Btc Transaction Computer software (Particular tutorial)

Cryptocurrency can be a high-risk, high-reward game for those willing to deal with the volatility. Can we use AI to help us make predictions about Bitcoin’s future price? In this video, i’ll show you how to build a simple Bitcoin trading bot using an LSTM neural network in Keras. Along the way I’ll explain why we use LSTM networks through code and animations, as well as a review of the vanishing gradient problem.

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Welcome to the next tutorial covering deep learning with Python, Tensorflow, and Keras. We’ve been working on a cryptocurrency price movement prediction recurrent neural network, focusing mainly on the pre-processing that we’ve got to do. In this tutorial, we’re going to be finishing up by building our model and training it.

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39 Antworten auf „Btc Transaction Computer software (Particular tutorial)“

  1. Hey Siraj, great video! Quick question for my notes; should the feedback connection in 6:04 be in between the input and output layers and not after both? and if not, why? With other RNN's I've seen it placed in between.

  2. Great deal for me trading bitcoin as a beginner, I thought trusting someone with such huge amount was wrong but after I saw few positive comments about Jim oddain I decided to contact him and it all turns out great on my portfolio, lately after seen the chart analysis on bitcoin I know my payment was coming through anytime soon. Just received mine and I want to keep more people coming to Jim.
    Email address “”

  3. I get this error on step 7. Can someone share any insights ? Thank you in advance. Siraj you're the Man.

    > Testing model on 87070 data rows with 870 steps
    NotFoundError Traceback (most recent call last)
    <ipython-input-7-2b74f10ce00c> in <module>
    11 predictions = model.predict_generator(
    12 generator_strip_xy(data_gen_test, true_values),
    —> 13 steps=steps_test
    14 )

    ~/anaconda3/envs/JaunkerBot/lib/python3.6/site-packages/keras/legacy/ in wrapper(*args, **kwargs)
    89 warnings.warn('Update your `' + object_name + '` call to the ' +
    90 'Keras 2 API: ' + signature, stacklevel=2)
    —> 91 return func(*args, **kwargs)
    92 wrapper._original_function = func
    93 return wrapper

    ~/anaconda3/envs/JaunkerBot/lib/python3.6/site-packages/keras/engine/ in predict_generator(self, generator, steps, max_queue_size, workers, use_multiprocessing, verbose)
    1520 workers=workers,
    1521 use_multiprocessing=use_multiprocessing,
    -> 1522 verbose=verbose)

    ~/anaconda3/envs/JaunkerBot/lib/python3.6/site-packages/keras/engine/ in predict_generator(model, generator, steps, max_queue_size, workers, use_multiprocessing, verbose)
    451 x = generator_output
    –> 453 outs = model.predict_on_batch(x)
    454 outs = to_list(outs)

    ~/anaconda3/envs/JaunkerBot/lib/python3.6/site-packages/keras/engine/ in predict_on_batch(self, x)
    1272 ins = x
    1273 self._make_predict_function()
    -> 1274 outputs = self.predict_function(ins)
    1275 return unpack_singleton(outputs)

    ~/anaconda3/envs/JaunkerBot/lib/python3.6/site-packages/keras/backend/ in __call__(self, inputs)
    2713 return self._legacy_call(inputs)
    -> 2715 return self._call(inputs)
    2716 else:
    2717 if py_any(is_tensor(x) for x in inputs):

    ~/anaconda3/envs/JaunkerBot/lib/python3.6/site-packages/keras/backend/ in _call(self, inputs)
    2669 feed_symbols,
    2670 symbol_vals,
    -> 2671 session)
    2672 if self.run_metadata:
    2673 fetched = self._callable_fn(*array_vals, run_metadata=self.run_metadata)

    ~/anaconda3/envs/JaunkerBot/lib/python3.6/site-packages/keras/backend/ in _make_callable(self, feed_arrays, feed_symbols, symbol_vals, session)
    2621 callable_opts.run_options.CopyFrom(self.run_options)
    2622 # Create callable.
    -> 2623 callable_fn = session._make_callable_from_options(callable_opts)
    2624 # Cache parameters corresponding to the generated callable, so that
    2625 # we can detect future mismatches and refresh the callable.

    ~/anaconda3/envs/JaunkerBot/lib/python3.6/site-packages/tensorflow/python/client/ in _make_callable_from_options(self, callable_options)
    1469 """
    1470 self._extend_graph()
    -> 1471 return BaseSession._Callable(self, callable_options)

    ~/anaconda3/envs/JaunkerBot/lib/python3.6/site-packages/tensorflow/python/client/ in __init__(self, session, callable_options)
    1423 with errors.raise_exception_on_not_ok_status() as status:
    1424 self._handle = tf_session.TF_SessionMakeCallable(
    -> 1425 session._session, options_ptr, status)
    1426 finally:
    1427 tf_session.TF_DeleteBuffer(options_ptr)

    ~/anaconda3/envs/JaunkerBot/lib/python3.6/site-packages/tensorflow/python/framework/ in __exit__(self, type_arg, value_arg, traceback_arg)
    526 None, None,
    527 compat.as_text(c_api.TF_Message(self.status.status)),
    –> 528 c_api.TF_GetCode(self.status.status))
    529 # Delete the underlying status object from memory otherwise it stays alive
    530 # as there is a reference to status from this from the traceback due to

    NotFoundError: PruneForTargets: Some target nodes not found: group_deps

  4. If you are doing audio please include Wavenet from Google. I specifically wanted to see how do you explain the receptive fields in Wavenet because I did not understand that very wel.

  5. I'm getting an error when running the code from CMD

    , line 2, in <module>

    from sklearn import preprocessing

    ModuleNotFoundError: No module named 'sklearn'

    Any idea what the issues is? sklearn is already installed in my conda env

  6. Anyone got an unusual AttributeError: 'str' object has no attribute 'shape'? It's so weird that the error message points to the last line of 'callbacks=[tensorboard, checkpoint]' as the location of this problem, although adding a line of code to check the shape of the training data all the way until the error line shows a correct data type of numpy array.

  7. SentDex – I think there is an error due to the labels not being arrays, but instead being lists and trying to label X arrays. This was the case when I tried to implement the function and I had to return np.array(x) and nparray(y) – thoughts?

  8. Hi. Sentdex, if you ask me, this whole series about market prices, need a re-evaluation and recording, this is a big potential use case for neural network, but many of the things you do here are not the best cases to work with. I would really appreciate if you start a whole new series and not only work with prices but also add some indicators to the whole game. Use two types of prediction: regression and classification. Let us see how each one of the two will work. At the end, we can even use both and predict both directions and use both predictions to have better results in real-time data. It's up to you if you want to drop sometime on this project and redo the whole thing without some of the issues we read in the comments or not. But I think the audience for this is not small.

  9. Hi,
    I am replicating all this code and now I would like to build a trading strategy based on the predicted results.
    Or maybe it would be nice to give weights to each of the 4 cryptos and build a portfolio, compute the returns and compare it to a benchmark. Could someone help me please?

  10. Hey Sentdex, there indeed is an error in your code… I don't know if anyone pointed it out to you already. When instantiating the ModelCheckpoint you accidentally missplaced a parantheses.

  11. Please help, im getting this error
    TypeError: The added layer must be an instance of class Layer. Found: <class 'tensorflow.python.keras.layers.normalization.BatchNormalization'>

    Tensorflow 1.14

  12. Thanks a lot… Your videos are not boring and I really like to watch them and learn new things with them… just out of curiosity how many cups do you have…

  13. Lol this is nearly impossible to follow with all the f-strings and other things, that is not what they were invented for. Really frustrating to try to generalize to other datasets!!!!!!!!

  14. Hey! you are doing such a GREAT JOB with your videos!!! BUT may I point out that 0.5 for a binary prediction is … random pick? 
    Actually it makes sense, as you shuffled your data, when the whole point of a RNN is to take the past into account… 
    My guess is that the prediction is a bit over the 0.5 as the level of the other currencies at a given time might help a bit.

  15. @7:00 or so when the neural network layers are designed, can anyone enlighten me as to how the parameters are chosen- specifically the number of layers and the dimensions of 128? I can see that trial and error could be straightforwardly applied at first to find the best combination (though this wouldn’t necessarily prove that you have the best solution, so maybe someone has a way to definitively find the best solution), but also that as you add more parameters eg by having multiple neural networks working in parallel, the number of models to be trialed becomes increases combinatorially- is there any general heuristics for nn perhaps that I have not come across? Thanks if anyone can respond to this

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