Practicing wash sell model

Wash+sell+model+-+take+2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import date, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "WASHSALE_DAY_DELTA = 30\n",
    "\n",
    "\n",
    "class StockEstimation:\n",
    "    \"\"\" Modeled by Sanghee\"\"\"\n",
    "    \n",
    "    def get_max_profic(self):\n",
    "        return 10000\n",
    "\n",
    "\n",
    "class WashSaleFunctions:\n",
    "    \"\"\" Written by James\"\"\"\n",
    "    \n",
    "    def calc_wash_sale_period(self):\n",
    "        \"\"\"\n",
    "        Requirement:\n",
    "        \n",
    "        a. sell_date\n",
    "        b. repurchase_date\n",
    "        \"\"\"\n",
    "        before = self.get_wash_sale_before()\n",
    "        after = self.get_wash_sale_after()\n",
    "        if before < self.repurchase_date and \\\n",
    "            self.repurchase_date < after:\n",
    "            return True\n",
    "        else:\n",
    "            return False\n",
    "        \n",
    "    def get_wash_sale_before(self):\n",
    "        return self.sell_date - timedelta(WASHSALE_DAY_DELTA)\n",
    "    \n",
    "    def get_wash_sale_after(self):\n",
    "        return self.sell_date + timedelta(WASHSALE_DAY_DELTA) \n",
    "    \n",
    "\n",
    "\n",
    "class TradeUnit(WashSaleFunctions, StockEstimation):\n",
    "    \n",
    "    def __init__(self, type_, name, quantity, price, \n",
    "                 buy_date, sell_date, repurchase_date):\n",
    "        \n",
    "        assert isinstance(buy_date, date), \"Date type required\"\n",
    "        assert isinstance(sell_date, date), \"Date type required\"\n",
    "        assert isinstance(repurchase_date, date), \"Date type required\"\n",
    "        \n",
    "        self.type_ = type_\n",
    "        self.name = name\n",
    "        self.quantity = quantity\n",
    "        self.price = price\n",
    "        self.buy_date = buy_date\n",
    "        self.sell_date = sell_date\n",
    "        self.repurchase_date = repurchase_date\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class HealthTaxUnit(WashSaleFunctions):\n",
    "    \n",
    "    def __init__(self, buy_date, sell_date, repurchase_date):\n",
    "        \n",
    "        assert isinstance(buy_date, date), \"Date type required\"\n",
    "        assert isinstance(sell_date, date), \"Date type required\"\n",
    "        assert isinstance(repurchase_date, date), \"Date type required\"\n",
    "        \n",
    "        self.buy_date = buy_date\n",
    "        self.sell_date = sell_date\n",
    "        self.repurchase_date = repurchase_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "health = HealthTaxUnit(\n",
    "    date(2017, 01, 01),\n",
    "    date(2017, 01, 02),\n",
    "    date(2017, 01, 15)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health.calc_wash_sale_period()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2017, 1, 1)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health.buy_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "items = []\n",
    "for i in range(10):\n",
    "    item = [\"stock\", \"samsung\", i, 100.05, date(2017, 01, 01), date(2017, 01, 01), date(2017, 01, 01)]\n",
    "    items.append(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "results = []\n",
    "for item in items:\n",
    "    tu = TradeUnit(*item)\n",
    "    results.append(tu.calc_wash_sale_period())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[True, True, True, True, True, True, True, True, True, True]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.TradeUnit instance at 0x10e90d680>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TradeUnit(*a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "samsung_wash_sale_true = TradeUnit(\n",
    "    \"stock\",\n",
    "    \"Samsung\",\n",
    "    30,\n",
    "    100.05,\n",
    "    date(2017, 01, 01),\n",
    "    date(2017, 01, 02),\n",
    "    date(2017, 01, 15)\n",
    ")\n",
    "\n",
    "samsung_wash_sale_false = TradeUnit(\n",
    "    \"stock\",\n",
    "    \"Samsung\",\n",
    "    30,\n",
    "    100.05,\n",
    "    date(2017, 01, 01),\n",
    "    date(2017, 01, 02),\n",
    "    date(2017, 05, 15)\n",
    ")\n",
    "\n",
    "assert samsung_wash_sale_true.calc_wash_sale_period() == True\n",
    "assert samsung_wash_sale_false.calc_wash_sale_period() == False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samsung_wash_sale_false.get_max_profic()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2017, 2, 1)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samsung_wash_sale_true.get_wash_sale_after()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samsung_wash_sale_true.calc_wash_sale_period()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Before:', True)\n",
      "('After:', True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samsung.calc_wash_sale_period()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() takes exactly 8 arguments (7 given)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-79-09ff50271d15>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;36m100.05\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2017\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2017\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m02\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m )\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: __init__() takes exactly 8 arguments (7 given)"
     ]
    }
   ],
   "source": [
    "samsung = TradeUnit(\n",
    "    \"stock\",\n",
    "    \"Samsung\",\n",
    "    30,\n",
    "    100.05,\n",
    "    date(2017, 01, 01),\n",
    "    date(2017, 01, 02)\n",
    ")\n",
    "\n",
    "LG = TradeUnit(\n",
    "     \"Stock\",\n",
    "     \"LG\",\n",
    "      100,\n",
    "      200,\n",
    "      date(2016, 03, 05),\n",
    "      date(2016, 12, 25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Before:', True)\n",
      "('After:', True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LG.calc_wash_sale_period()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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Wash+sell+model+-+take1.ipynb