experiments/notebooks/pcv_management_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment Notebook: PCV Management"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "* [Experiment Summary](#Experiment-Summary)\n",
    "* [Experiment Assumptions](#Experiment-Assumptions)\n",
    "* [Experiment Setup](#Experiment-Setup)\n",
    "* [Analysis 1: ...](#Analysis-1:-...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment Summary \n",
    "\n",
    "The purpose of this notebook is to...\n",
    "\n",
    "# Experiment Assumptions\n",
    "\n",
    "See [assumptions document](../../ASSUMPTIONS.md) for further details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment Setup\n",
    "\n",
    "We begin with several experiment-notebook-level preparatory setup operations:\n",
    "\n",
    "* Import relevant dependencies\n",
    "* Import relevant experiment templates\n",
    "* Create copies of experiments\n",
    "* Configure and customize experiments \n",
    "\n",
    "Analysis-specific setup operations are handled in their respective notebook sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the setup module:\n",
    "# * sets up the Python path\n",
    "# * runs shared notebook configuration methods, such as loading IPython modules\n",
    "import setup\n",
    "\n",
    "import copy\n",
    "import logging\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "\n",
    "import experiments.notebooks.visualizations as visualizations\n",
    "from experiments.run import run\n",
    "from experiments.utils import display_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.io as pio\n",
    "png_renderer = pio.renderers[\"png\"]\n",
    "png_renderer.width = 1200\n",
    "png_renderer.height = 500\n",
    "# png_renderer.scale = 1\n",
    "\n",
    "pio.renderers.default = \"png\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from operator import lt, gt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enable/disable logging\n",
    "logger = logging.getLogger()\n",
    "logger.disabled = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import experiment templates\n",
    "import experiments.default_experiment as default_experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inspect experiment template\n",
    "display_code(default_experiment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis 1: Target Stable PCV Ratio Policy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{Analysis Description}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter_overrides = {\n",
    "    \"target_rebalancing_condition\": [gt, lt], # Simulate decrease and increase of stable PCV\n",
    "    \"target_stable_pcv_ratio\": [0.2, 0.5], # Simulate decrease and increase of stable PCV\n",
    "    # Disable policy by setting to `None`\n",
    "    \"target_stable_backing_ratio\": [None],\n",
    "    \"rebalancing_period\": [int(365 / 4)],\n",
    "    \"yield_withdrawal_period\": [None],  # Disable yield-withdrawal policy\n",
    "    \"yield_reinvest_period\": [None],  # Disable yield-reinvestment policy\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a simulation for each analysis\n",
    "simulation_1 = copy.deepcopy(default_experiment.experiment.simulations[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Experiment configuration\n",
    "simulation_1.model.initial_state.update({})\n",
    "\n",
    "# Override default experiment System Parameters\n",
    "simulation_1.model.params.update(parameter_overrides)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analysis-specific setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Experiment execution\n",
    "df, exceptions = run(simulation_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Post-processing and visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = df.query('subset==0').plot(\n",
    "    x='timestep',\n",
    "    y=['total_volatile_asset_pcv', 'total_stable_asset_pcv', 'total_pcv'],\n",
    "    title=\"Policy decreases stable PCV ratio to a target of 20%\"\n",
    ")\n",
    "\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"Timestamp\",\n",
    "    yaxis_title=\"USD Value of PCV\",\n",
    "#     autosize=False,\n",
    "#     width=1000,\n",
    "#     height=675,\n",
    "    legend=dict(\n",
    "        yanchor=\"top\",\n",
    "        y=0.99,\n",
    "        xanchor=\"right\",\n",
    "        x=0.99\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = df.query('subset==1').plot(\n",
    "    x='timestep',\n",
    "    y=['total_volatile_asset_pcv', 'total_stable_asset_pcv', 'total_pcv'],\n",
    "    title=\"Policy increases stable PCV ratio to a target of 50%\"\n",
    ")\n",
    "\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"Timestamp\",\n",
    "    yaxis_title=\"USD Value of PCV\",\n",
    "#     autosize=False,\n",
    "#     width=1000,\n",
    "#     height=675,\n",
    "    legend=dict(\n",
    "        yanchor=\"top\",\n",
    "        y=0.99,\n",
    "        xanchor=\"right\",\n",
    "        x=0.99\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.query('subset==0')['stable_pcv_ratio'].plot(\n",
    "    title=\"Policy decreases stable PCV ratio to a target of 20%\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.query('subset==0')['stable_backing_ratio'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.query('subset==1')['stable_pcv_ratio'].plot(\n",
    "    title=\"Policy increases stable PCV ratio to a target of 50%\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.query('subset==1')['stable_backing_ratio'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCV Constituents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Note:</b> Filter by appropriate subset here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='timestep', y='volatile_yield_bearing_pcv_deposit_balance', color='subset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='timestep', y='volatile_idle_pcv_deposit_balance', color='subset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='timestep', y='stable_yield_bearing_pcv_deposit_balance', color='subset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='timestep', y='stable_idle_pcv_deposit_balance', color='subset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='timestep', y='total_stable_asset_pcv_balance', color='subset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='timestep', y='total_volatile_asset_pcv_balance', color='subset')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='timestep', y='collateralization_ratio', color='subset')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis 2: Target Stable Backing Ratio Policy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{Analysis Description}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter_overrides = {\n",
    "    \"target_rebalancing_condition\": [gt, lt], # Simulate decrease and increase of stable PCV\n",
    "    # Disable policy by setting to None\n",
    "    \"target_stable_pcv_ratio\": [None], # Simulate decrease and increase of stable PCV\n",
    "    \"target_stable_backing_ratio\": [0.3, 0.8], # Simulate decrease and increase of stable backing\n",
    "    \"rebalancing_period\": [int(365 / 4)],\n",
    "    \"yield_withdrawal_period\": [None],  # Disable yield-withdrawal policy\n",
    "    \"yield_reinvest_period\": [None],  # Disable yield-reinvestment policy\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a simulation for each analysis\n",
    "simulation_2 = copy.deepcopy(default_experiment.experiment.simulations[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Experiment configuration\n",
    "simulation_2.model.initial_state.update({})\n",
    "\n",
    "# Override default experiment System Parameters\n",
    "simulation_2.model.params.update(parameter_overrides)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analysis-specific setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Experiment execution\n",
    "df, exceptions = run(simulation_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Post-processing and visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = df.query('subset==0').plot(\n",
    "    x='timestep',\n",
    "    y=['total_volatile_asset_pcv', 'total_stable_asset_pcv', 'total_pcv'],\n",
    "    title=\"Policy decreases Stable Backing Ratio to a target of 30%\"\n",
    ")\n",
    "\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"Timestamp\",\n",
    "    yaxis_title=\"USD Value of PCV\",\n",
    "#     autosize=False,\n",
    "#     width=1000,\n",
    "#     height=675,\n",
    "    legend=dict(\n",
    "        yanchor=\"top\",\n",
    "        y=0.99,\n",
    "        xanchor=\"right\",\n",
    "        x=0.99\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = df.query('subset==1').plot(\n",
    "    x='timestep',\n",
    "    y=['total_volatile_asset_pcv', 'total_stable_asset_pcv', 'total_pcv'],\n",
    "    title=\"Policy increases Stable Backing Ratio to a target of 80%\"\n",
    ")\n",
    "\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"Timestamp\",\n",
    "    yaxis_title=\"USD Value of PCV\",\n",
    "#     autosize=False,\n",
    "#     width=1000,\n",
    "#     height=675,\n",
    "    legend=dict(\n",
    "        yanchor=\"top\",\n",
    "        y=0.99,\n",
    "        xanchor=\"right\",\n",
    "        x=0.99\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.query('subset==0')['total_user_circulating_fei'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.query('subset==0')['stable_backing_ratio'].plot(\n",
    "    title=\"Policy decreases Stable Backing ratio to a target of 30%\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.query('subset==0')['stable_pcv_ratio'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.query('subset==1')['stable_backing_ratio'].plot(\n",
    "    title=\"Policy increases stable backing ratio to a target of 80%\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.query('subset==1')['stable_pcv_ratio'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCV Constituents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Note:</b> Filter by appropriate subset here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='timestep', y='volatile_yield_bearing_pcv_deposit_balance', color='subset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='timestep', y='volatile_idle_pcv_deposit_balance', color='subset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='timestep', y='stable_yield_bearing_pcv_deposit_balance', color='subset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='timestep', y='stable_idle_pcv_deposit_balance', color='subset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='timestep', y='total_stable_asset_pcv_balance', color='subset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='timestep', y='total_volatile_asset_pcv_balance', color='subset')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='timestep', y='collateralization_ratio', color='subset')"
   ]
  }
 ],
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