tokenomics.ipynb

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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from helpers import generate_random_payments, convert_to_df, process_ingress_payments\n",
    "from models.tokens import ERC20Token\n",
    "from models.dao import Bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#build contracts set\n",
    "token = ERC20Token()\n",
    "bkt_one = Bucket(token=token, max_volume=100000, name=\"bkt_01\", withdraw_begin=datetime.now())\n",
    "bkt_two = Bucket(token=token, max_volume=200000, name=\"bkt_02\", withdraw_begin=datetime.now())\n",
    "bkt_three = Bucket(token=token, max_volume=300000, name=\"bkt_03\", withdraw_begin=datetime.now())\n",
    "bkt_four = Bucket(token=token, name=\"bkt_04\", withdraw_begin=datetime.now())\n",
    "\n",
    "bkt_one.set_overflow_bucket(bkt_two)\n",
    "bkt_one.set_overflow_bucket(bkt_two)\n",
    "bkt_one.set_overflow_bucket(bkt_two)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate random payments\n",
    "payments = generate_random_payments()\n",
    "# process each payment and calculate how buckets get filled\n",
    "processed = process_ingress_payments(payments, token, bkt_one, bkt_two, bkt_three, bkt_four)\n",
    "columns = processed[0]\n",
    "dataframe = convert_to_df(processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>bkt_01</th>\n",
       "      <th>bkt_02</th>\n",
       "      <th>bkt_03</th>\n",
       "      <th>bkt_04</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 03:36:46</th>\n",
       "      <td>6121</td>\n",
       "      <td>6121</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05 15:12:32</th>\n",
       "      <td>1714</td>\n",
       "      <td>7835</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06 10:13:45</th>\n",
       "      <td>7458</td>\n",
       "      <td>15293</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-10 05:15:14</th>\n",
       "      <td>5881</td>\n",
       "      <td>21174</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-13 13:51:39</th>\n",
       "      <td>4116</td>\n",
       "      <td>25290</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "                     income  bkt_01  bkt_02  bkt_03  bkt_04\n",
       "date                                                       \n",
       "2019-01-01 03:36:46    6121    6121       0       0       0\n",
       "2019-01-05 15:12:32    1714    7835       0       0       0\n",
       "2019-01-06 10:13:45    7458   15293       0       0       0\n",
       "2019-01-10 05:15:14    5881   21174       0       0       0\n",
       "2019-01-13 13:51:39    4116   25290       0       0       0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the first records of df\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.graph_objs as go\n",
    "dframe = dataframe.groupby(lambda x : x.strftime('%y-%m-%d'), sort=False).agg('sum')\n",
    "dframe['cum_sum'] = dframe.income.cumsum()\n",
    "\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "\n",
    "init_notebook_mode(connected=True)\n",
    "trace1 = {\n",
    "    'x': dframe.index,\n",
    "    'y': dframe.income,\n",
    "    'name': 'Income',\n",
    "    'type': 'bar',\n",
    "    'yaxis': 'y'\n",
    "}\n",
    "trace2 = {\n",
    "    'x': dframe.index, \n",
    "    'y': dframe.cum_sum,\n",
    "    'name': 'Total',\n",
    "    'type': 'scatter',\n",
    "    'yaxis': 'y2',\n",
    "    'mode':'lines',\n",
    "    'line': {\n",
    "        'shape': 'hvh',\n",
    "    }\n",
    "}\n",
    "\n",
    "layout = {\n",
    "    'xaxis': {\n",
    "        'title': 'X axis'\n",
    "    },\n",
    "    'yaxis': {\n",
    "        'title': 'Y Trace',\n",
    "        'side': 'left',\n",
    "        'zeroline': True\n",
    "    },\n",
    "    'yaxis2': {\n",
    "        'title': 'Y Total',\n",
    "        'side': 'right',\n",
    "        'overlaying': 'y',\n",
    "    },\n",
    "    'barmode': 'group',\n",
    "    'title': 'Payments and raised funds graph', \n",
    "}\n",
    "\n",
    "data = [trace1, trace2]\n",
    "\n",
    "iplot({'data': data, 'layout': layout})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>bkt_01</th>\n",
       "      <th>bkt_02</th>\n",
       "      <th>bkt_03</th>\n",
       "      <th>bkt_04</th>\n",
       "      <th>cum_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19-01</th>\n",
       "      <td>62820</td>\n",
       "      <td>62820</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-02</th>\n",
       "      <td>50250</td>\n",
       "      <td>100000</td>\n",
       "      <td>13070</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-03</th>\n",
       "      <td>63034</td>\n",
       "      <td>100000</td>\n",
       "      <td>76104</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>176104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-04</th>\n",
       "      <td>109331</td>\n",
       "      <td>100000</td>\n",
       "      <td>185435</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>285435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-05</th>\n",
       "      <td>50075</td>\n",
       "      <td>100000</td>\n",
       "      <td>200000</td>\n",
       "      <td>35510</td>\n",
       "      <td>0</td>\n",
       "      <td>335510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-06</th>\n",
       "      <td>97209</td>\n",
       "      <td>100000</td>\n",
       "      <td>200000</td>\n",
       "      <td>132719</td>\n",
       "      <td>0</td>\n",
       "      <td>432719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-07</th>\n",
       "      <td>109124</td>\n",
       "      <td>100000</td>\n",
       "      <td>200000</td>\n",
       "      <td>241843</td>\n",
       "      <td>0</td>\n",
       "      <td>541843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-08</th>\n",
       "      <td>89244</td>\n",
       "      <td>100000</td>\n",
       "      <td>200000</td>\n",
       "      <td>300000</td>\n",
       "      <td>31087</td>\n",
       "      <td>631087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-09</th>\n",
       "      <td>85446</td>\n",
       "      <td>100000</td>\n",
       "      <td>200000</td>\n",
       "      <td>300000</td>\n",
       "      <td>116533</td>\n",
       "      <td>716533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-10</th>\n",
       "      <td>139000</td>\n",
       "      <td>100000</td>\n",
       "      <td>200000</td>\n",
       "      <td>300000</td>\n",
       "      <td>255533</td>\n",
       "      <td>855533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-11</th>\n",
       "      <td>79314</td>\n",
       "      <td>100000</td>\n",
       "      <td>200000</td>\n",
       "      <td>300000</td>\n",
       "      <td>334847</td>\n",
       "      <td>934847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-12</th>\n",
       "      <td>65153</td>\n",
       "      <td>100000</td>\n",
       "      <td>200000</td>\n",
       "      <td>300000</td>\n",
       "      <td>400000</td>\n",
       "      <td>1000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       income  bkt_01  bkt_02  bkt_03  bkt_04  cum_sum\n",
       "19-01   62820   62820       0       0       0    62820\n",
       "19-02   50250  100000   13070       0       0   113070\n",
       "19-03   63034  100000   76104       0       0   176104\n",
       "19-04  109331  100000  185435       0       0   285435\n",
       "19-05   50075  100000  200000   35510       0   335510\n",
       "19-06   97209  100000  200000  132719       0   432719\n",
       "19-07  109124  100000  200000  241843       0   541843\n",
       "19-08   89244  100000  200000  300000   31087   631087\n",
       "19-09   85446  100000  200000  300000  116533   716533\n",
       "19-10  139000  100000  200000  300000  255533   855533\n",
       "19-11   79314  100000  200000  300000  334847   934847\n",
       "19-12   65153  100000  200000  300000  400000  1000000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# aggregation pattern declares which column should be summarized \n",
    "# and which should have latest value after\n",
    "aggpattern = { columns[1] : 'sum' }\n",
    "for k in columns[2:]:\n",
    "    aggpattern[k] = 'last'\n",
    "    \n",
    "# Aggregate by year+month\n",
    "grouped_and_aggregated_df = dataframe.groupby(lambda x : x.strftime('%y-%m'), sort=False).agg(aggpattern)\n",
    "grouped_and_aggregated_df\n",
    "# Add accumulated summary for investments\n",
    "grouped_and_aggregated_df['cum_sum'] = grouped_and_aggregated_df.income.cumsum()\n",
    "grouped_and_aggregated_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from random import randint\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "traces = []\n",
    "for c in columns[1:]:\n",
    "    trace = {\n",
    "        'x': grouped_and_aggregated_df.index,\n",
    "        'y': grouped_and_aggregated_df[c],\n",
    "        'name': c,\n",
    "    'type': 'bar',\n",
    "    'yaxis': 'y'\n",
    "    }\n",
    "    traces.append(trace)\n",
    "\n",
    "trace_cumsum = {\n",
    "    'x': grouped_and_aggregated_df.index, \n",
    "    'y': grouped_and_aggregated_df.cum_sum,\n",
    "    'name': 'Total',\n",
    "    'type': 'scatter',\n",
    "    'yaxis': 'y2',\n",
    "    'mode':'markers+lines',\n",
    "    'line': {\n",
    "        'shape': 'hvh',\n",
    "    }\n",
    "}\n",
    "traces.append(trace_cumsum)\n",
    "\n",
    "layout = {\n",
    "    'xaxis': {\n",
    "        'title': 'X axis'\n",
    "    },\n",
    "    'yaxis': {\n",
    "        'title': 'Y Buckets',\n",
    "        'side': 'left',\n",
    "        'zeroline': True\n",
    "    },\n",
    "    'yaxis2': {\n",
    "        'title': 'Y Total invest',\n",
    "        'side': 'right',\n",
    "        'overlaying': 'y',\n",
    "    },\n",
    "    'barmode': 'group',\n",
    "    'title': 'Investments and buckets filling', \n",
    "}\n",
    "\n",
    "iplot({'data': traces, 'layout': layout})"
   ]
  }
 ],
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