example_downloads_for_es_gc_cl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from typing import List\n",
    "\n",
    "import pandas as pd\n",
    "import pytz\n",
    "from ib_insync import IB, util\n",
    "\n",
    "from ib_insync_options.etl.ibkr_etl import MarketDataType\n",
    "from ib_insync_options.utils.date_utils import calc_effective_date_of_dt\n",
    "from ib_insync_options.utils.networking import get_ibkr_host_ip\n",
    "from ib_insync_options.etl.ibkr_etl import IbkrFutureOptionData, IbkrDownloader\n",
    "from ib_insync_options.etl.instrument_multiplexer import InstrumentMultiplexer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# required for jupyter notebook\n",
    "util.startLoop()\n",
    "ib = IB()\n",
    "\n",
    "host = get_ibkr_host_ip()\n",
    "ib.connect(host, 7496, clientId=3, timeout=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save(gd: IbkrFutureOptionData, df: pd.DataFrame):\n",
    "    \"\"\"\n",
    "    Save gd results/df to the ibkr cache.\n",
    "\n",
    "    ```\n",
    "    save(gd, gd.v.enrich_option_tickers_df_with_price)\n",
    "    ```\n",
    "\n",
    "    Args:\n",
    "        gd (IbkrFutureOptionData): _description_\n",
    "        df (pd.DataFrame): _description_\n",
    "\n",
    "    Returns:\n",
    "        _type_: _description_\n",
    "    \"\"\"\n",
    "    df = df[df.edt.notnull()].copy()\n",
    "\n",
    "    utc_now = datetime.now(pytz.utc)\n",
    "    mt_now = utc_now.astimezone(pytz.timezone(\"US/Mountain\"))\n",
    "    data_effective_date = calc_effective_date_of_dt(mt_now)\n",
    "    # data_effective_date_str: str = data_effective_date.strftime(\"%Y-%m-%d\")\n",
    "\n",
    "    assert len(df.symbol.unique()) == 1\n",
    "    ticker = df.symbol.unique()[0]\n",
    "\n",
    "    # NOTE\n",
    "    # _ibkr_cache_write - writes a table called ibkr_option_core_facet, in a database called ib_insync_options_dev\n",
    "    # f\"postgresql+psycopg2://postgres:@localhost:5432/ib_insync_options_dev\"\n",
    "    # if you need to change the db connection string, see ib_insync_options/etl/db_ops.py, gen_engine()\n",
    "\n",
    "    return InstrumentMultiplexer._ibkr_cache_write(\n",
    "        asset_class=gd.v.asset_category,\n",
    "        ticker=ticker,\n",
    "        effective_date=data_effective_date,\n",
    "        df=df,\n",
    "    )\n",
    "\n",
    "\n",
    "def command_execute_graph_download(\n",
    "    ib: IB,\n",
    "    market_data_type: MarketDataType = MarketDataType.LIVE,\n",
    "    symbol: str = \"GC\",\n",
    "    dte_max: int = 30,\n",
    "    pre_fetch_option_filters: List[dict] = None,\n",
    "):\n",
    "    \"\"\"\n",
    "    Download futures option data for a given underlying symbol.\n",
    "\n",
    "    Steps:\n",
    "    1. initialize the graph data object\n",
    "    2. initialize the underlying symbol and pre-fetch filters\n",
    "    3. initialize the ibkr downloader\n",
    "    4. fetch the option chain for the underlying\n",
    "    5. compute the option chain df\n",
    "    6. fetch the underlying df\n",
    "    7. enrich the option chain df with the underlying tickers\n",
    "    8. apply fetch filters\n",
    "    9. generate qualified futures options\n",
    "    10. fetch tickers for qualified futures options\n",
    "    11. generate option tickers df\n",
    "    12. enrich option tickers df with the underlying price (because IBKR will drop the underlying price)\n",
    "\n",
    "    Example,\n",
    "    ```\n",
    "    pre_fetch_option_filters_gc = [\n",
    "        {\"moneyness_gte\": 95.0},\n",
    "        {\"moneyness_lte\": 105.0},\n",
    "        {\"strike_modulus_eq\": 10.0},\n",
    "    ]\n",
    "    gd = command_execute_graph_download(\n",
    "        ib=ib,\n",
    "        symbol=\"GC\",\n",
    "        dte_max=30,\n",
    "        pre_fetch_option_filters=pre_fetch_option_filters_gc\n",
    "    )\n",
    "    save(gd, gd.v.enrich_option_tickers_df_with_price)\n",
    "    ```\n",
    "\n",
    "    Args:\n",
    "        ib (IB): ib_insync IB object\n",
    "        symbol (str, optional): underlying symbol. Defaults to \"GC\".\n",
    "        dte_max (int, optional): max DTE. Defaults to 30.\n",
    "        pre_fetch_option_filters (List[dict], optional): pre-fetch option filters. Defaults to None.\n",
    "        existing_graph_data (IbkrFutureOptionData, optional): existing graph data object. Defaults to None.\n",
    "\n",
    "    Returns:\n",
    "        IbkrFutureOptionData: graph data object\n",
    "    \"\"\"\n",
    "    # graph data object\n",
    "    gd = IbkrFutureOptionData()\n",
    "\n",
    "    # initialize the underlying symbol and pre-fetch filters\n",
    "    gd.add_node(\"underlying_symbol\", value=symbol)\n",
    "    gd.add_node(\n",
    "        name=\"pre_fetch_option_filters\",\n",
    "        value=pre_fetch_option_filters,\n",
    "    )\n",
    "    gd.add_node(\"dte_max\", value=dte_max)\n",
    "    gd.compute_all()\n",
    "\n",
    "    # initialize the ibkr downloader\n",
    "    ibkr_downloader = IbkrDownloader(ib, market_data_type=market_data_type)\n",
    "\n",
    "    # fetch the option chain for the underlying\n",
    "    raw_option_chain = ibkr_downloader.fetch_option_chain_for_underlying(symbol)\n",
    "    gd.add_node(\"raw_option_chain\", value=raw_option_chain)\n",
    "    gd.compute(\"option_chain_df\")\n",
    "\n",
    "    # compute the option chain df\n",
    "    gd.compute_all()\n",
    "\n",
    "    xdf = gd.v.filter_option_chain_by_max_expiry_date\n",
    "    underlying_df = ibkr_downloader.fetch_underlying_df(option_chain_df=xdf)\n",
    "    gd.add_node(\"underlying_df\", value=underlying_df)\n",
    "    gd.compute(\"underlying_df_calc_price\")\n",
    "    gd.compute(\"enrich_option_chain_df_with_underlying_tickers\")\n",
    "\n",
    "    # apply fetch filters so only get the desired futures options\n",
    "    gd.compute(\"apply_fetch_filters\")\n",
    "\n",
    "    qualified_futures_options = ibkr_downloader.gen_qualified_futures_options(\n",
    "        option_chain_df=gd.v.apply_fetch_filters\n",
    "    )\n",
    "    gd.add_node(\"qualified_futures_options\", value=qualified_futures_options)\n",
    "\n",
    "    futures_option_tickers = ibkr_downloader.fetch_tickers_for_contracts(\n",
    "        qualified_futures_options\n",
    "    )\n",
    "    gd.add_node(\"futures_option_tickers\", value=futures_option_tickers)\n",
    "    df = ibkr_downloader.gen_option_tickers_df(tickers=futures_option_tickers)\n",
    "    gd.add_node(\"option_tickers_df\", value=df)\n",
    "\n",
    "    gd.compute(\"gen_qualified_fops_df\")\n",
    "\n",
    "    gd.compute(\"enrich_option_tickers_df_with_price\")\n",
    "\n",
    "    return gd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fetch_option_filters_es = [\n",
    "    {\"moneyness_gte\": 98.0},\n",
    "    {\"moneyness_lte\": 102.0},\n",
    "    {\"strike_modulus_eq\": 50.0},\n",
    "]\n",
    "gd = command_execute_graph_download(\n",
    "    ib=ib,\n",
    "    symbol=\"ES\",\n",
    "    dte_max=21,\n",
    "    pre_fetch_option_filters=fetch_option_filters_es,\n",
    "    # use frozen market data when market is closed\n",
    "    market_data_type=MarketDataType.FROZEN,\n",
    "    # use live market data when market is open\n",
    "    # market_data_type=MarketDataType.LIVE\n",
    ")\n",
    "\n",
    "save(gd, gd.v.enrich_option_tickers_df_with_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_fetch_option_filters_cl = [\n",
    "    {\"moneyness_gte\": 95.0},\n",
    "    {\"moneyness_lte\": 105.0},\n",
    "    {\"strike_modulus_eq\": 5.0},\n",
    "]\n",
    "gd = command_execute_graph_download(\n",
    "    ib=ib,\n",
    "    symbol=\"CL\",\n",
    "    dte_max=90,\n",
    "    pre_fetch_option_filters=pre_fetch_option_filters_cl,\n",
    "    market_data_type=MarketDataType.FROZEN,\n",
    ")\n",
    "save(gd, gd.v.enrich_option_tickers_df_with_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_fetch_option_filters_gc = [\n",
    "    {\"moneyness_gte\": 95.0},\n",
    "    {\"moneyness_lte\": 105.0},\n",
    "    {\"strike_modulus_eq\": 10.0},\n",
    "]\n",
    "gd = command_execute_graph_download(\n",
    "    ib=ib,\n",
    "    symbol=\"GC\",\n",
    "    dte_max=90,\n",
    "    pre_fetch_option_filters=pre_fetch_option_filters_gc,\n",
    "    market_data_type=MarketDataType.FROZEN,\n",
    ")\n",
    "save(gd, gd.v.enrich_option_tickers_df_with_price)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "finx-api-fop",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}