Convert OHLCV 1-min data to other intraday timeframe

data_utils.py

import math
import pandas


# ======================================================================================================================
def convert_data_for_timeframe(price_data, target_timeframe):
    """
    Convert 1-min price data to a new timeframe.
    Reference Links
       * https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.resample.html
       * https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases
       
    :param price_data:  List of objects. Each object has the following fields: open, high, low, close, volume, datetime. Example:
         {
            "open": 168.99,
            "high": 169.0908,
            "low": 168.96,
            "close": 169.05,
            "volume": 108369,
            "datetime": 1683821460000
        },
    :param target_timeframe: The new timeframe. Example: "5m", "15m", "60m"
    :return:
    """
    # Some basic validation
    if not target_timeframe.endswith("m"):
        return None

    # Convert list of objects to panda DataFrame
    df = pandas.DataFrame.from_records(price_data)

    # Convert unix time in ms to python datetime
    df["date"] = pandas.to_datetime(df['datetime'], unit='ms', origin='unix')

    # Make the python datetime as index
    df = df.set_index("date")

    aggregation_dict = {
        'open': 'first',
        'high': 'max',
        'low': 'min',
        'close': 'last',
        'volume': 'sum',
    }
    # get panda timeframe. panda timeframe examples: 5min, 15min, 60min
    panda_time_frame = target_timeframe.replace("m", "min")

    # Resample the data to the new timeframe using the aggregation functions
    df2 = df.resample(panda_time_frame).agg(aggregation_dict)

    # Get the list of objects from panda DataFrame
    bars2 = df2.reset_index().values.tolist()
    out_candles = []
    for bar in bars2:
        candle = {}
        candle["datetime"] = int(bar[0].timestamp() * 1000)
        # candle["_debug"] = bar[0].strftime('%Y-%m-%dT%H:%M:%S')
        candle["open"] = bar[1]
        candle["high"] = bar[2]
        candle["low"] = bar[3]
        candle["close"] = bar[4]
        candle["volume"] = bar[5]
        if math.isnan(candle["open"]) or  math.isnan(candle["high"]) or math.isnan(candle["low"]) or math.isnan(candle["close"]):
            # Skip it
            continue
        out_candles.append(candle)

    return out_candles