1491 lines
48 KiB
Plaintext
1491 lines
48 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "0b5ca901",
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"metadata": {},
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"outputs": [],
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"source": [
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"import requests\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from datetime import datetime\n",
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"import time\n",
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"import json\n",
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"import valkey\n",
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"from dataclasses import dataclass, field, asdict\n",
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"import modules.structs as structs\n",
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"import modules.aster_auth as aster_auth\n",
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"import modules.manual_leverage as leverage\n",
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"VAL_KEY = valkey.Valkey(host='localhost', port=6379, db=0, decode_responses=True)\n",
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"df_leverage_by_exch = pd.DataFrame(data=leverage.LEVERAGE_BY_EXCH)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 101,
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"id": "20665e82",
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"metadata": {},
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"outputs": [],
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"source": [
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"### ********** TODO: ADD IN VOLUME DATA TO FILTER MKTS ###"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 102,
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"id": "53647b40",
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"metadata": {},
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"outputs": [],
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"source": [
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"# ### ASTER Historical FR ###\n",
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"# params = {\n",
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"# 'limit': 1000,\n",
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"# }\n",
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"# r = json.loads(requests.get('https://fapi.asterdex.com/fapi/v3/fundingRate', params=params).text)\n",
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"# df_aster_hist_fr = pd.DataFrame(r)\n",
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"# df_aster_hist_fr['funding_rate_ts_dt'] = pd.to_datetime(df_aster_hist_fr['fundingTime'], unit='ms')\n",
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"# df_aster_hist_fr = df_aster_hist_fr.sort_values(by='fundingTime', ascending=True).drop_duplicates(subset=['symbol'], keep='last')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 103,
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"id": "1f3b65ee",
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"metadata": {},
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"outputs": [],
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"source": [
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"# ### ASTER Current FR ###\n",
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"# r = json.loads(requests.get('https://fapi.asterdex.com/fapi/v3/fundingInfo').text)\n",
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"# df_aster_current_fr = pd.DataFrame(r)\n",
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"# df_aster_current_fr['funding_rate_ts_dt'] = pd.to_datetime(df_aster_current_fr['time'], unit='ms')\n",
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"# df_aster_current_fr['funding_rate'] = df_aster_current_fr['interestRate']\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 104,
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"id": "5176d5b1",
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"metadata": {},
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"outputs": [],
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"source": [
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"### ASTER EXCHANGE INFO ###\n",
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"fut_acct_exchangeInfo: dict = {\n",
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" \"url\": \"/fapi/v3/exchangeInfo\",\n",
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" \"method\": \"GET\",\n",
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" \"params\": {}\n",
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"}\n",
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"r: dict = await aster_auth.post_authenticated_url(fut_acct_exchangeInfo) # ty:ignore[invalid-assignment]\n",
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"df_aster_exch_info = pd.DataFrame(r['symbols'])\n",
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"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",
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"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",
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"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",
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"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",
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"\n",
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"fut_acct_ticker_stats: dict = {\n",
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" \"url\": \"/fapi/v3/ticker/24hr\",\n",
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" \"method\": \"GET\",\n",
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" \"params\": {}\n",
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"}\n",
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"r: dict = await aster_auth.post_authenticated_url(fut_acct_ticker_stats) # ty:ignore[invalid-assignment]\n",
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"df_aster_ticker_stats = pd.DataFrame(r)\n",
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"df_aster_ticker_stats['last_trade_ts_ast'] = df_aster_ticker_stats['closeTime']\n",
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"\n",
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"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",
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"df_aster_exch_info['daily_volume'] = df_aster_exch_info['daily_volume'].astype(float)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 105,
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"id": "e33ec721",
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"metadata": {},
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"outputs": [],
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"source": [
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"### Extended Current FR ###\n",
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"r = json.loads(requests.get('https://api.starknet.extended.exchange/api/v1/info/markets').text)\n",
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"df_extend_current_mkt_stats = pd.DataFrame(r['data'])\n",
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"\n",
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"df_extend_current_mkt_stats['funding_rate'] = df_extend_current_mkt_stats['marketStats'].apply(lambda x: x.get('fundingRate',{}))\n",
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"df_extend_current_mkt_stats['funding_rate_ts'] = df_extend_current_mkt_stats['marketStats'].apply(lambda x: x.get('nextFundingRate',{}))\n",
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"df_extend_current_mkt_stats['daily_volume'] = df_extend_current_mkt_stats['marketStats'].apply(lambda x: x.get('dailyVolume',{})).astype(float)\n",
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"df_extend_current_mkt_stats['min_order_size'] = df_extend_current_mkt_stats['tradingConfig'].apply(lambda x: x.get('minOrderSize',{}))\n",
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"df_extend_current_mkt_stats['min_price'] = df_extend_current_mkt_stats['tradingConfig'].apply(lambda x: x.get('minPriceChange',{}))\n",
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"df_extend_current_mkt_stats['min_notional'] = 0\n",
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"df_extend_current_mkt_stats['min_lot_size'] = df_extend_current_mkt_stats['tradingConfig'].apply(lambda x: x.get('minOrderSizeChange',{}))\n",
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"\n",
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"df_extend_current_mkt_stats['max_leverage'] = df_extend_current_mkt_stats['tradingConfig'].apply(lambda x: x.get('maxLeverage',{}))\n",
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"\n",
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"\n",
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"# 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",
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"# df_extend_current_fr['funding_rate_ts_dt'] = pd.to_datetime(df_extend_current_fr['funding_rate_ts'], unit='ms')\n",
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"# df_extend_current_fr = df_extend_current_fr.loc[df_extend_current_fr['status']=='ACTIVE',:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 106,
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"id": "62815940",
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"metadata": {},
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"outputs": [],
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"source": [
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"### ASTER Current FR from Mark Price Req ###\n",
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"# r = json.loads(requests.get('https://fapi.asterdex.com/fapi/v3/exchangeInfo').text)\n",
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"# df_aster_current_mkt_stats = pd.DataFrame(r)\n",
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"# ### ASTER Current FR from Mark Price Req ###\n",
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"# r = json.loads(requests.get('https://fapi.asterdex.com/fapi/v3/premiumIndex').text)\n",
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"# df_aster_current_mkt_stats = pd.DataFrame(r)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 107,
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"id": "271a67c1",
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"metadata": {},
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"outputs": [],
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"source": [
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"### ASTER CURRENT FR - WS ###\n",
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"df_aster_current_fr = pd.DataFrame(json.loads(VAL_KEY.get('fund_rate_aster_all'))) # ty:ignore[invalid-argument-type]\n",
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"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",
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"df_aster_current_fr['funding_rate_updated_dt'] = pd.to_datetime(df_aster_current_fr['funding_rate_updated_ts_ms'], unit='ms')\n",
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"df_aster_current_fr['funding_rate'] = df_aster_current_fr['funding_rate'].astype(float)\n",
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"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",
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"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')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 108,
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"id": "1ce2fde4",
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"metadata": {},
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"outputs": [],
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"source": [
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"### EXTEND CURRENT FR - WS ###\n",
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"df_extended_current_fr = pd.DataFrame(json.loads(VAL_KEY.get('fund_rate_extended_all'))) # ty:ignore[invalid-argument-type]\n",
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"df_extended_current_fr = df_extended_current_fr[['symbol','funding_rate_updated_ts_ms','funding_rate']]\n",
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"df_extended_current_fr['funding_rate_updated_dt'] = pd.to_datetime(df_extended_current_fr['funding_rate_updated_ts_ms'], unit='ms')\n",
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"df_extended_current_fr['funding_rate'] = df_extended_current_fr['funding_rate'].astype(float)\n",
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"\n",
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"# 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",
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"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",
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"df_extended_current_fr = df_extended_current_fr.loc[df_extended_current_fr['status']=='ACTIVE',:]\n",
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"df_extended_current_fr['USDT_Symbol'] = df_extended_current_fr['assetName'] + 'USDT'\n",
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"df_extended_current_fr['time_delta_to_next_funding'] = pd.to_datetime(df_extended_current_fr['next_funding_ts'], unit='ms') - pd.Timestamp.now()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 109,
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"id": "ff88b413",
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"metadata": {},
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"outputs": [],
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"source": [
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"### COMBINED CURRENT FR - WS ###\n",
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"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",
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"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",
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"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",
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"df_comb_current_fr['net_funding_rate_abs'] = df_comb_current_fr['net_funding_rate'].abs()\n",
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"\n",
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"### NET MULT ###\n",
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"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",
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"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",
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"df_comb_current_fr_net['net_mult'] = df_comb_current_fr_net['net_mult'].round(2)\n",
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"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']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 110,
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"id": "f5ade993",
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"metadata": {},
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"outputs": [],
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"source": [
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"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",
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"df_best_fr_rate['hourly_dollars_per_1k'] = df_best_fr_rate['net_mult_x_net_fr_abs'] * 1000\n",
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"df_best_fr_rate['hourly_dollars_per_1k'] = df_best_fr_rate['hourly_dollars_per_1k'].round(2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 111,
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"id": "84bbc5a8",
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"metadata": {},
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"outputs": [],
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"source": [
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"last_trade_max_ts = []\n",
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"\n",
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"for index, row in df_best_fr_rate.iterrows():\n",
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" r = json.loads(requests.get(f'https://api.starknet.extended.exchange/api/v1/info/markets/{row['symbol_ext']}/trades').text)\n",
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" max_ts = max([t['T'] for t in r['data']])\n",
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" last_trade_max_ts.append({'symbol_ext':row['symbol_ext'],'last_trade_ts_ext': max_ts})\n",
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" time.sleep(0.01)\n",
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"\n",
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"df_best_fr_rate = df_best_fr_rate.merge(pd.DataFrame(last_trade_max_ts), on='symbol_ext', how='left')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 112,
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"id": "7bd849e4",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_best_fr_rate['last_trade_ts_dt_ast'] = pd.to_datetime(df_best_fr_rate['last_trade_ts_ast'], unit='ms')\n",
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"df_best_fr_rate['last_trade_ts_dt_ext'] = pd.to_datetime(df_best_fr_rate['last_trade_ts_ext'], unit='ms')\n",
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"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",
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"df_best_fr_rate = df_best_fr_rate.loc[( (datetime.now().timestamp()*1000 )-df_best_fr_rate['last_trade_ts_ext']) < (15*60*1000) ]"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 113,
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||
"id": "48d093ce",
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||
"metadata": {},
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||
"outputs": [
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||
{
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||
"data": {
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||
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
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||
"columns": [
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||
{
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||
"name": "index",
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||
"rawType": "int64",
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||
"type": "integer"
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||
},
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||
{
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||
"name": "symbol_ext",
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||
"rawType": "str",
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||
"type": "string"
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||
},
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||
{
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||
"name": "symbol_ast",
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||
"rawType": "str",
|
||
"type": "string"
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||
},
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||
{
|
||
"name": "daily_volume_ext",
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||
"rawType": "float64",
|
||
"type": "float"
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||
},
|
||
{
|
||
"name": "daily_volume_ast",
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||
"rawType": "float64",
|
||
"type": "float"
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||
},
|
||
{
|
||
"name": "min_price_ext",
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||
"rawType": "str",
|
||
"type": "string"
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||
},
|
||
{
|
||
"name": "min_price_ast",
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||
"rawType": "str",
|
||
"type": "string"
|
||
},
|
||
{
|
||
"name": "min_order_size_ext",
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||
"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",
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||
"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",
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||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "max_leverage_ast",
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||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "lh_asset_ext",
|
||
"rawType": "str",
|
||
"type": "string"
|
||
},
|
||
{
|
||
"name": "lh_asset_ast",
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||
"rawType": "str",
|
||
"type": "string"
|
||
},
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||
{
|
||
"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"
|
||
},
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
" <td>1.20</td>\n",
|
||
" <td>1777838943428</td>\n",
|
||
" <td>2026-05-03 20:09:19.171</td>\n",
|
||
" <td>2026-05-03 20:09:03.428</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>HYPE-USD</td>\n",
|
||
" <td>HYPEUSDT</td>\n",
|
||
" <td>2.557237e+07</td>\n",
|
||
" <td>3.517136e+06</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>USDT</td>\n",
|
||
" <td>0.001114</td>\n",
|
||
" <td>0.000013</td>\n",
|
||
" <td>0.000013</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1.777839e+12</td>\n",
|
||
" <td>1.11</td>\n",
|
||
" <td>1777838941321</td>\n",
|
||
" <td>2026-05-03 20:08:46.707</td>\n",
|
||
" <td>2026-05-03 20:09:01.321</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>XRP-USD</td>\n",
|
||
" <td>XRPUSDT</td>\n",
|
||
" <td>5.624797e+06</td>\n",
|
||
" <td>7.717252e+06</td>\n",
|
||
" <td>0.0001</td>\n",
|
||
" <td>0.0143</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>USDT</td>\n",
|
||
" <td>0.000867</td>\n",
|
||
" <td>0.000013</td>\n",
|
||
" <td>0.000013</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1.777839e+12</td>\n",
|
||
" <td>0.87</td>\n",
|
||
" <td>1777838873023</td>\n",
|
||
" <td>2026-05-03 20:09:16.056</td>\n",
|
||
" <td>2026-05-03 20:07:53.023</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>BNB-USD</td>\n",
|
||
" <td>BNBUSDT</td>\n",
|
||
" <td>3.508321e+06</td>\n",
|
||
" <td>7.656350e+06</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" <td>0.010</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" <td>0.001</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>USDT</td>\n",
|
||
" <td>0.000867</td>\n",
|
||
" <td>0.000013</td>\n",
|
||
" <td>0.000013</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1.777839e+12</td>\n",
|
||
" <td>0.87</td>\n",
|
||
" <td>1777838862018</td>\n",
|
||
" <td>2026-05-03 20:09:17.563</td>\n",
|
||
" <td>2026-05-03 20:07:42.018</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>DOGE-USD</td>\n",
|
||
" <td>DOGEUSDT</td>\n",
|
||
" <td>2.224083e+06</td>\n",
|
||
" <td>1.313191e+07</td>\n",
|
||
" <td>0.00001</td>\n",
|
||
" <td>0.002440</td>\n",
|
||
" <td>100</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>USDT</td>\n",
|
||
" <td>0.000780</td>\n",
|
||
" <td>0.000013</td>\n",
|
||
" <td>0.000013</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1.777839e+12</td>\n",
|
||
" <td>0.78</td>\n",
|
||
" <td>1777838861853</td>\n",
|
||
" <td>2026-05-03 20:09:18.745</td>\n",
|
||
" <td>2026-05-03 20:07:41.853</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>ASTER-USD</td>\n",
|
||
" <td>ASTERUSDT</td>\n",
|
||
" <td>3.798975e+05</td>\n",
|
||
" <td>3.579841e+07</td>\n",
|
||
" <td>0.00001</td>\n",
|
||
" <td>0.00010</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>USDT</td>\n",
|
||
" <td>0.000487</td>\n",
|
||
" <td>0.000013</td>\n",
|
||
" <td>0.000013</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1.777839e+12</td>\n",
|
||
" <td>0.49</td>\n",
|
||
" <td>1777838861663</td>\n",
|
||
" <td>2026-05-03 20:09:16.077</td>\n",
|
||
" <td>2026-05-03 20:07:41.663</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>CHIP-USD</td>\n",
|
||
" <td>CHIPUSDT</td>\n",
|
||
" <td>6.015818e+05</td>\n",
|
||
" <td>3.690379e+05</td>\n",
|
||
" <td>0.000001</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>USDT</td>\n",
|
||
" <td>0.000483</td>\n",
|
||
" <td>0.000053</td>\n",
|
||
" <td>0.000053</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>1.777839e+12</td>\n",
|
||
" <td>0.48</td>\n",
|
||
" <td>1777838894261</td>\n",
|
||
" <td>2026-05-03 20:07:13.850</td>\n",
|
||
" <td>2026-05-03 20:08:14.261</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>SOL-USD</td>\n",
|
||
" <td>SOLUSDT</td>\n",
|
||
" <td>1.099378e+07</td>\n",
|
||
" <td>2.819154e+07</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" <td>0.4200</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>USDT</td>\n",
|
||
" <td>0.000467</td>\n",
|
||
" <td>0.000007</td>\n",
|
||
" <td>0.000007</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1.777839e+12</td>\n",
|
||
" <td>0.47</td>\n",
|
||
" <td>1777838903681</td>\n",
|
||
" <td>2026-05-03 20:09:19.552</td>\n",
|
||
" <td>2026-05-03 20:08:23.681</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>BTC-USD</td>\n",
|
||
" <td>BTCUSDT</td>\n",
|
||
" <td>1.906997e+08</td>\n",
|
||
" <td>3.317879e+08</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.0001</td>\n",
|
||
" <td>0.001</td>\n",
|
||
" <td>0.00001</td>\n",
|
||
" <td>0.001</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>USDT</td>\n",
|
||
" <td>0.000450</td>\n",
|
||
" <td>0.000006</td>\n",
|
||
" <td>-0.000006</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1.777839e+12</td>\n",
|
||
" <td>0.45</td>\n",
|
||
" <td>1777838940017</td>\n",
|
||
" <td>2026-05-03 20:09:19.137</td>\n",
|
||
" <td>2026-05-03 20:09:00.017</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>WLFI-USD</td>\n",
|
||
" <td>WLFIUSDT</td>\n",
|
||
" <td>1.185166e+05</td>\n",
|
||
" <td>2.231255e+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>USDT</td>\n",
|
||
" <td>0.000186</td>\n",
|
||
" <td>0.000013</td>\n",
|
||
" <td>0.000013</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>1.777839e+12</td>\n",
|
||
" <td>0.19</td>\n",
|
||
" <td>1777838640653</td>\n",
|
||
" <td>2026-05-03 20:08:40.826</td>\n",
|
||
" <td>2026-05-03 20:04:00.653</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>12 rows × 29 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" symbol_ext symbol_ast daily_volume_ext daily_volume_ast min_price_ext \\\n",
|
||
"0 LIT-USD LITUSDT 1.530863e+06 5.573442e+05 0.0001 \n",
|
||
"1 ZEC-USD ZECUSDT 5.395748e+06 1.986811e+06 0.001 \n",
|
||
"2 ETH-USD ETHUSDT 6.379490e+07 2.158012e+08 0.1 \n",
|
||
"3 HYPE-USD HYPEUSDT 2.557237e+07 3.517136e+06 0.001 \n",
|
||
"5 XRP-USD XRPUSDT 5.624797e+06 7.717252e+06 0.0001 \n",
|
||
"6 BNB-USD BNBUSDT 3.508321e+06 7.656350e+06 0.01 \n",
|
||
"7 DOGE-USD DOGEUSDT 2.224083e+06 1.313191e+07 0.00001 \n",
|
||
"9 ASTER-USD ASTERUSDT 3.798975e+05 3.579841e+07 0.00001 \n",
|
||
"10 CHIP-USD CHIPUSDT 6.015818e+05 3.690379e+05 0.000001 \n",
|
||
"11 SOL-USD SOLUSDT 1.099378e+07 2.819154e+07 0.01 \n",
|
||
"12 BTC-USD BTCUSDT 1.906997e+08 3.317879e+08 1 \n",
|
||
"14 WLFI-USD WLFIUSDT 1.185166e+05 2.231255e+06 0.00001 \n",
|
||
"\n",
|
||
" min_price_ast min_order_size_ext min_order_size_ast min_lot_size_ext \\\n",
|
||
"0 0.0001000 10 1 1 \n",
|
||
"1 0.0100 0.1 0.001 0.1 \n",
|
||
"2 0.01 0.01 0.001 0.001 \n",
|
||
"3 0.00100 0.1 0.01 0.01 \n",
|
||
"5 0.0143 10 0.1 1 \n",
|
||
"6 0.010 0.01 0.01 0.001 \n",
|
||
"7 0.002440 100 1 10 \n",
|
||
"9 0.00010 10 0.01 1 \n",
|
||
"10 0.0000100 100 1 10 \n",
|
||
"11 0.4200 0.1 0.01 0.01 \n",
|
||
"12 1 0.0001 0.001 0.00001 \n",
|
||
"14 0.0001000 100 1 10 \n",
|
||
"\n",
|
||
" min_lot_size_ast ... rh_asset_ast net_mult_x_net_fr_abs \\\n",
|
||
"0 1 ... USDT 0.002113 \n",
|
||
"1 0.001 ... USDT 0.001506 \n",
|
||
"2 0.001 ... USDT 0.001200 \n",
|
||
"3 0.01 ... USDT 0.001114 \n",
|
||
"5 0.1 ... USDT 0.000867 \n",
|
||
"6 0.01 ... USDT 0.000867 \n",
|
||
"7 1 ... USDT 0.000780 \n",
|
||
"9 0.01 ... USDT 0.000487 \n",
|
||
"10 1 ... USDT 0.000483 \n",
|
||
"11 0.01 ... USDT 0.000467 \n",
|
||
"12 0.001 ... USDT 0.000450 \n",
|
||
"14 1 ... USDT 0.000186 \n",
|
||
"\n",
|
||
" net_funding_rate_abs net_funding_rate next_funding_at_same_time \\\n",
|
||
"0 0.000063 0.000063 True \n",
|
||
"1 0.000085 0.000085 True \n",
|
||
"2 0.000016 -0.000016 False \n",
|
||
"3 0.000013 0.000013 False \n",
|
||
"5 0.000013 0.000013 False \n",
|
||
"6 0.000013 0.000013 False \n",
|
||
"7 0.000013 0.000013 False \n",
|
||
"9 0.000013 0.000013 False \n",
|
||
"10 0.000053 0.000053 True \n",
|
||
"11 0.000007 0.000007 False \n",
|
||
"12 0.000006 -0.000006 False \n",
|
||
"14 0.000013 0.000013 False \n",
|
||
"\n",
|
||
" last_trade_ts_ast hourly_dollars_per_1k last_trade_ts_ext \\\n",
|
||
"0 1.777839e+12 2.11 1777838893573 \n",
|
||
"1 1.777839e+12 1.51 1777838958681 \n",
|
||
"2 1.777839e+12 1.20 1777838943428 \n",
|
||
"3 1.777839e+12 1.11 1777838941321 \n",
|
||
"5 1.777839e+12 0.87 1777838873023 \n",
|
||
"6 1.777839e+12 0.87 1777838862018 \n",
|
||
"7 1.777839e+12 0.78 1777838861853 \n",
|
||
"9 1.777839e+12 0.49 1777838861663 \n",
|
||
"10 1.777839e+12 0.48 1777838894261 \n",
|
||
"11 1.777839e+12 0.47 1777838903681 \n",
|
||
"12 1.777839e+12 0.45 1777838940017 \n",
|
||
"14 1.777839e+12 0.19 1777838640653 \n",
|
||
"\n",
|
||
" last_trade_ts_dt_ast last_trade_ts_dt_ext \n",
|
||
"0 2026-05-03 20:09:15.491 2026-05-03 20:08:13.573 \n",
|
||
"1 2026-05-03 20:09:16.400 2026-05-03 20:09:18.681 \n",
|
||
"2 2026-05-03 20:09:19.171 2026-05-03 20:09:03.428 \n",
|
||
"3 2026-05-03 20:08:46.707 2026-05-03 20:09:01.321 \n",
|
||
"5 2026-05-03 20:09:16.056 2026-05-03 20:07:53.023 \n",
|
||
"6 2026-05-03 20:09:17.563 2026-05-03 20:07:42.018 \n",
|
||
"7 2026-05-03 20:09:18.745 2026-05-03 20:07:41.853 \n",
|
||
"9 2026-05-03 20:09:16.077 2026-05-03 20:07:41.663 \n",
|
||
"10 2026-05-03 20:07:13.850 2026-05-03 20:08:14.261 \n",
|
||
"11 2026-05-03 20:09:19.552 2026-05-03 20:08:23.681 \n",
|
||
"12 2026-05-03 20:09:19.137 2026-05-03 20:09:00.017 \n",
|
||
"14 2026-05-03 20:08:40.826 2026-05-03 20:04:00.653 \n",
|
||
"\n",
|
||
"[12 rows x 29 columns]"
|
||
]
|
||
},
|
||
"execution_count": 113,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_best_fr_rate"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "bbad7d21",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"### Candles for Midpoint Dispersion ###\n",
|
||
"# Aster\n",
|
||
"import modules.aster_auth as aster_auth\n",
|
||
"symbol = 'BTCUSDT'\n",
|
||
"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(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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "421577f1",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Extend\n",
|
||
"symbol_ext = 'BTC-USD'\n",
|
||
"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}/trades', params=params).text)\n",
|
||
"df_candles_extended = pd.DataFrame(r)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "f83feeda",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
|
||
"columns": [
|
||
{
|
||
"name": "index",
|
||
"rawType": "str",
|
||
"type": "string"
|
||
},
|
||
{
|
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"# 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",
|
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|
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|
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|
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"ASTER = structs.Perpetual_Exchange(\n",
|
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|
||
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|
||
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|
||
" symbol_asset_separator = '',\n",
|
||
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|
||
" min_price=float(df_best_fr_rate['min_price_ast'][0]),\n",
|
||
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|
||
" min_lot_size=float(df_best_fr_rate['min_lot_size_ast'][0]),\n",
|
||
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|
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")\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",
|
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")"
|
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]
|
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{
|
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"cell_type": "code",
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"execution_count": null,
|
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"id": "185c04e9",
|
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"metadata": {},
|
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"outputs": [],
|
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"source": []
|
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},
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{
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|
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"id": "b7a08468",
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|
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"outputs": [],
|
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|
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"id": "ca6b3810",
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|
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|
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|
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|
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}
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],
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"metadata": {
|
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"display_name": "py_313",
|
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"language": "python",
|
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"name": "python3"
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},
|
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"name": "ipython",
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