# 手把手：教你如何用深度学习模型预测加密货币价格

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http://www.jakob-aungiers.com/articles/a/Multidimensional-LSTM-Networks-to-Predict-Bitcoin-Price

http://colah.github.io/posts/2015-08-Understanding-LSTMs/

http://blog.echen.me/2017/05/30/exploring-lstms/

http://www.bioinf.jku.at/publications/older/2604.pdf

Jupyter (Python) 笔记https://raw.githubusercontent.com/dashee87/blogScripts/master/Jupyter/2017-11-20-predicting-cryptocurrency-prices-with-deep-learning.ipynb

http://www.jakob-aungiers.com/articles/a/Multidimensional-LSTM-Networks-to-Predict-Bitcoin-Price

import pandas as pd

import time

import seaborn as sns

import matplotlib.pyplot as plt

import datetime

import numpy as np

# get market info for bitcoin from the start of 2016 to the current day

# convert the date string to the correct date format

bitcoin_market_info = bitcoin_market_info.assign(Date=pd.to_datetime(bitcoin_market_info['Date']))

# when Volume is equal to '-' convert it to 0

bitcoin_market_info.loc[bitcoin_market_info['Volume']=="-",'Volume']=0

# convert to int

bitcoin_market_info['Volume'] = bitcoin_market_info['Volume'].astype('int64')

# look at the first few rows

bitcoin_market_info.head()

https://coinmarketcap.com/currencies/bitcoin/historical-data/

https://medium.com/@binsumi/neural-networks-and-bitcoin-d452bfd7757e

https://blog.statsbot.co/time-series-prediction-using-recurrent-neural-networks-lstms-807fa6ca7f

（译者注：如果在时过境迁之后，加密货币的价格接近月球的高度，那么所有不在OmiseGo区块链中的加密货币会一直升值）

*本篇文章不涉及财务建议，也不应该做财务建议使用。尽管加密货币的投资在长时间的范围看肯定会增值，但它们也可能会贬值。

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