How to forecast using the Tensorflow model?

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.

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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.