{ "cells": [ { "cell_type": "code", "execution_count": 155, "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": 156, "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": 157, "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": 158, "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", "df_aster_ticker_stats['last_trade_px_ast'] = df_aster_ticker_stats['lastPrice'].astype(float)\n", "\n", "df_aster_exch_info = df_aster_exch_info.merge(df_aster_ticker_stats[['symbol','quoteVolume','last_trade_ts_ast','last_trade_px_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": 159, "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": 160, "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": 161, "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', 'last_trade_px_ast']], on='symbol', how='left')" ] }, { "cell_type": "code", "execution_count": 162, "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": 163, "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": 164, "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','last_trade_px_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": 165, "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", " px = np.median([float(t['p']) for t in r['data'] if t['T']==max_ts])\n", " last_trade_max_ts.append({'symbol_ext':row['symbol_ext'],'last_trade_ts_ext': max_ts, 'last_trade_px_ext': float(px)})\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": 166, "id": "7bd849e4", "metadata": {}, "outputs": [], "source": [ "df_best_fr_rate['current_ast_over_ext_ratio'] = ( df_best_fr_rate['last_trade_px_ast'] / df_best_fr_rate['last_trade_px_ext'] ) - 1\n", "\n", "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": 167, "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": 168, "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", " buy_ratio_std = float(df['med_ratio_aster_over_extend'].std())\n", " candles_ratios.append({'symbol_ext':row['symbol_ext'], 'buy_ratio_std': buy_ratio_std, '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), 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0DOGE-USDDOGEUSDT6.750571e+062.321757e+070.000010.0024401001101...0.1155203.2417780473631020.115940-0.0036232026-05-06 06:07:47.6002026-05-06 06:02:43.1020.001433-0.0011810.001181
1LIT-USDLITUSDT2.522778e+067.523452e+050.00010.000100010111...0.9681002.0117780476691810.968600-0.0005162026-05-06 06:07:21.0002026-05-06 06:07:49.1810.0026220.000299-0.000299
2HYPE-USDHYPEUSDT5.863991e+071.255636e+070.0010.001000.10.010.010.01...43.9590001.63177804765001143.966000-0.0001592026-05-06 06:07:47.1532026-05-06 06:07:30.0110.0018040.000131-0.000131
3BNB-USDBNBUSDT9.050764e+061.487197e+070.010.0100.010.010.0010.01...635.3200000.871778047650009634.8600000.0007252026-05-06 06:07:47.4952026-05-06 06:07:30.0090.0025530.001051-0.001051
4SOL-USDSOLUSDT2.257412e+076.243654e+070.010.42000.10.010.010.01...87.3200000.87177804760202787.450000-0.0014872026-05-06 06:07:47.8552026-05-06 06:06:42.0270.0007760.000116-0.000116
5SUI-USDSUIUSDT5.872095e+062.857091e+050.00010.000100100.110.1...0.9871000.7817780475761230.988900-0.0018202026-05-06 06:07:46.7002026-05-06 06:06:16.1230.0042260.000211-0.000211
6ETH-USDETHUSDT6.121374e+074.044987e+080.10.010.010.0010.0010.001...2361.6900000.6817780476709112361.3000000.0001652026-05-06 06:07:48.2502026-05-06 06:07:50.9110.0006850.000210-0.000210
74-USD4USDT3.209672e+053.135862e+050.000010.00000101001101...0.0133470.6717780475265530.0132700.0058032026-05-06 06:06:17.1832026-05-06 06:05:26.5530.0244500.004767-0.004767
8ASTER-USDASTERUSDT3.228248e+054.170694e+070.000010.00010100.0110.01...0.6782000.4917780471829450.680060-0.0027352026-05-06 06:07:48.2502026-05-06 05:59:42.9450.001276-0.0007420.000742
9CHIP-USDCHIPUSDT4.525915e+052.374932e+050.0000010.00001001001101...0.0542500.4817780474909110.054252-0.0000372026-05-06 06:06:17.6972026-05-06 06:04:50.9110.041605-0.0000990.000099
10XRP-USDXRPUSDT9.026962e+061.613476e+070.00010.0143100.110.1...1.4235000.2717780476231011.423600-0.0000702026-05-06 06:07:45.3562026-05-06 06:07:03.1010.0043790.000212-0.000212
11BTC-USDBTCUSDT1.828855e+089.794189e+08110.00010.0010.000010.001...81258.1000000.22177804767163781255.0000000.0000382026-05-06 06:07:46.6572026-05-06 06:07:51.6370.0009990.000097-0.000097
12ZEC-USDZECUSDT6.849535e+066.584711e+060.0010.01000.10.0010.10.001...545.9000000.011778047665513546.126000-0.0004142026-05-06 06:07:46.5302026-05-06 06:07:45.5130.0031990.000013-0.000013
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13 rows × 35 columns

\n", "
" ], "text/plain": [ " symbol_ext symbol_ast daily_volume_ext daily_volume_ast min_price_ext \\\n", "0 DOGE-USD DOGEUSDT 6.750571e+06 2.321757e+07 0.00001 \n", "1 LIT-USD LITUSDT 2.522778e+06 7.523452e+05 0.0001 \n", "2 HYPE-USD HYPEUSDT 5.863991e+07 1.255636e+07 0.001 \n", "3 BNB-USD BNBUSDT 9.050764e+06 1.487197e+07 0.01 \n", "4 SOL-USD SOLUSDT 2.257412e+07 6.243654e+07 0.01 \n", "5 SUI-USD SUIUSDT 5.872095e+06 2.857091e+05 0.0001 \n", "6 ETH-USD ETHUSDT 6.121374e+07 4.044987e+08 0.1 \n", "7 4-USD 4USDT 3.209672e+05 3.135862e+05 0.00001 \n", "8 ASTER-USD ASTERUSDT 3.228248e+05 4.170694e+07 0.00001 \n", "9 CHIP-USD CHIPUSDT 4.525915e+05 2.374932e+05 0.000001 \n", "10 XRP-USD XRPUSDT 9.026962e+06 1.613476e+07 0.0001 \n", "11 BTC-USD BTCUSDT 1.828855e+08 9.794189e+08 1 \n", "12 ZEC-USD ZECUSDT 6.849535e+06 6.584711e+06 0.001 \n", "\n", " min_price_ast min_order_size_ext min_order_size_ast min_lot_size_ext \\\n", "0 0.002440 100 1 10 \n", "1 0.0001000 10 1 1 \n", "2 0.00100 0.1 0.01 0.01 \n", "3 0.010 0.01 0.01 0.001 \n", "4 0.4200 0.1 0.01 0.01 \n", "5 0.000100 10 0.1 1 \n", "6 0.01 0.01 0.001 0.001 \n", "7 0.0000010 100 1 10 \n", "8 0.00010 10 0.01 1 \n", "9 0.0000100 100 1 10 \n", "10 0.0143 10 0.1 1 \n", "11 1 0.0001 0.001 0.00001 \n", "12 0.0100 0.1 0.001 0.1 \n", "\n", " min_lot_size_ast ... last_trade_px_ast hourly_dollars_per_1k \\\n", "0 1 ... 0.115520 3.24 \n", "1 1 ... 0.968100 2.01 \n", "2 0.01 ... 43.959000 1.63 \n", "3 0.01 ... 635.320000 0.87 \n", "4 0.01 ... 87.320000 0.87 \n", "5 0.1 ... 0.987100 0.78 \n", "6 0.001 ... 2361.690000 0.68 \n", "7 1 ... 0.013347 0.67 \n", "8 0.01 ... 0.678200 0.49 \n", "9 1 ... 0.054250 0.48 \n", "10 0.1 ... 1.423500 0.27 \n", "11 0.001 ... 81258.100000 0.22 \n", "12 0.001 ... 545.900000 0.01 \n", "\n", " last_trade_ts_ext last_trade_px_ext current_ast_over_ext_ratio \\\n", "0 1778047363102 0.115940 -0.003623 \n", "1 1778047669181 0.968600 -0.000516 \n", "2 1778047650011 43.966000 -0.000159 \n", "3 1778047650009 634.860000 0.000725 \n", "4 1778047602027 87.450000 -0.001487 \n", "5 1778047576123 0.988900 -0.001820 \n", "6 1778047670911 2361.300000 0.000165 \n", "7 1778047526553 0.013270 0.005803 \n", "8 1778047182945 0.680060 -0.002735 \n", "9 1778047490911 0.054252 -0.000037 \n", "10 1778047623101 1.423600 -0.000070 \n", "11 1778047671637 81255.000000 0.000038 \n", "12 1778047665513 546.126000 -0.000414 \n", "\n", " last_trade_ts_dt_ast last_trade_ts_dt_ext buy_ratio_std \\\n", "0 2026-05-06 06:07:47.600 2026-05-06 06:02:43.102 0.001433 \n", "1 2026-05-06 06:07:21.000 2026-05-06 06:07:49.181 0.002622 \n", "2 2026-05-06 06:07:47.153 2026-05-06 06:07:30.011 0.001804 \n", "3 2026-05-06 06:07:47.495 2026-05-06 06:07:30.009 0.002553 \n", "4 2026-05-06 06:07:47.855 2026-05-06 06:06:42.027 0.000776 \n", "5 2026-05-06 06:07:46.700 2026-05-06 06:06:16.123 0.004226 \n", "6 2026-05-06 06:07:48.250 2026-05-06 06:07:50.911 0.000685 \n", "7 2026-05-06 06:06:17.183 2026-05-06 06:05:26.553 0.024450 \n", "8 2026-05-06 06:07:48.250 2026-05-06 05:59:42.945 0.001276 \n", "9 2026-05-06 06:06:17.697 2026-05-06 06:04:50.911 0.041605 \n", "10 2026-05-06 06:07:45.356 2026-05-06 06:07:03.101 0.004379 \n", "11 2026-05-06 06:07:46.657 2026-05-06 06:07:51.637 0.000999 \n", "12 2026-05-06 06:07:46.530 2026-05-06 06:07:45.513 0.003199 \n", "\n", " buy_ratio_ext buy_ratio_ast \n", "0 -0.001181 0.001181 \n", "1 0.000299 -0.000299 \n", "2 0.000131 -0.000131 \n", "3 0.001051 -0.001051 \n", "4 0.000116 -0.000116 \n", "5 0.000211 -0.000211 \n", "6 0.000210 -0.000210 \n", "7 0.004767 -0.004767 \n", "8 -0.000742 0.000742 \n", "9 -0.000099 0.000099 \n", "10 0.000212 -0.000212 \n", "11 0.000097 -0.000097 \n", "12 0.000013 -0.000013 \n", "\n", "[13 rows x 35 columns]" ] }, "execution_count": 170, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_best_fr_rate" ] }, { "cell_type": "code", "execution_count": 171, "id": "bbad7d21", "metadata": {}, "outputs": [], "source": [ "### Candles for Midpoint Dispersion ###\n", "# Aster\n", "\n", "\n", "\n", "symbol = 'HYPEUSDT'\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 = 'HYPE-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": 172, "id": "799ffa9c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.float64(0.00013075388140060262)" ] }, "execution_count": 172, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_candles_comb['med_ratio_aster_over_extend'].median()" ] }, { "cell_type": "code", "execution_count": 184, "id": "75ac86aa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.float64(0.00015347678748833804)" ] }, "execution_count": 184, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_candles_comb.iloc[-60:]['med_ratio_aster_over_extend'].median()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "beff570b", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 173, "id": "9eb589c7", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'current_last_px_ratio'", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/py_313/lib/python3.13/site-packages/pandas/core/indexes/base.py:3641\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 3640\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3641\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_engine\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget_loc\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mcasted_key\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 3642\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:168\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m168\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n", "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:197\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m197\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n", "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7668\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m7668\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n", "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7676\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m7676\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n", "\u001b[31mKeyError\u001b[39m: 'current_last_px_ratio'", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[173]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m df_candles_comb[\u001b[33m'current_last_px_ratio'\u001b[39m].median()\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/py_313/lib/python3.13/site-packages/pandas/core/frame.py:4378\u001b[39m, in \u001b[36mDataFrame.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 4374\u001b[39m \n\u001b[32m 4375\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_single_key:\n\u001b[32m 4376\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m self.columns.nlevels > \u001b[32m1\u001b[39m:\n\u001b[32m 4377\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m self._getitem_multilevel(key)\n\u001b[32m-> \u001b[39m\u001b[32m4378\u001b[39m indexer = self.columns.get_loc(key)\n\u001b[32m 4379\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[32m 4380\u001b[39m indexer = [indexer]\n\u001b[32m 4381\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/py_313/lib/python3.13/site-packages/pandas/core/indexes/base.py:3648\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 3643\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m 3644\u001b[39m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc.Iterable)\n\u001b[32m 3645\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[32m 3646\u001b[39m ):\n\u001b[32m 3647\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3648\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m 3649\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[32m 3650\u001b[39m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[32m 3651\u001b[39m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[32m 3652\u001b[39m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[32m 3653\u001b[39m \u001b[38;5;28mself\u001b[39m._check_indexing_error(key)\n", "\u001b[31mKeyError\u001b[39m: 'current_last_px_ratio'" ] } ], "source": [ "df_candles_comb['current_last_px_ratio'].median()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 174, "id": "0182370c", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "variable=med_ratio_aster_over_extend
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value=%{y}", "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": [ "2026-05-05T06:10:00", "2026-05-05T06:11:00", "2026-05-05T06:12:00", "2026-05-05T06:13:00", "2026-05-05T06:14:00", "2026-05-05T06:15:00", "2026-05-05T06:16:00", "2026-05-05T06:17:00", "2026-05-05T06:18:00", "2026-05-05T06:19:00", "2026-05-05T06:20:00", "2026-05-05T06:21:00", "2026-05-05T06:22:00", "2026-05-05T06:23:00", "2026-05-05T06:24:00", "2026-05-05T06:25:00", "2026-05-05T06:26:00", "2026-05-05T06:27:00", "2026-05-05T06:28:00", "2026-05-05T06:29:00", "2026-05-05T06:30:00", "2026-05-05T06:31:00", "2026-05-05T06:32:00", "2026-05-05T06:33:00", "2026-05-05T06:34:00", "2026-05-05T06:35:00", "2026-05-05T06:36:00", "2026-05-05T06:37:00", "2026-05-05T06:38:00", "2026-05-05T06:39:00", "2026-05-05T06:40:00", "2026-05-05T06:41:00", 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oRUCHFtnO9+tFQNnO91Pj7UVAAiuHFtnuRUBKDAIrh+5FQKJFtvP97EVA6iYxCKzsRUCmm8QgsOpFQP7UeOkm6UVA1XjpJjHoRUB46SYxCOhFQCuHFtnO50VA16NwPQrnRUDTTWIQWOlFQObQItv57kVAz/dT46XvRUBEi2zn+/FFQG8Sg8DK8UVAGi/dJAbtRUAUrkfheuxFQHnpJjEI5EVArBxaZDvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCe76fGS99FQJ7vp8ZL30VAnu+nxkvfRUCgGi/dJN5FQKAaL90k3kVAoBov3STeRUCe76fGS99FQNNNYhBY5UVA4E+Nl27qRUC6SQwCK+dFQD4K16Nw4UVA7nw/NV7iRUD4U+Olm+RFQAIrhxbZ5kVAlBgEVg7pRUCiRbbz/exFQNv5fmq87EVAy6FFtvPpRUD8qfHSTeJFQJqZmZmZ2UVAiUFg5dDWRUCS7Xw/NdJFQARWDi2y2UVAeekmMQjcRUBzaJHtfNtFQG3n+6nx2kVAbef7qfHaRUBt5/up8dpFQG3n+6nx2kVAbef7qfHaRUBt5/up8dpFQG3n+6nx2kVAbef7qfHaRUBt5/up8dpFQG3n+6nx2kVAbef7qfHaRUBt5/up8dpFQG3n+6nx2kVAbef7qfHaRUBt5/up8dpFQG3n+6nx2kVAbef7qfHaRUBeukkMAttFQFCNl24S20VAUI2XbhLbRUBQjZduEttFQFCNl24S20VAUI2XbhLbRUBQjZduEttFQFCNl24S20VAUI2XbhLbRUBQjZduEttFQFCNl24S20VAUI2XbhLbRUBQjZduEttFQFCNl24S20VAUI2XbhLbRUBQjZduEttFQFCNl24S20VAUI2XbhLbRUBQjZduEttFQFCNl24S20VAUI2XbhLbRUBQjZduEttFQFCNl24S20VAUI2XbhLbRUBQjZduEttFQFCNl24S20VAUI2XbhLbRUBQjZduEttFQFCNl24S20VAUI2XbhLbRUBQjZduEttFQFCNl24S20VAUI2XbhLbRUBQjZduEttFQFCNl24S20VAUI2XbhLbRUBQjZduEttFQFCNl24S20VAUI2XbhLbRUBQjZduEttFQARWDi2y2UVAx0s3iUHYRUDHSzeJQdhFQMdLN4lB2EVAx0s3iUHYRUDHSzeJQdhFQMdLN4lB2EVAx0s3iUHYRUDHSzeJQdhFQMdLN4lB2EVAx0s3iUHYRUDHSzeJQdhFQMdLN4lB2EVAx0s3iUHYRUDHSzeJQdhFQMdLN4lB2EVAx0s3iUHYRUDHSzeJQdhFQMdLN4lB2EVAx0s3iUHYRUDHSzeJQdhFQMdLN4lB2EVAx0s3iUHYRUDHSzeJQdhFQMdLN4lB2EVAx0s3iUHYRUDHSzeJQdhFQMdLN4lB2EVAx0s3iUHYRUDHSzeJQdhFQMdLN4lB2EVAx0s3iUHYRUDHSzeJQdhFQKabxCCw2kVAhetRuB7dRUCF61G4Ht1FQIXrUbge3UVAhetRuB7dRUCF61G4Ht1FQGQ730+N+0VA7nw/NV4SRkDIdr6fGhNGQHWTGARWFkZA7FG4HoUTRkApXI/C9RBGQCCwcmiRDUZAqMZLN4kFRkBMN4lBYAVGQO58PzVeBkZAexSuR+ECRkCDwMqhRf5FQESLbOf7+UVA6iYxCKz4RUB1kxgEVvZFQHa+nxov9UVAgZVDi2zzRUDqJjEIrOxFQKwcWmQ740VAxSCwcmjlRUAK16NwPepFQKabxCCw7kVAAAAAAADsRUDsUbgehetFQJhuEoPA6kVAGy/dJAbtRUCuR+F6FO5FQBxaZDvf80VALbKd76f2RUD6fmq8dPdFQEJg5dAi90VAwvUoXI/2RUDkpZvEIPhFQCuHFtnO90VAarx0kxj4RUCJQWDl0PZFQNejcD0K90VALbKd76f2RUDGSzeJQfRFQLByaJHt9EVAI9v5fmr4RUAEVg4tsvlFQAwCK4cW/UVAYhBYObT8RUDXo3A9CvtFQI6XbhKD+EVAqvHSTWL4RUCoxks3ifVFQDEIrBxa9EVAIbByaJH1RUDl0CLb+fZFQPLSTWIQ+EVA8KfGSzf5RUAW2c73U/tFQFg5tMh2/kVAyXa+nxoDRkCLbOf7qQVGQLx0kxgECkZAukkMAisLRkCF61G4Hg1GQH0/NV66GUZAy6FFtvMhRkC28/3UeB1GQGIQWDm0IEZAxks3iUEgRkCBlUOLbCdGQPCnxks3JUZA+n5qvHQrRkBeukkMAi9GQOj7qfHSLUZAZmZmZmYqRkCHFtnO9ytGQMZLN4lBKEZA/Knx0k0mRkCmm8QgsCpGQLTIdr6fKkZA+FPjpZswRkBcj8L1KCxGQCPb+X5qMEZAd76fGi8tRkBuEoPAyilGQOomMQisJEZAUI2XbhIjRkA+CtejcCFGQMDKoUW2H0ZAnu+nxksfRkACK4cW2SJGQC2yne+nIkZAsp3vp8YfRkB6FK5H4RZGQFpkO99PFUZAz/dT46UfRkCwcmiR7SBGQJHtfD81GkZA6Pup8dIZRkA0MzMzMx9GQDMzMzMzI0ZAj8L1KFwjRkCYbhKDwCJGQPhT46WbJEZAGi/dJAYlRkDsUbgehSNGQEJg5dAiI0ZAokW28/0kRkD+1HjpJilGQA4tsp3vK0ZA6Pup8dItRkDNzMzMzCxGQGiR7Xw/MUZAQDVeukk0RkBMN4lBYDVGQARWDi2yOUZARIts5/s5RkC0yHa+nzpGQGZmZmZmOkZAQDVeukk4RkASg8DKoTVGQLgehetRNEZAMzMzMzMzRkDwp8ZLNzFGQP7UeOkmMUZA4E+Nl24uRkBeukkMAitGQNV46SYxKEZASOF6FK4nRkDjpZvEIChGQJ7vp8ZLK0ZA1XjpJjEwRkBqvHSTGDRGQNV46SYxNEZAAAAAAAA0RkAv3SQGgTFGQLKd76fGM0ZA8tJNYhA0RkBcj8L1KDRGQEJg5dAiM0ZAgZVDi2wzRkCcxCCwcjRGQAaBlUOLNEZAzMzMzMw0RkBcj8L1KDRGQLgehetRNEZAzMzMzMw0RkCQwvUoXDNGQOj7qfHSMUZA/tR46SYxRkDdJAaBlTNGQFyPwvUoNEZA3SQGgZUzRkDVeOkmMTRGQL6fGi/dNEZAgZVDi2wvRkBSuB6F6ylGQIgW2c73J0ZAMzMzMzMnRkCyne+nxiNGQNnO91PjIUZA9P3UeOkiRkAMAiuHFiVGQHe+nxovJUZAOrTIdr4fRkAnMQisHBpGQBkEVg4tFkZAeekmMQgURkCJQWDl0BJGQGIQWDm0FEZA3SQGgZUTRkDufD81XhJGQIlBYOXQEkZAKVyPwvUURkBYObTIdhJGQFK4HoXrEUZAdJMYBFYWRkByaJHtfBdGQO58PzVeEkZA/Knx0k0SRkBANV66SRhGQMh2vp8aG0ZAI9v5fmogRkDZzvdT4yFGQFYOLbKdH0ZAexSuR+EeRkA730+Nlx5GQEa28/3UIEZA001iEFglRkCoxks3iSVGQLKd76fGI0ZAAiuHFtkmRkDJdr6fGiNGQKjGSzeJIUZASOF6FK4jRkB46SYxCCRGQOj7qfHSJUZAtvP91HglRkBcj8L1KCRGQN4kBoGVI0ZApHA9CtcjRkB56SYxCCRGQITAyqFFIkZAmG4Sg8AiRkD4U+OlmyRGQIpBYOXQJkZASOF6FK4nRkAUrkfheiRGQHNoke18G0ZAdZMYBFYWRkAUrkfhehhGQBov3SQGGUZAWmQ7308VRkDAyqFFtg9GQL6fGi/dCEZABFYOLbIBRkACK4cW2f5FQEw3iUFg+UVArBxaZDv7RUC28/3UeAVGQI6XbhKD+EVAlBgEVg79RUBEi2zn+wFGQLbz/dR4CUZAsHJoke0QRkBEi2zn+xVGQOomMQisGEZA4noUrkcZRkCe76fGSxdGQJqZmZmZFUZABFYOLbIRRkC0yHa+nxJGQPT91HjpDkZA9ihcj8INRkD4U+OlmxBGQGiR7Xw/EUZAEFg5tMgSRkDIdr6fGhNGQMDKoUW2D0ZACtejcD0SRkBaZDvfTxFGQCQGgZVDE0ZAxks3iUEURkCsHFpkOxNGQOSlm8QgFEZA0SLb+X4WRkC8dJMYBBpGQNAi2/l+GkZAZmZmZmYeRkAv3SQGgSFGQMh2vp8aI0ZAUI2XbhIjRkAEVg4tsiFGQEa28/3UIEZAvp8aL90gRkBYObTIdiJGQIlBYOXQJkZAppvEILAmRkArhxbZzidGQEa28/3UKEZAfT81XropRkDEILByaClGQNnO91PjKUZAmG4Sg8AqRkCsHFpkOy9GQMQgsHJoMUZAVOOlm8Q0RkAW2c73UzdGQAAAAAAANEZAmG4Sg8AyRkDO91PjpS9GQEoMAiuHKkZAnMQgsHIoRkCe76fGSydGQLgehetRKEZAWmQ7308pRkDo+6nx0ilGQH9qvHSTJEZAjZduEoMkRkCoxks3iSVGQEa28/3UJEZABoGVQ4soRkDgT42XbipGQIKVQ4tsK0ZAOIlBYOUsRkDufD81XjZGQHA9CtejQEZA001iEFhFRkAW2c73U0dGQBkEVg4tRkZAHoXrUbhCRkB7FK5H4UZGQOj7qfHSSUZAjGzn+6lJRkCEwMqhRUpGQGDl0CLbSUZAuB6F61FERkCcxCCwckBGQClcj8L1QEZAAiuHFtlCRkBYObTIdj5GQCcxCKwcOkZATDeJQWA5RkDm0CLb+TpGQLgehetROEZA+n5qvHQ7RkCJQWDl0DpGQLbz/dR4NUZAvHSTGAQuRkBQjZduEidGQBBYObTIJkZAoBov3SQmRkCHFtnO9x9GQJqZmZmZIUZAJzEIrBweRkDEILByaBlGQDq0yHa+F0ZAxCCwcmgVRkBoke18PxFGQHsUrkfhDkZA8tJNYhAQRkCoxks3iRFGQI/C9ShcE0ZArBxaZDsTRkBzaJHtfA9GQFg5tMh2EkZAgZVDi2wXRkAfhetRuBpGQA4tsp3vG0ZA30+Nl24aRkAAAAAAABxGQBBYObTIIkZAukkMAisnRkCmm8QgsCZGQAwCK4cWJUZArkfhehQmRkDRItv5fiZGQFTjpZvEKEZAku18PzUmRkACK4cW2R5GQGDl0CLbGUZArkfhehQaRkACK4cW2RpGQBbZzvdTH0ZAfT81XrohRkDo+6nx0iVGQArXo3A9KkZAsHJoke0oRkCNl24SgyhGQJqZmZmZKUZAVOOlm8QoRkBkO99PjSdGQH9qvHSTKEZAeekmMQgsRkAhsHJokS1GQHjpJjEILEZAsHJoke0oRkCsHFpkOydGQCCwcmiRJUZAwvUoXI8mRkCuR+F6FCpGQJhuEoPAKkZAukkMAisvRkDsUbgehTNGQDDdJAaBMUZAlkOLbOczRkDjpZvEIDRGQHe+nxovMUZAokW28/00RkAcWmQ73zdGQDDdJAaBOUZAjGzn+6k5RkAQWDm0yDZGQJhuEoPAMkZAQmDl0CIvRkA+CtejcClGQJZDi2znI0ZAqMZLN4khRkBcj8L1KCBGQESLbOf7HUZAjZduEoMcRkA4iUFg5RhGQDiJQWDlFEZABoGVQ4sQRkAMAiuHFglGQAAAAAAACEZAcmiR7XwLRkAshxbZzg9GQITAyqFFFkZAbOf7qfEWRkA830+NlxZGQHnpJjEIGEZAexSuR+EaRkC28/3UeB1GQOxRuB6FG0ZAQDVeukkkRkCgGi/dJCJGQM73U+OlH0ZAJQaBlUMjRkBg5dAi2yFGQFCNl24SG0ZAObTIdr4XRkDeJAaBlRdGQFCNl24SF0ZAH4XrUbgSRkCiRbbz/QxGQIgW2c73B0ZAHVpkO98HRkBmZmZmZgZGQNnO91PjAUZAuB6F61EARkDsUbgehf9FQDMzMzMz/0VAPgrXo3ABRkBqvHSTGABGQF66SQwC/0VAyHa+nxr7RUBeukkMAvdFQBfZzvdT70VAWDm0yHbuRUD2KFyPwu1FQHNoke1870VAAAAAAADwRUCQwvUoXO9FQMh2vp8a70VAbOf7qfHqRUBSuB6F6+lFQNR46SYx6EVArkfhehTmRUD4U+Olm+RFQLgehetR4EVAQDVeukncRUA830+Nl9pFQGq8dJMY3EVAku18PzXeRUDdJAaBld9FQFyPwvUo3EVAK4cW2c7TRUBGtvP91NBFQEA1XrpJzEVA16NwPQrPRUBOYhBYOdBFQMh2vp8az0VAke18PzXSRUCgGi/dJNZFQA4tsp3v10VAqvHSTWLYRUBYObTIdtZFQDVeukkM1kVAVOOlm8TURUBuEoPAytVFQLgehetR1EVAH4XrUbjSRUAGgZVDi9BFQLx0kxgE0kVAL90kBoHRRUAGgZVDi9RFQBBYObTI1kVAXrpJDALXRUBGtvP91NhFQEa28/3U3EVAWDm0yHbaRUAyCKwcWthFQH0/NV662UVAYhBYObTYRUBI4XoUrttFQBov3SQG4UVA5KWbxCDkRUCS7Xw/NeJFQP7UeOkm4UVAQDVeuknkRUDGSzeJQeRFQHSTGARW4kVA7FG4HoXjRUAv3SQGgeVFQHWTGARW5kVAMzMzMzPjRUAUrkfheuRFQDzfT42X5kVAku18PzXqRUAkBoGVQ+tFQEA1XrpJ7EVA1XjpJjHsRUB6FK5H4epFQMDKoUW260VAlBgEVg7pRUBWDi2yneNFQB6F61G44kVA7nw/NV7iRUDgT42XbuZFQN9PjZdu5kVAQDVeuknkRUBGtvP91ORFQBSuR+F65EVASgwCK4fiRUCgGi/dJOJFQLgehetR4EVA30+Nl27iRUB46SYxCOhFQI2XbhKD6EVAObTIdr7rRUBcj8L1KOxFQLgehetR6EVARIts5/vlRUD4U+Olm+RFQPYoXI/C4UVAGARWDi3iRUAgsHJokeFFQDMzMzMz30VA5tAi2/naRUBcj8L1KNRFQKabxCCw0kVAsp3vp8bTRUAAAAAAANxFQJ7vp8ZL30VAku18PzXeRUCgGi/dJN5FQC/dJAaB3UVAqMZLN4ndRUAK16NwPdpFQEJg5dAi10VAarx0kxjYRUB2vp8aL9lFQLTIdr6f2kVAFK5H4XrYRUAOLbKd79dFQF66SQwC10VACKwcWmTXRUAW2c73U9dFQNV46SYx2EVA16NwPQrbRUDl0CLb+dpFQPp+arx020VApHA9CtfbRUAtsp3vp9pFQKwcWmQ720VAI9v5fmrcRUDb+X5qvOBFQFpkO99P5UVA5KWbxCDoRUAxCKwcWuxFQDiJQWDl8EVAnu+nxkvvRUAv3SQGgfFFQPLSTWIQ9EVAarx0kxj0RUAehetRuPJFQNV46SYx9EVAMzMzMzPzRUDazvdT4/FFQIgW2c7380VA9P3UeOn2RUD6fmq8dPdFQBgEVg4t9kVAgZVDi2z3RUD4U+Olm/RFQG4Sg8DK9UVA9P3UeOn2RUCQwvUoXPNFQBSuR+F67EVAWmQ730/tRUCoxks3ie1FQN4kBoGV70VAyqFFtvPxRUDNzMzMzPRFQLx0kxgE9kVACKwcWmT3RUCyne+nxvdFQJZDi2zn90VASOF6FK73RUCS7Xw/NfpFQCcxCKwc+kVASOF6FK77RUCe76fGS/tFQFYOLbKd+0VAXI/C9Sj8RUAAAAAAAPxFQJZDi2zn+0VA5tAi2/n6RUCS7Xw/NfpFQO58PzVe+kVAdJMYBFb6RUA6tMh2vvtFQKRwPQrX+0VAku18PzX6RUCKQWDl0PpFQIpBYOXQ+kVA6Pup8dL5RUDNzMzMzPhFQFyPwvUo+EVAqvHSTWL4RUD4U+Olm/hFQJDC9Shc+0VAMQisHFr8RUBU46WbxPxFQCPb+X5q/EVAqvHSTWL8RUDqJjEIrPxFQDeJQWDl/EVAFK5H4Xr8RUAnMQisHP5FQIPAyqFF/kVALbKd76f+RUBs5/up8f5FQPp+arx0/0VAbOf7qfH+RUDsUbgehf9FQE5iEFg5AEZAfT81Xrr9RUCDwMqhRfpFQFg5tMh28kVAsHJoke3sRUD6fmq8dOdFQKabxCCw5kVAcD0K16PoRUBeukkMAudFQMdLN4lB6EVAoBov3STiRUBCYOXQIttFQPhT46Wb2EVAItv5fmrYRUBU46WbxNhFQCcxCKwc0kVAJQaBlUPPRUDm0CLb+dZFQIPAyqFF3kVAFtnO91PfRUBg5dAi2+FFQKJFtvP95EVAdJMYBFbmRUA730+Nl+ZFQOF6FK5H6UVABoGVQ4vsRUD+1HjpJu1FQDq0yHa+60VA30+Nl27qRUDufD81Xu5FQB+F61G47kVA/Knx0k3yRUArhxbZzvNFQGzn+6nx9kVA6iYxCKz4RUDJdr6fGvdFQLgehetR+EVApHA9Ctf3RUCOl24Sg/hFQNEi2/l++kVAXrpJDAL7RUDufD81Xv5FQJhuEoPA+kVApHA9Ctf3RUBwPQrXo/RFQJzEILBy9EVAgZVDi2z3RUDD9Shcj/pFQObQItv5+kVAAAAAAAD8RUDFILByaP1FQK5H4XoU/kVAg8DKoUX+RUBwPQrXo/xFQNV46SYx/EVAUI2XbhL/RUAj2/l+agBGQE5iEFg5AEZAlBgEVg4BRkDZzvdT4wFGQBKDwMqhBUZAdJMYBFYKRkAAAAAAAAxGQFyPwvUoDEZAUrgehesJRkA830+NlwpGQFYOLbKdC0ZA001iEFgNRkACK4cW2Q5GQK5H4XoUEkZAuB6F61EQRkBs5/up8RJGQNV46SYxFEZAxks3iUEURkAGgZVDixBGQPYoXI/CCUZA9P3UeOkCRkDwp8ZLN/1FQDeJQWDl/EVAmG4Sg8D+RUB3vp8aLwFGQAIrhxbZAkZAg8DKoUUCRkDIdr6fGgNGQFg5tMh2AkZAarx0kxgERkBzaJHtfANGQFTjpZvEBEZADAIrhxYJRkCEwMqhRQpGQG4Sg8DKCUZA6Pup8dIJRkBU46WbxAxGQOSlm8QgEEZA3SQGgZUTRkBeukkMAhNGQLKd76fGE0ZAQmDl0CITRkDfT42XbhZGQI6XbhKDFEZA1XjpJjEURkCmm8QgsBJGQBbZzvdTE0ZADAIrhxYVRkAQWDm0yBJGQKAaL90kEkZA+n5qvHQLRkDqJjEIrAhGQL6fGi/dCEZAbOf7qfEGRkC4HoXrUQRGQOomMQisBEZAvHSTGAQCRkAj2/l+avxFQAaBlUOL+EVAYhBYObT0RUDO91PjpfNFQM/3U+Ol80VApHA9CtfzRUAMAiuHFvFFQNrO91Pj7UVA5/up8dLtRUCq8dJNYuxFQJZDi2zn60VA0CLb+X7qRUCe76fGS+dFQDeJQWDl6EVA4E+Nl27qRUBI4XoUrutFQLByaJHt7EVArBxaZDvrRUAv3SQGgelFQESLbOf76UVAeekmMQjsRUB/arx0k+xFQLx0kxgE6kVA9ihcj8LpRUCcxCCwcuhFQI6XbhKD6EVA9ihcj8LpRUB56SYxCOxFQJD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"white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "xaxis": { "anchor": "y", "domain": [ 0, 0.94 ] }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Price" } }, "yaxis2": { "anchor": "x", "overlaying": "y", "side": "right", "title": { "text": "Ratio" } } } } }, "metadata": {}, "output_type": "display_data" } ], "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": 56, "id": "fada6f1d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.float64(0.000125082440699531)" ] }, "execution_count": 56, "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": 57, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "open_dt=%{x}
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"output_type": "display_data" } ], "source": [ "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": 58, "id": "11df4770", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.float64(0.000125082440699531)" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_candles_comb['med_ratio_aster_over_extend'].median()" ] }, { "cell_type": "code", "execution_count": 48, "id": "89cbf119", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.float64(0.0003062005613676533)" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_candles_comb['med_ratio_aster_over_extend'].median()" ] }, { "cell_type": "code", "execution_count": 49, "id": "f83feeda", "metadata": {}, "outputs": [], "source": [ "# df_mp_by_hour = 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"ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "open_dt" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "volume_ast_sum" } } } } }, "metadata": {}, "output_type": "display_data" } ], "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": 193, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'symbol_ast': 'DOGEUSDT', 'symbol_extended': 'DOGE-USD'}" ] }, "execution_count": 193, "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": 195, "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": 199, "id": "185c04e9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 199, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from decimal import Decimal\n", "Decimal('10.00') > 0" ] }, { "cell_type": "code", "execution_count": 196, "id": "b7a08468", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Perpetual_Exchange(lh_asset='DOGE', rh_asset='USD', symbol='DOGE-USD', symbol_asset_separator='-', mult=50, initial_funding_rate=5.4e-05, min_price=1e-05, min_order_size=100.0, min_lot_size=10.0, min_notional=0.0, buy_ratio=0, notional_obj={}, notional_position=0, unrealized_pnl=0)" ] }, "execution_count": 196, "metadata": {}, "output_type": "execute_result" } ], "source": [ "EXTEND" ] }, { "cell_type": "code", "execution_count": 202, "id": "ca6b3810", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Decimal('-4.0')" ] }, "execution_count": 202, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Decimal(str('-2614')) % Decimal('10.0')" ] }, { "cell_type": "code", "execution_count": 204, "id": "fd1838f4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Decimal('-2610.0')" ] }, "execution_count": 204, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tail = Decimal(str('-2614'))\n", "min_lot_size = Decimal('10.0')\n", "tail = tail - (tail % min_lot_size)\n", "tail" ] }, { "cell_type": "code", "execution_count": null, "id": "dbfe6a5d", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "8dcc42a1", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "edc30ffb", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "cdcda9a8", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "py_313", "language": 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