def _create_mse_debug_chart(self, alphas, chosen_solution, mean_square_errors, min_se_solution):
mse_chart = LineChart()
mean_square_errors_paths = QFDataFrame(data=mean_square_errors, index=pd.Index(alphas))
mse_chart.add_decorator(TitleDecorator("Cross-validated avg. MSE of Elastic Net fit"))
for _, path in mean_square_errors_paths.iteritems():
mse_chart.add_decorator(DataElementDecorator(path))
mse_chart.add_decorator(VerticalLineDecorator(x=min_se_solution, linestyle='-.'))
mse_chart.add_decorator(VerticalLineDecorator(x=chosen_solution, linestyle='-'))
mse_chart.plot()
mse_chart.axes.invert_xaxis()
return mse_chart
def _create_coeffs_debug_chart(self, alphas, chosen_solution, coeffs_path, min_se_solution):
coeffs_chart = LineChart()
coefficients_paths = QFDataFrame(data=coeffs_path, index=pd.Index(alphas))
for _, path in coefficients_paths.iteritems():
coeffs_chart.add_decorator(DataElementDecorator(path))
coeffs_chart.add_decorator(TitleDecorator("Elastic Net (using Cross Validation)"))
coeffs_chart.add_decorator(VerticalLineDecorator(x=min_se_solution, linestyle='-.'))
coeffs_chart.add_decorator(VerticalLineDecorator(x=chosen_solution, linestyle='-'))
coeffs_chart.plot()
coeffs_chart.axes.invert_xaxis()
return coeffs_chart
def table_for_df(df: QFDataFrame,
frequency: Frequency = Frequency.DAILY) -> str:
"""
df
DataFrame of returns or prices of assets to be analysed
frequency
(optional) frequency of the returns or price sampling in the DataFrame. By default daily frequency is used
Returns
----------
returns a table similar to the one below:
Analysed period: start_date - end_date, using frequency data
Name total_ret cagr vol up_vol down_vol ...
Asset1 63.93 28.27 19.15 14.06 14.35 ...
Asset2 66.26 29.19 20.74 14.86 15.54 ...
Asset3 66.26 29.19 20.74 14.86 15.54 ...
... ... ... ... ... ... ...
"""
name_ta_list = [(name, TimeseriesAnalysis(asset_tms, frequency))
for name, asset_tms in df.iteritems()]
first_ta = name_ta_list[0][1]
result = "Analysed period: {} - {}, using {} data\n".format(
date_to_str(first_ta.start_date), date_to_str(first_ta.end_date),
frequency)
header_without_dates = ""
for value in first_ta.get_short_names()[2:]:
header_without_dates += '{:>12}'.format(value)
result += ("{:12} {}\n".format("Name", header_without_dates))
for name, ta in name_ta_list:
values = ""
for value in ta.get_measures()[2:]:
values += '{:>12}'.format(value)
result += ("{:12} {}\n".format(name.as_string(), values))
return result
def main():
# GENERATE DATA
regressors_and_fund_df = QFDataFrame(data=[[1, 3, 5], [2, 3, 1], [3, 1, 2],
[4, 2, 3], [5, 3, 4]],
index=pd.bdate_range(
start='2015-01-01', periods=5),
columns=['a', 'b', 'c'])
# add data to the chart and the legend
marker_props_template = {'alpha': 0.5}
stemline_props_template = {'linestyle': '-.', 'linewidth': 0.2}
baseline_props = {'visible': True}
colors = cycle(Chart.get_axes_colors())
chart = LineChart(start_x=str_to_date('2014-12-31'),
end_x=str_to_date('2015-01-08'))
legend = LegendDecorator()
for name, series in regressors_and_fund_df.iteritems():
marker_props = marker_props_template.copy()
stemline_props = stemline_props_template.copy()
color = next(colors)
marker_props['markeredgecolor'] = color
marker_props['markerfacecolor'] = color
stemline_props['color'] = color
data_elem = StemDecorator(series,
marker_props=marker_props,
stemline_props=stemline_props,
baseline_props=baseline_props)
chart.add_decorator(data_elem)
legend.add_entry(data_elem, name)
chart.add_decorator(legend)
chart.plot()
plt.show(block=True)