quant/code/.ipynb_checkpoints/Untitled2-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: '1.5 总获利'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-57-ee6c3b7ef107>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     14\u001b[0m         \u001b[0msell\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mindex_spa\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m         \u001b[1;31m#print(index_bchl,index_spa2)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m         \u001b[0mprofit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindex_bchl\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mindex_spa2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m         \u001b[0mbuy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msell\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mprofit\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m         \u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msell\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mbuy\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m0.002\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: '1.5 总获利'"
     ]
    }
   ],
   "source": [
    "df2=pd.DataFrame(columns=['i','value'])\n",
    "df3=pd.DataFrame(columns=['year','value'])\n",
    "f = open('C:/Users/lenovo/Desktop/000338-3.txt')\n",
    "i=0\n",
    "value=100\n",
    "year=2000\n",
    "for line in f.readlines():\n",
    "    index_spa=line.find(' ',3)\n",
    "    index_bchl=line.find('本次获利')\n",
    "    index_spa2=line.find(' ',index_bchl+6)\n",
    "    index_zhl=line.find('总获利')\n",
    "    index_year=line.find(' ',index_zhl+5)+1\n",
    "    if index_bchl>0:\n",
    "        sell=float(line[3:index_spa])\n",
    "        #print(index_bchl,index_spa2)\n",
    "        profit=float(line[index_bchl+5:index_spa2])\n",
    "        buy=sell-profit\n",
    "        value=value*(sell/buy-0.002)\n",
    "        i+=1\n",
    "        df2=df2.append({'i':i, 'value':value},ignore_index=True)\n",
    "        if year!=line[index_year:index_year+4]:\n",
    "            df3=df3.append({'year':int(line[index_year:index_year+4]), 'value':value},ignore_index=True)            \n",
    "            year=line[index_year:index_year+4]\n",
    "df3=df3.append({'year':2020, 'value':value},ignore_index=True)  \n",
    "f.close()\n",
    "print(value)\n",
    "df2.plot(x='i',y='value',figsize=(16,3))\n",
    "df3.plot(x='year',y='value',figsize=(16,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.39999999999999"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float('4.39999999999999')"
   ]
  }
 ],
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