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danwagnerco/net_prem_ticks.ipynb

Created October 10, 2024 11:47
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Experiment with the UW Net Premium Ticks endpoint to create single-chart and dashboard views
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net_prem_ticks.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div id=\"QOIsfE\"></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(\"QOIsfE\").appendChild(div);\n",
" };\n",
" var e = document.getElementById(\"QOIsfE\");\n",
" e.appendChild(script);\n",
" })()\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"import requests\n",
"import polars as pl\n",
"from lets_plot import *\n",
"LetsPlot.setup_html()\n",
"\n",
"uw_token = os.environ['UW_TOKEN']\n",
"headers = {'Accept': 'application/json, text/plain', 'Authorization': uw_token}"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"theme_colors = {\n",
" 'darkest_black': '#0d1117',\n",
" 'darker_black': '#161b22',\n",
" 'dark_black': '#21262d',\n",
" 'darkest_gray': '#89929b',\n",
" 'darker_gray': '#c6cdd5',\n",
" 'dark_gray': '#ecf2f8',\n",
" 'white': '#ffffff',\n",
" 'red': '#fa7970',\n",
" 'orange': '#faa356',\n",
" 'green': '#7ce38b',\n",
" 'sky_blue': '#a2d2fb',\n",
" 'blue': '#77bdfb',\n",
" 'purple': '#cea5fb'\n",
"}\n",
"\n",
"def theme_light(legend_position: str = 'bottom') -> ggplot:\n",
" darkest_black = '#0d1117'\n",
" darker_black = '#161b22'\n",
" dark_black = '#21262d'\n",
" white = '#ffffff'\n",
" return theme_none() + theme(\n",
" line=element_line(color=darkest_black, size=1),\n",
" rect=element_rect(color=darkest_black, fill=white, size=1),\n",
" text=element_text(color=darkest_black),\n",
" axis_ontop=True,\n",
" axis_ticks=element_line(color=darker_black),\n",
" legend_background=element_rect(size=1),\n",
" legend_position=legend_position,\n",
" panel_grid_major=element_line(color=dark_black, size=0.5, linetype='dashed'),\n",
" panel_grid_minor=element_blank(),\n",
" plot_title=element_text(hjust=0.5, color=darkest_black),\n",
" tooltip=element_rect(color=dark_black),\n",
" axis_tooltip=element_rect(color=darker_black)\n",
" )\n",
"\n",
"def expand_net_prem_ticks(data: list[dict], tz: str) -> pl.DataFrame:\n",
" \"\"\"\n",
" API documentation:\n",
" https://api.unusualwhales.com/docs#/operations/PublicApi.TickerController.net_prem_ticks\n",
" \n",
" Function:\n",
" Takes the raw 'data' list of dictionaries from the UW endpoint\n",
" then converts string time into timezone-aware datetimes and\n",
" calculates the cumulative sum of net call and put premiums\n",
" (in millions) to return a polars DataFrame.\n",
" \"\"\"\n",
" raw_df = pl.DataFrame(data)\n",
" return (\n",
" raw_df\n",
" .with_columns(\n",
" pl.col('tape_time').str.strptime(pl.Datetime, '%Y-%m-%dT%H:%M:%S%.fZ').alias('tape_dt')\n",
" )\n",
" .with_columns(\n",
" pl.col('tape_dt').dt.convert_time_zone(tz).alias('tape_dt_tz')\n",
" )\n",
" .with_columns(\n",
" pl.col('tape_dt_tz').dt.strftime('%H:%M').alias('tape_dt_tz_str')\n",
" )\n",
" .with_columns(\n",
" [\n",
" pl.col('net_call_premium').cast(pl.Float64),\n",
" pl.col('net_put_premium').cast(pl.Float64)\n",
" ]\n",
" )\n",
" .with_columns(\n",
" [\n",
" (\n",
" pl.col('net_call_premium').cum_sum() / 1_000_000\n",
" ).alias('cum_net_call_premium_in_millions'),\n",
" (\n",
" pl.col('net_put_premium').cum_sum() / 1_000_000\n",
" ).alias('cum_net_put_premium_in_millions')\n",
" ]\n",
" )\n",
" )\n",
"\n",
"def create_plot_df(df: pl.DataFrame) -> pl.DataFrame:\n",
" \"\"\"\n",
" Function:\n",
" Takes the expanded polars DataFrame and manipulate it down into\n",
" essentially one really-long DataFrame with with columns formatted\n",
" to be easy for lets-plot to display.\n",
" \"\"\"\n",
" c_df = (\n",
" df\n",
" .select(\n",
" ['tape_dt_tz', 'tape_dt_tz_str', 'cum_net_call_premium_in_millions']\n",
" )\n",
" .with_columns(\n",
" pl.lit('Call').alias('type')\n",
" )\n",
" .rename(\n",
" {'cum_net_call_premium_in_millions': 'net_premium'}\n",
" )\n",
" )\n",
" p_df = (\n",
" df\n",
" .select(\n",
" ['tape_dt_tz', 'tape_dt_tz_str', 'cum_net_put_premium_in_millions']\n",
" )\n",
" .with_columns(\n",
" pl.lit('Put').alias('type')\n",
" )\n",
" .rename(\n",
" {'cum_net_put_premium_in_millions': 'net_premium'}\n",
" )\n",
" )\n",
" return pl.concat([c_df, p_df])\n",
"\n",
"def create_plot(df: pl.DataFrame,\n",
" ticker: str,\n",
" tz: str,\n",
" color_mapping: dict[str, str],\n",
" size: int,\n",
" legend_position: str = 'bottom',\n",
" hide_axis_labels: bool = True) -> ggplot:\n",
" \"\"\"\n",
" Function:\n",
" Take the final 'long' polars DataFrame and return a lets-plot\n",
" geom_line chart showing the cumulative net call and put premiums\n",
" (in millions) for every minute of the cash session.\n",
" \"\"\"\n",
" p = ggplot(df, aes(x='tape_dt_tz_str', y='net_premium', color='type')) + \\\n",
" labs(x=f'Time ({tz})', y='Net Premium', title=f'{ticker} Net Premium (in $M)') + \\\n",
" scale_color_manual(values=color_mapping) + \\\n",
" geom_line(size=size) + \\\n",
" theme_light(legend_position=legend_position) + \\\n",
" scale_y_continuous(format='{.2f}M')\n",
" if hide_axis_labels:\n",
" p += theme(axis_title_x=element_blank(), axis_title_y=element_blank())\n",
" return p"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Create a Net Premium Flow plot for a single stock**\n",
"- Let's start with COIN, crypto names have been very volatile in the last two weeks\n",
"- Put traders (red line) were not very active, trading between $0 and -$1M all day\n",
"- Call traders (blue line) sold appx -$6.75M in premium over the ten-minute span from 11:13AM to 11:23AM Chicago time"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" <div id=\"I1O6vI\"></div>\n",
" <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
" (function() {\n",
" var plotSpec={\n",
"\"data\":{\n",
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"},\n",
"\"mapping\":{\n",
"\"x\":\"tape_dt_tz_str\",\n",
"\"y\":\"net_premium\",\n",
"\"color\":\"type\"\n",
"},\n",
"\"data_meta\":{\n",
"\"series_annotations\":[{\n",
"\"column\":\"tape_dt_tz\",\n",
"\"type\":\"datetime\"\n",
"}]\n",
"},\n",
"\"ggtitle\":{\n",
"\"text\":\"COIN Net Premium (in $M)\"\n",
"},\n",
"\"theme\":{\n",
"\"name\":\"none\",\n",
"\"line\":{\n",
"\"color\":\"#0d1117\",\n",
"\"size\":1.0,\n",
"\"blank\":false\n",
"},\n",
"\"rect\":{\n",
"\"fill\":\"#ffffff\",\n",
"\"color\":\"#0d1117\",\n",
"\"size\":1.0,\n",
"\"blank\":false\n",
"},\n",
"\"text\":{\n",
"\"color\":\"#0d1117\",\n",
"\"blank\":false\n",
"},\n",
"\"axis_ontop\":true,\n",
"\"axis_ticks\":{\n",
"\"color\":\"#161b22\",\n",
"\"blank\":false\n",
"},\n",
"\"legend_background\":{\n",
"\"size\":1.0,\n",
"\"blank\":false\n",
"},\n",
"\"legend_position\":\"none\",\n",
"\"panel_grid_major\":{\n",
"\"color\":\"#21262d\",\n",
"\"size\":0.5,\n",
"\"linetype\":\"dashed\",\n",
"\"blank\":false\n",
"},\n",
"\"panel_grid_minor\":{\n",
"\"blank\":true\n",
"},\n",
"\"plot_title\":{\n",
"\"color\":\"#0d1117\",\n",
"\"hjust\":0.5,\n",
"\"blank\":false\n",
"},\n",
"\"axis_tooltip\":{\n",
"\"color\":\"#161b22\",\n",
"\"blank\":false\n",
"},\n",
"\"tooltip\":{\n",
"\"color\":\"#21262d\",\n",
"\"blank\":false\n",
"},\n",
"\"axis_title_x\":{\n",
"\"blank\":true\n",
"},\n",
"\"axis_title_y\":{\n",
"\"blank\":true\n",
"}\n",
"},\n",
"\"kind\":\"plot\",\n",
"\"scales\":[{\n",
"\"name\":\"Time (America/Chicago)\",\n",
"\"aesthetic\":\"x\"\n",
"},{\n",
"\"name\":\"Net Premium\",\n",
"\"aesthetic\":\"y\"\n",
"},{\n",
"\"aesthetic\":\"color\",\n",
"\"breaks\":[\"Call\",\"Put\"],\n",
"\"values\":[\"#77bdfb\",\"#fa7970\"]\n",
"},{\n",
"\"aesthetic\":\"y\",\n",
"\"format\":\"{.2f}M\"\n",
"}],\n",
"\"layers\":[{\n",
"\"geom\":\"line\",\n",
"\"mapping\":{\n",
"},\n",
"\"data_meta\":{\n",
"},\n",
"\"size\":1.0,\n",
"\"data\":{\n",
"}\n",
"}],\n",
"\"metainfo_list\":[]\n",
"};\n",
" var plotContainer = document.getElementById(\"I1O6vI\");\n",
" window.letsPlotCall(function() {{\n",
" LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
" }});\n",
" })();\n",
" </script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ticker = 'COIN'\n",
"\n",
"color_mapping = {\n",
" 'Call': theme_colors['blue'],\n",
" 'Put': theme_colors['red']\n",
"}\n",
"url = f'https://api.unusualwhales.com/api/stock/{ticker}/net-prem-ticks'\n",
"r = requests.get(url, headers=headers)\n",
"if r.status_code == 200:\n",
" df = expand_net_prem_ticks(r.json()['data'], 'America/Chicago')\n",
" plot_df = create_plot_df(df)\n",
" p = create_plot(\n",
" plot_df,\n",
" ticker,\n",
" 'America/Chicago',\n",
" color_mapping,\n",
" 1,\n",
" legend_position='none',\n",
" hide_axis_labels=True\n",
" )\n",
"else:\n",
" print(f'Error fetching data for {ticker}')\n",
"p.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Create a Net Premium Flow plot dashboard for a group of 8 single stocks on Wed Oct 9th**\n",
"- This time let's go with the MAGMANTN (META, AAPL, GOOGL, MSFT, AMZN, NFLX, TSLA, and NVDA) group\n",
"- We will use `gggrid` to group the net cumulative premiums for each of the 8 charts together\n",
"- Call sellers stepped up early in both META and GOOGL, their call premium never recovered\n",
"- Big step-change up in MSFT Call buying at 9:45AM Chicago time but sellers battled back to roughly zero\n",
"- Call buyers consistent all day long in AMZN\n",
"- Steady Call buying in TSLA until about 12:30PM Chicago time before Call sellers steadily brought that cumulative number back down\n",
"- Steady Put selling in NVDA consistently throughout the day, big -$55M Call selling in the first 15 minutes of the session and the cumulative Call number never recovered"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"\"name\":\"grid\"\n",
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"\"figures\":[{\n",
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,\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\",\"Put\"]\n",
"},\n",
"\"mapping\":{\n",
"\"x\":\"tape_dt_tz_str\",\n",
"\"y\":\"net_premium\",\n",
"\"color\":\"type\"\n",
"},\n",
"\"data_meta\":{\n",
"\"series_annotations\":[{\n",
"\"column\":\"tape_dt_tz\",\n",
"\"type\":\"datetime\"\n",
"}]\n",
"},\n",
"\"ggtitle\":{\n",
"\"text\":\"NVDA Net Premium (in $M)\"\n",
"},\n",
"\"theme\":{\n",
"\"name\":\"none\",\n",
"\"line\":{\n",
"\"color\":\"#0d1117\",\n",
"\"size\":1.0,\n",
"\"blank\":false\n",
"},\n",
"\"rect\":{\n",
"\"fill\":\"#ffffff\",\n",
"\"color\":\"#0d1117\",\n",
"\"size\":1.0,\n",
"\"blank\":false\n",
"},\n",
"\"text\":{\n",
"\"color\":\"#0d1117\",\n",
"\"blank\":false\n",
"},\n",
"\"axis_ontop\":true,\n",
"\"axis_ticks\":{\n",
"\"color\":\"#161b22\",\n",
"\"blank\":false\n",
"},\n",
"\"legend_background\":{\n",
"\"size\":1.0,\n",
"\"blank\":false\n",
"},\n",
"\"legend_position\":\"none\",\n",
"\"panel_grid_major\":{\n",
"\"color\":\"#21262d\",\n",
"\"size\":0.5,\n",
"\"linetype\":\"dashed\",\n",
"\"blank\":false\n",
"},\n",
"\"panel_grid_minor\":{\n",
"\"blank\":true\n",
"},\n",
"\"plot_title\":{\n",
"\"color\":\"#0d1117\",\n",
"\"hjust\":0.5,\n",
"\"blank\":false\n",
"},\n",
"\"axis_tooltip\":{\n",
"\"color\":\"#161b22\",\n",
"\"blank\":false\n",
"},\n",
"\"tooltip\":{\n",
"\"color\":\"#21262d\",\n",
"\"blank\":false\n",
"},\n",
"\"axis_title_x\":{\n",
"\"blank\":true\n",
"},\n",
"\"axis_title_y\":{\n",
"\"blank\":true\n",
"}\n",
"},\n",
"\"kind\":\"plot\",\n",
"\"scales\":[{\n",
"\"name\":\"Time (America/Chicago)\",\n",
"\"aesthetic\":\"x\"\n",
"},{\n",
"\"name\":\"Net Premium\",\n",
"\"aesthetic\":\"y\"\n",
"},{\n",
"\"aesthetic\":\"color\",\n",
"\"breaks\":[\"Call\",\"Put\"],\n",
"\"values\":[\"#77bdfb\",\"#fa7970\"]\n",
"},{\n",
"\"aesthetic\":\"y\",\n",
"\"format\":\"{.2f}M\"\n",
"}],\n",
"\"layers\":[{\n",
"\"geom\":\"line\",\n",
"\"mapping\":{\n",
"},\n",
"\"data_meta\":{\n",
"},\n",
"\"size\":1.0,\n",
"\"data\":{\n",
"}\n",
"}],\n",
"\"metainfo_list\":[]\n",
"}]\n",
"};\n",
" var plotContainer = document.getElementById(\"pAwp2c\");\n",
" window.letsPlotCall(function() {{\n",
" LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
" }});\n",
" })();\n",
" </script>"
],
"text/plain": [
"<lets_plot.plot.subplots.SupPlotsSpec at 0x7f002ae6b8b0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tickers = ['META', 'AAPL', 'GOOGL', 'MSFT', 'AMZN', 'NFLX', 'TSLA', 'NVDA']\n",
"\n",
"dfs = []\n",
"plot_dfs = []\n",
"plots = []\n",
"color_mapping = {\n",
" 'Call': theme_colors['blue'],\n",
" 'Put': theme_colors['red']\n",
"}\n",
"for ticker in tickers:\n",
" url = f'https://api.unusualwhales.com/api/stock/{ticker}/net-prem-ticks'\n",
" r = requests.get(url, headers=headers)\n",
"\n",
" if r.status_code == 200:\n",
" df = expand_net_prem_ticks(r.json()['data'], 'America/Chicago')\n",
" dfs.append(df)\n",
" plot_df = create_plot_df(df)\n",
" plot_dfs.append(plot_df)\n",
" p = create_plot(\n",
" plot_df,\n",
" ticker,\n",
" 'America/Chicago',\n",
" color_mapping,\n",
" 1,\n",
" legend_position='none',\n",
" hide_axis_labels=True\n",
" )\n",
" plots.append(p)\n",
" else:\n",
" print(f'Error fetching data for {ticker}')\n",
"gggrid(plots, ncol=4)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "dans-magic-house-uvW-KuUb-py3.10",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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