Cryptocurrency-predicting RNN release that Intense Learning w/ Python, TensorFlow and Keras p.8

Welcome to part 8 of the Deep Learning with Python, Keras, and Tensorflow series. In this tutorial, we’re going to work on using a recurrent neural network to predict against a time-series dataset, which is going to be cryptocurrency prices.

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20 Antworten auf „Cryptocurrency-predicting RNN release that Intense Learning w/ Python, TensorFlow and Keras p.8“

  1. Find a Forex or something like that that offers api / build an api over that and let him play with a demo account. If it works try with a true one

  2. probably not better, but it allows you to change the columns easily by dropping them using the list.

    names = ["time", "low", "high", "open", "close", "volume"]

    ratios = ["BCH-USD", "BTC-USD", "ETH-USD", "LTC-USD"]

    x = 0

    df_dict = {}

    for ratio in ratios:

    df = pd.read_csv(f"{ratio}.csv", names = names).drop(columns = names[1:4])

    df_dict[f"dataframe{x}"] = df

    x += 1

    if x > len(ratios) – 1:



    df_dict["dataframe0"] = pd.merge(df_dict["dataframe0"], df, on='time', how='left', suffixes = (ratio, ratios[x]))

    main_df = df_dict["dataframe0"]

  3. Any idea of how this happened?:
    OSError: Unable to create file (unable to open file: name = 'models/RNN_Final-01-0.559.model', errno = 2, error message = 'No such file or directory', flags = 13, o_flags = 302) Thanks.

  4. My simple version to make the main_df:

    files = ["LTC-USD", "BTC-USD", "ETH-USD", "BCH-USD"]

    main_list = []

    for file in files:

    df = pd.read_csv(f"crypto_data/{file}.csv", names=["open", "low", "high", f"{file}_close", f"{file}_volume"])

    main_list.append(df[[f"{file}_close", f"{file}_volume"]])

    main_df = pd.concat(main_list, axis=1).dropna()

  5. Please feel free to do as many finance videos as you want! No complaints from me on this topic. Would be curious what other [sequential] data sets are being analyzed via RNNs…

  6. Just as a side note: Financial date usually come in OHLC format (open, high, low, close). Your data is probably the same but I could be wrong. I love your videos by the way..

  7. Hey man, first off thanks for all you do! Really enjoy all your content. We don't need to understand the minute by minute data… But I really would like to understand what the OPEN, and CLOSE is.. if it's OPEN and CLOSE for every minute or HIGH and LOW for every minute why is the CLOSE not carried over to the next rows OPEN column..?

    Also, did you use a free service to gather the DATA or did you pay for it?

  8. If you are the fan of OS Library Check this Out:-

    import pandas as pd
    import os

    SEQ_LEN = 60 #This is for 60 Mins

    DIR_DATA = "crypto_data/"

    def classification_for_stock(current_price,future_price):
    if float(current_price) < float(future_price):
    return 1
    return 0

    #Importing Out Data(Saved by WEB SCRAPING)
    files_name = ['BTC-USD','LTC-USD','ETH-USA','BCH-USD']
    files_path = ['BTC-USD.csv','LTC-USD.csv','ETH-USD.csv','BCH-USD.csv']

    df_main = pd.DataFrame()

    for nums,file in enumerate(files_path):
    all_datasets = os.path.join(DIR_DATA,file)
    data1 = pd.read_csv(all_datasets,names=['time','low','high','open','close','volume'])
    data1 = data1[[f'{files_name[nums]}_close',f'{files_name[nums]}_volume']]
    if bool(df_main.empty) != False:
    df_main = data1
    df_main = df_main.join(data1)
    Same Code Just Some Alterations 😉

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