def test_get_price_change(self):
""" Make sure the price change is calculated correctly."""
# Copied a row from EUR_USD raw file.
row = ['2006-07-20', '1.26278', '1.26953', '1.26234', '1.269'] + \
['1.26293', '1.27015', '1.26249', '1.27', '13111']
price_change = util.get_price_change(row, 10000)
# Check the calculation.
self.assertEqual(price_change, '60.7')
return
def transform_row(row, next_row, pip_factor):
""" Return the transformed row with features and target variable.
Args:
row: list of Strings. A row from the raw data file representing
today's candle.
next_row: list of Strings. The row representing tomorrow's candle.
pip_factor: int. The multiplier for calculating pip from price.
Returns:
data_point: list of floats. Combine the features and the target.
"""
features = list_to_features(row, pip_factor)
target = util.get_price_change(next_row, pip_factor)
data_point = features + [target]
return data_point
def dry_run(self, pred, **kwargs):
""" Do a dry run of strategy Euler as if the strategy was put in place.
Return the day-to-day balance during the run, and produce a report
on the profitability of this strategy over the given period.
Args:
pred: np.vector. Predicted daily price changes for days in the
last part of the test data.
kwargs: named arguments, including:
print_result: boolean. Whether to print dry run report.
export_plot: string. Name of the plot to be saved.
Returns:
balance: np.array. Accumulated profit/loss of every day.
"""
# Write report title.
report = "\nDry run report: {0}\n\n".format(self.instrument)
report += str(self.params) + '\n'
report += '=' * 80 + '\n'
# Initialize the balance.
test_size = pred.size
balance = np.zeros_like(pred)
# Now go through the actual daily candles and figure out actual PL.
data = self.test_data[-test_size:]
for i in range(len(data)):
# Fetch the row.
row = data[i]
# Fetch the predicted and actual price change.
predicted = pred[i]
actual = util.get_price_change(row, self.pip_factor)
# Figure out the action we take and the units.
units = self.parse_units(predicted)
controls = self.parse_controls()
# Calculate profit/loss for this day.
p_l = util.get_profit_loss(row, units, **controls)
# Update daily profit/loss
balance[i] = balance[i - 1] + p_l
# Add to the report. row[0] is date.
report += util.format_row(row[0], units, predicted, actual, p_l)
# Add final total profit/loss to the report.
report += "Total profit/loss: {0}".format(balance[-1])
# Export the graphs if asked.
if 'export_plot' in kwargs:
# Do some plots.
common.plot(balance, kwargs['export_plot'])
# Print the report if asked.
if 'print_result' in kwargs and kwargs['print_result']:
print(report)
return balance
