I have created tensorflow program in order to for the close prices of the forex. I have successfully created the predcitions but failed understand the way to forecast the values for the future. See the following is my prediction function:
test_pred_list = [] def testAndforecast(xTest1,yTest1): # test_pred_list = 0 truncated_backprop_length = 3 with tf.Session() as sess: # train_writer = tf.summary.FileWriter('logs', sess.graph) tf.global_variables_initializer().run() counter = 0 # saver.restore(sess, "models\\model2298.ckpt") try: with open ("Checkpointcounter.txt","r") as file: value = file.read() except FileNotFoundError: print("First Time Running Training!....") if(tf.train.checkpoint_exists("models\\model"+value+".ckpt")): saver.restore(sess, "models\\model"+value+".ckpt") print("models\\model"+value+".ckpt Session Loaded for Testing") for test_idx in range(len(xTest1) - truncated_backprop_length): testBatchX = xTest1[test_idx:test_idx+truncated_backprop_length,:].reshape((1,truncated_backprop_length,num_features)) testBatchY = yTest1[test_idx:test_idx+truncated_backprop_length].reshape((1,truncated_backprop_length,1)) #_current_state = np.zeros((batch_size,state_size)) feed = {batchX_placeholder : testBatchX, batchY_placeholder : testBatchY} #Test_pred contains 'window_size' predictions, we want the last one _last_state,_last_label,test_pred = sess.run([last_state,last_label,prediction],feed_dict=feed) test_pred_list.append(test_pred[-1][-1]) #The last one
Here is the complete jupyter and datasets for test and train:
My repository with code.
Kindly, help me how I can forecast the close values for the future. Please do not share something related to predictions as I have tried. Kindly, let me know something that will forecast without any support just on the basis of training what I have given.
I hope to hear soon.
If I understand your question correctly, by forecasting you mean predicting multiple closing prices in future (for example next 5 closing prices from current state). I went through your jupyter notebook. In short, you can not easily do that.
Right now your code takes the last three positions defined by multiple futures (open/low/high/close prices and some indicators values). Based on that you predict next closing price. If you would like to predict even further position, you would have to create an "artificial" position based on the predicted closing price. Here you can approximate that open price is same as previous closing, but you can only guess high and low prices. Then you would calculate other futures/values (from indicators) and use this position with previous two to predict next closing price. You can continue like this for future steps.
The issue is in the open/low/high prices because you can only approximate them. You could remove them from data, retrain the model, and make predictions without them, but they may be necessary for indicators calculations.
I somehow compressed your code here to show the approach of predicting all OHLC prices:
# Data xTrain = datasetTrain[ ["open", "high", "low", "close", "k", "d", "atr", "macdmain", "macdsgnal", "bbup", "bbmid", "bblow"]].as_matrix() yTrain = datasetTrain[["open", "high", "low", "close"]].as_matrix() # Settings batch_size = 1 num_batches = 1000 truncated_backprop_length = 3 state_size = 12 num_features = 12 num_classes = 4 # Graph batchX_placeholder = tf.placeholder( dtype=tf.float32, shape=[None, truncated_backprop_length, num_features], name='data_ph') batchY_placeholder = tf.placeholder( dtype=tf.float32, shape=[None, num_classes], name='target_ph') cell = tf.contrib.rnn.BasicRNNCell(num_units=state_size) states_series, current_state = tf.nn.dynamic_rnn( cell=cell, inputs=batchX_placeholder, dtype=tf.float32) states_series = tf.transpose(states_series, [1,0,2]) last_state = tf.gather( params=states_series, indices=states_series.get_shape()[0]-1) weight = tf.Variable(tf.truncated_normal([state_size, num_classes])) bias = tf.Variable(tf.constant(0.1, shape=[num_classes])) prediction = tf.matmul(last_state, weight) + bias loss = tf.reduce_mean(tf.squared_difference(last_label, prediction)) train_step = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss) # Training for batch_idx in range(num_batches): start_idx = batch_idx end_idx = start_idx + truncated_backprop_length batchX = xTrain[start_idx:end_idx,:].reshape(batch_size, truncated_backprop_length, num_features) batchY = yTrain[end_idx].reshape(batch_size, truncated_backprop_length, num_classes) feed = {batchX_placeholder: batchX, batchY_placeholder: batchY} _loss, _train_step, _pred, _last_label,_prediction = sess.run( fetches=[loss, train_step, prediction, last_label, prediction], feed_dict=feed)
I think it is not important to write the whole code plus I don't know how are the indicators calculated. Also you should change way of data feeding because right now it only works with batches os size 1.
