{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0b5ca901",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import time\n",
    "import json\n",
    "import valkey\n",
    "from dataclasses import dataclass, field, asdict\n",
    "import modules.structs as structs\n",
    "import modules.aster_auth as aster_auth\n",
    "import modules.manual_leverage as leverage\n",
    "VAL_KEY = valkey.Valkey(host='localhost', port=6379, db=0, decode_responses=True)\n",
    "df_leverage_by_exch = pd.DataFrame(data=leverage.LEVERAGE_BY_EXCH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "53647b40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ### ASTER Historical FR ###\n",
    "# params = {\n",
    "#     'limit': 1000,\n",
    "# }\n",
    "# r = json.loads(requests.get('https://fapi.asterdex.com/fapi/v3/fundingRate', params=params).text)\n",
    "# df_aster_hist_fr = pd.DataFrame(r)\n",
    "# df_aster_hist_fr['funding_rate_ts_dt'] = pd.to_datetime(df_aster_hist_fr['fundingTime'], unit='ms')\n",
    "# df_aster_hist_fr = df_aster_hist_fr.sort_values(by='fundingTime', ascending=True).drop_duplicates(subset=['symbol'], keep='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1f3b65ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ### ASTER Current FR ###\n",
    "# r = json.loads(requests.get('https://fapi.asterdex.com/fapi/v3/fundingInfo').text)\n",
    "# df_aster_current_fr = pd.DataFrame(r)\n",
    "# df_aster_current_fr['funding_rate_ts_dt'] = pd.to_datetime(df_aster_current_fr['time'], unit='ms')\n",
    "# df_aster_current_fr['funding_rate'] = df_aster_current_fr['interestRate']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5176d5b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "### ASTER EXCHANGE INFO ###\n",
    "fut_acct_exchangeInfo: dict = {\n",
    "    \"url\": \"/fapi/v3/exchangeInfo\",\n",
    "    \"method\": \"GET\",\n",
    "    \"params\": {}\n",
    "}\n",
    "r: dict = await aster_auth.post_authenticated_url(fut_acct_exchangeInfo)  # ty:ignore[invalid-assignment]\n",
    "df_aster_exch_info = pd.DataFrame(r['symbols'])\n",
    "df_aster_exch_info['min_order_size'] = df_aster_exch_info['filters'].apply(lambda x: [f for f in x if f.get('filterType', None) == 'LOT_SIZE'][0]['minQty'] )\n",
    "df_aster_exch_info['min_price'] = df_aster_exch_info['filters'].apply(lambda x: [f for f in x if f.get('filterType', None) == 'PRICE_FILTER'][0]['minPrice'] )\n",
    "df_aster_exch_info['min_notional'] = df_aster_exch_info['filters'].apply(lambda x: [f for f in x if f.get('filterType', None) == 'MIN_NOTIONAL'][0]['notional'] )\n",
    "df_aster_exch_info['min_lot_size'] = df_aster_exch_info['filters'].apply(lambda x: [f for f in x if f.get('filterType', None) == 'LOT_SIZE'][0]['stepSize'] )\n",
    "\n",
    "fut_acct_ticker_stats: dict = {\n",
    "    \"url\": \"/fapi/v3/ticker/24hr\",\n",
    "    \"method\": \"GET\",\n",
    "    \"params\": {}\n",
    "}\n",
    "r: dict = await aster_auth.post_authenticated_url(fut_acct_ticker_stats)  # ty:ignore[invalid-assignment]\n",
    "df_aster_ticker_stats = pd.DataFrame(r)\n",
    "df_aster_ticker_stats['last_trade_ts_ast'] = df_aster_ticker_stats['closeTime']\n",
    "\n",
    "df_aster_exch_info = df_aster_exch_info.merge(df_aster_ticker_stats[['symbol','quoteVolume','last_trade_ts_ast']].rename({'quoteVolume':'daily_volume'}, axis=1), on='symbol', how='left')\n",
    "df_aster_exch_info['daily_volume'] = df_aster_exch_info['daily_volume'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e33ec721",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Extended Current FR ###\n",
    "r = json.loads(requests.get('https://api.starknet.extended.exchange/api/v1/info/markets').text)\n",
    "df_extend_current_mkt_stats = pd.DataFrame(r['data'])\n",
    "\n",
    "df_extend_current_mkt_stats['funding_rate'] = df_extend_current_mkt_stats['marketStats'].apply(lambda x: x.get('fundingRate',{}))\n",
    "df_extend_current_mkt_stats['funding_rate_ts'] = df_extend_current_mkt_stats['marketStats'].apply(lambda x: x.get('nextFundingRate',{}))\n",
    "df_extend_current_mkt_stats['daily_volume'] = df_extend_current_mkt_stats['marketStats'].apply(lambda x: x.get('dailyVolume',{})).astype(float)\n",
    "df_extend_current_mkt_stats['min_order_size'] = df_extend_current_mkt_stats['tradingConfig'].apply(lambda x: x.get('minOrderSize',{}))\n",
    "df_extend_current_mkt_stats['min_price'] = df_extend_current_mkt_stats['tradingConfig'].apply(lambda x: x.get('minPriceChange',{}))\n",
    "df_extend_current_mkt_stats['min_notional'] = 0\n",
    "df_extend_current_mkt_stats['min_lot_size'] = df_extend_current_mkt_stats['tradingConfig'].apply(lambda x: x.get('minOrderSizeChange',{}))\n",
    "\n",
    "df_extend_current_mkt_stats['max_leverage'] = df_extend_current_mkt_stats['tradingConfig'].apply(lambda x: x.get('maxLeverage',{}))\n",
    "\n",
    "\n",
    "# df_extend_current_fr = df_extend_current_mkt_stats[['status','name','assetName','collateralAssetName','category','min_order_size','min_price','max_leverage','funding_rate','funding_rate_ts']]\n",
    "# df_extend_current_fr['funding_rate_ts_dt'] = pd.to_datetime(df_extend_current_fr['funding_rate_ts'], unit='ms')\n",
    "# df_extend_current_fr = df_extend_current_fr.loc[df_extend_current_fr['status']=='ACTIVE',:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "62815940",
   "metadata": {},
   "outputs": [],
   "source": [
    "### ASTER Current FR from Mark Price Req ###\n",
    "# r = json.loads(requests.get('https://fapi.asterdex.com/fapi/v3/exchangeInfo').text)\n",
    "# df_aster_current_mkt_stats = pd.DataFrame(r)\n",
    "# ### ASTER Current FR from Mark Price Req ###\n",
    "# r = json.loads(requests.get('https://fapi.asterdex.com/fapi/v3/premiumIndex').text)\n",
    "# df_aster_current_mkt_stats = pd.DataFrame(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "271a67c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "### ASTER CURRENT FR - WS ###\n",
    "df_aster_current_fr = pd.DataFrame(json.loads(VAL_KEY.get('fund_rate_aster_all')))  # ty:ignore[invalid-argument-type]\n",
    "df_aster_current_fr = df_aster_current_fr[['s','E','r','T']].rename({'s':'symbol','E':'funding_rate_updated_ts_ms','r':'funding_rate','T':'next_funding_ts'}, axis=1)\n",
    "df_aster_current_fr['funding_rate_updated_dt'] = pd.to_datetime(df_aster_current_fr['funding_rate_updated_ts_ms'], unit='ms')\n",
    "df_aster_current_fr['funding_rate'] = df_aster_current_fr['funding_rate'].astype(float)\n",
    "df_aster_current_fr['time_delta_to_next_funding'] = pd.to_datetime(df_aster_current_fr['next_funding_ts'], unit='ms') - pd.Timestamp.now()\n",
    "df_aster_current_fr = df_aster_current_fr.merge(df_aster_exch_info[['symbol','daily_volume','min_order_size','min_price','min_lot_size','min_notional','last_trade_ts_ast']], on='symbol', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1ce2fde4",
   "metadata": {},
   "outputs": [],
   "source": [
    "### EXTEND CURRENT FR - WS ###\n",
    "df_extended_current_fr = pd.DataFrame(json.loads(VAL_KEY.get('fund_rate_extended_all')))  # ty:ignore[invalid-argument-type]\n",
    "df_extended_current_fr = df_extended_current_fr[['symbol','funding_rate_updated_ts_ms','funding_rate']]\n",
    "df_extended_current_fr['funding_rate_updated_dt'] = pd.to_datetime(df_extended_current_fr['funding_rate_updated_ts_ms'], unit='ms')\n",
    "df_extended_current_fr['funding_rate'] = df_extended_current_fr['funding_rate'].astype(float)\n",
    "\n",
    "# df_extended_current_fr = df_extended_current_fr.merge(df_extend_current_mkt_stats[['name','assetName','status', 'funding_rate_ts','max_leverage']].rename({'name':'symbol','funding_rate_ts':'next_funding_ts'}, axis=1), on='symbol', how='left')\n",
    "df_extended_current_fr = df_extended_current_fr.merge(df_extend_current_mkt_stats[['name','assetName','status', 'funding_rate_ts','min_order_size','min_price','min_lot_size','min_notional','daily_volume']].rename({'name':'symbol','funding_rate_ts':'next_funding_ts'}, axis=1), on='symbol', how='left')\n",
    "df_extended_current_fr = df_extended_current_fr.loc[df_extended_current_fr['status']=='ACTIVE',:]\n",
    "df_extended_current_fr['USDT_Symbol'] = df_extended_current_fr['assetName'] + 'USDT'\n",
    "df_extended_current_fr['time_delta_to_next_funding'] = pd.to_datetime(df_extended_current_fr['next_funding_ts'], unit='ms') - pd.Timestamp.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ff88b413",
   "metadata": {},
   "outputs": [],
   "source": [
    "### COMBINED CURRENT FR - WS ###\n",
    "df_comb_current_fr = df_extended_current_fr.merge(df_aster_current_fr, left_on='USDT_Symbol', right_on='symbol', how='inner', suffixes=('_ext', '_ast'))\n",
    "df_comb_current_fr['next_funding_at_same_time'] = (abs(df_comb_current_fr['time_delta_to_next_funding_ext'].dt.total_seconds() - df_comb_current_fr['time_delta_to_next_funding_ast'].dt.total_seconds()) / 60) < 1\n",
    "df_comb_current_fr['net_funding_rate'] = (df_comb_current_fr[['funding_rate_ext', 'funding_rate_ast']].max(axis=1) - df_comb_current_fr[['funding_rate_ext', 'funding_rate_ast']].min(axis=1)).where(df_comb_current_fr['next_funding_at_same_time'], df_comb_current_fr['funding_rate_ext'])\n",
    "df_comb_current_fr['net_funding_rate_abs'] = df_comb_current_fr['net_funding_rate'].abs()\n",
    "\n",
    "### NET MULT ###\n",
    "df_comb_current_fr_net = df_comb_current_fr.merge(df_leverage_by_exch.loc[df_leverage_by_exch['exchange']=='EXTEND'], left_on='assetName', right_on='lh_asset').merge(df_leverage_by_exch.loc[df_leverage_by_exch['exchange']=='ASTER'], left_on='assetName', right_on='lh_asset', suffixes=('_ext', '_ast'))\n",
    "df_comb_current_fr_net['net_mult'] = 1 / ( ( 0.5 / df_comb_current_fr_net['max_leverage_ext'] ) + ( 0.5 / df_comb_current_fr_net['max_leverage_ast'] ) )\n",
    "df_comb_current_fr_net['net_mult'] = df_comb_current_fr_net['net_mult'].round(2)\n",
    "df_comb_current_fr_net['net_mult_x_net_fr_abs'] = df_comb_current_fr_net['net_funding_rate_abs'] * df_comb_current_fr_net['net_mult']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f5ade993",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_best_fr_rate = df_comb_current_fr_net[['symbol_ext','symbol_ast','daily_volume_ext','daily_volume_ast','min_price_ext','min_price_ast','min_order_size_ext','min_order_size_ast','min_lot_size_ext','min_lot_size_ast','min_notional_ext','min_notional_ast','funding_rate_ext','funding_rate_ast','max_leverage_ext','max_leverage_ast','lh_asset_ext','lh_asset_ast','rh_asset_ext','rh_asset_ast','net_mult_x_net_fr_abs','net_funding_rate_abs','net_funding_rate','next_funding_at_same_time','last_trade_ts_ast']].sort_values(by='net_mult_x_net_fr_abs', ascending=False).reset_index(drop=True)\n",
    "df_best_fr_rate['hourly_dollars_per_1k'] = df_best_fr_rate['net_mult_x_net_fr_abs'] * 1000\n",
    "df_best_fr_rate['hourly_dollars_per_1k'] = df_best_fr_rate['hourly_dollars_per_1k'].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "84bbc5a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "last_trade_max_ts = []\n",
    "\n",
    "for index, row in df_best_fr_rate.iterrows():\n",
    "    r = json.loads(requests.get(f'https://api.starknet.extended.exchange/api/v1/info/markets/{row['symbol_ext']}/trades').text)\n",
    "    max_ts = max([t['T'] for t in r['data']])\n",
    "    last_trade_max_ts.append({'symbol_ext':row['symbol_ext'],'last_trade_ts_ext': max_ts})\n",
    "    time.sleep(0.01)\n",
    "\n",
    "df_best_fr_rate = df_best_fr_rate.merge(pd.DataFrame(last_trade_max_ts), on='symbol_ext', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7bd849e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_best_fr_rate['last_trade_ts_dt_ast'] = pd.to_datetime(df_best_fr_rate['last_trade_ts_ast'], unit='ms')\n",
    "df_best_fr_rate['last_trade_ts_dt_ext'] = pd.to_datetime(df_best_fr_rate['last_trade_ts_ext'], unit='ms')\n",
    "df_best_fr_rate = df_best_fr_rate.loc[( (datetime.now().timestamp()*1000 )-df_best_fr_rate['last_trade_ts_ast']) < (3*60*1000) ]\n",
    "df_best_fr_rate = df_best_fr_rate.loc[( (datetime.now().timestamp()*1000 )-df_best_fr_rate['last_trade_ts_ext']) < (15*60*1000) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "86549660",
   "metadata": {},
   "outputs": [],
   "source": [
    "import modules.aster_auth as aster_auth\n",
    "import modules.utils as utils\n",
    "\n",
    "async def get_candles(symbol: str) -> pd.DataFrame:\n",
    "    ### Candles for Midpoint Dispersion ###\n",
    "    # Aster\n",
    "    symbol_ast = utils.symbol_to_aster_fmt(symbol)\n",
    "    aster_candles = {\n",
    "        \"url\": \"/fapi/v3/klines\",\n",
    "        \"method\": \"GET\",\n",
    "        \"params\": {\n",
    "            'symbol': symbol_ast,\n",
    "            'interval': '1m',\n",
    "            'limit':'1440'\n",
    "        }\n",
    "    }\n",
    "    j = await aster_auth.post_authenticated_url(aster_candles)\n",
    "    df_candles_aster = pd.DataFrame(j, columns=['open_ts','open_px','high_px','low_px','close_px','volume','close_ts','quote_asset_volume','count_trades','taker_buy_base_asset_volume','taker_buy_quote_asset_volume','_drop'])\n",
    "    df_candles_aster = df_candles_aster[['open_px', 'low_px', 'high_px', 'close_px', 'volume', 'open_ts']]\n",
    "    df_candles_aster[['open_px', 'low_px', 'high_px', 'close_px', 'volume']] = df_candles_aster[['open_px', 'low_px', 'high_px', 'close_px', 'volume']].astype(float)\n",
    "\n",
    "    df_candles_aster['med_px'] = ( df_candles_aster['high_px'] + df_candles_aster['low_px'] ) / 2\n",
    "    df_candles_aster['typical_px'] = ( df_candles_aster['open_px'] + df_candles_aster['high_px'] + df_candles_aster['low_px'] + df_candles_aster['close_px'] ) / 4\n",
    "\n",
    "    # Extend\n",
    "    symbol_ext = utils.symbol_to_extend_fmt(symbol)\n",
    "    ext_params = {\n",
    "        'interval':'1m',\n",
    "        'limit':1440,\n",
    "    }\n",
    "    r = json.loads(requests.get(f'https://api.starknet.extended.exchange/api/v1/info/candles/{symbol_ext}/trades', params=ext_params).text)\n",
    "    df_candles_extended = pd.DataFrame(r['data'])\n",
    "    df_candles_extended = df_candles_extended.rename({'o':'open_px','l':'low_px','h':'high_px','c':'close_px','v':'volume','T':'open_ts'}, axis=1)\n",
    "    df_candles_extended[['open_px', 'low_px', 'high_px', 'close_px', 'volume']] = df_candles_extended[['open_px', 'low_px', 'high_px', 'close_px', 'volume']].astype(float)\n",
    "    df_candles_extended['med_px'] = ( df_candles_extended['high_px'] + df_candles_extended['low_px'] ) / 2\n",
    "    df_candles_extended['typical_px'] = ( df_candles_extended['open_px'] + df_candles_extended['high_px'] + df_candles_extended['low_px'] + df_candles_extended['close_px'] ) / 4\n",
    "\n",
    "    df_candles_comb = df_candles_aster.merge(df_candles_extended, on='open_ts', how='inner', suffixes=('_ast','_ext'))\n",
    "    df_candles_comb['open_dt'] = pd.to_datetime(df_candles_comb['open_ts'], unit='ms')\n",
    "    df_candles_comb['med_ratio_aster_over_extend'] = ( df_candles_comb['med_px_ast'] / df_candles_comb['med_px_ext'] ) - 1\n",
    "\n",
    "    return df_candles_comb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3da1ef8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "candles_ratios = []\n",
    "\n",
    "for index, row in df_best_fr_rate.iterrows():\n",
    "    df = await get_candles(symbol=row['symbol_ext'])\n",
    "    buy_ratio_ext = float(df['med_ratio_aster_over_extend'].median())\n",
    "    candles_ratios.append({'symbol_ext':row['symbol_ext'], 'buy_ratio_ext':buy_ratio_ext,'buy_ratio_ast':buy_ratio_ext*-1})\n",
    "\n",
    "df_best_fr_rate = df_best_fr_rate.merge(pd.DataFrame(candles_ratios), on='symbol_ext', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "symbol_ext",
         "rawType": "str",
         "type": "string"
        },
        {
         "name": "symbol_ast",
         "rawType": "str",
         "type": "string"
        },
        {
         "name": "daily_volume_ext",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "daily_volume_ast",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "min_price_ext",
         "rawType": "str",
         "type": "string"
        },
        {
         "name": "min_price_ast",
         "rawType": "str",
         "type": "string"
        },
        {
         "name": "min_order_size_ext",
         "rawType": "str",
         "type": "string"
        },
        {
         "name": "min_order_size_ast",
         "rawType": "str",
         "type": "string"
        },
        {
         "name": "min_lot_size_ext",
         "rawType": "str",
         "type": "string"
        },
        {
         "name": "min_lot_size_ast",
         "rawType": "str",
         "type": "string"
        },
        {
         "name": "min_notional_ext",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "min_notional_ast",
         "rawType": "str",
         "type": "string"
        },
        {
         "name": "funding_rate_ext",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "funding_rate_ast",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "max_leverage_ext",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "max_leverage_ast",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "lh_asset_ext",
         "rawType": "str",
         "type": "string"
        },
        {
         "name": "lh_asset_ast",
         "rawType": "str",
         "type": "string"
        },
        {
         "name": "rh_asset_ext",
         "rawType": "str",
         "type": "string"
        },
        {
         "name": "rh_asset_ast",
         "rawType": "str",
         "type": "string"
        },
        {
         "name": "net_mult_x_net_fr_abs",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "net_funding_rate_abs",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "net_funding_rate",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "next_funding_at_same_time",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "last_trade_ts_ast",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "hourly_dollars_per_1k",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "last_trade_ts_ext",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "last_trade_ts_dt_ast",
         "rawType": "datetime64[ms]",
         "type": "datetime"
        },
        {
         "name": "last_trade_ts_dt_ext",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>symbol_ext</th>\n",
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       "      <th>daily_volume_ext</th>\n",
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       "      <td>2026-05-04 23:07:52.912</td>\n",
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       "      <td>12.75</td>\n",
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       "      <td>2026-05-04 23:16:40.931</td>\n",
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       "      <td>0.000127</td>\n",
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       "      <td>1.150000e-04</td>\n",
       "      <td>1.150000e-04</td>\n",
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       "      <td>2026-05-04 23:15:44.932</td>\n",
       "      <td>0.000233</td>\n",
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       "      <td>8.700000e-05</td>\n",
       "      <td>8.700000e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>1.777937e+12</td>\n",
       "      <td>5.22</td>\n",
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       "      <td>2026-05-04 23:16:40.123</td>\n",
       "      <td>2026-05-04 23:11:27.673</td>\n",
       "      <td>-0.000405</td>\n",
       "      <td>0.000405</td>\n",
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       "      <td>6.291000e-05</td>\n",
       "      <td>6.291000e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>1.777937e+12</td>\n",
       "      <td>4.19</td>\n",
       "      <td>1777936411533</td>\n",
       "      <td>2026-05-04 23:16:40.008</td>\n",
       "      <td>2026-05-04 23:13:31.533</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XMR-USD</td>\n",
       "      <td>XMRUSDT</td>\n",
       "      <td>1.649243e+06</td>\n",
       "      <td>3.507893e+05</td>\n",
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       "      <td>7.744000e-05</td>\n",
       "      <td>7.744000e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>1.777937e+12</td>\n",
       "      <td>2.58</td>\n",
       "      <td>1777936419604</td>\n",
       "      <td>2026-05-04 23:15:59.400</td>\n",
       "      <td>2026-05-04 23:13:39.604</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ETH-USD</td>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>1.616961e+08</td>\n",
       "      <td>6.561522e+08</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>3.223000e-05</td>\n",
       "      <td>3.223000e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>1.777937e+12</td>\n",
       "      <td>2.42</td>\n",
       "      <td>1777936565323</td>\n",
       "      <td>2026-05-04 23:16:40.075</td>\n",
       "      <td>2026-05-04 23:16:05.323</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>-0.000278</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <td>4USDT</td>\n",
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       "      <td>4.548348e+05</td>\n",
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       "      <td>2.265000e-04</td>\n",
       "      <td>2.265000e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>1.777937e+12</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1777936522376</td>\n",
       "      <td>2026-05-04 23:16:15.650</td>\n",
       "      <td>2026-05-04 23:15:22.376</td>\n",
       "      <td>0.001436</td>\n",
       "      <td>-0.001436</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HYPE-USD</td>\n",
       "      <td>HYPEUSDT</td>\n",
       "      <td>4.365432e+07</td>\n",
       "      <td>1.053062e+07</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.00100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.197000e-05</td>\n",
       "      <td>2.197000e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>1.777937e+12</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1777936590010</td>\n",
       "      <td>2026-05-04 23:16:40.796</td>\n",
       "      <td>2026-05-04 23:16:30.010</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>0.000182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ENA-USD</td>\n",
       "      <td>ENAUSDT</td>\n",
       "      <td>1.142030e+07</td>\n",
       "      <td>4.009133e+05</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.0000100</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.700000e-05</td>\n",
       "      <td>3.700000e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>1.777937e+12</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1777936576866</td>\n",
       "      <td>2026-05-04 23:16:33.650</td>\n",
       "      <td>2026-05-04 23:16:16.866</td>\n",
       "      <td>-0.001241</td>\n",
       "      <td>0.001241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>WLFI-USD</td>\n",
       "      <td>WLFIUSDT</td>\n",
       "      <td>7.601354e+05</td>\n",
       "      <td>4.159099e+06</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.0001000</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000e-05</td>\n",
       "      <td>6.000000e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>1.777937e+12</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1777935796212</td>\n",
       "      <td>2026-05-04 23:16:25.617</td>\n",
       "      <td>2026-05-04 23:03:16.212</td>\n",
       "      <td>0.002205</td>\n",
       "      <td>-0.002205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LIT-USD</td>\n",
       "      <td>LITUSDT</td>\n",
       "      <td>1.854604e+06</td>\n",
       "      <td>1.350735e+06</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001000</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>True</td>\n",
       "      <td>1.777937e+12</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1777936593781</td>\n",
       "      <td>2026-05-04 23:16:10.643</td>\n",
       "      <td>2026-05-04 23:16:33.781</td>\n",
       "      <td>0.000296</td>\n",
       "      <td>-0.000296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ZEC-USD</td>\n",
       "      <td>ZECUSDT</td>\n",
       "      <td>6.512462e+06</td>\n",
       "      <td>4.174327e+06</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>True</td>\n",
       "      <td>1.777937e+12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1777936396208</td>\n",
       "      <td>2026-05-04 23:16:31.300</td>\n",
       "      <td>2026-05-04 23:13:16.208</td>\n",
       "      <td>-0.000177</td>\n",
       "      <td>0.000177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symbol_ext symbol_ast  daily_volume_ext  daily_volume_ast min_price_ext  \\\n",
       "0     BNB-USD    BNBUSDT      8.095373e+06      2.796749e+07          0.01   \n",
       "1     BTC-USD    BTCUSDT      4.883520e+08      1.481693e+09             1   \n",
       "2     SOL-USD    SOLUSDT      2.509109e+07      9.187277e+07          0.01   \n",
       "3    DOGE-USD   DOGEUSDT      2.591347e+06      2.816025e+07       0.00001   \n",
       "4     XRP-USD    XRPUSDT      1.344608e+07      1.667650e+07        0.0001   \n",
       "5     XMR-USD    XMRUSDT      1.649243e+06      3.507893e+05          0.01   \n",
       "6     ETH-USD    ETHUSDT      1.616961e+08      6.561522e+08           0.1   \n",
       "7       4-USD      4USDT      3.027195e+05      4.548348e+05       0.00001   \n",
       "8    HYPE-USD   HYPEUSDT      4.365432e+07      1.053062e+07         0.001   \n",
       "9     ENA-USD    ENAUSDT      1.142030e+07      4.009133e+05       0.00001   \n",
       "10   WLFI-USD   WLFIUSDT      7.601354e+05      4.159099e+06       0.00001   \n",
       "11    LIT-USD    LITUSDT      1.854604e+06      1.350735e+06        0.0001   \n",
       "12    ZEC-USD    ZECUSDT      6.512462e+06      4.174327e+06         0.001   \n",
       "\n",
       "   min_price_ast min_order_size_ext min_order_size_ast min_lot_size_ext  \\\n",
       "0          0.010               0.01               0.01            0.001   \n",
       "1              1             0.0001              0.001          0.00001   \n",
       "2         0.4200                0.1               0.01             0.01   \n",
       "3       0.002440                100                  1               10   \n",
       "4         0.0143                 10                0.1                1   \n",
       "5           0.01                0.1              0.001             0.01   \n",
       "6           0.01               0.01              0.001            0.001   \n",
       "7      0.0000010                100                  1               10   \n",
       "8        0.00100                0.1               0.01             0.01   \n",
       "9      0.0000100                100                  1               10   \n",
       "10     0.0001000                100                  1               10   \n",
       "11     0.0001000                 10                  1                1   \n",
       "12        0.0100                0.1              0.001              0.1   \n",
       "\n",
       "   min_lot_size_ast  ...  net_funding_rate_abs net_funding_rate  \\\n",
       "0              0.01  ...          2.132000e-04     2.132000e-04   \n",
       "1             0.001  ...          1.699800e-04     1.699800e-04   \n",
       "2              0.01  ...          1.150000e-04     1.150000e-04   \n",
       "3                 1  ...          8.700000e-05     8.700000e-05   \n",
       "4               0.1  ...          6.291000e-05     6.291000e-05   \n",
       "5             0.001  ...          7.744000e-05     7.744000e-05   \n",
       "6             0.001  ...          3.223000e-05     3.223000e-05   \n",
       "7                 1  ...          2.265000e-04     2.265000e-04   \n",
       "8              0.01  ...          2.197000e-05     2.197000e-05   \n",
       "9                 1  ...          3.700000e-05     3.700000e-05   \n",
       "10                1  ...          6.000000e-05     6.000000e-05   \n",
       "11                1  ...          5.000000e-07     5.000000e-07   \n",
       "12            0.001  ...          5.000000e-07     5.000000e-07   \n",
       "\n",
       "    next_funding_at_same_time  last_trade_ts_ast  hourly_dollars_per_1k  \\\n",
       "0                        True       1.777937e+12                  14.21   \n",
       "1                        True       1.777937e+12                  12.75   \n",
       "2                        True       1.777937e+12                   7.67   \n",
       "3                        True       1.777937e+12                   5.22   \n",
       "4                        True       1.777937e+12                   4.19   \n",
       "5                        True       1.777937e+12                   2.58   \n",
       "6                        True       1.777937e+12                   2.42   \n",
       "7                        True       1.777937e+12                   2.06   \n",
       "8                        True       1.777937e+12                   1.88   \n",
       "9                        True       1.777937e+12                   1.23   \n",
       "10                       True       1.777937e+12                   0.86   \n",
       "11                       True       1.777937e+12                   0.02   \n",
       "12                       True       1.777937e+12                   0.01   \n",
       "\n",
       "    last_trade_ts_ext    last_trade_ts_dt_ast    last_trade_ts_dt_ext  \\\n",
       "0       1777936072912 2026-05-04 23:16:38.200 2026-05-04 23:07:52.912   \n",
       "1       1777936599985 2026-05-04 23:16:40.931 2026-05-04 23:16:39.985   \n",
       "2       1777936544932 2026-05-04 23:16:39.229 2026-05-04 23:15:44.932   \n",
       "3       1777936287673 2026-05-04 23:16:40.123 2026-05-04 23:11:27.673   \n",
       "4       1777936411533 2026-05-04 23:16:40.008 2026-05-04 23:13:31.533   \n",
       "5       1777936419604 2026-05-04 23:15:59.400 2026-05-04 23:13:39.604   \n",
       "6       1777936565323 2026-05-04 23:16:40.075 2026-05-04 23:16:05.323   \n",
       "7       1777936522376 2026-05-04 23:16:15.650 2026-05-04 23:15:22.376   \n",
       "8       1777936590010 2026-05-04 23:16:40.796 2026-05-04 23:16:30.010   \n",
       "9       1777936576866 2026-05-04 23:16:33.650 2026-05-04 23:16:16.866   \n",
       "10      1777935796212 2026-05-04 23:16:25.617 2026-05-04 23:03:16.212   \n",
       "11      1777936593781 2026-05-04 23:16:10.643 2026-05-04 23:16:33.781   \n",
       "12      1777936396208 2026-05-04 23:16:31.300 2026-05-04 23:13:16.208   \n",
       "\n",
       "   buy_price_ext buy_price_ast  \n",
       "0       0.001123     -0.001123  \n",
       "1       0.000127     -0.000127  \n",
       "2       0.000233     -0.000233  \n",
       "3      -0.000405      0.000405  \n",
       "4       0.000000     -0.000000  \n",
       "5      -0.000003      0.000003  \n",
       "6       0.000278     -0.000278  \n",
       "7       0.001436     -0.001436  \n",
       "8      -0.000182      0.000182  \n",
       "9      -0.001241      0.001241  \n",
       "10      0.002205     -0.002205  \n",
       "11      0.000296     -0.000296  \n",
       "12     -0.000177      0.000177  \n",
       "\n",
       "[13 rows x 31 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_best_fr_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbad7d21",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Candles for Midpoint Dispersion ###\n",
    "# Aster\n",
    "\n",
    "\n",
    "\n",
    "symbol = 'LITUSDT'\n",
    "aster_candles = {\n",
    "    \"url\": \"/fapi/v3/klines\",\n",
    "    \"method\": \"GET\",\n",
    "    \"params\": {\n",
    "        'symbol': symbol,\n",
    "        'interval': '1m',\n",
    "        'limit':'1440'\n",
    "    }\n",
    "}\n",
    "j = await aster_auth.post_authenticated_url(aster_candles)\n",
    "df_candles_aster = pd.DataFrame(j, columns=['open_ts','open_px','high_px','low_px','close_px','volume','close_ts','quote_asset_volume','count_trades','taker_buy_base_asset_volume','taker_buy_quote_asset_volume','_drop'])\n",
    "df_candles_aster = df_candles_aster[['open_px', 'low_px', 'high_px', 'close_px', 'volume', 'open_ts']]\n",
    "df_candles_aster[['open_px', 'low_px', 'high_px', 'close_px', 'volume']] = df_candles_aster[['open_px', 'low_px', 'high_px', 'close_px', 'volume']].astype(float)\n",
    "\n",
    "df_candles_aster['med_px'] = ( df_candles_aster['high_px'] + df_candles_aster['low_px'] ) / 2\n",
    "df_candles_aster['typical_px'] = ( df_candles_aster['open_px'] + df_candles_aster['high_px'] + df_candles_aster['low_px'] + df_candles_aster['close_px'] ) / 4\n",
    "\n",
    "# Extend\n",
    "symbol_ext = 'LIT-USD'\n",
    "ext_params = {\n",
    "    'interval':'1m',\n",
    "    'limit':1440,\n",
    "}\n",
    "r = json.loads(requests.get(f'https://api.starknet.extended.exchange/api/v1/info/candles/{symbol_ext}/trades', params=ext_params).text)\n",
    "df_candles_extended = pd.DataFrame(r['data'])\n",
    "df_candles_extended = df_candles_extended.rename({'o':'open_px','l':'low_px','h':'high_px','c':'close_px','v':'volume','T':'open_ts'}, axis=1)\n",
    "df_candles_extended[['open_px', 'low_px', 'high_px', 'close_px', 'volume']] = df_candles_extended[['open_px', 'low_px', 'high_px', 'close_px', 'volume']].astype(float)\n",
    "df_candles_extended['med_px'] = ( df_candles_extended['high_px'] + df_candles_extended['low_px'] ) / 2\n",
    "df_candles_extended['typical_px'] = ( df_candles_extended['open_px'] + df_candles_extended['high_px'] + df_candles_extended['low_px'] + df_candles_extended['close_px'] ) / 4\n",
    "\n",
    "df_candles_comb = df_candles_aster.merge(df_candles_extended, on='open_ts', how='inner', suffixes=('_ast','_ext'))\n",
    "df_candles_comb['open_dt'] = pd.to_datetime(df_candles_comb['open_ts'], unit='ms')\n",
    "df_candles_comb['med_ratio_aster_over_extend'] = ( df_candles_comb['med_px_ast'] / df_candles_comb['med_px_ext'] ) - 1\n",
    "df_candles_comb['typical_ratio_aster_over_extend'] = ( df_candles_comb['typical_px_ast'] / df_candles_comb['typical_px_ext'] ) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "799ffa9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.00027310069856325913)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_candles_comb['med_ratio_aster_over_extend'].median() * df_candles_comb['med_px_ext'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2bb2d8f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.941825)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_candles_comb['med_px_ext'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________Start___________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________VK Loads Time: 5000.38 ___________\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "loop_start = time.time()\n",
    "print('__________Start___________')\n",
    "\n",
    "time.sleep(5)\n",
    "\n",
    "print(f'__________VK Loads Time: {(time.time() - loop_start)*1000:.2f} ___________')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7211dfcf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0182370c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "variable=med_ratio_aster_over_extend<br>open_dt=%{x}<br>value=%{y}<extra></extra>",
         "legendgroup": "med_ratio_aster_over_extend",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "med_ratio_aster_over_extend",
         "showlegend": true,
         "type": "scattergl",
         "x": [
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          "2026-05-03T22:47:00",
          "2026-05-03T22:48:00",
          "2026-05-03T22:49:00",
          "2026-05-03T22:50:00",
          "2026-05-03T22:51:00",
          "2026-05-03T22:52:00",
          "2026-05-03T22:53:00",
          "2026-05-03T22:54:00",
          "2026-05-03T22:55:00",
          "2026-05-03T22:56:00",
          "2026-05-03T22:57:00",
          "2026-05-03T22:58:00",
          "2026-05-03T22:59:00",
          "2026-05-03T23:00:00",
          "2026-05-03T23:01:00",
          "2026-05-03T23:02:00",
          "2026-05-03T23:03:00",
          "2026-05-03T23:04:00",
          "2026-05-03T23:05:00",
          "2026-05-03T23:06:00",
          "2026-05-03T23:07:00",
          "2026-05-03T23:08:00",
          "2026-05-03T23:09:00",
          "2026-05-03T23:10:00",
          "2026-05-03T23:11:00",
          "2026-05-03T23:12:00",
          "2026-05-03T23:13:00",
          "2026-05-03T23:14:00",
          "2026-05-03T23:15:00",
          "2026-05-03T23:16:00",
          "2026-05-03T23:17:00",
          "2026-05-03T23:18:00",
          "2026-05-03T23:19:00",
          "2026-05-03T23:20:00",
          "2026-05-03T23:21:00",
          "2026-05-03T23:22:00",
          "2026-05-03T23:23:00",
          "2026-05-03T23:24:00",
          "2026-05-03T23:25:00",
          "2026-05-03T23:26:00",
          "2026-05-03T23:27:00",
          "2026-05-03T23:28:00",
          "2026-05-03T23:29:00",
          "2026-05-03T23:30:00",
          "2026-05-03T23:31:00",
          "2026-05-03T23:32:00",
          "2026-05-03T23:33:00",
          "2026-05-03T23:34:00",
          "2026-05-03T23:35:00",
          "2026-05-03T23:36:00",
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          "2026-05-03T23:43:00",
          "2026-05-03T23:44:00",
          "2026-05-03T23:45:00",
          "2026-05-03T23:46:00",
          "2026-05-03T23:47:00",
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         "yaxis": "y"
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          "dtype": "f8"
         },
         "yaxis": "y"
        }
       ],
       "layout": {
        "legend": {
         "title": {
          "text": "variable"
         },
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
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           "bgcolor": "#E5ECF6",
           "caxis": {
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            "linecolor": "white",
            "ticks": ""
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          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
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           "linecolor": "white",
           "ticks": "",
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           "zerolinewidth": 2
          },
          "yaxis": {
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     },
     "metadata": {},
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   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "px.line(df_candles_comb, y=['med_ratio_aster_over_extend','typical_ratio_aster_over_extend'], x='open_dt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a9bd45c0",
   "metadata": {},
   "outputs": [
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         },
         "yaxis": "y2"
        }
       ],
       "layout": {
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
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              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
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            },
            "type": "bar"
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              "width": 0.5
             },
             "pattern": {
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            },
            "type": "barpolar"
           }
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            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
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            "baxis": {
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             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
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          },
          "scene": {
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           "ticks": "",
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        },
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         "anchor": "x",
         "overlaying": "y",
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         }
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      }
     },
     "metadata": {},
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   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "# 1. Create figure with secondary y-axis\n",
    "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "\n",
    "# 2. Add traces\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=df_candles_comb['open_dt'], y=df_candles_comb['med_px_ast'], name=\"Aster Px\"),\n",
    "    secondary_y=False\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=df_candles_comb['open_dt'], y=df_candles_comb['med_px_ext'], name=\"Extend Px\"),\n",
    "    secondary_y=False\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=df_candles_comb['open_dt'], y=df_candles_comb['med_ratio_aster_over_extend']*10000, name=\"Ast/Ext\"),\n",
    "    secondary_y=True\n",
    ")\n",
    "\n",
    "\n",
    "# 3. Update axes titles\n",
    "fig.update_yaxes(title_text=\"Price\", secondary_y=False)\n",
    "fig.update_yaxes(title_text=\"Ratio\", secondary_y=True)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fada6f1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.00032550317365598325)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_candles_comb['med_ratio_aster_over_extend'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "869d70b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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         "yaxis": "y"
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       ],
       "layout": {
        "legend": {
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
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            "error_x": {
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            "error_y": {
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            "marker": {
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             },
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             "gridcolor": "white",
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             "startlinecolor": "#2a3f5f"
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    "import plotly.express as px\n",
    "\n",
    "px.line(df_candles_comb, y='med_ratio_aster_over_extend', x='open_dt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "11df4770",
   "metadata": {},
   "outputs": [
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     "data": {
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     "execution_count": 10,
     "metadata": {},
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   "execution_count": 11,
   "id": "89cbf119",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 11,
     "metadata": {},
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    "df_candles_comb['med_ratio_aster_over_extend'].median()"
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  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f83feeda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_mp_by_hour = df_candles_comb.groupby(df_candles_comb['open_dt'].dt.year).agg({'med_ratio_aster_over_extend':['min','max','median'],'volume_ast':'sum','volume_ext':'sum'})\n",
    "# df_mp_by_hour = df_candles_comb.agg({'med_ratio_aster_over_extend':['min','max','median'],'volume_ast':'sum','volume_ext':'sum'})\n",
    "df_mp_by_hour = df_candles_comb.agg({'med_ratio_aster_over_extend':['min','max','median']}).T\n",
    "# df_mp_by_hour.columns = ['_'.join(col).strip() for col in df_mp_by_hour.columns.values]\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ed1f5af",
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   "outputs": [
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     "execution_count": 37,
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  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9bac094e",
   "metadata": {},
   "outputs": [],
   "source": [
    "extend_buy_signal = {\n",
    "    'median': df_mp_by_hour['median'].iloc[0],\n",
    "    'min': df_mp_by_hour['min'].iloc[0],\n",
    "    'max': df_mp_by_hour['max'].iloc[0],\n",
    "}"
   ]
  },
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   "execution_count": null,
   "id": "99da860f",
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   "outputs": [],
   "source": []
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   "source": []
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   "execution_count": null,
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   "execution_count": 24,
   "id": "9db1fc65",
   "metadata": {},
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   ],
   "source": [
    "px.line(df_mp_by_hour, y='volume_ast_sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a9a28ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'symbol_ast': 'CHIPUSDT', 'symbol_extended': 'CHIP-USD'}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# min_daily_volume = 100_000\n",
    "# df_best_fr_rate = df_best_fr_rate.loc[ (df_best_fr_rate['daily_volume_ast']>=min_daily_volume) & (df_best_fr_rate['daily_volume_ext']>min_daily_volume) ,:].reset_index(drop=True)\n",
    "\n",
    "\n",
    "best_next_funding_pair = {'symbol_ast':df_best_fr_rate['symbol_ast'][0],'symbol_extended':df_best_fr_rate['symbol_ext'][0]}\n",
    "best_next_funding_pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "125ee5ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "ASTER = structs.Perpetual_Exchange(\n",
    "    mult = int(df_best_fr_rate['max_leverage_ast'][0]),\n",
    "    lh_asset = df_best_fr_rate['lh_asset_ast'][0],\n",
    "    rh_asset = df_best_fr_rate['rh_asset_ast'][0],\n",
    "    symbol_asset_separator = '',\n",
    "    initial_funding_rate=float(df_best_fr_rate['funding_rate_ast'][0]),\n",
    "    min_price=float(df_best_fr_rate['min_price_ast'][0]),\n",
    "    min_order_size=float(df_best_fr_rate['min_order_size_ast'][0]),\n",
    "    min_lot_size=float(df_best_fr_rate['min_lot_size_ast'][0]),\n",
    "    min_notional=float(df_best_fr_rate['min_notional_ast'][0]),\n",
    ")\n",
    "            \n",
    "EXTEND = structs.Perpetual_Exchange(\n",
    "    mult = int(df_best_fr_rate['max_leverage_ext'][0]),\n",
    "    lh_asset = df_best_fr_rate['lh_asset_ext'][0],\n",
    "    rh_asset = df_best_fr_rate['rh_asset_ext'][0],\n",
    "    symbol_asset_separator = '-',\n",
    "    initial_funding_rate=float(df_best_fr_rate['funding_rate_ext'][0]),\n",
    "    min_price=float(df_best_fr_rate['min_price_ext'][0]),\n",
    "    min_order_size=float(df_best_fr_rate['min_order_size_ext'][0]),\n",
    "    min_lot_size=float(df_best_fr_rate['min_lot_size_ext'][0]),\n",
    "    min_notional=float(df_best_fr_rate['min_notional_ext'][0]),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "185c04e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "execution_count": null,
   "id": "b7a08468",
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "execution_count": null,
   "id": "ca6b3810",
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdcda9a8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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