{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "eae7849d-bb6f-4016-879c-46b21353097e",
   "metadata": {},
   "source": [
    "# A EURUSD market for IRS, cross-currency and FX volatility\n",
    "\n",
    "In this notebook we demonstrate the code for *rateslib* to build:\n",
    "\n",
    "- local currency interest rate curves in EUR and USD from RFR swaps,\n",
    "- collateral curves accounting for the cross-currency basis and FX swap points,\n",
    "- volatility surface priced from FX volatility products.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8195cc4e-dcaf-492a-b3b0-218ac28550e3",
   "metadata": {},
   "source": [
    "### Input market data\n",
    "\n",
    "First things first we need market data for the interest rates curves and forward FX curves.\n",
    "This data was observed on 28th May 2024."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e25b47f0-de95-4f47-9de2-9e50c9799a05",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rateslib import *\n",
    "import numpy as np\n",
    "from pandas import DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e352b0b9-ff4b-4528-9237-2deba2fc0bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "fxr = FXRates({\"eurusd\": 1.0867}, settlement=dt(2024, 5, 30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3713f62-9278-41f6-a956-4a6a103776d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "mkt_data = DataFrame(\n",
    "    data=[['1w', 3.9035,5.3267,3.33,],\n",
    "          ['2w', 3.9046,5.3257,6.37,],\n",
    "          ['3w',3.8271,5.3232,9.83,],\n",
    "          ['1m',3.7817,5.3191,13.78,],\n",
    "          ['2m',3.7204,5.3232,30.04,],\n",
    "          ['3m',3.667,5.3185,45.85,-2.5],\n",
    "          ['4m',3.6252,5.3307,61.95,],\n",
    "          ['5m',3.587,5.3098,78.1,],\n",
    "          ['6m',3.5803,5.3109,94.25,-3.125],\n",
    "          ['7m',3.5626,5.301,110.82,],\n",
    "          ['8m',3.531,5.2768,130.45,],\n",
    "          ['9m',3.5089,5.2614,145.6,-7.25],\n",
    "          ['10m',3.4842,5.2412,162.05,],\n",
    "          ['11m',3.4563,5.2144,178,],\n",
    "          ['1y',3.4336,5.1936,None,-6.75],\n",
    "          ['15m',3.3412,5.0729,None,-6.75],\n",
    "          ['18m',3.2606,4.9694,None,-6.75],\n",
    "          ['21m',3.1897,4.8797,None,-7.75],\n",
    "          ['2y',3.1283,4.8022,None,-7.875],\n",
    "          ['3y',2.9254,4.535,None,-9],\n",
    "          ['4y',2.81,4.364,None,-10.125],\n",
    "          ['5y',2.7252,4.256,None,-11.125],\n",
    "          ['6y',2.6773,4.192,None,-12.125],\n",
    "          ['7y',2.6541,4.151,None,-13],\n",
    "          ['8y',2.6431,4.122,None,-13.625],\n",
    "          ['9y',2.6466,4.103,None,-14.25],\n",
    "          ['10y',2.6562,4.091,None,-14.875],\n",
    "          ['12y',2.6835,4.084,None,-16.125],\n",
    "          ['15y',2.7197,4.08,None,-17],\n",
    "          ['20y',2.6849,4.04,None,-16],\n",
    "          ['25y',2.6032,3.946,None,-12.75],\n",
    "          ['30y',2.5217,3.847,None,-9.5]],\n",
    "    columns=[\"tenor\", \"estr\", \"sofr\", \"fx_swap\", \"xccy\"],\n",
    ")\n",
    "mkt_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f91c7283-fb3d-434d-8b33-68a095e8606c",
   "metadata": {},
   "source": [
    "### Solving rates curves and FX forwards curve\n",
    "\n",
    "We will create all *Curves* and solve them all using the *Solver*. It is possible to solve everything simultaneously in a\n",
    "single *Solver* but this is less efficient than decoupling the known separable components, and using multiple *Solvers* in a\n",
    "dependency chain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d3dcc1a-a4e3-46c4-a32c-841a4820da89",
   "metadata": {},
   "outputs": [],
   "source": [
    "eur = Curve(\n",
    "    nodes={\n",
    "        dt(2024, 5, 28): 1.0,\n",
    "        **{add_tenor(dt(2024, 5, 30), _, \"F\", \"tgt\"): 1.0 for _ in mkt_data[\"tenor\"]}\n",
    "    },\n",
    "    calendar=\"tgt\",\n",
    "    interpolation=\"log_linear\",\n",
    "    convention=\"act360\",\n",
    "    id=\"estr\",\n",
    ")\n",
    "usd = Curve(\n",
    "    nodes={\n",
    "        dt(2024, 5, 28): 1.0,\n",
    "        **{add_tenor(dt(2024, 5, 30), _, \"F\", \"nyc\"): 1.0 for _ in mkt_data[\"tenor\"]}\n",
    "    },\n",
    "    calendar=\"nyc\",\n",
    "    interpolation=\"log_linear\",\n",
    "    convention=\"act360\",\n",
    "    id=\"sofr\",\n",
    ")\n",
    "eurusd = Curve(\n",
    "    nodes={\n",
    "        dt(2024, 5, 28): 1.0,\n",
    "        **{add_tenor(dt(2024, 5, 30), _, \"F\", \"tgt\"): 1.0 for _ in mkt_data[\"tenor\"]}\n",
    "    },\n",
    "    interpolation=\"log_linear\",\n",
    "    convention=\"act360\",\n",
    "    id=\"eurusd\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf83c99-896c-4974-831c-d29cb127b61a",
   "metadata": {},
   "source": [
    "With *Curves* created but not necessarily calibrated we can design the FXForwards market mapping:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4b3cc9c-4962-4a65-b934-35a02b5ed33a",
   "metadata": {},
   "outputs": [],
   "source": [
    "fxf = FXForwards(\n",
    "    fx_rates=fxr,\n",
    "    fx_curves={\"eureur\": eur, \"eurusd\": eurusd, \"usdusd\": usd}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "416dc324-69b0-438a-85be-7a40d1c68969",
   "metadata": {},
   "source": [
    "The *Instruments* used to solve the ESTR curve are ESTR swaps and the SOFR curve are SOFR swaps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "560268b8-b321-4614-9f0b-b8c0c99964b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "estr_swaps = [IRS(dt(2024, 5, 30), _, spec=\"eur_irs\", curves=\"estr\") for _ in mkt_data[\"tenor\"]]\n",
    "estr_rates = mkt_data[\"estr\"].tolist()\n",
    "labels = mkt_data[\"tenor\"].to_list()\n",
    "sofr_swaps =  [IRS(dt(2024, 5, 30), _, spec=\"usd_irs\", curves=\"sofr\") for _ in mkt_data[\"tenor\"]]\n",
    "sofr_rates = mkt_data[\"sofr\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dab3168e-26ef-4676-842e-8f04ac48461b",
   "metadata": {},
   "outputs": [],
   "source": [
    "eur_solver = Solver(\n",
    "    curves=[eur],\n",
    "    instruments=estr_swaps,\n",
    "    s=estr_rates,\n",
    "    fx=fxf,\n",
    "    instrument_labels=labels,\n",
    "    id=\"eur\",\n",
    ")\n",
    "usd_solver = Solver(\n",
    "    curves=[usd],\n",
    "    instruments=sofr_swaps,\n",
    "    s=sofr_rates,\n",
    "    fx=fxf,\n",
    "    instrument_labels=labels,\n",
    "    id=\"usd\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6343067d-fe16-4887-95bc-f36bc7fe187d",
   "metadata": {},
   "source": [
    "The cross currency curve use a combination of *FXSwaps* and *XCS*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f52f54e1-bad4-4a6c-82f6-98432e69442d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fxswaps = [FXSwap(dt(2024, 5, 30), _, pair=\"eurusd\", curves=[None, \"eurusd\", None, \"sofr\"]) for _ in mkt_data[\"tenor\"][0:14]]\n",
    "fxswap_rates = mkt_data[\"fx_swap\"][0:14].tolist()\n",
    "xcs = [XCS(dt(2024, 5, 30), _, spec=\"eurusd_xcs\", curves=[\"estr\", \"eurusd\", \"sofr\", \"sofr\"]) for _ in mkt_data[\"tenor\"][14:]]\n",
    "xcs_rates = mkt_data[\"xccy\"][14:].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "706c0dd6-60ad-405b-989c-837bb8e5f2a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "fx_solver = Solver(\n",
    "    pre_solvers=[eur_solver, usd_solver],\n",
    "    curves=[eurusd],\n",
    "    instruments=fxswaps + xcs,\n",
    "    s=fxswap_rates + xcs_rates,\n",
    "    fx=fxf,\n",
    "    instrument_labels=labels,\n",
    "    id=\"eurusd_xccy\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8dcfd5f-c601-4bdf-84de-124bbb083b67",
   "metadata": {},
   "source": [
    "## Solving an FX Vol Surface\n",
    "\n",
    "Next we will use the market FX volatility quotes to build a surface. These prices are all expressed in log-normal vol terms under normal market conventions and the instruments 1Y or less use spot unadjusted delta and those longer than 1y use forward undajusted delta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78a4c4bf-2ed1-4beb-b0f7-8db0e0b263e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "vol_data = DataFrame(\n",
    "    data=[\n",
    "        ['1w',4.535,-0.047,0.07,-0.097,0.252],\n",
    "        ['2w',5.168,-0.082,0.077,-0.165,0.24],\n",
    "        ['3w',5.127,-0.175,0.07,-0.26,0.233],\n",
    "        ['1m',5.195,-0.2,0.07,-0.295,0.235],\n",
    "        ['2m',5.237,-0.28,0.087,-0.535,0.295],\n",
    "        ['3m',5.257,-0.363,0.1,-0.705,0.35],\n",
    "        ['4m',5.598,-0.47,0.123,-0.915,0.422],\n",
    "        ['5m',5.776,-0.528,0.133,-1.032,0.463],\n",
    "        ['6m',5.92,-0.565,0.14,-1.11,0.49],\n",
    "        ['9m',6.01,-0.713,0.182,-1.405,0.645],\n",
    "        ['1y',6.155,-0.808,0.23,-1.585,0.795],\n",
    "        ['18m',6.408,-0.812,0.248,-1.588,0.868],\n",
    "        ['2y',6.525,-0.808,0.257,-1.58,0.9],\n",
    "        ['3y',6.718,-0.733,0.265,-1.45,0.89],\n",
    "        ['4y',7.025,-0.665,0.265,-1.31,0.885],\n",
    "        ['5y',7.26,-0.62,0.26,-1.225,0.89],\n",
    "        ['6y',7.508,-0.516,0.27,-0.989,0.94],\n",
    "        ['7y',7.68,-0.442,0.278,-0.815,0.975],\n",
    "        ['10y',8.115,-0.267,0.288,-0.51,1.035],\n",
    "        ['15y',8.652,-0.325,0.362,-0.4,1.195],\n",
    "        ['20y',8.651,-0.078,0.343,-0.303,1.186],\n",
    "        ['25y',8.65,-0.029,0.342,-0.218,1.178],\n",
    "        ['30y',8.65,0.014,0.341,-0.142,1.171],\n",
    "    ],\n",
    "    columns=[\"tenor\", \"atm\", \"25drr\", \"25dbf\", \"10drr\", \"10dbf\"]\n",
    ")\n",
    "vol_data[\"expiry\"] = [add_tenor(dt(2024, 5, 28), _, \"MF\", \"tgt\") for _ in vol_data[\"tenor\"]]\n",
    "vol_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7ad062f-8386-49a7-a8cb-333b577b9e1b",
   "metadata": {},
   "source": [
    "A *Surface* is defined by given expiries and delta grdipoints. All vol values are initially set to 5.0, and will be calibrated by the \n",
    "*Instruments*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca180225-c2d4-438a-9d11-f9d829d4ff92",
   "metadata": {},
   "outputs": [],
   "source": [
    "surface = FXDeltaVolSurface(\n",
    "    eval_date=dt(2024, 5, 28),\n",
    "    expiries=vol_data[\"expiry\"],\n",
    "    delta_indexes=[0.1, 0.25, 0.5, 0.75, 0.9],\n",
    "    node_values=np.ones((23, 5))*5.0,\n",
    "    delta_type=\"forward\",\n",
    "    id=\"eurusd_vol\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e773b098-7f41-41d9-8e9c-908c6cec81b6",
   "metadata": {},
   "source": [
    "Define the instruments and their rates for 1Y or less:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "459da8b9-413e-480a-9869-62f2189b9977",
   "metadata": {},
   "outputs": [],
   "source": [
    "fx_args = dict(\n",
    "    pair=\"eurusd\", \n",
    "    curves=[None, \"eurusd\", None, \"sofr\"], \n",
    "    calendar=\"tgt\", \n",
    "    delivery_lag=2, \n",
    "    payment_lag=2,\n",
    "    eval_date=dt(2024, 5, 28),\n",
    "    modifier=\"MF\",\n",
    "    premium_ccy=\"usd\",\n",
    "    vol=\"eurusd_vol\",\n",
    ")\n",
    "\n",
    "instruments_le_1y, rates_le_1y, labels_le_1y = [], [], []\n",
    "for row in range(11):\n",
    "    instruments_le_1y.extend([\n",
    "        FXStraddle(strike=\"atm_delta\", expiry=vol_data[\"expiry\"][row], delta_type=\"spot\", **fx_args),\n",
    "        FXRiskReversal(strike=[\"-25d\", \"25d\"], expiry=vol_data[\"expiry\"][row], delta_type=\"spot\", **fx_args),\n",
    "        FXBrokerFly(strike=[\"-25d\", \"atm_delta\", \"25d\"], expiry=vol_data[\"expiry\"][row], delta_type=\"spot\", **fx_args),\n",
    "        FXRiskReversal(strike=[\"-10d\", \"10d\"], expiry=vol_data[\"expiry\"][row], delta_type=\"spot\", **fx_args),\n",
    "        FXBrokerFly(strike=[\"-10d\", \"atm_delta\", \"10d\"], expiry=vol_data[\"expiry\"][row], delta_type=\"spot\", **fx_args),\n",
    "    ])\n",
    "    rates_le_1y.extend([vol_data[\"atm\"][row], vol_data[\"25drr\"][row], vol_data[\"25dbf\"][row], vol_data[\"10drr\"][row], vol_data[\"10dbf\"][row]])\n",
    "    labels_le_1y.extend([f\"atm_{row}\", f\"25drr_{row}\", f\"25dbf_{row}\", f\"10drr_{row}\", f\"10dbf_{row}\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57229c3e-1879-4981-8cb6-36fc5cccb06c",
   "metadata": {},
   "source": [
    "Also define the instruments and rates for greater than 1Y:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5bb6c03-b28c-48cb-bd4b-b814cb41cfdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "instruments_gt_1y, rates_gt_1y, labels_gt_1y = [], [], []\n",
    "for row in range(11, 23):\n",
    "    instruments_gt_1y.extend([\n",
    "        FXStraddle(strike=\"atm_delta\", expiry=vol_data[\"expiry\"][row], delta_type=\"forward\", **fx_args),\n",
    "        FXRiskReversal(strike=[\"-25d\", \"25d\"], expiry=vol_data[\"expiry\"][row], delta_type=\"forward\", **fx_args),\n",
    "        FXBrokerFly(strike=[\"-25d\", \"atm_delta\", \"25d\"], expiry=vol_data[\"expiry\"][row], delta_type=\"forward\", **fx_args),\n",
    "        FXRiskReversal(strike=[\"-10d\", \"10d\"], expiry=vol_data[\"expiry\"][row], delta_type=\"forward\", **fx_args),\n",
    "        FXBrokerFly(strike=[\"-10d\", \"atm_delta\", \"10d\"], expiry=vol_data[\"expiry\"][row], delta_type=\"forward\", **fx_args),\n",
    "    ])\n",
    "    rates_gt_1y.extend([vol_data[\"atm\"][row], vol_data[\"25drr\"][row], vol_data[\"25dbf\"][row], vol_data[\"10drr\"][row], vol_data[\"10dbf\"][row]])\n",
    "    labels_gt_1y.extend([f\"atm_{row}\", f\"25drr_{row}\", f\"25dbf_{row}\", f\"10drr_{row}\", f\"10dbf_{row}\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc52314d-837d-4a51-a2ee-aa0cda11f28c",
   "metadata": {},
   "source": [
    "Now solve for all calibrating instruments and rates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5eaff434-c158-4c21-af2c-4531d1f840ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "surface_solver = Solver(\n",
    "    surfaces=[surface],\n",
    "    instruments=instruments_le_1y+instruments_gt_1y,\n",
    "    s=rates_le_1y+rates_gt_1y,\n",
    "    instrument_labels=labels_le_1y+labels_gt_1y,\n",
    "    fx=fxf,\n",
    "    pre_solvers=[fx_solver],\n",
    "    id=\"eurusd_vol\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92e1e04b-121f-4de2-9cb1-00cd716fdacd",
   "metadata": {},
   "source": [
    "### 3D Surface Plot and Cross-sectional Smiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98ccdc8c-1738-4144-aaaa-e0bf62817be2",
   "metadata": {},
   "outputs": [],
   "source": [
    "surface.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "963077a0-249f-4632-a593-16c806dbc09f",
   "metadata": {},
   "outputs": [],
   "source": [
    "surface.smiles[0].plot(comparators=surface.smiles[1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d447f20-17cf-48e0-88cf-2cb10421dda8",
   "metadata": {},
   "source": [
    "## Calculating a generic option price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd193993-a065-402a-9654-0faf0eb9e676",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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