def test_duration_of_drawdowns(self):
series_of_max_drawdowns, duration_of_drawdowns = list_of_max_drawdowns(
self.test_dd_prices_tms)
drawdowns = [0.3, 0.75, 0.25]
durations = [93, 62, 1]
assert_lists_equal(durations, duration_of_drawdowns)
assert_lists_equal(drawdowns, series_of_max_drawdowns)
def create_dd_probability_chart_3d(prices_tms: QFSeries) -> SurfaceChart3D:
"""Create a 3d drawdowns probability chart"""
def count_dd_above_threshold(drawdown_series: Sequence, threshold: float):
return sum(1 for dd in drawdown_series if dd > threshold)
drawdowns, duration_of_drawdowns = list_of_max_drawdowns(prices_tms)
bear_marker_definitions = np.arange(0.15, 0.55,
0.05) # rows -> will be the Y axis
all_examined_dds = np.arange(0.01, 0.20,
0.01) # columns -> will be the X axis
percentage_ending_in_bear_market = np.ones(
(len(bear_marker_definitions), len(all_examined_dds))) * 100
for i, bear_marker_definition in enumerate(bear_marker_definitions):
nr_of_bear_markets = count_dd_above_threshold(drawdowns,
bear_marker_definition)
for j, examined_dd in enumerate(all_examined_dds):
if examined_dd <= bear_marker_definition:
number_of_dds_above = count_dd_above_threshold(
drawdowns, examined_dd)
percentage = nr_of_bear_markets / number_of_dds_above * 100
percentage_ending_in_bear_market[i, j] = percentage
chart = SurfaceChart3D(all_examined_dds, bear_marker_definitions,
percentage_ending_in_bear_market)
chart.set_axes_names('examined drawdown', 'bear market definition')
chart.set_title('Percentage of drawdowns ending in bear market')
return chart
def avg_drawdown_duration(prices_tms: QFSeries) -> float:
"""
Finds the average duration of a drawdown for the given timeseries of prices.
Parameters
----------
prices_tms
timeseries of prices
Returns
-------
avg_duration
average duration of a drawdown for the given timeseries of prices expressed in days
"""
from qf_lib.common.utils.returns.list_of_max_drawdowns import list_of_max_drawdowns
series_of_max_drawdowns, duration_of_drawdowns = list_of_max_drawdowns(prices_tms)
if len(duration_of_drawdowns) > 0:
return mean(duration_of_drawdowns)
else:
return 0.0
def create_dd_probability_chart(
prices_tms: QFSeries,
bear_market_definition: float = 0.2) -> Tuple[Chart, Chart]:
"""Creates drawdowns probability chart.
Parameters
----------
prices_tms: QFSeries
timeseries of prices
bear_market_definition: float
definition of bear market threshold
Returns
-------
Tuple[Chart, Chart]
Returns two charts - one showing the probability of drawdowns going beyond a certain level and one showing the
marginal increase of probability of drawdowns going beyond the given level.
"""
def count_dd_above_threshold(drawdown_series: Sequence, threshold: float):
return sum(1 for dd in drawdown_series if dd > threshold)
drawdowns, duration_of_drawdowns = list_of_max_drawdowns(prices_tms)
examined_dds = np.arange(0.01, bear_market_definition, 0.005)
percentage_ending_in_bear_market = []
nr_of_bear_markets = count_dd_above_threshold(drawdowns,
bear_market_definition)
for examined_dd in examined_dds:
number_of_dds_above = count_dd_above_threshold(drawdowns, examined_dd)
percentage = nr_of_bear_markets / number_of_dds_above * 100
percentage_ending_in_bear_market.append(percentage)
chart = LineChart()
chart.add_decorator(
ScatterDecorator(examined_dds * 100,
percentage_ending_in_bear_market,
edgecolors='black'))
chart.add_decorator(
TitleDecorator("Percentage of drawdowns going beyond {:2.0f}%".format(
bear_market_definition * 100)))
axis_dec = AxesLabelDecorator(
"examined drawdown [%]",
"chance that drawdown will go beyond {:2.0f}% in [%]".format(
bear_market_definition * 100))
chart.add_decorator(axis_dec)
x_axis_values = examined_dds * 100
prob_of_dd_chart = LineChart()
prob_of_dd_chart.add_decorator(
ScatterDecorator(x_axis_values,
percentage_ending_in_bear_market,
edgecolors='black'))
prob_of_dd_chart.add_decorator(
TitleDecorator("Percentage of drawdowns going beyond {:2.0f}%".format(
bear_market_definition * 100)))
axis_dec = AxesLabelDecorator(
"examined drawdown [%]",
"chance that drawdown will go beyond {:2.0f}% in [%]".format(
bear_market_definition * 100))
prob_of_dd_chart.add_decorator(axis_dec)
marginal_increase_in_prob_chart = LineChart()
diff = np.diff([0] + percentage_ending_in_bear_market)
marginal_increase_in_prob_chart.add_decorator(
ScatterDecorator(x_axis_values, diff, edgecolors='black'))
marginal_increase_in_prob_chart.add_decorator(
TitleDecorator(
"Marginal increase of probability of drawdowns going beyond {:2.0f}%"
.format(bear_market_definition * 100)))
axis_dec = AxesLabelDecorator(
"examined drawdown [%]",
"Marginal increase of chance that drawdown will go beyond {:2.0f}% in [%]"
.format(bear_market_definition * 100))
marginal_increase_in_prob_chart.add_decorator(axis_dec)
return prob_of_dd_chart, marginal_increase_in_prob_chart