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Funding_Rate/engine_dispersion.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 183,
"id": "25697582",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from pprint import pprint\n",
"from sqlalchemy import create_engine, text\n",
"import plotly.express as px\n",
"import requests\n",
"import json\n",
"import time\n",
"from datetime import datetime\n",
"import plotly.graph_objects as go\n",
"from plotly.subplots import make_subplots\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 184,
"id": "cd9ecfde",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Connection successful\n"
]
}
],
"source": [
"### MYSQL ###\n",
"ENGINE = create_engine('mysql+pymysql://root:pwd@localhost/fund_rate')\n",
"try:\n",
" with ENGINE.connect() as conn:\n",
" print(\"Connection successful\")\n",
"except Exception as e:\n",
" print(f\"Connection failed: {e}\") "
]
},
{
"cell_type": "code",
"execution_count": 185,
"id": "60cc2366",
"metadata": {},
"outputs": [],
"source": [
"def funding_rate_hist_extend(market: str, startTime: int | None = None, endTime: int | None = None ):\n",
" if startTime is None:\n",
" startTime = int(round(datetime.now().timestamp() * 1000)) - int(1000 * 60 * 60 * 24 * 30)\n",
" if endTime is None:\n",
" endTime = int(round(datetime.now().timestamp() * 1000))\n",
" \n",
" r = requests.get(f\"https://api.starknet.extended.exchange/api/v1/info/{market}/funding?startTime={startTime}&endTime={endTime}\")\n",
" return json.loads(r.text)"
]
},
{
"cell_type": "code",
"execution_count": 186,
"id": "55ae5271",
"metadata": {},
"outputs": [],
"source": [
"def ratio_trades_to_midbars(df: pd.DataFrame, ts_col: str, is_buyer_maker_col: str, price_col: str = 'price', qty_col: str = 'qty', freq: str ='s') -> tuple[pd.DataFrame, pd.DataFrame]:\n",
" df_maker = df.loc[df[is_buyer_maker_col]==1, :].groupby(pd.Grouper(key=ts_col, freq=freq)).agg({'price':['min','median','max'], 'qty':'sum'}) # Resting on the bid (buyer is maker): ASK [Taker] [Seller] -- Crossed into --> BID [Maker] [Buyer]\n",
" df_taker = df.loc[df[is_buyer_maker_col]==0, :].groupby(pd.Grouper(key=ts_col, freq=freq)).agg({'price':['min','median','max'], 'qty':'sum'}) # Resting on the ask (seller is maker): BID [Taker] [Buyer] -- Crossed into --> ASK [Maker] [Seller]\n",
" df_maker.columns = ['_'.join(col).strip() for col in df_maker.columns.to_flat_index()]\n",
" df_taker.columns = ['_'.join(col).strip() for col in df_taker.columns.to_flat_index()]\n",
" \n",
" return df_maker, df_taker"
]
},
{
"cell_type": "code",
"execution_count": 187,
"id": "2b7232e5",
"metadata": {},
"outputs": [],
"source": [
"def get_24hrs_midbars(table_name: str, freq='min', ts_col='timestamp_trade', is_buyer_maker_col='is_buyer_mkt_maker'):\n",
" df = pd.read_sql(\n",
" text(f'''\n",
" SELECT *\n",
" FROM {table_name}\n",
" '''),\n",
" con=ENGINE\n",
" )\n",
" df['timestamp_trade_dt'] = pd.to_datetime(df[ts_col], unit='ms')\n",
"\n",
" df_maker, df_taker = ratio_trades_to_midbars(\n",
" df=df,\n",
" freq=freq,\n",
" ts_col='timestamp_trade_dt',\n",
" is_buyer_maker_col=is_buyer_maker_col\n",
" )\n",
"\n",
" return df_maker.dropna(), df_taker.dropna()\n"
]
},
{
"cell_type": "code",
"execution_count": 188,
"id": "f1174bae",
"metadata": {},
"outputs": [],
"source": [
"df_fr = funding_rate_hist_extend(market='ETH-USD')\n",
"df_fr = pd.DataFrame(df_fr['data']).rename({'m':'market','f':'funding_rate','T':'timestamp'}, axis=1)\n",
"df_fr['timestamp_dt'] = pd.to_datetime(df_fr['timestamp'], unit='ms')"
]
},
{
"cell_type": "code",
"execution_count": 189,
"id": "068a54b6",
"metadata": {},
"outputs": [],
"source": [
"df_aster_trades_by_sec_maker, df_aster_trades_by_sec_taker = get_24hrs_midbars(\n",
" table_name='fr_aster_mkt_trades',\n",
")\n",
"\n",
"df_extend_trades_by_sec_maker, df_extend_trades_by_sec_taker = get_24hrs_midbars(\n",
" table_name='fr_extended_mkt_trades',\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 190,
"id": "2862e7e3",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "timestamp_trade_dt=%{x}<br>qty_sum=%{y}<extra></extra>",
"legendgroup": "",
"line": {
"color": "#636efa",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
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"orientation": "v",
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},
{
"cell_type": "code",
"execution_count": 192,
"id": "56f94403",
"metadata": {},
"outputs": [],
"source": [
"df_comb_trades_by_sec = df_aster_trades_by_sec_taker[['price_median']].merge(df_extend_trades_by_sec_maker[['price_median']], left_index=True, right_index=True, suffixes=('_aster','_extend'))"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {},
"outputs": [],
"source": [
"df_comb_trades_by_sec['ratio'] = df_comb_trades_by_sec['price_median_aster'] / df_comb_trades_by_sec['price_median_extend']"
]
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{
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"execution_count": 194,
"id": "663d036f",
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"source": [
"# df_comb_trades_by_sec = df_comb_trades_by_sec.reset_index().groupby(pd.Grouper(key='timestamp_trade_dt', freq='h')).agg({'price_median_aster': 'median', 'price_median_extend': 'median', 'ratio': 'median'}) # Resting on the ask (seller is maker): BID [Taker] [Buyer] -- Crossed into --> ASK [Maker] [Seller]"
]
},
{
"cell_type": "code",
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"id": "66c5a128",
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{
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"execution_count": 195,
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{
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"metadata": {},
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},
{
"cell_type": "code",
"execution_count": 197,
"id": "bf9af769",
"metadata": {},
"outputs": [],
"source": [
"min_time = max([round(df_comb_trades_by_sec.index.min().timestamp()*1000), int(round(df_fr['timestamp'].min()))])"
]
},
{
"cell_type": "code",
"execution_count": 198,
"id": "a0b4ab50",
"metadata": {},
"outputs": [],
"source": [
"df_comb_trades_by_sec_plot = df_comb_trades_by_sec.loc[df_comb_trades_by_sec.index.values.astype(np.int64) > min_time,:]\n",
"df_fr_plot = df_fr.loc[df_fr['timestamp'] > min_time,:]"
]
},
{
"cell_type": "code",
"execution_count": 199,
"id": "b724d368",
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},
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],
"source": [
"fig = go.Figure()\n",
"\n",
"# Add traces bound to different Y-axes\n",
"fig.add_trace(go.Scatter(x=df_comb_trades_by_sec_plot.index, y=df_comb_trades_by_sec_plot['ratio'], name=\"Ratio\")) # Funding Rate\n",
"fig.add_trace(go.Scatter(x=df_fr_plot['timestamp_dt'], y=df_fr_plot['funding_rate'], name=\"FR\", yaxis='y2')) # Ratio\n",
"# fig.add_trace(go.Scatter(x=[1, 2, 3], y=[400, 500, 600], name=\"Y3\", yaxis='y3')) # \n",
"\n",
"fig.update_layout(\n",
" yaxis=dict(title=\"Ratio\", side=\"right\"),\n",
" yaxis2=dict(title=\"Funding Rate\", side=\"left\", overlaying=\"y\",),\n",
" template='plotly_dark'\n",
" # yaxis3=dict(title=\"Tertiary Y (Right)\", overlaying=\"y\", side=\"right\", position=0.8)\n",
")\n",
"\n",
"fig.add_hline(\n",
" y=1, \n",
" line_dash=\"dot\", \n",
" line_color=\"white\",\n",
" label=dict(\n",
" text=\"^ Ratio_Buy\",\n",
" textposition=\"end\",\n",
" font=dict(size=16, color=\"white\")\n",
" )\n",
")\n",
"\n",
"# Create a range of hours\n",
"start_time = int(min_time)\n",
"end_time = (datetime.now().timestamp())\n",
"hours = pd.date_range(start=start_time, end=end_time, freq='h')\n",
"# Add a vertical line for each hour\n",
"for hour in hours:\n",
" fig.add_vline(x=hour.strftime('%Y-%m-%d %H:%M:%S'), \n",
" line=dict(color=\"gray\", width=1), \n",
" opacity=0.5)\n",
"fig.show()\n"
]
},
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