From 2516cfbc72d23a7656715314ea94cc34d4c994ee Mon Sep 17 00:00:00 2001 From: Danglewood <85772166+deeleeramone@users.noreply.github.com> Date: Tue, 13 Feb 2024 09:48:52 -0800 Subject: [PATCH] Update USD Liquidity Example Notebook for V4 (#5902) * Update USD Liquidity Example Notebook for V4 * delete some v3 text in the copperToGold * Update README.md * findSymbols preview image * Google Colab preview image * financialStatements preview * content tsx file * ### title * convert tsx to json * indent size * double quote keys.json * trailing commas * Update content.json * patch --------- Co-authored-by: Igor Radovanovic <74266147+IgorWounds@users.noreply.github.com> Co-authored-by: James Maslek --- examples/README.md | 11 + examples/content.json | 44 + examples/copperToGoldRatio.ipynb | 640 +- examples/financialStatements.webp | Bin 0 -> 12770 bytes examples/findSymbols.webp | Bin 0 -> 80348 bytes examples/googleColab.webp | Bin 0 -> 6108 bytes examples/usdLiquidityIndex.ipynb | 10206 ++++++++++++++++ .../due_diligence/dd_controller.py | 44 - .../due_diligence/tokenterminal_model.py | 4 +- 9 files changed, 10498 insertions(+), 451 deletions(-) create mode 100644 examples/content.json create mode 100644 examples/financialStatements.webp create mode 100644 examples/findSymbols.webp create mode 100644 examples/googleColab.webp create mode 100644 examples/usdLiquidityIndex.ipynb diff --git a/examples/README.md b/examples/README.md index 484cf64f42c..8a496fabdd5 100644 --- a/examples/README.md +++ b/examples/README.md @@ -56,3 +56,14 @@ This notebook shows you how you can use OpenbB Platform as functions in an LLM b - Convert all OpenBB Platform functions to LLM tools - Build a basic Langchain agent that can utilize function calling - Run the agent + +### usdLiquidityIndex + +This notebook demonstrates how to query the Federal Reserve Economic Database and recreate the USD Liquidity Index. + +- Search FRED for series IDs. +- Load multiple series as a single call. +- Unpacking the data response from the FRED query. +- Perform arithmetic operations on a DataFrame. +- Normalization methods for a series or DataFrame. +- Simple processes for creating charts. diff --git a/examples/content.json b/examples/content.json new file mode 100644 index 00000000000..7c5d748bd1e --- /dev/null +++ b/examples/content.json @@ -0,0 +1,44 @@ +[ + { + "title": "Install in Google Colab", + "url": "https://github.com/OpenBB-finance/OpenBBTerminal/blob/develop/examples/googleColab.ipynb", + "img":"https://raw.githubusercontent.com/OpenBB-finance/OpenBBTerminal/develop/examples/googleColab.webp", + "description": + "Install the OpenBB Platform in Google Colab and get started pulling data and creating visualizations." + }, + { + "title": "Find Symbols", + "url": "https://github.com/OpenBB-finance/OpenBBTerminal/blob/develop/examples/findSymbols.ipynb", + "img": "https://raw.githubusercontent.com/OpenBB-finance/OpenBBTerminal/develop/examples/findSymbols.webp", + "description": + "An introduction to discovering, finding, screening, and searching symbols using different sources." + }, + { + "title": "Load Historical Price Data", + "url": "https://github.com/OpenBB-finance/OpenBBTerminal/blob/develop/examples/loadHistoricalPriceData.ipynb", + "img": "https://my.openbb.co/assets/images/sdk/examples/loadHistoricalPriceData.webp", + "description": + "Loading data with different intervals and sources, ticker symbology, load data from other asset classes, load multiple tickers in one go, draw lines on plotly." + }, + { + "title": "Copper To Gold Ratio", + "url": "https://github.com/OpenBB-finance/OpenBBTerminal/blob/develop/examples/copperToGoldRatio.ipynb", + "img": "https://my.openbb.co/assets/images/sdk/examples/copperToGoldRatio.webp", + "description": + "Calculate copper to gold ratio, load front-month future prices, 10-year constant maturity vs treasury bill, basic dataframe operations, plotting on 2 y-axis." + }, + { + "title": "USD Liquidity Index", + "url": "https://github.com/OpenBB-finance/OpenBBTerminal/blob/develop/examples/usdLiquidityIndex.ipynb", + "img": "https://my.openbb.co/assets/images/sdk/examples/usdLiquidityIndex.webp", + "description": + "Query the Federal Reserve Economic Database and recreate the USD Liquidity Index, load multiple data series, basic operations on a dataframe, normalization methods, and creating custom chart." + }, + { + "title": "Financial Statements", + "url": "https://github.com/OpenBB-finance/OpenBBTerminal/blob/develop/examples/financialStatements.ipynb", + "img": "https://raw.githubusercontent.com/OpenBB-finance/OpenBBTerminal/develop/examples/financialStatements.webp", + "description": + "Get started with financial statements in the OpenBB Platform. This notebook compares the data from different providers and demonstrates how to access items within the three main financial statements - balance, cash, and income." + } +] diff --git a/examples/copperToGoldRatio.ipynb b/examples/copperToGoldRatio.ipynb index e2ccf423114..cb5dd96fecc 100644 --- a/examples/copperToGoldRatio.ipynb +++ b/examples/copperToGoldRatio.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -118,7 +118,7 @@ "2000-09-04 273.299988 0.912" ] }, - "execution_count": 63, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -174,16 +174,16 @@ " \n", " \n", " \n", - " 2023-11-20\n", - " 2002.199951\n", - " 3.7965\n", - " 0.001896\n", + " 2023-12-04\n", + " 1998.300049\n", + " 3.820\n", + " 0.001912\n", " \n", " \n", - " 2023-11-27\n", - " 2011.800049\n", - " 3.7625\n", - " 0.001870\n", + " 2023-12-11\n", + " 2021.099976\n", + " 3.885\n", + " 0.001922\n", " \n", " \n", "\n", @@ -192,11 +192,11 @@ "text/plain": [ " Gold Copper Copper/Gold Ratio\n", "date \n", - "2023-11-20 2002.199951 3.7965 0.001896\n", - "2023-11-27 2011.800049 3.7625 0.001870" + "2023-12-04 1998.300049 3.820 0.001912\n", + "2023-12-11 2021.099976 3.885 0.001922" ] }, - "execution_count": 64, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -217,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -254,16 +254,16 @@ " \n", " \n", " \n", - " 2023-11-20\n", - " 2002.199951\n", - " 3.7965\n", - " 1.896164\n", + " 2023-12-04\n", + " 1998.300049\n", + " 3.820\n", + " 1.911625\n", " \n", " \n", - " 2023-11-27\n", - " 2011.800049\n", - " 3.7625\n", - " 1.870216\n", + " 2023-12-11\n", + " 2021.099976\n", + " 3.885\n", + " 1.922221\n", " \n", " \n", "\n", @@ -272,11 +272,11 @@ "text/plain": [ " Gold Copper Copper/Gold Ratio\n", "date \n", - "2023-11-20 2002.199951 3.7965 1.896164\n", - "2023-11-27 2011.800049 3.7625 1.870216" + "2023-12-04 1998.300049 3.820 1.911625\n", + "2023-12-11 2021.099976 3.885 1.922221" ] }, - "execution_count": 65, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -297,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -365,7 +365,7 @@ "2000-09-04 5.68 " ] }, - "execution_count": 67, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -394,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1621,7 +1621,9 @@ "2023-11-06", "2023-11-13", "2023-11-20", - "2023-11-27" + "2023-11-27", + "2023-12-04", + "2023-12-11" ], "y": [ 3.2093862764241465, @@ -2837,7 +2839,9 @@ 1.8542377903946718, 1.8861021388593557, 1.8961642494062623, - 1.8702157055197284 + 1.8874939255873286, + 1.9116248010317485, + 1.9222205914563706 ] }, { @@ -4057,7 +4061,9 @@ "2023-11-06", "2023-11-13", "2023-11-20", - "2023-11-27" + "2023-11-27", + "2023-12-04", + "2023-12-11" ], "y": [ 5.78, @@ -5273,6 +5279,8 @@ 4.67, 4.63, 4.42, + 4.39, + null, null ] } @@ -5289,14 +5297,14 @@ "bar": [ { "error_x": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "error_y": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "#E5ECF6", "width": 0.5 }, "pattern": { @@ -5312,7 +5320,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "#E5ECF6", "width": 0.5 }, "pattern": { @@ -5324,38 +5332,21 @@ "type": "barpolar" } ], - "candlestick": [ - { - "decreasing": { - "fillcolor": "#e4003a", - "line": { - "color": "#e4003a" - } - }, - "increasing": { - "fillcolor": "#00ACFF", - "line": { - "color": "#00ACFF" - } - }, - "type": "candlestick" - } - ], "carpet": [ { "aaxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" }, "baxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" }, "type": "carpet" } @@ -5673,10 +5664,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -5723,8 +5714,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -5829,18 +5821,18 @@ { "cells": { "fill": { - "color": "#506784" + "color": "#EBF0F8" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "header": { "fill": { - "color": "#2a3f5f" + "color": "#C8D4E3" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "type": "table" @@ -5849,10 +5841,9 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, - "arrowwidth": 1, - "showarrow": false + "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { @@ -5994,194 +5985,125 @@ ] }, "colorway": [ - "#ffed00", - "#ef7d00", - "#e4003a", - "#c13246", - "#822661", - "#48277c", - "#005ca9", - "#00aaff", - "#9b30d9", - "#af005f", - "#5f00af", - "#af87ff" + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" ], - "dragmode": "pan", "font": { - "color": "#f2f5fa", - "family": "Fira Code", - "size": 18 + "color": "#2a3f5f" }, "geo": { - "bgcolor": "rgb(17,17,17)", - "lakecolor": "rgb(17,17,17)", - "landcolor": "rgb(17,17,17)", + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", "showlakes": true, "showland": true, - "subunitcolor": "#506784" + "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, - "hovermode": "x", - "legend": { - "bgcolor": "rgba(0, 0, 0, 0.5)", - "font": { - "size": 15 - }, - "x": 0.01, - "xanchor": "left", - "y": 0.99, - "yanchor": "top" - }, - "legend2": { - "bgcolor": "rgba(0, 0, 0, 0.5)", - "font": { - "size": 15 - } - }, - "legend3": { - "bgcolor": "rgba(0, 0, 0, 0.5)", - "font": { - "size": 15 - } - }, - "legend4": { - "bgcolor": "rgba(0, 0, 0, 0.5)", - "font": { - "size": 15 - } - }, - "legend5": { - "bgcolor": "rgba(0, 0, 0, 0.5)", - "font": { - "size": 15 - } - }, + "hovermode": "closest", "mapbox": { - "style": "dark" + "style": "light" }, - "paper_bgcolor": "#000000", - "plot_bgcolor": "#000000", + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "white", + "linecolor": "white", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "#E5ECF6", "radialaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "white", + "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "white", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "white" }, "yaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "white", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "white" }, "zaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "white", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "white" } }, "shapedefaults": { "line": { - "color": "#f2f5fa" + "color": "#2a3f5f" } }, - "sliderdefaults": { - "bgcolor": "#C8D4E3", - "bordercolor": "rgb(17,17,17)", - "borderwidth": 1, - "tickwidth": 0 - }, "ternary": { "aaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "white", + "linecolor": "white", "ticks": "" }, "baxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "white", + "linecolor": "white", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "#E5ECF6", "caxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "white", + "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, - "updatemenudefaults": { - "bgcolor": "#506784", - "borderwidth": 0 - }, "xaxis": { "automargin": true, - "autorange": true, - "gridcolor": "#283442", - "linecolor": "#F5EFF3", - "mirror": true, - "rangeslider": { - "visible": false - }, - "showgrid": true, - "showline": true, - "tick0": 1, - "tickfont": { - "size": 14 - }, - "ticks": "outside", + "gridcolor": "white", + "linecolor": "white", + "ticks": "", "title": { - "standoff": 20 + "standoff": 15 }, - "zeroline": false, - "zerolinecolor": "#283442", + "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { - "anchor": "x", "automargin": true, - "fixedrange": false, - "gridcolor": "#283442", - "linecolor": "#F5EFF3", - "mirror": true, - "showgrid": true, - "showline": true, - "side": "right", - "tick0": 0.5, - "ticks": "outside", + "gridcolor": "white", + "linecolor": "white", + "ticks": "", "title": { - "standoff": 20 + "standoff": 15 }, - "zeroline": false, - "zerolinecolor": "#283442", + "zerolinecolor": "white", "zerolinewidth": 2 } } @@ -6245,7 +6167,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -6286,20 +6208,20 @@ " \n", " \n", " \n", - " 2023-11-20\n", - " 2002.199951\n", - " 3.7965\n", - " 1.896164\n", - " 4.42\n", - " 0.130023\n", + " 2023-12-04\n", + " 1998.300049\n", + " 3.820\n", + " 1.911625\n", + " NaN\n", + " 0.131083\n", " \n", " \n", - " 2023-11-27\n", - " 2011.800049\n", - " 3.7625\n", - " 1.870216\n", + " 2023-12-11\n", + " 2021.099976\n", + " 3.885\n", + " 1.922221\n", " NaN\n", - " 0.128243\n", + " 0.131809\n", " \n", " \n", "\n", @@ -6308,21 +6230,21 @@ "text/plain": [ " Gold Copper Copper/Gold Ratio \\\n", "date \n", - "2023-11-20 2002.199951 3.7965 1.896164 \n", - "2023-11-27 2011.800049 3.7625 1.870216 \n", + "2023-12-04 1998.300049 3.820 1.911625 \n", + "2023-12-11 2021.099976 3.885 1.922221 \n", "\n", " US 10-Year Constant Maturity \\\n", "date \n", - "2023-11-20 4.42 \n", - "2023-11-27 NaN \n", + "2023-12-04 NaN \n", + "2023-12-11 NaN \n", "\n", " Copper/Gold Ratio per Ounce (x1000) % \n", "date \n", - "2023-11-20 0.130023 \n", - "2023-11-27 0.128243 " + "2023-12-04 0.131083 \n", + "2023-12-11 0.131809 " ] }, - "execution_count": 78, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -6336,27 +6258,15 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Each y-axis requires its own DataFrame, so let's make one for each next." + "Now let's draw it!" ] }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "y1 = pd.DataFrame(data[\"Copper/Gold Ratio per Ounce (x1000) %\"])\n", - "y2 = pd.DataFrame(data[\"US 10-Year Constant Maturity\"])\n", - "fig = show_plot(y1, y2, external_axes = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 85, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -7583,7 +7493,9 @@ "2023-11-06", "2023-11-13", "2023-11-20", - "2023-11-27" + "2023-11-27", + "2023-12-04", + "2023-12-11" ], "y": [ 3.2093862764241465, @@ -8799,7 +8711,9 @@ 1.8542377903946718, 1.8861021388593557, 1.8961642494062623, - 1.8702157055197284 + 1.8874939255873286, + 1.9116248010317485, + 1.9222205914563706 ], "yaxis": "y" }, @@ -10020,7 +9934,9 @@ "2023-11-06", "2023-11-13", "2023-11-20", - "2023-11-27" + "2023-11-27", + "2023-12-04", + "2023-12-11" ], "y": [ 5.78, @@ -11236,6 +11152,8 @@ 4.67, 4.63, 4.42, + 4.39, + null, null ], "yaxis": "y2" @@ -11256,14 +11174,14 @@ "bar": [ { "error_x": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "error_y": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "#E5ECF6", "width": 0.5 }, "pattern": { @@ -11279,7 +11197,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "#E5ECF6", "width": 0.5 }, "pattern": { @@ -11291,38 +11209,21 @@ "type": "barpolar" } ], - "candlestick": [ - { - "decreasing": { - "fillcolor": "#e4003a", - "line": { - "color": "#e4003a" - } - }, - "increasing": { - "fillcolor": "#00ACFF", - "line": { - "color": "#00ACFF" - } - }, - "type": "candlestick" - } - ], "carpet": [ { "aaxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" }, "baxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" }, "type": "carpet" } @@ -11640,10 +11541,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -11690,8 +11591,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -11796,18 +11698,18 @@ { "cells": { "fill": { - "color": "#506784" + "color": "#EBF0F8" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "header": { "fill": { - "color": "#2a3f5f" + "color": "#C8D4E3" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "type": "table" @@ -11816,10 +11718,9 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, - "arrowwidth": 1, - "showarrow": false + "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { @@ -11961,194 +11862,125 @@ ] }, "colorway": [ - "#ffed00", - "#ef7d00", - "#e4003a", - "#c13246", - "#822661", - "#48277c", - "#005ca9", - "#00aaff", - "#9b30d9", - "#af005f", - "#5f00af", - "#af87ff" + "#636efa", + "#EF553B", + "#00cc96", + 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@@ -0,0 +1,10206 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Calculating the USD Liquidity Index with the OpenBB Platform\n", + "\n", + "This popular indicator is made from a simple subtraction of three FRED series that are published every Wednesday, and is often overlayed with risk assets like the S&P 500 Index or Bitcoin. The OpenBB SDK is well suited for this task, let's take a look to create this index.\n", + "\n", + "The formula is defined as:\n", + "\n", + "```console\n", + "WALCL (All Liabilities) – WLRRAL (RRP) – WDTGAL (TGA)\n", + "```\n", + "\n", + "To get these data series, we will use the `openbb-fred` data extension and the `economy` module. First thing is to import the Python interface, and we will also import Pandas to conduct some DataFrame operations." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "from openbb import obb\n", + "from pandas import DataFrame\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are two `fred` functions in the `openbb-economy` router:\n", + "\n", + "- `obb.economy.fred_search()`\n", + "- `obb.economy.fred_series()`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OBBject\n", + "\n", + "id: 06580cab-5bdc-7e28-8000-632a5d8dc92f\n", + "results: [{'date': datetime.date(2002, 12, 18), 'WALCL': 719542.0, 'WLRRAL': 21905....\n", + "provider: fred\n", + "warnings: [{'category': 'UserWarning', 'message': '{\"WALCL\": {\"title\": \"Assets: Tot...\n", + "chart: None\n", + "extra: {'metadata': {'arguments': {'provider_choices': {'provider': None}, 'standar..." + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = obb.economy.fred_series([\"WALCL\", \"WLRRAL\", \"WDTGAL\", \"SP500\"])\n", + "\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is metadata from each series in the warnings of the response object. It can be recovered as a JSON dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['WALCL', 'WLRRAL', 'WDTGAL', 'SP500'])" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'Assets: Total Assets: Total Assets (Less Eliminations from Consolidation): Wednesday Level'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'Millions of U.S. Dollars'" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "metadata = json.loads(data.warnings[0].message)\n", + "\n", + "display(metadata.keys())\n", + "display(metadata[\"WALCL\"].get(\"title\"))\n", + "display(metadata[\"WALCL\"].get(\"units\"))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Querying FRED\n", + "\n", + "If we didn't already know the ID for the series, we can search with:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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series_idtitleobservation_startobservation_endfrequencyfrequency_shortunitsunits_shortseasonal_adjustmentseasonal_adjustment_shortlast_updatednotespopularitygroup_popularity
0WALCLAssets: Total Assets: Total Assets (Less Elimi...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:34:03-06:00None9594
1H41RESPPALDKNWWAssets: Liquidity and Credit Facilities: Loans...2002-12-182023-12-13WeeklyWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:35:03-06:00None7373
2TREASTAssets: Securities Held Outright: U.S. Treasur...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:35:02-06:00The total face value of U.S. Treasury securiti...7272
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" + ], + "text/plain": [ + " series_id ... group_popularity\n", + "0 WALCL ... 94\n", + "1 H41RESPPALDKNWW ... 73\n", + "2 TREAST ... 72\n", + "\n", + "[3 rows x 14 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The first result is the series we are looking for as the starting value.\n", + "\n", + "obb.economy.fred_search(\"Wednesday Levels\").to_df().head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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series_idtitleobservation_startobservation_endfrequencyfrequency_shortunitsunits_shortseasonal_adjustmentseasonal_adjustment_shortlast_updatednotespopularitygroup_popularity
0WLRRALLiabilities and Capital: Liabilities: Reverse ...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:34:09-06:00Reverse repurchase agreements are transactions...6464
1WLRRAFOIALLiabilities and Capital: Liabilities: Reverse ...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:34:12-06:00Reverse repurchase agreements are transactions...4040
2WLRRAOLLiabilities and Capital: Liabilities: Reverse ...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:34:16-06:00None2424
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" + ], + "text/plain": [ + " series_id ... group_popularity\n", + "0 WLRRAL ... 64\n", + "1 WLRRAFOIAL ... 40\n", + "2 WLRRAOL ... 24\n", + "\n", + "[3 rows x 14 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Adding \"Reverse Repo\" to the search returns the second series in the equation, as the first result.\n", + "\n", + "obb.economy.fred_search(\"Wednesday Levels Reverse Repo\").to_df().head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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series_idtitleobservation_startobservation_endfrequencyfrequency_shortunitsunits_shortseasonal_adjustmentseasonal_adjustment_shortlast_updatednotespopularitygroup_popularity
0WDTGALLiabilities and Capital: Liabilities: Deposits...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:34:08-06:00This account is the primary operational accoun...6565
1D2WLTGALLiabilities and Capital: Liabilities: Deposits...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:34:10-06:00None6262
2WLDLCLLiabilities and Capital: Liabilities: Deposits...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:34:13-06:00This item is the sum of \"Term deposits held by...2323
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" + ], + "text/plain": [ + " series_id ... group_popularity\n", + "0 WDTGAL ... 65\n", + "1 D2WLTGAL ... 62\n", + "2 WLDLCL ... 23\n", + "\n", + "[3 rows x 14 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Refining the search for the Treasury General Account, returns the final series in the equation, as the first result.\n", + "\n", + "obb.economy.fred_search(\"Wednesday Levels Treasury General\").to_df().head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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series_idtitleobservation_startobservation_endfrequencyfrequency_shortunitsunits_shortseasonal_adjustmentseasonal_adjustment_shortlast_updatednotespopularitygroup_popularity
0SP500S&P 5002013-12-162023-12-15Daily, CloseDIndexIndexNot Seasonally AdjustedNSA2023-12-15 19:11:01-06:00The observations for the S&P 500 represent the...8383
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" + ], + "text/plain": [ + " series_id title ... popularity group_popularity\n", + "0 SP500 S&P 500 ... 83 83\n", + "\n", + "[1 rows x 14 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Several major equity indices are published to FRED, S&P 500 is one of them.\n", + "\n", + "obb.economy.fred_search(\"SP500\").to_df().head(2)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By looking at the descriptions, we can confirm that all three Federal Reserve series are numbers as `Millions of USD`. If they were not all equivalent, some adjustments would need to be made before applying the equation." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'WALCL: Millions of U.S. Dollars'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'WLRRAL: Millions of U.S. Dollars'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'WDTGAL: Millions of U.S. Dollars'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'SP500: Index'" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for id in metadata:\n", + " display(f\"{id}: {metadata[id]['units']}\")\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inspecting the time series element shows that the S&P 500 data (as published to FRED) does not extend as far back as the others. Let's drop the NaN values and start the time series at a common starting point, which is approximately ten years ago." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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WALCLWLRRALWDTGALSP500
date
2002-12-18719542.021905.06595.0NaN
2002-12-25732059.020396.04662.0NaN
2003-01-01730994.021091.04420.0NaN
2003-01-08723762.018709.05490.0NaN
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" + ], + "text/plain": [ + " WALCL WLRRAL WDTGAL SP500\n", + "date \n", + "2002-12-18 719542.0 21905.0 6595.0 NaN\n", + "2002-12-25 732059.0 20396.0 4662.0 NaN\n", + "2003-01-01 730994.0 21091.0 4420.0 NaN\n", + "2003-01-08 723762.0 18709.0 5490.0 NaN" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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WALCLWLRRALWDTGALSP500
date
2013-12-184008062.0111966.0116998.01810.65
2014-01-084028185.0150726.0110758.01837.49
2014-01-154071528.0156266.087926.01848.38
2014-01-224097914.0201954.096724.01844.86
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" + ], + "text/plain": [ + " WALCL WLRRAL WDTGAL SP500\n", + "date \n", + "2013-12-18 4008062.0 111966.0 116998.0 1810.65\n", + "2014-01-08 4028185.0 150726.0 110758.0 1837.49\n", + "2014-01-15 4071528.0 156266.0 87926.0 1848.38\n", + "2014-01-22 4097914.0 201954.0 96724.0 1844.86" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(data.to_df().head(4))\n", + "display(data.to_df().dropna().head(4))\n", + "\n", + "# We'll create a new DataFrame object with the dropped rows.\n", + "liquidity_index = DataFrame(data.to_df().dropna())" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Applying the formula will simply be a matter of subtracting the first, three, columns." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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WALCLWLRRALWDTGALSP500USD Liquidity Index
date
2023-11-227810814.01271679.0693007.04556.625846128.0
2023-11-297796145.01254156.0753570.04550.585788419.0
2023-12-067737385.01184528.0667678.04549.345885179.0
2023-12-137739566.01152232.0630618.04707.095956716.0
\n", + "
" + ], + "text/plain": [ + " WALCL WLRRAL WDTGAL SP500 USD Liquidity Index\n", + "date \n", + "2023-11-22 7810814.0 1271679.0 693007.0 4556.62 5846128.0\n", + "2023-11-29 7796145.0 1254156.0 753570.0 4550.58 5788419.0\n", + "2023-12-06 7737385.0 1184528.0 667678.0 4549.34 5885179.0\n", + "2023-12-13 7739566.0 1152232.0 630618.0 4707.09 5956716.0" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "liquidity_index[\"USD Liquidity Index\"] = (liquidity_index[\"WALCL\"] - liquidity_index[\"WLRRAL\"] - liquidity_index[\"WDTGAL\"])\n", + "\n", + "liquidity_index.tail(4)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that there are two items to compare, let's draw it!" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, 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Index vs. S&P 500 Index", + "x": 0.5, + "y": 0.9 + }, + "yaxis": { + "position": 0, + "showgrid": false, + "side": "left", + "title": { + "font": { + "size": 12 + }, + "text": "USD Liquidity Index (Billions)" + } + }, + "yaxis2": { + "overlaying": "y", + "position": 1, + "side": "right", + "title": { + "font": { + "size": 12 + }, + "text": "S&P 500 Index" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "fig = go.Figure()\n", + "\n", + "fig.add_scatter(\n", + " x=liquidity_index.index, y=liquidity_index[\"USD Liquidity Index\"]/1000, name=\"USD Liquidity Index (Billions)\", yaxis=\"y1\")\n", + "\n", + "fig.add_scatter(\n", + " x=liquidity_index.index, y=liquidity_index[\"SP500\"], name=\"S&P 500 Index\", yaxis=\"y2\")\n", + "\n", + "fig.update_layout(\n", + " yaxis=dict(title=\"USD Liquidity Index (Billions)\",side=\"left\", position=0, titlefont=dict(size=12), showgrid=False),\n", + " yaxis2=dict(title=\"S&P 500 Index\", side=\"right\", overlaying=\"y\", position=1, titlefont=dict(size=12)),\n", + " title=\"USD Liquidity Index vs. S&P 500 Index\",\n", + " title_y=0.90,\n", + " title_x=0.5,\n", + " autosize=True,\n", + ")\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To draw them both on the same y-axis, they will need to be normalized. There are several methods for normalizing a series, the fourth function in the block below paramaterizes a few of them, making it easy to A/B them." + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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USD Liquidity IndexSP500
date
2023-11-290.7213510.920274
2023-12-060.7484890.919866
2023-12-130.7685530.971734
\n", + "
" + ], + "text/plain": [ + " USD Liquidity Index SP500\n", + "date \n", + "2023-11-29 0.721351 0.920274\n", + "2023-12-06 0.748489 0.919866\n", + "2023-12-13 0.768553 0.971734" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_axis = liquidity_index[[\"USD Liquidity Index\", \"SP500\"]]\n", + "\n", + "def absolute_maximum_scale(series):\n", + " return series / series.abs().max()\n", + "\n", + "def min_max_scaling(series):\n", + " return (series - series.min()) / (series.max() - series.min())\n", + "\n", + "def z_score_standardization(series):\n", + " return (series - series.mean()) / series.std()\n", + "\n", + "methods = {\"z\": z_score_standardization, \"m\": min_max_scaling, \"a\": absolute_maximum_scale}\n", + "\n", + "def normalize(data: DataFrame, method: str = \"z\") -> DataFrame:\n", + " for col in data.columns:\n", + " data.loc[:, col] = methods[f\"{method}\"](data.loc[:, col])\n", + "\n", + " return data\n", + "\n", + "normalized = normalize(y_axis, method = \"m\")\n", + "\n", + "normalized.tail(3)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now they can be easily plotted using the built-in `DataFrame.plot` method." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "variable=USD Liquidity Index
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"fig.add_scatter(\n", + " x=normalized.index, y=normalized[\"SP500\"], name=\"S&P 500 Index\")\n", + "\n", + "fig.update_layout(\n", + " title=\"USD Liquidity Index vs. S&P 500 Index (Normalized)\",\n", + " title_y=0.90,\n", + " title_x=0.5,\n", + " autosize=True,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The combinations are endless and we love seeing your creations, tag us on social media with your custom indexes and indicators." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "obb", + "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": "3.11.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/openbb_terminal/cryptocurrency/due_diligence/dd_controller.py b/openbb_terminal/cryptocurrency/due_diligence/dd_controller.py index 83eb87c8b69..147dfdc708a 100644 --- a/openbb_terminal/cryptocurrency/due_diligence/dd_controller.py +++ b/openbb_terminal/cryptocurrency/due_diligence/dd_controller.py @@ -69,7 +69,6 @@ class DueDiligenceController(CryptoBaseController): "change", "nonzero", "eb", - "funot", "desc", ] @@ -195,7 +194,6 @@ class DueDiligenceController(CryptoBaseController): mt.add_cmd("ps") mt.add_cmd("mcapdom") mt.add_cmd("mt") - mt.add_cmd("funot") mt.add_info("_contributors_") mt.add_cmd("team") @@ -1858,48 +1856,6 @@ class DueDiligenceController(CryptoBaseController): region=ns_parser.region, ) - @log_start_end(log=logger) - def call_funot(self, other_args): - """Process fun command""" - parser = argparse.ArgumentParser( - prog="funot", - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - description="""Display fundamental metric over time [Source: Token Terminal]""", - ) - parser.add_argument( - "-m", - "--metric", - default="", - choices=tokenterminal_model.METRICS, - dest="metric", - help="Choose metric of interest", - ) - parser.add_argument( - "-p", - "--project", - required="-h" not in other_args, - choices=tokenterminal_model.get_project_ids(), - dest="project", - help="Choose project of interest", - ) - - if other_args and other_args[0][0] != "-": - other_args.insert(0, "-p") - - ns_parser = self.parse_known_args_and_warn( - parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES - ) - if ns_parser: - tokenterminal_view.display_fundamental_metric_from_project_over_time( - metric=ns_parser.metric, - project=ns_parser.project, - export=ns_parser.export, - sheet_name=( - " ".join(ns_parser.sheet_name) if ns_parser.sheet_name else None - ), - ) - @log_start_end(log=logger) def call_desc(self, other_args): """Process desc command""" diff --git a/openbb_terminal/cryptocurrency/due_diligence/tokenterminal_model.py b/openbb_terminal/cryptocurrency/due_diligence/tokenterminal_model.py index e5f7507af37..5d6d5c7ed80 100644 --- a/openbb_terminal/cryptocurrency/due_diligence/tokenterminal_model.py +++ b/openbb_terminal/cryptocurrency/due_diligence/tokenterminal_model.py @@ -192,9 +192,7 @@ def get_project_ids() -> List[str]: # check if its a dict - which would be a successful api call - # might need to add error checking here later if they messed up the API key though. - if isinstance(PROJECTS_DATA, dict): - return [project["project_id"] for project in PROJECTS_DATA] - return PROJECTS_DATA + return ["none"] @log_start_end(log=logger)