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    dt = date(int(dttxt[0:4]), int(dttxt[5:7]), int(dttxt[8:10]))

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    • S
      shoaibmalek21 last edited by

      I want to convert the date format from this %Y-%m-%d to
      %m-%d-%Y . Please mention how.

      1 Reply Last reply Reply Quote 0
      • S
        shoaibmalek21 last edited by

        Error dt = date(int(dttxt[0:4]), int(dttxt[5:7]), int(dttxt[8:10])). The date format of this %Y-%m-%d but i need %m-%d-%Y . Please mention how.

        CSV Data,

        11/3/1994,2.7,2.75,2.675,2.71,89999.99598
        11/7/1994,2.67,2.67,2.651,2.651,19999.99911
        11/8/1994,2.75,2.775,2.75,2.775,69999.99687
        11/10/1994,2.8,2.8,2.8,2.8,9999.999553

        run-out 1 Reply Last reply Reply Quote 0
        • run-out
          run-out @shoaibmalek21 last edited by

          @shoaibmalek21
          Have a look here for date formats.

          And here for backtrader instructions.

          You could try the following:

          data = btfeeds.GenericCSVData(
    dataname='mydata.csv',

    fromdate=datetime.datetime(2000, 1, 1),
    todate=datetime.datetime(2000, 12, 31),

    nullvalue=0.0,

    dtformat=('%-m-%-d-%Y'),
 
    datetime=0,
    time=1,
    high=2,
    low=3,
    open=4,
    close=5,
    volume=6,
    openinterest=-1
)
          1 Reply Last reply Reply Quote 1
          • S
            shoaibmalek21 last edited by

            Thanks a lot @run-out , it's helpful :)

            data_string[found.end():])
            ValueError: unconverted data remains: AM

            I want to use both Date formats. Anyone please help me.

            CSV Data,

            11/3/1994 00:00:00,2.7,2.75,2.675,2.71,89999.99598
            11/7/1994 12:00:00 AM,2.67,2.67,2.651,2.651,19999.99911

            1 Reply Last reply Reply Quote 0
            • run-out
              run-out last edited by

              Both date formats in the same dataset and column?

              1 Reply Last reply Reply Quote 0
              • S
                shoaibmalek21 last edited by

                Yes, @run-out

                1 Reply Last reply Reply Quote 0
                • vladisld
                  vladisld last edited by

                  IMHO it is easier to just pre-process the csv file to bring all the dates to the same format and only then feed it to backtrader.

                  1 Reply Last reply Reply Quote 2
                  • A
                    ab_trader last edited by

                    If I understand correctly, you are trying to write the class for your own data feed. Probably this piece:

                            dttxt = linetokens[next(i)]
        dt = date(int(dttxt[0:4]), int(dttxt[5:7]), int(dttxt[8:10]))

                    The following might work in your case for daily only data (from your 1st post)

                    dtlst = dttxt.split('/')
dt = date(int(dtlst[2]), int(dtlst[0]), int(dtlst[1]))
                    S 1 Reply Last reply Reply Quote 1
                    • S
                      shoaibmalek21 @ab_trader last edited by

                      Thanks, @ab_trader

                      Now, I want to use both Date format. Both date formats in the same dataset and column. It's able to import only one format Please help me how to import both.

                      Error: data_string[found.end():])
                      ValueError: unconverted data remains: AM

                      CSV Data,

                      11/3/1994 00:00:00,2.7,2.75,2.675,2.71,89999.99598
                      11/7/1994 12:00:00 AM,2.67,2.67,2.651,2.651,19999.99911
                      11/8/1994 00:00:00,2.7,2.75,2.675,2.71,89999.99598
                      11/9/1994 12:00:00 AM,2.67,2.67,2.651,2.651,19999.99911

                      run-out 1 Reply Last reply Reply Quote 0
                      • A
                        ab_trader last edited by

                        I am not sure what do you mean saying both. If you have both daily and intraday bars in the same file, than split them to have two separate files.

                        Take a look on how the csv data feeds parsed in the original bt package, this will be the best approach I think.

                        1 Reply Last reply Reply Quote 1
                        • run-out
                          run-out @shoaibmalek21 last edited by

                          @shoaibmalek21 said in dt = date(int(dttxt[0:4]), int(dttxt[5:7]), int(dttxt[8:10])):

                          Now, I want to use both Date format.

                          You are going to have to pre-process this. I recommend using pandas for the job, because you will end up with a dataframe you can just import into backtrader.

                          First step, let's import the csv file. I have added header titles. I've also added a 'PM' for thoroughness.

                          import pandas as pd

df = pd.read_csv("mix_index.csv")
print(df)
               datetime   open   high   low   close    volume
0     11/3/1994 00:00:00  2.700  2.750 2.675   2.710 89999.996
1  11/7/1994 12:00:00 AM  2.670  2.670 2.651   2.651 19999.999
2     11/8/1994 00:00:00  2.700  2.750 2.675   2.710 89999.996
3  11/9/1994 12:00:00 PM  2.670  2.670 2.651   2.651 19999.999

                          Next, we are going to select the subrows with 'AM' or 'PM' and use pandas to_datetime to convert these to usable dates and times.

                          # Select the AM/PM rows and convert to datetime into a new column 'newdt'

df.loc[df["datetime"].str.contains("|".join(["AM", "PM"])), "newdt"] = pd.to_datetime(
    df.loc[df["datetime"].str.contains("|".join(["AM", "PM"])), "datetime"]
)

print(df)
 	datetime 	        open 	high 	low 	close 	volume 	        newdt
0 	11/3/1994 00:00:00 	2.700 	2.750 	2.675 	2.710 	89999.996 	NaT
1 	11/7/1994 12:00:00 AM 	2.670 	2.670 	2.651 	2.651 	19999.999 	1994-11-07 00:00:00
2 	11/8/1994 00:00:00 	2.700 	2.750 	2.675 	2.710 	89999.996 	NaT
3 	11/9/1994 12:00:00 PM 	2.670 	2.670 	2.651 	2.651 	19999.999 	1994-11-09 12:00:00

                          Next with the other rows.

                          # Use '~' to select the non-AM/PM rows and convert to datetime into a new column 'newdt'

df.loc[~df["datetime"].str.contains("|".join(["AM", "PM"])), "newdt"] = pd.to_datetime(
    df.loc[~df["datetime"].str.contains("|".join(["AM", "PM"])), "datetime"]
)

print(df)
 	datetime 	        open 	high 	low 	close 	volume 	        newdt
0 	11/3/1994 00:00:00 	2.700 	2.750 	2.675 	2.710 	89999.996 	1994-11-03 00:00:00
1 	11/7/1994 12:00:00 AM 	2.670 	2.670 	2.651 	2.651 	19999.999 	1994-11-07 00:00:00
2 	11/8/1994 00:00:00 	2.700 	2.750 	2.675 	2.710 	89999.996 	1994-11-08 00:00:00
3 	11/9/1994 12:00:00 PM 	2.670 	2.670 	2.651 	2.651 	19999.999 	1994-11-09 12:00:00

                          Then clean up.

                          # Clean up, delete the original offending 'datetime' column, rename the new column to
# the original datetime name, set the datetime to index and sort for good measure.
del df["datetime"]
df = df.rename(columns={"newdt": "datetime"})
df = df.set_index("datetime").sort_index()
print(df)
                      open   high   low   close    volume
datetime                                                 
1994-11-03 00:00:00  2.700  2.750 2.675   2.710 89999.996
1994-11-07 00:00:00  2.670  2.670 2.651   2.651 19999.999
1994-11-08 00:00:00  2.700  2.750 2.675   2.710 89999.996
1994-11-09 12:00:00  2.670  2.670 2.651   2.651 19999.999

                          You can now import this into backtrader using the pandas data feed module.

                          # Pass it to the backtrader datafeed and add it to the cerebro
 data = bt.feeds.PandasData(dataname=df)
 cerebro.adddata(data)
                          S 1 Reply Last reply Reply Quote 1
                          • S
                            shoaibmalek21 @run-out last edited by

                            Your help & support is greatly appreciated. Thanks a bunch! @run-out

                            1 Reply Last reply Reply Quote 1
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