Funding_Rate/algo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d1eed397",
   "metadata": {},
   "outputs": [],
   "source": [
    "import modules.structs as structs\n",
    "import json\n",
    "from dataclasses import dataclass, asdict\n",
    "import valkey\n",
    "\n",
    "with open('algo_config.json', 'r', encoding='utf-8') as file:\n",
    "    ALGO_CONFIG = json.load(file, object_hook=lambda d: structs.Algo_Config(**d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c6151613",
   "metadata": {},
   "outputs": [],
   "source": [
    "VAL_KEY = valkey.Valkey(host='localhost', port=6379, db=0, decode_responses=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d83c61e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config_update = {\n",
    "    'Min_Time_To_Funding_Minutes': 7,\n",
    "    # 'Allow_Ordering_Aster': True,\n",
    "    # 'Allow_Ordering_Extend': True,\n",
    "    'Loop_Sleep_Sec': 0.00,\n",
    "#     'Flip_Side_For_Testing': False,\n",
    "#     'Price_Worsener_Extend': 0.0,\n",
    "    'Log_Summary_Each_Loop': False,\n",
    "    'Print_Summary_Each_Loop': False,\n",
    "}\n",
    "VAL_KEY.publish('fr_orchestrator_input', json.dumps(config_update))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "45fae761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json.loads(VAL_KEY.get('fr_orchestrator_output'), object_hook=lambda d: structs.Algo_Config(**d)).Loop_Sleep_Sec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98c500cc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2cf3325",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a0df43de",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos = json.loads(VAL_KEY.get('fr_aster_user_positions'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ca526c8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'timestamp_arrival': 1777303258987,\n",
       "  'timestamp_msg': 1777303258979,\n",
       "  'timestamp_transaction': 1777303258950,\n",
       "  'event_reason_type': 'ORDER',\n",
       "  'symbol': 'ETHUSDT',\n",
       "  'position_amount': 0.226,\n",
       "  'entry_price': 2284.28,\n",
       "  'accumulated_realized_pre_fees': 8.24392002,\n",
       "  'unrealized_pnl': 0.0,\n",
       "  'margin_type': 'cross',\n",
       "  'isolated_wallet': 0.0,\n",
       "  'position_side': 'BOTH'}]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f788b6df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2026-04-27 15:20:58.987000')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.to_datetime(1777303258987, unit='ms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "855f980b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2026-04-27 15:20:58.979000')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.to_datetime(1777303258979, unit='ms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2026-04-27 15:20:58.950000')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.to_datetime(1777303258950, unit='ms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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