notebook.community

Edit and run 





In [1]:



    


import pandas as pd
import os
import time
from datetime import datetime
from matplotlib.dates import date2num
import sqlite3



    











In [119]:



    


# Create NEW SQL database
connect = sqlite3.connect('Yahoo_sqlite.db')
cursor = connect.cursor()


PATH = '/Users/insuyu/GitHub/historical.data/yahoo.csv/'
#PATH = '/Users/insuyu/GitHub/python/11. Sqlite3/'


# For All Files in PATH
nFiles = 0
for filename in os.listdir(PATH):
    # Find only *.csv files
    if filename.endswith(".csv"):
        print ("[%d] %s" % (nFiles, filename))
        # Load CVS to Database
        DB = pd.read_csv(PATH+filename)
    
        # Generate Moving Average Data
        DB['MA20'] = pd.stats.moments.rolling_mean(DB['Adj Close'], 20)
        DB['MA30'] = pd.stats.moments.rolling_mean(DB['Adj Close'], 30)
        #DB['MA30'] = pd.stats.moments.rolling_mean(DB['Adj Close'], 30)
        #DB['MA60'] = pd.stats.moments.rolling_mean(DB['Adj Close'], 60)
        #DB['MA120'] = pd.stats.moments.rolling_mean(DB['Adj Close'], 120)
        DB['MA200']     = pd.stats.moments.rolling_mean(DB['Adj Close'], 200)
        DB['MA240']     = pd.stats.moments.rolling_mean(DB['Adj Close'], 240)
        DB['DateStamp'] = [ date2num(datetime.strptime(date,"%Y-%m-%d")) for date in DB['Date'] ]
        
        ###########################################################################
        Code = filename[:-4] # Trading Code
        
        # Create a New Table for Code
        # DateStamp(REAL) | Date(TEXT) | Open(REAL) | High(REAL) | Low(REAL) 
        #    | Close(REAL) | Volume(REAL) | AdjClose(REAL) | MA20(REAL) | MA30(REAL) | MA200(REAL) | MA240 (REAL)
        command = "CREATE TABLE KS%s(DateStamp INT, Date TEXT, Open REAL, High REAL, Low REAL,  \
                        Close REAL, Volume INT, AdjClose REAL, MA20 REAL, MA30 REAL, MA200 REAL, MA240 REAL)" % Code
        print(command)
        cursor.execute(command)
        
        command = "INSERT INTO KS%s(DateStamp, Date, Open, High, Low, Close, Volume, AdjClose, MA20, MA30, MA200, MA240) \
        VALUES (?,?,?,?,?,?,?,?,?,?,?,?)" % Code
        print(command)
        
        for i in range(len(DB)):
            DateStamp = DB['DateStamp'][i]
            Date = DB['Date'][i]
            Open = DB['Open'][i]
            High = DB['High'][i]
            Low = DB['Low'][i] 
            Close = DB['Close'][i]
            Volume = DB['Volume'][i]
            AdjClose = DB['Adj Close'][i]
            MA20 = DB['MA20'][i]
            MA30 = DB['MA30'][i]
            MA200 = DB['MA200'][i]
            MA240 = DB['MA240'][i]
            
            cursor.execute(command,(DateStamp, Date, Open, High, Low, Close, Volume, AdjClose, MA20, MA30, MA200, MA240) )
            connect.commit()
        nFiles += 1



    


















    






[0] 000020.csv
CREATE TABLE KS000020(DateStamp INT, Date TEXT, Open REAL, High REAL, Low REAL,                          Close REAL, Volume REAL, AdjClose REAL, MA20 REAL, MA30 REAL, MA200 REAL, MA240 REAL)
INSERT INTO KS000020(DateStamp, Date, Open, High, Low, Close, Volume, AdjClose, MA20, MA30, MA200, MA240)         VALUES (?,?,?,?,?,?,?,?,?,?,?,?)










    






---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-119-82360804f457> in <module>()
     47             Volume = DB['Volume'][i]
     48             AdjClose = DB['Adj Close'][i]
---> 49             MA20 = DB['MA20'][i]
     50             MA30 = DB['MA30'][i]
     51             MA200 = DB['MA200'][i]

/Users/insuyu/anaconda/lib/python3.4/site-packages/pandas/core/frame.py in __getitem__(self, key)
   1795             return self._getitem_multilevel(key)
   1796         else:
-> 1797             return self._getitem_column(key)
   1798 
   1799     def _getitem_column(self, key):

/Users/insuyu/anaconda/lib/python3.4/site-packages/pandas/core/frame.py in _getitem_column(self, key)
   1802         # get column
   1803         if self.columns.is_unique:
-> 1804             return self._get_item_cache(key)
   1805 
   1806         # duplicate columns & possible reduce dimensionaility

/Users/insuyu/anaconda/lib/python3.4/site-packages/pandas/core/generic.py in _get_item_cache(self, item)
   1082         res = cache.get(item)
   1083         if res is None:
-> 1084             values = self._data.get(item)
   1085             res = self._box_item_values(item, values)
   1086             cache[item] = res

/Users/insuyu/anaconda/lib/python3.4/site-packages/pandas/core/internals.py in get(self, item, fastpath)
   2849 
   2850             if not isnull(item):
-> 2851                 loc = self.items.get_loc(item)
   2852             else:
   2853                 indexer = np.arange(len(self.items))[isnull(self.items)]

/Users/insuyu/anaconda/lib/python3.4/site-packages/pandas/core/index.py in get_loc(self, key, method)
   1570         """
   1571         if method is None:
-> 1572             return self._engine.get_loc(_values_from_object(key))
   1573 
   1574         indexer = self.get_indexer([key], method=method)

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3824)()

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3704)()

pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12280)()

pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12231)()

KeyError: 'MA20'

Convert a string "2015-8-1" to 73468.0





In [29]:



    


PATH = '/Users/insuyu/GitHub/python/12. CSV2SQL/'
# conversion "%Y-%m-%d" string to date number
DB = pd.read_csv(PATH+'003490.csv')
date = DB['Date'][20]
date2num(datetime.strptime(date,"%Y-%m-%d"))



    


















    
Out[29]:








730151.0

Add DateStamp to DB





In [31]:



    


DB['MA20'] = pd.stats.moments.rolling_mean(DB['Adj Close'], 20)
DB['DateStamp'] = [ date2num(datetime.strptime(date,"%Y-%m-%d")) for date in DB['Date'] ]
DB



    


















    
Out[31]:










  
    

      
      Date
      Open
      High
      Low
      Close
      Volume
      Adj Close
      MA30
      MA60
      MA120
      MA240
      MA20
      DateStamp
    

  
  
    

      0
      2000-01-04
      10700.02
      11800.04
      10700.02
      11800.04
      1810000
      14564.59
      NaN
      NaN
      NaN
      NaN
      NaN
      730123
    

    

      1
      2000-01-05
      11599.98
      13099.96
      11099.98
      13099.96
      2130200
      16169.06
      NaN
      NaN
      NaN
      NaN
      NaN
      730124
    

    

      2
      2000-01-06
      13000.01
      13000.01
      11299.97
      11599.98
      1499700
      14317.66
      NaN
      NaN
      NaN
      NaN
      NaN
      730125
    

    

      3
      2000-01-07
      12000.02
      12250.02
      11400.00
      12000.02
      1283400
      14811.42
      NaN
      NaN
      NaN
      NaN
      NaN
      730126
    

    

      4
      2000-01-10
      11800.04
      12200.00
      11649.99
      11750.02
      840700
      14502.86
      NaN
      NaN
      NaN
      NaN
      NaN
      730129
    

    

      5
      2000-01-11
      11599.98
      12099.97
      11549.96
      11800.04
      994200
      14564.59
      NaN
      NaN
      NaN
      NaN
      NaN
      730130
    

    

      6
      2000-01-12
      11599.98
      11700.01
      11099.98
      11099.98
      710800
      13700.52
      NaN
      NaN
      NaN
      NaN
      NaN
      730131
    

    

      7
      2000-01-13
      10900.00
      11500.03
      10900.00
      11450.01
      672100
      14132.56
      NaN
      NaN
      NaN
      NaN
      NaN
      730132
    

    

      8
      2000-01-14
      11899.99
      11899.99
      11049.97
      11150.00
      617800
      13762.26
      NaN
      NaN
      NaN
      NaN
      NaN
      730133
    

    

      9
      2000-01-17
      10900.00
      11000.03
      10299.98
      10750.04
      775100
      13268.59
      NaN
      NaN
      NaN
      NaN
      NaN
      730136
    

    

      10
      2000-01-18
      10500.04
      10700.02
      10150.01
      10200.03
      798300
      12589.72
      NaN
      NaN
      NaN
      NaN
      NaN
      730137
    

    

      11
      2000-01-19
      10100.00
      10200.03
      9100.01
      9200.04
      1061500
      11355.45
      NaN
      NaN
      NaN
      NaN
      NaN
      730138
    

    

      12
      2000-01-20
      8799.99
      9100.01
      8400.03
      8590.03
      877800
      10602.52
      NaN
      NaN
      NaN
      NaN
      NaN
      730139
    

    

      13
      2000-01-21
      8710.03
      9489.98
      8400.03
      8699.96
      1246600
      10738.22
      NaN
      NaN
      NaN
      NaN
      NaN
      730140
    

    

      14
      2000-01-24
      8509.97
      8989.99
      8509.97
      8600.01
      1090900
      10614.85
      NaN
      NaN
      NaN
      NaN
      NaN
      730143
    

    

      15
      2000-01-25
      8499.98
      8520.04
      8300.00
      8300.00
      641100
      10244.55
      NaN
      NaN
      NaN
      NaN
      NaN
      730144
    

    

      16
      2000-01-26
      8350.02
      8720.02
      8150.03
      8550.00
      891500
      10553.12
      NaN
      NaN
      NaN
      NaN
      NaN
      730145
    

    

      17
      2000-01-27
      8619.99
      9520.02
      8550.00
      9450.03
      1121600
      11664.02
      NaN
      NaN
      NaN
      NaN
      NaN
      730146
    

    

      18
      2000-01-28
      9499.97
      9640.03
      9150.02
      9340.02
      745500
      11528.22
      NaN
      NaN
      NaN
      NaN
      NaN
      730147
    

    

      19
      2000-01-31
      9150.02
      9590.01
      8900.03
      9499.97
      742900
      11725.65
      NaN
      NaN
      NaN
      NaN
      12770.5215
      730150
    

    

      20
      2000-02-01
      9900.01
      10650.01
      9710.02
      10599.99
      1593000
      13083.39
      NaN
      NaN
      NaN
      NaN
      12696.4615
      730151
    

    

      21
      2000-02-02
      10599.99
      11150.00
      10100.00
      10750.04
      1205200
      13268.59
      NaN
      NaN
      NaN
      NaN
      12551.4380
      730152
    

    

      22
      2000-02-03
      10750.04
      11000.03
      10500.04
      10500.04
      565700
      12960.02
      NaN
      NaN
      NaN
      NaN
      12483.5560
      730153
    

    

      23
      2000-02-04
      10500.04
      10500.04
      10500.04
      10500.04
      0
      12960.02
      NaN
      NaN
      NaN
      NaN
      12390.9860
      730154
    

    

      24
      2000-02-07
      10299.98
      10900.00
      10100.00
      10500.04
      1116600
      12960.02
      NaN
      NaN
      NaN
      NaN
      12313.8440
      730157
    

    

      25
      2000-02-08
      10299.98
      10700.02
      10150.01
      10599.99
      607500
      13083.39
      NaN
      NaN
      NaN
      NaN
      12239.7840
      730158
    

    

      26
      2000-02-09
      10500.04
      10750.04
      10100.00
      10150.01
      839300
      12527.99
      NaN
      NaN
      NaN
      NaN
      12181.1575
      730159
    

    

      27
      2000-02-10
      9999.96
      9999.96
      9030.02
      9309.98
      993900
      11491.15
      NaN
      NaN
      NaN
      NaN
      12049.0870
      730160
    

    

      28
      2000-02-11
      9009.96
      9499.97
      8749.98
      8970.01
      1106200
      11071.54
      NaN
      NaN
      NaN
      NaN
      11914.5510
      730161
    

    

      29
      2000-02-14
      8550.00
      9299.99
      8550.00
      8610.00
      772800
      10627.18
      12648.124000
      NaN
      NaN
      NaN
      11782.4805
      730164
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      4003
      2015-06-09
      39400.00
      40450.00
      39350.00
      39750.00
      608000
      39750.00
      42530.000000
      46153.333333
      46097.916667
      42082.708333
      41307.5000
      735758
    

    

      4004
      2015-06-10
      39950.00
      40000.00
      37800.00
      38050.00
      1023500
      38050.00
      42231.666667
      45997.500000
      46019.166667
      42098.958333
      41007.5000
      735759
    

    

      4005
      2015-06-11
      38000.00
      38750.00
      37450.00
      38100.00
      612800
      38100.00
      41975.000000
      45831.666667
      45940.833333
      42115.208333
      40730.0000
      735760
    

    

      4006
      2015-06-12
      38600.00
      39050.00
      37200.00
      37500.00
      546000
      37500.00
      41698.333333
      45654.166667
      45866.250000
      42132.916667
      40440.0000
      735761
    

    

      4007
      2015-06-15
      36950.00
      36950.00
      35150.00
      35200.00
      817100
      35200.00
      41351.666667
      45454.166667
      45769.583333
      42138.125000
      40120.0000
      735764
    

    

      4008
      2015-06-16
      35200.00
      35850.00
      34800.00
      35750.00
      634700
      35750.00
      41023.333333
      45237.500000
      45672.083333
      42146.875000
      39855.0000
      735765
    

    

      4009
      2015-06-17
      35750.00
      35800.00
      34800.00
      35600.00
      535000
      35600.00
      40780.000000
      45009.166667
      45573.333333
      42157.291667
      39447.5000
      735766
    

    

      4010
      2015-06-18
      35800.00
      36200.00
      35350.00
      35700.00
      509000
      35700.00
      40513.333333
      44809.166667
      45475.416667
      42163.958333
      39052.5000
      735767
    

    

      4011
      2015-06-19
      36000.00
      38150.00
      35800.00
      37850.00
      1011600
      37850.00
      40295.000000
      44645.833333
      45414.583333
      42177.708333
      38747.5000
      735768
    

    

      4012
      2015-06-22
      38750.00
      39700.00
      38750.00
      39350.00
      835800
      39350.00
      40103.333333
      44489.166667
      45357.500000
      42195.833333
      38517.5000
      735771
    

    

      4013
      2015-06-23
      39350.00
      39750.00
      38650.00
      39700.00
      494900
      39700.00
      39965.000000
      44341.666667
      45307.916667
      42217.291667
      38397.5000
      735772
    

    

      4014
      2015-06-24
      39750.00
      40450.00
      39300.00
      39950.00
      418900
      39950.00
      39828.333333
      44162.500000
      45278.333333
      42238.541667
      38332.5000
      735773
    

    

      4015
      2015-06-25
      39650.00
      40250.00
      39300.00
      39800.00
      278700
      39800.00
      39700.000000
      43970.833333
      45249.166667
      42259.166667
      38270.0000
      735774
    

    

      4016
      2015-06-26
      39800.00
      39800.00
      38800.00
      39100.00
      296900
      39100.00
      39560.000000
      43740.833333
      45197.500000
      42276.875000
      38202.5000
      735775
    

    

      4017
      2015-06-29
      38400.00
      38750.00
      37750.00
      37900.00
      380500
      37900.00
      39436.666667
      43489.166667
      45139.166667
      42288.541667
      38155.0000
      735778
    

    

      4018
      2015-06-30
      37750.00
      40600.00
      37700.00
      40500.00
      632700
      40500.00
      39418.333333
      43287.500000
      45098.750000
      42307.500000
      38250.0000
      735779
    

    

      4019
      2015-07-01
      40600.00
      42300.00
      39650.00
      41850.00
      807000
      41850.00
      39355.000000
      43106.666667
      45064.166667
      42334.166667
      38407.5000
      735780
    

    

      4020
      2015-07-02
      42500.00
      43850.00
      41900.00
      42900.00
      1340700
      42900.00
      39331.666667
      42953.333333
      45045.416667
      42362.916667
      38622.5000
      735781
    

    

      4021
      2015-07-03
      42950.00
      43450.00
      41450.00
      41500.00
      583100
      41500.00
      39250.000000
      42773.333333
      45019.583333
      42391.458333
      38747.5000
      735782
    

    

      4022
      2015-07-06
      40800.00
      42600.00
      40700.00
      41200.00
      587800
      41200.00
      39158.333333
      42575.000000
      44996.250000
      42420.625000
      38862.5000
      735785
    

    

      4023
      2015-07-07
      42600.00
      44500.00
      42050.00
      43550.00
      1506500
      43550.00
      39206.666667
      42425.833333
      44986.250000
      42462.291667
      39052.5000
      735786
    

    

      4024
      2015-07-08
      43550.00
      43900.00
      41900.00
      42000.00
      601500
      42000.00
      39231.666667
      42275.833333
      44961.250000
      42500.625000
      39250.0000
      735787
    

    

      4025
      2015-07-09
      41000.00
      41500.00
      38950.00
      39700.00
      1608600
      39700.00
      39186.666667
      42106.666667
      44930.833333
      42526.875000
      39330.0000
      735788
    

    

      4026
      2015-07-10
      40100.00
      40150.00
      39500.00
      39750.00
      512700
      39750.00
      39163.333333
      41952.500000
      44895.416667
      42555.208333
      39442.5000
      735789
    

    

      4027
      2015-07-13
      40050.00
      40550.00
      39750.00
      40450.00
      452200
      40450.00
      39216.666667
      41800.000000
      44871.250000
      42585.625000
      39705.0000
      735792
    

    

      4028
      2015-07-14
      40700.00
      40800.00
      39400.00
      39450.00
      657700
      39450.00
      39245.000000
      41652.500000
      44829.583333
      42612.500000
      39890.0000
      735793
    

    

      4029
      2015-07-15
      40450.00
      40500.00
      39400.00
      39450.00
      886900
      39450.00
      39270.000000
      41510.000000
      44788.750000
      42637.083333
      40082.5000
      735794
    

    

      4030
      2015-07-16
      39000.00
      40300.00
      38700.00
      39450.00
      9033400
      39450.00
      39298.333333
      41344.166667
      44728.333333
      42661.875000
      40270.0000
      735795
    

    

      4031
      2015-07-17
      39550.00
      40000.00
      38250.00
      39000.00
      1149500
      39000.00
      39298.333333
      41206.666667
      44657.500000
      42684.791667
      40327.5000
      735796
    

    

      4032
      2015-07-20
      39050.00
      39400.00
      38100.00
      38450.00
      828900
      38450.00
      39283.333333
      41044.166667
      44588.333333
      42703.750000
      40282.5000
      735799
    

  



4033 rows × 13 columns

Access SQLite data





In [58]:



    


connect = sqlite3.connect('Yahoo_sqlite.db')
cursor = connect.cursor()



    











In [59]:



    


sql = "SELECT * FROM YAHOO WHERE Date =?"

for row in cursor.execute(sql, [('2015-06-15')]):
    print (row)



    


















    






('003490', 735764, '2015-06-15', 36950.0, 36950.0, 35150.0, 35200.0, 817100, 35200.0, 40119.999999999956, 41351.66666666669, 43575.00000000003, 42138.124999999905)
('005930', 735764, '2015-06-15', 1255000.0, 1274000.0, 1255000.0, 1270000.0, 161300, 1270000.0, 1312749.9999999988, 1323700.0000000005, 1307059.9780500038, 1299703.0276250048)

















In [60]:



    


for row in cursor.execute("SELECT * FROM YAHOO WHERE Date > '2015-07-01' "):
    print (row)



    


















    






('003490', 735781, '2015-07-02', 42500.0, 43850.0, 41900.0, 42900.0, 1340700, 42900.0, 38622.499999999956, 39331.66666666669, 43631.25000000003, 42362.91666666658)
('003490', 735782, '2015-07-03', 42950.0, 43450.0, 41450.0, 41500.0, 583100, 41500.0, 38747.499999999956, 39250.00000000002, 43664.00000000003, 42391.45833333324)
('003490', 735785, '2015-07-06', 40800.0, 42600.0, 40700.0, 41200.0, 587800, 41200.0, 38862.499999999956, 39158.33333333336, 43691.75000000003, 42420.624999999905)
('003490', 735786, '2015-07-07', 42600.0, 44500.0, 42050.0, 43550.0, 1506500, 43550.0, 39052.499999999956, 39206.66666666669, 43729.50000000003, 42462.29166666658)
('003490', 735787, '2015-07-08', 43550.0, 43900.0, 41900.0, 42000.0, 601500, 42000.0, 39249.999999999956, 39231.66666666669, 43765.00000000003, 42500.624999999905)
('003490', 735788, '2015-07-09', 41000.0, 41500.0, 38950.0, 39700.0, 1608600, 39700.0, 39329.999999999956, 39186.66666666669, 43787.75000000003, 42526.874999999905)
('003490', 735789, '2015-07-10', 40100.0, 40150.0, 39500.0, 39750.0, 512700, 39750.0, 39442.499999999956, 39163.33333333336, 43810.75000000003, 42555.20833333324)
('003490', 735792, '2015-07-13', 40050.0, 40550.0, 39750.0, 40450.0, 452200, 40450.0, 39704.999999999956, 39216.66666666669, 43837.75000000003, 42585.624999999905)
('003490', 735793, '2015-07-14', 40700.0, 40800.0, 39400.0, 39450.0, 657700, 39450.0, 39889.999999999956, 39245.00000000002, 43859.50000000003, 42612.499999999905)
('003490', 735794, '2015-07-15', 40450.0, 40500.0, 39400.0, 39450.0, 886900, 39450.0, 40082.499999999956, 39270.00000000002, 43880.75000000003, 42637.08333333324)
('003490', 735795, '2015-07-16', 39000.0, 40300.0, 38700.0, 39450.0, 9033400, 39450.0, 40269.999999999956, 39298.33333333336, 43902.00000000003, 42661.874999999905)
('003490', 735796, '2015-07-17', 39550.0, 40000.0, 38250.0, 39000.0, 1149500, 39000.0, 40327.499999999956, 39298.33333333336, 43919.50000000003, 42684.79166666658)
('003490', 735799, '2015-07-20', 39050.0, 39400.0, 38100.0, 38450.0, 828900, 38450.0, 40282.499999999956, 39283.33333333336, 43932.75000000003, 42703.749999999905)
('005930', 735781, '2015-07-02', 1286000.0, 1304000.0, 1285000.0, 1299000.0, 164400, 1299000.0, 1281849.9999999988, 1294433.3333333337, 1313837.9104500038, 1296961.581041671)
('005930', 735782, '2015-07-03', 1287000.0, 1294000.0, 1267000.0, 1268000.0, 142600, 1268000.0, 1278199.9999999988, 1291700.0000000005, 1314338.3671500038, 1296939.2252500046)
('005930', 735785, '2015-07-06', 1253000.0, 1260000.0, 1223000.0, 1230000.0, 201900, 1230000.0, 1273999.9999999988, 1287700.0000000005, 1314599.5450000037, 1296655.8718750044)
('005930', 735786, '2015-07-07', 1220000.0, 1259000.0, 1220000.0, 1240000.0, 249500, 1240000.0, 1271899.9999999988, 1283633.3333333337, 1314964.9296000036, 1296418.2917083378)
('005930', 735787, '2015-07-08', 1240000.0, 1251000.0, 1232000.0, 1239000.0, 235600, 1239000.0, 1270749.9999999988, 1281133.3333333337, 1315463.2949500037, 1296242.2500000044)
('005930', 735788, '2015-07-09', 1230000.0, 1265000.0, 1226000.0, 1265000.0, 280600, 1265000.0, 1271149.9999999988, 1279666.6666666672, 1316165.5786000036, 1296215.6073333377)
('005930', 735789, '2015-07-10', 1257000.0, 1266000.0, 1248000.0, 1259000.0, 191400, 1259000.0, 1270249.9999999988, 1278066.6666666672, 1316837.8622500035, 1296328.2275000042)
('005930', 735792, '2015-07-13', 1250000.0, 1272000.0, 1245000.0, 1266000.0, 153400, 1266000.0, 1270049.9999999988, 1277233.3333333337, 1317495.8670500035, 1296387.882916671)
('005930', 735793, '2015-07-14', 1265000.0, 1270000.0, 1221000.0, 1225000.0, 399700, 1225000.0, 1268549.9999999988, 1274633.3333333337, 1317894.6651000036, 1296293.1312916712)
('005930', 735794, '2015-07-15', 1225000.0, 1238000.0, 1224000.0, 1235000.0, 167400, 1235000.0, 1267599.9999999988, 1273366.6666666672, 1318501.1554500037, 1296248.2594583377)
('005930', 735795, '2015-07-16', 1223000.0, 1287000.0, 1223000.0, 1282000.0, 223400, 1282000.0, 1268449.9999999988, 1271533.3333333337, 1319342.6458000035, 1296411.540666671)
('005930', 735796, '2015-07-17', 1300000.0, 1311000.0, 1278000.0, 1305000.0, 297200, 1305000.0, 1270399.9999999988, 1270333.3333333337, 1320422.3332500036, 1296670.6552083378)
('005930', 735799, '2015-07-20', 1291000.0, 1304000.0, 1273000.0, 1275000.0, 128900, 1275000.0, 1270099.9999999988, 1269033.3333333337, 1321337.2370500036, 1296849.9420416711)

















In [63]:



    


for row in cursor.execute("SELECT * FROM YAHOO WHERE Date > '2015-07-01' AND Date < '2015-07-08'"):
    print (row)



    


















    






('003490', 735781, '2015-07-02', 42500.0, 43850.0, 41900.0, 42900.0, 1340700, 42900.0, 38622.499999999956, 39331.66666666669, 43631.25000000003, 42362.91666666658)
('003490', 735782, '2015-07-03', 42950.0, 43450.0, 41450.0, 41500.0, 583100, 41500.0, 38747.499999999956, 39250.00000000002, 43664.00000000003, 42391.45833333324)
('003490', 735785, '2015-07-06', 40800.0, 42600.0, 40700.0, 41200.0, 587800, 41200.0, 38862.499999999956, 39158.33333333336, 43691.75000000003, 42420.624999999905)
('003490', 735786, '2015-07-07', 42600.0, 44500.0, 42050.0, 43550.0, 1506500, 43550.0, 39052.499999999956, 39206.66666666669, 43729.50000000003, 42462.29166666658)
('005930', 735781, '2015-07-02', 1286000.0, 1304000.0, 1285000.0, 1299000.0, 164400, 1299000.0, 1281849.9999999988, 1294433.3333333337, 1313837.9104500038, 1296961.581041671)
('005930', 735782, '2015-07-03', 1287000.0, 1294000.0, 1267000.0, 1268000.0, 142600, 1268000.0, 1278199.9999999988, 1291700.0000000005, 1314338.3671500038, 1296939.2252500046)
('005930', 735785, '2015-07-06', 1253000.0, 1260000.0, 1223000.0, 1230000.0, 201900, 1230000.0, 1273999.9999999988, 1287700.0000000005, 1314599.5450000037, 1296655.8718750044)
('005930', 735786, '2015-07-07', 1220000.0, 1259000.0, 1220000.0, 1240000.0, 249500, 1240000.0, 1271899.9999999988, 1283633.3333333337, 1314964.9296000036, 1296418.2917083378)

















In [62]:



    


for row in cursor.execute("SELECT * FROM YAHOO WHERE DateStamp > 735788"):
    print (row)



    


















    






('003490', 735789, '2015-07-10', 40100.0, 40150.0, 39500.0, 39750.0, 512700, 39750.0, 39442.499999999956, 39163.33333333336, 43810.75000000003, 42555.20833333324)
('003490', 735792, '2015-07-13', 40050.0, 40550.0, 39750.0, 40450.0, 452200, 40450.0, 39704.999999999956, 39216.66666666669, 43837.75000000003, 42585.624999999905)
('003490', 735793, '2015-07-14', 40700.0, 40800.0, 39400.0, 39450.0, 657700, 39450.0, 39889.999999999956, 39245.00000000002, 43859.50000000003, 42612.499999999905)
('003490', 735794, '2015-07-15', 40450.0, 40500.0, 39400.0, 39450.0, 886900, 39450.0, 40082.499999999956, 39270.00000000002, 43880.75000000003, 42637.08333333324)
('003490', 735795, '2015-07-16', 39000.0, 40300.0, 38700.0, 39450.0, 9033400, 39450.0, 40269.999999999956, 39298.33333333336, 43902.00000000003, 42661.874999999905)
('003490', 735796, '2015-07-17', 39550.0, 40000.0, 38250.0, 39000.0, 1149500, 39000.0, 40327.499999999956, 39298.33333333336, 43919.50000000003, 42684.79166666658)
('003490', 735799, '2015-07-20', 39050.0, 39400.0, 38100.0, 38450.0, 828900, 38450.0, 40282.499999999956, 39283.33333333336, 43932.75000000003, 42703.749999999905)
('005930', 735789, '2015-07-10', 1257000.0, 1266000.0, 1248000.0, 1259000.0, 191400, 1259000.0, 1270249.9999999988, 1278066.6666666672, 1316837.8622500035, 1296328.2275000042)
('005930', 735792, '2015-07-13', 1250000.0, 1272000.0, 1245000.0, 1266000.0, 153400, 1266000.0, 1270049.9999999988, 1277233.3333333337, 1317495.8670500035, 1296387.882916671)
('005930', 735793, '2015-07-14', 1265000.0, 1270000.0, 1221000.0, 1225000.0, 399700, 1225000.0, 1268549.9999999988, 1274633.3333333337, 1317894.6651000036, 1296293.1312916712)
('005930', 735794, '2015-07-15', 1225000.0, 1238000.0, 1224000.0, 1235000.0, 167400, 1235000.0, 1267599.9999999988, 1273366.6666666672, 1318501.1554500037, 1296248.2594583377)
('005930', 735795, '2015-07-16', 1223000.0, 1287000.0, 1223000.0, 1282000.0, 223400, 1282000.0, 1268449.9999999988, 1271533.3333333337, 1319342.6458000035, 1296411.540666671)
('005930', 735796, '2015-07-17', 1300000.0, 1311000.0, 1278000.0, 1305000.0, 297200, 1305000.0, 1270399.9999999988, 1270333.3333333337, 1320422.3332500036, 1296670.6552083378)
('005930', 735799, '2015-07-20', 1291000.0, 1304000.0, 1273000.0, 1275000.0, 128900, 1275000.0, 1270099.9999999988, 1269033.3333333337, 1321337.2370500036, 1296849.9420416711)

Content source: 3WiseMen/python

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