diff --git a/content/python.ipynb b/content/python.ipynb deleted file mode 100644 index 689e883..0000000 --- a/content/python.ipynb +++ /dev/null @@ -1,721 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# A Python kernel backed by Pyodide\n", - "\n", - "![](https://raw.githubusercontent.com/pyodide/pyodide/master/docs/_static/img/pyodide-logo.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import pyodide_kernel\n", - "pyodide_kernel.__version__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simple code execution" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "a = 3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "a" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "b = 89\n", - "\n", - "def sq(x):\n", - " return x * x\n", - "\n", - "sq(b)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "print" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Redirected streams" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "print(\"Error !!\", file=sys.stderr)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Error handling" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true, - "trusted": true - }, - "outputs": [], - "source": [ - "\"Hello\"\n", - "\n", - "def dummy_function():\n", - " import missing_module" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "dummy_function()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Code completion" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### press `tab` to see what is available in `sys` module" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from sys import " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Code inspection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### using the question mark" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "?print" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### by pressing `shift+tab`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "print(" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Input support" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "name = await input('Enter your name: ')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "'Hello, ' + name" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Rich representation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import display, Markdown, HTML, JSON, Latex" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## HTML" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "print('Before display')\n", - "\n", - "s = '

HTML Title

'\n", - "display(HTML(s))\n", - "\n", - "print('After display')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Markdown" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "Markdown('''\n", - "# Title\n", - "\n", - "**in bold**\n", - "\n", - "~~Strikthrough~~\n", - "''')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pandas DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from string import ascii_uppercase as letters\n", - "from IPython.display import display\n", - "\n", - "df = pd.DataFrame(np.random.randint(0, 100, size=(100, len(letters))), columns=list(letters))\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Show the same DataFrame " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## IPython.display module" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import clear_output, display, update_display\n", - "from asyncio import sleep" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Update display" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "class Square:\n", - " color = 'PeachPuff'\n", - " def _repr_html_(self):\n", - " return '''\n", - "
\n", - "
''' % self.color\n", - "square = Square()\n", - "\n", - "display(square, display_id='some-square')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "square.color = 'OliveDrab'\n", - "update_display(square, display_id='some-square')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Clear output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "print(\"hello\")\n", - "await sleep(3)\n", - "clear_output() # will flicker when replacing \"hello\" with \"goodbye\"\n", - "print(\"goodbye\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "print(\"hello\")\n", - "await sleep(3)\n", - "clear_output(wait=True) # prevents flickering\n", - "print(\"goodbye\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Display classes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import HTML\n", - "HTML('''\n", - "
\n", - "
''')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import Math\n", - "Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import Latex\n", - "Latex(r\"\"\"\\begin{eqnarray}\n", - "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", - "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", - "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", - "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n", - "\\end{eqnarray}\"\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import ProgressBar\n", - "\n", - "for i in ProgressBar(10):\n", - " await sleep(0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import JSON\n", - "JSON(['foo', {'bar': ('baz', None, 1.0, 2)}], metadata={}, expanded=True, root='test')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from IPython.display import GeoJSON\n", - "GeoJSON(\n", - " data={\n", - " \"type\": \"Feature\",\n", - " \"geometry\": {\n", - " \"type\": \"Point\",\n", - " \"coordinates\": [11.8, -45.04]\n", - " }\n", - " }, url_template=\"http://s3-eu-west-1.amazonaws.com/whereonmars.cartodb.net/{basemap_id}/{z}/{x}/{y}.png\",\n", - " layer_options={\n", - " \"basemap_id\": \"celestia_mars-shaded-16k_global\",\n", - " \"attribution\" : \"Celestia/praesepe\",\n", - " \"tms\": True,\n", - " \"minZoom\" : 0,\n", - " \"maxZoom\" : 5\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Network requests and JSON" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import json\n", - "from js import fetch" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "res = await fetch('https://httpbin.org/get')\n", - "text = await res.text()\n", - "obj = json.loads(text) \n", - "JSON(obj)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Sympy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "from sympy import Integral, sqrt, symbols, init_printing\n", - "\n", - "init_printing()\n", - "\n", - "x = symbols('x')\n", - "\n", - "Integral(sqrt(1 / x), x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Magics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import os\n", - "os.listdir()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "%cd /home" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "%pwd" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "current_path = %pwd\n", - "print(current_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "%%writefile test.txt\n", - "\n", - "This will create a new file. \n", - "With the text that you see here." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "%history" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "import time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "trusted": true - }, - "outputs": [], - "source": [ - "%%timeit \n", - "\n", - "time.sleep(0.1)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (Pyodide)", - "language": "python", - "name": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "python", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/content/rateslib sofr curve.ipynb b/content/rateslib sofr curve.ipynb new file mode 100644 index 0000000..34e1b35 --- /dev/null +++ b/content/rateslib sofr curve.ipynb @@ -0,0 +1,1166 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "59b97624-c832-4d96-ada4-2c1f26d9f1ca", + "metadata": {}, + "source": [ + "## Construct a SOFR curve and value an IRS\n", + "\n", + "This notebook illustrates how a SOFR curve can be calibrated against input instruments, then used to price and risk an interest rate swap.\n", + "\n", + "All code taken from https://rateslib.readthedocs.io/en/1.1.x/z_swpm.html\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "1df0a125-3436-4f35-9304-51b4cf0b86a4", + "metadata": {}, + "outputs": [], + "source": [ + "# Install rateslib if running on pyodide.\n", + "import platform\n", + "if platform.system() == 'Emscripten':\n", + " import micropip\n", + " await micropip.install('rateslib')\n", + " package_list = micropip.list()\n", + " package_list[\"rateslib\"]\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "b4c11275-a75b-41a7-b3e1-06ed10105876", + "metadata": {}, + "source": [ + "We first set-up the market data inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "cb214882-5398-413b-a9c7-e17c4b317117", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TermRateTermination
01W5.301112023-08-28
12W5.304242023-09-05
23W5.306572023-09-11
31M5.311002023-09-21
42M5.348002023-10-23
53M5.380252023-11-21
64M5.409152023-12-21
75M5.430782024-01-22
86M5.442352024-02-21
97M5.449502024-03-21
108M5.448782024-04-22
119M5.441002024-05-21
1210M5.427302024-06-21
1311M5.407472024-07-22
1412M5.383902024-08-21
1518M5.091952025-02-21
162Y4.857852025-08-21
173Y4.518452026-08-21
184Y4.317052027-08-23
\n", + "
" + ], + "text/plain": [ + " Term Rate Termination\n", + "0 1W 5.30111 2023-08-28\n", + "1 2W 5.30424 2023-09-05\n", + "2 3W 5.30657 2023-09-11\n", + "3 1M 5.31100 2023-09-21\n", + "4 2M 5.34800 2023-10-23\n", + "5 3M 5.38025 2023-11-21\n", + "6 4M 5.40915 2023-12-21\n", + "7 5M 5.43078 2024-01-22\n", + "8 6M 5.44235 2024-02-21\n", + "9 7M 5.44950 2024-03-21\n", + "10 8M 5.44878 2024-04-22\n", + "11 9M 5.44100 2024-05-21\n", + "12 10M 5.42730 2024-06-21\n", + "13 11M 5.40747 2024-07-22\n", + "14 12M 5.38390 2024-08-21\n", + "15 18M 5.09195 2025-02-21\n", + "16 2Y 4.85785 2025-08-21\n", + "17 3Y 4.51845 2026-08-21\n", + "18 4Y 4.31705 2027-08-23" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datetime import datetime as dt\n", + "import pandas as pd\n", + "import rateslib as rl\n", + "\n", + "as_of_date = dt(2023, 8, 17)\n", + "effective_date = rl.add_tenor(as_of_date, \"2b\", \"F\", \"nyc\")\n", + "\n", + "data = pd.DataFrame({\n", + " \"Term\": [\"1W\", \"2W\", \"3W\", \"1M\", \"2M\", \"3M\", \"4M\", \"5M\", \"6M\", \"7M\", \"8M\", \"9M\", \"10M\", \"11M\", \"12M\", \"18M\", \"2Y\", \"3Y\", \"4Y\"],\n", + " \"Rate\": [5.30111, 5.30424, 5.30657, 5.31100, 5.34800, 5.38025, 5.40915, 5.43078, 5.44235, 5.44950, 5.44878, 5.44100, 5.42730, 5.40747, 5.3839, 5.09195, 4.85785, 4.51845, 4.31705],\n", + "})\n", + "data[\"Termination\"] = [rl.add_tenor(effective_date, tenor, \"F\", \"nyc\") for tenor in data[\"Term\"]]\n", + "data\n" + ] + }, + { + "cell_type": "markdown", + "id": "132c2b29-ef68-48b2-a19c-8d8555aecbcc", + "metadata": {}, + "source": [ + "A visual representation of the inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e0f0f53c-3047-4f1c-a489-a78d41ad8acc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.Series(data.Rate.values, data.Termination).plot(style='+-');\n" + ] + }, + { + "cell_type": "markdown", + "id": "9139df3f-31ff-4221-b06f-30e2477e0ea5", + "metadata": {}, + "source": [ + "### Curve structure\n", + "\n", + "We create a curve with node dates matching the inputs, give the interpolation information." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b6bd1a71-dff7-4a15-a17b-54ed45351008", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sofr_curve = rl.Curve(\n", + " id=\"sofr_curve\",\n", + " convention=\"Act360\",\n", + " calendar=\"nyc\",\n", + " modifier=\"MF\",\n", + " interpolation=\"log_linear\",\n", + " nodes={\n", + " **{as_of_date: 1.0}, # <- this is today's DF,\n", + " **{term_dt.to_pydatetime(): 1.0 for term_dt in data[\"Termination\"]},\n", + " }\n", + ")\n", + "sofr_curve\n" + ] + }, + { + "cell_type": "markdown", + "id": "d64d3dfe-2c00-4943-80a5-d2dc86663b5e", + "metadata": {}, + "source": [ + "### Create a Solver\n", + "\n", + "Calibrate the curve to the input instruments using a Solver." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "1b8c010f-009c-4b20-906f-042820390093", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SUCCESS: `func_tol` reached after 5 iterations (levenberg_marquardt) , `f_val`: 3.116744256634176e-17, `time`: 0.0655s\n" + ] + } + ], + "source": [ + "instruments = [\n", + " rl.IRS(termination=term.to_pydatetime(), \n", + " effective=effective_date,\n", + " spec=\"usd_irs\",\n", + " curves=\"sofr_curve\"\n", + " ) for term in data[\"Termination\"]\n", + "]\n", + "\n", + "solver = rl.Solver(\n", + " curves=[sofr_curve],\n", + " instruments=instruments,\n", + " s=data[\"Rate\"],\n", + " instrument_labels=data[\"Term\"],\n", + " id=\"us_rates\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "b06dc8ec-793e-42d6-a919-f57f92ef61a6", + "metadata": {}, + "source": [ + "### Curve results\n", + "\n", + "Can now read the discount factors from the calibrated curve." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "580bcf24-378c-4d90-be17-d6e6f37efc8f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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184Y4.317052027-08-230.842731
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" + ], + "text/plain": [ + " Term Rate Termination DF\n", + "0 1W 5.30111 2023-08-28 0.998382\n", + "1 2W 5.30424 2023-09-05 0.997208\n", + "2 3W 5.30657 2023-09-11 0.996327\n", + "3 1M 5.31100 2023-09-21 0.994862\n", + "4 2M 5.34800 2023-10-23 0.990145\n", + "5 3M 5.38025 2023-11-21 0.985856\n", + "6 4M 5.40915 2023-12-21 0.981421\n", + "7 5M 5.43078 2024-01-22 0.976721\n", + "8 6M 5.44235 2024-02-21 0.972364\n", + "9 7M 5.44950 2024-03-21 0.968194\n", + "10 8M 5.44878 2024-04-22 0.963676\n", + "11 9M 5.44100 2024-05-21 0.959670\n", + "12 10M 5.42730 2024-06-21 0.955477\n", + "13 11M 5.40747 2024-07-22 0.951395\n", + "14 12M 5.38390 2024-08-21 0.947546\n", + "15 18M 5.09195 2025-02-21 0.926160\n", + "16 2Y 4.85785 2025-08-21 0.907898\n", + "17 3Y 4.51845 2026-08-21 0.874241\n", + "18 4Y 4.31705 2027-08-23 0.842731" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"DF\"] = [float(sofr_curve[term]) for term in data[\"Termination\"]]\n", + "data" + ] + }, + { + "cell_type": "markdown", + "id": "ecaa0445-9211-4810-94e1-2123ad914064", + "metadata": {}, + "source": [ + "### Pricing an Instrument\n", + "\n", + "We create an Interest Rate Swap to price." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7fdde96a-2e65-4423-b7fa-fb79199706f4", + "metadata": {}, + "outputs": [], + "source": [ + "irs = rl.IRS(\n", + " effective=dt(2023, 11, 21),\n", + " termination=dt(2025, 2, 21),\n", + " notional=-100e6,\n", + " fixed_rate=5.40,\n", + " curves=\"sofr_curve\",\n", + " spec=\"usd_irs\",\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c9fb1e26-5b7d-4111-811e-461d03ad1888", + "metadata": {}, + "source": [ + "Then use the solver to price it." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c428f696-0a6a-4a2a-9646-67484608430c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "456622.09860395174" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pv = irs.npv(solver=solver)\n", + "pv.real" + ] + }, + { + "cell_type": "markdown", + "id": "ae18df01-a8db-4183-9940-a24b2fa558d8", + "metadata": {}, + "source": [ + "### Delta\n", + "\n", + "The solver can now be used to get delta senstivites," + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9a6091c0-ea89-4170-b66b-37d2a0b0a586", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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local_ccyusd
display_ccyusd
typesolverlabel
instrumentsus_rates1W-5.068355e-01
2W4.448341e-14
3W3.262071e-13
1M-6.819837e-13
2M6.608491e-13
3M2.484503e+03
4M2.835389e-13
5M-1.242682e-12
6M6.003902e+01
7M-6.609346e-01
8M9.208455e-13
9M-2.533433e-12
10M-1.728135e-12
11M-1.077498e-12
12M3.042953e-02
18M-1.442208e+04
2Y-1.265049e+00
3Y1.130177e-04
4Y-1.172195e-08
\n", + "
" + ], + "text/plain": [ + "local_ccy usd\n", + "display_ccy usd\n", + "type solver label \n", + "instruments us_rates 1W -5.068355e-01\n", + " 2W 4.448341e-14\n", + " 3W 3.262071e-13\n", + " 1M -6.819837e-13\n", + " 2M 6.608491e-13\n", + " 3M 2.484503e+03\n", + " 4M 2.835389e-13\n", + " 5M -1.242682e-12\n", + " 6M 6.003902e+01\n", + " 7M -6.609346e-01\n", + " 8M 9.208455e-13\n", + " 9M -2.533433e-12\n", + " 10M -1.728135e-12\n", + " 11M -1.077498e-12\n", + " 12M 3.042953e-02\n", + " 18M -1.442208e+04\n", + " 2Y -1.265049e+00\n", + " 3Y 1.130177e-04\n", + " 4Y -1.172195e-08" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "delta = irs.delta(solver=solver)\n", + "delta" + ] + }, + { + "cell_type": "markdown", + "id": "8f6e7b43-bdd1-4918-968e-f126306f3ffe", + "metadata": {}, + "source": [ + "Show the strip delta graphically." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ef0d5e2b-3a47-4210-b210-a60f7c0156ea", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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TypePeriodCcyAcc StartAcc EndPaymentConventionDCFNotionalDFCollateralRateSpreadCashflowNPVFX RateNPV Ccy
leg10FixedPeriodStubUSD2023-11-212024-02-212024-02-23act3600.255556-100000000.00.972076None5.400000NaN1.380000e+061.341464e+061.01.341464e+06
1FixedPeriodRegularUSD2024-02-212025-02-212025-02-25act3601.016667-100000000.00.925752None5.400000NaN5.490000e+065.082380e+061.05.082380e+06
leg20FloatPeriodStubUSD2023-11-212024-02-212024-02-23act3600.255556100000000.00.972076None5.4297930.0-1.387614e+06-1.348865e+061.0-1.348865e+06
1FloatPeriodRegularUSD2024-02-212025-02-212025-02-25act3601.016667100000000.00.925752None4.9069780.0-4.988761e+06-4.618357e+061.0-4.618357e+06
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" + ], + "text/plain": [ + " Type Period Ccy Acc Start Acc End Payment Convention \\\n", + "leg1 0 FixedPeriod Stub USD 2023-11-21 2024-02-21 2024-02-23 act360 \n", + " 1 FixedPeriod Regular USD 2024-02-21 2025-02-21 2025-02-25 act360 \n", + "leg2 0 FloatPeriod Stub USD 2023-11-21 2024-02-21 2024-02-23 act360 \n", + " 1 FloatPeriod Regular USD 2024-02-21 2025-02-21 2025-02-25 act360 \n", + "\n", + " DCF Notional DF Collateral Rate Spread \\\n", + "leg1 0 0.255556 -100000000.0 0.972076 None 5.400000 NaN \n", + " 1 1.016667 -100000000.0 0.925752 None 5.400000 NaN \n", + "leg2 0 0.255556 100000000.0 0.972076 None 5.429793 0.0 \n", + " 1 1.016667 100000000.0 0.925752 None 4.906978 0.0 \n", + "\n", + " Cashflow NPV FX Rate NPV Ccy \n", + "leg1 0 1.380000e+06 1.341464e+06 1.0 1.341464e+06 \n", + " 1 5.490000e+06 5.082380e+06 1.0 5.082380e+06 \n", + "leg2 0 -1.387614e+06 -1.348865e+06 1.0 -1.348865e+06 \n", + " 1 -4.988761e+06 -4.618357e+06 1.0 -4.618357e+06 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "irs.cashflows(solver=solver)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}