bitcoin_prediction
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This is the code for "Bitcoin Prediction" by Siraj Raval on Youtube
Hi Siraj, just wondering where the merge_data.csv comes from in the engine.py ? Its being read into a data frame but I cant see it being used anywhere else?
Is it possible to add additional indicators like macd, rssi, ma (moving average) indicators to network so it could better predict movement?
Running LSTM_V1.ipynb ``` data2 = pd.read_csv(filepath_or_buffer="sentiment6.txt") data2.info() data2.dropna(axis=1,how='all') data2['sentiment'] = data2.sentiment.astype(float) ``` Results in: `AttributeError: 'DataFrame' object has no attribute 'sentiment'` sentiment6.txt starts with: 20171114,0.38021943904690936 20171113,0.38021943904690936 20171112,0.38021943904690936 20171111,0.38021943904690936 20171110,0.38021943904690936 20171109,0.38021943904690936...
There should be a requirements files, lots of lib should be installed.
Great lib, great video. Thanks. Its very timely that you've realised this work. I'm wondering, how much code did you change from Sapphirine's work? Is the goal of this lib...
I didn't find any correlation between tweeter sentiment and bitcoin prices. Take a look at this notebook: https://github.com/flamoedo/Bitcoin-sentiment-correlation/blob/master/Bitcoin%20daily%20returns%20correlation.ipynb
Hi! Were i can find 'merged_data.csv' for ipynb usage (`data = pd.read_csv("merged_data.csv")`)?
What do you think of the sample size of 100 (and the samples twitter selects for you in your search) you get from all twitter data? Is this kind of...
Do you have to make sure that tweets aren't analysed twice or more?