       "      <th>date</th>\n",
       "      <th>net_amount</th>\n",
       "      <th>description</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20241218000000000::d8d0cfa4-5263-45dc-922f-259...</td>\n",
       "      <td>FEE</td>\n",
       "      <td>TAF</td>\n",
       "      <td>2024-12-18</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>TAF fee for proceed of 844 shares (8 trades) o...</td>\n",
       "      <td>executed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20241218000000000::280a7b24-9664-4eaf-acc6-e12...</td>\n",
       "      <td>FEE</td>\n",
       "      <td>REG</td>\n",
       "      <td>2024-12-18</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>REG fee for proceed of $23354.98 on 2024-12-18...</td>\n",
       "      <td>executed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20241217000000000::d5049a34-99e5-4bc7-8623-92e...</td>\n",
       "      <td>FEE</td>\n",
       "      <td>REG</td>\n",
       "      <td>2024-12-17</td>\n",
       "      <td>-3.55</td>\n",
       "      <td>REG fee for proceed of $127652.86 on 2024-12-1...</td>\n",
       "      <td>executed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20241217000000000::31b1304e-b947-4935-8a2f-bd9...</td>\n",
       "      <td>FEE</td>\n",
       "      <td>TAF</td>\n",
       "      <td>2024-12-17</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>TAF fee for proceed of 1535 shares (5 trades) ...</td>\n",
       "      <td>executed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20241216000000000::bce28164-5e3e-4805-8bb4-bfb...</td>\n",
       "      <td>FEE</td>\n",
       "      <td>REG</td>\n",
       "      <td>2024-12-16</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>REG fee for proceed of $8257.82 on 2024-12-16 ...</td>\n",
       "      <td>executed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>20240606000000000::00a672de-6c9b-476b-ad63-f9c...</td>\n",
       "      <td>FEE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-06-06</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>TAF fee for proceed of 5561 shares (12 trades)...</td>\n",
       "      <td>executed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>20240603000000000::85796647-02bd-4713-a377-241...</td>\n",
       "      <td>FEE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-06-03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>TAF fee for proceed of 14 shares (2 trades) on...</td>\n",
       "      <td>executed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>20240603000000000::4cf843a8-de5e-4191-81af-5b2...</td>\n",
       "      <td>FEE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-06-03</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>REG fee for proceed of $15879.55 on 2024-06-03...</td>\n",
       "      <td>executed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>20240513000000000::3697788f-038f-4542-838f-8e3...</td>\n",
       "      <td>FEE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-05-13</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>TAF fee for proceed of 100 shares (7 trades) o...</td>\n",
       "      <td>executed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>20240513000000000::266e3e2c-966a-4346-8c17-9e5...</td>\n",
       "      <td>FEE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-05-13</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>REG fee for proceed of $1216.00 on 2024-05-13 ...</td>\n",
       "      <td>executed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   id activity_type  \\\n",
       "0   20241218000000000::d8d0cfa4-5263-45dc-922f-259...           FEE   \n",
       "1   20241218000000000::280a7b24-9664-4eaf-acc6-e12...           FEE   \n",
       "2   20241217000000000::d5049a34-99e5-4bc7-8623-92e...           FEE   \n",
       "3   20241217000000000::31b1304e-b947-4935-8a2f-bd9...           FEE   \n",
       "4   20241216000000000::bce28164-5e3e-4805-8bb4-bfb...           FEE   \n",
       "..                                                ...           ...   \n",
       "93  20240606000000000::00a672de-6c9b-476b-ad63-f9c...           FEE   \n",
       "94  20240603000000000::85796647-02bd-4713-a377-241...           FEE   \n",
       "95  20240603000000000::4cf843a8-de5e-4191-81af-5b2...           FEE   \n",
       "96  20240513000000000::3697788f-038f-4542-838f-8e3...           FEE   \n",
       "97  20240513000000000::266e3e2c-966a-4346-8c17-9e5...           FEE   \n",
       "\n",
       "   activity_sub_type        date net_amount  \\\n",
       "0                TAF  2024-12-18      -0.15   \n",
       "1                REG  2024-12-18      -0.65   \n",
       "2                REG  2024-12-17      -3.55   \n",
       "3                TAF  2024-12-17      -0.26   \n",
       "4                REG  2024-12-16      -0.23   \n",
       "..               ...         ...        ...   \n",
       "93               NaN  2024-06-06      -0.81   \n",
       "94               NaN  2024-06-03      -0.01   \n",
       "95               NaN  2024-06-03      -0.13   \n",
       "96               NaN  2024-05-13      -0.02   \n",
       "97               NaN  2024-05-13      -0.01   \n",
       "\n",
       "                                          description    status  \n",
       "0   TAF fee for proceed of 844 shares (8 trades) o...  executed  \n",
       "1   REG fee for proceed of $23354.98 on 2024-12-18...  executed  \n",
       "2   REG fee for proceed of $127652.86 on 2024-12-1...  executed  \n",
       "3   TAF fee for proceed of 1535 shares (5 trades) ...  executed  \n",
       "4   REG fee for proceed of $8257.82 on 2024-12-16 ...  executed  \n",
       "..                                                ...       ...  \n",
       "93  TAF fee for proceed of 5561 shares (12 trades)...  executed  \n",
       "94  TAF fee for proceed of 14 shares (2 trades) on...  executed  \n",
       "95  REG fee for proceed of $15879.55 on 2024-06-03...  executed  \n",
       "96  TAF fee for proceed of 100 shares (7 trades) o...  executed  \n",
       "97  REG fee for proceed of $1216.00 on 2024-05-13 ...  executed  \n",
       "\n",
       "[98 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity = 'FEE'\n",
    "endpoint = f'/account/activities/{activity}'\n",
    "params = {'after':'2023-11-30','until':'2024-12-20'}\n",
    "df_fees = pd.DataFrame(alpaca.trading_client.get(path=endpoint, data=params))\n",
    "df_fees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'FILL' # Order fills (both partial and full fills)\n",
    "'TRANS' # Cash transactions (both CSD and CSW)\n",
    "'MISC' # Miscellaneous or rarely used activity types (All types except those in TRANS, DIV, or FILL)\n",
    "'ACATC' # ACATS IN/OUT (Cash)\n",
    "'ACATS' # ACATS IN/OUT (Securities)\n",
    "'CFEE' # Crypto fee\n",
    "'CSD' # Cash deposit(+)\n",
    "'CSW' # Cash withdrawal(-)\n",
    "'DIV' # Dividends\n",
    "'DIVCGL' # Dividend (capital gain long term)\n",
    "'DIVCGS' # Dividend (capital gain short term)\n",
    "'DIVFEE' # Dividend fee\n",
    "'DIVFT'     # Dividend adjusted (Foreign Tax Withheld)\n",
    "'DIVNRA'    # Dividend adjusted (NRA Withheld)\n",
    "'DIVROC'    # Dividend return of capital\n",
    "'DIVTW'    # Dividend adjusted (Tefra Withheld)\n",
    "'DIVTXEX'    # Dividend (tax exempt)\n",
    "'FEE'    # Fee denominated in USD\n",
    "'INT'    # Interest (credit/margin)\n",
    "'INTNRA'    # Interest adjusted (NRA Withheld)\n",
    "'INTTW'    # Interest adjusted (Tefra Withheld)\n",
    "'JNL'    # Journal entry\n",
    "'JNLC'    # Journal entry (cash)\n",
    "'JNLS'    # Journal entry (stock)\n",
    "'MA'    # Merger/Acquisition\n",
    "'NC'    # Name change\n",
    "'OPASN'    # Option assignment\n",
    "'OPEXP'    # Option expiration\n",
    "'OPXRC'    # Option exercise\n",
    "'PTC'    # Pass Thru Charge\n",
    "'PTR'    # Pass Thru Rebate\n",
    "'REORG'    # Reorg CA\n",
    "'SC'    # Symbol change\n",
    "'SSO'    # Stock spinoff\n",
    "'SSP'    # Stock split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Portfolio dynamic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpaca.trading_client.get_portfolio_history()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submit Orders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Understanding orders: https://docs.alpaca.markets/docs/orders-at-alpaca\n",
    "# Nice overview of different order types: https://alpaca.markets/learn/13-order-types-you-should-know-about/\n",
    "\n",
    "# time-in-force options for various order types: https://docs.alpaca.markets/docs/orders-at-alpaca#order-types-vs-supported-time-in-force\n",
    "\n",
    "# Trading is allowed from 4am till 8pm ET (10am - 2am Munich Time)\n",
    "\n",
    "# Rules for submitting orders for extended hours: https://docs.alpaca.markets/docs/orders-at-alpaca#extended-hours-trading\n",
    "    # extended hours order must be DAY limit orders: extended_hours=True & type='limit' & time_in_force=TimeInForce.DAY\n",
    "        # A limit orders with a limit price that significantly exceeds the current market price will be rejected\n",
    "        # Any other order types, including market orders, will be rejected\n",
    "\n",
    "# Orders not eligible for extended hours submitted after 4:00pm ET will be queued up for release the next trading day.\n",
    "\n",
    "# Your extended hour order will be processed and filled immediately.\n",
    "        # not eligible orders: submitted between 4:00pm - 7:00pm ET will be rejected.\n",
    "        # orders submitted after 7:00pm ET will be queued and eligible for execution at the time of the next market open"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from alpaca.trading.enums import OrderSide, TimeInForce, OrderStatus, OrderType, OrderClass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimeInForce:['CLS', 'DAY', 'FOK', 'GTC', 'IOC', 'OPG']\n",
      "OrderSide:['BUY', 'SELL']\n",
      "OrderClass:['BRACKET', 'OCO', 'OTO', 'SIMPLE']\n",
      "OrderType:['LIMIT', 'MARKET', 'STOP', 'STOP_LIMIT', 'TRAILING_STOP']\n",
      "OrderStatus:['ACCEPTED', 'ACCEPTED_FOR_BIDDING', 'CALCULATED', 'CANCELED', 'DONE_FOR_DAY', 'EXPIRED', 'FILLED', 'HELD', 'NEW', 'PARTIALLY_FILLED', 'PENDING_CANCEL', 'PENDING_NEW', 'PENDING_REPLACE', 'PENDING_REVIEW', 'REJECTED', 'REPLACED', 'STOPPED', 'SUSPENDED']\n"
     ]
    }
   ],
   "source": [
    "classes = [TimeInForce, OrderSide, OrderClass, OrderType, OrderStatus]  # Replace with your actual classes\n",
    "for cls in classes:\n",
    "    filtered_members = [member for member in dir(cls) if not member.startswith('__')]\n",
    "    print(f\"{cls.__name__}:{filtered_members}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time in force: https://docs.alpaca.markets/docs/orders-at-alpaca#time-in-force\n",
    "    '''\n",
    "    gtc - good-till-cancelled\n",
    "            > aged order policy that will automatically cancel GTC orders 90 days\n",
    "                > List/Get Orders API endpoint has a field titled expires_at, which provides information on the expiration\n",
    "    day - eligible for execution only on the day it is live (9:30am - 4:00pm ET)\n",
    "    opg - use it to submit “market on open” (MOO) and “limit on open” (LOO) orders: must be submitted after 7:00pm and before 9:28am\n",
    "    cls - use it to submit “market on close” (MOC) and “limit on close” (LOC) orders\n",
    "    ioc - Immediate Or Cancel (IOC) - all or part of the order to be executed immediately\n",
    "    fok - Fill or Kill (FOK)\n",
    "    '''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bracket Order: https://docs.alpaca.markets/docs/orders-at-alpaca#bracket-orders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Orders & Positions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Order Statuses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ACCEPTED',\n",
       " 'ACCEPTED_FOR_BIDDING',\n",
       " 'CALCULATED',\n",
       " 'CANCELED',\n",
       " 'DONE_FOR_DAY',\n",
       " 'EXPIRED',\n",
       " 'FILLED',\n",
       " 'HELD',\n",
       " 'NEW',\n",
       " 'PARTIALLY_FILLED',\n",
       " 'PENDING_CANCEL',\n",
       " 'PENDING_NEW',\n",
       " 'PENDING_REPLACE',\n",
       " 'PENDING_REVIEW',\n",
       " 'REJECTED',\n",
       " 'REPLACED',\n",
       " 'STOPPED',\n",
       " 'SUSPENDED',\n",
       " '__class__',\n",
       " '__doc__',\n",
       " '__members__',\n",
       " '__module__']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from alpaca.trading.enums import OrderStatus\n",
    "\n",
    "dir(OrderStatus)\n",
    "\n",
    "# full list of statuses: https://alpaca.markets/docs/trading-on-alpaca/orders/#order-lifecycle\n",
    "\n",
    "# Updates on orders states at Alpaca will be sent over the streaming interface\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting buy orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>strategy</th>\n",
       "      <th>symbol</th>\n",
       "      <th>invest(k)</th>\n",
       "      <th>buytime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
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       "      <td>5.1</td>\n",
       "      <td>20.12 16:57:26</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>PBH</td>\n",
       "      <td>5.2</td>\n",
       "      <td>20.12 16:57:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>PBH</td>\n",
       "      <td>5.5</td>\n",
       "      <td>20.12 16:57:26</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>PBH</td>\n",
       "      <td>5.9</td>\n",
       "      <td>20.12 16:57:27</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>AVB</td>\n",
       "      <td>5.7</td>\n",
       "      <td>20.12 16:16:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>PBH</td>\n",
       "      <td>6.2</td>\n",
       "      <td>20.12 16:57:26</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>AVB</td>\n",
       "      <td>6.2</td>\n",
       "      <td>20.12 16:16:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>PBH</td>\n",
       "      <td>6.7</td>\n",
       "      <td>20.12 16:57:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>AVB</td>\n",
       "      <td>6.6</td>\n",
       "      <td>20.12 16:16:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>WD</td>\n",
       "      <td>7.5</td>\n",
       "      <td>20.12 15:36:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>ANDE</td>\n",
       "      <td>7.5</td>\n",
       "      <td>20.12 15:40:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>AVB</td>\n",
       "      <td>7.5</td>\n",
       "      <td>20.12 16:16:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>CNSL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>MU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>14</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>ANDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>ANDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>OpenRangeBreakout</td>\n",
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       "      <td>OpenRangeBreakout</td>\n",
       "      <td>ANDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>ANDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>ANDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>ANDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>OpenRangeBreakout</td>\n",
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       "      <td>OpenRangeBreakout</td>\n",
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       "      <td>OpenRangeBreakout</td>\n",
       "      <td>ANDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "             strategy symbol  invest(k)         buytime\n",
       "0   OpenRangeBreakout    PBH        5.1  20.12 16:57:26\n",
       "1   OpenRangeBreakout    PBH        5.2  20.12 16:57:26\n",
       "2   OpenRangeBreakout    PBH        5.5  20.12 16:57:26\n",
       "3   OpenRangeBreakout    PBH        5.9  20.12 16:57:27\n",
       "4   OpenRangeBreakout    AVB        5.7  20.12 16:16:53\n",
       "5   OpenRangeBreakout    PBH        6.2  20.12 16:57:26\n",
       "6   OpenRangeBreakout    AVB        6.2  20.12 16:16:53\n",
       "7   OpenRangeBreakout    PBH        6.7  20.12 16:57:26\n",
       "8   OpenRangeBreakout    AVB        6.6  20.12 16:16:53\n",
       "9   OpenRangeBreakout     WD        7.5  20.12 15:36:14\n",
       "10  OpenRangeBreakout   ANDE        7.5  20.12 15:40:52\n",
       "11  OpenRangeBreakout    AVB        7.5  20.12 16:16:53\n",
       "12  OpenRangeBreakout   CNSL        NaN             NaN\n",
       "13  OpenRangeBreakout     MU        NaN             NaN\n",
       "14  OpenRangeBreakout   ANDE        NaN             NaN\n",
       "15  OpenRangeBreakout   ANDE        NaN             NaN\n",
       "16  OpenRangeBreakout   ANDE        NaN             NaN\n",
       "17  OpenRangeBreakout   ANDE        NaN             NaN\n",
       "18  OpenRangeBreakout   ANDE        NaN             NaN\n",
       "19  OpenRangeBreakout   ANDE        NaN             NaN\n",
       "20  OpenRangeBreakout   ANDE        NaN             NaN\n",
       "21  OpenRangeBreakout   ANDE        NaN             NaN\n",
       "22  OpenRangeBreakout   ANDE        NaN             NaN\n",
       "23  OpenRangeBreakout   ANDE        NaN             NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from alpaca.trading.requests import GetOrdersRequest\n",
    "from alpaca.trading.enums import QueryOrderStatus\n",
    "date_filter = (pd.Timestamp.now()- pd.Timedelta(30, \"days\")) # orders for last 30 days\n",
    "orders = alpaca.trading_client.get_orders(filter=GetOrdersRequest(status=QueryOrderStatus.CLOSED,after = date_filter))\n",
    "bought_orders = [o for o in orders if o.side == \"buy\"]\n",
    "bought_orders_df = pd.DataFrame(alpaca._orders_to_dictionary(bought_orders))\n",
    "bought_orders_df['filled_avg_price'] = round(bought_orders_df['filled_avg_price'].astype(float),2)\n",
    "bought_orders_df['filled_qty'] = bought_orders_df['filled_qty'].astype(int)\n",
    "bought_orders_df['invest(k)'] = round((bought_orders_df['filled_qty'] * bought_orders_df['filled_avg_price'])/1000,1)\n",
    "bought_orders_df['buytime'] = bought_orders_df['filled_at'].dt.tz_convert('Europe/Berlin').dt.tz_localize(None).dt.strftime('%d.%m %H:%M:%S')\n",
    "bought_orders_df['strategy'] = bought_orders_df['client_order_id'].str.split('_').str[0]\n",
    "columns_to_keep = ['strategy', 'symbol','invest(k)','buytime']\n",
    "bought_orders_df = bought_orders_df[columns_to_keep]\n",
    "bought_orders_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting open orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 0 open buy limit orders, 7 open sell limit orders and 0 other open orders.\n"
     ]
    }
   ],
   "source": [
    "from alpaca.trading.requests import GetOrdersRequest\n",
    "from alpaca.trading.enums import QueryOrderStatus\n",
    "orders = alpaca.trading_client.get_orders(filter=GetOrdersRequest(status=QueryOrderStatus.OPEN))\n",
    "open_buy_limit_orders = [o for o in orders if o.side == \"buy\" and o.order_type == \"limit\"]\n",
    "open_sell_limit_orders = [o for o in orders if o.side == \"sell\" and o.order_type == \"limit\"]\n",
    "open_other_orders = [o for o in orders if o.order_type != \"limit\"]\n",
    "print(f'There are {len(open_buy_limit_orders)} open buy limit orders, {len(open_sell_limit_orders)} open sell limit orders and {len(open_other_orders)} other open orders.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get current positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-01-17 23:19:24,423 - root - INFO - Details (buytime, buyprice, qty, strategy) about bought positions (15) were extracted. - [utils_for_alpaca.py:986]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>market_value</th>\n",
       "      <th>unrealized_plpc</th>\n",
       "      <th>filled_at</th>\n",
       "      <th>filled_avg_price</th>\n",
       "      <th>filled_qty</th>\n",
       "      <th>strat</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SPY</td>\n",
       "      <td>597.22</td>\n",
       "      <td>0.167745</td>\n",
       "      <td>2024-08-05 15:30:12.830152</td>\n",
       "      <td>511.43</td>\n",
       "      <td>1</td>\n",
       "      <td>c52eb28c-1294-460c-80c6-72b50c8ab4f8</td>\n",
       "      <td>165</td>\n",
       "      <td>0.6</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SMAR</td>\n",
       "      <td>2145.86</td>\n",
       "      <td>0.156224</td>\n",
       "      <td>2024-07-23 16:30:25.924043</td>\n",
       "      <td>48.84</td>\n",
       "      <td>38</td>\n",
       "      <td>BuyStreaksAt11</td>\n",
       "      <td>178</td>\n",
       "      <td>2.1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>VITL</td>\n",
       "      <td>7543.20</td>\n",
       "      <td>0.154835</td>\n",
       "      <td>2024-12-12 15:30:34.487996</td>\n",
       "      <td>38.88</td>\n",
       "      <td>168</td>\n",
       "      <td>BuyStreaksAt11</td>\n",
       "      <td>36</td>\n",
       "      <td>7.5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TPR</td>\n",
       "      <td>27128.20</td>\n",
       "      <td>0.095673</td>\n",
       "      <td>2024-12-16 16:19:30.080496</td>\n",
       "      <td>65.15</td>\n",
       "      <td>103</td>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>32</td>\n",
       "      <td>27.1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANDE</td>\n",
       "      <td>8125.20</td>\n",
       "      <td>0.088235</td>\n",
       "      <td>2024-12-20 15:40:52.276478</td>\n",
       "      <td>40.80</td>\n",
       "      <td>183</td>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>28</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BKU</td>\n",
       "      <td>1951.18</td>\n",
       "      <td>0.052048</td>\n",
       "      <td>2024-07-23 16:30:12.661888</td>\n",
       "      <td>37.85</td>\n",
       "      <td>49</td>\n",
       "      <td>BuyStreaksAt11</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALTR</td>\n",
       "      <td>79826.43</td>\n",
       "      <td>0.021936</td>\n",
       "      <td>2024-12-19 18:33:13.816129</td>\n",
       "      <td>108.04</td>\n",
       "      <td>64</td>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>29</td>\n",
       "      <td>79.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CALM</td>\n",
       "      <td>456.52</td>\n",
       "      <td>0.023312</td>\n",
       "      <td>2024-12-17 15:30:38.175639</td>\n",
       "      <td>111.53</td>\n",
       "      <td>4</td>\n",
       "      <td>BuyStreaksAt11</td>\n",
       "      <td>31</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CNP</td>\n",
       "      <td>520.96</td>\n",
       "      <td>0.021971</td>\n",
       "      <td>2024-12-17 15:30:30.128961</td>\n",
       "      <td>31.86</td>\n",
       "      <td>16</td>\n",
       "      <td>BuyStreaksAt11</td>\n",
       "      <td>31</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AVB</td>\n",
       "      <td>25740.52</td>\n",
       "      <td>-0.007552</td>\n",
       "      <td>2024-12-20 16:16:53.418603</td>\n",
       "      <td>219.80</td>\n",
       "      <td>34</td>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>28</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PBH</td>\n",
       "      <td>34339.46</td>\n",
       "      <td>-0.006197</td>\n",
       "      <td>2024-12-20 16:57:27.192230</td>\n",
       "      <td>79.07</td>\n",
       "      <td>74</td>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>28</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RCM</td>\n",
       "      <td>23625.81</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2024-11-15 15:54:09.530995</td>\n",
       "      <td>14.31</td>\n",
       "      <td>796</td>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>63</td>\n",
       "      <td>23.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BA</td>\n",
       "      <td>510.93</td>\n",
       "      <td>-0.019460</td>\n",
       "      <td>2024-12-17 15:30:44.795303</td>\n",
       "      <td>173.69</td>\n",
       "      <td>3</td>\n",
       "      <td>BuyStreaksAt11</td>\n",
       "      <td>31</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>WD</td>\n",
       "      <td>7341.18</td>\n",
       "      <td>-0.024355</td>\n",
       "      <td>2024-12-20 15:36:14.178809</td>\n",
       "      <td>97.72</td>\n",
       "      <td>77</td>\n",
       "      <td>OpenRangeBreakout</td>\n",
       "      <td>28</td>\n",
       "      <td>7.3</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADEA</td>\n",
       "      <td>465.12</td>\n",
       "      <td>-0.116883</td>\n",
       "      <td>2024-12-17 15:30:43.427283</td>\n",
       "      <td>14.63</td>\n",
       "      <td>36</td>\n",
       "      <td>BuyStreaksAt11</td>\n",
       "      <td>31</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symbol  market_value  unrealized_plpc                  filled_at  \\\n",
       "11    SPY        597.22         0.167745 2024-08-05 15:30:12.830152   \n",
       "10   SMAR       2145.86         0.156224 2024-07-23 16:30:25.924043   \n",
       "13   VITL       7543.20         0.154835 2024-12-12 15:30:34.487996   \n",
       "12    TPR      27128.20         0.095673 2024-12-16 16:19:30.080496   \n",
       "2    ANDE       8125.20         0.088235 2024-12-20 15:40:52.276478   \n",
       "5     BKU       1951.18         0.052048 2024-07-23 16:30:12.661888   \n",
       "1    ALTR      79826.43         0.021936 2024-12-19 18:33:13.816129   \n",
       "6    CALM        456.52         0.023312 2024-12-17 15:30:38.175639   \n",
       "7     CNP        520.96         0.021971 2024-12-17 15:30:30.128961   \n",
       "3     AVB      25740.52        -0.007552 2024-12-20 16:16:53.418603   \n",
       "8     PBH      34339.46        -0.006197 2024-12-20 16:57:27.192230   \n",
       "9     RCM      23625.81         0.000000 2024-11-15 15:54:09.530995   \n",
       "4      BA        510.93        -0.019460 2024-12-17 15:30:44.795303   \n",
       "14     WD       7341.18        -0.024355 2024-12-20 15:36:14.178809   \n",
       "0    ADEA        465.12        -0.116883 2024-12-17 15:30:43.427283   \n",
       "\n",
       "    filled_avg_price  filled_qty                                 strat  days  \\\n",
       "11            511.43           1  c52eb28c-1294-460c-80c6-72b50c8ab4f8   165   \n",
       "10             48.84          38                        BuyStreaksAt11   178   \n",
       "13             38.88         168                        BuyStreaksAt11    36   \n",
       "12             65.15         103                     OpenRangeBreakout    32   \n",
       "2              40.80         183                     OpenRangeBreakout    28   \n",
       "5              37.85          49                        BuyStreaksAt11   178   \n",
       "1             108.04          64                     OpenRangeBreakout    29   \n",
       "6             111.53           4                        BuyStreaksAt11    31   \n",
       "7              31.86          16                        BuyStreaksAt11    31   \n",
       "3             219.80          34                     OpenRangeBreakout    28   \n",
       "8              79.07          74                     OpenRangeBreakout    28   \n",
       "9              14.31         796                     OpenRangeBreakout    63   \n",
       "4             173.69           3                        BuyStreaksAt11    31   \n",
       "14             97.72          77                     OpenRangeBreakout    28   \n",
       "0              14.63          36                        BuyStreaksAt11    31   \n",
       "\n",
       "    value  pl%  \n",
       "11    0.6   16  \n",
       "10    2.1   15  \n",
       "13    7.5   15  \n",
       "12   27.1    9  \n",
       "2     8.1    8  \n",
       "5     2.0    5  \n",
       "1    79.8    2  \n",
       "6     0.5    2  \n",
       "7     0.5    2  \n",
       "3    25.7    0  \n",
       "8    34.3    0  \n",
       "9    23.6    0  \n",
       "4     0.5   -1  \n",
       "14    7.3   -2  \n",
       "0     0.5  -11  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# details: https://docs.alpaca.markets/docs/position-average-entry-price-calculation#which-method-is-alpaca-using\n",
    "\n",
    "positions = alpaca.trading_client.get_all_positions()\n",
    "current_positions_df = pd.DataFrame(alpaca._orders_to_dictionary(positions))\n",
    "cols_needed = ['symbol','market_value','unrealized_plpc']\n",
    "current_positions_df = current_positions_df[cols_needed].copy()\n",
    "\n",
    "current_positions_symbols = [p.symbol for p in positions]\n",
    "buy_orders_dict = alpaca._get_orders_info(current_positions_symbols, datefilter=300)\n",
    "buy_orders_df = pd.DataFrame.from_dict(buy_orders_dict, orient='index', columns=['filled_at', 'filled_avg_price', 'filled_qty', 'strat'])\n",
    "buy_orders_df.reset_index(inplace=True)\n",
    "buy_orders_df.rename(columns={'index': 'symbol'}, inplace=True)\n",
    "\n",
    "current_positions_df = current_positions_df.merge(buy_orders_df, on='symbol', how='left')\n",
    "current_positions_df['filled_at'] = current_positions_df['filled_at'].dt.tz_convert('Europe/Berlin').dt.tz_localize(None)\n",
    "current_date = dt.datetime.now()\n",
    "current_positions_df['days'] = (current_date - current_positions_df['filled_at']).dt.days\n",
    "for column in ['market_value','unrealized_plpc']:\n",
    "    current_positions_df[column] = pd.to_numeric(current_positions_df[column], errors='coerce')\n",
    "current_positions_df['value'] = round(current_positions_df['market_value'] / 1000, 1)\n",
    "current_positions_df['pl%'] = (current_positions_df['unrealized_plpc'] * 100).astype(int)\n",
    "current_positions_df = current_positions_df.sort_values(by='pl%',ascending=False)\n",
    "\n",
    "current_positions_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trading_client.get_open_position('CALM').qty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time needed for update position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "            \n",
    "# (1) First get the current position qty\n",
    "\n",
    "symbol = 'SPY'\n",
    "positions_list = trading_client.get_all_positions()\n",
    "positions_df = pd.DataFrame([position for position in positions_list])\n",
    "\n",
    "if positions_df.query('symbol==@symbol').empty:\n",
    "    initial_qty = 0\n",
    "else:\n",
    "    initial_qty = positions_df.query('symbol==@symbol').qty.astype('int').iat[0]\n",
    "\n",
    "# (2) place an order\n",
    "market_order_data = MarketOrderRequest(symbol=symbol,qty=1,side=OrderSide.BUY,time_in_force=TimeInForce.DAY)\n",
    "my_order = trading_client.submit_order(market_order_data)\n",
    "\n",
    "# (3) Check time for filling\n",
    "status = None\n",
    "while status != 'filled':\n",
    "    my_order = trading_client.get_order_by_id(my_order.get('id'))\n",
    "    status = my_order.get('status')\n",
    "\n",
    "created_at = pd.to_datetime(my_order.get('created_at')).tz_convert(None)\n",
    "filled_at = pd.to_datetime(my_order.get('filled_at')).tz_convert(None)\n",
    "current_time = pd.to_datetime('now', utc=True).tz_convert(None)\n",
    "\n",
    "delta_fill_time = (current_time - created_at).total_seconds()\n",
    "delta_poll_time = (current_time - filled_at).total_seconds()\n",
    "\n",
    "print(f'{delta_fill_time} seconds to fill and {delta_poll_time} seconds polled after fill and status is {status}')\n",
    "\n",
    "# (4) Check when in portoflio\n",
    "filled_in_portfolio = False\n",
    "while not filled_in_portfolio:\n",
    "    positions_list = trading_client.get_all_positions()\n",
    "    positions_df = pd.DataFrame([position for position in positions_list])\n",
    "\n",
    "if positions_df.query('symbol==@symbol').empty:\n",
    "    new_qty = 0\n",
    "else:\n",
    "    new_qty = positions_df.query('symbol==@symbol').qty.astype('int').iat[0]\n",
    "\n",
    "if new_qty > initial_qty:\n",
    "    filled_in_portfolio = True\n",
    "    status = 'in portfolio'\n",
    "else:\n",
    "    filled_in_portfolio = False\n",
    "    status = 'not in portfolio yet'    \n",
    "\n",
    "current_time = pd.to_datetime('now', utc=True).tz_convert(None)\n",
    "delta_time = (current_time - filled_at).total_seconds()\n",
    "print(f'{delta_time} seconds after fill and status is {status}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Drawdowns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for every symbol in positions, check current drawdown and sell if critical\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:23,102 - root - INFO - 152 rows were downloaded for TASK to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for TASK is -0.51%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:23,351 - root - INFO - 149 rows were downloaded for UTHR to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for UTHR is -0.69%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:23,595 - root - INFO - 157 rows were downloaded for DAY to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for DAY is -0.57%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:23,841 - root - INFO - 161 rows were downloaded for RVMD to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for RVMD is -1.46%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:24,085 - root - INFO - 168 rows were downloaded for ATEC to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for ATEC is -1.58%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:24,336 - root - INFO - 170 rows were downloaded for EA to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for EA is -1.31%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:24,588 - root - INFO - 169 rows were downloaded for MUFG to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for MUFG is -0.35%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:24,825 - root - INFO - 165 rows were downloaded for PAYC to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for PAYC is -2.62%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:25,058 - root - INFO - 163 rows were downloaded for EXEL to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for EXEL is -1.00%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:25,302 - root - INFO - 168 rows were downloaded for APPN to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for APPN is -2.93%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:25,555 - root - INFO - 389 rows were downloaded for SUM to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for SUM is -2.33%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:26,670 - root - INFO - 5392 rows were downloaded for CBU to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for CBU is -5.24%\n",
      "We need to sell CBU (30 stocks) as current drawdown (-5.24%) bigger than allowed (-5.00%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:28,045 - root - INFO - 6131 rows were downloaded for IAC to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for IAC is -1.76%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:29,387 - root - INFO - 6178 rows were downloaded for EQR to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for EQR is -7.78%\n",
      "We need to sell EQR (26 stocks) as current drawdown (-7.78%) bigger than allowed (-5.00%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:30,405 - root - INFO - 4863 rows were downloaded for MBIN to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for MBIN is -23.24%\n",
      "We need to sell MBIN (37 stocks) as current drawdown (-23.24%) bigger than allowed (-5.00%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:31,720 - root - INFO - 5671 rows were downloaded for MLNK to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for MLNK is -3.10%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:33,043 - root - INFO - 6073 rows were downloaded for CTO to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for CTO is -0.83%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:34,239 - root - INFO - 5948 rows were downloaded for ELME to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for ELME is -5.16%\n",
      "We need to sell ELME (111 stocks) as current drawdown (-5.16%) bigger than allowed (-5.00%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:35,909 - root - INFO - 6381 rows were downloaded for SMAR to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for SMAR is -1.11%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:36,565 - root - INFO - 2397 rows were downloaded for MCBS to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for MCBS is -4.25%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:37,975 - root - INFO - 6150 rows were downloaded for CBOE to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for CBOE is -7.48%\n",
      "We need to sell CBOE (9 stocks) as current drawdown (-7.48%) bigger than allowed (-5.00%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 14:49:39,547 - root - INFO - 6078 rows were downloaded for BKU to get current drawdown. - [utils_for_alpaca.py:599]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current drawdown for BKU is -4.60%\n"
     ]
    }
   ],
   "source": [
    "positions = alpaca.trading_client.get_all_positions()\n",
    "current_positions_symbols = [p.symbol for p in positions]\n",
    "buy_orders_dict = alpaca._get_orders_info(current_positions_symbols)\n",
    "\n",
    "# Define a function to calculate drawdown\n",
    "def calculate_drawdown(prices):\n",
    "    max_prices = prices.cummax()\n",
    "    drawdown = (prices - max_prices) / max_prices\n",
    "    return drawdown\n",
    "\n",
    "positions_dict_loss = {}\n",
    "for symbol, details in buy_orders_dict.items():\n",
    "    filled_at = details[0]\n",
    "    data_df = alpaca.get_history_minute_single(symbol, filled_at)\n",
    "    data_df['drawdown'] = data_df['close'].transform(calculate_drawdown)\n",
    "    current_drawdown = data_df.iloc[-1]['drawdown'] # the rows are sorted from oldest to newest, so we take last row\n",
    "    print(f'Current drawdown for {symbol} is {current_drawdown:.2%}')\n",
    "    if current_drawdown < -alpaca.SimpleStopLoss:\n",
    "        owned = int(alpaca.trading_client.get_open_position(symbol).qty)\n",
    "        print(f'We need to sell {symbol} ({owned} stocks) as current drawdown ({current_drawdown:.2%}) bigger than allowed ({-alpaca.SimpleStopLoss:.2%})')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assets in scope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Asset enums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CRYPTO',\n",
       " 'US_EQUITY',\n",
       " 'US_OPTION',\n",
       " '__class__',\n",
       " '__doc__',\n",
       " '__members__',\n",
       " '__module__']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from alpaca.trading.enums import AssetClass, AssetStatus, AssetExchange\n",
    "dir(AssetClass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ACTIVE', 'INACTIVE', '__class__', '__doc__', '__members__', '__module__']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(AssetStatus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AMEX',\n",
       " 'ARCA',\n",
       " 'BATS',\n",
       " 'CBSE',\n",
       " 'CRYPTO',\n",
       " 'EMPTY',\n",
       " 'ERSX',\n",
       " 'FTXU',\n",
       " 'GNSS',\n",
       " 'NASDAQ',\n",
       " 'NYSE',\n",
       " 'NYSEARCA',\n",
       " 'OTC',\n",
       " '__class__',\n",
       " '__doc__',\n",
       " '__members__',\n",
       " '__module__']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(AssetExchange)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get ok stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2788"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_stocks():\n",
    "    assets = alpaca.trading_client.get_all_assets(GetAssetsRequest(asset_class=AssetClass.US_EQUITY, status= AssetStatus.ACTIVE))\n",
    "    exclude_strings = ['Etf', 'ETF', 'Lp', 'L.P', 'Fund', 'Trust', 'Depositary', 'Depository', 'Note', 'Reit', 'REIT']\n",
    "    assets_in_scope = [asset.symbol for asset in assets\n",
    "                        if asset.exchange != 'OTC' # OTC stocks play by different rules than Exchange Traded stocks (often referred to as NMS). \n",
    "                                                    # Data reporting is even different. \n",
    "                                                    # It’s not that one shouldn’t trade OTC stocks but be aware they have different rules. \n",
    "                                                    # Perhaps separate OTC stocks in your algo if you wish to trade these. \n",
    "                                                    # More about OTC: https://docs.alpaca.markets/docs/orders-at-alpaca#otc-positions\n",
    "                        and asset.shortable\n",
    "                        and asset.tradable\n",
    "                        and asset.marginable # if a stock is not marginable that means it cannot be used as collateral for margin. \n",
    "                                                # As an example, if one had equity of $10,000 one could buy $10,000 of marginable stock and still have $10,000 left in buying power. \n",
    "                                                # However, if one were to buy $10,000 of non marginable stock the buying power would be $0 (ie no funds can be margined against that stock). \n",
    "                                                # This has ramifications for RegT buying power and Fed margin calls. Best to stay away from these unless your algo carefully monitors RegT buying power. \n",
    "                                                # FYI there are currently no tradable exchange traded stocks which are not marginable (so this doesn’t really limit the universe). However, all OTC stocks are non marginable (another reason to perhaps not consider OTC stocks)\n",
    "                        and asset.fractionable # indirectly filters out a lot of small volatile stocks:  \n",
    "                                                # Alpaca Trading Team manually reviews stocks to qualify as ‘fractionable’. \n",
    "                                                # This is entirely an Alpaca designation and other brokers may have different ‘fractionable’ stocks\n",
    "                        and asset.easy_to_borrow # Alpaca currently uses its clearing firms ‘Easy To Borrow’ list and assumes everything else is Hard To Borrow. \n",
    "                        and asset.maintenance_margin_requirement == 30\n",
    "                        and not (any(ex_string in asset.name for ex_string in exclude_strings))]\n",
    "    return assets_in_scope\n",
    "len(get_stocks())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check spread of assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>spread</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HI</td>\n",
       "      <td>0.023005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ASTS</td>\n",
       "      <td>0.002868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JKHY</td>\n",
       "      <td>0.006103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LOVE</td>\n",
       "      <td>0.041589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALIT</td>\n",
       "      <td>0.026087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2782</th>\n",
       "      <td>AME</td>\n",
       "      <td>0.007992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2783</th>\n",
       "      <td>CSV</td>\n",
       "      <td>0.478852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2784</th>\n",
       "      <td>TGT</td>\n",
       "      <td>0.002171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2785</th>\n",
       "      <td>CCJ</td>\n",
       "      <td>0.001161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2786</th>\n",
       "      <td>TD</td>\n",
       "      <td>0.041167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2787 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symbol    spread\n",
       "0        HI  0.023005\n",
       "1      ASTS  0.002868\n",
       "2      JKHY  0.006103\n",
       "3      LOVE  0.041589\n",
       "4      ALIT  0.026087\n",
       "...     ...       ...\n",
       "2782    AME  0.007992\n",
       "2783    CSV  0.478852\n",
       "2784    TGT  0.002171\n",
       "2785    CCJ  0.001161\n",
       "2786     TD  0.041167\n",
       "\n",
       "[2787 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assets = get_stocks()\n",
    "latest_multisymbol_quotes = alpaca.stock_client.get_stock_latest_quote(StockLatestQuoteRequest(symbol_or_symbols=assets, feed=DataFeed.SIP))\n",
    "rows = []\n",
    "# TODO: check the bidask repo: https://github.com/eguidotti/bidask/tree/main/python\n",
    "for symbol, info in latest_multisymbol_quotes.items():\n",
    "    try:\n",
    "        spread = (info.ask_price - info.bid_price) / info.ask_price\n",
    "        rows.append({'symbol': symbol, 'spread': spread})\n",
    "    except:\n",
    "        continue\n",
    "df_spreads = pd.DataFrame(rows)\n",
    "df_spreads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IPO Assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{   'asset_class': <AssetClass.US_EQUITY: 'us_equity'>,\n",
       "    'attributes': [],\n",
       "    'easy_to_borrow': False,\n",
       "    'exchange': <AssetExchange.OTC: 'OTC'>,\n",
       "    'fractionable': False,\n",
       "    'id': UUID('27debfaf-b63f-40f5-a585-0423b96662ce'),\n",
       "    'maintenance_margin_requirement': 100.0,\n",
       "    'marginable': False,\n",
       "    'min_order_size': None,\n",
       "    'min_trade_increment': None,\n",
       "    'name': 'NorthView Acquisition Corporation Red Wt Exp  08/02/2027',\n",
       "    'price_increment': None,\n",
       "    'shortable': False,\n",
       "    'status': <AssetStatus.ACTIVE: 'active'>,\n",
       "    'symbol': 'NVACW',\n",
       "    'tradable': False}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assets = alpaca.trading_client.get_all_assets(GetAssetsRequest(asset_class=AssetClass.US_EQUITY, status= AssetStatus.ACTIVE))\n",
    "# assets_in_scope = [asset.symbol for asset in assets if asset.exchange != 'OTC']\n",
    "assets[1]\n",
    "\n",
    "# https://docs.alpaca.markets/docs/orders-at-alpaca#ipo-symbols\n",
    "\n",
    "# IS it only in broker client?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Strategies profitability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Orders for the last 90 days (max 500 orders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days_of_stat = 90\n",
    "date_filter = (pd.Timestamp.now()- pd.Timedelta(days_of_stat, \"days\")) # if it a stock in position, it has been bought with max_holding_time\n",
    "request_params = GetOrdersRequest(status=QueryOrderStatus.CLOSED, after = date_filter, limit = 500)\n",
    "orders_data = trading_client.get_orders(filter=request_params)\n",
    "# some tech stuff as the orders_data are of strange type ----------------------------\n",
    "orders_dicts = []\n",
    "for entry in orders_data:\n",
    "    entry_dict = {}  # Create a new dictionary for each entry\n",
    "    for attribute, value in vars(entry).items():\n",
    "        entry_dict[attribute] = value  # Populate the dictionary\n",
    "    orders_dicts.append(entry_dict)  # Append the populated dictionary to the list\n",
    "len(orders_dicts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting current positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "positions = trading_client.get_all_positions()\n",
    "position_dicts = []\n",
    "for entry in positions:\n",
    "    entry_dict = {}  # Create a new dictionary for each entry\n",
    "    for attribute, value in vars(entry).items():\n",
    "        entry_dict[attribute] = value  # Populate the dictionary\n",
    "    position_dicts.append(entry_dict)  # Append the populated dictionary to the list\n",
    "current_positions = pd.DataFrame(position_dicts)\n",
    "current_positions\n",
    "cols_needed = ['symbol','qty','side','current_price']\n",
    "current_positions = current_positions[cols_needed].copy()\n",
    "current_positions['current_price'] = round(current_positions['current_price'].astype(float),2)\n",
    "current_positions['qty'] = current_positions['qty'].astype(int)\n",
    "current_positions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Orders as dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders = pd.DataFrame(orders_dicts)\n",
    "needed_cols = ['symbol','client_order_id', 'side','filled_at', 'filled_qty', 'filled_avg_price']\n",
    "orders = orders[needed_cols].copy()\n",
    "orders['filled_at'] = orders['filled_at'].dt.tz_convert('Europe/Berlin').dt.tz_localize(None)\n",
    "# orders['filled_at'] = orders['filled_at'].dt.tz_convert('Europe/Berlin').dt.strftime('%Y-%m-%d %H:%M')\n",
    "orders['filled_avg_price'] = round(orders['filled_avg_price'].astype(float),2)\n",
    "orders['filled_qty'] = orders['filled_qty'].astype(int)\n",
    "orders['strategy'] = orders['client_order_id'].str.split('_').str[0]\n",
    "orders.loc[orders['strategy'].str[8] == '-', 'strategy'] = 'manual' # get rows where the 9th character in 'strategy' is \"-\" and update those rows to \"manual\"\n",
    "# Sort the DataFrame by filled_at to ensure chronological order\n",
    "orders.sort_values(by=['filled_at'], inplace=True)\n",
    "orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders.to_excel('orders.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import deque\n",
    "\n",
    "# Initialize dictionaries to hold the FIFO queue for each stock (long and short)\n",
    "long_positions = {}\n",
    "short_positions = {}\n",
    "\n",
    "results = [] # Initialize a list to store the results\n",
    "\n",
    "for index, row in orders.iterrows():\n",
    "    symbol = row['symbol']\n",
    "    side = row['side']\n",
    "    filled_qty = row['filled_qty']\n",
    "    filled_avg_price = row['filled_avg_price']\n",
    "    filled_at = row['filled_at']\n",
    "    strategy = row['strategy']\n",
    "    \n",
    "    if side == OrderSide.BUY:\n",
    "        # Check if closing a short position\n",
    "        if symbol in short_positions and short_positions[symbol]:\n",
    "            qty_to_buy = filled_qty\n",
    "            while qty_to_buy > 0 and short_positions[symbol]:\n",
    "                sell_qty, sell_price, sell_filled_at, sell_strategy = short_positions[symbol].popleft()\n",
    "                \n",
    "                if sell_qty > qty_to_buy:\n",
    "                    short_positions[symbol].appendleft((sell_qty - qty_to_buy, sell_price, sell_filled_at, sell_strategy))\n",
    "                    results.append({\n",
    "                        'symbol': symbol,\n",
    "                        'filled_qty': qty_to_buy,\n",
    "                        'buy_filled_at': filled_at,\n",
    "                        'filled_avg_price_buy': filled_avg_price,\n",
    "                        'filled_avg_price_sell': sell_price,\n",
    "                        'profitability': (sell_price - filled_avg_price) * qty_to_buy,\n",
    "                        'strategy': sell_strategy,\n",
    "                        'days_in_portfolio': (filled_at - sell_filled_at).days,\n",
    "                        'status': 'realized'\n",
    "                    })\n",
    "                    qty_to_buy = 0\n",
    "                else:\n",
    "                    results.append({\n",
    "                        'symbol': symbol,\n",
    "                        'filled_qty': sell_qty,\n",
    "                        'buy_filled_at': filled_at,\n",
    "                        'filled_avg_price_buy': filled_avg_price,\n",
    "                        'filled_avg_price_sell': sell_price,\n",
    "                        'profitability': (sell_price - filled_avg_price) * sell_qty,\n",
    "                        'strategy': sell_strategy,\n",
    "                        'days_in_portfolio': (filled_at - sell_filled_at).days,\n",
    "                        'status': 'realized'\n",
    "                    })\n",
    "                    qty_to_buy -= sell_qty\n",
    "        else:\n",
    "            # Add the buy order to the FIFO queue for the stock\n",
    "            if symbol not in long_positions:\n",
    "                long_positions[symbol] = deque()\n",
    "            long_positions[symbol].append((filled_qty, filled_avg_price, filled_at, strategy))\n",
    "\n",
    "    elif side == OrderSide.SELL:\n",
    "        # Check if closing a long position\n",
    "        if symbol in long_positions and long_positions[symbol]:\n",
    "            qty_to_sell = filled_qty\n",
    "            while qty_to_sell > 0 and long_positions[symbol]:\n",
    "                buy_qty, buy_price, buy_filled_at, buy_strategy = long_positions[symbol].popleft()\n",
    "                \n",
    "                if buy_qty > qty_to_sell:\n",
    "                    long_positions[symbol].appendleft((buy_qty - qty_to_sell, buy_price, buy_filled_at, buy_strategy))\n",
    "                    results.append({\n",
    "                        'symbol': symbol,\n",
    "                        'filled_qty': qty_to_sell,\n",
    "                        'buy_filled_at': buy_filled_at,\n",
    "                        'filled_avg_price_buy': buy_price,\n",
    "                        'filled_avg_price_sell': filled_avg_price,\n",
    "                        'profitability': (filled_avg_price - buy_price) * qty_to_sell,\n",
    "                        'strategy': buy_strategy,\n",
    "                        'days_in_portfolio': (filled_at - buy_filled_at).days,\n",
    "                        'status': 'realized'\n",
    "                    })\n",
    "                    qty_to_sell = 0\n",
    "                else:\n",
    "                    results.append({\n",
    "                        'symbol': symbol,\n",
    "                        'filled_qty': buy_qty,\n",
    "                        'buy_filled_at': buy_filled_at,\n",
    "                        'filled_avg_price_buy': buy_price,\n",
    "                        'filled_avg_price_sell': filled_avg_price,\n",
    "                        'profitability': (filled_avg_price - buy_price) * buy_qty,\n",
    "                        'strategy': buy_strategy,\n",
    "                        'days_in_portfolio': (filled_at - buy_filled_at).days,\n",
    "                        'status': 'realized'\n",
    "                    })\n",
    "                    qty_to_sell -= buy_qty            \n",
    "        else:\n",
    "            # Add the sell order to the FIFO queue for short positions\n",
    "            if symbol not in short_positions:\n",
    "                short_positions[symbol] = deque()\n",
    "            short_positions[symbol].append((filled_qty, filled_avg_price, filled_at, strategy))\n",
    "\n",
    "# Append remaining unsold stocks to results and use current_price for profitability calculation\n",
    "for symbol, queue in long_positions.items():\n",
    "    if symbol in current_positions['symbol'].values:\n",
    "        current_price = current_positions[current_positions['symbol'] == symbol]['current_price'].values[0]\n",
    "        while queue:\n",
    "            buy_qty, buy_price, buy_filled_at, buy_strategy = queue.popleft()\n",
    "            results.append({\n",
    "                'symbol': symbol,\n",
    "                'filled_qty': buy_qty,\n",
    "                'buy_filled_at': buy_filled_at,\n",
    "                'filled_avg_price_buy': buy_price,\n",
    "                'filled_avg_price_sell': current_price,\n",
    "                'profitability': (current_price - buy_price) * buy_qty,\n",
    "                'strategy': buy_strategy,\n",
    "                'days_in_portfolio': (dt.datetime.now() - buy_filled_at).days,\n",
    "                'status': 'unrealized'\n",
    "            })\n",
    "\n",
    "for symbol, queue in short_positions.items():\n",
    "    if symbol in current_positions['symbol'].values:\n",
    "        current_price = current_positions[current_positions['symbol'] == symbol]['current_price'].values[0]\n",
    "        while queue:\n",
    "            sell_qty, sell_price, sell_filled_at, sell_strategy = queue.popleft()\n",
    "            results.append({\n",
    "                'symbol': symbol,\n",
    "                'filled_qty': sell_qty,\n",
    "                'buy_filled_at': dt.datetime.now(),\n",
    "                'filled_avg_price_buy': current_price,\n",
    "                'filled_avg_price_sell': sell_price,\n",
    "                'profitability': (sell_price - current_price) * sell_qty,\n",
    "                'strategy': sell_strategy,\n",
    "                'days_in_portfolio': (dt.datetime.now() - sell_filled_at).days,\n",
    "                'status': 'unrealized'\n",
    "            })\n",
    "\n",
    "# Convert results to DataFrame\n",
    "result_df = pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# result_df.to_excel('merged.xlsx')\n",
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>strategy</th>\n",
       "      <th>total_profit</th>\n",
       "      <th>number_of_trades</th>\n",
       "      <th>average_profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BuyStreaksAt11</td>\n",
       "      <td>34.07</td>\n",
       "      <td>44</td>\n",
       "      <td>0.774318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BuyTopPremarket</td>\n",
       "      <td>-1145.62</td>\n",
       "      <td>4</td>\n",
       "      <td>-286.405000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>manual</td>\n",
       "      <td>3151.50</td>\n",
       "      <td>4</td>\n",
       "      <td>787.875000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          strategy  total_profit  number_of_trades  average_profit\n",
       "0   BuyStreaksAt11         34.07                44        0.774318\n",
       "1  BuyTopPremarket      -1145.62                 4     -286.405000\n",
       "2           manual       3151.50                 4      787.875000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df.groupby('strategy').agg(\n",
    "    total_profit=('profitability', 'sum'),\n",
    "    number_of_trades=('profitability', 'count'),\n",
    "    average_profit=('profitability', 'mean')\n",
    ").reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update Relative Volume GH File"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In my strat_config repo I have an csv file with average trade volumes for different stock. I use Openning-Range-Breakout strategy.\n",
    "\n",
    "It is quicker to read the prepared file than to do recalculations. As I don't really need to do recalculation every time the strategy is run, I prefer to periodically (e.g. once a month) to update the csv file from here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import utils_for_alpaca\n",
    "alpaca = utils_for_alpaca.MyAlpaca(os.environ['ALPACA_API_KEY_PAPER'],os.environ['ALPACA_API_SECRET_PAPER'])\n",
    "alpaca.update_database()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LowPass Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LowPass (2-pole digital lowpass filter): suppresses high frequencies in the data series and thus attenuates all cycles that are shorter than the cutoff period. \n",
    "\n",
    "Compared to an EMA (which is in fact a 1-pole lowpass filter), cycles longer than the cutoff period are unaffected and passed without lag. This allows the LowPass function to react much faster and generate signals earlier than moving averages.\n",
    "\n",
    "Replacing moving averages by lowpass filters improves most algorithms. MA is bad for trend finding. It lags a lotbehind the prices => the signals are just not timely for a good trade (the trend is already over when my moving average finally bends up or down)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dateutil.relativedelta import relativedelta\n",
    "clock = alpaca.trading_client.get_clock()\n",
    "start_day = clock.timestamp.date() - relativedelta(days=10)\n",
    "data_df = alpaca.get_history_minute_single('AAPL', start_day, window=10)\n",
    "data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Formula"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ATR for Stop-Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['XLB', 'XLE', 'XLF', 'XLI', 'XLK', 'XLP', 'XLY', 'XLU', 'XLV']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Universes.Spiders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-15 21:42:54,724 - root - INFO - Starting concurrent download of hour data across 9 tickers for 12 days ... - [utils_for_alpaca.py:481]\n",
      "2024-11-15 21:42:56,322 - root - INFO - Done downloading hour data. 270 rows collected. - [utils_for_alpaca.py:493]\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>trade_count</th>\n",
       "      <th>vwap</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2024-11-04 11:00:00</td>\n",
       "      <td>93.93</td>\n",
       "      <td>93.950</td>\n",
       "      <td>93.115</td>\n",
       "      <td>93.390</td>\n",
       "      <td>1881240.0</td>\n",
       "      <td>19233.0</td>\n",
       "      <td>93.508072</td>\n",
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       "      <th>1</th>\n",
       "      <td>XLB</td>\n",
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       "      <td>14994.0</td>\n",
       "      <td>93.429882</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2024-11-04 15:00:00</td>\n",
       "      <td>93.42</td>\n",
       "      <td>93.550</td>\n",
       "      <td>93.330</td>\n",
       "      <td>93.540</td>\n",
       "      <td>1532957.0</td>\n",
       "      <td>11387.0</td>\n",
       "      <td>93.442497</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2024-11-05 11:00:00</td>\n",
       "      <td>93.62</td>\n",
       "      <td>94.000</td>\n",
       "      <td>93.610</td>\n",
       "      <td>93.950</td>\n",
       "      <td>878936.0</td>\n",
       "      <td>11186.0</td>\n",
       "      <td>93.834754</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2024-11-05 13:00:00</td>\n",
       "      <td>93.96</td>\n",
       "      <td>94.055</td>\n",
       "      <td>93.620</td>\n",
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       "      <td>216.96</td>\n",
       "      <td>217.250</td>\n",
       "      <td>215.810</td>\n",
       "      <td>216.330</td>\n",
       "      <td>572407.0</td>\n",
       "      <td>10132.0</td>\n",
       "      <td>216.305522</td>\n",
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       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>XLY</td>\n",
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       "      <td>215.000</td>\n",
       "      <td>215.370</td>\n",
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       "      <td>14151.0</td>\n",
       "      <td>215.491792</td>\n",
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       "      <td>XLY</td>\n",
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       "      <td>213.48</td>\n",
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       "      <th>268</th>\n",
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       "      <td>2024-11-15 13:00:00</td>\n",
       "      <td>213.44</td>\n",
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       "      <td>2024-11-15 15:00:00</td>\n",
       "      <td>212.98</td>\n",
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       "<p>270 rows × 9 columns</p>\n",
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       "    symbol           timestamp    open     high      low    close     volume  \\\n",
       "0      XLB 2024-11-04 11:00:00   93.93   93.950   93.115   93.390  1881240.0   \n",
       "1      XLB 2024-11-04 13:00:00   93.38   93.550   93.150   93.415  1423924.0   \n",
       "2      XLB 2024-11-04 15:00:00   93.42   93.550   93.330   93.540  1532957.0   \n",
       "3      XLB 2024-11-05 11:00:00   93.62   94.000   93.610   93.950   878936.0   \n",
       "4      XLB 2024-11-05 13:00:00   93.96   94.055   93.620   93.670   538998.0   \n",
       "..     ...                 ...     ...      ...      ...      ...        ...   \n",
       "265    XLY 2024-11-14 13:00:00  216.96  217.250  215.810  216.330   572407.0   \n",
       "266    XLY 2024-11-14 15:00:00  216.32  216.320  215.000  215.370   941038.0   \n",
       "267    XLY 2024-11-15 11:00:00  213.48  214.530  213.050  213.460   700871.0   \n",
       "268    XLY 2024-11-15 13:00:00  213.44  213.490  212.460  212.975   965155.0   \n",
       "269    XLY 2024-11-15 15:00:00  212.98  213.660  212.945  213.320   492889.0   \n",
       "\n",
       "     trade_count        vwap  \n",
       "0        19233.0   93.508072  \n",
       "1        14994.0   93.429882  \n",
       "2        11387.0   93.442497  \n",
       "3        11186.0   93.834754  \n",
       "4         7905.0   93.843818  \n",
       "..           ...         ...  \n",
       "265      10132.0  216.305522  \n",
       "266      14151.0  215.491792  \n",
       "267      11157.0  213.693828  \n",
       "268      13035.0  212.932340  \n",
       "269       7667.0  213.415837  \n",
       "\n",
       "[270 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days = 14\n",
    "df_2hour = alpaca.get_history(symbols=Universes.Spiders,periods=3 * days, FrameLength=2, frame = 'hour')\n",
    "df_2hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kalmar\\AppData\\Local\\Temp\\ipykernel_27176\\211800909.py:18: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  df_2hour = df_2hour.groupby('symbol').apply(calculate_tr_and_atr).reset_index(drop=True)\n"
     ]
    },
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       "      <td>93.950</td>\n",
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       "      <th>1</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2024-11-04 13:00:00</td>\n",
       "      <td>93.38</td>\n",
       "      <td>93.550</td>\n",
       "      <td>93.150</td>\n",
       "      <td>93.415</td>\n",
       "      <td>1423924.0</td>\n",
       "      <td>14994.0</td>\n",
       "      <td>93.429882</td>\n",
       "      <td>93.390</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.617500</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2024-11-04 15:00:00</td>\n",
       "      <td>93.42</td>\n",
       "      <td>93.550</td>\n",
       "      <td>93.330</td>\n",
       "      <td>93.540</td>\n",
       "      <td>1532957.0</td>\n",
       "      <td>11387.0</td>\n",
       "      <td>93.442497</td>\n",
       "      <td>93.415</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.485000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2024-11-05 11:00:00</td>\n",
       "      <td>93.62</td>\n",
       "      <td>94.000</td>\n",
       "      <td>93.610</td>\n",
       "      <td>93.950</td>\n",
       "      <td>878936.0</td>\n",
       "      <td>11186.0</td>\n",
       "      <td>93.834754</td>\n",
       "      <td>93.540</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.478750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2024-11-05 13:00:00</td>\n",
       "      <td>93.96</td>\n",
       "      <td>94.055</td>\n",
       "      <td>93.620</td>\n",
       "      <td>93.670</td>\n",
       "      <td>538998.0</td>\n",
       "      <td>7905.0</td>\n",
       "      <td>93.843818</td>\n",
       "      <td>93.950</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.470000</td>\n",
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       "      <th>...</th>\n",
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       "      <th>265</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2024-11-14 13:00:00</td>\n",
       "      <td>216.96</td>\n",
       "      <td>217.250</td>\n",
       "      <td>215.810</td>\n",
       "      <td>216.330</td>\n",
       "      <td>572407.0</td>\n",
       "      <td>10132.0</td>\n",
       "      <td>216.305522</td>\n",
       "      <td>217.000</td>\n",
       "      <td>1.440</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.190</td>\n",
       "      <td>1.440</td>\n",
       "      <td>2.041421</td>\n",
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       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2024-11-14 15:00:00</td>\n",
       "      <td>216.32</td>\n",
       "      <td>216.320</td>\n",
       "      <td>215.000</td>\n",
       "      <td>215.370</td>\n",
       "      <td>941038.0</td>\n",
       "      <td>14151.0</td>\n",
       "      <td>215.491792</td>\n",
       "      <td>216.330</td>\n",
       "      <td>1.320</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1.330</td>\n",
       "      <td>1.330</td>\n",
       "      <td>1.904993</td>\n",
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       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2024-11-15 11:00:00</td>\n",
       "      <td>213.48</td>\n",
       "      <td>214.530</td>\n",
       "      <td>213.050</td>\n",
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       "      <td>700871.0</td>\n",
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       "      <td>215.370</td>\n",
       "      <td>1.480</td>\n",
       "      <td>0.840</td>\n",
       "      <td>2.320</td>\n",
       "      <td>2.320</td>\n",
       "      <td>1.984993</td>\n",
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       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2024-11-15 13:00:00</td>\n",
       "      <td>213.44</td>\n",
       "      <td>213.490</td>\n",
       "      <td>212.460</td>\n",
       "      <td>212.975</td>\n",
       "      <td>965155.0</td>\n",
       "      <td>13035.0</td>\n",
       "      <td>212.932340</td>\n",
       "      <td>213.460</td>\n",
       "      <td>1.030</td>\n",
       "      <td>0.030</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.030</td>\n",
       "      <td>2.001421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2024-11-15 15:00:00</td>\n",
       "      <td>212.98</td>\n",
       "      <td>213.660</td>\n",
       "      <td>212.945</td>\n",
       "      <td>213.320</td>\n",
       "      <td>492889.0</td>\n",
       "      <td>7667.0</td>\n",
       "      <td>213.415837</td>\n",
       "      <td>212.975</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.715</td>\n",
       "      <td>1.656779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>270 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    symbol           timestamp    open     high      low    close     volume  \\\n",
       "0      XLB 2024-11-04 11:00:00   93.93   93.950   93.115   93.390  1881240.0   \n",
       "1      XLB 2024-11-04 13:00:00   93.38   93.550   93.150   93.415  1423924.0   \n",
       "2      XLB 2024-11-04 15:00:00   93.42   93.550   93.330   93.540  1532957.0   \n",
       "3      XLB 2024-11-05 11:00:00   93.62   94.000   93.610   93.950   878936.0   \n",
       "4      XLB 2024-11-05 13:00:00   93.96   94.055   93.620   93.670   538998.0   \n",
       "..     ...                 ...     ...      ...      ...      ...        ...   \n",
       "265    XLY 2024-11-14 13:00:00  216.96  217.250  215.810  216.330   572407.0   \n",
       "266    XLY 2024-11-14 15:00:00  216.32  216.320  215.000  215.370   941038.0   \n",
       "267    XLY 2024-11-15 11:00:00  213.48  214.530  213.050  213.460   700871.0   \n",
       "268    XLY 2024-11-15 13:00:00  213.44  213.490  212.460  212.975   965155.0   \n",
       "269    XLY 2024-11-15 15:00:00  212.98  213.660  212.945  213.320   492889.0   \n",
       "\n",
       "     trade_count        vwap  Prior Close  High_Low  High_PriorClose  \\\n",
       "0        19233.0   93.508072          NaN     0.835              NaN   \n",
       "1        14994.0   93.429882       93.390     0.400            0.160   \n",
       "2        11387.0   93.442497       93.415     0.220            0.135   \n",
       "3        11186.0   93.834754       93.540     0.390            0.460   \n",
       "4         7905.0   93.843818       93.950     0.435            0.105   \n",
       "..           ...         ...          ...       ...              ...   \n",
       "265      10132.0  216.305522      217.000     1.440            0.250   \n",
       "266      14151.0  215.491792      216.330     1.320            0.010   \n",
       "267      11157.0  213.693828      215.370     1.480            0.840   \n",
       "268      13035.0  212.932340      213.460     1.030            0.030   \n",
       "269       7667.0  213.415837      212.975     0.715            0.685   \n",
       "\n",
       "     Low_PriorClose     TR       ATR  \n",
       "0               NaN  0.835  0.835000  \n",
       "1             0.240  0.400  0.617500  \n",
       "2             0.085  0.220  0.485000  \n",
       "3             0.070  0.460  0.478750  \n",
       "4             0.330  0.435  0.470000  \n",
       "..              ...    ...       ...  \n",
       "265           1.190  1.440  2.041421  \n",
       "266           1.330  1.330  1.904993  \n",
       "267           2.320  2.320  1.984993  \n",
       "268           1.000  1.030  2.001421  \n",
       "269           0.030  0.715  1.656779  \n",
       "\n",
       "[270 rows x 15 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate TR and ATR per symbol\n",
    "def calculate_tr_and_atr(group, period=14):\n",
    "    group['Prior Close'] = group['close'].shift() # Shift 'close' to get the previous close\n",
    "\n",
    "    # Calculate True Range components\n",
    "    group['High_Low'] = group['high'] - group['low']\n",
    "    group['High_PriorClose'] = (group['high'] - group['Prior Close']).abs()\n",
    "    group['Low_PriorClose'] = (group['low'] - group['Prior Close']).abs()   \n",
    "    group['TR'] = group[['High_Low', 'High_PriorClose', 'Low_PriorClose']].max(axis=1)\n",
    "    \n",
    "    # for trasparency I included 4 steps above, but the 1 below could be used instead\n",
    "    # group['TR'] = group[['high', 'Prior Close']].max(axis=1) - group[['low', 'Prior Close']].min(axis=1) # Calculate True Range (TR)\n",
    "\n",
    "    group['ATR'] = group['TR'].rolling(window=period, min_periods=1).mean() # ATR as rolling mean of TR    \n",
    "    return group\n",
    "\n",
    "# Apply the function to each group of symbols\n",
    "df_2hour = df_2hour.groupby('symbol').apply(calculate_tr_and_atr).reset_index(drop=True)\n",
    "df_2hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'XLB': 90.24,\n",
       " 'XLE': 93.65,\n",
       " 'XLF': 49.42,\n",
       " 'XLI': 137.3,\n",
       " 'XLK': 225.34,\n",
       " 'XLP': 79.41,\n",
       " 'XLU': 78.14,\n",
       " 'XLV': 140.25,\n",
       " 'XLY': 210.01}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_atr_df = df_2hour.groupby('symbol').last().reset_index() # the rows are sorted from oldest to newest, so we take last row\n",
    "current_atr_df['stop_loss_price'] = round(current_atr_df['close'] - 2 * current_atr_df['ATR'],2) # Calculate stop_loss_price as close - 2 * ATR\n",
    "stop_loss_dict = current_atr_df.set_index('symbol')['stop_loss_price'].to_dict()\n",
    "stop_loss_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Open Range Breakout Strategy (with relative volumes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import utils_for_alpaca\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpaca = utils_for_alpaca.MyAlpaca(os.environ['ALPACA_API_KEY_PAPER'],os.environ['ALPACA_API_SECRET_PAPER'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting latest volume data for currently-in-scope tickers \n",
    "all_tickers = alpaca.get_ok_alpaca_stocks(spread_limit = 0.015)\n",
    "df_current = alpaca.get_history(symbols=all_tickers, periods=1, FrameLength=5, frame = 'min', only_for_today=True)\n",
    "df_current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-05 22:51:43,169 - root - INFO - Successfully read stocks_db.csv from GitHub - [utils_for_alpaca.py:89]\n"
     ]
    }
   ],
   "source": [
    "# reading current historical file with volumes from GH\n",
    "from github import Github\n",
    "import pandas as pd\n",
    "repo = Github(os.getenv('github_strat_token')).get_user().get_repo(os.getenv('dedicated_repo'))        \n",
    "history_volumes_df = pd.DataFrame()\n",
    "history_volumes_df, sha = utils_for_alpaca._read_csv_from_github(repo, os.getenv('gh_csv_stocks_db'))\n",
    "columns_to_convert = ['volume', 'trade_count']\n",
    "history_volumes_df[columns_to_convert] = history_volumes_df[columns_to_convert].apply(pd.to_numeric, errors='coerce').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding historical average volumes to the df with current volumes\n",
    "cols= ['symbol', 'volume', 'trade_count']\n",
    "result_df = pd.merge(df_current[cols], history_volumes_df[cols], on=['symbol'], how='inner', suffixes=('_latest','_avg'))\n",
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating volume ratio selecting tickers with highest volume ratio\n",
    "result_df['volume_ratio'] = result_df['volume_latest'] / result_df['volume_avg']\n",
    "result_df['trade_count_ratio'] = result_df['trade_count_latest'] / result_df['trade_count_avg']\n",
    "result_df = result_df.sort_values(by='volume_ratio', ascending=False)\n",
    "ticker_rel_vol = result_df.head(alpaca.stocks_per_run).symbol.unique().tolist()\n",
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting today's high price for our filtered-by-volume tickers\n",
    "# As I plan to run the strategy at 9:35 - it will be the high for the first 5min of trading\n",
    "from alpaca.data.enums import DataFeed\n",
    "from alpaca.data.requests import StockSnapshotRequest\n",
    "snap = alpaca.stock_client.get_stock_snapshot(StockSnapshotRequest(symbol_or_symbols=ticker_rel_vol, feed = DataFeed.SIP))\n",
    "snapshot_data = {stock: [snapshot.daily_bar.high, snapshot.daily_bar.low,]\n",
    "                for stock, snapshot in snap.items() if snapshot and snapshot.daily_bar\n",
    "                }\n",
    "snapshot_columns=['today_high', 'today_low']\n",
    "snapshot_df = pd.DataFrame(snapshot_data.values(), snapshot_data.keys(), columns=snapshot_columns)\n",
    "snapshot_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prices & Quotes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Historical Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-01-19 10:50:57,875 - root - INFO - Starting concurrent download of day data across 9 tickers for 3 days ... - [utils_for_alpaca.py:494]\n",
      "2025-01-19 10:50:58,432 - root - INFO - Done downloading day data. 9 rows collected. - [utils_for_alpaca.py:508]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>trade_count</th>\n",
       "      <th>vwap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-17</td>\n",
       "      <td>88.34</td>\n",
       "      <td>88.900</td>\n",
       "      <td>88.0000</td>\n",
       "      <td>88.33</td>\n",
       "      <td>5794592.0</td>\n",
       "      <td>40521.0</td>\n",
       "      <td>88.483792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-17</td>\n",
       "      <td>93.24</td>\n",
       "      <td>94.290</td>\n",
       "      <td>92.9100</td>\n",
       "      <td>93.96</td>\n",
       "      <td>11949825.0</td>\n",
       "      <td>93948.0</td>\n",
       "      <td>93.925451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-17</td>\n",
       "      <td>49.91</td>\n",
       "      <td>50.320</td>\n",
       "      <td>49.7300</td>\n",
       "      <td>50.23</td>\n",
       "      <td>43693111.0</td>\n",
       "      <td>91335.0</td>\n",
       "      <td>50.144941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-17</td>\n",
       "      <td>137.81</td>\n",
       "      <td>138.265</td>\n",
       "      <td>137.2075</td>\n",
       "      <td>137.75</td>\n",
       "      <td>6869566.0</td>\n",
       "      <td>63163.0</td>\n",
       "      <td>137.791080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-17</td>\n",
       "      <td>234.66</td>\n",
       "      <td>234.940</td>\n",
       "      <td>233.0800</td>\n",
       "      <td>234.11</td>\n",
       "      <td>3371692.0</td>\n",
       "      <td>49555.0</td>\n",
       "      <td>234.184389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-17</td>\n",
       "      <td>76.94</td>\n",
       "      <td>77.400</td>\n",
       "      <td>76.8500</td>\n",
       "      <td>77.24</td>\n",
       "      <td>10525009.0</td>\n",
       "      <td>44529.0</td>\n",
       "      <td>77.214605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-17</td>\n",
       "      <td>78.68</td>\n",
       "      <td>79.200</td>\n",
       "      <td>78.4300</td>\n",
       "      <td>78.84</td>\n",
       "      <td>7334541.0</td>\n",
       "      <td>58414.0</td>\n",
       "      <td>78.886877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-17</td>\n",
       "      <td>140.94</td>\n",
       "      <td>141.400</td>\n",
       "      <td>140.1800</td>\n",
       "      <td>140.18</td>\n",
       "      <td>7668273.0</td>\n",
       "      <td>58035.0</td>\n",
       "      <td>140.821277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-17</td>\n",
       "      <td>229.03</td>\n",
       "      <td>230.660</td>\n",
       "      <td>228.6600</td>\n",
       "      <td>229.22</td>\n",
       "      <td>1942339.0</td>\n",
       "      <td>33310.0</td>\n",
       "      <td>229.713226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol  timestamp    open     high       low   close      volume  \\\n",
       "0    XLB 2025-01-17   88.34   88.900   88.0000   88.33   5794592.0   \n",
       "1    XLE 2025-01-17   93.24   94.290   92.9100   93.96  11949825.0   \n",
       "2    XLF 2025-01-17   49.91   50.320   49.7300   50.23  43693111.0   \n",
       "3    XLI 2025-01-17  137.81  138.265  137.2075  137.75   6869566.0   \n",
       "4    XLK 2025-01-17  234.66  234.940  233.0800  234.11   3371692.0   \n",
       "5    XLP 2025-01-17   76.94   77.400   76.8500   77.24  10525009.0   \n",
       "6    XLU 2025-01-17   78.68   79.200   78.4300   78.84   7334541.0   \n",
       "7    XLV 2025-01-17  140.94  141.400  140.1800  140.18   7668273.0   \n",
       "8    XLY 2025-01-17  229.03  230.660  228.6600  229.22   1942339.0   \n",
       "\n",
       "   trade_count        vwap  \n",
       "0      40521.0   88.483792  \n",
       "1      93948.0   93.925451  \n",
       "2      91335.0   50.144941  \n",
       "3      63163.0  137.791080  \n",
       "4      49555.0  234.184389  \n",
       "5      44529.0   77.214605  \n",
       "6      58414.0   78.886877  \n",
       "7      58035.0  140.821277  \n",
       "8      33310.0  229.713226  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symbols = ['XLB', 'XLE', 'XLF', 'XLI', 'XLK', 'XLP', 'XLY', 'XLU', 'XLV']\n",
    "df_day = alpaca.get_history(symbols=symbols, periods=1, FrameLength=1, frame = 'day')\n",
    "df_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-01-19 10:51:15,643 - root - INFO - Starting concurrent download of min data across 9 tickers for 3 days ... - [utils_for_alpaca.py:494]\n",
      "2025-01-19 10:51:17,353 - root - INFO - Done downloading min data. 45 rows collected. - [utils_for_alpaca.py:508]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>symbol</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>trade_count</th>\n",
       "      <th>vwap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>88.3300</td>\n",
       "      <td>88.3500</td>\n",
       "      <td>88.3300</td>\n",
       "      <td>88.350</td>\n",
       "      <td>257543.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>88.330008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-17 15:59:00</td>\n",
       "      <td>88.3350</td>\n",
       "      <td>88.3700</td>\n",
       "      <td>88.3200</td>\n",
       "      <td>88.350</td>\n",
       "      <td>271770.0</td>\n",
       "      <td>1060.0</td>\n",
       "      <td>88.348370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-17 15:58:00</td>\n",
       "      <td>88.3800</td>\n",
       "      <td>88.3899</td>\n",
       "      <td>88.3200</td>\n",
       "      <td>88.340</td>\n",
       "      <td>203285.0</td>\n",
       "      <td>712.0</td>\n",
       "      <td>88.348816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-17 15:57:00</td>\n",
       "      <td>88.3650</td>\n",
       "      <td>88.3850</td>\n",
       "      <td>88.3650</td>\n",
       "      <td>88.375</td>\n",
       "      <td>67800.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>88.375193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-17 15:56:00</td>\n",
       "      <td>88.3600</td>\n",
       "      <td>88.3700</td>\n",
       "      <td>88.3500</td>\n",
       "      <td>88.370</td>\n",
       "      <td>39276.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>88.362489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>93.9600</td>\n",
       "      <td>93.9600</td>\n",
       "      <td>93.9000</td>\n",
       "      <td>93.900</td>\n",
       "      <td>416840.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>93.959561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-17 15:59:00</td>\n",
       "      <td>94.0100</td>\n",
       "      <td>94.0400</td>\n",
       "      <td>93.9600</td>\n",
       "      <td>93.960</td>\n",
       "      <td>364805.0</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>93.999643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-17 15:58:00</td>\n",
       "      <td>94.0699</td>\n",
       "      <td>94.0800</td>\n",
       "      <td>94.0099</td>\n",
       "      <td>94.010</td>\n",
       "      <td>205618.0</td>\n",
       "      <td>1466.0</td>\n",
       "      <td>94.055749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-17 15:57:00</td>\n",
       "      <td>94.0400</td>\n",
       "      <td>94.0800</td>\n",
       "      <td>94.0300</td>\n",
       "      <td>94.065</td>\n",
       "      <td>93525.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>94.053905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-17 15:56:00</td>\n",
       "      <td>94.1050</td>\n",
       "      <td>94.1300</td>\n",
       "      <td>94.0200</td>\n",
       "      <td>94.040</td>\n",
       "      <td>99727.0</td>\n",
       "      <td>822.0</td>\n",
       "      <td>94.070408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>50.2300</td>\n",
       "      <td>50.2300</td>\n",
       "      <td>50.2100</td>\n",
       "      <td>50.230</td>\n",
       "      <td>830536.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>50.229936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-17 15:59:00</td>\n",
       "      <td>50.2300</td>\n",
       "      <td>50.2500</td>\n",
       "      <td>50.2100</td>\n",
       "      <td>50.220</td>\n",
       "      <td>1412563.0</td>\n",
       "      <td>1826.0</td>\n",
       "      <td>50.236346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-17 15:58:00</td>\n",
       "      <td>50.2599</td>\n",
       "      <td>50.2600</td>\n",
       "      <td>50.2300</td>\n",
       "      <td>50.235</td>\n",
       "      <td>660008.0</td>\n",
       "      <td>1165.0</td>\n",
       "      <td>50.249678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-17 15:57:00</td>\n",
       "      <td>50.2350</td>\n",
       "      <td>50.2600</td>\n",
       "      <td>50.2300</td>\n",
       "      <td>50.255</td>\n",
       "      <td>308970.0</td>\n",
       "      <td>703.0</td>\n",
       "      <td>50.246721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-17 15:56:00</td>\n",
       "      <td>50.2400</td>\n",
       "      <td>50.2500</td>\n",
       "      <td>50.2200</td>\n",
       "      <td>50.235</td>\n",
       "      <td>320867.0</td>\n",
       "      <td>752.0</td>\n",
       "      <td>50.235933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>137.7500</td>\n",
       "      <td>137.7500</td>\n",
       "      <td>137.7500</td>\n",
       "      <td>137.750</td>\n",
       "      <td>151463.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>137.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-17 15:59:00</td>\n",
       "      <td>137.7150</td>\n",
       "      <td>137.7975</td>\n",
       "      <td>137.7150</td>\n",
       "      <td>137.730</td>\n",
       "      <td>214507.0</td>\n",
       "      <td>1625.0</td>\n",
       "      <td>137.763625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-17 15:58:00</td>\n",
       "      <td>137.7550</td>\n",
       "      <td>137.7800</td>\n",
       "      <td>137.6950</td>\n",
       "      <td>137.720</td>\n",
       "      <td>102153.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>137.730851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-17 15:57:00</td>\n",
       "      <td>137.7000</td>\n",
       "      <td>137.7600</td>\n",
       "      <td>137.6800</td>\n",
       "      <td>137.760</td>\n",
       "      <td>50825.0</td>\n",
       "      <td>513.0</td>\n",
       "      <td>137.723146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-17 15:56:00</td>\n",
       "      <td>137.7600</td>\n",
       "      <td>137.7750</td>\n",
       "      <td>137.6650</td>\n",
       "      <td>137.685</td>\n",
       "      <td>57431.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>137.704403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>234.1100</td>\n",
       "      <td>234.1100</td>\n",
       "      <td>234.1100</td>\n",
       "      <td>234.110</td>\n",
       "      <td>148636.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>234.110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-17 15:59:00</td>\n",
       "      <td>233.9750</td>\n",
       "      <td>234.1900</td>\n",
       "      <td>233.9750</td>\n",
       "      <td>234.120</td>\n",
       "      <td>106154.0</td>\n",
       "      <td>1334.0</td>\n",
       "      <td>234.094193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-17 15:58:00</td>\n",
       "      <td>234.0800</td>\n",
       "      <td>234.1100</td>\n",
       "      <td>233.9700</td>\n",
       "      <td>233.970</td>\n",
       "      <td>53829.0</td>\n",
       "      <td>698.0</td>\n",
       "      <td>234.032137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-17 15:57:00</td>\n",
       "      <td>234.1800</td>\n",
       "      <td>234.1800</td>\n",
       "      <td>234.0300</td>\n",
       "      <td>234.090</td>\n",
       "      <td>37382.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>234.085569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-17 15:56:00</td>\n",
       "      <td>234.2400</td>\n",
       "      <td>234.2600</td>\n",
       "      <td>234.0700</td>\n",
       "      <td>234.170</td>\n",
       "      <td>36317.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>234.118485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>77.2400</td>\n",
       "      <td>77.2400</td>\n",
       "      <td>77.2400</td>\n",
       "      <td>77.240</td>\n",
       "      <td>828875.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>77.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-17 15:59:00</td>\n",
       "      <td>77.2600</td>\n",
       "      <td>77.3000</td>\n",
       "      <td>77.2450</td>\n",
       "      <td>77.250</td>\n",
       "      <td>420211.0</td>\n",
       "      <td>1158.0</td>\n",
       "      <td>77.274517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-17 15:58:00</td>\n",
       "      <td>77.3350</td>\n",
       "      <td>77.3500</td>\n",
       "      <td>77.2525</td>\n",
       "      <td>77.255</td>\n",
       "      <td>666237.0</td>\n",
       "      <td>1822.0</td>\n",
       "      <td>77.288458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-17 15:57:00</td>\n",
       "      <td>77.2950</td>\n",
       "      <td>77.3400</td>\n",
       "      <td>77.2900</td>\n",
       "      <td>77.335</td>\n",
       "      <td>77209.0</td>\n",
       "      <td>436.0</td>\n",
       "      <td>77.312980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-17 15:56:00</td>\n",
       "      <td>77.3050</td>\n",
       "      <td>77.3100</td>\n",
       "      <td>77.2600</td>\n",
       "      <td>77.300</td>\n",
       "      <td>201701.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>77.282303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>78.8400</td>\n",
       "      <td>78.8400</td>\n",
       "      <td>78.8400</td>\n",
       "      <td>78.840</td>\n",
       "      <td>165749.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>78.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-17 15:59:00</td>\n",
       "      <td>78.8600</td>\n",
       "      <td>78.8999</td>\n",
       "      <td>78.8100</td>\n",
       "      <td>78.820</td>\n",
       "      <td>304101.0</td>\n",
       "      <td>1583.0</td>\n",
       "      <td>78.865284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-17 15:58:00</td>\n",
       "      <td>78.8874</td>\n",
       "      <td>78.9000</td>\n",
       "      <td>78.8500</td>\n",
       "      <td>78.860</td>\n",
       "      <td>153132.0</td>\n",
       "      <td>733.0</td>\n",
       "      <td>78.873646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-17 15:57:00</td>\n",
       "      <td>78.8700</td>\n",
       "      <td>78.8900</td>\n",
       "      <td>78.8500</td>\n",
       "      <td>78.885</td>\n",
       "      <td>57689.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>78.870741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-17 15:56:00</td>\n",
       "      <td>78.8800</td>\n",
       "      <td>78.9100</td>\n",
       "      <td>78.8500</td>\n",
       "      <td>78.870</td>\n",
       "      <td>83131.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>78.878455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>140.1800</td>\n",
       "      <td>140.1800</td>\n",
       "      <td>140.0900</td>\n",
       "      <td>140.090</td>\n",
       "      <td>392918.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>140.179790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-17 15:59:00</td>\n",
       "      <td>140.2100</td>\n",
       "      <td>140.2850</td>\n",
       "      <td>140.2000</td>\n",
       "      <td>140.210</td>\n",
       "      <td>238348.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>140.238839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-17 15:58:00</td>\n",
       "      <td>140.3100</td>\n",
       "      <td>140.3300</td>\n",
       "      <td>140.1900</td>\n",
       "      <td>140.210</td>\n",
       "      <td>216296.0</td>\n",
       "      <td>1416.0</td>\n",
       "      <td>140.257367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-17 15:57:00</td>\n",
       "      <td>140.2800</td>\n",
       "      <td>140.3200</td>\n",
       "      <td>140.2550</td>\n",
       "      <td>140.315</td>\n",
       "      <td>55180.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>140.287439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-17 15:56:00</td>\n",
       "      <td>140.2950</td>\n",
       "      <td>140.3250</td>\n",
       "      <td>140.2600</td>\n",
       "      <td>140.290</td>\n",
       "      <td>66312.0</td>\n",
       "      <td>536.0</td>\n",
       "      <td>140.287434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>229.2200</td>\n",
       "      <td>229.2200</td>\n",
       "      <td>229.1300</td>\n",
       "      <td>229.220</td>\n",
       "      <td>41598.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>229.219564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-17 15:59:00</td>\n",
       "      <td>229.1600</td>\n",
       "      <td>229.3500</td>\n",
       "      <td>229.1600</td>\n",
       "      <td>229.210</td>\n",
       "      <td>66646.0</td>\n",
       "      <td>860.0</td>\n",
       "      <td>229.268374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-17 15:58:00</td>\n",
       "      <td>229.1900</td>\n",
       "      <td>229.2400</td>\n",
       "      <td>229.1000</td>\n",
       "      <td>229.150</td>\n",
       "      <td>39858.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>229.182980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-17 15:57:00</td>\n",
       "      <td>229.1500</td>\n",
       "      <td>229.1800</td>\n",
       "      <td>229.0700</td>\n",
       "      <td>229.180</td>\n",
       "      <td>32184.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>229.126769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-17 15:56:00</td>\n",
       "      <td>229.3000</td>\n",
       "      <td>229.3000</td>\n",
       "      <td>229.0900</td>\n",
       "      <td>229.175</td>\n",
       "      <td>17046.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>229.150086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symbol           timestamp      open      high       low    close  \\\n",
       "0     XLB 2025-01-17 16:00:00   88.3300   88.3500   88.3300   88.350   \n",
       "1     XLB 2025-01-17 15:59:00   88.3350   88.3700   88.3200   88.350   \n",
       "2     XLB 2025-01-17 15:58:00   88.3800   88.3899   88.3200   88.340   \n",
       "3     XLB 2025-01-17 15:57:00   88.3650   88.3850   88.3650   88.375   \n",
       "4     XLB 2025-01-17 15:56:00   88.3600   88.3700   88.3500   88.370   \n",
       "5     XLE 2025-01-17 16:00:00   93.9600   93.9600   93.9000   93.900   \n",
       "6     XLE 2025-01-17 15:59:00   94.0100   94.0400   93.9600   93.960   \n",
       "7     XLE 2025-01-17 15:58:00   94.0699   94.0800   94.0099   94.010   \n",
       "8     XLE 2025-01-17 15:57:00   94.0400   94.0800   94.0300   94.065   \n",
       "9     XLE 2025-01-17 15:56:00   94.1050   94.1300   94.0200   94.040   \n",
       "10    XLF 2025-01-17 16:00:00   50.2300   50.2300   50.2100   50.230   \n",
       "11    XLF 2025-01-17 15:59:00   50.2300   50.2500   50.2100   50.220   \n",
       "12    XLF 2025-01-17 15:58:00   50.2599   50.2600   50.2300   50.235   \n",
       "13    XLF 2025-01-17 15:57:00   50.2350   50.2600   50.2300   50.255   \n",
       "14    XLF 2025-01-17 15:56:00   50.2400   50.2500   50.2200   50.235   \n",
       "15    XLI 2025-01-17 16:00:00  137.7500  137.7500  137.7500  137.750   \n",
       "16    XLI 2025-01-17 15:59:00  137.7150  137.7975  137.7150  137.730   \n",
       "17    XLI 2025-01-17 15:58:00  137.7550  137.7800  137.6950  137.720   \n",
       "18    XLI 2025-01-17 15:57:00  137.7000  137.7600  137.6800  137.760   \n",
       "19    XLI 2025-01-17 15:56:00  137.7600  137.7750  137.6650  137.685   \n",
       "20    XLK 2025-01-17 16:00:00  234.1100  234.1100  234.1100  234.110   \n",
       "21    XLK 2025-01-17 15:59:00  233.9750  234.1900  233.9750  234.120   \n",
       "22    XLK 2025-01-17 15:58:00  234.0800  234.1100  233.9700  233.970   \n",
       "23    XLK 2025-01-17 15:57:00  234.1800  234.1800  234.0300  234.090   \n",
       "24    XLK 2025-01-17 15:56:00  234.2400  234.2600  234.0700  234.170   \n",
       "25    XLP 2025-01-17 16:00:00   77.2400   77.2400   77.2400   77.240   \n",
       "26    XLP 2025-01-17 15:59:00   77.2600   77.3000   77.2450   77.250   \n",
       "27    XLP 2025-01-17 15:58:00   77.3350   77.3500   77.2525   77.255   \n",
       "28    XLP 2025-01-17 15:57:00   77.2950   77.3400   77.2900   77.335   \n",
       "29    XLP 2025-01-17 15:56:00   77.3050   77.3100   77.2600   77.300   \n",
       "30    XLU 2025-01-17 16:00:00   78.8400   78.8400   78.8400   78.840   \n",
       "31    XLU 2025-01-17 15:59:00   78.8600   78.8999   78.8100   78.820   \n",
       "32    XLU 2025-01-17 15:58:00   78.8874   78.9000   78.8500   78.860   \n",
       "33    XLU 2025-01-17 15:57:00   78.8700   78.8900   78.8500   78.885   \n",
       "34    XLU 2025-01-17 15:56:00   78.8800   78.9100   78.8500   78.870   \n",
       "35    XLV 2025-01-17 16:00:00  140.1800  140.1800  140.0900  140.090   \n",
       "36    XLV 2025-01-17 15:59:00  140.2100  140.2850  140.2000  140.210   \n",
       "37    XLV 2025-01-17 15:58:00  140.3100  140.3300  140.1900  140.210   \n",
       "38    XLV 2025-01-17 15:57:00  140.2800  140.3200  140.2550  140.315   \n",
       "39    XLV 2025-01-17 15:56:00  140.2950  140.3250  140.2600  140.290   \n",
       "40    XLY 2025-01-17 16:00:00  229.2200  229.2200  229.1300  229.220   \n",
       "41    XLY 2025-01-17 15:59:00  229.1600  229.3500  229.1600  229.210   \n",
       "42    XLY 2025-01-17 15:58:00  229.1900  229.2400  229.1000  229.150   \n",
       "43    XLY 2025-01-17 15:57:00  229.1500  229.1800  229.0700  229.180   \n",
       "44    XLY 2025-01-17 15:56:00  229.3000  229.3000  229.0900  229.175   \n",
       "\n",
       "       volume  trade_count        vwap  \n",
       "0    257543.0          5.0   88.330008  \n",
       "1    271770.0       1060.0   88.348370  \n",
       "2    203285.0        712.0   88.348816  \n",
       "3     67800.0        365.0   88.375193  \n",
       "4     39276.0        264.0   88.362489  \n",
       "5    416840.0         22.0   93.959561  \n",
       "6    364805.0       2170.0   93.999643  \n",
       "7    205618.0       1466.0   94.055749  \n",
       "8     93525.0        919.0   94.053905  \n",
       "9     99727.0        822.0   94.070408  \n",
       "10   830536.0         14.0   50.229936  \n",
       "11  1412563.0       1826.0   50.236346  \n",
       "12   660008.0       1165.0   50.249678  \n",
       "13   308970.0        703.0   50.246721  \n",
       "14   320867.0        752.0   50.235933  \n",
       "15   151463.0          4.0  137.750000  \n",
       "16   214507.0       1625.0  137.763625  \n",
       "17   102153.0        797.0  137.730851  \n",
       "18    50825.0        513.0  137.723146  \n",
       "19    57431.0        583.0  137.704403  \n",
       "20   148636.0          9.0  234.110000  \n",
       "21   106154.0       1334.0  234.094193  \n",
       "22    53829.0        698.0  234.032137  \n",
       "23    37382.0        528.0  234.085569  \n",
       "24    36317.0        560.0  234.118485  \n",
       "25   828875.0          9.0   77.240000  \n",
       "26   420211.0       1158.0   77.274517  \n",
       "27   666237.0       1822.0   77.288458  \n",
       "28    77209.0        436.0   77.312980  \n",
       "29   201701.0        682.0   77.282303  \n",
       "30   165749.0         10.0   78.840000  \n",
       "31   304101.0       1583.0   78.865284  \n",
       "32   153132.0        733.0   78.873646  \n",
       "33    57689.0        377.0   78.870741  \n",
       "34    83131.0        559.0   78.878455  \n",
       "35   392918.0         12.0  140.179790  \n",
       "36   238348.0       1627.0  140.238839  \n",
       "37   216296.0       1416.0  140.257367  \n",
       "38    55180.0        482.0  140.287439  \n",
       "39    66312.0        536.0  140.287434  \n",
       "40    41598.0         10.0  229.219564  \n",
       "41    66646.0        860.0  229.268374  \n",
       "42    39858.0        588.0  229.182980  \n",
       "43    32184.0        445.0  229.126769  \n",
       "44    17046.0        293.0  229.150086  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1min = alpaca.get_history(symbols=symbols, periods=5, FrameLength=1)\n",
    "df_1min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-01-19 10:53:09,802 - root - INFO - Starting concurrent download of min data across 9 tickers for 3 days ... - [utils_for_alpaca.py:494]\n",
      "2025-01-19 10:53:10,308 - root - INFO - Done downloading min data. 45 rows collected. - [utils_for_alpaca.py:508]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>symbol</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>trade_count</th>\n",
       "      <th>vwap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>88.330</td>\n",
       "      <td>88.3500</td>\n",
       "      <td>88.3300</td>\n",
       "      <td>88.3300</td>\n",
       "      <td>258214.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>88.330008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-17 15:45:00</td>\n",
       "      <td>88.395</td>\n",
       "      <td>88.4180</td>\n",
       "      <td>88.3150</td>\n",
       "      <td>88.3500</td>\n",
       "      <td>1050040.0</td>\n",
       "      <td>5342.0</td>\n",
       "      <td>88.361868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-17 15:30:00</td>\n",
       "      <td>88.400</td>\n",
       "      <td>88.4600</td>\n",
       "      <td>88.3750</td>\n",
       "      <td>88.3900</td>\n",
       "      <td>274319.0</td>\n",
       "      <td>1876.0</td>\n",
       "      <td>88.409764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-17 15:15:00</td>\n",
       "      <td>88.365</td>\n",
       "      <td>88.4150</td>\n",
       "      <td>88.3300</td>\n",
       "      <td>88.4100</td>\n",
       "      <td>115383.0</td>\n",
       "      <td>862.0</td>\n",
       "      <td>88.368922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>88.420</td>\n",
       "      <td>88.4200</td>\n",
       "      <td>88.2900</td>\n",
       "      <td>88.3650</td>\n",
       "      <td>181229.0</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>88.341328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>93.960</td>\n",
       "      <td>94.0003</td>\n",
       "      <td>93.9000</td>\n",
       "      <td>94.0000</td>\n",
       "      <td>430379.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>93.959495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-17 15:45:00</td>\n",
       "      <td>94.090</td>\n",
       "      <td>94.1600</td>\n",
       "      <td>93.9600</td>\n",
       "      <td>93.9600</td>\n",
       "      <td>1413811.0</td>\n",
       "      <td>10682.0</td>\n",
       "      <td>94.062843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-17 15:30:00</td>\n",
       "      <td>94.000</td>\n",
       "      <td>94.1100</td>\n",
       "      <td>93.9700</td>\n",
       "      <td>94.0850</td>\n",
       "      <td>559131.0</td>\n",
       "      <td>3968.0</td>\n",
       "      <td>94.047458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-17 15:15:00</td>\n",
       "      <td>93.995</td>\n",
       "      <td>94.0250</td>\n",
       "      <td>93.9350</td>\n",
       "      <td>94.0051</td>\n",
       "      <td>327502.0</td>\n",
       "      <td>2737.0</td>\n",
       "      <td>93.993719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>93.900</td>\n",
       "      <td>94.0100</td>\n",
       "      <td>93.8050</td>\n",
       "      <td>94.0000</td>\n",
       "      <td>358671.0</td>\n",
       "      <td>2601.0</td>\n",
       "      <td>93.892006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>50.230</td>\n",
       "      <td>50.2400</td>\n",
       "      <td>50.1600</td>\n",
       "      <td>50.2200</td>\n",
       "      <td>1112075.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>50.221318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-17 15:45:00</td>\n",
       "      <td>50.250</td>\n",
       "      <td>50.2600</td>\n",
       "      <td>50.1950</td>\n",
       "      <td>50.2200</td>\n",
       "      <td>6859916.0</td>\n",
       "      <td>11202.0</td>\n",
       "      <td>50.234481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-17 15:30:00</td>\n",
       "      <td>50.295</td>\n",
       "      <td>50.3200</td>\n",
       "      <td>50.2500</td>\n",
       "      <td>50.2500</td>\n",
       "      <td>1902158.0</td>\n",
       "      <td>5085.0</td>\n",
       "      <td>50.292553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-17 15:15:00</td>\n",
       "      <td>50.244</td>\n",
       "      <td>50.3000</td>\n",
       "      <td>50.2300</td>\n",
       "      <td>50.2950</td>\n",
       "      <td>1469451.0</td>\n",
       "      <td>3793.0</td>\n",
       "      <td>50.279282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>50.230</td>\n",
       "      <td>50.2700</td>\n",
       "      <td>50.1700</td>\n",
       "      <td>50.2450</td>\n",
       "      <td>1661161.0</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>50.208818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>137.750</td>\n",
       "      <td>137.7500</td>\n",
       "      <td>137.6900</td>\n",
       "      <td>137.6900</td>\n",
       "      <td>152133.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>137.749913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-17 15:45:00</td>\n",
       "      <td>137.775</td>\n",
       "      <td>137.8470</td>\n",
       "      <td>137.6650</td>\n",
       "      <td>137.7300</td>\n",
       "      <td>946158.0</td>\n",
       "      <td>8079.0</td>\n",
       "      <td>137.749963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-17 15:30:00</td>\n",
       "      <td>137.730</td>\n",
       "      <td>137.8300</td>\n",
       "      <td>137.7300</td>\n",
       "      <td>137.7650</td>\n",
       "      <td>177205.0</td>\n",
       "      <td>2109.0</td>\n",
       "      <td>137.790143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-17 15:15:00</td>\n",
       "      <td>137.760</td>\n",
       "      <td>137.8100</td>\n",
       "      <td>137.6950</td>\n",
       "      <td>137.7400</td>\n",
       "      <td>167992.0</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>137.745734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>137.780</td>\n",
       "      <td>137.7800</td>\n",
       "      <td>137.6300</td>\n",
       "      <td>137.7550</td>\n",
       "      <td>216524.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>137.703550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>234.110</td>\n",
       "      <td>234.5200</td>\n",
       "      <td>234.1100</td>\n",
       "      <td>234.1400</td>\n",
       "      <td>199853.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>234.129401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-17 15:45:00</td>\n",
       "      <td>234.390</td>\n",
       "      <td>234.6000</td>\n",
       "      <td>233.9700</td>\n",
       "      <td>234.1200</td>\n",
       "      <td>473496.0</td>\n",
       "      <td>7538.0</td>\n",
       "      <td>234.208745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-17 15:30:00</td>\n",
       "      <td>234.180</td>\n",
       "      <td>234.4500</td>\n",
       "      <td>234.1400</td>\n",
       "      <td>234.3900</td>\n",
       "      <td>93967.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>234.325381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-17 15:15:00</td>\n",
       "      <td>234.150</td>\n",
       "      <td>234.2700</td>\n",
       "      <td>233.9900</td>\n",
       "      <td>234.1700</td>\n",
       "      <td>70458.0</td>\n",
       "      <td>1449.0</td>\n",
       "      <td>234.145563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>234.120</td>\n",
       "      <td>234.2600</td>\n",
       "      <td>233.8400</td>\n",
       "      <td>234.0783</td>\n",
       "      <td>94454.0</td>\n",
       "      <td>1617.0</td>\n",
       "      <td>233.997683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>77.240</td>\n",
       "      <td>77.4400</td>\n",
       "      <td>77.1900</td>\n",
       "      <td>77.3200</td>\n",
       "      <td>949618.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>77.257999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-17 15:45:00</td>\n",
       "      <td>77.260</td>\n",
       "      <td>77.3500</td>\n",
       "      <td>77.2300</td>\n",
       "      <td>77.2500</td>\n",
       "      <td>2595928.0</td>\n",
       "      <td>7290.0</td>\n",
       "      <td>77.282782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-17 15:30:00</td>\n",
       "      <td>77.270</td>\n",
       "      <td>77.3400</td>\n",
       "      <td>77.2550</td>\n",
       "      <td>77.2600</td>\n",
       "      <td>315259.0</td>\n",
       "      <td>1877.0</td>\n",
       "      <td>77.294884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-17 15:15:00</td>\n",
       "      <td>77.225</td>\n",
       "      <td>77.2800</td>\n",
       "      <td>77.2100</td>\n",
       "      <td>77.2650</td>\n",
       "      <td>213177.0</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>77.237516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>77.205</td>\n",
       "      <td>77.2300</td>\n",
       "      <td>77.1600</td>\n",
       "      <td>77.2250</td>\n",
       "      <td>285385.0</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>77.186683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>78.840</td>\n",
       "      <td>79.0000</td>\n",
       "      <td>78.5835</td>\n",
       "      <td>78.8700</td>\n",
       "      <td>268435.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>78.850700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-17 15:45:00</td>\n",
       "      <td>78.855</td>\n",
       "      <td>78.9100</td>\n",
       "      <td>78.8100</td>\n",
       "      <td>78.8200</td>\n",
       "      <td>1163477.0</td>\n",
       "      <td>6980.0</td>\n",
       "      <td>78.858639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-17 15:30:00</td>\n",
       "      <td>78.870</td>\n",
       "      <td>78.9800</td>\n",
       "      <td>78.8501</td>\n",
       "      <td>78.8501</td>\n",
       "      <td>353556.0</td>\n",
       "      <td>2805.0</td>\n",
       "      <td>78.916907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-17 15:15:00</td>\n",
       "      <td>78.850</td>\n",
       "      <td>78.8900</td>\n",
       "      <td>78.7950</td>\n",
       "      <td>78.8765</td>\n",
       "      <td>177234.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>78.845188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>78.830</td>\n",
       "      <td>78.8900</td>\n",
       "      <td>78.7700</td>\n",
       "      <td>78.8450</td>\n",
       "      <td>141144.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>78.822873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>140.180</td>\n",
       "      <td>140.5400</td>\n",
       "      <td>140.0900</td>\n",
       "      <td>140.5400</td>\n",
       "      <td>393538.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>140.179905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-17 15:45:00</td>\n",
       "      <td>140.595</td>\n",
       "      <td>140.6450</td>\n",
       "      <td>140.1900</td>\n",
       "      <td>140.2100</td>\n",
       "      <td>906464.0</td>\n",
       "      <td>7365.0</td>\n",
       "      <td>140.316320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-17 15:30:00</td>\n",
       "      <td>140.710</td>\n",
       "      <td>140.8600</td>\n",
       "      <td>140.5650</td>\n",
       "      <td>140.5900</td>\n",
       "      <td>250749.0</td>\n",
       "      <td>2378.0</td>\n",
       "      <td>140.725885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-17 15:15:00</td>\n",
       "      <td>140.800</td>\n",
       "      <td>140.9000</td>\n",
       "      <td>140.6800</td>\n",
       "      <td>140.7100</td>\n",
       "      <td>354195.0</td>\n",
       "      <td>2197.0</td>\n",
       "      <td>140.772503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>140.650</td>\n",
       "      <td>140.8000</td>\n",
       "      <td>140.6100</td>\n",
       "      <td>140.7900</td>\n",
       "      <td>481130.0</td>\n",
       "      <td>3453.0</td>\n",
       "      <td>140.687106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-17 16:00:00</td>\n",
       "      <td>229.220</td>\n",
       "      <td>229.2200</td>\n",
       "      <td>229.1300</td>\n",
       "      <td>229.2200</td>\n",
       "      <td>41935.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>229.219567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-17 15:45:00</td>\n",
       "      <td>229.715</td>\n",
       "      <td>229.7900</td>\n",
       "      <td>229.0700</td>\n",
       "      <td>229.2100</td>\n",
       "      <td>351460.0</td>\n",
       "      <td>5543.0</td>\n",
       "      <td>229.380104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-17 15:30:00</td>\n",
       "      <td>229.510</td>\n",
       "      <td>229.9800</td>\n",
       "      <td>229.5100</td>\n",
       "      <td>229.7200</td>\n",
       "      <td>90835.0</td>\n",
       "      <td>2137.0</td>\n",
       "      <td>229.766411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-17 15:15:00</td>\n",
       "      <td>229.760</td>\n",
       "      <td>230.1300</td>\n",
       "      <td>229.5400</td>\n",
       "      <td>229.5700</td>\n",
       "      <td>98018.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>229.877858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>230.290</td>\n",
       "      <td>230.2900</td>\n",
       "      <td>229.6400</td>\n",
       "      <td>229.7600</td>\n",
       "      <td>94349.0</td>\n",
       "      <td>1544.0</td>\n",
       "      <td>229.896097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symbol           timestamp     open      high       low     close  \\\n",
       "0     XLB 2025-01-17 16:00:00   88.330   88.3500   88.3300   88.3300   \n",
       "1     XLB 2025-01-17 15:45:00   88.395   88.4180   88.3150   88.3500   \n",
       "2     XLB 2025-01-17 15:30:00   88.400   88.4600   88.3750   88.3900   \n",
       "3     XLB 2025-01-17 15:15:00   88.365   88.4150   88.3300   88.4100   \n",
       "4     XLB 2025-01-17 15:00:00   88.420   88.4200   88.2900   88.3650   \n",
       "5     XLE 2025-01-17 16:00:00   93.960   94.0003   93.9000   94.0000   \n",
       "6     XLE 2025-01-17 15:45:00   94.090   94.1600   93.9600   93.9600   \n",
       "7     XLE 2025-01-17 15:30:00   94.000   94.1100   93.9700   94.0850   \n",
       "8     XLE 2025-01-17 15:15:00   93.995   94.0250   93.9350   94.0051   \n",
       "9     XLE 2025-01-17 15:00:00   93.900   94.0100   93.8050   94.0000   \n",
       "10    XLF 2025-01-17 16:00:00   50.230   50.2400   50.1600   50.2200   \n",
       "11    XLF 2025-01-17 15:45:00   50.250   50.2600   50.1950   50.2200   \n",
       "12    XLF 2025-01-17 15:30:00   50.295   50.3200   50.2500   50.2500   \n",
       "13    XLF 2025-01-17 15:15:00   50.244   50.3000   50.2300   50.2950   \n",
       "14    XLF 2025-01-17 15:00:00   50.230   50.2700   50.1700   50.2450   \n",
       "15    XLI 2025-01-17 16:00:00  137.750  137.7500  137.6900  137.6900   \n",
       "16    XLI 2025-01-17 15:45:00  137.775  137.8470  137.6650  137.7300   \n",
       "17    XLI 2025-01-17 15:30:00  137.730  137.8300  137.7300  137.7650   \n",
       "18    XLI 2025-01-17 15:15:00  137.760  137.8100  137.6950  137.7400   \n",
       "19    XLI 2025-01-17 15:00:00  137.780  137.7800  137.6300  137.7550   \n",
       "20    XLK 2025-01-17 16:00:00  234.110  234.5200  234.1100  234.1400   \n",
       "21    XLK 2025-01-17 15:45:00  234.390  234.6000  233.9700  234.1200   \n",
       "22    XLK 2025-01-17 15:30:00  234.180  234.4500  234.1400  234.3900   \n",
       "23    XLK 2025-01-17 15:15:00  234.150  234.2700  233.9900  234.1700   \n",
       "24    XLK 2025-01-17 15:00:00  234.120  234.2600  233.8400  234.0783   \n",
       "25    XLP 2025-01-17 16:00:00   77.240   77.4400   77.1900   77.3200   \n",
       "26    XLP 2025-01-17 15:45:00   77.260   77.3500   77.2300   77.2500   \n",
       "27    XLP 2025-01-17 15:30:00   77.270   77.3400   77.2550   77.2600   \n",
       "28    XLP 2025-01-17 15:15:00   77.225   77.2800   77.2100   77.2650   \n",
       "29    XLP 2025-01-17 15:00:00   77.205   77.2300   77.1600   77.2250   \n",
       "30    XLU 2025-01-17 16:00:00   78.840   79.0000   78.5835   78.8700   \n",
       "31    XLU 2025-01-17 15:45:00   78.855   78.9100   78.8100   78.8200   \n",
       "32    XLU 2025-01-17 15:30:00   78.870   78.9800   78.8501   78.8501   \n",
       "33    XLU 2025-01-17 15:15:00   78.850   78.8900   78.7950   78.8765   \n",
       "34    XLU 2025-01-17 15:00:00   78.830   78.8900   78.7700   78.8450   \n",
       "35    XLV 2025-01-17 16:00:00  140.180  140.5400  140.0900  140.5400   \n",
       "36    XLV 2025-01-17 15:45:00  140.595  140.6450  140.1900  140.2100   \n",
       "37    XLV 2025-01-17 15:30:00  140.710  140.8600  140.5650  140.5900   \n",
       "38    XLV 2025-01-17 15:15:00  140.800  140.9000  140.6800  140.7100   \n",
       "39    XLV 2025-01-17 15:00:00  140.650  140.8000  140.6100  140.7900   \n",
       "40    XLY 2025-01-17 16:00:00  229.220  229.2200  229.1300  229.2200   \n",
       "41    XLY 2025-01-17 15:45:00  229.715  229.7900  229.0700  229.2100   \n",
       "42    XLY 2025-01-17 15:30:00  229.510  229.9800  229.5100  229.7200   \n",
       "43    XLY 2025-01-17 15:15:00  229.760  230.1300  229.5400  229.5700   \n",
       "44    XLY 2025-01-17 15:00:00  230.290  230.2900  229.6400  229.7600   \n",
       "\n",
       "       volume  trade_count        vwap  \n",
       "0    258214.0          6.0   88.330008  \n",
       "1   1050040.0       5342.0   88.361868  \n",
       "2    274319.0       1876.0   88.409764  \n",
       "3    115383.0        862.0   88.368922  \n",
       "4    181229.0       1236.0   88.341328  \n",
       "5    430379.0         50.0   93.959495  \n",
       "6   1413811.0      10682.0   94.062843  \n",
       "7    559131.0       3968.0   94.047458  \n",
       "8    327502.0       2737.0   93.993719  \n",
       "9    358671.0       2601.0   93.892006  \n",
       "10  1112075.0        242.0   50.221318  \n",
       "11  6859916.0      11202.0   50.234481  \n",
       "12  1902158.0       5085.0   50.292553  \n",
       "13  1469451.0       3793.0   50.279282  \n",
       "14  1661161.0       3067.0   50.208818  \n",
       "15   152133.0          8.0  137.749913  \n",
       "16   946158.0       8079.0  137.749963  \n",
       "17   177205.0       2109.0  137.790143  \n",
       "18   167992.0       1880.0  137.745734  \n",
       "19   216524.0       1952.0  137.703550  \n",
       "20   199853.0        168.0  234.129401  \n",
       "21   473496.0       7538.0  234.208745  \n",
       "22    93967.0       1968.0  234.325381  \n",
       "23    70458.0       1449.0  234.145563  \n",
       "24    94454.0       1617.0  233.997683  \n",
       "25   949618.0        186.0   77.257999  \n",
       "26  2595928.0       7290.0   77.282782  \n",
       "27   315259.0       1877.0   77.294884  \n",
       "28   213177.0       1315.0   77.237516  \n",
       "29   285385.0       1516.0   77.186683  \n",
       "30   268435.0        223.0   78.850700  \n",
       "31  1163477.0       6980.0   78.858639  \n",
       "32   353556.0       2805.0   78.916907  \n",
       "33   177234.0       1660.0   78.845188  \n",
       "34   141144.0       1463.0   78.822873  \n",
       "35   393538.0         18.0  140.179905  \n",
       "36   906464.0       7365.0  140.316320  \n",
       "37   250749.0       2378.0  140.725885  \n",
       "38   354195.0       2197.0  140.772503  \n",
       "39   481130.0       3453.0  140.687106  \n",
       "40    41935.0         14.0  229.219567  \n",
       "41   351460.0       5543.0  229.380104  \n",
       "42    90835.0       2137.0  229.766411  \n",
       "43    98018.0       2006.0  229.877858  \n",
       "44    94349.0       1544.0  229.896097  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_15min = alpaca.get_history(symbols=symbols,periods=5)\n",
    "df_15min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-01-19 10:53:53,123 - root - INFO - Starting concurrent download of hour data across 9 tickers for 3 days ... - [utils_for_alpaca.py:494]\n",
      "2025-01-19 10:53:53,288 - root - INFO - Done downloading hour data. 45 rows collected. - [utils_for_alpaca.py:508]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>trade_count</th>\n",
       "      <th>vwap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>88.420</td>\n",
       "      <td>88.530</td>\n",
       "      <td>88.2500</td>\n",
       "      <td>88.2500</td>\n",
       "      <td>1879386.0</td>\n",
       "      <td>9325.0</td>\n",
       "      <td>88.362478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-17 13:00:00</td>\n",
       "      <td>88.710</td>\n",
       "      <td>88.710</td>\n",
       "      <td>88.4000</td>\n",
       "      <td>88.4250</td>\n",
       "      <td>921668.0</td>\n",
       "      <td>6953.0</td>\n",
       "      <td>88.544645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-17 11:00:00</td>\n",
       "      <td>88.680</td>\n",
       "      <td>88.900</td>\n",
       "      <td>88.6000</td>\n",
       "      <td>88.7150</td>\n",
       "      <td>1365338.0</td>\n",
       "      <td>9985.0</td>\n",
       "      <td>88.748003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-16 15:00:00</td>\n",
       "      <td>87.560</td>\n",
       "      <td>87.770</td>\n",
       "      <td>87.4900</td>\n",
       "      <td>87.5600</td>\n",
       "      <td>1874277.0</td>\n",
       "      <td>11096.0</td>\n",
       "      <td>87.695127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XLB</td>\n",
       "      <td>2025-01-16 13:00:00</td>\n",
       "      <td>87.310</td>\n",
       "      <td>87.630</td>\n",
       "      <td>87.2750</td>\n",
       "      <td>87.5700</td>\n",
       "      <td>1189313.0</td>\n",
       "      <td>12269.0</td>\n",
       "      <td>87.420317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>93.900</td>\n",
       "      <td>94.160</td>\n",
       "      <td>93.8050</td>\n",
       "      <td>93.9800</td>\n",
       "      <td>3101831.0</td>\n",
       "      <td>20154.0</td>\n",
       "      <td>94.017246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-17 13:00:00</td>\n",
       "      <td>94.105</td>\n",
       "      <td>94.175</td>\n",
       "      <td>93.7600</td>\n",
       "      <td>93.9100</td>\n",
       "      <td>2419654.0</td>\n",
       "      <td>18879.0</td>\n",
       "      <td>93.964426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-17 11:00:00</td>\n",
       "      <td>94.030</td>\n",
       "      <td>94.290</td>\n",
       "      <td>93.8900</td>\n",
       "      <td>94.1050</td>\n",
       "      <td>2327809.0</td>\n",
       "      <td>20543.0</td>\n",
       "      <td>94.095618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-16 15:00:00</td>\n",
       "      <td>93.230</td>\n",
       "      <td>93.325</td>\n",
       "      <td>93.0500</td>\n",
       "      <td>93.1789</td>\n",
       "      <td>3274699.0</td>\n",
       "      <td>21633.0</td>\n",
       "      <td>93.204385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>XLE</td>\n",
       "      <td>2025-01-16 13:00:00</td>\n",
       "      <td>92.940</td>\n",
       "      <td>93.270</td>\n",
       "      <td>92.9200</td>\n",
       "      <td>93.2400</td>\n",
       "      <td>2577975.0</td>\n",
       "      <td>20748.0</td>\n",
       "      <td>93.046406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>50.230</td>\n",
       "      <td>50.320</td>\n",
       "      <td>50.1600</td>\n",
       "      <td>50.2500</td>\n",
       "      <td>13255013.0</td>\n",
       "      <td>23491.0</td>\n",
       "      <td>50.244372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-17 13:00:00</td>\n",
       "      <td>50.205</td>\n",
       "      <td>50.240</td>\n",
       "      <td>50.1300</td>\n",
       "      <td>50.2300</td>\n",
       "      <td>5315489.0</td>\n",
       "      <td>16285.0</td>\n",
       "      <td>50.192858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-17 11:00:00</td>\n",
       "      <td>50.065</td>\n",
       "      <td>50.270</td>\n",
       "      <td>50.0235</td>\n",
       "      <td>50.2000</td>\n",
       "      <td>9695376.0</td>\n",
       "      <td>21009.0</td>\n",
       "      <td>50.179832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-16 15:00:00</td>\n",
       "      <td>49.555</td>\n",
       "      <td>49.845</td>\n",
       "      <td>49.5500</td>\n",
       "      <td>49.7500</td>\n",
       "      <td>10632357.0</td>\n",
       "      <td>25662.0</td>\n",
       "      <td>49.703556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>XLF</td>\n",
       "      <td>2025-01-16 13:00:00</td>\n",
       "      <td>49.635</td>\n",
       "      <td>49.660</td>\n",
       "      <td>49.5300</td>\n",
       "      <td>49.5573</td>\n",
       "      <td>6659465.0</td>\n",
       "      <td>19424.0</td>\n",
       "      <td>49.593356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>137.780</td>\n",
       "      <td>138.004</td>\n",
       "      <td>137.5000</td>\n",
       "      <td>137.7100</td>\n",
       "      <td>1772246.0</td>\n",
       "      <td>14308.0</td>\n",
       "      <td>137.748755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-17 13:00:00</td>\n",
       "      <td>138.050</td>\n",
       "      <td>138.055</td>\n",
       "      <td>137.7500</td>\n",
       "      <td>137.7800</td>\n",
       "      <td>981037.0</td>\n",
       "      <td>10677.0</td>\n",
       "      <td>137.929063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-17 11:00:00</td>\n",
       "      <td>138.010</td>\n",
       "      <td>138.265</td>\n",
       "      <td>137.9700</td>\n",
       "      <td>138.0400</td>\n",
       "      <td>1276851.0</td>\n",
       "      <td>13741.0</td>\n",
       "      <td>138.116745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-16 15:00:00</td>\n",
       "      <td>136.720</td>\n",
       "      <td>137.130</td>\n",
       "      <td>136.6500</td>\n",
       "      <td>136.6500</td>\n",
       "      <td>1760894.0</td>\n",
       "      <td>16695.0</td>\n",
       "      <td>136.916766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>XLI</td>\n",
       "      <td>2025-01-16 13:00:00</td>\n",
       "      <td>136.845</td>\n",
       "      <td>136.990</td>\n",
       "      <td>136.5400</td>\n",
       "      <td>136.7300</td>\n",
       "      <td>1105408.0</td>\n",
       "      <td>13021.0</td>\n",
       "      <td>136.790329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>234.120</td>\n",
       "      <td>234.600</td>\n",
       "      <td>233.8400</td>\n",
       "      <td>234.1000</td>\n",
       "      <td>932943.0</td>\n",
       "      <td>12750.0</td>\n",
       "      <td>234.172103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-17 13:00:00</td>\n",
       "      <td>234.540</td>\n",
       "      <td>234.914</td>\n",
       "      <td>234.0000</td>\n",
       "      <td>234.1700</td>\n",
       "      <td>572901.0</td>\n",
       "      <td>9599.0</td>\n",
       "      <td>234.467275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-17 11:00:00</td>\n",
       "      <td>234.300</td>\n",
       "      <td>234.670</td>\n",
       "      <td>233.8301</td>\n",
       "      <td>234.5350</td>\n",
       "      <td>830682.0</td>\n",
       "      <td>10901.0</td>\n",
       "      <td>234.290263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-16 15:00:00</td>\n",
       "      <td>232.220</td>\n",
       "      <td>232.430</td>\n",
       "      <td>230.0000</td>\n",
       "      <td>230.0900</td>\n",
       "      <td>775117.0</td>\n",
       "      <td>11190.0</td>\n",
       "      <td>231.051480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>XLK</td>\n",
       "      <td>2025-01-16 13:00:00</td>\n",
       "      <td>232.490</td>\n",
       "      <td>232.750</td>\n",
       "      <td>231.0950</td>\n",
       "      <td>232.2800</td>\n",
       "      <td>561725.0</td>\n",
       "      <td>10280.0</td>\n",
       "      <td>232.001420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>77.205</td>\n",
       "      <td>77.440</td>\n",
       "      <td>77.1300</td>\n",
       "      <td>77.2000</td>\n",
       "      <td>4398296.0</td>\n",
       "      <td>12352.0</td>\n",
       "      <td>77.266850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-17 13:00:00</td>\n",
       "      <td>77.320</td>\n",
       "      <td>77.345</td>\n",
       "      <td>77.1650</td>\n",
       "      <td>77.2000</td>\n",
       "      <td>2116023.0</td>\n",
       "      <td>10003.0</td>\n",
       "      <td>77.236170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-17 11:00:00</td>\n",
       "      <td>77.250</td>\n",
       "      <td>77.400</td>\n",
       "      <td>77.2200</td>\n",
       "      <td>77.3250</td>\n",
       "      <td>1390015.0</td>\n",
       "      <td>8836.0</td>\n",
       "      <td>77.306041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-16 15:00:00</td>\n",
       "      <td>76.640</td>\n",
       "      <td>76.875</td>\n",
       "      <td>76.6050</td>\n",
       "      <td>76.7400</td>\n",
       "      <td>4464342.0</td>\n",
       "      <td>14765.0</td>\n",
       "      <td>76.780054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>XLP</td>\n",
       "      <td>2025-01-16 13:00:00</td>\n",
       "      <td>76.490</td>\n",
       "      <td>76.665</td>\n",
       "      <td>76.4200</td>\n",
       "      <td>76.6400</td>\n",
       "      <td>1663880.0</td>\n",
       "      <td>11478.0</td>\n",
       "      <td>76.553762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>78.830</td>\n",
       "      <td>79.000</td>\n",
       "      <td>78.5835</td>\n",
       "      <td>78.8500</td>\n",
       "      <td>2198676.0</td>\n",
       "      <td>13366.0</td>\n",
       "      <td>78.863452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-17 13:00:00</td>\n",
       "      <td>79.130</td>\n",
       "      <td>79.130</td>\n",
       "      <td>78.8150</td>\n",
       "      <td>78.8350</td>\n",
       "      <td>1259775.0</td>\n",
       "      <td>11540.0</td>\n",
       "      <td>78.944201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-17 11:00:00</td>\n",
       "      <td>78.980</td>\n",
       "      <td>79.200</td>\n",
       "      <td>78.9250</td>\n",
       "      <td>79.1300</td>\n",
       "      <td>1368468.0</td>\n",
       "      <td>12341.0</td>\n",
       "      <td>79.108633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-16 15:00:00</td>\n",
       "      <td>78.485</td>\n",
       "      <td>78.760</td>\n",
       "      <td>78.3750</td>\n",
       "      <td>78.6600</td>\n",
       "      <td>2994992.0</td>\n",
       "      <td>19144.0</td>\n",
       "      <td>78.626814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>XLU</td>\n",
       "      <td>2025-01-16 13:00:00</td>\n",
       "      <td>78.370</td>\n",
       "      <td>78.690</td>\n",
       "      <td>78.3650</td>\n",
       "      <td>78.4800</td>\n",
       "      <td>2513828.0</td>\n",
       "      <td>19751.0</td>\n",
       "      <td>78.541264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>140.650</td>\n",
       "      <td>140.900</td>\n",
       "      <td>140.0900</td>\n",
       "      <td>140.4600</td>\n",
       "      <td>2398806.0</td>\n",
       "      <td>15532.0</td>\n",
       "      <td>140.471795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-17 13:00:00</td>\n",
       "      <td>141.120</td>\n",
       "      <td>141.120</td>\n",
       "      <td>140.6200</td>\n",
       "      <td>140.6500</td>\n",
       "      <td>2119904.0</td>\n",
       "      <td>15258.0</td>\n",
       "      <td>140.878546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-17 11:00:00</td>\n",
       "      <td>141.160</td>\n",
       "      <td>141.225</td>\n",
       "      <td>140.9100</td>\n",
       "      <td>141.1100</td>\n",
       "      <td>1200583.0</td>\n",
       "      <td>11157.0</td>\n",
       "      <td>141.066857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-16 15:00:00</td>\n",
       "      <td>141.090</td>\n",
       "      <td>141.340</td>\n",
       "      <td>140.7950</td>\n",
       "      <td>140.9700</td>\n",
       "      <td>2402130.0</td>\n",
       "      <td>19072.0</td>\n",
       "      <td>141.106388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>XLV</td>\n",
       "      <td>2025-01-16 13:00:00</td>\n",
       "      <td>140.910</td>\n",
       "      <td>141.225</td>\n",
       "      <td>140.7000</td>\n",
       "      <td>141.0900</td>\n",
       "      <td>1390496.0</td>\n",
       "      <td>14333.0</td>\n",
       "      <td>140.944973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-17 15:00:00</td>\n",
       "      <td>230.290</td>\n",
       "      <td>230.290</td>\n",
       "      <td>229.0700</td>\n",
       "      <td>229.1200</td>\n",
       "      <td>676844.0</td>\n",
       "      <td>11246.0</td>\n",
       "      <td>229.550387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-17 13:00:00</td>\n",
       "      <td>229.930</td>\n",
       "      <td>230.660</td>\n",
       "      <td>229.8300</td>\n",
       "      <td>230.3239</td>\n",
       "      <td>422413.0</td>\n",
       "      <td>6358.0</td>\n",
       "      <td>230.390226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-17 11:00:00</td>\n",
       "      <td>229.730</td>\n",
       "      <td>230.410</td>\n",
       "      <td>229.6400</td>\n",
       "      <td>229.9308</td>\n",
       "      <td>341448.0</td>\n",
       "      <td>7258.0</td>\n",
       "      <td>230.026949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-16 15:00:00</td>\n",
       "      <td>226.040</td>\n",
       "      <td>226.520</td>\n",
       "      <td>225.5000</td>\n",
       "      <td>225.5000</td>\n",
       "      <td>714817.0</td>\n",
       "      <td>10902.0</td>\n",
       "      <td>226.173909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>XLY</td>\n",
       "      <td>2025-01-16 13:00:00</td>\n",
       "      <td>226.210</td>\n",
       "      <td>226.480</td>\n",
       "      <td>225.1800</td>\n",
       "      <td>226.0800</td>\n",
       "      <td>553944.0</td>\n",
       "      <td>9486.0</td>\n",
       "      <td>225.765562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symbol           timestamp     open     high       low     close  \\\n",
       "0     XLB 2025-01-17 15:00:00   88.420   88.530   88.2500   88.2500   \n",
       "1     XLB 2025-01-17 13:00:00   88.710   88.710   88.4000   88.4250   \n",
       "2     XLB 2025-01-17 11:00:00   88.680   88.900   88.6000   88.7150   \n",
       "3     XLB 2025-01-16 15:00:00   87.560   87.770   87.4900   87.5600   \n",
       "4     XLB 2025-01-16 13:00:00   87.310   87.630   87.2750   87.5700   \n",
       "5     XLE 2025-01-17 15:00:00   93.900   94.160   93.8050   93.9800   \n",
       "6     XLE 2025-01-17 13:00:00   94.105   94.175   93.7600   93.9100   \n",
       "7     XLE 2025-01-17 11:00:00   94.030   94.290   93.8900   94.1050   \n",
       "8     XLE 2025-01-16 15:00:00   93.230   93.325   93.0500   93.1789   \n",
       "9     XLE 2025-01-16 13:00:00   92.940   93.270   92.9200   93.2400   \n",
       "10    XLF 2025-01-17 15:00:00   50.230   50.320   50.1600   50.2500   \n",
       "11    XLF 2025-01-17 13:00:00   50.205   50.240   50.1300   50.2300   \n",
       "12    XLF 2025-01-17 11:00:00   50.065   50.270   50.0235   50.2000   \n",
       "13    XLF 2025-01-16 15:00:00   49.555   49.845   49.5500   49.7500   \n",
       "14    XLF 2025-01-16 13:00:00   49.635   49.660   49.5300   49.5573   \n",
       "15    XLI 2025-01-17 15:00:00  137.780  138.004  137.5000  137.7100   \n",
       "16    XLI 2025-01-17 13:00:00  138.050  138.055  137.7500  137.7800   \n",
       "17    XLI 2025-01-17 11:00:00  138.010  138.265  137.9700  138.0400   \n",
       "18    XLI 2025-01-16 15:00:00  136.720  137.130  136.6500  136.6500   \n",
       "19    XLI 2025-01-16 13:00:00  136.845  136.990  136.5400  136.7300   \n",
       "20    XLK 2025-01-17 15:00:00  234.120  234.600  233.8400  234.1000   \n",
       "21    XLK 2025-01-17 13:00:00  234.540  234.914  234.0000  234.1700   \n",
       "22    XLK 2025-01-17 11:00:00  234.300  234.670  233.8301  234.5350   \n",
       "23    XLK 2025-01-16 15:00:00  232.220  232.430  230.0000  230.0900   \n",
       "24    XLK 2025-01-16 13:00:00  232.490  232.750  231.0950  232.2800   \n",
       "25    XLP 2025-01-17 15:00:00   77.205   77.440   77.1300   77.2000   \n",
       "26    XLP 2025-01-17 13:00:00   77.320   77.345   77.1650   77.2000   \n",
       "27    XLP 2025-01-17 11:00:00   77.250   77.400   77.2200   77.3250   \n",
       "28    XLP 2025-01-16 15:00:00   76.640   76.875   76.6050   76.7400   \n",
       "29    XLP 2025-01-16 13:00:00   76.490   76.665   76.4200   76.6400   \n",
       "30    XLU 2025-01-17 15:00:00   78.830   79.000   78.5835   78.8500   \n",
       "31    XLU 2025-01-17 13:00:00   79.130   79.130   78.8150   78.8350   \n",
       "32    XLU 2025-01-17 11:00:00   78.980   79.200   78.9250   79.1300   \n",
       "33    XLU 2025-01-16 15:00:00   78.485   78.760   78.3750   78.6600   \n",
       "34    XLU 2025-01-16 13:00:00   78.370   78.690   78.3650   78.4800   \n",
       "35    XLV 2025-01-17 15:00:00  140.650  140.900  140.0900  140.4600   \n",
       "36    XLV 2025-01-17 13:00:00  141.120  141.120  140.6200  140.6500   \n",
       "37    XLV 2025-01-17 11:00:00  141.160  141.225  140.9100  141.1100   \n",
       "38    XLV 2025-01-16 15:00:00  141.090  141.340  140.7950  140.9700   \n",
       "39    XLV 2025-01-16 13:00:00  140.910  141.225  140.7000  141.0900   \n",
       "40    XLY 2025-01-17 15:00:00  230.290  230.290  229.0700  229.1200   \n",
       "41    XLY 2025-01-17 13:00:00  229.930  230.660  229.8300  230.3239   \n",
       "42    XLY 2025-01-17 11:00:00  229.730  230.410  229.6400  229.9308   \n",
       "43    XLY 2025-01-16 15:00:00  226.040  226.520  225.5000  225.5000   \n",
       "44    XLY 2025-01-16 13:00:00  226.210  226.480  225.1800  226.0800   \n",
       "\n",
       "        volume  trade_count        vwap  \n",
       "0    1879386.0       9325.0   88.362478  \n",
       "1     921668.0       6953.0   88.544645  \n",
       "2    1365338.0       9985.0   88.748003  \n",
       "3    1874277.0      11096.0   87.695127  \n",
       "4    1189313.0      12269.0   87.420317  \n",
       "5    3101831.0      20154.0   94.017246  \n",
       "6    2419654.0      18879.0   93.964426  \n",
       "7    2327809.0      20543.0   94.095618  \n",
       "8    3274699.0      21633.0   93.204385  \n",
       "9    2577975.0      20748.0   93.046406  \n",
       "10  13255013.0      23491.0   50.244372  \n",
       "11   5315489.0      16285.0   50.192858  \n",
       "12   9695376.0      21009.0   50.179832  \n",
       "13  10632357.0      25662.0   49.703556  \n",
       "14   6659465.0      19424.0   49.593356  \n",
       "15   1772246.0      14308.0  137.748755  \n",
       "16    981037.0      10677.0  137.929063  \n",
       "17   1276851.0      13741.0  138.116745  \n",
       "18   1760894.0      16695.0  136.916766  \n",
       "19   1105408.0      13021.0  136.790329  \n",
       "20    932943.0      12750.0  234.172103  \n",
       "21    572901.0       9599.0  234.467275  \n",
       "22    830682.0      10901.0  234.290263  \n",
       "23    775117.0      11190.0  231.051480  \n",
       "24    561725.0      10280.0  232.001420  \n",
       "25   4398296.0      12352.0   77.266850  \n",
       "26   2116023.0      10003.0   77.236170  \n",
       "27   1390015.0       8836.0   77.306041  \n",
       "28   4464342.0      14765.0   76.780054  \n",
       "29   1663880.0      11478.0   76.553762  \n",
       "30   2198676.0      13366.0   78.863452  \n",
       "31   1259775.0      11540.0   78.944201  \n",
       "32   1368468.0      12341.0   79.108633  \n",
       "33   2994992.0      19144.0   78.626814  \n",
       "34   2513828.0      19751.0   78.541264  \n",
       "35   2398806.0      15532.0  140.471795  \n",
       "36   2119904.0      15258.0  140.878546  \n",
       "37   1200583.0      11157.0  141.066857  \n",
       "38   2402130.0      19072.0  141.106388  \n",
       "39   1390496.0      14333.0  140.944973  \n",
       "40    676844.0      11246.0  229.550387  \n",
       "41    422413.0       6358.0  230.390226  \n",
       "42    341448.0       7258.0  230.026949  \n",
       "43    714817.0      10902.0  226.173909  \n",
       "44    553944.0       9486.0  225.765562  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2hour = alpaca.get_history(symbols=symbols, periods=5, FrameLength=2, frame = 'hour')\n",
    "df_2hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Snapshot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price time</th>\n",
       "      <th>price</th>\n",
       "      <th>today_open</th>\n",
       "      <th>today_close</th>\n",
       "      <th>yest_close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>XLU</th>\n",
       "      <td>2025-01-17 20:00:00.006298</td>\n",
       "      <td>78.84</td>\n",
       "      <td>78.68</td>\n",
       "      <td>78.84</td>\n",
       "      <td>78.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLY</th>\n",
       "      <td>2025-01-17 20:00:00.003525</td>\n",
       "      <td>229.22</td>\n",
       "      <td>229.03</td>\n",
       "      <td>229.22</td>\n",
       "      <td>226.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLP</th>\n",
       "      <td>2025-01-17 20:00:00.004451</td>\n",
       "      <td>77.24</td>\n",
       "      <td>76.94</td>\n",
       "      <td>77.24</td>\n",
       "      <td>76.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLE</th>\n",
       "      <td>2025-01-17 20:00:00.006246</td>\n",
       "      <td>93.96</td>\n",
       "      <td>93.24</td>\n",
       "      <td>93.96</td>\n",
       "      <td>93.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLK</th>\n",
       "      <td>2025-01-17 20:00:00.006800</td>\n",
       "      <td>234.11</td>\n",
       "      <td>234.66</td>\n",
       "      <td>234.11</td>\n",
       "      <td>230.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLI</th>\n",
       "      <td>2025-01-17 20:00:00.002148</td>\n",
       "      <td>137.75</td>\n",
       "      <td>137.81</td>\n",
       "      <td>137.75</td>\n",
       "      <td>136.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLV</th>\n",
       "      <td>2025-01-17 20:00:00.001613</td>\n",
       "      <td>140.18</td>\n",
       "      <td>140.94</td>\n",
       "      <td>140.18</td>\n",
       "      <td>141.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLB</th>\n",
       "      <td>2025-01-17 20:00:00.001880</td>\n",
       "      <td>88.33</td>\n",
       "      <td>88.34</td>\n",
       "      <td>88.33</td>\n",
       "      <td>87.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLF</th>\n",
       "      <td>2025-01-17 20:00:00.006586</td>\n",
       "      <td>50.23</td>\n",
       "      <td>49.91</td>\n",
       "      <td>50.23</td>\n",
       "      <td>49.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    price time   price  today_open  today_close  yest_close\n",
       "XLU 2025-01-17 20:00:00.006298   78.84       78.68        78.84       78.73\n",
       "XLY 2025-01-17 20:00:00.003525  229.22      229.03       229.22      226.05\n",
       "XLP 2025-01-17 20:00:00.004451   77.24       76.94        77.24       76.80\n",
       "XLE 2025-01-17 20:00:00.006246   93.96       93.24        93.96       93.24\n",
       "XLK 2025-01-17 20:00:00.006800  234.11      234.66       234.11      230.50\n",
       "XLI 2025-01-17 20:00:00.002148  137.75      137.81       137.75      136.93\n",
       "XLV 2025-01-17 20:00:00.001613  140.18      140.94       140.18      141.13\n",
       "XLB 2025-01-17 20:00:00.001880   88.33       88.34        88.33       87.76\n",
       "XLF 2025-01-17 20:00:00.006586   50.23       49.91        50.23       49.81"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symbols = ['XLB', 'XLE', 'XLF', 'XLI', 'XLK', 'XLP', 'XLY', 'XLU', 'XLV']\n",
    "snap = alpaca.stock_client.get_stock_snapshot(StockSnapshotRequest(symbol_or_symbols=symbols, feed = DataFeed.SIP))\n",
    "snapshot_data = {stock: [\n",
    "                        snapshot.latest_trade.timestamp,                        \n",
    "                        snapshot.latest_trade.price, \n",
    "                        snapshot.daily_bar.open,\n",
    "                        snapshot.daily_bar.close,\n",
    "                        snapshot.previous_daily_bar.close,\n",
    "                        ]\n",
    "                for stock, snapshot in snap.items() if snapshot and snapshot.daily_bar and snapshot.previous_daily_bar\n",
    "                }\n",
    "snapshot_columns=['price time', 'price', 'today_open', 'today_close','yest_close']\n",
    "snapshot_df = pd.DataFrame(snapshot_data.values(), snapshot_data.keys(), columns=snapshot_columns)\n",
    "snapshot_df['price time'] = snapshot_df['price time'].dt.tz_convert('America/New_York').dt.tz_localize(None) # convert from UTC to ET and remove +00:00 from datetime\n",
    "snapshot_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Steps of a strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpaca = utils_for_alpaca.MyAlpaca(os.environ['ALPACA_API_KEY_PAPER'],os.environ['ALPACA_API_SECRET_PAPER'],'BuyTopPremarket')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "amount_to_invest = alpaca.strategy_buypower()\n",
    "\n",
    "tickers_universe = alpaca.get_strategy_universe()\n",
    "snapshot_df = alpaca.get_tkrs_snapshot_df(tickers_universe)\n",
    "column_focus = 'POST'\n",
    "tickers_to_buy_df = snapshot_df.sort_values(by=column_focus, ascending=False).head(alpaca.stocks_per_run)\n",
    "tickers_to_buy_list = tickers_to_buy_df.symbol.unique().tolist()\n",
    "tickers_to_buy_dict = utils_for_alpaca.create_dict_from_dataframe(tickers_to_buy_df,column_focus)\n",
    "# investments = alpaca.allocate_capital(amount_to_invest, tickers=tickers_to_buy)\n",
    "investments = alpaca.allocate_capital(amount_to_invest, method = 'manual', manual_weights=tickers_to_buy_dict)\n",
    "alpaca.open_selected_positions(investments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpaca = utils_for_alpaca.MyAlpaca(os.environ['ALPACA_API_KEY_PAPER'],os.environ['ALPACA_API_SECRET_PAPER'],'BuyStreaksAt11')\n",
    "if alpaca.is_positive():\n",
    "    amount_to_invest = alpaca.strategy_buypower()\n",
    "    tickers_universe = alpaca.get_strategy_universe()\n",
    "    df_day = alpaca.get_history(symbols=tickers_universe,periods=10, FrameLength=1, frame = 'day')\n",
    "    df = alpaca.add_columns(df_day)\n",
    "    latest_date = df['timestamp'].max()\n",
    "    latest_date_rows = df[df['timestamp'] == latest_date].copy()\n",
    "    max_streak = latest_date_rows['streak'].max()\n",
    "    tickers_to_buy_prelim = latest_date_rows[latest_date_rows['streak'] == max_streak].symbol.unique().tolist()\n",
    "    tickers_to_buy = alpaca.is_positive(tickers_to_buy_prelim) # 2nd filter - which are up today\n",
    "    investments = alpaca.allocate_capital(amount_to_invest, tickers=tickers_to_buy)\n",
    "    alpaca.open_selected_positions(investments)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time conversions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "snapshot_df['price time'] = snapshot_df['price time'].dt.tz_convert('America/New_York').dt.tz_localize(None) # convert from UTC to ET and remove +00:00 from datetime\n",
    "snapshot_df['price time short'] = snapshot_df['price time'].dt.strftime('%H:%M')\n",
    "\n",
    "now_time = SPY['SPY'].daily_bar.timestamp.strftime('%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Corporate Actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://docs.alpaca.markets/reference/corporateactions-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from alpaca.data.enums import CorporateActionsType\n",
    "from alpaca.data.requests import CorporateActionsRequest\n",
    "from alpaca.data.historical.corporate_actions import CorporateActionsClient\n",
    "\n",
    "API_KEY_PAPER = os.environ['ALPACA_API_KEY_PAPER']\n",
    "API_SECRET_PAPER = os.environ['ALPACA_API_SECRET_PAPER']\n",
    "corp_client = CorporateActionsClient(API_KEY_PAPER,API_SECRET_PAPER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CASH_DIVIDEND',\n",
       " 'CASH_MERGER',\n",
       " 'FORWARD_SPLIT',\n",
       " 'NAME_CHANGE',\n",
       " 'REDEMPTION',\n",
       " 'REVERSE_SPLIT',\n",
       " 'RIGHTS_DISTRIBUTION',\n",
       " 'SPIN_OFF',\n",
       " 'STOCK_AND_CASH_MERGER',\n",
       " 'STOCK_DIVIDEND',\n",
       " 'STOCK_MERGER',\n",
       " 'UNIT_SPLIT',\n",
       " 'WORTHLESS_REMOVAL',\n",
       " '__class__',\n",
       " '__doc__',\n",
       " '__members__',\n",
       " '__module__']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(CorporateActionsType)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{   'data': {   'cash_dividends': [   {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2024, 12, 31),\n",
       "                                          'foreign': True,\n",
       "                                          'payable_date': datetime.date(2025, 1, 20),\n",
       "                                          'process_date': datetime.date(2025, 1, 20),\n",
       "                                          'rate': 0.050485,\n",
       "                                          'record_date': datetime.date(2024, 12, 31),\n",
       "                                          'special': False,\n",
       "                                          'symbol': 'BIRDF'},\n",
       "                                      {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2024, 12, 31),\n",
       "                                          'foreign': False,\n",
       "                                          'payable_date': datetime.date(2025, 1, 20),\n",
       "                                          'process_date': datetime.date(2025, 1, 20),\n",
       "                                          'rate': 1.1142,\n",
       "                                          'record_date': datetime.date(2024, 12, 31),\n",
       "                                          'special': False,\n",
       "                                          'symbol': 'CRNG'},\n",
       "                                      {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2024, 12, 31),\n",
       "                                          'foreign': False,\n",
       "                                          'payable_date': datetime.date(2025, 1, 20),\n",
       "                                          'process_date': datetime.date(2025, 1, 20),\n",
       "                                          'rate': 0.52,\n",
       "                                          'record_date': datetime.date(2024, 12, 31),\n",
       "                                          'special': True,\n",
       "                                          'symbol': 'CRNG'},\n",
       "                                      {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2024, 12, 31),\n",
       "                                          'foreign': True,\n",
       "                                          'payable_date': datetime.date(2025, 1, 20),\n",
       "                                          'process_date': datetime.date(2025, 1, 20),\n",
       "                                          'rate': 0.085,\n",
       "                                          'record_date': datetime.date(2024, 12, 31),\n",
       "                                          'special': False,\n",
       "                                          'symbol': 'DHTRF'},\n",
       "                                      {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2025, 1, 13),\n",
       "                                          'foreign': False,\n",
       "                                          'payable_date': datetime.date(2025, 1, 20),\n",
       "                                          'process_date': datetime.date(2025, 1, 20),\n",
       "                                          'rate': 1.0,\n",
       "                                          'record_date': datetime.date(2025, 1, 13),\n",
       "                                          'special': False,\n",
       "                                          'symbol': 'DMFG'},\n",
       "                                      {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2025, 1, 10),\n",
       "                                          'foreign': False,\n",
       "                                          'payable_date': datetime.date(2025, 1, 20),\n",
       "                                          'process_date': datetime.date(2025, 1, 20),\n",
       "                                          'rate': 27.0,\n",
       "                                          'record_date': datetime.date(2025, 1, 10),\n",
       "                                          'special': True,\n",
       "                                          'symbol': 'DTRL'},\n",
       "                                      {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2025, 1, 10),\n",
       "                                          'foreign': False,\n",
       "                                          'payable_date': datetime.date(2025, 1, 20),\n",
       "                                          'process_date': datetime.date(2025, 1, 20),\n",
       "                                          'rate': 0.171875,\n",
       "                                          'record_date': datetime.date(2025, 1, 10),\n",
       "                                          'special': False,\n",
       "                                          'symbol': 'FATBP'},\n",
       "                                      {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2024, 12, 27),\n",
       "                                          'foreign': False,\n",
       "                                          'payable_date': datetime.date(2025, 1, 20),\n",
       "                                          'process_date': datetime.date(2025, 1, 20),\n",
       "                                          'rate': 0.22125,\n",
       "                                          'record_date': datetime.date(2024, 12, 27),\n",
       "                                          'special': False,\n",
       "                                          'symbol': 'FMAO'},\n",
       "                                      {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2025, 1, 3),\n",
       "                                          'foreign': False,\n",
       "                                          'payable_date': datetime.date(2025, 1, 20),\n",
       "                                          'process_date': datetime.date(2025, 1, 20),\n",
       "                                          'rate': 0.09,\n",
       "                                          'record_date': datetime.date(2025, 1, 3),\n",
       "                                          'special': False,\n",
       "                                          'symbol': 'HLIO'},\n",
       "                                      {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2025, 1, 6),\n",
       "                                          'foreign': True,\n",
       "                                          'payable_date': datetime.date(2025, 1, 20),\n",
       "                                          'process_date': datetime.date(2025, 1, 20),\n",
       "                                          'rate': 0.1591175,\n",
       "                                          'record_date': datetime.date(2025, 1, 6),\n",
       "                                          'special': False,\n",
       "                                          'symbol': 'TCLAF'},\n",
       "                                      {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2025, 1, 6),\n",
       "                                          'foreign': True,\n",
       "                                          'payable_date': datetime.date(2025, 1, 20),\n",
       "                                          'process_date': datetime.date(2025, 1, 20),\n",
       "                                          'rate': 0.1591175,\n",
       "                                          'record_date': datetime.date(2025, 1, 6),\n",
       "                                          'special': False,\n",
       "                                          'symbol': 'TCLCF'},\n",
       "                                      {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2025, 1, 6),\n",
       "                                          'foreign': False,\n",
       "                                          'payable_date': datetime.date(2025, 1, 20),\n",
       "                                          'process_date': datetime.date(2025, 1, 20),\n",
       "                                          'rate': 0.5475,\n",
       "                                          'record_date': datetime.date(2025, 1, 6),\n",
       "                                          'special': False,\n",
       "                                          'symbol': 'XEL'}],\n",
       "                'reverse_splits': [   {   'corporate_action_type': 'reverse_splits',\n",
       "                                          'ex_date': datetime.date(2025, 1, 20),\n",
       "                                          'new_rate': 1.0,\n",
       "                                          'old_rate': 6.0,\n",
       "                                          'payable_date': datetime.date(2025, 1, 20),\n",
       "                                          'process_date': datetime.date(2025, 1, 20),\n",
       "                                          'record_date': datetime.date(2025, 1, 20),\n",
       "                                          'symbol': 'CAMZF'}]}}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Today's actions\n",
    "corp_request = CorporateActionsRequest()\n",
    "corp_client.get_corporate_actions(corp_request)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{   'data': {   'cash_dividends': [   {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2023, 9, 6),\n",
       "                                          'foreign': False,\n",
       "                                          'payable_date': datetime.date(2023, 9, 28),\n",
       "                                          'process_date': datetime.date(2023, 9, 28),\n",
       "                                          'rate': 0.04,\n",
       "                                          'record_date': datetime.date(2023, 9, 7),\n",
       "                                          'special': False,\n",
       "                                          'symbol': 'NVDA'},\n",
       "                                      {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2023, 12, 5),\n",
       "                                          'foreign': False,\n",
       "                                          'payable_date': datetime.date(2023, 12, 28),\n",
       "                                          'process_date': datetime.date(2023, 12, 28),\n",
       "                                          'rate': 0.04,\n",
       "                                          'record_date': datetime.date(2023, 12, 6),\n",
       "                                          'special': False,\n",
       "                                          'symbol': 'NVDA'},\n",
       "                                      {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2024, 3, 5),\n",
       "                                          'foreign': False,\n",
       "                                          'payable_date': datetime.date(2024, 3, 27),\n",
       "                                          'process_date': datetime.date(2024, 3, 27),\n",
       "                                          'rate': 0.04,\n",
       "                                          'record_date': datetime.date(2024, 3, 6),\n",
       "                                          'special': False,\n",
       "                                          'symbol': 'NVDA'},\n",
       "                                      {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2024, 6, 11),\n",
       "                                          'foreign': False,\n",
       "                                          'payable_date': datetime.date(2024, 6, 28),\n",
       "                                          'process_date': datetime.date(2024, 6, 28),\n",
       "                                          'rate': 0.01,\n",
       "                                          'record_date': datetime.date(2024, 6, 11),\n",
       "                                          'special': False,\n",
       "                                          'symbol': 'NVDA'},\n",
       "                                      {   'corporate_action_type': 'cash_dividends',\n",
       "                                          'due_bill_off_date': None,\n",
       "                                          'due_bill_on_date': None,\n",
       "                                          'ex_date': datetime.date(2024, 9, 12),\n",
       "                                          'foreign': False,\n",
       "                                          'payable_date': datetime.date(2024, 10, 3),\n",
       "                                          'process_date': datetime.date(2024, 10, 3),\n",
       "                                          'rate': 0.01,\n",
       "                                          'record_date': datetime.date(2024, 9, 12),\n",
       "                                          'special': False,\n",
       "                                          'symbol': 'NVDA'}],\n",
       "                'forward_splits': [   {   'corporate_action_type': 'forward_splits',\n",
       "                                          'due_bill_redemption_date': datetime.date(2024, 6, 10),\n",
       "                                          'ex_date': datetime.date(2024, 6, 10),\n",
       "                                          'new_rate': 10.0,\n",
       "                                          'old_rate': 1.0,\n",
       "                                          'payable_date': datetime.date(2024, 6, 10),\n",
       "                                          'process_date': datetime.date(2024, 6, 10),\n",
       "                                          'record_date': datetime.date(2024, 6, 7),\n",
       "                                          'symbol': 'NVDA'}]}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corp_request = CorporateActionsRequest(\n",
    "    symbols=['NVDA'],\n",
    "    start = '2023-09-11', # 90 days\n",
    "    end = '2024-12-12', # 90 days\n",
    "    limit=3000)\n",
    "corp_client.get_corporate_actions(corp_request)"
   ]
  }
 ],
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