experiments/notebooks/pcv_yield_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment Notebook: PCV Yield"
   ]
  },
  {
   "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": [
    "# 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": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots"
   ]
  },
  {
   "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": [
    "parameter_overrides = {\n",
    "    # Disable policy by setting to `None`\n",
    "    # Only one can be enabled at a time\n",
    "    \"yield_withdrawal_period\": [int(365/4)],\n",
    "    \"yield_reinvest_period\": [None],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Experiment configuration\n",
    "simulation_1.model.initial_state.update({})\n",
    "\n",
    "simulation_1.model.params.update(parameter_overrides)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis 1: Yield Management Policies (Withdraw and Reinvest - see PSUBs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{Analysis Description}"
   ]
  },
  {
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Post-processing and visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Yield on volatile PCV Deposit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create figure with secondary y-axis\n",
    "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "\n",
    "# Add traces\n",
    "fig.add_trace(\n",
    "    go.Scatter(y=df.volatile_yield_bearing_pcv_deposit_yield_accrued, name=\"Volatile deposit yield-bearing yield accrued\"),\n",
    "    secondary_y=False,\n",
    ")\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Scatter(y=df.volatile_idle_pcv_deposit_balance, name=\"Volatile deposit idle balance\"),\n",
    "    secondary_y=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create figure with secondary y-axis\n",
    "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "\n",
    "# Add traces\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=df.timestamp, y=df.volatile_yield_bearing_pcv_deposit_yield_value, name=\"Volatile deposit yield-bearing yield value\"),\n",
    "    secondary_y=False,\n",
    ")\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=df.timestamp, y=df.volatile_idle_pcv_deposit_asset_value, name=\"Volatile deposit idle value\"),\n",
    "    secondary_y=True,\n",
    ")\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"USD Value of Yield-Bearing Volatile Deposit Balance and Yield Value\",\n",
    "    xaxis_title=\"Timestamp\",\n",
    "    yaxis_title=\"USD Value of Deposit Balance and Yield\",\n",
    "    autosize=False,\n",
    "    width=1000,\n",
    "    height=675,\n",
    "    legend=dict(\n",
    "        yanchor=\"top\",\n",
    "        y=0.99,\n",
    "        xanchor=\"right\",\n",
    "        x=0.92\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.update_xaxes(title='Timestamp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Yield on stable PCV Deposit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create figure with secondary y-axis\n",
    "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "\n",
    "# Add traces\n",
    "fig.add_trace(\n",
    "    go.Scatter(y=df.stable_yield_bearing_pcv_deposit_yield_accrued, name=\"Stable deposit yield-bearing yield accrued\"),\n",
    "    secondary_y=False,\n",
    ")\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Scatter(y=df.stable_idle_pcv_deposit_balance, name=\"Stable deposit idle balance\"),\n",
    "    secondary_y=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create figure with secondary y-axis\n",
    "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "\n",
    "# Add traces\n",
    "fig.add_trace(\n",
    "    go.Scatter(y=df.stable_yield_bearing_pcv_deposit_yield_value, name=\"Stable deposit yield-bearing yield value\"),\n",
    "    secondary_y=False,\n",
    ")\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Scatter(y=df.stable_idle_pcv_deposit_asset_value, name=\"Stable deposit idle value\"),\n",
    "    secondary_y=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(\n",
    "    x='timestep',\n",
    "    y=['total_volatile_asset_pcv', 'total_stable_asset_pcv', 'total_pcv'],\n",
    ")"
   ]
  }
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