130 lines
40 KiB
Plaintext
130 lines
40 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import random\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1604.8379034318823\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x16ec73bbb38>"
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]
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},
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"execution_count": 58,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1152x216 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1152x216 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"df2=pd.DataFrame(columns=['i','value'])\n",
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"df3=pd.DataFrame(columns=['year','value'])\n",
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"f = open('C:/Users/lenovo/Desktop/000338-3.txt')\n",
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"i=0\n",
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"value=100\n",
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"year=2000\n",
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"for line in f.readlines():\n",
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" index_spa=line.find(' ',3)\n",
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" index_bchl=line.find('本次获利')\n",
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" index_spa2=line.find(' ',index_bchl+6)\n",
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" index_zhl=line.find('总获利')\n",
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" index_year=line.find(' ',index_zhl+5)+1\n",
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" if index_bchl>0:\n",
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" sell=float(line[3:index_spa])\n",
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" #print(index_bchl,index_spa2)\n",
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" profit=float(line[index_bchl+5:index_spa2])\n",
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" buy=sell-profit\n",
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" value=value*(sell/buy-0.002)\n",
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" i+=1\n",
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" df2=df2.append({'i':i, 'value':value},ignore_index=True)\n",
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" if year!=line[index_year:index_year+4]:\n",
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" df3=df3.append({'year':int(line[index_year:index_year+4]), 'value':value},ignore_index=True) \n",
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" year=line[index_year:index_year+4]\n",
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"df3=df3.append({'year':2020, 'value':value},ignore_index=True) \n",
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"f.close()\n",
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"print(value)\n",
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"df2.plot(x='i',y='value',figsize=(16,3))\n",
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"df3.plot(x='year',y='value',figsize=(16,3))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"4.39999999999999"
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]
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},
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"execution_count": 40,
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"metadata": {},
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"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"float('4.39999999999999')"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python [conda root]",
|
||
|
|
"language": "python",
|
||
|
|
"name": "conda-root-py"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.6.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|