├── # 验证小市值结合盈利指标过滤策略.ipynb
├── .gitignore
├── 002475.csv
├── 600256.csv
├── README.md
├── fft-filter.ipynb
├── getstockcsv.py
├── newfeature.py
├── singlelstm.ipynb
├── singlelstm2.ipynb
├── sklearn机器学习单票回测.ipynb
├── xmm.csv
└── 保存K线数据到数据库.ipynb


/# 验证小市值结合盈利指标过滤策略.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# 验证小市值结合盈利指标过滤策略"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 1,
 13 |    "metadata": {
 14 |     "ExecuteTime": {
 15 |      "end_time": "2019-10-07T01:19:15.137015Z",
 16 |      "start_time": "2019-10-07T01:19:13.865910Z"
 17 |     }
 18 |    },
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "#常规库引用\n",
 22 |     "import datetime\n",
 23 |     "import tushare as ts\n",
 24 |     "# import rqalpha_data\n",
 25 |     "import pandas as pd\n",
 26 |     "import numpy as np\n",
 27 |     "import tushare as ts\n",
 28 |     "import baostock as bs\n",
 29 |     "import talib as talib\n",
 30 |     "import seaborn as sns\n",
 31 |     "import math\n",
 32 |     "import statsmodels.tsa.stattools as stattool\n",
 33 |     "import matplotlib.pyplot as plt \n",
 34 |     "plt.rcParams['figure.figsize'] = (18.0, 6.0)\n",
 35 |     "from sqlalchemy import create_engine\n",
 36 |     "\n",
 37 |     "engine = create_engine(\"mysql+pymysql://{}:{}@{}/{}?charset={}\".format('root', '12345678', '127.0.0.1:3306', 'finance','UTF8MB4'))\n",
 38 |     "conn = engine.connect()#创建连接"
 39 |    ]
 40 |   },
 41 |   {
 42 |    "cell_type": "code",
 43 |    "execution_count": 30,
 44 |    "metadata": {
 45 |     "ExecuteTime": {
 46 |      "end_time": "2019-10-07T02:01:50.459824Z",
 47 |      "start_time": "2019-10-07T02:00:38.800072Z"
 48 |     }
 49 |    },
 50 |    "outputs": [
 51 |     {
 52 |      "name": "stdout",
 53 |      "output_type": "stream",
 54 |      "text": [
 55 |       "(1332913, 24)\n",
 56 |       "(228430, 24)\n"
 57 |      ]
 58 |     }
 59 |    ],
 60 |    "source": [
 61 |     "datas=pd.DataFrame()\n",
 62 |     "#flowmkt<=40,大概是50%分位数，为了减少数据量提前过滤\n",
 63 |     "for sscode in ['000','001','002','300','600','601','603'][:]:\n",
 64 |     "    datas=pd.concat([datas,pd.read_sql('select * from k'+sscode+' where date>\"2016-01-01\" \\\n",
 65 |     "                                       AND isST=0 AND eprofit>1  AND flowmkt<=60  AND esale>0 \\\n",
 66 |     "                                       AND pctChg>-9.7 AND pctChg<=9.7 AND turn>=0.5 order by date \\\n",
 67 |     "                                       ',con=conn)],axis=0)\n",
 68 |     "print(datas.shape)\n",
 69 |     "datas=datas[datas.groupby('date')['flowmkt'].rank()<=250]\n",
 70 |     "print(datas.shape)\n"
 71 |    ]
 72 |   },
 73 |   {
 74 |    "cell_type": "code",
 75 |    "execution_count": 31,
 76 |    "metadata": {
 77 |     "ExecuteTime": {
 78 |      "end_time": "2019-10-07T02:01:51.961805Z",
 79 |      "start_time": "2019-10-07T02:01:51.635186Z"
 80 |     }
 81 |    },
 82 |    "outputs": [
 83 |     {
 84 |      "name": "stdout",
 85 |      "output_type": "stream",
 86 |      "text": [
 87 |       "(228430, 15)\n",
 88 |       "(182782, 15) (228430, 15)\n"
 89 |      ]
 90 |     }
 91 |    ],
 92 |    "source": [
 93 |     "n=200\n",
 94 |     "sdata=datas\n",
 95 |     "# sdata['amo']=sdata.close/sdata.emoney\n",
 96 |     "sdata['ape']=sdata.close/sdata.eprofit\n",
 97 |     "sdata['asa']=sdata.close/sdata.esale\n",
 98 |     "sdata['result2']=sdata.result\n",
 99 |     "sdata.drop(['close','open','preclose','high','low','amount','volume','isST','eb','emoney','esale','result'],axis=1,inplace=True)\n",
100 |     "print(datas.shape)\n",
101 |     "datas=datas[datas.groupby('date')['ape'].rank()<=n]\n",
102 |     "print(datas.shape,sdata.shape)"
103 |    ]
104 |   },
105 |   {
106 |    "cell_type": "code",
107 |    "execution_count": 39,
108 |    "metadata": {
109 |     "ExecuteTime": {
110 |      "end_time": "2019-10-07T02:03:06.363597Z",
111 |      "start_time": "2019-10-07T02:03:05.816182Z"
112 |     }
113 |    },
114 |    "outputs": [
115 |     {
116 |      "name": "stdout",
117 |      "output_type": "stream",
118 |      "text": [
119 |       "[-193.11, -110.04, -40.03, 88.52]\n",
120 |       "[343.01, 399.55, 122.52, 94.81]\n",
121 |       "[396.47, 439.14, 184.96, 81.99]\n",
122 |       "[419.43, 486.3, 126.51, 72.1]\n",
123 |       "[249.88, 350.18, 53.26, 74.1]\n"
124 |      ]
125 |     },
126 |     {
127 |      "data": {
128 |       "image/png": 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\n",
129 |       "text/plain": [
130 |        "<Figure size 432x288 with 1 Axes>"
131 |       ]
132 |      },
133 |      "metadata": {
134 |       "needs_background": "light"
135 |      },
136 |      "output_type": "display_data"
137 |     }
138 |    ],
139 |    "source": [
140 |     "stock_data=datas[:-40].copy()\n",
141 |     "\n",
142 |     "output=pd.DataFrame()\n",
143 |     "output['monthresult']=sdata.groupby('date')['result5'].mean()\n",
144 |     "# print(stock_data.shape,stock_data.columns)\n",
145 |     "i,j,k=int(n*0.95),0.3,2\n",
146 |     "turnmin,turnmax,volrmin,volrmax=2,8,6,20\n",
147 |     "stock_data=stock_data[stock_data['turn']>turnmin]  \n",
148 |     "stock_data=stock_data[stock_data['turn']<turnmax]\n",
149 |     "stock_data=stock_data[stock_data['volratio']>volrmin*0.1]  \n",
150 |     "stock_data=stock_data[stock_data['volratio']<volrmax*0.1]\n",
151 |     "stock_data=stock_data[stock_data.groupby('date')['asa'].rank()<i]  \n",
152 |     "stock_data=stock_data[stock_data.groupby('date')['ape'].rank()<int(j*i)]  \n",
153 |     "stock_data=stock_data[stock_data.groupby('date')['flowmkt'].rank()<=k]\n",
154 |     "\n",
155 |     "#计算选中的股票在下月的涨幅\n",
156 |     "output['line_benchmark']=(0.01*output['monthresult']/5+1).cumprod()-1\n",
157 |     "plt.plot(output['line_benchmark'],label='benchmark')\n",
158 |     "for mm in [2,5,10,20,40]:\n",
159 |     "    output['selectresult']=stock_data.groupby('date')['result'+str(mm)].mean()\n",
160 |     "    output['line']=(0.01*output['selectresult']/mm+1).cumprod()-1\n",
161 |     "    plt.plot(output['line'],label=str(mm))\n",
162 |     "    plt.legend()\n",
163 |     "\n",
164 |     "    sharpratio=(output.line.dropna(axis=0)[-1]-0.050)/output.line.std()  #以5%作为无风险收益率\n",
165 |     "    inforatio=(output.line[-1]-output.line_benchmark[-1])/output.line.std()\n",
166 |     "    rj = np.argmax(np.maximum.accumulate(output.line)-output.line)\n",
167 |     "    backd = (output.line[np.argmax(output.line[:rj])] - output.line[rj])\n",
168 |     "    print([round(100*sharpratio,2),round(100*inforatio,2),round(100*output.line[-1],2),round(100*backd,2)])"
169 |    ]
170 |   },
171 |   {
172 |    "cell_type": "markdown",
173 |    "metadata": {},
174 |    "source": [
175 |     "## 由此可见ape、asa、flowmkt三个指标结合（舍去前最突出的几个），配合量比、换手率指标，5-10日调仓，远远跑赢大盘"
176 |    ]
177 |   },
178 |   {
179 |    "cell_type": "markdown",
180 |    "metadata": {},
181 |    "source": [
182 |     "### 搜索最佳参数"
183 |    ]
184 |   },
185 |   {
186 |    "cell_type": "code",
187 |    "execution_count": 40,
188 |    "metadata": {
189 |     "ExecuteTime": {
190 |      "end_time": "2019-10-07T02:05:30.709007Z",
191 |      "start_time": "2019-10-07T02:05:03.920183Z"
192 |     }
193 |    },
194 |    "outputs": [
195 |     {
196 |      "name": "stderr",
197 |      "output_type": "stream",
198 |      "text": [
199 |       "/Users/hongyuouyang/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:56: FutureWarning: \n",
200 |       "The current behaviour of 'Series.argmax' is deprecated, use 'idxmax'\n",
201 |       "instead.\n",
202 |       "The behavior of 'argmax' will be corrected to return the positional\n",
203 |       "maximum in the future. For now, use 'series.values.argmax' or\n",
204 |       "'np.argmax(np.array(values))' to get the position of the maximum\n",
205 |       "row.\n",
206 |       "  return getattr(obj, method)(*args, **kwds)\n"
207 |      ]
208 |     },
209 |     {
210 |      "ename": "KeyboardInterrupt",
211 |      "evalue": "",
212 |      "output_type": "error",
213 |      "traceback": [
214 |       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
215 |       "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
216 |       "\u001b[0;32m<ipython-input-40-95804ce1711a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m                             \u001b[0mstock_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstock_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstock_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'turn'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0mturnmax\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m                             \u001b[0mstock_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstock_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstock_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'volratio'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0mvolrmin\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m                             \u001b[0mstock_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstock_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstock_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'volratio'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0mvolrmax\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m                             \u001b[0mstock_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstock_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstock_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'asa'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrank\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m                             \u001b[0mstock_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstock_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstock_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ape'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrank\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
217 |       "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
218 |      ]
219 |     }
220 |    ],
221 |    "source": [
222 |     "for turnmin in range(0,4,1):#4\n",
223 |     "    for turnmax in range(8,20,3):#4\n",
224 |     "        for volrmin in range(5,10,1):#4\n",
225 |     "            for volrmax in range(20,28,2):#4\n",
226 |     "                results=[]\n",
227 |     "                for i in [0.5,0.6,0.7,0.8,0.9]:#8\n",
228 |     "                    for j in [0.5,0.6,0.7,0.8,0.9]:#5\n",
229 |     "                        for k in range(1,5,1): #4\n",
230 |     "\n",
231 |     "                            stock_data=datas[:-40].copy()\n",
232 |     "                            stock_data=stock_data[stock_data['turn']>turnmin]  \n",
233 |     "                            stock_data=stock_data[stock_data['turn']<turnmax]\n",
234 |     "                            stock_data=stock_data[stock_data['volratio']>volrmin*0.1]  \n",
235 |     "                            stock_data=stock_data[stock_data['volratio']<volrmax*0.1]\n",
236 |     "                            stock_data=stock_data[stock_data.groupby('date')['asa'].rank()<int(n*i)]  \n",
237 |     "                            stock_data=stock_data[stock_data.groupby('date')['ape'].rank()<int(j*n*i)]  \n",
238 |     "#                             print(stock_data.shape)\n",
239 |     "                            stock_data=stock_data[stock_data.groupby('date')['flowmkt'].rank()<=k]\n",
240 |     "                            #计算选中的股票在下月的涨幅\n",
241 |     "# \n",
242 |     "                            backtests=[]\n",
243 |     "                            for mm in [2,5,10,20,40]:\n",
244 |     "                                output['selectresult']=stock_data.groupby('date')['result'+str(mm)].mean()#输出选择股票和资金曲线\n",
245 |     "                                output['line'+str(mm)]=(0.01*output['selectresult']/mm+1).cumprod()-1\n",
246 |     "#                                 plt.plot(output['line'+str(mm)],label=str(mm))\n",
247 |     "#                                 plt.legend()\n",
248 |     "                                output['selectresult']=stock_data.groupby('date')['result'+str(mm)].mean()\n",
249 |     "                                output['line']=(0.01*output['selectresult']/mm+1).cumprod()-1\n",
250 |     "                                sharpratio=(output.line.dropna(axis=0)[-1]-0.050)/output.line.std()  #以5%作为无风险收益率\n",
251 |     "                                inforatio=(output.line[-1]-output.line_benchmark[-1])/output.line.std()\n",
252 |     "                                rj = np.argmax(np.maximum.accumulate(output.line)-output.line)\n",
253 |     "                                backd = (output.line[np.argmax(output.line[:rj])] - output.line[rj])\n",
254 |     "                                backtests.append([round(100*sharpratio,2),round(100*inforatio,2),round(100*output.line[-1],2),round(100*backd,2)])\n",
255 |     "                            backtests=np.array(backtests).reshape(20)\n",
256 |     "                            para=np.array([n,n*i,n*i*j,k,turnmin,turnmax,volrmin,volrmax])\n",
257 |     "                            res=np.r_[para,backtests]\n",
258 |     "                            results.append(res)\n",
259 |     "                m=pd.DataFrame(results,columns=['n','i','j','k','turnmin','turnmax','volrmin','volrmax','sha2','inf2',\n",
260 |     "                                'profit2','backd2','sha5','inf5','profit5','backd5','sha10','inf10','profit10','backd10',\n",
261 |     "                                'sha20','inf20','profit20','backd20','sha40','inf40','profit40','backd40'])\n",
262 |     "                m.to_sql(name='minimkt2',if_exists='append',con=conn)\n",
263 |     "                print(turnmin,turnmax,volrmin,volrmax,'ok',backtests[-1])\n",
264 |     "\n"
265 |    ]
266 |   },
267 |   {
268 |    "cell_type": "code",
269 |    "execution_count": null,
270 |    "metadata": {
271 |     "ExecuteTime": {
272 |      "start_time": "2019-10-07T00:48:21.698Z"
273 |     }
274 |    },
275 |    "outputs": [
276 |     {
277 |      "name": "stderr",
278 |      "output_type": "stream",
279 |      "text": [
280 |       "/Users/hongyuouyang/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:56: FutureWarning: \n",
281 |       "The current behaviour of 'Series.argmax' is deprecated, use 'idxmax'\n",
282 |       "instead.\n",
283 |       "The behavior of 'argmax' will be corrected to return the positional\n",
284 |       "maximum in the future. For now, use 'series.values.argmax' or\n",
285 |       "'np.argmax(np.array(values))' to get the position of the maximum\n",
286 |       "row.\n",
287 |       "  return getattr(obj, method)(*args, **kwds)\n"
288 |      ]
289 |     },
290 |     {
291 |      "name": "stdout",
292 |      "output_type": "stream",
293 |      "text": [
294 |       "3 8 6 20 ok 61.76\n",
295 |       "2 8 6 20 ok 66.75\n",
296 |       "1 8 6 20 ok 67.6\n",
297 |       "0 8 6 20 ok 63.45\n",
298 |       "3 8 6 22 ok 61.3\n",
299 |       "2 8 6 22 ok 8.7\n"
300 |      ]
301 |     }
302 |    ],
303 |    "source": [
304 |     "import threading\n",
305 |     "threads=[]\n",
306 |     "jlen=0\n",
307 |     "\n",
308 |     "for turnmin in range(0,4,1):#4\n",
309 |     "    threads.append(threading.Thread(target=touchpara,args=(turnmin,)))\n",
310 |     "    threads[jlen].start()\n",
311 |     "    jlen+=1\n",
312 |     "\n",
313 |     "for i in range(jlen):\n",
314 |     "    threads[i].join()\n",
315 |     "# touchpara()"
316 |    ]
317 |   }
318 |  ],
319 |  "metadata": {
320 |   "hide_input": false,
321 |   "kernelspec": {
322 |    "display_name": "Python 3",
323 |    "language": "python",
324 |    "name": "python3"
325 |   },
326 |   "language_info": {
327 |    "codemirror_mode": {
328 |     "name": "ipython",
329 |     "version": 3
330 |    },
331 |    "file_extension": ".py",
332 |    "mimetype": "text/x-python",
333 |    "name": "python",
334 |    "nbconvert_exporter": "python",
335 |    "pygments_lexer": "ipython3",
336 |    "version": "3.7.4"
337 |   },
338 |   "latex_envs": {
339 |    "LaTeX_envs_menu_present": true,
340 |    "autoclose": false,
341 |    "autocomplete": true,
342 |    "bibliofile": "biblio.bib",
343 |    "cite_by": "apalike",
344 |    "current_citInitial": 1,
345 |    "eqLabelWithNumbers": true,
346 |    "eqNumInitial": 1,
347 |    "hotkeys": {
348 |     "equation": "Ctrl-E",
349 |     "itemize": "Ctrl-I"
350 |    },
351 |    "labels_anchors": false,
352 |    "latex_user_defs": false,
353 |    "report_style_numbering": false,
354 |    "user_envs_cfg": false
355 |   },
356 |   "toc": {
357 |    "base_numbering": 1,
358 |    "nav_menu": {},
359 |    "number_sections": true,
360 |    "sideBar": true,
361 |    "skip_h1_title": false,
362 |    "title_cell": "Table of Contents",
363 |    "title_sidebar": "Contents",
364 |    "toc_cell": false,
365 |    "toc_position": {},
366 |    "toc_section_display": true,
367 |    "toc_window_display": false
368 |   },
369 |   "varInspector": {
370 |    "cols": {
371 |     "lenName": 16,
372 |     "lenType": 16,
373 |     "lenVar": 40
374 |    },
375 |    "kernels_config": {
376 |     "python": {
377 |      "delete_cmd_postfix": "",
378 |      "delete_cmd_prefix": "del ",
379 |      "library": "var_list.py",
380 |      "varRefreshCmd": "print(var_dic_list())"
381 |     },
382 |     "r": {
383 |      "delete_cmd_postfix": ") ",
384 |      "delete_cmd_prefix": "rm(",
385 |      "library": "var_list.r",
386 |      "varRefreshCmd": "cat(var_dic_list()) "
387 |     }
388 |    },
389 |    "types_to_exclude": [
390 |     "module",
391 |     "function",
392 |     "builtin_function_or_method",
393 |     "instance",
394 |     "_Feature"
395 |    ],
396 |    "window_display": false
397 |   }
398 |  },
399 |  "nbformat": 4,
400 |  "nbformat_minor": 2
401 | }
402 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | 
  9 | # Distribution / packaging
 10 | .Python
 11 | build/
 12 | develop-eggs/
 13 | dist/
 14 | downloads/
 15 | eggs/
 16 | .eggs/
 17 | lib/
 18 | lib64/
 19 | parts/
 20 | sdist/
 21 | var/
 22 | wheels/
 23 | *.egg-info/
 24 | .installed.cfg
 25 | *.egg
 26 | MANIFEST
 27 | 
 28 | # PyInstaller
 29 | #  Usually these files are written by a python script from a template
 30 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 31 | *.manifest
 32 | *.spec
 33 | 
 34 | # Installer logs
 35 | pip-log.txt
 36 | pip-delete-this-directory.txt
 37 | 
 38 | # Unit test / coverage reports
 39 | htmlcov/
 40 | .tox/
 41 | .coverage
 42 | .coverage.*
 43 | .cache
 44 | nosetests.xml
 45 | coverage.xml
 46 | *.cover
 47 | .hypothesis/
 48 | .pytest_cache/
 49 | 
 50 | # Translations
 51 | *.mo
 52 | *.pot
 53 | 
 54 | # Django stuff:
 55 | *.log
 56 | local_settings.py
 57 | db.sqlite3
 58 | 
 59 | # Flask stuff:
 60 | instance/
 61 | .webassets-cache
 62 | 
 63 | # Scrapy stuff:
 64 | .scrapy
 65 | 
 66 | # Sphinx documentation
 67 | docs/_build/
 68 | 
 69 | # PyBuilder
 70 | target/
 71 | 
 72 | # Jupyter Notebook
 73 | .ipynb_checkpoints
 74 | 
 75 | # pyenv
 76 | .python-version
 77 | 
 78 | # celery beat schedule file
 79 | celerybeat-schedule
 80 | 
 81 | # SageMath parsed files
 82 | *.sage.py
 83 | 
 84 | # Environments
 85 | .env
 86 | .venv
 87 | env/
 88 | venv/
 89 | ENV/
 90 | env.bak/
 91 | venv.bak/
 92 | 
 93 | # Spyder project settings
 94 | .spyderproject
 95 | .spyproject
 96 | 
 97 | # Rope project settings
 98 | .ropeproject
 99 | 
100 | # mkdocs documentation
101 | /site
102 | 
103 | # mypy
104 | .mypy_cache/
105 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # ML_stock_qt
2 | 基于机器学习算法的A股股票走势预测
3 | 


--------------------------------------------------------------------------------
/fft-filter.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# 用滤波器进行滤波，并与平均线对比效果\n"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 3,
 13 |    "metadata": {
 14 |     "ExecuteTime": {
 15 |      "end_time": "2019-08-07T23:07:00.143661Z",
 16 |      "start_time": "2019-08-07T23:07:00.139283Z"
 17 |     },
 18 |     "code_folding": []
 19 |    },
 20 |    "outputs": [],
 21 |    "source": [
 22 |     "#常规库引用\n",
 23 |     "import datetime\n",
 24 |     "import tushare as ts\n",
 25 |     "# import rqalpha_data\n",
 26 |     "import pandas as pd\n",
 27 |     "import numpy as np\n",
 28 |     "import tushare as ts\n",
 29 |     "import baostock as bs\n",
 30 |     "import talib as talib\n",
 31 |     "import seaborn as sns\n",
 32 |     "from sklearn.externals import joblib   #保存模型方法\n",
 33 |     "#保存模型\n",
 34 |     "#joblib.dump(model,'rf.model')\n",
 35 |     "#加载模型\n",
 36 |     "#model=joblib.load('rf.model')\n",
 37 |     "\n",
 38 |     "import math\n",
 39 |     "import statsmodels.tsa.stattools as stattool\n",
 40 |     "import matplotlib.pyplot as plt \n",
 41 |     "plt.rcParams['figure.figsize'] = (18.0, 6.0)"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "markdown",
 46 |    "metadata": {},
 47 |    "source": [
 48 |     "## 函数库"
 49 |    ]
 50 |   },
 51 |   {
 52 |    "cell_type": "code",
 53 |    "execution_count": 27,
 54 |    "metadata": {
 55 |     "ExecuteTime": {
 56 |      "end_time": "2019-08-07T23:24:12.237133Z",
 57 |      "start_time": "2019-08-07T23:24:12.230800Z"
 58 |     }
 59 |    },
 60 |    "outputs": [],
 61 |    "source": [
 62 |     "def pre(code):\n",
 63 |     "    pcode='sh.'+code if code[0]=='6' else 'sz.'+code\n",
 64 |     "    return pcode\n",
 65 |     "def getDataFromTushare(code,start_date,end_date):\n",
 66 |     "    price=ts.get_hist_data(code,start='2012-01-01',end='2019-04-30')\n",
 67 |     "    return price\n",
 68 |     "def getDataFromBaostock(code,start_date,end_date):\n",
 69 |     "    lg = bs.login()\n",
 70 |     "    print(pre(code))\n",
 71 |     "    rs = bs.query_history_k_data_plus(pre(code),\n",
 72 |     "        \"date,code,open,high,low,close,preclose,volume,amount\",\n",
 73 |     "        start_date=start_date, end_date=end_date,\n",
 74 |     "        frequency=\"d\", adjustflag=\"3\")\n",
 75 |     "    #### 打印结果集 ####\n",
 76 |     "    data_list = []\n",
 77 |     "    while (rs.error_code == '0') & rs.next():\n",
 78 |     "        # 获取一条记录，将记录合并在一起\n",
 79 |     "        data_list.append(rs.get_row_data())\n",
 80 |     "    result = pd.DataFrame(data_list, columns=rs.fields)\n",
 81 |     "\n",
 82 |     "\n",
 83 |     "    bs.logout()\n",
 84 |     "    return result"
 85 |    ]
 86 |   },
 87 |   {
 88 |    "cell_type": "markdown",
 89 |    "metadata": {},
 90 |    "source": [
 91 |     "## 读取数据"
 92 |    ]
 93 |   },
 94 |   {
 95 |    "cell_type": "code",
 96 |    "execution_count": 29,
 97 |    "metadata": {
 98 |     "ExecuteTime": {
 99 |      "end_time": "2019-08-07T23:24:32.994422Z",
100 |      "start_time": "2019-08-07T23:24:32.742763Z"
101 |     }
102 |    },
103 |    "outputs": [
104 |     {
105 |      "name": "stdout",
106 |      "output_type": "stream",
107 |      "text": [
108 |       "login success!\n",
109 |       "sz.000001\n",
110 |       "logout success!\n"
111 |      ]
112 |     },
113 |     {
114 |      "data": {
115 |       "text/html": [
116 |        "<div>\n",
117 |        "<style scoped>\n",
118 |        "    .dataframe tbody tr th:only-of-type {\n",
119 |        "        vertical-align: middle;\n",
120 |        "    }\n",
121 |        "\n",
122 |        "    .dataframe tbody tr th {\n",
123 |        "        vertical-align: top;\n",
124 |        "    }\n",
125 |        "\n",
126 |        "    .dataframe thead th {\n",
127 |        "        text-align: right;\n",
128 |        "    }\n",
129 |        "</style>\n",
130 |        "<table border=\"1\" class=\"dataframe\">\n",
131 |        "  <thead>\n",
132 |        "    <tr style=\"text-align: right;\">\n",
133 |        "      <th></th>\n",
134 |        "      <th>date</th>\n",
135 |        "      <th>code</th>\n",
136 |        "      <th>open</th>\n",
137 |        "      <th>high</th>\n",
138 |        "      <th>low</th>\n",
139 |        "      <th>close</th>\n",
140 |        "      <th>preclose</th>\n",
141 |        "      <th>volume</th>\n",
142 |        "      <th>amount</th>\n",
143 |        "    </tr>\n",
144 |        "  </thead>\n",
145 |        "  <tbody>\n",
146 |        "    <tr>\n",
147 |        "      <th>0</th>\n",
148 |        "      <td>2015-03-02</td>\n",
149 |        "      <td>sz.000001</td>\n",
150 |        "      <td>14.0300</td>\n",
151 |        "      <td>14.0900</td>\n",
152 |        "      <td>13.8700</td>\n",
153 |        "      <td>14.0300</td>\n",
154 |        "      <td>13.9900</td>\n",
155 |        "      <td>101879700</td>\n",
156 |        "      <td>1423603264.0000</td>\n",
157 |        "    </tr>\n",
158 |        "    <tr>\n",
159 |        "      <th>1</th>\n",
160 |        "      <td>2015-03-03</td>\n",
161 |        "      <td>sz.000001</td>\n",
162 |        "      <td>13.9800</td>\n",
163 |        "      <td>13.9800</td>\n",
164 |        "      <td>13.5900</td>\n",
165 |        "      <td>13.6000</td>\n",
166 |        "      <td>14.0300</td>\n",
167 |        "      <td>105947629</td>\n",
168 |        "      <td>1457305040.0000</td>\n",
169 |        "    </tr>\n",
170 |        "    <tr>\n",
171 |        "      <th>2</th>\n",
172 |        "      <td>2015-03-04</td>\n",
173 |        "      <td>sz.000001</td>\n",
174 |        "      <td>13.6200</td>\n",
175 |        "      <td>13.7200</td>\n",
176 |        "      <td>13.5000</td>\n",
177 |        "      <td>13.5700</td>\n",
178 |        "      <td>13.6000</td>\n",
179 |        "      <td>81497304</td>\n",
180 |        "      <td>1108145424.0000</td>\n",
181 |        "    </tr>\n",
182 |        "    <tr>\n",
183 |        "      <th>3</th>\n",
184 |        "      <td>2015-03-05</td>\n",
185 |        "      <td>sz.000001</td>\n",
186 |        "      <td>13.5000</td>\n",
187 |        "      <td>13.5300</td>\n",
188 |        "      <td>13.2900</td>\n",
189 |        "      <td>13.3800</td>\n",
190 |        "      <td>13.5700</td>\n",
191 |        "      <td>82860485</td>\n",
192 |        "      <td>1109151968.0000</td>\n",
193 |        "    </tr>\n",
194 |        "    <tr>\n",
195 |        "      <th>4</th>\n",
196 |        "      <td>2015-03-06</td>\n",
197 |        "      <td>sz.000001</td>\n",
198 |        "      <td>13.3700</td>\n",
199 |        "      <td>13.5100</td>\n",
200 |        "      <td>13.3500</td>\n",
201 |        "      <td>13.4400</td>\n",
202 |        "      <td>13.3800</td>\n",
203 |        "      <td>55704774</td>\n",
204 |        "      <td>748959120.0000</td>\n",
205 |        "    </tr>\n",
206 |        "  </tbody>\n",
207 |        "</table>\n",
208 |        "</div>"
209 |       ],
210 |       "text/plain": [
211 |        "         date       code     open     high      low    close preclose  \\\n",
212 |        "0  2015-03-02  sz.000001  14.0300  14.0900  13.8700  14.0300  13.9900   \n",
213 |        "1  2015-03-03  sz.000001  13.9800  13.9800  13.5900  13.6000  14.0300   \n",
214 |        "2  2015-03-04  sz.000001  13.6200  13.7200  13.5000  13.5700  13.6000   \n",
215 |        "3  2015-03-05  sz.000001  13.5000  13.5300  13.2900  13.3800  13.5700   \n",
216 |        "4  2015-03-06  sz.000001  13.3700  13.5100  13.3500  13.4400  13.3800   \n",
217 |        "\n",
218 |        "      volume           amount  \n",
219 |        "0  101879700  1423603264.0000  \n",
220 |        "1  105947629  1457305040.0000  \n",
221 |        "2   81497304  1108145424.0000  \n",
222 |        "3   82860485  1109151968.0000  \n",
223 |        "4   55704774   748959120.0000  "
224 |       ]
225 |      },
226 |      "execution_count": 29,
227 |      "metadata": {},
228 |      "output_type": "execute_result"
229 |     }
230 |    ],
231 |    "source": [
232 |     "code='000001'\n",
233 |     "start_date='2015-03-01'\n",
234 |     "end_date='2019-03-01'\n",
235 |     "price=getDataFromBaostock(code,start_date,end_date)\n",
236 |     "price.head()"
237 |    ]
238 |   },
239 |   {
240 |    "cell_type": "code",
241 |    "execution_count": 73,
242 |    "metadata": {
243 |     "ExecuteTime": {
244 |      "end_time": "2019-08-07T23:58:08.995969Z",
245 |      "start_time": "2019-08-07T23:58:08.766082Z"
246 |     }
247 |    },
248 |    "outputs": [
249 |     {
250 |      "data": {
251 |       "text/plain": [
252 |        "<matplotlib.legend.Legend at 0x1c2f8e1e10>"
253 |       ]
254 |      },
255 |      "execution_count": 73,
256 |      "metadata": {},
257 |      "output_type": "execute_result"
258 |     },
259 |     {
260 |      "data": {
261 |       "image/png": 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\n",
262 |       "text/plain": [
263 |        "<Figure size 1296x432 with 1 Axes>"
264 |       ]
265 |      },
266 |      "metadata": {
267 |       "needs_background": "light"
268 |      },
269 |      "output_type": "display_data"
270 |     }
271 |    ],
272 |    "source": [
273 |     "from scipy import signal\n",
274 |     "data=pd.to_numeric(price['close'])\n",
275 |     "b, a = signal.butter(1, 0.1, 'lowpass')  \n",
276 |     "# b1, a1 = signal.butter(8, 0.1, 'lowpass')  \n",
277 |     "# wn=2*滤波频率/采样频率，如果是五日平均，则采用2*1/5=0.4\n",
278 |     "meandata=data.rolling(10).mean().shift(-5)\n",
279 |     "filtedData = signal.filtfilt(b, a, data)       #data为要过滤的信号\n",
280 |     "filtedData1 = signal.filtfilt(b, a, data[:-10])       #data为要过滤的信号\n",
281 |     "plt.plot(filtedData[-100:],label='filter')\n",
282 |     "plt.plot(filtedData1[-90:],label='filter')\n",
283 |     "plt.plot(meandata.values[-100:],label='mean')\n",
284 |     "plt.plot(data.values[-100:],label='orig')\n",
285 |     "plt.legend()"
286 |    ]
287 |   },
288 |   {
289 |    "cell_type": "code",
290 |    "execution_count": null,
291 |    "metadata": {
292 |     "ExecuteTime": {
293 |      "end_time": "2019-08-07T23:27:43.750018Z",
294 |      "start_time": "2019-08-07T23:27:43.495314Z"
295 |     }
296 |    },
297 |    "outputs": [],
298 |    "source": []
299 |   }
300 |  ],
301 |  "metadata": {
302 |   "hide_input": false,
303 |   "kernelspec": {
304 |    "display_name": "Python 3",
305 |    "language": "python",
306 |    "name": "python3"
307 |   },
308 |   "language_info": {
309 |    "codemirror_mode": {
310 |     "name": "ipython",
311 |     "version": 3
312 |    },
313 |    "file_extension": ".py",
314 |    "mimetype": "text/x-python",
315 |    "name": "python",
316 |    "nbconvert_exporter": "python",
317 |    "pygments_lexer": "ipython3",
318 |    "version": "3.7.4"
319 |   },
320 |   "latex_envs": {
321 |    "LaTeX_envs_menu_present": true,
322 |    "autoclose": false,
323 |    "autocomplete": true,
324 |    "bibliofile": "biblio.bib",
325 |    "cite_by": "apalike",
326 |    "current_citInitial": 1,
327 |    "eqLabelWithNumbers": true,
328 |    "eqNumInitial": 1,
329 |    "hotkeys": {
330 |     "equation": "Ctrl-E",
331 |     "itemize": "Ctrl-I"
332 |    },
333 |    "labels_anchors": false,
334 |    "latex_user_defs": false,
335 |    "report_style_numbering": false,
336 |    "user_envs_cfg": false
337 |   },
338 |   "toc": {
339 |    "base_numbering": 1,
340 |    "nav_menu": {},
341 |    "number_sections": true,
342 |    "sideBar": true,
343 |    "skip_h1_title": false,
344 |    "title_cell": "Table of Contents",
345 |    "title_sidebar": "Contents",
346 |    "toc_cell": false,
347 |    "toc_position": {},
348 |    "toc_section_display": true,
349 |    "toc_window_display": false
350 |   },
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358 |     "python": {
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360 |      "delete_cmd_prefix": "del ",
361 |      "library": "var_list.py",
362 |      "varRefreshCmd": "print(var_dic_list())"
363 |     },
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365 |      "delete_cmd_postfix": ") ",
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367 |      "library": "var_list.r",
368 |      "varRefreshCmd": "cat(var_dic_list()) "
369 |     }
370 |    },
371 |    "types_to_exclude": [
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373 |     "function",
374 |     "builtin_function_or_method",
375 |     "instance",
376 |     "_Feature"
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379 |   }
380 |  },
381 |  "nbformat": 4,
382 |  "nbformat_minor": 2
383 | }
384 | 


--------------------------------------------------------------------------------
/getstockcsv.py:
--------------------------------------------------------------------------------
  1 | import newfeature
  2 | import numpy as np
  3 | import pandas as pd
  4 | import tushare as ts
  5 | import baostock as bs
  6 | import talib
  7 | import time
  8 | import bcolz
  9 | 
 10 | today=time.strftime('%Y-%m-%d',time.localtime(time.time()))
 11 | 
 12 | def get_quantiles(data,x): 
 13 |     bins=np.r_[-1e100,[np.round(np.quantile(data, i/x),3) for i in range(1,x)],1e100]  # [负无穷，data中result这列的1,1/2,1/3...，正无穷]
 14 |     return bins
 15 | 
 16 | def precode(c): #在代码后载入sz，sh，6开头代表上证，加sh.前缀
 17 |     return 'sh.'+c  if c[0:1]=='6' else 'sz.'+c 
 18 | 
 19 | 
 20 | def cut_to1(alpha,minx=-1,maxx=1):
 21 |     alpha=alpha.copy()
 22 |     alpha[alpha>maxx]=maxx  # 涨幅超过多少，则截取， 应当是在对异常值进行处理
 23 |     alpha[alpha<minx]=minx  # 跌幅超过多少，则截取
 24 |     return alpha
 25 | 
 26 | 
 27 | def getbaostock(symbol, start='2015-01-01', end='2020-10-25', ktype='D',index=0):
 28 |     # index=0代表大盘，1代表个股
 29 |     infields="date,close,open,preclose,low,high,turn,volume,amount,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST" if index==0 else "date,open,high,low,close,preclose,volume,amount,pctChg"
 30 |     rs = bs.query_history_k_data_plus(symbol, infields,start_date=start, end_date=end,
 31 |                                     frequency=ktype, adjustflag="2")
 32 |     
 33 |     data_list = []
 34 |     while (rs.error_code == '0') & rs.next():
 35 |         data_list.append(rs.get_row_data())
 36 |     result = pd.DataFrame(data_list, columns=rs.fields)
 37 |     if len(result)>50:
 38 |     
 39 |         result=result.sort_values('date').set_index('date').fillna(0)
 40 |         result=result[result['volume']!='0']  # 剔除交易量为0数据（即停牌日期数据）
 41 |         result=result[result['volume']!='']
 42 |         result=result.astype('float32')
 43 |         if index==0:  # 拿个股数据，才执行
 44 |             result=result[result['psTTM']>0]  # 剔除动态市盈率小于0数据
 45 |             
 46 |             # 特征工程，通过老特征组合，获得新增特征
 47 |             # ？？？？？？？
 48 |             result['flowmkt']=0.637*np.arctan(np.log(0.00000001*result['close']*result['volume']/result['turn']))
 49 |             result['everyprofit']=0.637*np.arctan(10/result.peTTM)
 50 |             result['turn']=0.637*np.arctan((result.turn/10)**0.2)
 51 |             result['everypb']=0.637*np.arctan(2/result.pbMRQ)
 52 |             result['eversale']=0.637*np.arctan(2/np.sqrt(result.psTTM))
 53 |             result['evermoney']=0.637*np.arctan(20/result.pcfNcfTTM)
 54 |             result.fillna(0,inplace=True)
 55 |             # result.fillna(0,inplace=True)
 56 | 
 57 |     # result['p_change']=result['pctChg']
 58 |     # print(symbol,result.shape)
 59 | 
 60 |         return result 
 61 |     else:
 62 |         return []
 63 | 
 64 | def saveData(path,symbol,data,szzs,days,binscount,r=1,isfeature=1):
 65 |      """
 66 |      path:文件存储路径
 67 |      symbol：股票代码
 68 |      data：个股数据
 69 |      szzs：上证指数数据
 70 |      days：默认为5，代表交易日
 71 |      
 72 |        
 73 |      """
 74 |     #如果是用talib、newfeatures计算的数据，需要解除前120条数据
 75 |     data.dropna(axis=0,inplace=True)
 76 |     # data['amount']=0.25*(data.open+data.close+data.high+data.low)*data['volume']
 77 |     data_price=data.values
 78 | 
 79 |     # 特征工程，组合生成新特征（有些特征往上证指数szzs数据加，又通过szzs新特征算出个股新特征，有些往个股数据加）
 80 |     data['szpctchg1']=18*szzs['close']/szzs['close'].shift(1)-18  # 今日收盘/昨日收盘 - 1  即18*(当日涨幅 - 1)
 81 |     data['szpctchg2']=19*szzs['close'].rolling(2).mean()/szzs['close'].shift(2)-19  # 19*(前3日收盘价均值(含当日)/前2日收盘价 - 1)
 82 |     data['szpctchg4']=10*szzs['close'].rolling(2).mean()/szzs['close'].shift(4)-10  # 10*(前3日收盘价均值(含当日)/前4日收盘价 - 1)
 83 |     data['szpctchg8']=10*szzs['close'].rolling(4).mean()/szzs['close'].shift(8)-10  # 10*(前4日收盘价均值(含当日)/前8日收盘价 - 1)
 84 |     data['szpctchg15']=8*szzs['close'].rolling(5).mean()/szzs['close'].shift(15)-8
 85 |     data['szpctchg30']=6*szzs['close'].rolling(10).mean()/szzs['close'].shift(30)-6
 86 | 
 87 | 
 88 |     szzs['sma5']=szzs['close'].rolling(5).mean() # 大盘5日均线
 89 |     szzs['sma10']=szzs['close'].rolling(10).mean()
 90 |     szzs['sma20']=szzs['close'].rolling(20).mean()
 91 |     szzs['sma40']=szzs['close'].rolling(40).mean()
 92 |     szzs['sma80']=szzs['close'].rolling(80).mean()
 93 | 
 94 |     szzs['max20']=szzs['high'].rolling(20).max().shift()  # 大盘20日最高价
 95 |     szzs['max60']=szzs['high'].rolling(60).max().shift()
 96 |     szzs['max120']=szzs['high'].rolling(120).max().shift()
 97 | 
 98 |     szzs['min20']=szzs['low'].rolling(20).min().shift()  # 大盘20日最低价
 99 |     szzs['min60']=szzs['low'].rolling(60).min().shift()
100 |     szzs['min120']=szzs['low'].rolling(120).min().shift()
101 | 
102 |     data['zs20_min']=cut_to1(2*szzs['close']/szzs['min20']-2)###  大盘2倍的20日内较最低价的涨幅（涨幅超50截取，或跌幅超50%被截取）
103 |     data['zs60_min']=cut_to1(2*szzs['close']/szzs['min60']-2)###
104 |     data['zs120_min']=cut_to1(2*szzs['close']/szzs['min120']-2)###
105 | 
106 |     data['zs20_max']=cut_to1(4*szzs['close']/szzs['max20']-4)###  大盘4倍的20日内较最高价的跌幅(涨幅超过25%截取，跌幅超过25%截取)
107 |     data['zs60_max']=cut_to1(4*szzs['close']/szzs['max60']-4)###
108 |     data['zs120_max']=cut_to1(4*szzs['close']/szzs['max120']-4)###
109 | 
110 |     data['zs5_d']=cut_to1(20*szzs['sma5'].diff()/szzs['sma5'].shift())  # 大盘5日均线差分/昨日5日均线值，涨跌幅限制-5% ~ 5%截取
111 |     data['zs10_d']=cut_to1(40*szzs['sma10'].diff()/szzs['sma10'].shift())
112 |     data['zs20_d']=cut_to1(60*szzs['sma20'].diff()/szzs['sma20'].shift())
113 |     data['zs40_d']=cut_to1(80*szzs['sma40'].diff()/szzs['sma40'].shift())
114 |     data['zs80_d']=cut_to1(100*szzs['sma80'].diff()/szzs['sma80'].shift())
115 | 
116 |     data['zsr5']=cut_to1(10*szzs['close']/szzs['sma5']-10)  # 大盘收盘价偏离5日均线的幅度，涨跌幅限制-10% ~ 10%截取
117 |     data['zsr10']=cut_to1(8*szzs['close']/szzs['sma10']-8)  
118 |     data['zsr20']=cut_to1(6*szzs['close']/szzs['sma20']-6)
119 |     data['zsr40']=cut_to1(6*szzs['close']/szzs['sma40']-6)
120 |     data['zsr80']=cut_to1(4*szzs['close']/szzs['sma80']-4)
121 |     
122 |     szzs['r5_20']=(4*szzs['sma5']/szzs['sma20']-4)  # 大盘5日均线偏离20日均线幅度，涨跌幅限制-25% ~ 25%截取
123 |     szzs['r10_40']=(4*szzs['sma10']/szzs['sma40']-4)
124 |     szzs['r20_80']=(4*szzs['sma20']/szzs['sma80']-4)
125 |     szzs['r5_40']=(4*szzs['sma5']/szzs['sma40']-4)
126 |     
127 |     data['zsr5_20_d']=cut_to1(10*szzs['r5_20'].diff())  # 大盘5日均线较20日均线的偏移幅度的变化值，反应5日和20日均偏离的剧烈程度
128 |     data['zsr10_40_d']=cut_to1(20*szzs['r10_40'].diff())
129 |     data['zsr20_80_d']=cut_to1(20*szzs['r20_80'].diff())
130 |     data['zsr5_20']=cut_to1(szzs['r5_20'])
131 |     data['zsr10_40']=cut_to1(1*szzs['r10_40'])
132 |     data['zsr20_80']=cut_to1(1*szzs['r20_80'])
133 |     data['zsr5_40']=cut_to1(1*szzs['r5_40'])
134 | 
135 | 
136 |     szzs['v5']=(10*szzs['volume']/(0.1+szzs['volume'].rolling(5).mean())) # 大盘交易量较5日均交易量均值偏移幅度，0.1是考虑到rolling前几个值为null
137 |     szzs['v10']=(10*szzs['volume']/(0.1+szzs['volume'].rolling(10).mean()))  # 衡量交易量较平均值异常增减的情况
138 |     szzs['v20']=(10*szzs['volume']/(0.1+szzs['volume'].rolling(20).mean()))
139 |     szzs['v40']=(10*szzs['volume']/(0.1+szzs['volume'].rolling(40).mean()))
140 |     szzs['v80']=(10*szzs['volume']/(0.1+szzs['volume'].rolling(80).mean()))
141 |     
142 |     data['zsv3_9']=(10*szzs['volume'].rolling(3).mean()/(0.1+szzs['volume'].rolling(9).mean()))  # 大盘3日交易量较9日均交易量偏移幅度
143 |     data['zsv5_20']=(10*szzs['volume'].rolling(5).mean()/(0.1+szzs['volume'].rolling(20).mean()))
144 |     data['zsv9_50']=(10*szzs['volume'].rolling(9).mean()/(0.1+szzs['volume'].rolling(50).mean()))
145 |     
146 |     data['zsv5_d']=0.6366*np.arctan(0.2*szzs['v5'].diff())  # 交易量较交易量均线的偏置幅度变化，衡量交易量的变动情况
147 |     data['zsv10_d']=0.6366*np.arctan(0.1*szzs['v10'].diff())  # arctan()用于归一化，把任何取值范围数据压缩到0-1
148 |     data['zsv20_d']=0.6366*np.arctan(0.2*szzs['v20'].diff())  # 为何用0.6366？？？？？？
149 |     data['zsv40_d']=0.6366*np.arctan(0.2*szzs['v40'].diff())
150 |     data['zsv80_d']=0.6366*np.arctan(0.2*szzs['v80'].diff())
151 |     
152 |     data['zsv3_9_d']=0.6366*np.arctan(2*data['zsv3_9'].diff())
153 |     data['zsv5_20_d']=0.6366*np.arctan(2*data['zsv5_20'].diff())
154 |     data['zsv9_50_d']=0.6366*np.arctan(2*data['zsv9_50'].diff())
155 |     
156 |     data['zsv3_9']=0.6366*np.arctan(0.2*data['zsv3_9'])
157 |     data['zsv5_20']=0.6366*np.arctan(0.2*data['zsv5_20'])
158 |     data['zsv9_50']=0.6366*np.arctan(0.2*data['zsv9_50'])
159 |     data['zsv5']=0.6366*np.arctan(0.2*szzs['v5'])
160 |     data['zsv10']=0.6366*np.arctan(0.2*szzs['v10'])
161 |     data['zsv20']=0.6366*np.arctan(0.2*szzs['v20'])
162 |     data['zsv40']=0.6366*np.arctan(0.2*szzs['v40'])
163 |     data['zsv80']=0.6366*np.arctan(0.2*szzs['v80'])
164 | 
165 |     # 大盘收盘价macd
166 |     szzs['dif'], szzs['dea'],szzs['hist'] = talib.MACD(szzs['close'].astype(float).values, fastperiod=12, slowperiod=26, signalperiod=9)
167 |     data['zsdif']=cut_to1(0.01*szzs['dif'])  # macd的dif线，上下限100截取
168 |     data['zsdea']=cut_to1(0.01*szzs['dea'])
169 | 
170 |     data['zshist_d']=cut_to1(0.1*szzs['hist'].diff())  # macd红绿柱的差分
171 |     data['zshist']=cut_to1(0.02*szzs['hist'])
172 | 
173 |     
174 |     # 提取标签值    
175 |     for i in [2,4,7,10]:,
176 |         data['result']=data['close'].shift(-i*r)/data['close']-1  #　ｒ＝１，个股未来2日涨幅(收盘价算)，4日后涨幅，7日后涨幅，10日后涨幅
177 |         # data['result2zs']=data['result']-szzs['close'].shift(-i*r)/szzs['close']
178 |         data['resultmax']=data['close'].rolling(i*r).max().shift(-i*r)/data['close']-1  # 个股未来(2,4,7,10日)最高涨幅(收盘价算)
179 |         data['resultmin']=data['close'].rolling(i*r).min().shift(-i*r)/data['close']-1  # 个股未来(2,4,7,10日)最大跌幅
180 |         data['resultrel']=data['resultmax']+data['resultmin']  # 个股未来(2,4,7,10日)振幅
181 |         remax=data['close'].rolling(i*r).max().shift(-i*r)  # 个股未来(2,4,7,10日)最高价
182 |         remin=data['close'].rolling(i*r).min().shift(-i*r)  # 个股未来(2,4,7,10日)最低价
183 |         reavg=remax-remin  # 个股未来(2,4,7,10日)最高最低价差
184 |         
185 |         if i>2:
186 |             # 每种标签值，对应不同的买入信号
187 |             # 最高最低价均分5份or3份，收盘价低于最低价到1/5的值的权重为4,而1/5到1/3的值是2，中间是0，2/3到5/4的值是-2，4/5到1的值是-4
188 |             buysignal=2*(data['close']<(remin+reavg/5))+2*(data['close']<(remin+reavg/3))-2*(data['close']>(remax-reavg/3))-2*(data['close']>(remax-reavg/5))
189 |             # 进一步约束买入信号，振幅要大于3%。然后如果振幅大于5%，且收盘价还在高低价差下方五分之一内，则buysignal所有权重再加1
190 |             # 最前面加4，因为权重最小值就是4，相当于把权重全部转为正数
191 |             data['resultbuy'+str(i)]=4+(reavg/data['close']>0.03)*(buysignal)+1*(reavg/data['close']>0.05)*(data['close']<(remin+reavg/5))
192 |             # data[(data['resultmax']-data['resultrel']/4)<0].loc[:,'buyorsale'+str(i)]=-1
193 |         # 获取result的1,1/2,1/3...1/9分位值
194 |         bins=get_quantiles(data['result'].dropna(),10)  # data["result"]之前是依据shift获取，因此有null值
195 |         data['resultclass'+str(i)]=pd.cut(data['result'], bins, right=False,labels=False)  # 新建结果类别列，以历史涨幅走势来分类别
196 |         # bins=get_quantiles(data['resultmin'].dropna(),10)
197 |         # data['resultmin'+i]=pd.cut(data['resultmin'], bins, right=False,labels=False)
198 |         # bins=get_quantiles(data['resultmax'].dropna(),10)
199 |         # data['resultmax'+i]=pd.cut(data['resultmax'], bins, right=False,labels=False)
200 |         bins=get_quantiles(data['resultrel'].dropna(),10)
201 |         data['resultrelclass'+str(i)]=pd.cut(data['resultrel'], bins, right=False,labels=False)  # 新建结果类别列，以历振幅走势来分类别
202 | 
203 |     if isfeature==0: 
204 |     ###这是不用feature的操作
205 |         start=0
206 |         data=data[start:].drop(['ma5','ma10','ma20','result','resultmax','resultmin','resultrel','peTTM','pbMRQ','psTTM',
207 |                                                                   'pcfNcfTTM','p_change','price_change'],axis=1)
208 | 
209 | 
210 | ###下面是用features的操作
211 |     elif isfeature==1: 
212 |         start=120
213 |         data=newfeature.getStockCharacter(data).astype('float16')
214 |         # data=data[start:].drop(['ma5','ma10','ma20','v_ma5','v_ma10','v_ma20','result','p_change','price_change'],axis=1)
215 |         data=data[start:].drop(['peTTM','pbMRQ','psTTM','pcfNcfTTM','result','resultmax','resultmin','resultrel'],axis=1)
216 |         # data.drop(['pctChg'],axis=1,inplace=True)
217 |     return data
218 | 
219 | import os, tarfile
220 | #一次性打包整个根目录。空子目录会被打包。
221 | #如果只打包不压缩，将"w:gz"参数改为"w:"或"w"即可。
222 | def make_targz(output_filename, source_dir):
223 |     with tarfile.open(output_filename, "w:gz") as tar:
224 |       tar.add(source_dir, arcname=os.path.basename(source_dir))
225 | #逐个添加文件打包，未打包空子目录。可过滤文件。
226 | #如果只打包不压缩，将"w:gz"参数改为"w:"或"w"即可。
227 | def make_targz_one_by_one(output_filename, source_dir):
228 |     tar = tarfile.open(output_filename,"w:gz")
229 |     for root,dir,files in os.walk(source_dir):
230 |       for file in files:
231 |           pathfile = os.path.join(root, file)
232 |           tar.add(pathfile)
233 |     tar.close()
234 | 
235 | 
236 | bs.login()
237 | 
238 | 
239 | 
240 | path='/www/stocks/'
241 | path='/Users/hongyuouyang/python/finance/stockdata/'
242 | days=5
243 | r=1
244 | sequence_length=10
245 | stocks=ts.get_stock_basics().sort_index()
246 | stocks.to_csv(path+'stocks.csv')
247 | stocks=stocks.index.values
248 | 
249 | # szzs=ts.get_hist_data('sh').sort_values('date')
250 | szzs=getbaostock('sh.000001','2016-01-01', today, 'D',1)
251 | j=0
252 | for symbol in stocks[:]:  # symbol代表6位股票代码
253 |     if not(os.path.exists(path+symbol)):
254 | 
255 |         # data=ts.get_hist_data(symbol)
256 | 
257 | 
258 |         #拼合数据生成bcolz文件
259 |         data=getbaostock(precode(symbol),'2016-01-01', today, 'D',0)  # 返回该股票2016年至今的日K数据，df格式，包含十多项指标
260 |         if data is None:
261 |             print(symbol,'no data')
262 |         else:
263 |             if len(data) > 300:
264 |                 print(j,symbol,data.shape)
265 |                 j += 1
266 |                 data=data.sort_values('date')  # 升序排列，日期从2016-现在排序
267 |                 datanew=saveData(path,symbol,data,szzs,days,10,r).astype('float16')
268 |                 if j==1:
269 |                     datanew.head(1).to_csv(path+'data.csv')
270 | 
271 |             	datanew.to_csv(path+symbol+'.csv')  # 保存每一只股票的数据到各自的csv文件（包含各种指标及特征）
272 | 
273 | 
274 | # make_targz('/www/wwwroot/fina.ouyanghome.com/public/stockdata.tar.gz', path)
275 | # print('data done')
276 | # bs.logout()
277 | 
278 | #存磁盘


--------------------------------------------------------------------------------
/newfeature.py:
--------------------------------------------------------------------------------
  1 | # -*- coding:utf8 -*-
  2 | import pandas as pd
  3 | import talib 
  4 | # import ta
  5 | import numpy as np
  6 | 
  7 | from scipy.stats import rankdata
  8 | import scipy as sp
  9 | 
 10 | from sklearn import preprocessing
 11 | abs_scaler = preprocessing.MaxAbsScaler()
 12 | import copy
 13 | 
 14 | 
 15 | def sigmoid(X,useStatus):
 16 |     if useStatus:
 17 |         return 1.0 / (1 + np.exp((0.001*X)));
 18 |     else:
 19 |         return (X)
 20 |     
 21 |     
 22 | # TSRANK 函数
 23 | def func_rank(na):
 24 |     return rankdata(na)[-1]/rankdata(na).max()
 25 | 
 26 | 
 27 | # DECAYLINEAR函数
 28 | def func_decaylinear(na):
 29 |     n = len(na)
 30 |     decay_weights = np.arange(1,n+1,1) 
 31 |     decay_weights = decay_weights / decay_weights.sum()
 32 | 
 33 |     return (na * decay_weights).sum()
 34 | 
 35 | 
 36 | # HIGHDAY 函数
 37 | def func_highday(na):
 38 |     return len(na) - na.argmax()
 39 | 
 40 | 
 41 | # LOWDAY 函数 
 42 | def func_lowday(na):
 43 |     return len(na) - na.argmin()
 44 | 
 45 | 
 46 | def cut_to1(alpha,minx=-1,maxx=1):
 47 |     alpha=alpha.copy()
 48 |     alpha[alpha>maxx]=maxx 
 49 |     alpha[alpha<minx]=minx 
 50 |     return alpha
 51 | 
 52 | 
 53 | def arbr(df):
 54 |     df['HO']=df.high-df.open
 55 |     df['OL']=df.open-df.low
 56 |     df['HCY']=df.high-df.close.shift()
 57 |     df['CYL']=df.close.shift()-df.low
 58 |     df['MID']=0.5*(df.high+df.low)
 59 |     df['uppwer']=df.high-df['MID']
 60 |     df['downpwer']=df['MID']-df.low
 61 |     #计算AR、BR指标
 62 |     df['AR']=talib.SUM(df.HO, timeperiod=26)/(1+talib.SUM(df.OL, timeperiod=26))-1
 63 |     df['BR']=talib.SUM(df.HCY, timeperiod=26)/(1+talib.SUM(df.CYL, timeperiod=26))-1
 64 |     df['CR']=talib.SUM(df.uppwer, timeperiod=26)-talib.SUM(df.downpwer, timeperiod=26)-1
 65 | 
 66 |     return df['AR'],df['BR'],df['BR']
 67 | 
 68 | 
 69 | def CCI(data, ndays): 
 70 |     TP = (data['high'] + data['low'] + data['close']) / 3 
 71 |     CCI = pd.Series((TP - TP.rolling(ndays).mean()) / (0.015 * TP.rolling(ndays).std()),
 72 |     name = 'CCI') 
 73 |     data = data.join(CCI) 
 74 |     return data
 75 | 
 76 | 
 77 | def EVM(data, ndays): 
 78 |     print(data['high'] + data['low'])
 79 |     dm = ((data['high'] + data['low'])) - ((data['high'].shift(1) + data['low'].shift(1)))
 80 |     r=data['high'] - data['low']
 81 |     r[r==0]=0.0001
 82 |     br = (data['volume'] ) / r
 83 |     EVM = 0.5*dm / br 
 84 |     EVM_MA = -pd.Series(EVM.rolling(ndays).mean(), name = 'EVM') 
 85 |     data = data.join(EVM_MA) 
 86 |     return data
 87 | 
 88 | 
 89 | def ForceIndex(data, ndays):
 90 |     FI = pd.Series(data['close'].diff(ndays) * data['volume'], name = 'ForceIndex') 
 91 |     data = data.join(FI) 
 92 |     return data
 93 | 
 94 | 
 95 | def my_ROC(data,n):
 96 |     N = data['close'].diff(n)
 97 |     D = data['close'].shift(n)
 98 |     ROC = pd.Series(N/D,name='myRoc')
 99 |     data = data.join(ROC)
100 |     return data 
101 | 
102 | 
103 | def sunup(df):
104 |     df['sunup']=1*(df['close']>df['open'])
105 |     return df['sunup']
106 | 
107 | 
108 | def upratio(df,days):
109 |     df['sunup']=sunup(df)
110 |     upratio=df['sunup'].rolling(days).sum()/days
111 |     return upratio
112 | 
113 | 
114 | def trans_features(stockk):  
115 |     """该函数主要用于继续新增各种个股的特征，增加了各种常用技术指标入EMA，布林线等"""
116 |     # stockk就是融合了各种特征列，标签列的df格式的data数据
117 |     # stockk[['open','close','low','high']]=stockk[['open','close','low','high']]/stockk['close'].iloc[-1]
118 |     
119 |     # 特征工程，继续增加新特征   
120 |     stockk['preclose']=stockk['close'].shift()
121 |     stockk['volume']=stockk['volume']/1000000  # 个股交易量数据缩放
122 |     stockk['amount']=stockk['amount']/1000000
123 |     stockk['pctChg']=cut_to1(8*stockk['close'].diff()/stockk['close'].shift())  # 当日涨幅
124 |     # stockk['pctChg2']=(1+stockk['pctChg']).rolling(2).cumprod()-1
125 |     stockk['pctChg2']=cut_to1(5*(stockk['close']/stockk['close'].shift(2))-5)  # 过去2日涨幅
126 |     stockk['pctChg4']=cut_to1(4*(stockk['close']/stockk['close'].shift(5))-4)
127 |     stockk['pctChg8']=cut_to1(2*(stockk['close']/stockk['close'].shift(8))-2)
128 |     stockk['pctChg15']=cut_to1(2*(stockk['close']/stockk['close'].shift(15))-2)
129 |     stockk['pctChg30']=cut_to1(2*(stockk['close']/stockk['close'].shift(15))-2)
130 |     stockk['avg_price']=stockk['amount']/(0.01+stockk['volume'])  # 平均成交价，成交额/交易量
131 | 
132 |     stockk['sma5']=stockk['close'].rolling(5).mean()  # 个股前5日均价
133 |     stockk['sma10']=stockk['close'].rolling(10).mean()
134 |     stockk['sma20']=stockk['close'].rolling(20).mean()
135 |     stockk['sma40']=stockk['close'].rolling(40).mean()
136 |     stockk['sma80']=stockk['close'].rolling(80).mean()
137 | 
138 |     stockk['max20']=stockk['high'].rolling(20).max().shift()  # 个股前20日最高价(不含当日)
139 |     stockk['max60']=stockk['high'].rolling(60).max().shift()
140 |     stockk['max120']=stockk['high'].rolling(120).max().shift()
141 | 
142 |     stockk['min20']=stockk['low'].rolling(20).min().shift()  # 个股前20日最低价(不含当日)
143 |     stockk['min60']=stockk['low'].rolling(60).min().shift()
144 |     stockk['min120']=stockk['low'].rolling(120).min().shift()
145 | 
146 |     stockk['20_min']=cut_to1(1*stockk['close']/stockk['min20']-0.9)### (个股20日内最大涨幅 + 10%)，涨幅超110%，则截断。----10%有何用？？？？？？
147 |     stockk['60_min']=cut_to1(1*stockk['close']/stockk['min60']-0.9)###
148 |     stockk['120_min']=cut_to1(1*stockk['close']/stockk['min120']-0.9)###
149 | 
150 |     stockk['20_max']=cut_to1(2*stockk['close']/stockk['max20']-2)### 2*(个股20日内最大跌幅), 跌幅超50%，则截断。
151 |     stockk['60_max']=cut_to1(2*stockk['close']/stockk['max60']-2)### （即便昨天是max20，今天最多涨10%，不会截断）
152 |     stockk['120_max']=cut_to1(2*stockk['close']/stockk['max120']-2)###
153 | 
154 |     stockk['5_d']=cut_to1(20*stockk['sma5'].diff()/stockk['sma5'].shift())  # 个股5日均线涨幅，±5%截断
155 |     stockk['10_d']=cut_to1(40*stockk['sma10'].diff()/stockk['sma10'].shift())
156 |     stockk['20_d']=cut_to1(60*stockk['sma20'].diff()/stockk['sma20'].shift())
157 |     stockk['40_d']=cut_to1(80*stockk['sma40'].diff()/stockk['sma40'].shift())
158 |     stockk['80_d']=cut_to1(100*stockk['sma80'].diff()/stockk['sma80'].shift())
159 | 
160 |     stockk['r5']=cut_to1(6*stockk['close']/stockk['sma5']-6)  # 个股收盘价较5日均线偏移幅度（±16.6%截断）
161 |     stockk['r10']=cut_to1(4*stockk['close']/stockk['sma10']-4)
162 |     stockk['r20']=cut_to1(4*stockk['close']/stockk['sma20']-4)
163 |     stockk['r40']=cut_to1(3*stockk['close']/stockk['sma40']-3)
164 |     stockk['r80']=cut_to1(2*stockk['close']/stockk['sma80']-2)
165 |     
166 |     stockk['r5_20']=(3*stockk['sma5']/stockk['sma20']-3)  # 个股5日均线较20日均线偏移幅度(下面语句会进行±33.3%截断)
167 |     stockk['r10_40']=(2*stockk['sma10']/stockk['sma40']-2)
168 |     stockk['r20_80']=(2*stockk['sma20']/stockk['sma80']-2)
169 |     stockk['r5_40']=(2*stockk['sma5']/stockk['sma40']-2)
170 |     
171 |     stockk['r5_20_d']=cut_to1(10*stockk['r5_20'].diff())  # 5日20日均线偏移幅度变动量（±10%截断）----不太可能一天变动10%吧！
172 |     stockk['r10_40_d']=cut_to1(20*stockk['r10_40'].diff())
173 |     stockk['r20_80_d']=cut_to1(20*stockk['r20_80'].diff())
174 |     
175 |     stockk['r5_20']=cut_to1(stockk['r5_20'])  # ±33.3%截断
176 |     stockk['r10_40']=cut_to1(stockk['r10_40'])
177 |     stockk['r20_80']=cut_to1(stockk['r20_80'])
178 |     stockk['r5_40']=cut_to1(stockk['r5_40'])
179 | 
180 | 
181 |     stockk['day_price'] = 0.5*(stockk['open']+stockk['close'])/stockk['volume']  # (开盘价-收盘价)的平均值除以交易量
182 | 
183 |     stockk['v5']=(10*stockk['volume']/(0.1+stockk['volume'].rolling(5).mean()))  # 交易量/5日交易量均值，0.1是避免出现0交易量，导致报错
184 |     stockk['v10']=(10*stockk['volume']/(0.1+stockk['volume'].rolling(10).mean()))
185 |     stockk['v20']=(10*stockk['volume']/(0.1+stockk['volume'].rolling(20).mean()))
186 |     stockk['v40']=(10*stockk['volume']/(0.1+stockk['volume'].rolling(40).mean()))
187 |     stockk['v80']=(10*stockk['volume']/(0.1+stockk['volume'].rolling(80).mean()))
188 |     
189 |     stockk['v3_9']=(10*stockk['volume'].rolling(3).mean()/(0.1+stockk['volume'].rolling(9).mean()))  # 交易量3日均/交易量9日均
190 |     stockk['v5_20']=(10*stockk['volume'].rolling(5).mean()/(0.1+stockk['volume'].rolling(20).mean()))
191 |     stockk['v9_50']=(10*stockk['volume'].rolling(9).mean()/(0.1+stockk['volume'].rolling(50).mean()))
192 |     
193 |     stockk['v5_d']=0.6366*np.arctan(0.1*stockk['v5'].diff())  # 用arctan()进行数据归一化，交易量过大，用0.1压一压，避难arctan()饱和
194 |     stockk['v10_d']=0.6366*np.arctan(0.1*stockk['v10'].diff())
195 |     stockk['v20_d']=0.6366*np.arctan(0.1*stockk['v20'].diff())
196 |     stockk['v40_d']=0.6366*np.arctan(0.1*stockk['v40'].diff())
197 |     stockk['v80_d']=0.6366*np.arctan(0.1*stockk['v80'].diff())
198 |     
199 |     stockk['v3_9_d']=0.6366*np.arctan(1*stockk['v3_9'].diff())
200 |     stockk['v5_20_d']=0.6366*np.arctan(1*stockk['v5_20'].diff())
201 |     stockk['v9_50_d']=0.6366*np.arctan(1*stockk['v9_50'].diff())
202 |     
203 |     stockk['v3_9']=0.6366*np.arctan(0.1*stockk['v3_9'])
204 |     stockk['v5_20']=0.6366*np.arctan(0.1*stockk['v5_20'])
205 |     stockk['v9_50']=0.6366*np.arctan(0.1*stockk['v9_50'])
206 |     
207 |     stockk['v5']=0.6366*np.arctan(0.1*stockk['v5'])
208 |     stockk['v10']=0.6366*np.arctan(0.1*stockk['v10'])
209 |     stockk['v20']=0.6366*np.arctan(0.1*stockk['v20'])
210 |     stockk['v40']=0.6366*np.arctan(0.1*stockk['v40'])
211 |     stockk['v80']=0.6366*np.arctan(0.1*stockk['v80'])
212 | 
213 |     # 个股macd(12,26,9)
214 |     stockk['dif'], stockk['dea'],stockk['hist'] = talib.MACD(stockk['close'].astype(float).values, fastperiod=12, slowperiod=26, signalperiod=9)
215 |     stockk['dif']=cut_to1(0.7*stockk['dif'])  # 个股dif截断怎么用1.42吗，超过1.42的dif就没了？？？
216 |     stockk['dea']=cut_to1(0.7*stockk['dea'])
217 |     stockk['hist']=(1*stockk['hist'])
218 | 
219 |     stockk['hist_d']=cut_to1(4*stockk['hist'].diff())
220 |     stockk['hist']=cut_to1(stockk['hist'])
221 | 
222 | 
223 |     #计算几个指标相对前收盘的涨幅
224 |     stockk['highratio']=cut_to1(10*(stockk['high'])/stockk['close'].shift()-10.2) # 当日最高涨幅（为何设10.2？？？）
225 |     stockk['lowratio']=cut_to1(10*(stockk['low'])/stockk['close'].shift()-9.8)
226 |     stockk['openratio']=cut_to1(20*(stockk['open'])/stockk['close'].shift()-20) # 当日开盘涨幅（±5%截断，不合适吧，开盘涨停的没了？？？）
227 | 
228 |     #talib均线指标
229 |     stockk['tema']=0.6366*np.arctan(0.1*talib.TEMA(stockk['close'],timeperiod=30)**0.5)  # 三重移动平均线
230 | 
231 |     stockk['trima']=0.6366*np.arctan(0.1*talib.TRIMA(stockk['close'],timeperiod=30)**0.5)  # 三角移动平均线
232 | 
233 |     stockk['wma']=0.6366*np.arctan(0.1*talib.WMA(stockk['close'],timeperiod=30)**0.5)  # 加权移动平均线
234 |     
235 |     stockk['t3']=0.6366*np.arctan(0.1*talib.T3(stockk['close'],timeperiod=5, vfactor=0)**0.5)  # 超短线使用的三重移动平均线
236 |      
237 |     #talib量价指标
238 |     stockk['natr']=cut_to1(-0.2+0.1*talib.NATR(stockk['high'],stockk['low'],stockk['close'],timeperiod=14))
239 |     stockk['adline'] = 0.6366*np.arctan(0.005*talib.AD(stockk['high'],stockk['low'],stockk['close'],stockk['volume'])-0.5)
240 |     stockk['adosc'] = 0.6366*np.arctan(0.1*talib.ADOSC(stockk['high'],stockk['low'],stockk['close'],stockk['volume'], fastperiod=3, slowperiod=10))
241 |     stockk['obv'] =0.6366*np.arctan(0.001*talib.OBV(stockk['close'],stockk['volume']))
242 |     
243 |     ##静态指标
244 |     stockk['beta'] = 0.6366*np.arctan(talib.BETA(stockk['high'],stockk['low'], timeperiod=5))
245 |     stockk['correl'] = cut_to1(-3*talib.CORREL(stockk['high'],stockk['low'], timeperiod=30)+3)
246 |     stockk['linearreg'] = 0.6366*np.arctan(0.1*talib.LINEARREG(stockk['close'], timeperiod=14)-1)
247 |     stockk['lineangle'] = 0.6366*np.arctan(0.1*talib.LINEARREG_ANGLE(stockk['close'], timeperiod=14))
248 |     # stockk['lineslope'] =0.1*talib.LINEARREG_SLOPE(stockk['close'],  timeperiod=14)
249 |     stockk['var'] =  0.6366*np.arctan(30*talib.VAR(stockk['close'],  timeperiod=5,nbdev=1))
250 |     stockk['TSF'] =  0.6366*np.arctan(2*talib.TSF(stockk['close'], timeperiod=14))
251 |     stockk['lineintercept'] = 0.6366*np.arctan(0.05*talib.LINEARREG_INTERCEPT(stockk['close'], timeperiod=14))
252 |     stockk['stddev'] =cut_to1((talib.STDDEV(stockk['close'], timeperiod=5,nbdev=1))**0.25-1)
253 | 
254 |     ###动量指标
255 |     stockk['sar']  = 0.6366*np.arctan(0.1*talib.SAR(stockk['high'], stockk['low'], acceleration=0, maximum=0))
256 |     stockk['adx'] = cut_to1(0.015*talib.ADX(stockk['high'],stockk['low'],stockk['close'], timeperiod=14))
257 |     stockk['adxr'] = cut_to1(0.015*talib.ADXR(stockk['high'],stockk['low'],stockk['close'],timeperiod=14))
258 |     stockk['apo'] = cut_to1(0.5*talib.APO(stockk['close'], fastperiod=12, slowperiod=26, matype=0))
259 |     stockk['ardown'], stockk['arup']= talib.AROON(stockk['high'],stockk['low'],timeperiod=14)
260 |     stockk['ardown']=0.02*stockk['ardown']-1
261 |     stockk['arup']=0.02*stockk['arup']-1
262 |     stockk['arosc'] = 0.01*talib.AROONOSC(stockk['high'],stockk['low'],timeperiod=14)
263 |     stockk['bop'] = talib.BOP(stockk['open'],stockk['high'],stockk['low'],stockk['close'])
264 |     stockk['cci'] = 0.004*talib.CCI(stockk['high'],stockk['low'],stockk['close'],timeperiod=14)
265 |     stockk['cci_d']=0.637*np.arctan(1*stockk['cci'].diff()/(0.1+stockk['cci'].shift()))
266 | 
267 |     return stockk
268 | 
269 | 
270 | def getStockCharacter(stock_data):
271 |     # stockk=getK(stock_data)
272 |     stockk=trans_features(stock_data)
273 | 
274 |     stockk=stockk.drop(['max20','max60','max120','min20','min60',
275 |         'min120','sma5','sma10','sma20','sma40','sma80',
276 |         "volume",'preclose','amount','avg_price','day_price'],axis=1) 
277 |                         #,'HO','OL','uppwer','downpwer'
278 | 
279 |     return stockk


--------------------------------------------------------------------------------
/保存K线数据到数据库.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "## 保存数据到mysql数据库，包含全部个股数据"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {
 13 |     "ExecuteTime": {
 14 |      "end_time": "2019-08-08T11:36:30.955410Z",
 15 |      "start_time": "2019-08-08T11:36:30.952804Z"
 16 |     }
 17 |    },
 18 |    "source": [
 19 |     "### 基础引用初始化"
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "code",
 24 |    "execution_count": 2,
 25 |    "metadata": {
 26 |     "ExecuteTime": {
 27 |      "end_time": "2019-10-20T07:15:14.676285Z",
 28 |      "start_time": "2019-10-20T07:15:14.195976Z"
 29 |     }
 30 |    },
 31 |    "outputs": [],
 32 |    "source": [
 33 |     "#常规引用\n",
 34 |     "import time\n",
 35 |     "import tushare as ts\n",
 36 |     "import pandas as pd\n",
 37 |     "import numpy as np\n",
 38 |     "import baostock as bs\n",
 39 |     "\n",
 40 |     "today=time.strftime('%Y-%m-%d',time.localtime(time.time()))\n"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "code",
 45 |    "execution_count": 10,
 46 |    "metadata": {
 47 |     "ExecuteTime": {
 48 |      "end_time": "2019-10-20T07:15:15.947809Z",
 49 |      "start_time": "2019-10-20T07:15:15.855557Z"
 50 |     }
 51 |    },
 52 |    "outputs": [],
 53 |    "source": [
 54 |     "# from sqlalchemy import create_engine\n",
 55 |     "\n",
 56 |     "# #获取股票代码清\n",
 57 |     "# engine = create_engine(\"mysql+pymysql://{}:{}@{}/{}?charset={}\".format('root', 'qqq375701956', '127.0.0.1:3306', 'finance','utf8'))\n",
 58 |     "# conn = engine.connect()#创建连接\n"
 59 |    ]
 60 |   },
 61 |   {
 62 |    "cell_type": "markdown",
 63 |    "metadata": {},
 64 |    "source": [
 65 |     "### baostock接口调用函数\n",
 66 |     "适用于多天数据为更新，所花时间较长\n",
 67 |     "\n"
 68 |    ]
 69 |   },
 70 |   {
 71 |    "cell_type": "markdown",
 72 |    "metadata": {},
 73 |    "source": [
 74 |     "#### 获取股票列表"
 75 |    ]
 76 |   },
 77 |   {
 78 |    "cell_type": "code",
 79 |    "execution_count": 11,
 80 |    "metadata": {
 81 |     "ExecuteTime": {
 82 |      "end_time": "2019-10-20T07:15:19.786504Z",
 83 |      "start_time": "2019-10-20T07:15:19.272555Z"
 84 |     },
 85 |     "scrolled": true
 86 |    },
 87 |    "outputs": [
 88 |     {
 89 |      "data": {
 90 |       "text/html": [
 91 |        "<div>\n",
 92 |        "<style scoped>\n",
 93 |        "    .dataframe tbody tr th:only-of-type {\n",
 94 |        "        vertical-align: middle;\n",
 95 |        "    }\n",
 96 |        "\n",
 97 |        "    .dataframe tbody tr th {\n",
 98 |        "        vertical-align: top;\n",
 99 |        "    }\n",
100 |        "\n",
101 |        "    .dataframe thead th {\n",
102 |        "        text-align: right;\n",
103 |        "    }\n",
104 |        "</style>\n",
105 |        "<table border=\"1\" class=\"dataframe\">\n",
106 |        "  <thead>\n",
107 |        "    <tr style=\"text-align: right;\">\n",
108 |        "      <th></th>\n",
109 |        "      <th>name</th>\n",
110 |        "      <th>industry</th>\n",
111 |        "      <th>area</th>\n",
112 |        "      <th>pe</th>\n",
113 |        "      <th>outstanding</th>\n",
114 |        "      <th>totals</th>\n",
115 |        "      <th>totalAssets</th>\n",
116 |        "      <th>liquidAssets</th>\n",
117 |        "      <th>fixedAssets</th>\n",
118 |        "      <th>reserved</th>\n",
119 |        "      <th>...</th>\n",
120 |        "      <th>bvps</th>\n",
121 |        "      <th>pb</th>\n",
122 |        "      <th>timeToMarket</th>\n",
123 |        "      <th>undp</th>\n",
124 |        "      <th>perundp</th>\n",
125 |        "      <th>rev</th>\n",
126 |        "      <th>profit</th>\n",
127 |        "      <th>gpr</th>\n",
128 |        "      <th>npr</th>\n",
129 |        "      <th>holders</th>\n",
130 |        "    </tr>\n",
131 |        "    <tr>\n",
132 |        "      <th>code</th>\n",
133 |        "      <th></th>\n",
134 |        "      <th></th>\n",
135 |        "      <th></th>\n",
136 |        "      <th></th>\n",
137 |        "      <th></th>\n",
138 |        "      <th></th>\n",
139 |        "      <th></th>\n",
140 |        "      <th></th>\n",
141 |        "      <th></th>\n",
142 |        "      <th></th>\n",
143 |        "      <th></th>\n",
144 |        "      <th></th>\n",
145 |        "      <th></th>\n",
146 |        "      <th></th>\n",
147 |        "      <th></th>\n",
148 |        "      <th></th>\n",
149 |        "      <th></th>\n",
150 |        "      <th></th>\n",
151 |        "      <th></th>\n",
152 |        "      <th></th>\n",
153 |        "      <th></th>\n",
154 |        "    </tr>\n",
155 |        "  </thead>\n",
156 |        "  <tbody>\n",
157 |        "    <tr>\n",
158 |        "      <td>000001</td>\n",
159 |        "      <td>平安银行</td>\n",
160 |        "      <td>银行</td>\n",
161 |        "      <td>深圳</td>\n",
162 |        "      <td>9.61</td>\n",
163 |        "      <td>194.06</td>\n",
164 |        "      <td>194.06</td>\n",
165 |        "      <td>37076.83</td>\n",
166 |        "      <td>0.00</td>\n",
167 |        "      <td>105.87</td>\n",
168 |        "      <td>808.16</td>\n",
169 |        "      <td>...</td>\n",
170 |        "      <td>13.82</td>\n",
171 |        "      <td>1.13</td>\n",
172 |        "      <td>19910403</td>\n",
173 |        "      <td>1152.93</td>\n",
174 |        "      <td>5.94</td>\n",
175 |        "      <td>18.80</td>\n",
176 |        "      <td>15.47</td>\n",
177 |        "      <td>0.00</td>\n",
178 |        "      <td>22.94</td>\n",
179 |        "      <td>299958.0</td>\n",
180 |        "    </tr>\n",
181 |        "    <tr>\n",
182 |        "      <td>000002</td>\n",
183 |        "      <td>万 科Ａ</td>\n",
184 |        "      <td>全国地产</td>\n",
185 |        "      <td>深圳</td>\n",
186 |        "      <td>12.55</td>\n",
187 |        "      <td>97.15</td>\n",
188 |        "      <td>113.02</td>\n",
189 |        "      <td>16387.63</td>\n",
190 |        "      <td>13581.85</td>\n",
191 |        "      <td>124.12</td>\n",
192 |        "      <td>119.31</td>\n",
193 |        "      <td>...</td>\n",
194 |        "      <td>14.80</td>\n",
195 |        "      <td>1.82</td>\n",
196 |        "      <td>19910129</td>\n",
197 |        "      <td>981.54</td>\n",
198 |        "      <td>8.68</td>\n",
199 |        "      <td>27.21</td>\n",
200 |        "      <td>30.43</td>\n",
201 |        "      <td>35.99</td>\n",
202 |        "      <td>8.15</td>\n",
203 |        "      <td>290546.0</td>\n",
204 |        "    </tr>\n",
205 |        "  </tbody>\n",
206 |        "</table>\n",
207 |        "<p>2 rows × 22 columns</p>\n",
208 |        "</div>"
209 |       ],
210 |       "text/plain": [
211 |        "        name industry area     pe  outstanding  totals  totalAssets  \\\n",
212 |        "code                                                                  \n",
213 |        "000001  平安银行       银行   深圳   9.61       194.06  194.06     37076.83   \n",
214 |        "000002  万 科Ａ     全国地产   深圳  12.55        97.15  113.02     16387.63   \n",
215 |        "\n",
216 |        "        liquidAssets  fixedAssets  reserved  ...   bvps    pb  timeToMarket  \\\n",
217 |        "code                                         ...                              \n",
218 |        "000001          0.00       105.87    808.16  ...  13.82  1.13      19910403   \n",
219 |        "000002      13581.85       124.12    119.31  ...  14.80  1.82      19910129   \n",
220 |        "\n",
221 |        "           undp  perundp    rev  profit    gpr    npr   holders  \n",
222 |        "code                                                             \n",
223 |        "000001  1152.93     5.94  18.80   15.47   0.00  22.94  299958.0  \n",
224 |        "000002   981.54     8.68  27.21   30.43  35.99   8.15  290546.0  \n",
225 |        "\n",
226 |        "[2 rows x 22 columns]"
227 |       ]
228 |      },
229 |      "execution_count": 11,
230 |      "metadata": {},
231 |      "output_type": "execute_result"
232 |     }
233 |    ],
234 |    "source": [
235 |     "\n",
236 |     "stocks=ts.get_stock_basics().sort_index()\n",
237 |     "stocks.head(2)"
238 |    ]
239 |   },
240 |   {
241 |    "cell_type": "code",
242 |    "execution_count": 12,
243 |    "metadata": {
244 |     "ExecuteTime": {
245 |      "end_time": "2019-10-20T07:15:23.392378Z",
246 |      "start_time": "2019-10-20T07:15:23.383645Z"
247 |     }
248 |    },
249 |    "outputs": [
250 |     {
251 |      "name": "stdout",
252 |      "output_type": "stream",
253 |      "text": [
254 |       "(3747, 22)\n",
255 |       "(3683, 22)\n"
256 |      ]
257 |     }
258 |    ],
259 |    "source": [
260 |     "print(stocks.shape)\n",
261 |     "\n",
262 |     "stocks=stocks[stocks.index.str[:3]!='688']  # 剔除科创板股票(688开头)\n",
263 |     "print(stocks.shape)\n",
264 |     "# stocks.index.str[0:1]"
265 |    ]
266 |   },
267 |   {
268 |    "cell_type": "markdown",
269 |    "metadata": {},
270 |    "source": [
271 |     "#### 调用接口函数"
272 |    ]
273 |   },
274 |   {
275 |    "cell_type": "code",
276 |    "execution_count": 22,
277 |    "metadata": {
278 |     "ExecuteTime": {
279 |      "end_time": "2019-09-21T01:01:16.424265Z",
280 |      "start_time": "2019-09-21T01:01:16.414424Z"
281 |     },
282 |     "code_folding": []
283 |    },
284 |    "outputs": [],
285 |    "source": [
286 |     "#调用接口函数\n",
287 |     "def addcode(c): #在代码后载入sz，sh\n",
288 |     "    return c+'.sh'  if c[0:1]=='6' else c+'.sz' \n",
289 |     "def precode(c): #在代码后载入sz，sh\n",
290 |     "    return 'sh.'+c  if c[0:1]=='6' else 'sz.'+c \n",
291 |     "\n",
292 |     "def getBaostock(pcode,start_date,end_date,datatype):\n",
293 |     "#     print(code)\n",
294 |     "    rs = bs.query_history_k_data_plus(pcode,\n",
295 |     "    \"date,code,close,open,low,high,preclose,volume,amount,turn,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST\",  #preclose,turn,\n",
296 |     "    start_date=start_date, end_date=end_date,\n",
297 |     "    frequency=datatype, adjustflag=\"3\")#复权类型选择123\n",
298 |     "    #### 打印结果集 ####\n",
299 |     "    data_list = []\n",
300 |     "    while (rs.error_code == '0') & rs.next():\n",
301 |     "        # 获取一条记录，将记录合并在一起\n",
302 |     "        data_list.append(rs.get_row_data())\n",
303 |     "    df=pd.DataFrame(data_list, columns=rs.fields)\n",
304 |     "    df.close=pd.to_numeric(pd.to_numeric(df.close, downcast='float')*100,downcast='integer')  # 收盘价*100转整型数\n",
305 |     "    df.open=pd.to_numeric(pd.to_numeric(df.open, downcast='float')*100,downcast='integer')\n",
306 |     "    df.low=pd.to_numeric(pd.to_numeric(df.low, downcast='float')*100,downcast='integer')\n",
307 |     "    df.high=pd.to_numeric(pd.to_numeric(df.high, downcast='float')*100,downcast='integer')\n",
308 |     "    df.preclose=pd.to_numeric(pd.to_numeric(df.preclose, downcast='float')*100,downcast='integer')\n",
309 |     "    df.turn=pd.to_numeric(df.turn, downcast='float')\n",
310 |     "    df['flowmkt']=0.001*df.close*df.volume/df.turn\n",
311 |     "    df['pctChg']=100*df.close/df.preclose-100\n",
312 |     "    df['ep']=100/pd.to_numeric(df.peTTM, downcast='float')\n",
313 |     "    df['eb']=100/pd.to_numeric(df.pbMRQ, downcast='float')\n",
314 |     "    df.amount=pd.to_numeric(df.amount, downcast='float')/10000\n",
315 |     "    df.volume=pd.to_numeric(df.volume,downcast='float')/10000\n",
316 |     "    df.fillna(method = 'ffill',inplace=True)\n",
317 |     "    df.dropna(inplace=True)\n",
318 |     "    return df"
319 |    ]
320 |   },
321 |   {
322 |    "cell_type": "markdown",
323 |    "metadata": {},
324 |    "source": [
325 |     "#### 依次添加baostock获取的数据到mysql\n"
326 |    ]
327 |   },
328 |   {
329 |    "cell_type": "code",
330 |    "execution_count": 23,
331 |    "metadata": {
332 |     "ExecuteTime": {
333 |      "start_time": "2019-09-21T00:59:29.946Z"
334 |     }
335 |    },
336 |    "outputs": [],
337 |    "source": [
338 |     "# def getStocksData(codes,start_date,issql=0):\n",
339 |     "#     dfdata=pd.DataFrame()\n",
340 |     "#     for code in codes:\n",
341 |     "# #         try:\n",
342 |     "#         df=getBaostock(precode(code),start_date,today,'d')\n",
343 |     "#         if issql==1:\n",
344 |     "#             if df.shape[0]>0 :\n",
345 |     "#                 df['code']=code\n",
346 |     "#                 df.to_sql(name='k'+code[0:4], con=conn, if_exists='append', index=False)\n",
347 |     "\n",
348 |     "#             else:\n",
349 |     "#                 print(code+' empty data')\n",
350 |     "#         else:\n",
351 |     "#             df=pd.DataFrame()\n",
352 |     "#             dfdata.append(df)\n",
353 |     "# #         except:\n",
354 |     "# #             print(code,'error')\n",
355 |     "            \n",
356 |     "# import threading\n",
357 |     "\n",
358 |     "# start_time=time.time()\n",
359 |     "# threads=[]\n",
360 |     "# codes=stocks.index[:]\n",
361 |     "# m=20\n",
362 |     "# start_date='2010-01-01'\n",
363 |     "# for i in range(1):\n",
364 |     "#     lg = bs.login()\n",
365 |     "\n",
366 |     "#     threads.append(threading.Thread(target=getStocksData,args=(codes[i*m:i*m+m],start_date,1,)))\n",
367 |     "#     threads[i].start()\n",
368 |     "#     bs.logout()\n",
369 |     "\n",
370 |     "# thread2.join()\n",
371 |     "\n",
372 |     "# for i in range(4):\n",
373 |     "#     threads[i].join()\n",
374 |     "#     getStocksData(codes,issql=0)"
375 |    ]
376 |   },
377 |   {
378 |    "cell_type": "code",
379 |    "execution_count": 24,
380 |    "metadata": {
381 |     "ExecuteTime": {
382 |      "end_time": "2019-09-21T01:07:57.225329Z",
383 |      "start_time": "2019-09-21T01:07:55.458802Z"
384 |     },
385 |     "code_folding": []
386 |    },
387 |    "outputs": [
388 |     {
389 |      "name": "stdout",
390 |      "output_type": "stream",
391 |      "text": [
392 |       "login success!\n",
393 |       "login respond  msg:success\n",
394 |       "0       2371.000000\n",
395 |       "1       2330.000000\n",
396 |       "2       2290.000000\n",
397 |       "3       2265.000000\n",
398 |       "4       2260.000000\n",
399 |       "           ...     \n",
400 |       "2399    1645.000122\n",
401 |       "2400    1641.000000\n",
402 |       "2401    1585.000000\n",
403 |       "2402    1586.000000\n",
404 |       "2403    1559.000000\n",
405 |       "Name: close, Length: 2404, dtype: float32\n"
406 |      ]
407 |     },
408 |     {
409 |      "ename": "TypeError",
410 |      "evalue": "can't multiply sequence by non-int of type 'float'",
411 |      "output_type": "error",
412 |      "traceback": [
413 |       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
414 |       "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
415 |       "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops\\__init__.py\u001b[0m in \u001b[0;36mna_op\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m    967\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 968\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr_rep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0meval_kwargs\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    969\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
416 |       "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py\u001b[0m in \u001b[0;36mevaluate\u001b[1;34m(op, op_str, a, b, use_numexpr, **eval_kwargs)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0muse_numexpr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 221\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_evaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0meval_kwargs\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    222\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
417 |       "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py\u001b[0m in \u001b[0;36m_evaluate_numexpr\u001b[1;34m(op, op_str, a, b, truediv, reversed, **eval_kwargs)\u001b[0m\n\u001b[0;32m    126\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 127\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\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    128\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
418 |       "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py\u001b[0m in \u001b[0;36m_evaluate_standard\u001b[1;34m(op, op_str, a, b, **eval_kwargs)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\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     71\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
419 |       "\u001b[1;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'float'",
420 |       "\nDuring handling of the above exception, another exception occurred:\n",
421 |       "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
422 |       "\u001b[1;32m<ipython-input-24-7b6e0842db38>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;31m#         print(i,'of',len(stocks),code)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;31m#     try:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m         \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgetBaostock\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstart_date\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtoday\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'd'\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     15\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m         \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
423 |       "\u001b[1;32m<ipython-input-22-d60485321273>\u001b[0m in \u001b[0;36mgetBaostock\u001b[1;34m(pcode, start_date, end_date, datatype)\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreclose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreclose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdowncast\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'float'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdowncast\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'integer'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mturn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mturn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdowncast\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'float'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m     \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'flowmkt'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.001\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvolume\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'pctChg'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreclose\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ep'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpeTTM\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdowncast\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'float'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
424 |       "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops\\__init__.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(left, right)\u001b[0m\n\u001b[0;32m   1046\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1047\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1048\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mna_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrvalues\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   1049\u001b[0m         return construct_result(\n\u001b[0;32m   1050\u001b[0m             \u001b[0mleft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mleft\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mres_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
425 |       "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops\\__init__.py\u001b[0m in \u001b[0;36mna_op\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m    968\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr_rep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0meval_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    969\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 970\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmasked_arith_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\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    971\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    972\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mmissing\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_fill_zeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
426 |       "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops\\__init__.py\u001b[0m in \u001b[0;36mmasked_arith_op\u001b[1;34m(x, y, op)\u001b[0m\n\u001b[0;32m    445\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    446\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 447\u001b[1;33m                 \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxrav\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myrav\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\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    448\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    449\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
427 |       "\u001b[1;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'float'"
428 |      ]
429 |     }
430 |    ],
431 |    "source": [
432 |     "### 登陆系统 ####\n",
433 |     "lg = bs.login()\n",
434 |     "# 显示登陆返回信息\n",
435 |     "start_date='2010-01-01'\n",
436 |     "print('login respond  msg:'+lg.error_msg)\n",
437 |     "i=0\n",
438 |     "symbols=stocks.index[:2]\n",
439 |     "# symbols=['']\n",
440 |     "for code in symbols:\n",
441 |     "        i+=1\n",
442 |     "#     if i%100==0:\n",
443 |     "#         print(i,'of',len(stocks),code)\n",
444 |     "#     try:\n",
445 |     "        df=getBaostock(precode(code),start_date,today,'d')\n",
446 |     "        print(df)\n",
447 |     "        df.code=code\n",
448 |     "        print(df)\n",
449 |     "\n",
450 |     "        if df.shape[0]>0:\n",
451 |     "            df.to_sql(name='k'+code[0:4], con=conn, if_exists='append', index=False)\n",
452 |     "    #             df.to_csv(code+'.csv')\n",
453 |     "    #         mcodes.append(code)\n",
454 |     "        else:\n",
455 |     "            print(code+' empty data')\n",
456 |     "#     except:\n",
457 |     "#         print(code,'error')\n",
458 |     "bs.logout()\n",
459 |     "df.head()"
460 |    ]
461 |   },
462 |   {
463 |    "cell_type": "markdown",
464 |    "metadata": {},
465 |    "source": [
466 |     "### 获取tushare当日全部数据到mysql\n",
467 |     "适用于当日数据更新，所花时间较短"
468 |    ]
469 |   },
470 |   {
471 |    "cell_type": "code",
472 |    "execution_count": null,
473 |    "metadata": {
474 |     "ExecuteTime": {
475 |      "end_time": "2019-08-11T13:52:47.508085Z",
476 |      "start_time": "2019-08-11T13:52:11.078734Z"
477 |     }
478 |    },
479 |    "outputs": [],
480 |    "source": [
481 |     "kdata=ts.get_today_all()\n",
482 |     "jdata=kdata[['code','trade','open','settlement','low','high','turnoverratio','volume','amount']]\n",
483 |     "jdata.columns=['code','close','open','preclose','low','high','turn','volume','amount']  # 列名不一样，重新设置一下\n",
484 |     "jdata['date']=today\n",
485 |     "# jdata.to_csv(today+'.csv')\n"
486 |    ]
487 |   },
488 |   {
489 |    "cell_type": "code",
490 |    "execution_count": null,
491 |    "metadata": {
492 |     "ExecuteTime": {
493 |      "end_time": "2019-08-11T13:53:05.263038Z",
494 |      "start_time": "2019-08-11T13:53:05.168443Z"
495 |     },
496 |     "scrolled": true
497 |    },
498 |    "outputs": [],
499 |    "source": [
500 |     "# jdata=kdata[['code','trade','open','settlement','low','high','turnoverratio','volume','amount']]\n",
501 |     "# jdata.columns=['code','close','open','preclose','low','high','turn','volume','amount']\n",
502 |     "# jdata['date']=today\n",
503 |     "# jdata['date']='2019-08-09'\n",
504 |     "jdata.head()\n"
505 |    ]
506 |   },
507 |   {
508 |    "cell_type": "code",
509 |    "execution_count": null,
510 |    "metadata": {
511 |     "ExecuteTime": {
512 |      "end_time": "2019-08-11T13:53:38.222560Z",
513 |      "start_time": "2019-08-11T13:53:16.487770Z"
514 |     }
515 |    },
516 |    "outputs": [],
517 |    "source": [
518 |     "for i in range(jdata.shape[0]):\n",
519 |     "    if i%500==1:\n",
520 |     "        print(i , 'of',jdata.shape[0],jdata.code[i])\n",
521 |     "#     print(jdata.code[i])\n",
522 |     "    jdata.iloc[i:i+1].to_sql(name='k'+jdata.code[i][0:4], con=conn, if_exists='append', index=False)"
523 |    ]
524 |   },
525 |   {
526 |    "cell_type": "markdown",
527 |    "metadata": {},
528 |    "source": [
529 |     "## 保存各类指数数据到数据库"
530 |    ]
531 |   },
532 |   {
533 |    "cell_type": "code",
534 |    "execution_count": null,
535 |    "metadata": {
536 |     "ExecuteTime": {
537 |      "end_time": "2019-08-18T07:24:20.376007Z",
538 |      "start_time": "2019-08-18T07:24:20.371426Z"
539 |     },
540 |     "code_folding": []
541 |    },
542 |    "outputs": [],
543 |    "source": [
544 |     "##调用接口函数\n",
545 |     "### 登陆系统 ####\n",
546 |     "def get_index(codes,start_date):\n",
547 |     "    lg = bs.login()\n",
548 |     "    for code in codes:\n",
549 |     "        # 显示登陆返回信息\n",
550 |     "        df=getBaostock(code,start_date,today)\n",
551 |     "        df.code=code\n",
552 |     "\n",
553 |     "        if df.shape[0]>0:\n",
554 |     "            df.to_sql(name='k'+code[:4].replace('.',''), con=conn, if_exists='append', index=False)\n",
555 |     "        else:\n",
556 |     "            print(code+' empty data')\n",
557 |     "    bs.logout()\n",
558 |     "    print('save to mysql ok')\n",
559 |     "\n"
560 |    ]
561 |   },
562 |   {
563 |    "cell_type": "code",
564 |    "execution_count": null,
565 |    "metadata": {
566 |     "ExecuteTime": {
567 |      "end_time": "2019-08-08T12:32:46.455232Z",
568 |      "start_time": "2019-08-08T12:32:46.382107Z"
569 |     },
570 |     "code_folding": []
571 |    },
572 |    "outputs": [],
573 |    "source": [
574 |     "# aindxname=pd.read_csv(u'/Users/hongyuouyang/python/finance/indexname.csv')\n",
575 |     "# aindxname.to_sql(name='indexname', con=conn, if_exists='append', index=False)\n"
576 |    ]
577 |   },
578 |   {
579 |    "cell_type": "code",
580 |    "execution_count": null,
581 |    "metadata": {
582 |     "ExecuteTime": {
583 |      "end_time": "2019-08-18T07:24:43.031708Z",
584 |      "start_time": "2019-08-18T07:24:22.837224Z"
585 |     }
586 |    },
587 |    "outputs": [],
588 |    "source": [
589 |     "start_date='2019-01-01'\n",
590 |     "\n",
591 |     "aindxname=pd.read_sql('select * from indexname',con=conn)\n",
592 |     "get_index(aindxname.code.values,start_date)"
593 |    ]
594 |   }
595 |  ],
596 |  "metadata": {
597 |   "hide_input": false,
598 |   "kernelspec": {
599 |    "display_name": "Python 3",
600 |    "language": "python",
601 |    "name": "python3"
602 |   },
603 |   "language_info": {
604 |    "codemirror_mode": {
605 |     "name": "ipython",
606 |     "version": 3
607 |    },
608 |    "file_extension": ".py",
609 |    "mimetype": "text/x-python",
610 |    "name": "python",
611 |    "nbconvert_exporter": "python",
612 |    "pygments_lexer": "ipython3",
613 |    "version": "3.7.4"
614 |   },
615 |   "latex_envs": {
616 |    "LaTeX_envs_menu_present": true,
617 |    "autoclose": false,
618 |    "autocomplete": true,
619 |    "bibliofile": "biblio.bib",
620 |    "cite_by": "apalike",
621 |    "current_citInitial": 1,
622 |    "eqLabelWithNumbers": true,
623 |    "eqNumInitial": 1,
624 |    "hotkeys": {
625 |     "equation": "Ctrl-E",
626 |     "itemize": "Ctrl-I"
627 |    },
628 |    "labels_anchors": false,
629 |    "latex_user_defs": false,
630 |    "report_style_numbering": false,
631 |    "user_envs_cfg": false
632 |   },
633 |   "toc": {
634 |    "base_numbering": 1,
635 |    "nav_menu": {},
636 |    "number_sections": true,
637 |    "sideBar": true,
638 |    "skip_h1_title": false,
639 |    "title_cell": "Table of Contents",
640 |    "title_sidebar": "Contents",
641 |    "toc_cell": false,
642 |    "toc_position": {},
643 |    "toc_section_display": true,
644 |    "toc_window_display": false
645 |   },
646 |   "varInspector": {
647 |    "cols": {
648 |     "lenName": 16,
649 |     "lenType": 16,
650 |     "lenVar": 40
651 |    },
652 |    "kernels_config": {
653 |     "python": {
654 |      "delete_cmd_postfix": "",
655 |      "delete_cmd_prefix": "del ",
656 |      "library": "var_list.py",
657 |      "varRefreshCmd": "print(var_dic_list())"
658 |     },
659 |     "r": {
660 |      "delete_cmd_postfix": ") ",
661 |      "delete_cmd_prefix": "rm(",
662 |      "library": "var_list.r",
663 |      "varRefreshCmd": "cat(var_dic_list()) "
664 |     }
665 |    },
666 |    "types_to_exclude": [
667 |     "module",
668 |     "function",
669 |     "builtin_function_or_method",
670 |     "instance",
671 |     "_Feature"
672 |    ],
673 |    "window_display": false
674 |   }
675 |  },
676 |  "nbformat": 4,
677 |  "nbformat_minor": 2
678 | }
679 | 


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