（转）The Road to TensorFlowStephen Smith's BlogIntroductionImport LibrariesGet Stock Market DataNormalize the DataRe-arrange the Data for TensorFlowAccuracyTensorFlow VariablesTensorFlow ModelTraining ModelRun the ModelMake a PredictionResultsSummaryIn

Introduction

Well after a long journey through Linux, Python, Python Libraries, the Stock Market, an Introduction to Neural Networks and training Neural Networks we are now ready to look at a complete Python example to predict the stock market.

I placed the full source code listing on my Google Drive here. As described in the previous articles you will need to run this on a Mac or on Linux (could be a virtual image) with Python and TensorFlow installed. You will also need to have the various libraries that are imported at the top of the source file installed or you will get an error when you go to run it. I would suggest getting the source file to play with, Python is very fussy about indentation, so copy/paste from the article may introduce indentation errors caused by the blog formatting.

The Neural Network we are running here is a simple feed forward network with four hidden layers and uses the hyperbolic tangent as the activation function in each case. This is a very simple model so don’t use it to invest with real money. Hopefully this article gives a flavour for how to create and train a Neural Network using TensorFlow. Then in future articles we can discuss the limitation of this model and how to improve it.

Import Libraries

First we import all the various libraries we will be using, note tensorflow and numpy as being particularly important.

# Copyright 2016 Stephen Smith

import time

import math
import os
from datetime import date
from datetime import timedelta
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import pandas as pd
import pandas_datareader as pdr
from pandas_datareader import data, wb
from six.moves import cPickle as pickle
from yahoo_finance import Share

Get Stock Market Data

Next we get the stock market data. If the file stocks.pickle exists we assume we’ve previously saved this file and use it. Otherwise we get the data from Yahoo Finance using a Web Service call, made via the Pandas DataReader. We only keep the adjusted close column and we fill in any NaN’s with the first value we saw (this really only applies to Visa in this case). The data will all be in a standard Pandas data frame after this.

# Choose amount of historical data to use NHistData
NHistData = 30
TrainDataSetSize = 3000

# Load the Dow 30 stocks from Yahoo into a Pandas datasheet

dow30 = ['AXP', 'AAPL', 'BA', 'CAT', 'CSCO', 'CVX', 'DD', 'XOM',
         'GE', 'GS', 'HD', 'IBM', 'INTC', 'JNJ', 'KO', 'JPM',
         'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PFE', 'PG',
         'TRV', 'UNH', 'UTX', 'VZ', 'V', 'WMT', 'DIS']

num_stocks = len(dow30)

trainData = None
loadNew = False

# If stocks.pickle exists then this contains saved stock data, so use this,
# else use the Pandas DataReader to get the stock data and then pickle it.
stock_filename = 'stocks.pickle'
if os.path.exists(stock_filename):
    try:
        with open(stock_filename, 'rb') as f:
            trainData = pickle.load(f)
    except Exception as e:
      print('Unable to process data from', stock_filename, ':', e)
      raise           
    print('%s already present - Skipping requesting/pickling.' % stock_filename)
else:
    # Get the historical data. Make the date range quite a bit bigger than
    # TrainDataSetSize since there are no quotes for weekends and holidays. This
    # ensures we have enough data.

    f = pdr.data.DataReader(dow30, 'yahoo',
        date.today()-timedelta(days=TrainDataSetSize*2+5), date.today())
    cleanData = f.ix['Adj Close']
    trainData = pd.DataFrame(cleanData)
    trainData.fillna(method='backfill', inplace=True)
    loadNew = True
    print('Pickling %s.' % stock_filename)
    try:
        with open(stock_filename, 'wb') as f:
          pickle.dump(trainData, f, pickle.HIGHEST_PROTOCOL)
    except Exception as e:
        print('Unable to save data to', stock_filename, ':', e)

Normalize the Data

We then normalize the data and remember the factor we used so we can de-normalize the results at the end.

# Normalize the data by dividing each price by the first price for a stock.
# This way all the prices start together at 1.
# Remember the normalizing factors so we can go back to real stock prices
# for our final predictions.
factors = np.ndarray(shape=( num_stocks ), dtype=np.float32)
i = 0
for symbol in dow30:
    factors[i] = trainData[symbol][0]
    trainData[symbol] = trainData[symbol]/trainData[symbol][0]
    i = i + 1

Re-arrange the Data for TensorFlow

Now we need to build up our training data, test data and validation data. We need to format this as input arrays for the Neural Network. Looking at this code, I think true Python programmers will accuse me of being a C programmer (which I am), since I do this all with loops. I’m sure a more experience Python programmer could accomplish this quicker with more array operations. This part of the code is quite slow so we pickle it, so if we re-run with the saved stock data, we can also use saved training data.

# Configure how much of the data to use for training, testing and validation.

usableData = len(trainData.index) - NHistData + 1
#numTrainData =  int(0.6 * usableData)
#numValidData =  int(0.2 * usableData
#numTestData = usableData - numTrainData - numValidData - 1
numTrainData = usableData - 1
numValidData = 0
numTestData = 0

train_dataset = np.ndarray(shape=(numTrainData - 1,
    num_stocks * NHistData), dtype=np.float32)
train_labels = np.ndarray(shape=(numTrainData - 1, num_stocks),
    dtype=np.float32)
valid_dataset = np.ndarray(shape=(max(0, numValidData - 1),
    num_stocks * NHistData), dtype=np.float32)
valid_labels = np.ndarray(shape=(max(0, numValidData - 1),
    num_stocks), dtype=np.float32)
test_dataset = np.ndarray(shape=(max(0, numTestData - 1),
    num_stocks * NHistData), dtype=np.float32)
test_labels = np.ndarray(shape=(max(0, numTestData - 1),
    num_stocks), dtype=np.float32)
final_row = np.ndarray(shape=(1, num_stocks * NHistData),
    dtype=np.float32)
final_row_prices = np.ndarray(shape=(1, num_stocks * NHistData),
    dtype=np.float32)

# Build the taining datasets in the correct format with the matching labels.
# So if calculate based on last 30 stock prices then the desired
# result is the 31st. So note that the first 29 data points can't be used.
# Rather than use the stock price, use the pricing deltas.
pickle_file = "traindata.pickle"
if loadNew == True or not os.path.exists(pickle_file):
    for i in range(1, numTrainData):
        for j in range(num_stocks):
            for k in range(NHistData):
                train_dataset[i-1][j * NHistData + k] = (trainData[dow30[j]][i + k]
                    - trainData[dow30[j]][i + k - 1])
            train_labels[i-1][j] = (trainData[dow30[j]][i + NHistData]
                - trainData[dow30[j]][i + NHistData - 1])  

    for i in range(1, numValidData):
        for j in range(num_stocks):
            for k in range(NHistData):
                valid_dataset[i-1][j * NHistData + k] = (trainData[dow30[j]][i + k + numTrainData]
                    - trainData[dow30[j]][i + k + numTrainData - 1])
            valid_labels[i-1][j] = (trainData[dow30[j]][i + NHistData + numTrainData]
                - trainData[dow30[j]][i + NHistData + numTrainData - 1])

    for i in range(1, numTestData):
        for j in range(num_stocks):
            for k in range(NHistData):
                test_dataset[i-1][j * NHistData + k] = (trainData[dow30[j]][i + k + numTrainData + numValidData]
                    - trainData[dow30[j]][i + k + numTrainData + numValidData - 1])
            test_labels[i-1][j] = (trainData[dow30[j]][i + NHistData + numTrainData + numValidData]
                - trainData[dow30[j]][i + NHistData + numTrainData + numValidData - 1])

    try:
      f = open(pickle_file, 'wb')
      save = {
        'train_dataset': train_dataset,
        'train_labels': train_labels,
        'valid_dataset': valid_dataset,
        'valid_labels': valid_labels,
        'test_dataset': test_dataset,
        'test_labels': test_labels,
        }
      pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)
      f.close()
    except Exception as e:
      print('Unable to save data to', pickle_file, ':', e)
      raise

else:
    with open(pickle_file, 'rb') as f:
      save = pickle.load(f)
      train_dataset = save['train_dataset']
      train_labels = save['train_labels']
      valid_dataset = save['valid_dataset']
      valid_labels = save['valid_labels']
      test_dataset = save['test_dataset']
      test_labels = save['test_labels']
      del save  # hint to help gc free up memory   

for j in range(num_stocks):
    for k in range(NHistData):
            final_row_prices[0][j * NHistData + k] = (trainData[dow30[j]][k + len(trainData.index - NHistData]
            final_row[0][j * NHistData + k] = (trainData[dow30[j]][k + len(trainData.index) - NHistData]
                - trainData[dow30[j]][k + len(trainData.index) - NHistData - 1])

print('Training set', train_dataset.shape, train_labels.shape)
print('Validation set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)

Accuracy

We now setup an accuracy function that is only used to report how we are doing during training. This isn’t used by the training algorithm. It roughly shows what percentage of predictions are within some tolerance.

# This accuracy function is used for reporting progress during training, it isn't actually
# used for training.
def accuracy(predictions, labels):
  err = np.sum( np.isclose(predictions, labels, 0.0, 0.005) ) / (predictions.shape[0] * predictions.shape[1])
  return (100.0 * err)

TensorFlow Variables

We now start setting up TensorFlow by creating our graph and defining our datasets and variables.

batch_size = 4
num_hidden = 16
num_labels = num_stocks

graph = tf.Graph()

# input is 30 days of dow 30 prices normalized to be between 0 and 1.
# output is 30 values for normalized next day price change of dow stocks
# use a 4 level neural network to compute this.

with graph.as_default():

  # Input data.
  tf_train_dataset = tf.placeholder(
    tf.float32, shape=(batch_size, num_stocks * NHistData))
  tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
  tf_valid_dataset = tf.constant(valid_dataset)
  tf_test_dataset = tf.constant(test_dataset)
  tf_final_dataset = tf.constant(final_row)

  # Variables.
  layer1_weights = tf.Variable(tf.truncated_normal(
      [NHistData * num_stocks, num_hidden], stddev=0.05))
  layer1_biases = tf.Variable(tf.zeros([num_hidden]))
  layer2_weights = tf.Variable(tf.truncated_normal(
      [num_hidden, num_hidden], stddev=0.05))
  layer2_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))
  layer3_weights = tf.Variable(tf.truncated_normal(
      [num_hidden, num_hidden], stddev=0.05))
  layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))
  layer4_weights = tf.Variable(tf.truncated_normal(
      [num_hidden, num_labels], stddev=0.05))
  layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))

TensorFlow Model

We now define our Neural Network model. Hyperbolic Tangent is our activation function and rest is matrix algebra as we described in previous articles.

  # Model.
  def model(data):
    hidden = tf.tanh(tf.matmul(data, layer1_weights) + layer1_biases)
    hidden = tf.tanh(tf.matmul(hidden, layer2_weights) + layer2_biases)
    hidden = tf.tanh(tf.matmul(hidden, layer3_weights) + layer3_biases)
    return tf.matmul(hidden, layer4_weights) + layer4_biases

Training Model

Now we setup the training model and the optimizer to use, namely gradient descent. We also define what are the correct answers to compare against.

  # Training computation.
  logits = model(tf_train_dataset)
  loss = tf.nn.l2_loss( tf.sub(logits, tf_train_labels))

  # Optimizer.
  optimizer = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
  # Predictions for the training, validation, and test data.
  train_prediction = logits
  valid_prediction = model(tf_valid_dataset)
  test_prediction = model(tf_test_dataset)
  next_prices = model(tf_final_dataset)

Run the Model

So far we have setup TensorFlow ready to go, but we haven’t calculated anything. This next set of code executes the training run. It will use the data we’ve provided in the configured batch size to train our network while printing out some intermediate information.

num_steps = 2052

with tf.Session(graph=graph) as session:
  tf.initialize_all_variables().run()
  print('Initialized')
  for step in range(num_steps):
    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
    batch_data = train_dataset[offset:(offset + batch_size), :]
    batch_labels = train_labels[offset:(offset + batch_size), :]
    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}
    _, l, predictions = session.run(
      [optimizer, loss, train_prediction], feed_dict=feed_dict)
    acc = accuracy(predictions, batch_labels)
    if (step % 100 == 0):
      print('Minibatch loss at step %d: %f' % (step, l))
      print('Minibatch accuracy: %.1f%%' % acc)
      if numValidData > 0:
          print('Validation accuracy: %.1f%%' % accuracy(
              valid_prediction.eval(), valid_labels))
  if numTestData > 0:        
      print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))

Make a Prediction

The final bit of code uses our trained model to make a prediction based on the last set of data we have (where we don’t know the right answer). If you get fresh stock market data for today, then the prediction will be for tomorrow’s price changes. If you run this late enough that Yahoo has updated its prices for the day, then you will get some real errors for comparison. Note that Yahoo is very slow and erratic about doing this, so be careful when reading this table.

predictions = next_prices.eval() * factors
  print("Stock    Last Close  Predict Chg   Predict Next      Current     Current Chg       Error")
  i = 0
  for x in dow30:
      yhfeed = Share(x)
      currentPrice = float(yhfeed.get_price())
      print( "%-6s  %9.2f  %9.2f       %9.2f       %9.2f     %9.2f     %9.2f" % (x,
             final_row_prices[0][i * NHistData + NHistData - 1] * factors[i],
             predictions[0][i],
             final_row_prices[0][i * NHistData + NHistData - 1] * factors[i] + predictions[0][i],
             currentPrice,
             currentPrice - final_row_prices[0][i * NHistData + NHistData - 1] * factors[i],
             abs(predictions[0][i] - (currentPrice - final_row_prices[0][i * NHistData + NHistData - 1] * factors[i]))) )
      i = i + 1

Results

Below is a screenshot of one run predicting the stock changes for Sept. 22. Basically it didn’t do very well. We’ll talk about why and what to do about this in a future article. As you can see it is very conservative in its predictions.

Summary

This article shows the code for training and executing a very simple Neural Network using TensorFlow. Definitely don’t bet on the stock market based on this model, it is very simple at this point. We still need to add a number of elements to start making this into a useful model which we’ll look at in future articles.