YouTube网红小哥Siraj Raval系列视频又和大家见面啦!今天要讲的是加密货币价格预测,包含大量代码,还用一个视频详解具体步骤,不信你看了还学不会!
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时长22分钟
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预测加密货币价格其实很简单,用Python+Keras,再来一个循环神经网络(确切说是双向LSTM),只需要9步就可以了!比特币以太坊价格预测都不在话下。
这9个步骤是:
- 数据处理
- 建模
- 训练模型
- 测试模型
- 分析价格变化
- 分析价格百分比变化
- 比较预测值和实际数据
- 计算模型评估指标
- 结合在一起:可视化
数据处理
导入Keras、Scikit learn的metrics、numpy、pandas、matplotlib这些我们需要的库。
- ## Keras for deep learning
- from keras.layers.core import Dense, Activation, Dropout
- from keras.layers.recurrent import LSTM
- from keras.layers import Bidirectional
- from keras.models import Sequential
- ## Scikit learn for mapping metrics
- from sklearn.metrics import mean_squared_error
- #for logging
- import time
- ##matrix math
- import numpy as np
- import math
- ##plotting
- import matplotlib.pyplot as plt
- ##data processing
- import pandas as pd
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首先,要对数据进行归一化处理。关于数据处理的原则,有张大图,大家可以在大数据文摘公众号后台对话框内回复“加密货币”查看高清图。
- def load_data(filename, sequence_length):
- """
- Loads the bitcoin data
- Arguments:
- filename -- A string that represents where the .csv file can be located
- sequence_length -- An integer of how many days should be looked at in a row
- Returns:
- X_train -- A tensor of shape (2400, 49, 35) that will be inputed into the model to train it
- Y_train -- A tensor of shape (2400,) that will be inputed into the model to train it
- X_test -- A tensor of shape (267, 49, 35) that will be used to test the model's proficiency
- Y_test -- A tensor of shape (267,) that will be used to check the model's predictions
- Y_daybefore -- A tensor of shape (267,) that represents the price of bitcoin the day before each Y_test value
- unnormalized_bases -- A tensor of shape (267,) that will be used to get the true prices from the normalized ones
- window_size -- An integer that represents how many days of X values the model can look at at once
- """
- #Read the data file
- raw_data = pd.read_csv(filename, dtype = float).values
- #Change all zeros to the number before the zero occurs
- for x in range(0, raw_data.shape[0]):
- for y in range(0, raw_data.shape[1]):
- if(raw_data[x][y] == 0):
- raw_data[x][y] = raw_data[x-1][y]
- #Convert the file to a list
- data = raw_data.tolist()
- #Convert the data to a 3D array (a x b x c)
- #Where a is the number of days, b is the window size, and c is the number of features in the data file
- result = []
- for index in range(len(data) - sequence_length):
- result.append(data[index: index + sequence_length])
-
- #Normalizing data by going through each window
- #Every value in the window is divided by the first value in the window, and then 1 is subtracted
- d0 = np.array(result)
- dr = np.zeros_like(d0)
- dr[:,1:,:] = d0[:,1:,:] / d0[:,0:1,:] - 1
- #Keeping the unnormalized prices for Y_test
- #Useful when graphing bitcoin price over time later
- start = 2400
- end = int(dr.shape[0] + 1)
- unnormalized_bases = d0[start:end,0:1,20]
- #Splitting data set into training (First 90% of data points) and testing data (last 10% of data points)
- split_line = round(0.9 * dr.shape[0])
- training_data = dr[:int(split_line), :]
- #Shuffle the data
- np.random.shuffle(training_data)
- #Training Data
- X_train = training_data[:, :-1]
- Y_train = training_data[:, -1]
- Y_train = Y_train[:, 20]
- #Testing data
- X_test = dr[int(split_line):, :-1]
- Y_test = dr[int(split_line):, 49, :]
- Y_test = Y_test[:, 20]
- #Get the day before Y_test's price
- Y_daybefore = dr[int(split_line):, 48, :]
- Y_daybefore = Y_daybefore[:, 20]
- #Get window size and sequence length
- sequence_length = sequence_length
- window_size = sequence_length - 1 #because the last value is reserved as the y value
- return X_train, Y_train, X_test, Y_test, Y_daybefore, unnormalized_bases, window_size
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建模
我们用到的是一个3层RNN,dropout率20%。
双向RNN基于这样的想法:时间t的输出不仅依赖于序列中的前一个元素,而且还可以取决于未来的元素。比如,要预测一个序列中缺失的单词,需要查看左侧和右侧的上下文。双向RNN是两个堆叠在一起的RNN,根据两个RNN的隐藏状态计算输出。
举个例子,这句话里缺失的单词gym要查看上下文才能知道(文摘菌:everyday?):
I go to the ( ) everyday to get fit.
- def initialize_model(window_size, dropout_value, activation_function, loss_function, optimizer):
- """
- Initializes and creates the model to be used
- Arguments:
- window_size -- An integer that represents how many days of X_values the model can look at at once
- dropout_value -- A decimal representing how much dropout should be incorporated at each level, in this case 0.2
- activation_function -- A string to define the activation_function, in this case it is linear
- loss_function -- A string to define the loss function to be used, in the case it is mean squared error
- optimizer -- A string to define the optimizer to be used, in the case it is adam
- Returns:
- model -- A 3 layer RNN with 100*dropout_value dropout in each layer that uses activation_function as its activation
- function, loss_function as its loss function, and optimizer as its optimizer
- """
- #Create a Sequential model using Keras
- model = Sequential()
- #First recurrent layer with dropout
- model.add(Bidirectional(LSTM(window_size, return_sequences=True), input_shape=(window_size, X_train.shape[-1]),))
- model.add(Dropout(dropout_value))
- #Second recurrent layer with dropout
- model.add(Bidirectional(LSTM((window_size*2), return_sequences=True)))
- model.add(Dropout(dropout_value))
- #Third recurrent layer
- model.add(Bidirectional(LSTM(window_size, return_sequences=False)))
- #Output layer (returns the predicted value)
- model.add(Dense(units=1))
- #Set activation function
- model.add(Activation(activation_function))
- #Set loss function and optimizer
- model.compile(loss=loss_function, optimizer=optimizer)
- return model
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训练模型
这里取batch size = 1024,epoch times = 100。我们需要最小化均方误差MSE。
- def fit_model(model, X_train, Y_train, batch_num, num_epoch, val_split):
- """
- Fits the model to the training data
- Arguments:
- model -- The previously initalized 3 layer Recurrent Neural Network
- X_train -- A tensor of shape (2400, 49, 35) that represents the x values of the training data
- Y_train -- A tensor of shape (2400,) that represents the y values of the training data
- batch_num -- An integer representing the batch size to be used, in this case 1024
- num_epoch -- An integer defining the number of epochs to be run, in this case 100
- val_split -- A decimal representing the proportion of training data to be used as validation data
- Returns:
- model -- The 3 layer Recurrent Neural Network that has been fitted to the training data
- training_time -- An integer representing the amount of time (in seconds) that the model was training
- """
- #Record the time the model starts training
- start = time.time()
- #Train the model on X_train and Y_train
- model.fit(X_train, Y_train, batch_size= batch_num, nb_epoch=num_epoch, validation_split= val_split)
- #Get the time it took to train the model (in seconds)
- training_time = int(math.floor(time.time() - start))
- return model, training_time
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测试模型
- def test_model(model, X_test, Y_test, unnormalized_bases):
- """
- Test the model on the testing data
- Arguments:
- model -- The previously fitted 3 layer Recurrent Neural Network
- X_test -- A tensor of shape (267, 49, 35) that represents the x values of the testing data
- Y_test -- A tensor of shape (267,) that represents the y values of the testing data
- unnormalized_bases -- A tensor of shape (267,) that can be used to get unnormalized data points
- Returns:
- y_predict -- A tensor of shape (267,) that represnts the normalized values that the model predicts based on X_test
- real_y_test -- A tensor of shape (267,) that represents the actual prices of bitcoin throughout the testing period
- real_y_predict -- A tensor of shape (267,) that represents the model's predicted prices of bitcoin
- fig -- A branch of the graph of the real predicted prices of bitcoin versus the real prices of bitcoin
- """
- #Test the model on X_Test
- y_predict = model.predict(X_test)
- #Create empty 2D arrays to store unnormalized values
- real_y_test = np.zeros_like(Y_test)
- real_y_predict = np.zeros_like(y_predict)
- #Fill the 2D arrays with the real value and the predicted value by reversing the normalization process
- for i in range(Y_test.shape[0]):
- y = Y_test[i]
- predict = y_predict[i]
- real_y_test[i] = (y+1)*unnormalized_bases[i]
- real_y_predict[i] = (predict+1)*unnormalized_bases[i]
- #Plot of the predicted prices versus the real prices
- fig = plt.figure(figsize=(10,5))
- ax = fig.add_subplot(111)
- ax.set_title("Bitcoin Price Over Time")
- plt.plot(real_y_predict, color = 'green', label = 'Predicted Price')
- plt.plot(real_y_test, color = 'red', label = 'Real Price')
- ax.set_ylabel("Price (USD)")
- ax.set_xlabel("Time (Days)")
- ax.legend()
- return y_predict, real_y_test, real_y_predict, fig
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分析价格变化
- def price_change(Y_daybefore, Y_test, y_predict):
- """
- Calculate the percent change between each value and the day before
- Arguments:
- Y_daybefore -- A tensor of shape (267,) that represents the prices of each day before each price in Y_test
- Y_test -- A tensor of shape (267,) that represents the normalized y values of the testing data
- y_predict -- A tensor of shape (267,) that represents the normalized y values of the model's predictions
- Returns:
- Y_daybefore -- A tensor of shape (267, 1) that represents the prices of each day before each price in Y_test
- Y_test -- A tensor of shape (267, 1) that represents the normalized y values of the testing data
- delta_predict -- A tensor of shape (267, 1) that represents the difference between predicted and day before values
- delta_real -- A tensor of shape (267, 1) that represents the difference between real and day before values
- fig -- A plot representing percent change in bitcoin price per day,
- """
- #Reshaping Y_daybefore and Y_test
- Y_daybefore = np.reshape(Y_daybefore, (-1, 1))
- Y_test = np.reshape(Y_test, (-1, 1))
- #The difference between each predicted value and the value from the day before
- delta_predict = (y_predict - Y_daybefore) / (1+Y_daybefore)
- #The difference between each true value and the value from the day before
- delta_real = (Y_test - Y_daybefore) / (1+Y_daybefore)
- #Plotting the predicted percent change versus the real percent change
- fig = plt.figure(figsize=(10, 6))
- ax = fig.add_subplot(111)
- ax.set_title("Percent Change in Bitcoin Price Per Day")
- plt.plot(delta_predict, color='green', label = 'Predicted Percent Change')
- plt.plot(delta_real, color='red', label = 'Real Percent Change')
- plt.ylabel("Percent Change")
- plt.xlabel("Time (Days)")
- ax.legend()
- plt.show()
- return Y_daybefore, Y_test, delta_predict, delta_real, fig
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分析价格百分比变化
- def binary_price(delta_predict, delta_real):
- """
- Converts percent change to a binary 1 or 0, where 1 is an increase and 0 is a decrease/no change
- Arguments:
- delta_predict -- A tensor of shape (267, 1) that represents the predicted percent change in price
- delta_real -- A tensor of shape (267, 1) that represents the real percent change in price
- Returns:
- delta_predict_1_0 -- A tensor of shape (267, 1) that represents the binary version of delta_predict
- delta_real_1_0 -- A tensor of shape (267, 1) that represents the binary version of delta_real
- """
- #Empty arrays where a 1 represents an increase in price and a 0 represents a decrease in price
- delta_predict_1_0 = np.empty(delta_predict.shape)
- delta_real_1_0 = np.empty(delta_real.shape)
- #If the change in price is greater than zero, store it as a 1
- #If the change in price is less than zero, store it as a 0
- for i in range(delta_predict.shape[0]):
- if delta_predict[i][0] > 0:
- delta_predict_1_0[i][0] = 1
- else:
- delta_predict_1_0[i][0] = 0
- for i in range(delta_real.shape[0]):
- if delta_real[i][0] > 0:
- delta_real_1_0[i][0] = 1
- else:
- delta_real_1_0[i][0] = 0
- return delta_predict_1_0, delta_real_1_0
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比较预测值和实际数据
- def find_positives_negatives(delta_predict_1_0, delta_real_1_0):
- """
- Finding the number of false positives, false negatives, true positives, true negatives
- Arguments:
- delta_predict_1_0 -- A tensor of shape (267, 1) that represents the binary version of delta_predict
- delta_real_1_0 -- A tensor of shape (267, 1) that represents the binary version of delta_real
- Returns:
- true_pos -- An integer that represents the number of true positives achieved by the model
- false_pos -- An integer that represents the number of false positives achieved by the model
- true_neg -- An integer that represents the number of true negatives achieved by the model
- false_neg -- An integer that represents the number of false negatives achieved by the model
- """
- #Finding the number of false positive/negatives and true positives/negatives
- true_pos = 0
- false_pos = 0
- true_neg = 0
- false_neg = 0
- for i in range(delta_real_1_0.shape[0]):
- real = delta_real_1_0[i][0]
- predicted = delta_predict_1_0[i][0]
- if real == 1:
- if predicted == 1:
- true_pos += 1
- else:
- false_neg += 1
- elif real == 0:
- if predicted == 0:
- true_neg += 1
- else:
- false_pos += 1
- return true_pos, false_pos, true_neg, false_neg
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计算模型评估指标
- def calculate_statistics(true_pos, false_pos, true_neg, false_neg, y_predict, Y_test):
- """
- Calculate various statistics to assess performance
- Arguments:
- true_pos -- An integer that represents the number of true positives achieved by the model
- false_pos -- An integer that represents the number of false positives achieved by the model
- true_neg -- An integer that represents the number of true negatives achieved by the model
- false_neg -- An integer that represents the number of false negatives achieved by the model
- Y_test -- A tensor of shape (267, 1) that represents the normalized y values of the testing data
- y_predict -- A tensor of shape (267, 1) that represents the normalized y values of the model's predictions
- Returns:
- precision -- How often the model gets a true positive compared to how often it returns a positive
- recall -- How often the model gets a true positive compared to how often is hould have gotten a positive
- F1 -- The weighted average of recall and precision
- Mean Squared Error -- The average of the squares of the differences between predicted and real values
- """
- precision = float(true_pos) / (true_pos + false_pos)
- recall = float(true_pos) / (true_pos + false_neg)
- F1 = float(2 * precision * recall) / (precision + recall)
- #Get Mean Squared Error
- MSE = mean_squared_error(y_predict.flatten(), Y_test.flatten())
- return precision, recall, F1, MSE
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结合在一起:可视化
终于可以看看我们的成果啦!
首先是预测价格vs实际价格:
- y_predict, real_y_test, real_y_predict, fig1 = test_model(model, X_test, Y_test, unnormalized_bases)
- #Show the plot
- plt.show(fig1)
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然后是预测的百分比变化vs实际的百分比变化,值得注意的是,这里的预测相对实际来说波动更大,这是模型可以提高的部分:
Y_daybefore, Y_test, delta_predict, delta_real, fig2 = price_change(Y_daybefore, Y_test, y_predict)
#Show the plot
plt.show(fig2)
最终模型表现是这样的:
Precision: 0.62
Recall: 0.553571428571
F1 score: 0.584905660377
Mean Squared Error: 0.0430756924477
怎么样,看完有没有跃跃欲试呢?
代码下载地址:
https://github.com/llSourcell/ethereum_future/blob/master/A%20Deep%20Learning%20Approach%20to%20Predicting%20Cryptocurrency%20Prices.ipynb
原视频地址:
https://www.youtube.com/watch?v=G5Mx7yYdEhE
作 者 | Siraj Raval 大数据文摘经授权译制
翻 译 | 糖竹子、狗小白、邓子稷
时间轴 | 韩振峰、Barbara、菜菜Tom
监 制 | 龙牧雪
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