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Chore: Make release 1.4.25
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‎gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/23_solve_vanna_&_volga.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-08-11T12:19:09.294055700Z",
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"start_time": "2025-08-11T12:19:03.376662500Z"
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}
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},
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"outputs": [],
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"source": [
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"from gs_quant.instrument import IRSwaption, IRSwap, IRCapFloor\n",
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"from gs_quant.common import AggregationLevel\n",
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"from gs_quant.markets.portfolio import Portfolio\n",
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"from gs_quant.risk import IRVanna, IRVolga\n",
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"from gs_quant.session import Environment, GsSession"
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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": 2,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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},
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"ExecuteTime": {
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"end_time": "2025-08-11T12:19:16.393303500Z",
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"start_time": "2025-08-11T12:19:11.738322600Z"
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}
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},
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"outputs": [],
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"source": [
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"# external users should substitute their client id and secret\n",
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"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Solve for Vanna and Volga\n",
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"Size swap to match swaption vanna"
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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": 3,
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"metadata": {
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"tags": [
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"Instrument - Solving - Size one trade by the risk of another trade"
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],
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"ExecuteTime": {
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"end_time": "2025-08-11T12:19:21.346721500Z",
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"start_time": "2025-08-11T12:19:18.268449900Z"
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}
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},
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"outputs": [],
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"source": [
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"port = Portfolio(\n",
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" (\n",
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" IRSwaption('Pay', '5y', 'USD', expiration_date='1y', buy_sell='Sell', name='payer_swaption'),\n",
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" IRSwap(\n",
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" 'Receive',\n",
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" '5y',\n",
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" 'USD',\n",
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" fixed_rate='atm',\n",
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" notional_amount='=solvefor([payer_swaption].risk.IRDeltaParallel,bp)',\n",
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" name='hedge1',\n",
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" ),\n",
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" )\n",
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")\n",
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"port.resolve()"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"port.calc(IRVanna).to_frame()"
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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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"outputs": [],
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"source": [
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"port.calc(IRVolga).to_frame()"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"Swaption (return) is non-linear (to interest change), that's why we can calculate Vanna and Volga for it, while IRSwap is linear and have it as zero."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"We can also check IRVanna and IRVolga with IRCapFloor instruments, which return new options for every defined period. "
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"cap = IRCapFloor(\n",
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" cap_floor=\"Cap\",\n",
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" notional_currency=\"USD\",\n",
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" notional_amount=1000,\n",
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" effective_date='2025-07-18',\n",
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" termination_date='2030-07-18',\n",
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" floating_rate_option='USD-LIBOR-BBA',\n",
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" floating_rate_designated_maturity='3m',\n",
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" floating_rate_frequency='3m',\n",
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" strike=0.015, # 1. 5% floor rate\n",
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")\n",
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"cap.resolve(in_place=False).to_dict()"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"cap.calc(IRVanna)"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"cap.calc(IRVanna(aggregation_level=AggregationLevel.Type))"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"cap.calc(IRVolga)"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"cap.calc(IRVolga(aggregation_level=AggregationLevel.Type))"
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],
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"metadata": {
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"collapsed": false
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎gs_quant/documentation/Contents.ipynb‎

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" - 📄 [20_fix_float_legs_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/20_fix_float_legs_price.ipynb)\n",
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" - 📄 [21_asset_swap_definition.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/21_asset_swap_definition.ipynb)\n",
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" - 📄 [22_cap_floor.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/22_cap_floor.ipynb)\n",
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" - 📄 [23_solve_vanna_&_volga.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/23_solve_vanna_&_volga.ipynb)\n",
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" - 📁 02_fx\n",
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" - 📄 [01_fx_fwd_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/01_fx_fwd_trade_construction.ipynb)\n",
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" - 📄 [02_fx_option_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/02_fx_option_trade_construction.ipynb)\n",

‎gs_quant/documentation/Index.ipynb‎

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" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/04_fx_binary_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/04_fx_binary_trade_construction.ipynb) cell 5\n",
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"- **Instrument - Solving - Size one trade by the risk of another trade** - \n",
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" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/19_solve_delta.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/19_solve_delta.ipynb) cell 3\n",
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" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/23_solve_vanna_&_volga.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/23_solve_vanna_&_volga.ipynb) cell 3\n",
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"- **Instrument - Solving - Solving FXVolSwap strike vol** - \n",
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" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/03_fx_vol_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/03_fx_vol_swap_trade_construction.ipynb) cell 10\n",
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"- **Instrument - Solving across multiple instruments** - \n",

‎gs_quant/documentation/README.md‎

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- 📄 [20_fix_float_legs_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/20_fix_float_legs_price.ipynb)
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- 📄 [21_asset_swap_definition.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/21_asset_swap_definition.ipynb)
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- 📄 [22_cap_floor.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/22_cap_floor.ipynb)
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- 📄 [23_solve_vanna_&_volga.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/23_solve_vanna_&_volga.ipynb)
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- 📁 02_fx
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- 📄 [01_fx_fwd_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/01_fx_fwd_trade_construction.ipynb)
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- 📄 [02_fx_option_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/02_fx_option_trade_construction.ipynb)

‎gs_quant/timeseries/helper.py‎

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) -> pd.DataFrame:
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tasks = [partial(ds.get_data, assetId=assets[i:i + batch_limit], start=start, end=end,
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return_type=None, limit=batch_limit, **kwargs) for i in range(0, len(assets), batch_limit)]
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return_type=None, **kwargs) for i in range(0, len(assets), batch_limit)]
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results = ThreadPoolManager.run_async(tasks)
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return pd.concat(results)
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