RNNs在股票价格预测的应用
前言
RNN和LSTMs在时态数据上表现特别好，这就是为什么他们在语音识别上是有效的。我们通过前25天的开高收低价格，去预测下一时刻的前收盘价。每个时间序列通过一个高斯分布和2层LSTM模型训练数据。文章分为两个版块，外汇价格预测和每日盘中价格预测(30分钟、15分钟、5分钟,等等)。源代码请在文末获取！
外汇预测（用英语描述）
a. Daily Data is pulled from Yahoo’s Data Reader
b. Only the training set is preprocessed because we create a separate test set later on
c. “model_forex” is the model for to build and train.
d. Create separate daily test set by specifying dates which start after your training set ends.
e. You can see “model_forex” is plugged in here for running the prediction
predicted_st = predict_standard(X_test_stock,y_test_stock, model_forex)
盘中预测（用英语描述）
a. Intraday Data is pulled from Google’s API. The second argument is the time in seconds (900 secs = 15 mins) and the third argument it the number of days, the max backtrack day for Googles API is 15 days I believe.
df = get_google_data(INTRA_DAY_TICKER, 900, 150)
b. Preprocess the full set of data and train test split it with “train_test_split_intra”
c. “model_intra” is the model for to build and train.
d. You can see “model_intra” is plugged in here for running the prediction
predicted_intra = predict_intra(X_test_intra,y_test_intra, model_intra)
代码展示
SITE = "http://en.wikipedia.org/wiki/List_of_S%26P_500_companies"
def scrape_list(site):
hdr = {'User-Agent': 'Mozilla/5.0'}
req = urllib2.Request(site, headers=hdr)
page = urllib2.urlopen(req)
soup = BeautifulSoup(page)
table = soup.find('table', {'class': 'wikitable sortable'})
sector_tickers = dict() for row in table.findAll('tr'):
col = row.findAll('td') if len(col) > 0:
sector = str(col[3].string.strip()).lower().replace(' ', '_')
ticker = str(col[0].string.strip()) if sector not in sector_tickers:
sector_tickers[sector] = list()
sector_tickers[sector].append(ticker) return sector_tickers
sector_tickers = scrape_list(SITE)
##Help functions to normalize and denormalize values
（省略）# Sequence Length, or # of days of tradingSEQ_LENGTH = 25
# Number of units in the two hidden (LSTM) layersN_HIDDEN = 256
#Number of attributes used for each trading daynum_attr = 4
#Out of those attribute how many are indicatorsnum_indicators = 0
#Variable to help define how far you want your y to reachREWARD_LAG = 1
#How many dats ahead do you want to predictLOOK_AHEAD = 5
#Window StrideSTRIDE = 1def _load_data(data, n_prev = SEQ_LENGTH):
docX, docY = [], [] for i in range(len(data)-n_prev):
x,y = norm(data.iloc[i:i+n_prev,:num_attr].as_matrix(),data.iloc[i+n_prev-1,num_attr:].as_matrix())
docX.append(x)
docY.append(y)
alsX = np.array(docX)
alsY = np.array(docY) return alsX, alsYdef _load_data_test(data, n_prev = SEQ_LENGTH):
docX, docY = [], []
num_sequences = (len(data)-n_prev+1)/STRIDE for i in range(num_sequences):
i = i*STRIDE
x = (data.iloc[i:i+n_prev,:num_attr].as_matrix())
y = (data.iloc[i+n_prev-1,num_attr:].as_matrix()) #x,y = norm(data.iloc[i:i+n_prev,:num_attr].as_matrix(),data.iloc[i+n_prev-1,num_attr:].as_matrix())
docX.append(x)
docY.append(y)
alsX = np.array(docX)
alsY = np.array(docY) return alsX, alsYdef _load_data_norm(data, n_prev = SEQ_LENGTH):
docX, docY = [], [] for i in range(len(data)-n_prev):
x = np.array((data.iloc[i:i+n_prev,:num_attr].as_matrix()))
y = np.array((data.iloc[i+n_prev-1,num_attr:].as_matrix()))
（省略）外汇数据
##Dataset on just single ticker to test performancesdf = data.DataReader('EUR=X', 'yahoo', datetime(2010,8,1), datetime(2014,8,1))
# df['RSI'] = ta.RSI(df.Close.values,timeperiod=14)# _,_, macdhist = ta.MACD(df.Close.values, fastperiod=12, slowperiod=26, signalperiod=9)# df['MACDHist'] = macdhist
##Add the predicted coloumn Y, as the last coloumn can be defined however you think is a good representation of a good decision
##Clean the rest of the Data Frame
y = []for i in range(0,len(df)): if i >= (len(df)- STRIDE):
y.append(None) else: if (REWARD_LAG > 1):
val = 0
for n in range(REWARD_LAG):
val = val + df['Close'][i+n+1]
val = val / float(REWARD_LAG)
y.append(val) else:
y.append(df['Close'][i+REWARD_LAG])
df['Y_Values'] =np.asarray(y)
df = df.dropna()#print (df)sliced_df = df.drop(['Adj Close','Volume'] ,axis=1)#print (sliced_df)#(X_train, y_train), (X_test, y_test) = train_test_split(sliced_df)(X_train, y_train) = train_test_split(sliced_df)
print(X_train[0],y_train[0])print (X_train.shape,y_train.shape)(array([[-0.76244909, -0.75153814, -1.36800657, -1.28695383], [-1.28305706, -1.17005084, -1.66649887, -1.50673145],
（省略）
盘中数据
def get_google_data(symbol, period, window):
url_root = 'http://www.google.com/finance/getprices?i='
url_root += str(period) + '&p=' + str(window)
url_root += 'd&f=d,o,h,l,c,v&df=cpct&q=' + symbol
print(url_root)
response = urllib2.urlopen(url_root)
data = response.read().split('
') #actual data starts at index = 7
#first line contains full timestamp,
#every other line is offset of period from timestamp
parsed_data = []
anchor_stamp = ''
end = len(data) for i in range(7, end):
cdata = data[i].split(',') if 'a' in cdata[0]: #first one record anchor timestamp
anchor_stamp = cdata[0].replace('a', '')
cts = int(anchor_stamp) else: try:
coffset = int(cdata[0])
cts = int(anchor_stamp) + (coffset * period)
parsed_data.append((dt.datetime.fromtimestamp(float(cts)), float(cdata[1]), float(cdata[2]), float(cdata[3]), float(cdata[4]), float(cdata[5]))) except: pass # for time zone offsets thrown into data
df = pd.DataFrame(parsed_data)
df.columns = ['ts', 'Open', 'High', 'Low', 'Close', 'Volume']
df.index = df.ts del df['ts'] return df盘中创建单独的数据集
df = get_google_data('AAPL', 900, 150)#print(df)plt.plot(df['Close'].values[:])
y = []for i in range(0,len(df)): if i >= (len(df)- REWARD_LAG):
y.append(None) else: if (REWARD_LAG > 1):
val = 0
for n in range(REWARD_LAG):
val = val + df['Close'][i+n+1]
val = val / float(REWARD_LAG)
y.append(val)
print('here') else:
y.append(df['Close'][i+REWARD_LAG])
df['Y_Values'] =np.asarray(y)
df = df.dropna()
sliced_df = df.drop(['Volume'] ,axis=1)#print(sliced_df)(X_train, y_train), (X_test, y_test) = train_test_split_intra(sliced_df)#print(X_train[0],y_train[0])print(len(X_train),len(X_test))#print(X_test[0],y_test[0])(1168, 108)
构建网络结构
model_intra = Sequential()
model_intra.add(LSTM(N_HIDDEN, return_sequences=True, activation='tanh', input_shape=(SEQ_LENGTH, num_attr)))#model_intra.add(LSTM(N_HIDDEN, return_sequences=True, activation='tanh'))model_intra.add(LSTM(N_HIDDEN, return_sequences=False, activation='tanh'))
model_intra.add(Dense(1,activation='linear'))
model_intra.compile(loss="mean_squared_error", optimizer='adam')
model_intra_full = Sequential()
model_intra_full.add(LSTM(N_HIDDEN, return_sequences=True, activation='tanh', input_shape=(SEQ_LENGTH, num_attr)))#model_intra_full.add(LSTM(N_HIDDEN, return_sequences=True, activation='tanh'))model_intra_full.add(LSTM(N_HIDDEN, return_sequences=False, activation='tanh'))
model_intra_full.add(Dense(1,activation='linear'))
model_intra_full.compile(loss="mean_squared_error", optimizer='adam')
model_forex = Sequential()
model_forex.add(LSTM(N_HIDDEN, return_sequences=True, activation='tanh', input_shape=(SEQ_LENGTH, num_attr)))#model_forex.add(LSTM(N_HIDDEN, return_sequences=True, activation='tanh'))model_forex.add(LSTM(N_HIDDEN, return_sequences=False, activation='tanh'))
model_forex.add(Dense(1,activation='linear'))
model_forex.compile(loss="mean_squared_error", optimizer符合模型的模型和参数
print(X_train.shape)
print(y_train.shape)(1018, 25, 4) (1018, 1)
model_intra.fit(X_train, y_train, batch_size=50, nb_epoch=Train on 1156 samples, validate on 12 samples Epoch 1/150 1156/1156 [==============================] - 1s - loss: 1.9575 - val_loss: 0.5494 Epoch 2/150 1156/1156 [==============================] - 1s - loss: 1.4731 - val_loss: 0.4006
（省略）
辅助绩效评估功能
#Function to normalize the test input then denormalize the result. Calculate the rmse of the predicted values on the test setdef predict(X_test,y_test, myModel):
predicted = [] for example in X_test:
x = copy.copy(example) #print (x)
x_norm, mn, mx = normalize(x)
toPred = []
toPred.append(x_norm)
add = np.array(toPred) #Predict for the standard model
predict_standard = myModel.predict(add)
pred_st = copy.copy(predict_standard)
y_real_st = deNormalizeY(pred_st,mn,mx)
predicted.append(y_real_st[0]) #Predict for the bidirectional model# predict_bidirectional = myModel.predict([add,add])# pred_bi = copy.copy(predict_bidirectional)# y_real_bi = deNormalizeY(pred_bi,mn,mx)# predicted.append(y_real_bi[0])（省略）df_test = data.DataReader('EUR=X', 'yahoo', datetime(2014,8,1), datetime(2015,8,1))# df_test['RSI'] = ta.RSI(df_test.Close.values,timeperiod=14)# _,_, macdhist = ta.MACD(df_test.Close.values, fastperiod=12, slowperiod=26, signalperiod=9)# df_test['MACDHist'] = macdhisty = []for i in range(0,len(df_test)): if i >= (len(df_test)- STRIDE): y.append(None) else: if (REWARD_LAG > 1):
val = 0
for n in range(REWARD_LAG):
val = val + df_test['Close'][i+n+1]
val = val / float(REWARD_LAG)
y.append(val) else:
y.append(df_test['Close'][i+REWARD_LAG])
（省略）MAE for LSTM is: [0.0035823152701196983] MAE for doing nothing is: [0.0045693478326778786] RMSE for LSTM is: [0.0050684837061917686] RMSE for doing nothing is: [0.0061416562709802761] Net profit for 0.0 threshhold is 245.261025777 making 234 trades Net profit for 0.001 threshhold is 242.673572498 making 201 trades （省略）
盘中交易评价和结果
def predict_intra(X_test, y_test, myModel):
print(len(X_test))
predicted = [] for example in X_test: #Transform the training example into gaussing distribution
x_norm, mean, std = normDist(np.array(example)) #Add examples to array to predict
toPred = []
toPred.append(x_norm)
add = np.array(toPred) #Predict these examples
predict_standard = myModel.predict(add)
pred = copy.copy(predict_standard)
y_real = deNormDist(pred,mean,std)
predicted.append(y_real[0]) return predicted
predicted_intra = predict_intra(X_test,y_test, model_intra)
plt.figure(figsize=(20,20))
plt.plot(y_test)
plt.plot(predicted_intra)
plt.show()MAE and RMSE 评估
sum_error = 0sum_error_donothing = 0for i in range(len(predicted_intra)): if i>0:
sum_error = sum_error + abs(predicted_intra[i] - y_test[i])
sum_error_donothing = sum_error_donothing + abs(predicted_intra[i] - y_test[i-1])
MAE_lstm = sum_error/len(predicted_intra)
MAE_donothing = sum_error_donothing/len(predicted_intra)
print("MAE for LSTM is: " + str(MAE_lstm))
print("MAE for doing nothing is: " + str(MAE_donothing))MAE for LSTM is: [0.091961468484759237] MAE for doing nothing is: [0.16699238882416201]
sum_error = 0sum_error_donothing = 0for i in range(len(predicted_intra)): if i>0:
sum_error = sum_error + (predicted_intra[i] - y_test[i])**2
sum_error_donothing = sum_error_donothing + (predicted_intra[i] - y_test[i-1])**2RMSE_lstm = (sum_error/len(predicted_intra))**(1.0/2.0)
RMSE_donothing = (sum_error_donothing/len(predicted_intra))**(1.0/2.0)
print("RMSE for LSTM is: " + str(RMSE_lstm))
print("RMSE for doing nothing is: " + str(RMSE_donoRMSE for LSTM is: [0.15719269057322682] RMSE for doing nothing is: [0.23207816758496383]
Policy的功能评价
net_profits = []
protits_per_trade = []for i in range(50):
THRESH = i/10000.0
LOT_SIZE = 100
net_profit = 0
num_trades = 0
for i in range(len(predicted_intra)): if i>1:
predicted_change = ((predicted_intra[i] / y_test[i-1]) - 1) #print(predicted_change)
actual_change = (predicted_intra[i] - y_test[i])*LOT_SIZE if predicted_change >= THRESH: #print("Buy")
net_profit = net_profit + actual_change
num_trades = num_trades + 1
（省略）
(array([327.67074597699519], dtype=object), 106) (array([322.81673063817777], dtype=object), 103)
plt.plot(net_profits)
plt.show()plt.plot(protits_per_trade)
plt.show()其他
buyTotal = 0sellTotal = 0correct = 0sellCorrect = 0buyCorrect = 0for i in range(len(predicted_st)):
realAnswer = y_test_stock[i][0][0] if predicted_st[i][1] > predicted_st[i][0]:
predicted = 0 #Buy
else:
predicted = 1 #Sell
if realAnswer == 0: ##This is where the actual answer is Buy:Up:[0,1]:0
buyTotal = buyTotal + 1
if predicted == realAnswer:
buyCorrect = buyCorrect + 1
correct = correct + 1（省略）(349, 730, 0.4780821917808219) (210, 382, 0.5497382198952879) (139, 348, 0.3994252873563218) 0.523287671233 0.476712328767
MMM AYI ALK ALLE(省略)
创造基线RMSE
totalCorrect = 0total = 0for stock in testing_dataframes[:50]:
X_test_stock, y_test_stock = _load_data_test(stock[1])
predicted_st = predict_standard(X_test_stock,y_test_stock, model)
buyTotal = 0
sellTotal = 0
correct = 0
sellCorrect = 0
buyCorrect = 0（省略）#Count the number of positive and the number of negative calls you got righttotalCorrect = 0total = 0buyTotal = 0sellTotal = 0correct = 0sellCorrect = 0buyCorrect = 0for i in range(len(predicted_st)):
realAnswer = y_test_stock[i][0][0] if predicted_st[i][1] > predicted_st[i][0]:
predicted = 0 #Buy
（省略）
(104, 235, 0.4425531914893617) (104, 104, 1.0) (0, 131, 0.0) 0.442553191489 0.557446808511
from sklearn.metrics import f1_score##Calculate F1 scoreactual = []
result = []for y in y_test_merged: if y[0] == 0:
actual.append(0) else:
actual.append(1)for y in predicted_st: if y[1] > y[0]:
result.append(0) else:
result.append(1)
score = f1_score(actual,result,average='weighted',pos_label=1)
print(score)0.498192044998
#Same percentage calculations but with a thresholdTHRESH = 0.1totalCorrect = 0total = 0noDecision = 0buyTotal = 0sellTotal = 0correct = 0sellCorrect = 0buyCorrect = 0for i in range(len(predicted_st)):
realAnswer = y_test_merged[i][0] if predicted_st[i][1] - THRESH > .5:
predicted = 0 #Buy
elif predicted_st[i][0] - THRESH > .5:
predicted = 1 #Sell
else:
predicted = 2 #Pass, do not count towards percentages because you make no decision if .6>x>.4（省略）(347, 750, 0.46266666666666667) (190, 351, 0.5413105413105413) (157, 399, 0.39348370927318294) If you just predicted all Up 0.468 If you just predicted all Down 0.532
thresholds = []
totalAcc = []
positiveAcc = []
negativeAcc = []##Graph this graph of the threshold vs accuracyfor i in range(10):
thresh = i/100.0
totalCorrect = 0
total = 0
noDecision = 0
buyTotal = 0
sellTotal = 0
correct = 0
sellCorrect = 0
buyCorrect = 0
for i in range(len(predicted_st)):
realAnswer = y_test_merged[i][0] if predicted_st[i][1] - thresh > .5:
predicted = 0 #Buy
elif predicted_st[i][0] - thresh > .5:
predicted = 1 #Sell
（省略） plt.plot(totalAcc)
plt.show()
plt.plot(positiveAcc)
plt.show()
plt.plot(negativeAcc)
plt.show()
通过测试表明，每日价格预测，外汇有更好的表现，比传统股票。因为他有更少的噪音。