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Created November 10, 2025 15:05
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Using interpolated 7d / 30d IV for ticker OXY to creating a rolling slope calculation with the Unusual Whales API
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tasty_historical_iv_slopes.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "6c4609e5",
"metadata": {},
"source": [
"Unusual Whales API endpoints we will use in this notebook:\n",
"\n",
"**Realized Volatility**\n",
"- Docs: [https://api.unusualwhales.com/docs#/operations/PublicApi.TickerController.realized_volatility](https://api.unusualwhales.com/docs#/operations/PublicApi.TickerController.realized_volatility)\n",
"- GET: `https://api.unusualwhales.com/api/stock/{ticker}/volatility/realized`\n",
"\n",
"**Interpolated IV**\n",
"- Docs: [https://api.unusualwhales.com/docs#/operations/PublicApi.TickerController.interpolated_iv](https://api.unusualwhales.com/docs#/operations/PublicApi.TickerController.interpolated_iv)\n",
"- GET: `https://api.unusualwhales.com/api/stock/{ticker}/interpolated-iv`"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b7b6a31b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div id=\"VLxxKs\"></div>\n",
" <script type=\"text/javascript\" data-lets-plot-script=\"library\">\n",
" if(!window.letsPlotCallQueue) {\n",
" window.letsPlotCallQueue = [];\n",
" }; \n",
" window.letsPlotCall = function(f) {\n",
" window.letsPlotCallQueue.push(f);\n",
" };\n",
" (function() {\n",
" var script = document.createElement(\"script\");\n",
" script.type = \"text/javascript\";\n",
" script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/[email protected]/js-package/distr/lets-plot.min.js\";\n",
" script.onload = function() {\n",
" window.letsPlotCall = function(f) {f();};\n",
" window.letsPlotCallQueue.forEach(function(f) {f();});\n",
" window.letsPlotCallQueue = [];\n",
" \n",
" };\n",
" script.onerror = function(event) {\n",
" window.letsPlotCall = function(f) {}; // noop\n",
" window.letsPlotCallQueue = [];\n",
" var div = document.createElement(\"div\");\n",
" div.style.color = 'darkred';\n",
" div.textContent = 'Error loading Lets-Plot JS';\n",
" document.getElementById(\"VLxxKs\").appendChild(div);\n",
" };\n",
" var e = document.getElementById(\"VLxxKs\");\n",
" e.appendChild(script);\n",
" })()\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"import time\n",
"import httpx # pip install httpx==0.27.2\n",
"import polars as pl # pip install polars==1.33.1\n",
"import lets_plot as lp # pip install lets-plot==4.3.3\n",
"lp.LetsPlot.setup_html()\n",
"\n",
"uw_token = os.getenv(\"UW_TOKEN\")\n",
"headers = {\n",
" \"Accept\": \"application/json, text/plain\",\n",
" \"Authorization\": uw_token\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "6c81e0db",
"metadata": {},
"source": [
"We need to collect trading dates for the last year, and using the Realized Volatility endpoint is a bit of a hack for that because its response is very convenient."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4154b3be",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['2024-11-11', '2024-11-12', '2024-11-13', '2024-11-14', '2024-11-15']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ticker = \"OXY\"\n",
"rv_url = f\"https://api.unusualwhales.com/api/stock/{ticker}/volatility/realized\"\n",
"params = {\"timeframe\": \"1Y\"}\n",
"rsp = httpx.get(rv_url, headers=headers, params=params)\n",
"trading_dates = [r[\"date\"] for r in rsp.json()[\"data\"]]\n",
"trading_dates[:5]"
]
},
{
"cell_type": "markdown",
"id": "a4f0aff6",
"metadata": {},
"source": [
"Now that we have the trading dates, we will hit the Interpolated IV endpoint for data."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2956731a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'date': '2025-05-12',\n",
" 'days': 1,\n",
" 'percentile': '0.709',\n",
" 'volatility': '0.363',\n",
" 'implied_move_perc': '0.023'},\n",
" {'date': '2025-05-12',\n",
" 'days': 5,\n",
" 'percentile': '0.697',\n",
" 'volatility': '0.359',\n",
" 'implied_move_perc': '0.028'},\n",
" {'date': '2025-05-12',\n",
" 'days': 7,\n",
" 'percentile': '0.72',\n",
" 'volatility': '0.351',\n",
" 'implied_move_perc': '0.032'},\n",
" {'date': '2025-05-12',\n",
" 'days': 14,\n",
" 'percentile': '0.656',\n",
" 'volatility': '0.323',\n",
" 'implied_move_perc': '0.042'},\n",
" {'date': '2025-05-12',\n",
" 'days': 30,\n",
" 'percentile': '0.569',\n",
" 'volatility': '0.31',\n",
" 'implied_move_perc': '0.06'}]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interpolated_ivs = []\n",
"for td in trading_dates:\n",
" url = f\"https://api.unusualwhales.com/api/stock/{ticker}/interpolated-iv\"\n",
" params = {\"date\": td}\n",
" rsp = httpx.get(url, headers=headers, params=params)\n",
" interpolated_ivs.extend(rsp.json()[\"data\"])\n",
" time.sleep(0.6) # Avoid hitting the basic 120 req/min limit\n",
"interpolated_ivs[:5]"
]
},
{
"cell_type": "markdown",
"id": "3e6ed9ea",
"metadata": {},
"source": [
"Convert this list of dictionaries into a dataframe for easier manipulation and plotting."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ded712ab",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div><style>\n",
".dataframe > thead > tr,\n",
".dataframe > tbody > tr {\n",
" text-align: right;\n",
" white-space: pre-wrap;\n",
"}\n",
"</style>\n",
"<small>shape: (1_143, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>days</th><th>percentile</th><th>volatility</th><th>implied_move_perc</th></tr><tr><td>str</td><td>i64</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;2025-05-12&quot;</td><td>1</td><td>&quot;0.709&quot;</td><td>&quot;0.363&quot;</td><td>&quot;0.023&quot;</td></tr><tr><td>&quot;2025-05-12&quot;</td><td>5</td><td>&quot;0.697&quot;</td><td>&quot;0.359&quot;</td><td>&quot;0.028&quot;</td></tr><tr><td>&quot;2025-05-12&quot;</td><td>7</td><td>&quot;0.72&quot;</td><td>&quot;0.351&quot;</td><td>&quot;0.032&quot;</td></tr><tr><td>&quot;2025-05-12&quot;</td><td>14</td><td>&quot;0.656&quot;</td><td>&quot;0.323&quot;</td><td>&quot;0.042&quot;</td></tr><tr><td>&quot;2025-05-12&quot;</td><td>30</td><td>&quot;0.569&quot;</td><td>&quot;0.31&quot;</td><td>&quot;0.06&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;2025-11-10&quot;</td><td>30</td><td>&quot;0.811&quot;</td><td>&quot;0.359&quot;</td><td>&quot;0.07&quot;</td></tr><tr><td>&quot;2025-11-10&quot;</td><td>60</td><td>&quot;0.502&quot;</td><td>&quot;0.33&quot;</td><td>&quot;0.091&quot;</td></tr><tr><td>&quot;2025-11-10&quot;</td><td>90</td><td>&quot;0.566&quot;</td><td>&quot;0.328&quot;</td><td>&quot;0.111&quot;</td></tr><tr><td>&quot;2025-11-10&quot;</td><td>180</td><td>&quot;0.815&quot;</td><td>&quot;0.345&quot;</td><td>&quot;0.162&quot;</td></tr><tr><td>&quot;2025-11-10&quot;</td><td>365</td><td>&quot;0.832&quot;</td><td>&quot;0.34&quot;</td><td>&quot;0.227&quot;</td></tr></tbody></table></div>"
],
"text/plain": [
"shape: (1_143, 5)\n",
"┌────────────┬──────┬────────────┬────────────┬───────────────────┐\n",
"│ date ┆ days ┆ percentile ┆ volatility ┆ implied_move_perc │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
"│ str ┆ i64 ┆ str ┆ str ┆ str │\n",
"╞════════════╪══════╪════════════╪════════════╪═══════════════════╡\n",
"│ 2025-05-12 ┆ 1 ┆ 0.709 ┆ 0.363 ┆ 0.023 │\n",
"│ 2025-05-12 ┆ 5 ┆ 0.697 ┆ 0.359 ┆ 0.028 │\n",
"│ 2025-05-12 ┆ 7 ┆ 0.72 ┆ 0.351 ┆ 0.032 │\n",
"│ 2025-05-12 ┆ 14 ┆ 0.656 ┆ 0.323 ┆ 0.042 │\n",
"│ 2025-05-12 ┆ 30 ┆ 0.569 ┆ 0.31 ┆ 0.06 │\n",
"│ … ┆ … ┆ … ┆ … ┆ … │\n",
"│ 2025-11-10 ┆ 30 ┆ 0.811 ┆ 0.359 ┆ 0.07 │\n",
"│ 2025-11-10 ┆ 60 ┆ 0.502 ┆ 0.33 ┆ 0.091 │\n",
"│ 2025-11-10 ┆ 90 ┆ 0.566 ┆ 0.328 ┆ 0.111 │\n",
"│ 2025-11-10 ┆ 180 ┆ 0.815 ┆ 0.345 ┆ 0.162 │\n",
"│ 2025-11-10 ┆ 365 ┆ 0.832 ┆ 0.34 ┆ 0.227 │\n",
"└────────────┴──────┴────────────┴────────────┴───────────────────┘"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_df = pl.DataFrame(interpolated_ivs)\n",
"raw_df"
]
},
{
"cell_type": "markdown",
"id": "c6cc901b",
"metadata": {},
"source": [
"Let's \"unmelt\" this dataframe into a \"wide\" dataframe so we can calculate the 7d / 30d slope."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "167df4d2",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div><style>\n",
".dataframe > thead > tr,\n",
".dataframe > tbody > tr {\n",
" text-align: right;\n",
" white-space: pre-wrap;\n",
"}\n",
"</style>\n",
"<small>shape: (127, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>slope_7_30</th></tr><tr><td>str</td><td>f64</td></tr></thead><tbody><tr><td>&quot;2025-05-12&quot;</td><td>0.132258</td></tr><tr><td>&quot;2025-05-13&quot;</td><td>0.10596</td></tr><tr><td>&quot;2025-05-14&quot;</td><td>0.07837</td></tr><tr><td>&quot;2025-05-15&quot;</td><td>0.045181</td></tr><tr><td>&quot;2025-05-16&quot;</td><td>-0.021672</td></tr><tr><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;2025-11-04&quot;</td><td>0.129032</td></tr><tr><td>&quot;2025-11-05&quot;</td><td>0.141566</td></tr><tr><td>&quot;2025-11-06&quot;</td><td>0.178571</td></tr><tr><td>&quot;2025-11-07&quot;</td><td>0.185393</td></tr><tr><td>&quot;2025-11-10&quot;</td><td>0.426184</td></tr></tbody></table></div>"
],
"text/plain": [
"shape: (127, 2)\n",
"┌────────────┬────────────┐\n",
"│ date ┆ slope_7_30 │\n",
"│ --- ┆ --- │\n",
"│ str ┆ f64 │\n",
"╞════════════╪════════════╡\n",
"│ 2025-05-12 ┆ 0.132258 │\n",
"│ 2025-05-13 ┆ 0.10596 │\n",
"│ 2025-05-14 ┆ 0.07837 │\n",
"│ 2025-05-15 ┆ 0.045181 │\n",
"│ 2025-05-16 ┆ -0.021672 │\n",
"│ … ┆ … │\n",
"│ 2025-11-04 ┆ 0.129032 │\n",
"│ 2025-11-05 ┆ 0.141566 │\n",
"│ 2025-11-06 ┆ 0.178571 │\n",
"│ 2025-11-07 ┆ 0.185393 │\n",
"│ 2025-11-10 ┆ 0.426184 │\n",
"└────────────┴────────────┘"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = (\n",
" raw_df\n",
" .with_columns(\n",
" pl.col(\"percentile\").cast(pl.Float64),\n",
" pl.col(\"volatility\").cast(pl.Float64),\n",
" pl.col(\"implied_move_perc\").cast(pl.Float64),\n",
" )\n",
" .pivot(\n",
" on=\"days\",\n",
" values=[\"percentile\", \"volatility\", \"implied_move_perc\"],\n",
" index=\"date\",\n",
" )\n",
" .with_columns(\n",
" (pl.col(\"volatility_7\")/pl.col(\"volatility_30\") - 1).alias(\"slope_7_30\"),\n",
" )\n",
" .select([\"date\", \"slope_7_30\"])\n",
" .sort(\"date\")\n",
")\n",
"df"
]
},
{
"cell_type": "markdown",
"id": "fed64333",
"metadata": {},
"source": [
"The next cell is \"boilerplate\" to make the plots look nice, scroll on by..."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0ab503a0",
"metadata": {},
"outputs": [],
"source": [
"UW_DARK_THEME = {\n",
" \"red\": \"#dc3545\",\n",
" \"yellow\": \"#ffc107\",\n",
" \"teal\": \"#20c997\",\n",
" \"blue_medium\": \"#53a5d5\",\n",
" \"blue_light\": \"#b5d5e1\",\n",
" \"black\": \"#161c2d\",\n",
" \"gray_medium\": \"#748196\",\n",
" \"gray_light\": \"#f9fbfd\",\n",
"}\n",
"\n",
"def uw_dark_theme(colors: dict, show_legend: bool=True):\n",
" \"\"\"Create a dark theme for lets-plot using UW colors.\"\"\"\n",
" t = lp.theme_none() + lp.theme(\n",
" plot_background=lp.element_rect(fill=colors[\"black\"]),\n",
" panel_background=lp.element_rect(fill=colors[\"black\"]),\n",
" panel_grid_major=lp.element_blank(),\n",
" panel_grid_minor=lp.element_blank(),\n",
" axis_ontop=True,\n",
" axis_ticks=lp.element_blank(),\n",
" axis_tooltip=lp.element_rect(color=colors[\"gray_light\"]),\n",
" tooltip=lp.element_rect(color=colors[\"gray_light\"], fill=colors[\"black\"]),\n",
" line=lp.element_line(color=colors[\"gray_medium\"], size=1),\n",
" rect=lp.element_rect(color=colors[\"black\"], fill=colors[\"black\"], size=2),\n",
" text=lp.element_text(color=colors[\"gray_light\"], size=10),\n",
" legend_background=lp.element_rect(\n",
" color=colors[\"gray_light\"], fill=colors[\"black\"], size=2\n",
" ),\n",
" plot_title=lp.element_text(hjust=0.5, size=16, color=colors[\"gray_light\"]),\n",
" )\n",
" if show_legend:\n",
" return t + lp.theme(legend_position=\"bottom\")\n",
" else:\n",
" return t + lp.theme(legend_position=\"none\")"
]
},
{
"cell_type": "markdown",
"id": "f7d0c2b8",
"metadata": {},
"source": [
"Finally we are ready to plot the `OXY` 7d / 30d interpolated IV slope over time."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "bbd98298",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" <div id=\"3us9b3\"></div>\n",
" <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
" (function() {\n",
" var plotSpec={\n",
"\"data\":{\n",
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"},\n",
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"\"y\":\"slope_7_30\",\n",
"\"color\":\"slope_7_30\"\n",
"},\n",
"\"data_meta\":{\n",
"},\n",
"\"ggtitle\":{\n",
"\"text\":\"OXY 7d to 30d IV Slope Over Time\"\n",
"},\n",
"\"theme\":{\n",
"\"name\":\"none\",\n",
"\"line\":{\n",
"\"color\":\"#748196\",\n",
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"},\n",
"\"rect\":{\n",
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"},\n",
"\"text\":{\n",
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"\"size\":10.0,\n",
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"},\n",
"\"axis_ontop\":true,\n",
"\"axis_ticks\":{\n",
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"},\n",
"\"legend_background\":{\n",
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"\"size\":2.0,\n",
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"},\n",
"\"panel_background\":{\n",
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"},\n",
"\"panel_grid_major\":{\n",
"\"blank\":true\n",
"},\n",
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"\"blank\":true\n",
"},\n",
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"\"blank\":false\n",
"},\n",
"\"plot_title\":{\n",
"\"color\":\"#f9fbfd\",\n",
"\"size\":16.0,\n",
"\"hjust\":0.5,\n",
"\"blank\":false\n",
"},\n",
"\"axis_tooltip\":{\n",
"\"color\":\"#f9fbfd\",\n",
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"},\n",
"\"tooltip\":{\n",
"\"fill\":\"#161c2d\",\n",
"\"color\":\"#f9fbfd\",\n",
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"},\n",
"\"legend_position\":\"none\"\n",
"},\n",
"\"kind\":\"plot\",\n",
"\"scales\":[{\n",
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"\"values\":[\"#ffc107\"]\n",
"}],\n",
"\"layers\":[{\n",
"\"geom\":\"line\",\n",
"\"mapping\":{\n",
"},\n",
"\"data_meta\":{\n",
"},\n",
"\"data\":{\n",
"}\n",
"}],\n",
"\"metainfo_list\":[]\n",
"};\n",
" var plotContainer = document.getElementById(\"3us9b3\");\n",
" window.letsPlotCall(function() {{\n",
" LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
" }});\n",
" })();\n",
" </script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"p = (\n",
" lp.ggplot(df)\n",
" + lp.aes(x=\"date\", y=\"slope_7_30\", color=\"slope_7_30\")\n",
" + lp.geom_line()\n",
" + lp.scale_color_manual(values=[UW_DARK_THEME[\"yellow\"]])\n",
" + lp.ggtitle(f\"{ticker} 7d to 30d IV Slope Over Time\")\n",
" + uw_dark_theme(UW_DARK_THEME, show_legend=False)\n",
")\n",
"p.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "dans-magic-house",
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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