Struggling with API V2 for get_barset

Alpaca Integration Applications
1

I recreated this two years ago which is what turned me on to Alpaca in the first place. It worked fine at the time.
https://hackernoon.com/forecasting-market-movements-using-tensorflow-fb73e614cd06

Then I got distracted for two years. I’m starting again recreating this, but I have no formal programming education so it’s a slog.

I managed to get through a few issues with changes between 2018 and now, but I’m stuck on this particular section of code:

returned_data = api.get_bars(
symbol,
barTimeframe,
start_dt=trainStartDate,
end_dt=evalEndDate).df

    # Processes all data into numpy arrays for use by talib
    timeList = np.array(returned_data.index)
    openList = np.array(returned_data.open, dtype=np.float64)
    highList = np.array(returned_data.high, dtype=np.float64)
    lowList = np.array(returned_data.low, dtype=np.float64)
    closeList = np.array(returned_data.close, dtype=np.float64)
    volumeList = np.array(returned_data.volume, dtype=np.float64)

get_bars is now get_barset - and I’ve managed to make that part work by changing to the below.

api.get_barset(ticker, barTimeframe, limit=None, start=trainStartDate, end=evalEndDate).df

However, only the timeList line beneath it executes, everything else chokes (doesn’t error, just won’t even execute a print statement beneath that line). Any help greatly appreciated.

2

This is a ping please help

3

Please keep in mind get_barset().df returns a multi-level column with (symbol, OHLCV), so you likely need to slice columns by symbol first.

4

Hi, i’m happy to share my function, the trick is that the dataframe is 3dimensional with the symbo, not just 2 dimensional.

def alpaca_get_15Min_bars(_symbol,_number):
_df=pd.DataFrame()
_temp=pd.DataFrame()
if _number<1000:
    _df = api.get_barset(_symbol, '15Min', limit=_number, start=None, end=None, after=None, until=None).df
else:
    _num_cycles, _residual = divmod(_number, 1000)
    if _residual == 0:
        _df = api.get_barset(_symbol, '15Min', limit=1000, start=None, end=None, after=None, until=None).df
        _num_cycles -=1
    else:
        _df = api.get_barset(_symbol, '15Min', limit=_residual, start=None, end=None, after=None, until=None).df    
    for i in range(1,_num_cycles+1):
        _temp = api.get_barset(_symbol, '15Min', limit=1000, start=None, end=None, after=None, until=_df.first_valid_index().isoformat()).df
        _df= pd.concat([_temp,_df,])
return _df[_symbol]
5

Hitoshi and Riodda,

I found both of these value and took something from of them. Either of you know why certain 15-minute candles are empty for some tickers but not others? Running the below code gives me what you see in the attached image.

def get_alpaca_data(api, sh, max_timestamps):
df_all_tickers = pd.DataFrame()
df_this_ticker = pd.DataFrame()
tickers = sh.worksheet(‘INPUTS’).col_values(3)
df_all_tickers = api.get_barset(tickers, ‘15Min’, limit=1000).df.transpose()
sh.worksheet(’').clear() set_with_dataframe(sh.worksheet('’), df_all_tickers, include_index=True)

asdfasdf
asdfasdf3184×1257 268 KB

6

Also df_this_ticker is since deleted after realizing I didn’t have to loop through the tickers.

7

Just pinging this one in case anyone has any thoughts.

8

Any luck on this anyone?

9

Not an expert but i guess it might depend from different market open times ? Do you have the same issue if you look at crypto assets ?

10 
def getBarsetLong(timeframe,symbol,limit):
        columns = ['Date', 'Open', 'High', 'Low',  'Close', 'Volume']
        res = []
        barset = api.get_barset(symbol,timeframe,limit)
        symbol_barset = barset[symbol]
        length = len(symbol_barset)
        for x in range(0,length):
            symbol_bars = symbol_barset[x]
            symbol_bars = symbol_bars._raw
            date = symbol_bars['t']
            date=datetime.datetime.fromtimestamp(date).strftime('%Y-%m-%d %H:%M:%S')
            close = symbol_bars['c']
            open = symbol_bars['o']
            volume = symbol_bars['v']
            high = symbol_bars['h']
            low = symbol_bars['l']
            list = [date, open, high, low, close, volume]
            res.append(list)
        df = pd.DataFrame(res, columns=columns)
        df.set_index('Date', inplace=True)
        return df

    start = '2019-04-15'
    end = '2020-04-15'
    start = datetime.datetime.strptime(start,"%Y-%m-%d")
    end = datetime.datetime.strptime(end,"%Y-%m-%d")
    limit=(end-start).days * 24
    if limit > 1000:
        limit = 1000

    sample = getBarsetLong('day', "WMT",limit)
    print(sample.tail(50))

"""
                Open      High       Low   Close    Volume
Date                                                              
2020-06-19 04:00:00  118.890  120.3000  117.7500  119.75  10821598
2020-06-22 04:00:00  120.970  122.0900  120.4000  121.68   8124144
2020-06-23 04:00:00  122.750  122.8500  120.9606  121.07   6072862
2020-06-24 04:00:00  120.740  121.1153  119.5800  120.35   6032080
2020-06-25 04:00:00  119.760  120.1600  118.3800  119.63   5735935
2020-06-26 04:00:00  119.600  120.8400  118.0200  118.31   5909472
2020-06-29 04:00:00  118.500  119.6800  118.2150  119.09   4637226
2020-06-30 04:00:00  119.220  120.1300  118.5400  119.66   5555898
2020-07-01 04:00:00  119.450  119.9200  118.6600  119.69   5718034
2020-07-02 04:00:00  120.090  120.8800  118.8600  119.21   5195854
2020-07-06 04:00:00  119.800  119.8700  118.2200  118.88   6617241
2020-07-07 04:00:00  118.450  127.5500  118.2200  126.97  28229924
"""
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