acs_harvest_calculator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "surgical-orange",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "extraordinary-extra",
   "metadata": {},
   "outputs": [],
   "source": [
    "capital=3473 # capital in $ of your FARM VAULT\n",
    "farm_apr_daily=0.0113  ## write the number you see in \"Farm Daily:\" in your FARM VAULT.  The script take care of the %\n",
    "farm_apr_daily=farm_apr_daily/100\n",
    "acs_vault_daily=0.101  ## write the number you see in \"Farm Daily:\" in your ACS VAULT.  The script take care of the %\n",
    "acs_vault_daily=acs_vault_daily/100 \n",
    "acs_value=8.6 ## value of a ACS token in $\n",
    "acs_fee=0.03 #harvesting fee\n",
    "period=365 ## I consider a period of one year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "polyphonic-booking",
   "metadata": {},
   "outputs": [],
   "source": [
    "### first we find after how many days we have a FARM worth to be harvested since there is a 0.03 fee\n",
    "min_day=1\n",
    "for days in range(100):\n",
    "  est_base=  (capital*farm_apr_daily*days)/acs_value\n",
    "  if(est_base>acs_fee):\n",
    "    min_day=days\n",
    "    break\n",
    "\n",
    "## in res_df we store the results for all the compounding interval we will compute\n",
    "res_df=pd.DataFrame()\n",
    "## \"comp_intervals\" is a list that represent the intervals we want to simulate, in days, between harvesting events\n",
    "## if we farm every 2 days what wold be the total ACS token in the vault?\n",
    "## if we farm every 3 days what would be .... \n",
    "## if we farm every 4 days...\n",
    "comp_intervals=range(min_day,period,1)\n",
    "\n",
    "for days_interval in comp_intervals:\n",
    "  ## in a \"period\" there are a certain number of compounding events\n",
    "  ## imagine we are testing an harvesting event every 30 days, in a year there woud be 12 events\n",
    "  ## 30, 60, 90.... until 360\n",
    "  aseq=range(days_interval, period, days_interval)\n",
    "\n",
    "  ##  the interest give by the FARM vault is \"capital*days_in_the_vault*farm_apr_daily\"\n",
    "  ## if we want to find the number of acs tokens we have to divide by acs_value and subtract the harvesting fee\n",
    "  harvest_val= (capital*days_interval*farm_apr_daily)/acs_value -acs_fee\n",
    "  \n",
    "  acs_vault=0\n",
    "\n",
    "  for i in aseq:\n",
    "    ## first we compute the interest we obtained in the ACS vault\n",
    "    for j in range(1,days_interval,1):\n",
    "      acs_interest=acs_vault*acs_vault_daily\n",
    "      acs_vault=acs_vault+acs_interest\n",
    "    \n",
    "    \n",
    "    ## then we add the farmed ACS, to the ACS vault\n",
    "    acs_vault=acs_vault+harvest_val\n",
    "  \n",
    "  \n",
    "  ## there may be additional days before the end of the period\n",
    "  ## as before, if we have a compounding event every 30 days, and we consider\n",
    "  ## a period of 365 days, there are 5 days that are not considered. We need to take\n",
    "  ## them into consideration\n",
    "  harvest_val=capital*(period-max(aseq))*farm_apr_daily/acs_value\n",
    "  for j in range(max(aseq),period,1):\n",
    "    acs_interest=acs_vault*acs_vault_daily\n",
    "    acs_vault=acs_vault+acs_interest\n",
    "  \n",
    "  acs_vault=acs_vault+acs_interest+harvest_val\n",
    "  \n",
    "  adf=pd.DataFrame([{\"comp_days\":days_interval, \"farm_acs\":acs_vault, \"harvest_val\":harvest_val}])\n",
    "    \n",
    "  res_df=pd.concat([res_df, adf], ignore_index=True)\n",
    "\n",
    "\n",
    "best_comp_day=res_df.loc[res_df.farm_acs == res_df.farm_acs.max(), 'comp_days']\n",
    "best_farm_val=res_df.loc[res_df.farm_acs == res_df.farm_acs.max(), 'harvest_val']\n",
    "best_vault_val=res_df.loc[res_df.farm_acs == res_df.farm_acs.max(), 'farm_acs']\n",
    "a_text=\"Harvest at:\"+str(best_comp_day.item())+ \" every \"+  str(round(best_farm_val.item(),2)) +\" ACS\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "executed-mattress",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Harvest at:41 every 1.69 ACS')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(res_df[\"comp_days\"], res_df[\"farm_acs\"])\n",
    "plt.scatter(best_comp_day, best_vault_val, color=\"red\" )\n",
    "plt.title(a_text)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}