intraday_data_fbp.py

def getIntradayPrices(crypto, n_hours, training_hours, mov_avg):
    """
    Gets crypto prices from now to N days ago and training amount will be in addition 
    to the number of days to train. (According to the EOD documentation: The maximum 
    periods between ‘from’ and ‘to’ are 120 days for 1-minute interval, 600 days 
    for 5-minute interval and 7200 days for 1 hour interval.)
    
    There also appears to be a time delay on the data of about 15-20 hours, which 
    is added to the from(ago) variable.
    """
    
    # Getting the time from N hours ago
    ago = datetime.utcnow() - timedelta(hours=n_hours+training_hours+15)
    
    # Getting the unix timestamp format for the intraday data from the API
    timestamp_unix = str(calendar.timegm(ago.utctimetuple()))
    
    # Retrieving price data
    resp = client.get_prices_intraday(
        crypto+'-USD.CC', 
        interval="1h", 
        from_=timestamp_unix
    )
    
    # Removing the last time period for uniformity if necessary
    if resp[-1]['datetime'][-5:-3] != "00":
        resp = resp[:-1]
    
    # Price formatting for the dataframe
    prices = pd.DataFrame(resp)
        
    # Filling NaNs with the most recent values for any missing data
    prices = prices.fillna(method='ffill')
    
    # Getting the N Day Moving Average and rounding the values for some light data preprocessing
    prices['MA'] = prices[['open']].rolling(
        window=mov_avg
    ).mean().apply(lambda x: round(x, 6))

    # Resetting format for FBP
    prices = prices.rename(
        columns={"datetime": "ds", "MA": "y"}
    )
    
    return prices