Need Help With Start Time In Specific RFC-3339 Time Format

Alpaca Market Data
1

I have some test code I use for grabbing bars but in this case, I am focusing on trades:

today = current_time.strftime("%Y-%m-%d")

print("\n")
print(“example TRADES WTIH CURRENT DATE”)
data = rest_api.get_trades(symbol, start=today, end=today, limit=10000)
print(data.df.to_string())
print("\n")

My problem is regarding start and end, I cannot figure out how to get trades say, for a start of 4:00PM EST to end at 4:01PM EST.

Having lots of trouble with this RFC-3339 formatting.

2

Have you tried using the .isoformat() function? It automatically converts the date into the standard/required format.

I just wrote a function that should help you:

import datetime as dt
import pytz

def get_todays_trades(_symbol):
    _currentTime = dt.datetime.now(pytz.timezone('US/Eastern'))

    #  As the market hasn't opened yet (at time of writing), I pushed the date back 3 days. The below line is just for testing purposes.
    _currentTime = _currentTime - dt.timedelta(days=3)

    #  Calculate the start and end time by modifying today's full timestamp with specific hours/minutes/seconds. I've used 4pm (16:00) to 4:01pm (16:01) for this example. You could use variables to make this more dynamic.
    _startTime = pd.Timestamp(year=_currentTime.year, month=_currentTime.month, day=_currentTime.day, hour=16, minute=00, second=00, tz='US/Eastern')
    _endTime = pd.Timestamp(year=_currentTime.year, month=_currentTime.month, day=_currentTime.day, hour=16, minute=00, second=1, tz='US/Eastern')

    print(f"\nToday's {_symbol} trades between {_startTime} and {_endTime}:\n")
    data = rest_api.get_trades(_symbol, start=_startTime.isoformat(), end=_endTime.isoformat())  # Don't forget to use .isoformat() !!!
    print(data.df)
    return data

#  Start:-----------------------
get_todays_trades('TSLA')

Output:

Today's TSLA trades between 2022-02-04 16:00:00-05:00 and 2022-02-04 16:01:00-05:00:
                                    exchange     price  ...      id tape
timestamp                                               ...             
2022-02-04 21:00:00+00:00                  D  923.3200  ...  172349    C
2022-02-04 21:00:00.076319488+00:00        P  922.8500  ...   73708    C
2022-02-04 21:00:00.204499262+00:00        Q  922.8600  ...  156873    C
2022-02-04 21:00:00.221063766+00:00        Q  923.1500  ...  156874    C
2022-02-04 21:00:00.534677+00:00           Z  923.0400  ...   40080    C
...                                      ...       ...  ...     ...  ...
2022-02-04 21:00:59.006858687+00:00        D  922.4200  ...   70843    C
2022-02-04 21:00:59.233357642+00:00        V  922.6700  ...   17313    C
2022-02-04 21:00:59.234561+00:00           Z  922.3600  ...   40086    C
2022-02-04 21:00:59.235063381+00:00        D  922.3123  ...   70844    C
2022-02-04 21:00:59.498821+00:00           D  922.8000  ...  170637    C

[299 rows x 6 columns]

Note that the dates in the output dataframe are in UTC timezone