rewards_staking.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "rewards_staking.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMMcaKSIae5HXFddqHO3n46",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vineet-codes/defi-exploration/blob/main/rewards_staking.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What is the impact of different reward mechanisms for incentivizing users to stake their tokens."
      ],
      "metadata": {
        "id": "B0Jx2VLwCCcy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')"
      ],
      "metadata": {
        "id": "NjxvWAjdVgFd"
      },
      "execution_count": 265,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 266,
      "metadata": {
        "id": "0iUh-zx-4nkb"
      },
      "outputs": [],
      "source": [
        "class OneStake() :\n",
        "    \n",
        "    def __init__(self, user, startTime, lockPeriod, amount):\n",
        "      self.user = user\n",
        "      self.startTime = startTime\n",
        "      self.lockPeriod = lockPeriod\n",
        "      self.amount= amount\n",
        "      self.lastTransferTime = startTime\n",
        "      assert self.lastTransferTime == self.startTime\n",
        "\n",
        "    def __repr__(self):\n",
        "      return f'{self.user}: has staked {self.amount} tokens at {self.startTime} locked for {self.lockPeriod}. Last transfer was {self.lastTransferTime}'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Stake():\n",
        "\n",
        "  def __init__(self, rewardStrategy = 0):\n",
        "    self.stakes = []\n",
        "    self.users = {}\n",
        "    self.currentTime = 0;\n",
        "    self.totalRewards_disbursed = 0\n",
        "    self.db = pd.DataFrame(columns = ['index', 'user', 'startTime', 'lockPeriod', 'amount', 'currentTime' , 'rewards'])\n",
        "    self.reward_time_history = []\n",
        "    self.rewardStrategy = rewardStrategy\n",
        "\n",
        "  # each stake position will be unique\n",
        "  def stake(self, user, startTime, lockPeriod, amount, lastTransferTime):\n",
        "    # assert self.currentTime == startTime\n",
        "    assert lockPeriod > 0\n",
        "    assert lastTransferTime >= startTime\n",
        "    assert amount > 0\n",
        "\n",
        "    stake_pos = OneStake(user, startTime, lockPeriod, amount)\n",
        "    self.stakes.append(stake_pos)\n",
        "    \n",
        "    index = len(self.stakes) - 1\n",
        "    \n",
        "    if len(self.users.get(user, [])) == 0:\n",
        "      self.users[user] = [index]\n",
        "    else: \n",
        "      self.users[user].append(index)\n",
        "\n",
        "    # print([index, user, startTime, lockPeriod, amount, self.currentTime, 0])\n",
        "    \n",
        "    # add to db\n",
        "    self.db.loc[len(self.db)] = [index, user, startTime, lockPeriod, amount, self.currentTime, 0]\n",
        "\n",
        "\n",
        "  def tick(self):\n",
        "    # tick the timer\n",
        "    \n",
        "    # calculate new state and update it\n",
        "    # process the db to calculate working balance and rewards for currentTime\n",
        "    for index, row in self.db.iterrows():\n",
        "      row['currentTime'] = self.currentTime\n",
        "      if(row['startTime'] + row['lockPeriod'] == row['currentTime']):\n",
        "        # print(row['user'], row['startTime'], row['lockPeriod'], row['currentTime'])\n",
        "        # stake complete, calculate reward and update reward given\n",
        "        # self.reward = self.amount * self.lockPeriod\n",
        "        if (self.rewardStrategy == 0):\n",
        "          row['rewards'] = self.reward0(row['amount'], row['lockPeriod'])\n",
        "        elif (self.rewardStrategy == 1): \n",
        "          row['rewards'] = self.reward1(row['amount'], row['lockPeriod'])\n",
        "\n",
        "        self.totalRewards_disbursed += row['rewards']\n",
        "    # print(f'Total rewards disbursed at t={self.currentTime} is {self.totalRewards_disbursed}')\n",
        "    self.reward_time_history.append(self.totalRewards_disbursed)\n",
        "    self.currentTime += 1\n",
        "\n",
        "  def move_time(self, by):\n",
        "    [self.tick() for i in range(by)]\n",
        "\n",
        "  def reward0(self, amount, lockPeriod):\n",
        "    # print(amount * lockPeriod)\n",
        "    return amount * lockPeriod\n",
        "\n",
        "  def reward1(self, amount, lockPeriod):\n",
        "    return amount + amount*(lockPeriod/60.)\n",
        "\n",
        "  def plot_reward_cumulative(self):\n",
        "    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(15,5))\n",
        "    ax.set_title(f'Cumulative Total YOP distributed: {self.totalRewards_disbursed} at t = {self.currentTime}')\n",
        "    ax.set_xlabel('time period')\n",
        "    ax.set_ylabel('YOP distributed quantity')\n",
        "    ax.plot(self.reward_time_history)"
      ],
      "metadata": {
        "id": "te2s7NdX5SBL"
      },
      "execution_count": 428,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ybcZsVW61fQG"
      },
      "execution_count": 428,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def cohort_stake_activity(stake_yop):\n",
        "  stake_yop.stake('alice', 1, 6, 300, 1)\n",
        "  stake_yop.stake('bob', 3, 10, 3000, 3)\n",
        "  stake_yop.stake('joh', 7, 8, 2000, 7)\n",
        "  stake_yop.stake('alice', 5, 6, 300, 5)\n",
        "\n",
        "  stake_yop.move_time(20)\n",
        "  stake_yop.plot_reward_cumulative()"
      ],
      "metadata": {
        "id": "FxEkPD8y7avE"
      },
      "execution_count": 429,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# reward function used amount * lockperiod\n",
        "stake_yop = Stake(0)\n",
        "cohort_stake_activity(stake_yop)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "6L9-4pb4pyzE",
        "outputId": "6e701cbb-feb2-461c-b209-ebde2675a70c"
      },
      "execution_count": 430,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reward used amount + amount * lockPeriod / 60\n",
        "stake_yop = Stake(1)\n",
        "cohort_stake_activity(stake_yop)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "ip7gM7BEoU7G",
        "outputId": "3e74c67c-1761-4f35-b57a-4603775e574b"
      },
      "execution_count": 431,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "uE01L8nskxcT"
      },
      "execution_count": 418,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "AyJAj51EkARV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}