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train_logistic_regression_v2.py
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"""
Training logistic regression v2:
using model to allocate funds, i.e. maximizing return without correct labels.
Things to work out:
1. price_data_raw percentage or pips? (percentage)
2. objective function normalization (yearly percentage return..? etc)
Other options to set:
np.set_printoptions(linewidth=75*3+5, edgeitems=6)
pl.rcParams.update({'font.size': 6})
"""
import numpy as np
import pylab as pl
import tensorflow as tf
from helpers.utils import extract_timeseries_from_oanda_data, train_test_validation_split
from helpers.utils import remove_nan_rows, get_data_batch
from models import logistic_regression
from helpers.get_features import get_features, min_max_scaling
# other-params
np.set_printoptions(linewidth=75*3+5, edgeitems=6)
pl.rcParams.update({'font.size': 6})
# hyper-params
batch_size = 1024
learning_rate = 0.002
drop_keep_prob = 0.3
value_moving_average = 50
split = (0.5, 0.3, 0.2)
plotting = False
saving = False
# load data
oanda_data = np.load('data\\EUR_USD_H1.npy')[-50000:]
price_data_raw = extract_timeseries_from_oanda_data(oanda_data, ['closeMid'])
input_data_raw, input_data_dummy = get_features(oanda_data)
price_data_raw = np.concatenate([[[0]],
(price_data_raw[1:] - price_data_raw[:-1]) / (price_data_raw[1:] + 1e-10)], axis=0)
# prepare data
input_data, price_data, input_data_dummy = remove_nan_rows([input_data_raw, price_data_raw, input_data_dummy])
input_data_scaled_no_dummies = (input_data - min_max_scaling[1, :]) / (min_max_scaling[0, :] - min_max_scaling[1, :])
input_data_scaled = np.concatenate([input_data_scaled_no_dummies, input_data_dummy], axis=1)
# split to train, test and cross validation
input_train, input_test, input_cv, price_train, price_test, price_cv = \
train_test_validation_split([input_data_scaled, price_data], split=split)
# get dims
_, input_dim = np.shape(input_data_scaled)
# forward-propagation
x, y, logits, y_, learning_r, drop_out = logistic_regression(input_dim, 3)
# tf cost and optimizer
price_h = tf.placeholder(tf.float32, [None, 1])
signals = tf.constant([[1., -1., 0.]])
cost = tf.reduce_mean(y_ * signals * price_h * 100) # * 24 * 251 # objective fun: annualized average hourly return
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
while True:
if step ==30000:
break
# train model
x_train, price_batch = get_data_batch([input_train[:-1], price_train[1:]], batch_size, sequential=False)
_, cost_train = sess.run([train_step, cost],
feed_dict={x: x_train, price_h: price_batch,
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})
y_test_pred, cost_test = sess.run([y_, cost], feed_dict={x: input_test[:-1], price_h: price_test[1:], drop_out: drop_keep_prob})
y_cv_pred = sess.run(y_, feed_dict={x: input_cv, drop_out: drop_keep_prob})
# get portfolio value
value_train = 1 + np.cumsum(np.sum(y_train_pred[:-1] * [1., -1., 0.] * price_train[1:], axis=1))
value_test = 1 + np.cumsum(np.sum(y_test_pred * [1., -1., 0.] * price_test[1:], axis=1))
value_cv = 1 + np.cumsum(np.sum(y_cv_pred[:-1] * [1., -1., 0.] * 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('Objective 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/lr-v2-avg_score{:.3f}'.format(current_score), global_step=step)
print('Model saved. Average score: {:.2f}'.format(current_score))
pl.figure(2)
pl.plot(value_train, linewidth=1)
pl.plot(value_test, linewidth=1)
pl.plot(value_cv, linewidth=1)
pl.pause(1e-10)