dt = date(int(dttxt[0:4]), int(dttxt[5:7]), int(dttxt[8:10]))
-
I want to convert the date format from this %Y-%m-%d to
%m-%d-%Y . Please mention how. -
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 -
@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 ) -
Thanks a lot @run-out , it's helpful :)
data_string[found.end():])
ValueError: unconverted data remains: AMI 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 -
Both date formats in the same dataset and column?
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Yes, @run-out
-
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.
-
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])) -
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: AMCSV 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 -
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
csvdata feeds parsed in the originalbtpackage, this will be the best approach I think. -
@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 19999.999Next, 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 12:00:00Next 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 12:00:00Then 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 19999.999You 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) -
Your help & support is greatly appreciated. Thanks a bunch! @run-out
