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train_logistic_regression_v3.py

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+"""
+Train logistic regression with many features, PCA, polynomials
+
+"""
+
+import numpy as np
+import pylab as plt
+from helpers.get_features import get_features_v2
+from helpers.utils import portfolio_value, price_to_binary_target, get_data_batch
+from helpers.utils import get_signal, remove_nan_rows, train_test_validation_split, plot_roc_curve
+from helpers.utils import min_max_scale, get_pca, get_poloynomials
+import tensorflow as tf
+from models import logistic_regression
+
+
+# hyper-params
+batch_size = 1024
+learning_rate = 0.002
+drop_keep_prob = 0.4
+value_moving_average = 50
+split = (0.5, 0.3, 0.2)
+plotting = False
+saving = False
+transaction_c = 0.000
+
+# load data
+oanda_data = np.load('data\\EUR_USD_H1.npy')[-50000:]
+y_data = price_to_binary_target(oanda_data, delta=0.000275)
+x_data = get_features_v2(oanda_data, time_periods=[10, 25, 50, 120, 256], return_numpy=False)
+
+# separate, rearrange and remove nans
+price = x_data['price'].as_matrix().reshape(-1, 1)
+price_change = x_data['price_delta'].as_matrix().reshape(-1, 1)
+x_data = x_data.drop(['price', 'price_delta'], axis=1).as_matrix()
+price, price_change, x_data, y_data = remove_nan_rows([price, price_change, x_data, y_data])
+
+# split to train, test and cross validation
+input_train, input_test, input_cv, output_train, output_test, output_cv, price_train, price_test, price_cv = \
+    train_test_validation_split([x_data, y_data, price_change], split=split)
+
+# pre-process data: scale, pca, polynomial
+input_train, input_test, input_cv = min_max_scale(input_train, input_test, input_cv, std_dev_threshold=2.5)
+# input_train, input_test, input_cv = get_pca(input_train, input_test, input_cv, threshold=0.01)
+input_train, input_test, input_cv = get_poloynomials(input_train, input_test, input_cv, degree=2)
+
+# get dims
+_, input_dim = np.shape(input_train)
+_, output_dim = np.shape(output_train)
+
+# forward-propagation
+x, y, logits, y_, learning_r, drop_out = logistic_regression(input_dim, output_dim)
+
+# tf cost and optimizer
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
+train_step = tf.train.AdamOptimizer(learning_r).minimize(cost)
+
+# init session
+cost_hist_train, cost_hist_test, value_hist_train, value_hist_test, value_hist_cv, value_hist_train_ma, \
+    value_hist_test_ma, value_hist_cv_ma, step, step_hist, saving_score = [], [], [], [], [], [], [], [], 0, [], 0.05
+saver = tf.train.Saver()
+init = tf.global_variables_initializer()
+sess = tf.Session()
+sess.run(init)
+
+# main loop
+for _ in range(5000):
+
+    if step % 1000 == 0:
+        learning_rate *= 0.8
+
+    # train model
+    x_train, y_train = get_data_batch([input_train, output_train], batch_size, sequential=False)
+    _, cost_train = sess.run([train_step, cost],
+                             feed_dict={x: x_train, y: y_train, learning_r: learning_rate, drop_out: drop_keep_prob})
+
+    # keep track of stuff
+    step += 1
+    if step % 10 == 0 or step == 1:
+
+        # get y_ predictions
+        y_train_pred = sess.run(y_, feed_dict={x: input_train, drop_out: drop_keep_prob})
+        y_test_pred, cost_test = sess.run([y_, cost], feed_dict={x: input_test, y: output_test, drop_out: drop_keep_prob})
+        y_cv_pred = sess.run(y_, feed_dict={x: input_cv, drop_out: drop_keep_prob})
+
+        # get portfolio value
+        signal_train, signal_test, signal_cv = get_signal(y_train_pred), get_signal(y_test_pred), get_signal(y_cv_pred)
+        value_train = portfolio_value(price_train, signal_train, trans_costs=transaction_c)
+        value_test = portfolio_value(price_test, signal_test, trans_costs=transaction_c)
+        value_cv = portfolio_value(price_cv, signal_cv, trans_costs=transaction_c)
+
+        # save history
+        step_hist.append(step)
+        cost_hist_train.append(cost_train)
+        cost_hist_test.append(cost_test)
+        value_hist_train.append(value_train[-1])
+        value_hist_test.append(value_test[-1])
+        value_hist_cv.append(value_cv[-1])
+        value_hist_train_ma.append(np.mean(value_hist_train[-value_moving_average:]))
+        value_hist_test_ma.append(np.mean(value_hist_test[-value_moving_average:]))
+        value_hist_cv_ma.append(np.mean(value_hist_cv[-value_moving_average:]))
+
+        print('Step {}: train {:.4f}, test {:.4f}'.format(step, cost_train, cost_test))
+
+        if plotting:
+
+            plt.figure(1, figsize=(3, 7), dpi=80, facecolor='w', edgecolor='k')
+
+            plt.subplot(211)
+            plt.title('cost function')
+            plt.plot(step_hist, cost_hist_train, color='darkorange', linewidth=0.3)
+            plt.plot(step_hist, cost_hist_test, color='dodgerblue', linewidth=0.3)
+
+            plt.subplot(212)
+            plt.title('Portfolio value')
+            plt.plot(step_hist, value_hist_train, color='darkorange', linewidth=0.3)
+            plt.plot(step_hist, value_hist_test, color='dodgerblue', linewidth=0.3)
+            plt.plot(step_hist, value_hist_cv, color='magenta', linewidth=1)
+            plt.plot(step_hist, value_hist_train_ma, color='tomato', linewidth=1.5)
+            plt.plot(step_hist, value_hist_test_ma, color='royalblue', linewidth=1.5)
+            plt.plot(step_hist, value_hist_cv_ma, color='black', linewidth=1.5)
+            plt.pause(1e-10)
+
+            # save if some complicated rules
+        if saving:
+            current_score = 0 if value_test[-1] < 0.01 or value_cv[-1] < 0.01 \
+                else np.average([value_test[-1], value_cv[-1]])
+            saving_score = current_score if saving_score < current_score else saving_score
+            if saving_score == current_score and saving_score > 0.1:
+                saver.save(sess, 'saved_models/lr-v1-avg_score{:.3f}'.format(current_score), global_step=step)
+                print('Model saved. Average score: {:.2f}'.format(current_score))
+
+                plt.figure(2)
+                plt.plot(value_train, linewidth=1)
+                plt.plot(value_test, linewidth=1)
+                plt.plot(value_cv, linewidth=1)
+                plt.pause(1e-10)
+
+# roc curve
+roc_auc_train, fpr_train, tpr_train = plot_roc_curve(y_train_pred, output_train)
+roc_auc_test, fpr_test, tpr_test = plot_roc_curve(y_test_pred, output_test)
+roc_auc_cv, fpr_cv, tpr_cv = plot_roc_curve(y_cv_pred, output_cv)
+
+plt.figure(2, figsize=(3, 3), dpi=80, facecolor='w', edgecolor='k')
+plt.plot(fpr_train, tpr_train, color='darkorange', lw=2, label='Train area: {:0.2f}'.format(roc_auc_train))
+plt.plot(fpr_test, tpr_test, color='dodgerblue', lw=2, label='Test area: {:0.2f}'.format(roc_auc_test))
+plt.plot(fpr_cv, tpr_cv, color='magenta', lw=2, label='CV area: {:0.2f}'.format(roc_auc_cv))
+plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
+plt.xlim([0.0, 1.0])
+plt.ylim([0.0, 1.0])
+plt.xlabel('False Positive Rate')
+plt.ylabel('True Positive Rate')
+plt.title('Receiver operating characteristic')
+plt.legend(loc="lower right")
+plt.show()

train_lstm_v3.py

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+"""
+Training lstm model v1
+"""
+
+import numpy as np
+import pylab as pl
+import tensorflow as tf
+from helpers.utils import price_to_binary_target, min_max_scale, get_poloynomials, get_pca, train_test_validation_split
+from helpers.utils import remove_nan_rows, get_signal, get_data_batch, get_lstm_input_output, plot_roc_curve
+from models import lstm_nn
+from helpers.get_features import get_features_v2
+
+
+# other-params
+np.set_printoptions(linewidth=75*3+5, edgeitems=6)
+pl.rcParams.update({'font.size': 6})
+
+# hyper-params
+batch_size = 1024
+learning_rate = 0.001
+drop_keep_prob = 0.6
+value_moving_average = 50
+split = (0.7, 0.2, 0.1)
+plotting = False
+saving = False
+time_steps = 5
+transaction_c = 0.000
+
+# load data
+oanda_data = np.load('data\\EUR_USD_H1.npy')[-50000:]
+y_data = price_to_binary_target(oanda_data, delta=0.000275)
+x_data = get_features_v2(oanda_data, time_periods=[10], return_numpy=False)
+
+# separate, rearrange and remove nans
+price = x_data['price'].as_matrix().reshape(-1, 1)
+price_change = x_data['price_delta'].as_matrix().reshape(-1, 1)
+x_data = x_data.drop(['price', 'price_delta'], axis=1).as_matrix()
+price, price_change, x_data, y_data = remove_nan_rows([price, price_change, x_data, y_data])
+
+# split to train,test and cross validation
+input_train, input_test, input_cv, output_train, output_test, output_cv, price_train, price_test, price_cv = \
+    train_test_validation_split([x_data, y_data, price_change], split=split)
+
+# pre-process data: scale, pca, polynomial
+input_train, input_test, input_cv = min_max_scale(input_train, input_test, input_cv, std_dev_threshold=2.5)
+input_train, input_test, input_cv = get_pca(input_train, input_test, input_cv, threshold=0.01)
+# input_train, input_test, input_cv = get_poloynomials(input_train, input_test, input_cv, degree=2)
+
+# prep lstm format
+input_train, output_train = get_lstm_input_output(input_train, output_train, time_steps=time_steps)
+input_test, output_test = get_lstm_input_output(input_test, output_test, time_steps=time_steps)
+input_cv, output_cv = get_lstm_input_output(input_cv, output_cv, time_steps=time_steps)
+
+price_train = price_train[-len(input_train):]
+price_test = price_test[-len(input_test):]
+price_cv = price_cv[-len(input_cv):]
+
+# get dims
+_, _, input_dim = np.shape(input_train)
+_, output_dim = np.shape(output_train)
+
+# forward-propagation
+x, y, logits, y_, learning_r, drop_out = lstm_nn(input_dim, output_dim, time_steps=time_steps, n_hidden=[8])
+
+# tf cost and optimizer
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
+train_step = tf.train.AdamOptimizer(learning_r).minimize(cost)
+
+# init session
+cost_hist_train, cost_hist_test, value_hist_train, value_hist_test, value_hist_cv, value_hist_train_ma, \
+    value_hist_test_ma, value_hist_cv_ma, step, step_hist, saving_score = [], [], [], [], [], [], [], [], 0, [], 0.05
+saver = tf.train.Saver()
+init = tf.global_variables_initializer()
+sess = tf.Session()
+sess.run(init)
+
+# train
+while True:
+
+    if step == 30000:
+        break
+
+    # train model
+    x_train, y_train = get_data_batch([input_train, output_train], batch_size, sequential=False)
+    _, cost_train = sess.run([train_step, cost],
+                             feed_dict={x: x_train, y: y_train, learning_r: learning_rate, drop_out: drop_keep_prob})
+
+    # keep track of stuff
+    step += 1
+    if step % 100 == 0 or step == 1:
+
+        # get y_ predictions
+        y_train_pred = sess.run(y_, feed_dict={x: input_train, drop_out: drop_keep_prob})
+        cost_test, y_test_pred = sess.run([cost, y_], feed_dict={x: input_test, y: output_test, drop_out: drop_keep_prob})
+        y_cv_pred = sess.run(y_, feed_dict={x: input_cv, y: output_cv, drop_out: drop_keep_prob})
+
+        # get portfolio value
+        signal_train, signal_test, signal_cv = get_signal(y_train_pred), get_signal(y_test_pred), get_signal(y_cv_pred)
+        value_train = 1 + np.cumsum(np.sum(signal_train[:-1] * price_train[1:], axis=1))
+        value_test = 1 + np.cumsum(np.sum(signal_test[:-1] * price_test[1:], axis=1))
+        value_cv = 1 + np.cumsum(np.sum(signal_cv[:-1] * price_cv[1:], axis=1))
+
+        # save history
+        step_hist.append(step)
+        cost_hist_train.append(cost_train)
+        cost_hist_test.append(cost_test)
+        value_hist_train.append(value_train[-1])
+        value_hist_test.append(value_test[-1])
+        value_hist_cv.append(value_cv[-1])
+        value_hist_train_ma.append(np.mean(value_hist_train[-value_moving_average:]))
+        value_hist_test_ma.append(np.mean(value_hist_test[-value_moving_average:]))
+        value_hist_cv_ma.append(np.mean(value_hist_cv[-value_moving_average:]))
+
+        print('Step {}: train {:.4f}, test {:.4f}'.format(step, cost_train, cost_test))
+
+        if plotting:
+
+            pl.figure(1, figsize=(3, 7), dpi=80, facecolor='w', edgecolor='k')
+
+            pl.subplot(211)
+            pl.title('cost function')
+            pl.plot(step_hist, cost_hist_train, color='darkorange', linewidth=0.3)
+            pl.plot(step_hist, cost_hist_test, color='dodgerblue', linewidth=0.3)
+
+            pl.subplot(212)
+            pl.title('Portfolio value')
+            pl.plot(step_hist, value_hist_train, color='darkorange', linewidth=0.3)
+            pl.plot(step_hist, value_hist_test, color='dodgerblue', linewidth=0.3)
+            pl.plot(step_hist, value_hist_cv, color='magenta', linewidth=1)
+            pl.plot(step_hist, value_hist_train_ma, color='tomato', linewidth=1.5)
+            pl.plot(step_hist, value_hist_test_ma, color='royalblue', linewidth=1.5)
+            pl.plot(step_hist, value_hist_cv_ma, color='black', linewidth=1.5)
+            pl.pause(1e-10)
+
+            # save if some complicated rules
+        if saving:
+            current_score = 0 if value_test[-1] < 0.01 or value_cv[-1] < 0.01 \
+                else np.average([value_test[-1], value_cv[-1]])
+            saving_score = current_score if saving_score < current_score else saving_score
+            if saving_score == current_score and saving_score > 0.05:
+                saver.save(sess, 'saved_models/lstm-v1-avg_score{:.3f}'.format(current_score), global_step=step)
+                print('Model saved. Average score: {:.2f}'.format(current_score))
+
+                pl.figure(2)
+                pl.plot(value_test, linewidth=0.2)
+                pl.plot(value_cv, linewidth=2)
+                pl.pause(1e-10)
+
+
+# roc curve
+roc_auc_train, fpr_train, tpr_train = plot_roc_curve(y_train_pred, output_train)
+roc_auc_test, fpr_test, tpr_test = plot_roc_curve(y_test_pred, output_test)
+roc_auc_cv, fpr_cv, tpr_cv = plot_roc_curve(y_cv_pred, output_cv)
+
+plt.figure(2, figsize=(3, 3), dpi=80, facecolor='w', edgecolor='k')
+plt.plot(fpr_train, tpr_train, color='darkorange', lw=2, label='Train area: {:0.2f}'.format(roc_auc_train))
+plt.plot(fpr_test, tpr_test, color='dodgerblue', lw=2, label='Test area: {:0.2f}'.format(roc_auc_test))
+plt.plot(fpr_cv, tpr_cv, color='magenta', lw=2, label='CV area: {:0.2f}'.format(roc_auc_cv))
+plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
+plt.xlim([0.0, 1.0])
+plt.ylim([0.0, 1.0])
+plt.xlabel('False Positive Rate')
+plt.ylabel('True Positive Rate')
+plt.title('Receiver operating characteristic')
+plt.legend(loc="lower right")
+plt.show()