大量代碼手把手教你如何預測加密貨幣的價格,有視頻喲

YouTube網紅小哥Siraj Raval系列視頻又和大家見面啦！今天要講的是加密貨幣價格預測，包含大量代碼，還用一個視頻詳解具體步驟，不信你看了還學不會！

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時長22分鐘

有中文字幕

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預測加密貨幣價格其實很簡單，用Python+Keras，再來一個循環神經網絡（確切說是雙向LSTM），只需要9步就可以了！比特幣以太坊價格預測都不在話下。

這9個步驟是：

數據處理建模訓練模型測試模型分析價格變化分析價格百分比變化比較預測值和實際數據計算模型評估指標結合在一起：可視化

數據處理

導入Keras、Scikit learn的metrics、numpy、pandas、matplotlib這些我們需要的庫。

## Keras for deep learningfrom keras.layers.core import Dense, Activation, Dropoutfrom keras.layers.recurrent import LSTMfrom keras.layers import Bidirectionalfrom keras.models import Sequential## Scikit learn for mapping metricsfrom sklearn.metrics import mean_squared_error#for loggingimport time##matrix mathimport numpy as npimport math##plottingimport matplotlib.pyplot as plt##data processingimport pandas as pd

首先，要對數據進行歸一化處理。關於數據處理的原則，有張大圖，大家可以在大數據文摘公眾號後臺對話框內回復「加密貨幣」查看高清圖。

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

建模

我們用到的是一個3層RNN，dropout率20%。

雙向RNN基於這樣的想法：時間t的輸出不僅依賴於序列中的前一個元素，而且還可以取決於未來的元素。比如，要預測一個序列中缺失的單詞，需要查看左側和右側的上下文。雙向RNN是兩個堆疊在一起的RNN，根據兩個RNN的隱藏狀態計算輸出。

舉個例子，這句話裡缺失的單詞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

訓練模型

這裡取batch size = 1024，epoch times = 100。我們需要最小化均方誤差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

測試模型

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

分析價格變化

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

分析價格百分比變化

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

比較預測值和實際數據

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

計算模型評估指標

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

結合在一起：可視化

終於可以看看我們的成果啦！

首先是預測價格vs實際價格：

y_predict, real_y_test, real_y_predict, fig1 = test_model(model, X_test, Y_test, unnormalized_bases)#Show the plotplt.show(fig1)

然後是預測的百分比變化vs實際的百分比變化，值得注意的是，這裡的預測相對實際來說波動更大，這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 plotplt.show(fig2)

最終模型表現是這樣的：

Precision: 0.62Recall: 0.553571428571F1 score: 0.584905660377Mean Squared Error: 0.0430756924477

怎麼樣，看完有沒有躍躍欲試呢？

作 者| Siraj Raval 大數據文摘經授權譯製

翻 譯| 糖竹子、狗小白、鄧子稷

時間軸| 韓振峰、Barbara、菜菜Tom

監 制| 龍牧雪