Traceback (most recent call last): File "C:\Users\alok.saw\Downloads\Deep-Trading-master\Deep-Trading-master\hyperparameters\hyper.py", line 164, in best = fmin(experiment, space, algo=tpe.suggest, max_evals=50, trials=trials) File "C:\Users\alok.saw\AppData\Local\Programs\Python\Python35\lib\site-packages\hyperopt\fmin.py", line 307, in fmin return_argmin=return_argmin, File "C:\Users\alok.saw\AppData\Local\Programs\Python\Python35\lib\site-packages\hyperopt\base.py", line 635, in fmin return_argmin=return_argmin) File "C:\Users\alok.saw\AppData\Local\Programs\Python\Python35\lib\site-packages\hyperopt\fmin.py", line 314, in fmin pass_expr_memo_ctrl=pass_expr_memo_ctrl) File "C:\Users\alok.saw\AppData\Local\Programs\Python\Python35\lib\site-packages\hyperopt\base.py", line 786, in init pyll.toposort(self.expr) File "C:\Users\alok.saw\AppData\Local\Programs\Python\Python35\lib\site-packages\hyperopt\pyll\base.py", line 715, in toposort assert order[-1] == expr TypeError: 'generator' object is not subscriptable

I'm working on a time-series classification on financial data (not regression, but similar).

I'm using sklearn.StandardScaler() although after reading all of your posts on Medium (thanks for the help!) I'm not entirely sure that I'm not screwing it up...

I'm doing something like this to create 'lagged' data for the time window I'm trying to classify:

def lag_data(df_data):
    for each in channels:
                features_to_add.append(pd.concat([
                df_data[[each]].shift(i).add_prefix("lag_{}_".format(i)) for i in range((lookforward*-1), lookback)], axis=1))
    return pd.concat(features_to_add, axis=1)

from from sklearn.preprocessing import StandardScaler
Instantiate scaler
scaler = StandardScaler()
# Scale the dataframe
df_scaled = pd.DataFrame(scaler.fit_transform(OHLCV_data_with_lag.values))

And then like this to prepare for Conv1D in Keras:

def X_to_Conv1D_arrays(X):
    # Convert X to 3D arrays
    X = np.array(X)
    
    # Reshape data for Conv1D
    X = X.reshape(X.shape[0], X.shape[1], 1)
    
    print("X: ", X.shape)
    print("X: ", type(X))

    return X

I've gotten some decent accuracy, but I'm wondering if this is a faulty way to prepare the data...

Error message: from . import np_utils ImportError: cannot import name np_utils

source: https://stackoverflow.com/questions/45060150/keras-cannot-import-name-np-utils - solution: pip install keras==2.0.2

`plt.figure() plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='best') plt.show()

plt.figure() plt.plot(history.history['acc']) plt.plot(history.history['val_acc']) plt.title('model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='best') plt.show()`

^ for me the above cell is not producing data on the graphs, just 2 blank plots. I was wondering what I am doing wrong?

Although the FORECAST constant is 1, the label is actually 2 days after. Check the codes between line 52 - 55.

Do a little experiment: x = [0, 1, 2, 3] x[0:3] x[3]

Hi Alex, I really like your tutorials and used them as a good example for starting own projects ;) but I think there is a major error in the preprocessing, performed by the split_into_XY - function, in the process_data modul in the multimodal project.

x_i = data_chng_train[i:i+window]
y_i = np.std(data_chng_train[i:i+window+forecast][3])

By using the above mentioned code, for generating the regression labels, the train data contain the labels!!! In general, the idea behind it, isn't clear to me. First, the code should be replaced with (that's for sure):

x_i = data_chng_train[i:i+window]
y_i = np.std(data_chng_train[i+window+forecast])

But on the other hand, i dont understand, why you are using the standard deviation along the specific axis?! Shouldn't it be:

x_i = data_chng_train[i:i+window]
y_i = data_chng_train[i+window+forecast][3]  #Using the close prize [3] as label

Then obviously all results substantially change and getting worse:

when I run habrahabr.ipynb, I get the error message about b'AAPL.csv' doesn't exist, may I have the 'AAPL.csv' file? thanks

FileNotFoundError Traceback (most recent call last) in () ----> 1 data = pd.read_csv('AAPL.csv')[::-1] 2 data = data.ix[:, 'Adj Close'].tolist() 3 4 # Uncomment below to use price change time series 5 # data = data.ix[:, 'Adj Close'].pct_change().dropna().tolist()

/home/smilewater/anaconda3/envs/tensorflow-gpu/lib/python3.6/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision) 653 skip_blank_lines=skip_blank_lines) 654 --> 655 return _read(filepath_or_buffer, kwds) 656 657 parser_f.name = name

/home/smilewater/anaconda3/envs/tensorflow-gpu/lib/python3.6/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds) 403 404 # Create the parser. --> 405 parser = TextFileReader(filepath_or_buffer, **kwds) 406 407 if chunksize or iterator:

/home/smilewater/anaconda3/envs/tensorflow-gpu/lib/python3.6/site-packages/pandas/io/parsers.py in init(self, f, engine, **kwds) 762 self.options['has_index_names'] = kwds['has_index_names'] 763 --> 764 self._make_engine(self.engine) 765 766 def close(self):

/home/smilewater/anaconda3/envs/tensorflow-gpu/lib/python3.6/site-packages/pandas/io/parsers.py in _make_engine(self, engine) 983 def _make_engine(self, engine='c'): 984 if engine == 'c': --> 985 self._engine = CParserWrapper(self.f, **self.options) 986 else: 987 if engine == 'python':

/home/smilewater/anaconda3/envs/tensorflow-gpu/lib/python3.6/site-packages/pandas/io/parsers.py in init(self, src, **kwds) 1603 kwds['allow_leading_cols'] = self.index_col is not False 1604 -> 1605 self._reader = parsers.TextReader(src, **kwds) 1606 1607 # XXX

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.cinit (pandas/_libs/parsers.c:4209)()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._setup_parser_source (pandas/_libs/parsers.c:8873)()

FileNotFoundError: File b'AAPL.csv' does not exist

Hi Alex,

Thanks for sharing your code. I am new to Python and CNN. I found your post on Medium and would like to try out your code, logic, and approaches on my own Mac OS. I am able to follow through your code in "Neural networks for algorithmic trading. Part One Simple time series forecasting.ipython" up to the following error.

TypeErrorTraceback (most recent call last) in () 6 X, Y = split_into_chunks(timeseries, TRAIN_SIZE, TARGET_TIME, LAG_SIZE, binary=False, scale=False) 7 X, Y = np.array(X), np.array(Y) ----> 8 X_train, X_test, Y_train, Y_test = create_Xt_Yt(X, Y, percentage=0.9) 9 10 Xp, Yp = split_into_chunks(timeseries, TRAIN_SIZE, TARGET_TIME, LAG_SIZE, binary=False, scale=False)

in create_Xt_Yt(X, y, percentage) 85 print ("len(X):", len(X)) 86 print ("len(y):", len(y)) ---> 87 X_train = X[0:len(X) * percentage] 88 Y_train = y[0:len(y) * percentage] 89

TypeError: slice indices must be integers or None or have an index method

Can you please help to shed some lights on this issue?

Thanks, Kiko

I'm not sure why no one has reported this before, so it makes me wonder if the problem is on my end. I got stuck trying to plot the very first chart, and it took some investigation to determine that load_snp_close() was quietly failing to read any data, because of the swallowed exception TypeError: a bytes-like object is required, not 'str'.

I had to change f = open('table.csv', 'rb').readlines()[1:] to f = open('table.csv', 'r').readlines()[1:]

After that, the data was read as expected.