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danwagnerco/net_premium_flow_dashboard.ipynb

Created November 19, 2025 01:32
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net_premium_flow_dashboard.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "585e6f35",
"metadata": {},
"source": [
"# Wealth Management Option Sentiment\n",
"\n",
"There are many ways to gauge sector sentiment but the flow of option premium is uniquely interesting because instead of yapping, you can actually see capital deployed.\n",
"\n",
"A dashboard that plots premium over days / weeks might signal inflection points in publicly-tradeable wealth management firms, or medtech companies, or real estate operators, etc.\n",
"\n",
"Scroll down past all the code to see a first-pass implementation targeting:\n",
"- **LPL Financial** (ticker: `LPLA`)\n",
"- **Ameriprise Financial** (ticker: `AMP`)\n",
"- **Raymond James** (ticker: `RJF`)\n",
"- **Stifel Financial** (ticker: `SF`)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "571489bd",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div id=\"c9owdT\"></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(\"c9owdT\").appendChild(div);\n",
" };\n",
" var e = document.getElementById(\"c9owdT\");\n",
" e.appendChild(script);\n",
" })()\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"import httpx\n",
"import polars as pl\n",
"import lets_plot as lp\n",
"lp.LetsPlot.setup_html()\n",
"\n",
"uw_token = os.getenv(\"UW_TOKEN\")\n",
"headers = {'Accept': 'application/json, text/plain', 'Authorization': uw_token}\n",
"\n",
"LEON_COLOR_THEME = {\n",
" \"red\": \"#dc3545\",\n",
" \"teal\": \"#20c997\",\n",
" \"navy\": \"#1E3A5F\",\n",
" \"black\": \"#000000\",\n",
" \"white\": \"#FFFFFF\",\n",
" \"gray_medium\": \"#3C3C3C\",\n",
" \"gray_light\": \"#D3D3D3\",\n",
"}\n",
"\n",
"color_mapping = {\n",
" \"cumsum_net_call_prem_in_mil\": LEON_COLOR_THEME[\"teal\"],\n",
" \"cumsum_net_put_prem_in_mil\": LEON_COLOR_THEME[\"red\"],\n",
" \"price\": LEON_COLOR_THEME[\"white\"],\n",
"}\n",
"\n",
"def leon_theme(colors: dict, show_legend: bool = True) -> lp.theme:\n",
" t = lp.theme_none() + lp.theme(\n",
" plot_background=lp.element_rect(fill=colors[\"navy\"]),\n",
" panel_background=lp.element_rect(fill=colors[\"navy\"]),\n",
" panel_grid_major=lp.element_blank(),\n",
" panel_grid_minor=lp.element_blank(),\n",
" axis_ontop=True,\n",
" axis_ticks=lp.element_line(color=colors[\"white\"], size=1),\n",
" axis_tooltip=lp.element_rect(color=colors[\"navy\"]),\n",
" tooltip=lp.element_rect(color=colors[\"gray_light\"], fill=colors[\"navy\"]),\n",
" line=lp.element_line(color=colors[\"white\"], size=1),\n",
" rect=lp.element_rect(color=colors[\"navy\"], fill=colors[\"navy\"], size=2),\n",
" text=lp.element_text(color=colors[\"white\"], size=10),\n",
" legend_background=lp.element_rect(\n",
" color=colors[\"gray_light\"], fill=colors[\"navy\"], size=2\n",
" ),\n",
" plot_title=lp.element_text(hjust=0.5, size=16, color=colors[\"white\"]),\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": "code",
"execution_count": 2,
"id": "ad332063",
"metadata": {},
"outputs": [],
"source": [
"tickers = [\"LPLA\", \"AMP\", \"RJF\", \"SF\"]\n",
"target_date = \"2025-11-18\"\n",
"\n",
"raw_npt_dfs = {}\n",
"for ticker in tickers:\n",
" url = f\"https://api.unusualwhales.com/api/stock/{ticker}/net-prem-ticks\"\n",
" params = {\"date\": target_date}\n",
" rsp = httpx.get(url, headers=headers, params=params)\n",
" if rsp.status_code == 200 and len(rsp.json()[\"data\"]) > 0:\n",
" raw_npt_dfs[ticker] = pl.DataFrame(rsp.json()[\"data\"])\n",
" else:\n",
" print(f\"{ticker} data not found for {target_date}\")\n",
"\n",
"clean_npt_dfs = {}\n",
"for ticker, raw_df in raw_npt_dfs.items():\n",
" clean_npt_dfs[ticker] = (\n",
" raw_df\n",
" .with_columns(\n",
" pl.col(\"net_call_premium\").cast(pl.Float64),\n",
" pl.col(\"net_put_premium\").cast(pl.Float64),\n",
" pl.col(\"tape_time\").cast(pl.Datetime)\n",
" )\n",
" .with_columns(\n",
" pl.col(\"tape_time\").dt.convert_time_zone(\n",
" \"America/New_York\"\n",
" ).alias(\"tape_time_tz\")\n",
" )\n",
" .with_columns(\n",
" pl.col(\"tape_time_tz\").dt.strftime(\"%H:%M\").alias(\"hms_str\")\n",
" )\n",
" .with_columns(\n",
" (\n",
" pl.col(\"net_call_premium\").cum_sum() / 1_000\n",
" ).alias(\"cumsum_net_call_prem_in_k\"),\n",
" (\n",
" pl.col(\"net_put_premium\").cum_sum() / 1_000\n",
" ).alias(\"cumsum_net_put_prem_in_k\")\n",
" )\n",
" )\n",
"\n",
"plot_ready_npt_dfs = {}\n",
"for ticker, clean_df in clean_npt_dfs.items():\n",
" plot_ready_npt_dfs[ticker] = (\n",
" clean_df\n",
" .unpivot(\n",
" index=[\"hms_str\"],\n",
" on=[\"cumsum_net_call_prem_in_k\", \"cumsum_net_put_prem_in_k\"],\n",
" variable_name=\"flow_type\",\n",
" value_name=\"cumsum_net_prem_in_k\"\n",
" )\n",
" )\n",
"\n",
"raw_ohlc_dfs = {}\n",
"candle_size = \"1m\"\n",
"for ticker in tickers:\n",
" url = f\"https://api.unusualwhales.com/api/stock/{ticker}/ohlc/{candle_size}\"\n",
" params = {\"date\": target_date}\n",
" rsp = httpx.get(url, headers=headers, params=params)\n",
" raw_ohlc_dfs[ticker] = pl.DataFrame(rsp.json()[\"data\"])\n",
"\n",
"clean_ohlc_dfs = {}\n",
"for ticker, raw_df in raw_ohlc_dfs.items():\n",
" start_date = clean_npt_dfs[ticker].select(pl.col(\"tape_time\").head(1)).item()\n",
" end_date = clean_npt_dfs[ticker].select(pl.col(\"tape_time\").tail(1)).item()\n",
" clean_ohlc_dfs[ticker] = (\n",
" raw_df\n",
" .with_columns(\n",
" pl.col(\"open\").cast(pl.Float64),\n",
" pl.col(\"high\").cast(pl.Float64),\n",
" pl.col(\"low\").cast(pl.Float64),\n",
" pl.col(\"close\").cast(pl.Float64),\n",
" pl.col(\"start_time\").cast(pl.Datetime)\n",
" )\n",
" .filter(\n",
" (pl.col(\"start_time\") >= start_date) & (pl.col(\"start_time\") <= end_date)\n",
" )\n",
" .with_columns(\n",
" pl.col(\"start_time\").dt.convert_time_zone(\n",
" \"America/New_York\"\n",
" ).alias(\"tape_time_tz\")\n",
" )\n",
" .with_columns(\n",
" pl.col(\"tape_time_tz\").dt.strftime(\"%H:%M\").alias(\"hms_str\")\n",
" )\n",
" .select(\n",
" [\n",
" \"start_time\",\n",
" \"tape_time_tz\",\n",
" \"hms_str\",\n",
" \"open\",\n",
" \"high\",\n",
" \"low\",\n",
" \"close\",\n",
" ]\n",
" )\n",
" .sort(\"tape_time_tz\", descending=False)\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6b1d3970",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" <div id=\"nu8qBq\"></div>\n",
" <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
" \n",
" (function() {\n",
" // ----------\n",
" \n",
" const forceImmediateRender = false;\n",
" const responsive = false;\n",
" \n",
" let sizing = {\n",
" width_mode: \"MIN\",\n",
" height_mode: \"SCALED\",\n",
" width: null, \n",
" height: null \n",
" };\n",
" \n",
" const preferredWidth = document.body.dataset.letsPlotPreferredWidth;\n",
" if (preferredWidth !== undefined) {\n",
" sizing = {\n",
" width_mode: 'FIXED',\n",
" height_mode: 'SCALED',\n",
" width: parseFloat(preferredWidth)\n",
" };\n",
" }\n",
" \n",
" const containerDiv = document.getElementById(\"nu8qBq\");\n",
" let fig = null;\n",
" \n",
" function renderPlot() {\n",
" if (fig === null) {\n",
" const plotSpec = {\n",
"\"ggsize\":{\n",
"\"width\":900.0,\n",
"\"height\":900.0\n",
"},\n",
"\"theme\":{\n",
"\"plot_background\":{\n",
"\"fill\":\"#1E3A5F\",\n",
"\"blank\":false\n",
"}\n",
"},\n",
"\"kind\":\"subplots\",\n",
"\"layout\":{\n",
"\"ncol\":2.0,\n",
"\"nrow\":2.0,\n",
"\"align\":true,\n",
"\"name\":\"grid\"\n",
"},\n",
"\"figures\":[{\n",
"\"theme\":{\n",
"\"plot_background\":{\n",
"\"fill\":\"#1E3A5F\",\n",
"\"blank\":false\n",
"}\n",
"},\n",
"\"kind\":\"subplots\",\n",
"\"layout\":{\n",
"\"ncol\":1.0,\n",
"\"nrow\":2.0,\n",
"\"align\":true,\n",
"\"name\":\"grid\"\n",
"},\n",
"\"figures\":[{\n",
"\"data\":{\n",
"\"hms_str\":[\"09:47\",\"09:51\",\"09:57\",\"10:16\",\"10:55\",\"11:11\",\"11:14\",\"11:19\",\"11:25\",\"13:08\",\"13:09\",\"13:22\",\"13:23\",\"13:28\",\"13:29\",\"13:47\",\"14:09\",\"14:11\",\"14:13\",\"14:17\",\"14:20\",\"14:22\",\"14:24\",\"14:43\",\"15:05\",\"15:16\",\"15:30\",\"15:54\",\"15:55\",\"15:58\",\"09:47\",\"09:51\",\"09:57\",\"10:16\",\"10:55\",\"11:11\",\"11:14\",\"11:19\",\"11:25\",\"13:08\",\"13:09\",\"13:22\",\"13:23\",\"13:28\",\"13:29\",\"13:47\",\"14:09\",\"14:11\",\"14:13\",\"14:17\",\"14:20\",\"14:22\",\"14:24\",\"14:43\",\"15:05\",\"15:16\",\"15:30\",\"15:54\",\"15:55\",\"15:58\"],\n",
"\"flow_type\":[\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_call_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\",\"cumsum_net_put_prem_in_k\"],\n",
"\"cumsum_net_prem_in_k\":[0.0,0.0,-1.084,-1.084,-0.124,3.236,3.236,3.236,3.236,8.211,8.211,7.932,7.7250000000000005,7.7250000000000005,7.7250000000000005,6.465,6.465,6.465,6.465,6.465,6.465,6.465,4.025,1.165,1.165,1.165,1.165,0.04,0.04,0.04,1.2,-11.35,-11.35,0.49,0.49,1.0,1.57,1.57,-2.2800000000000002,-2.2800000000000002,3.43,3.43,3.43,3.4450000000000003,3.59,3.59,3.553,13.013,13.013,6.523000000000001,5.623,8.373,9.533,9.533,8.183,6.853,5.523,10.243,10.943,11.643]\n",
"},\n",
"\"mapping\":{\n",
"\"x\":\"hms_str\",\n",
"\"y\":\"cumsum_net_prem_in_k\",\n",
"\"color\":\"flow_type\"\n",
"},\n",
"\"data_meta\":{\n",
"\"series_annotations\":[{\n",
"\"type\":\"str\",\n",
"\"column\":\"hms_str\"\n",
"},{\n",
"\"type\":\"str\",\n",
"\"column\":\"flow_type\"\n",
"},{\n",
"\"type\":\"float\",\n",
"\"column\":\"cumsum_net_prem_in_k\"\n",
"}]\n",
"},\n",
"\"ggtitle\":{\n",
"\"text\":\"2025-11-18: LPLA\"\n",
"},\n",
"\"guides\":{\n",
"\"x\":{\n",
"\"title\":\"Timestamp\"\n",
"},\n",
"\"y\":{\n",
"\"title\":\"Net Prem $K\"\n",
"}\n",
"},\n",
"\"theme\":{\n",
"\"name\":\"none\",\n",
"\"line\":{\n",
"\"color\":\"#FFFFFF\",\n",
"\"size\":1.0,\n",
"\"blank\":false\n",
"},\n",
"\"rect\":{\n",
"\"fill\":\"#1E3A5F\",\n",
"\"color\":\"#1E3A5F\",\n",
"\"size\":2.0,\n",
"\"blank\":false\n",
"},\n",
"\"text\":{\n",
"\"color\":\"#FFFFFF\",\n",
"\"size\":10.0,\n",
"\"blank\":false\n",
"},\n",
"\"axis_ontop\":true,\n",
"\"axis_ticks\":{\n",
"\"color\":\"#FFFFFF\",\n",
"\"size\":1.0,\n",
"\"blank\":false\n",
"},\n",
"\"legend_background\":{\n",
"\"fill\":\"#1E3A5F\",\n",
"\"color\":\"#D3D3D3\",\n",
"\"size\":2.0,\n",
"\"blank\":false\n",
"},\n",
"\"panel_background\":{\n",
"\"fill\":\"#1E3A5F\",\n",
"\"blank\":false\n",
"},\n",
"\"panel_grid_major\":{\n",
"\"blank\":true\n",
"},\n",
"\"panel_grid_minor\":{\n",
"\"blank\":true\n",
"},\n",
"\"plot_background\":{\n",
"\"fill\":\"#1E3A5F\",\n",
"\"blank\":false\n",
"},\n",
"\"plot_title\":{\n",
"\"color\":\"#FFFFFF\",\n",
"\"size\":16.0,\n",
"\"hjust\":0.5,\n",
"\"blank\":false\n",
"},\n",
"\"axis_tooltip\":{\n",
"\"color\":\"#1E3A5F\",\n",
"\"blank\":false\n",
"},\n",
"\"tooltip\":{\n",
"\"fill\":\"#1E3A5F\",\n",
"\"color\":\"#D3D3D3\",\n",
"\"blank\":false\n",
"},\n",
"\"legend_position\":\"none\",\n",
"\"axis_title_x\":{\n",
"\"blank\":true\n",
"}\n",
"},\n",
"\"kind\":\"plot\",\n",
"\"scales\":[{\n",
"\"aesthetic\":\"color\",\n",
"\"breaks\":[\"cumsum_net_call_prem_in_mil\",\"cumsum_net_put_prem_in_mil\",\"price\"],\n",
"\"values\":[\"#20c997\",\"#dc3545\",\"#FFFFFF\"]\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",
"\"spec_id\":\"1\"\n",
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"\"price\":[115.445,115.44,115.56,116.36,115.62,116.265,116.195,116.35,115.29,115.675,115.71,115.64,115.935,115.895,115.895,115.65,115.3,115.265,115.27,115.67,115.6365,115.705,115.88,116.23,116.34,116.07,116.09,115.98,116.02,116.0,116.02,116.0,115.62,115.63,116.025,116.0,116.18,115.91,115.915,115.7001,116.0,115.995,115.855,115.86,115.6601,115.81,116.12,116.145,116.27,116.38,116.38,116.385,116.23,116.15,116.315,116.505,116.4685,116.64,116.685,116.775,116.775,116.81,116.8,116.725,116.805,116.6,116.46,116.41,116.51,116.37,116.53,116.34,116.535,116.6811,116.5375,116.545,116.55,116.44,116.555,116.22,116.365,116.48,116.58,116.3201,116.555,116.5,116.49,116.495,116.38,116.73,116.58,116.66,116.375,116.39,116.54,116.43,116.4,116.31,116.235,116.26,116.15,116.26,116.32,116.32,116.565,116.91,116.885,116.86,116.86,117.07,117.025,116.83,116.96,116.88,117.275,117.28,117.1,117.265,117.13,117.24,117.575,117.585,117.585,117.58,117.47,117.66,117.55,117.4889,117.61,117.75,117.4,117.47,117.5675,117.64,117.5,117.38,117.29,117.24,117.505,117.25,117.08,117.15,117.13,117.305,117.46,117.525,117.405,117.4125,117.29,117.28,117.38,117.335,117.22,117.32,117.3,117.29,117.22,117.12,117.0,116.87,117.01,117.165,117.0,116.88,116.955,116.955,116.85,116.885,116.885,116.78,116.8,117.0,116.81,116.8164,116.84,116.91,116.975,116.92,116.79,116.845,116.89,116.94,116.885,116.72,116.74,116.75,116.81,116.82,116.78,116.91,117.04,116.9301,117.08,117.01,116.87,116.92,116.9,116.8,116.795,116.83,117.035,116.92,116.945,116.9,116.92,116.83,116.73,116.64,116.82,116.845,116.995,117.02,117.01,117.135,117.07,117.07,116.99,117.115,117.115,117.0,117.115,117.11,117.06,117.15,117.13,117.24,117.21,117.205,117.32,117.195,117.18,117.26,117.4,117.31,117.43,117.24,117.21,117.11,117.2,117.13,117.0717,117.1,117.12,117.26,117.31,117.265,117.19,117.18,117.15,117.19]\n",
"},\n",
"\"mapping\":{\n",
"\"x\":\"hms_str\",\n",
"\"y\":\"price\"\n",
"},\n",
"\"data_meta\":{\n",
"\"series_annotations\":[{\n",
"\"type\":\"str\",\n",
"\"column\":\"hms_str\"\n",
"},{\n",
"\"type\":\"str\",\n",
"\"column\":\"ohlc_type\"\n",
"},{\n",
"\"type\":\"float\",\n",
"\"column\":\"price\"\n",
"}]\n",
"},\n",
"\"ggtitle\":{\n",
"\"text\":\"SF Price\"\n",
"},\n",
"\"guides\":{\n",
"\"x\":{\n",
"\"title\":\"Timestamp\"\n",
"},\n",
"\"y\":{\n",
"\"title\":\"SF Price\"\n",
"}\n",
"},\n",
"\"theme\":{\n",
"\"name\":\"none\",\n",
"\"line\":{\n",
"\"color\":\"#FFFFFF\",\n",
"\"size\":1.0,\n",
"\"blank\":false\n",
"},\n",
"\"rect\":{\n",
"\"fill\":\"#1E3A5F\",\n",
"\"color\":\"#1E3A5F\",\n",
"\"size\":2.0,\n",
"\"blank\":false\n",
"},\n",
"\"text\":{\n",
"\"color\":\"#FFFFFF\",\n",
"\"size\":10.0,\n",
"\"blank\":false\n",
"},\n",
"\"axis_ontop\":true,\n",
"\"axis_ticks\":{\n",
"\"color\":\"#FFFFFF\",\n",
"\"size\":1.0,\n",
"\"blank\":false\n",
"},\n",
"\"legend_background\":{\n",
"\"fill\":\"#1E3A5F\",\n",
"\"color\":\"#D3D3D3\",\n",
"\"size\":2.0,\n",
"\"blank\":false\n",
"},\n",
"\"panel_background\":{\n",
"\"fill\":\"#1E3A5F\",\n",
"\"blank\":false\n",
"},\n",
"\"panel_grid_major\":{\n",
"\"blank\":true\n",
"},\n",
"\"panel_grid_minor\":{\n",
"\"blank\":true\n",
"},\n",
"\"plot_background\":{\n",
"\"fill\":\"#1E3A5F\",\n",
"\"blank\":false\n",
"},\n",
"\"plot_title\":{\n",
"\"color\":\"#FFFFFF\",\n",
"\"size\":16.0,\n",
"\"hjust\":0.5,\n",
"\"blank\":true\n",
"},\n",
"\"axis_tooltip\":{\n",
"\"color\":\"#1E3A5F\",\n",
"\"blank\":false\n",
"},\n",
"\"tooltip\":{\n",
"\"fill\":\"#1E3A5F\",\n",
"\"color\":\"#D3D3D3\",\n",
"\"blank\":false\n",
"},\n",
"\"legend_position\":\"none\",\n",
"\"axis_title_x\":{\n",
"\"blank\":true\n",
"}\n",
"},\n",
"\"kind\":\"plot\",\n",
"\"scales\":[],\n",
"\"layers\":[{\n",
"\"geom\":\"line\",\n",
"\"mapping\":{\n",
"},\n",
"\"data_meta\":{\n",
"},\n",
"\"size\":1.0,\n",
"\"color\":\"#FFFFFF\",\n",
"\"data\":{\n",
"}\n",
"}],\n",
"\"metainfo_list\":[],\n",
"\"spec_id\":\"8\"\n",
"}]\n",
"}]\n",
"};\n",
" window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
" } else {\n",
" fig.updateView({});\n",
" }\n",
" }\n",
" \n",
" const renderImmediately = \n",
" forceImmediateRender || (\n",
" sizing.width_mode === 'FIXED' && \n",
" (sizing.height_mode === 'FIXED' || sizing.height_mode === 'SCALED')\n",
" );\n",
" \n",
" if (renderImmediately) {\n",
" renderPlot();\n",
" }\n",
" \n",
" if (!renderImmediately || responsive) {\n",
" // Set up observer for initial sizing or continuous monitoring\n",
" var observer = new ResizeObserver(function(entries) {\n",
" for (let entry of entries) {\n",
" if (entry.contentBoxSize && \n",
" entry.contentBoxSize[0].inlineSize > 0) {\n",
" if (!responsive && observer) {\n",
" observer.disconnect();\n",
" observer = null;\n",
" }\n",
" renderPlot();\n",
" if (!responsive) {\n",
" break;\n",
" }\n",
" }\n",
" }\n",
" });\n",
" \n",
" observer.observe(containerDiv);\n",
" }\n",
" \n",
" // ----------\n",
" })();\n",
" \n",
" </script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_ready_ohlc_dfs = {}\n",
"for ticker, clean_df in clean_ohlc_dfs.items():\n",
" plot_ready_ohlc_dfs[ticker] = (\n",
" clean_df\n",
" .unpivot(\n",
" index=[\"hms_str\"],\n",
" on=[\"close\"],\n",
" variable_name=\"ohlc_type\",\n",
" value_name=\"price\"\n",
" )\n",
" )\n",
"\n",
"grouped_plots = {}\n",
"for ticker, plot_ready_npt_df in plot_ready_npt_dfs.items():\n",
" npt_plot = (\n",
" lp.ggplot(plot_ready_npt_df)\n",
" + lp.aes(x=\"hms_str\", y=\"cumsum_net_prem_in_k\", color=\"flow_type\")\n",
" + lp.geom_line(size=1)\n",
" + lp.scale_color_manual(values=color_mapping)\n",
" + lp.ggtitle(f\"{target_date}: {ticker}\")\n",
" + lp.xlab(\"Timestamp\")\n",
" + lp.ylab(\"Net Prem $K\")\n",
" + leon_theme(LEON_COLOR_THEME, show_legend=False)\n",
" + lp.theme(axis_title_x=lp.element_blank())\n",
" )\n",
"\n",
" price_plot = (\n",
" lp.ggplot(plot_ready_ohlc_dfs[ticker])\n",
" + lp.aes(x=\"hms_str\", y=\"price\")\n",
" + lp.geom_line(size=1, color=color_mapping[\"price\"])\n",
" + lp.ggtitle(f\"{ticker} Price\")\n",
" + lp.xlab(\"Timestamp\")\n",
" + lp.ylab(f\"{ticker} Price\")\n",
" + leon_theme(LEON_COLOR_THEME, show_legend=False)\n",
" + lp.theme(\n",
" axis_title_x=lp.element_blank(),\n",
" plot_title=lp.element_blank(),\n",
" )\n",
" )\n",
"\n",
" plots = [npt_plot, price_plot]\n",
" plot_grid = (\n",
" lp.gggrid(\n",
" plots,\n",
" ncol=1,\n",
" align=True\n",
" )\n",
" + lp.theme(plot_background=lp.element_rect(fill=LEON_COLOR_THEME[\"navy\"]))\n",
" )\n",
"\n",
" grouped_plots[ticker] = plot_grid\n",
"\n",
"final_plots = []\n",
"for _, plot_grid in grouped_plots.items():\n",
" final_plots.append(plot_grid)\n",
"\n",
"dashboard = (\n",
" lp.gggrid(final_plots, ncol=2, align=True)\n",
" + lp.ggsize(900, 900)\n",
" + lp.theme(plot_background=lp.element_rect(fill=LEON_COLOR_THEME[\"navy\"]))\n",
")\n",
"dashboard.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "leon-cap-demo",
"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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