def _add_up_and_down_trend_strength(self, prices_df: QFDataFrame):
def _down_trend_fun(df):
return down_trend_strength(df, self.use_next_open_instead_of_close)
def _up_trend_fun(df):
return up_trend_strength(df, self.use_next_open_instead_of_close)
up_trend_strength_tms = prices_df.rolling_time_window(window_length=self.window_len, step=1,
func=_down_trend_fun)
down_trend_strength_tms = prices_df.rolling_time_window(window_length=self.window_len, step=1,
func=_up_trend_fun)
chart = LineChart()
up_trend_elem = DataElementDecorator(up_trend_strength_tms)
down_trend_elem = DataElementDecorator(down_trend_strength_tms)
chart.add_decorator(up_trend_elem)
chart.add_decorator(down_trend_elem)
legend = LegendDecorator(legend_placement=Location.BEST, key='legend')
legend.add_entry(up_trend_elem, 'Up trend strength')
legend.add_entry(down_trend_elem, 'Down trend strength')
chart.add_decorator(legend)
title = "Strength of the up and down trend - rolling {} days".format(self.window_len)
title_decorator = TitleDecorator(title, key="title")
chart.add_decorator(title_decorator)
self._add_axes_position_decorator(chart)
self.document.add_element(ChartElement(chart, figsize=self.image_size, dpi=self.dpi))
def _add_gini_coefficient_chart(self):
chart = LineChart(rotate_x_axis=False)
position_decorator = AxesPositionDecorator(*self.full_image_axis_position)
chart.add_decorator(position_decorator)
# Get the assets history
assets_history = self.backtest_result.portfolio.positions_history()
assets_history = assets_history.applymap(
lambda x: x.total_exposure if isinstance(x, BacktestPositionSummary) else 0)
def gini(group):
# Function computing the Gini coefficients for each row
group = group.abs()
ranks = group.rank(ascending=True)
mean_value = group.mean()
samples = group.size
group = group * (2 * ranks - samples - 1)
return group.sum() / (samples * samples * mean_value)
assets_history = assets_history.stack(dropna=False).groupby(level=0).apply(gini).to_frame(name="Gini "
"coefficient")
assets_history_decorator = DataElementDecorator(assets_history)
chart.add_decorator(assets_history_decorator)
# Add title
title_decorator = TitleDecorator("Concentration of assets - Gini coefficient")
chart.add_decorator(title_decorator)
self.document.add_element(ChartElement(chart, figsize=self.full_image_size, dpi=self.dpi))
def create_gross_leverage_chart(gross_lev: QFSeries) -> LineChart:
"""
Creates a line chart showing gross leverage based on the specified gross leverage values.
Parameters
----------
gross_lev: QFSeries
Gross leverage as returned by the extract_rets_pos_txn_from_zipline function.
Returns
-------
LineChart
Created line chart
"""
result = LineChart()
result.add_decorator(
DataElementDecorator(gross_lev, linewidth=0.5, color="g"))
result.add_decorator(
HorizontalLineDecorator(gross_lev.mean(),
color="g",
linestyle="--",
linewidth=3))
result.add_decorator(TitleDecorator("Gross leverage"))
result.add_decorator(AxesLabelDecorator(y_label="Gross leverage"))
return result
def _add_assets_number_in_portfolio_chart(self):
chart = LineChart(rotate_x_axis=False)
chart.add_decorator(AxesPositionDecorator(*self.full_image_axis_position))
legend = LegendDecorator(key="legend_decorator")
positions_history = self.backtest_result.portfolio.positions_history()
# Find all not NaN values (not NaN values indicate that the position was open for this contract at that time)
# and count their number for each row (for each of the dates)
number_of_contracts = positions_history.notna().sum(axis=1)
number_of_contracts_decorator = DataElementDecorator(number_of_contracts)
chart.add_decorator(number_of_contracts_decorator)
legend.add_entry(number_of_contracts_decorator, "Contracts")
# Group tickers by name and for each name and date check if there was at least one position open with any
# of the corresponding tickers. Finally sum all the assets that had a position open on a certain date.
number_of_assets = positions_history.groupby(by=lambda ticker: ticker.name, axis='columns') \
.apply(lambda x: x.notna().any(axis=1)).sum(axis=1)
number_of_assets_decorator = DataElementDecorator(number_of_assets)
chart.add_decorator(number_of_assets_decorator)
legend.add_entry(number_of_assets_decorator, "Assets")
chart.add_decorator(TitleDecorator("Number of assets in the portfolio"))
chart.add_decorator(legend)
chart.add_decorator(AxesLabelDecorator(y_label="Number of contracts / assets"))
self.document.add_element(ChartElement(chart, figsize=self.full_image_size, dpi=self.dpi))
def cooks_distance_chart(self) -> LineChart:
cooks_dist = self.model.cooks_distance_tms
chart = LineChart()
colors = cycle(Chart.get_axes_colors())
color = next(colors)
marker_props = {'alpha': 0.7}
stemline_props = {'linestyle': '-.', 'linewidth': 0.2}
baseline_props = {'visible': False}
marker_props['markeredgecolor'] = color
marker_props['markerfacecolor'] = color
stemline_props['color'] = color
data_elem = StemDecorator(cooks_dist,
marker_props=marker_props,
stemline_props=stemline_props,
baseline_props=baseline_props)
chart.add_decorator(data_elem)
chart.add_decorator(TitleDecorator("Cook's Distance"))
chart.add_decorator(
AxesLabelDecorator(y_label="max change of coefficients"))
return chart
def __get_perf_chart(self, series_list, is_large_chart):
strategy = series_list[0].to_prices(
1) # the main strategy should be the first series
log_scale = True if strategy[
-1] > 10 else False # use log scale for returns above 1 000 %
if is_large_chart:
chart = LineChart(start_x=strategy.index[0],
end_x=strategy.index[-1],
log_scale=log_scale)
position_decorator = AxesPositionDecorator(
*self.full_image_axis_position)
chart.add_decorator(position_decorator)
else:
chart = LineChart(log_scale=log_scale, rotate_x_axis=True)
line_decorator = HorizontalLineDecorator(1, key="h_line", linewidth=1)
chart.add_decorator(line_decorator)
legend = LegendDecorator()
for series in series_list:
strategy_tms = series.to_prices(1)
series_elem = DataElementDecorator(strategy_tms)
chart.add_decorator(series_elem)
legend.add_entry(series_elem, strategy_tms.name)
chart.add_decorator(legend)
title_decorator = TitleDecorator("Strategy Performance", key="title")
chart.add_decorator(title_decorator)
return chart
def risk_contribution_chart(self) -> BarChart:
colors_palette = Chart.get_axes_colors()
tickers = self.model.input_data.regressors_df.columns.values
names = [self._get_security_name(ticker) for ticker in tickers]
risk_contributions = QFSeries(data=self.model.risk_contribution.values,
index=pd.Index(names))
index_translator = self._get_index_translator(labels=names)
bar_chart = BarChart(orientation=Orientation.Horizontal,
index_translator=index_translator,
thickness=self._bars_width,
align='center')
bar_chart.add_decorator(
DataElementDecorator(risk_contributions, color=colors_palette[1]))
bar_chart.add_decorator(TitleDecorator("Risk contribution"))
bar_chart.add_decorator(
AxesLabelDecorator(x_label="risk contribution [%]"))
bar_chart.add_decorator(
AxesFormatterDecorator(x_major=PercentageFormatter()))
labels = ('{:.2f}'.format(value * 100) for value in risk_contributions)
self._add_labels_for_bars(bar_chart,
risk_contributions,
labels,
margin=0.001)
return bar_chart
def _get_distribution_plot(self, data_series: SimpleReturnsSeries, title: str, bins: Union[int, str] = 50,
crop: bool = False):
colors = Chart.get_axes_colors()
if crop:
start_x = np.quantile(data_series, 0.01)
end_x = np.quantile(data_series, 0.99)
chart = HistogramChart(data_series, bins=bins, start_x=start_x, end_x=end_x)
else:
chart = HistogramChart(data_series, bins=bins)
# Only show whole numbers on the y-axis.
y_axis_locator = MaxNLocator(integer=True)
axes_locator_decorator = AxesLocatorDecorator(y_major=y_axis_locator, key="axes_locator")
chart.add_decorator(axes_locator_decorator)
# Add an average line.
avg_line = VerticalLineDecorator(data_series.mean(), color=colors[1],
key="average_line_decorator", linestyle="--", alpha=0.8)
chart.add_decorator(avg_line)
# Add a legend.
legend = LegendDecorator(key="legend_decorator")
legend.add_entry(avg_line, "Mean")
chart.add_decorator(legend)
# Add a title.
title_decorator = TitleDecorator(title, key="title")
chart.add_decorator(title_decorator)
chart.add_decorator(AxesLabelDecorator(title, "Occurrences"))
position_decorator = AxesPositionDecorator(*self.full_image_axis_position)
chart.add_decorator(position_decorator)
return chart
def historical_performance_chart(self) -> LineChart:
frequency = self.model.input_data.frequency
analysed_tms = self.model.input_data.analysed_tms
fitted_tms = self.model.fitted_tms
cumulative_fund_rets = analysed_tms.to_prices(initial_price=1.0,
frequency=frequency) - 1
cumulative_fit_rets = fitted_tms.to_prices(initial_price=1.0,
frequency=frequency) - 1
hist_performance_chart = LineChart()
fund_cummulative_rets_data_elem = DataElementDecorator(
cumulative_fund_rets)
fit_cummulative_rets_data_elem = DataElementDecorator(
cumulative_fit_rets)
legend_decorator = LegendDecorator(
legend_placement=Location.LOWER_RIGHT)
legend_decorator.add_entry(fund_cummulative_rets_data_elem,
self._get_security_name(analysed_tms.name))
legend_decorator.add_entry(fit_cummulative_rets_data_elem, 'Fit')
hist_performance_chart.add_decorator(fund_cummulative_rets_data_elem)
hist_performance_chart.add_decorator(fit_cummulative_rets_data_elem)
hist_performance_chart.add_decorator(
TitleDecorator("Historical Performance"))
hist_performance_chart.add_decorator(
AxesLabelDecorator(y_label="Cumulative return"))
hist_performance_chart.add_decorator(legend_decorator)
hist_performance_chart.add_decorator(
AxesFormatterDecorator(y_major=PercentageFormatter()))
return hist_performance_chart
def _create_chart(self, df: QFDataFrame, title: str):
chart = LineChart()
# Place legend on bottom right corner of plot
legend = LegendDecoratorCustomPosition(
legend_placement=AxisLocation.LOWER_LEFT)
cmap = get_cmap("tab10", len(df.columns))
# iterate columns
for column, label, color_index in zip(df.columns, df.columns.values,
range(0, cmap.N)):
data = df.loc[:, column]
data_value_decorator = DataElementDecorator(
data, color=cmap(color_index))
chart.add_decorator(data_value_decorator)
legend.add_entry(data_value_decorator, label)
chart.add_decorator(
AxesFormatterDecorator(
x_major=DateFormatter(fmt=str(DateFormat.YEAR_DOT_MONTH)),
y_major=StrMethodFormatter("{x:.2f}"),
))
chart.add_decorator(AxesPositionDecorator(*self.axis_position))
chart.add_decorator(TitleDecorator(title))
chart.add_decorator(legend)
return chart
def create_qq_chart(strategy: QFSeries) -> Chart:
colors = Chart.get_axes_colors()
strategy = strategy.to_log_returns()
# Normalize
strategy = strategy - strategy.mean()
strategy = strategy / strategy.std()
# Sort
strategy_values = sorted(strategy.values)
# Create benchmark
benchmark_values = list(range(1, len(strategy_values) + 1))
n = len(strategy_values) + 1
benchmark_values = map(lambda x: x / n, benchmark_values)
benchmark_values = list(map(lambda x: norm.ppf(x), benchmark_values))
# Figure out the limits.
maximum = max(max(benchmark_values), max(strategy_values))
result = LineChart(start_x=-maximum, end_x=maximum, upper_y=maximum, lower_y=-maximum)
result.add_decorator(ScatterDecorator(
benchmark_values, strategy_values, color=colors[0], alpha=0.6, edgecolors='black', linewidths=0.5))
result.add_decorator(VerticalLineDecorator(0, color='black', linewidth=1))
result.add_decorator(HorizontalLineDecorator(0, color='black', linewidth=1))
result.add_decorator(TitleDecorator("Normal Distribution Q-Q"))
result.add_decorator(AxesLabelDecorator("Normal Distribution Quantile", "Observed Quantile"))
# Add diagonal line.
result.add_decorator(DiagonalLineDecorator(color=colors[1]))
return result
def _get_histogram_chart(self):
colors = Chart.get_axes_colors()
chart = HistogramChart(self.returns_of_trades * 100) # expressed in %
# Format the x-axis so that its labels are shown as a percentage.
x_axis_formatter = FormatStrFormatter("%0.0f%%")
axes_formatter_decorator = AxesFormatterDecorator(x_major=x_axis_formatter, key="axes_formatter")
chart.add_decorator(axes_formatter_decorator)
# Only show whole numbers on the y-axis.
y_axis_locator = MaxNLocator(integer=True)
axes_locator_decorator = AxesLocatorDecorator(y_major=y_axis_locator, key="axes_locator")
chart.add_decorator(axes_locator_decorator)
# Add an average line.
avg_line = VerticalLineDecorator(self.returns_of_trades.values.mean(), color=colors[1],
key="average_line_decorator", linestyle="--", alpha=0.8)
chart.add_decorator(avg_line)
# Add a legend.
legend = LegendDecorator(key="legend_decorator")
legend.add_entry(avg_line, "Mean")
chart.add_decorator(legend)
# Add a title.
title = TitleDecorator("Distribution of Trades", key="title_decorator")
chart.add_decorator(title)
chart.add_decorator(AxesLabelDecorator("Return", "Occurrences"))
return chart
def create_returns_distribution(returns: QFSeries, frequency: Frequency = Frequency.MONTHLY, title: str = None) -> \
HistogramChart:
"""
Creates a new returns distribution histogram with the specified frequency.
Parameters
----------
returns: QFSeries
The returns series to use in the histogram.
frequency: Frequency
frequency of the returns after aggregation
title
title of the chart
Returns
-------
HistogramChart
A new ``HistogramChart`` instance.
"""
colors = Chart.get_axes_colors()
aggregate_returns = get_aggregate_returns(returns,
frequency,
multi_index=True).multiply(100)
chart = HistogramChart(aggregate_returns)
# Format the x-axis so that its labels are shown as a percentage.
x_axis_formatter = FormatStrFormatter("%.0f%%")
axes_formatter_decorator = AxesFormatterDecorator(x_major=x_axis_formatter,
key="axes_formatter")
chart.add_decorator(axes_formatter_decorator)
# Only show whole numbers on the y-axis.
y_axis_locator = MaxNLocator(integer=True)
axes_locator_decorator = AxesLocatorDecorator(y_major=y_axis_locator,
key="axes_locator")
chart.add_decorator(axes_locator_decorator)
# Add an average line.
avg_line = VerticalLineDecorator(aggregate_returns.values.mean(),
color=colors[1],
key="average_line_decorator",
linestyle="--",
alpha=0.8)
chart.add_decorator(avg_line)
# Add a legend.
legend = LegendDecorator(key="legend_decorator")
legend.add_entry(avg_line, "Mean")
chart.add_decorator(legend)
# Add a title.
if title is None:
title = "Distribution of " + str(frequency).capitalize() + " Returns"
title = TitleDecorator(title, key="title_decorator")
chart.add_decorator(title)
chart.add_decorator(AxesLabelDecorator("Returns", "Occurrences"))
return chart
def _add_concentration_of_portfolio_chart(self, top_assets_numbers: tuple = (1, 5)):
chart = LineChart(rotate_x_axis=False)
position_decorator = AxesPositionDecorator(*self.full_image_axis_position)
chart.add_decorator(position_decorator)
# Define a legend
legend = LegendDecorator(key="legend_decorator")
# Add y label
label_decorator = AxesLabelDecorator(y_label="Mean total exposure of top assets")
chart.add_decorator(label_decorator)
# Add top asset contribution
positions_history = self.backtest_result.portfolio.positions_history()
if positions_history.empty:
raise ValueError("No positions found in positions history")
positions_history = positions_history.applymap(
lambda x: x.total_exposure if isinstance(x, BacktestPositionSummary) else 0)
# Map all the single contracts onto tickers (including future tickers) and take the maximal total exposure for
# each of the groups - in case if two contracts for a single asset will be included in the open positions in
# the portfolio at any point of time, only one (with higher total exposure) will be considered while generating
# the top assets plot
def contract_to_ticker(c: Contract):
return self.backtest_result.portfolio.contract_ticker_mapper. \
contract_to_ticker(c, strictly_to_specific_ticker=False)
assets_history = positions_history.rename(columns=contract_to_ticker)
assets_history = assets_history.groupby(level=0, axis=1).apply(func=(
lambda x: x.abs().max(axis=1).astype(float)
))
for assets_number in top_assets_numbers:
# For each date (row), find the top_assets largest assets and compute the mean value of their market value
top_assets_mean_values = assets_history.stack(dropna=False).groupby(level=0).apply(
lambda group: group.nlargest(assets_number).mean()
).resample('D').last()
# Divide the computed mean values by the portfolio value, for each of the dates
top_assets_percentage_value = top_assets_mean_values / self.backtest_result.portfolio.portfolio_eod_series()
concentration_top_asset = DataElementDecorator(top_assets_percentage_value)
chart.add_decorator(concentration_top_asset)
# Add to legend
legend.add_entry(concentration_top_asset, "TOP {} assets".format(assets_number))
# Add title
title_decorator = TitleDecorator("Concentration of assets")
chart.add_decorator(title_decorator)
# Add legend
chart.add_decorator(legend)
self.document.add_element(ChartElement(chart, figsize=self.full_image_size, dpi=self.dpi))
def _crete_single_line_chart(self, measure_name, parameters, values, tickers):
result_series = QFSeries(data=values, index=parameters)
line_chart = LineChart()
data_element = DataElementDecorator(result_series)
line_chart.add_decorator(data_element)
line_chart.add_decorator(TitleDecorator(self._get_chart_title(tickers, measure_name)))
param_names = self._get_param_names()
line_chart.add_decorator(AxesLabelDecorator(x_label=param_names[0], y_label=measure_name))
self._resize_chart(line_chart)
return line_chart
def _get_leverage_chart(self,
leverage: QFSeries,
rotate_x_axis: bool = False):
chart = LineChart(rotate_x_axis=rotate_x_axis)
series_elem = DataElementDecorator(leverage)
chart.add_decorator(series_elem)
title_decorator = TitleDecorator("Leverage over time", key="title")
chart.add_decorator(title_decorator)
return chart
def _add_rolling_alpha_and_beta(self, timeseries_list):
freq = timeseries_list[0].get_frequency()
timeseries_list = [
tms.dropna().to_simple_returns() for tms in timeseries_list
]
df = pd.concat(timeseries_list, axis=1).fillna(0)
rolling_window_len = int(freq.value / 2) # 6M rolling
step = round(freq.value / 6) # 2M shift
legend = LegendDecorator()
chart = LineChart(start_x=df.index[0], end_x=df.index[-1])
line_decorator = HorizontalLineDecorator(0, key="h_line", linewidth=1)
chart.add_decorator(line_decorator)
def alpha_function(df_in_window):
strategy_returns = df_in_window.iloc[:, 0]
benchmark_returns = df_in_window.iloc[:, 1]
beta, alpha, _, _, _ = stats.linregress(benchmark_returns,
strategy_returns)
return beta, alpha
rolling = df.rolling_time_window(rolling_window_len, step,
alpha_function)
rolling = pd.DataFrame([[b, a] for b, a in rolling.values],
columns=["beta", "alpha"],
index=rolling.index)
rolling_element = DataElementDecorator(rolling["beta"])
chart.add_decorator(rolling_element)
legend.add_entry(rolling_element, "beta")
rolling_element = DataElementDecorator(rolling["alpha"],
use_secondary_axes=True)
chart.add_decorator(rolling_element)
legend.add_entry(rolling_element, "alpha")
chart.add_decorator(legend)
chart.add_decorator(
AxesFormatterDecorator(use_secondary_axes=True,
y_major=PercentageFormatter(".1f")))
# modify axes position to make secondary scale visible
axes_position = list(self.full_image_axis_position)
axes_position[2] = axes_position[2] - 0.07
position_decorator = AxesPositionDecorator(*axes_position)
chart.add_decorator(position_decorator)
title_str = "Rolling alpha and beta [{} {} samples]".format(
rolling_window_len, freq)
title_decorator = TitleDecorator(title_str, key="title")
chart.add_decorator(title_decorator)
self.document.add_element(
ChartElement(chart, figsize=self.full_image_size, dpi=self.dpi))
def _add_assets_number_in_portfolio_chart(self):
chart = LineChart(rotate_x_axis=False)
legend = LegendDecorator(key="legend_decorator")
position_decorator = AxesPositionDecorator(
*self.full_image_axis_position)
chart.add_decorator(position_decorator)
positions_history = self.backtest_result.portfolio.positions_history()
# Find all not NaN values (not NaN values indicate that the position was open for this contract at that time)
# and count their number for each row (for each of the dates)
number_of_contracts = positions_history.notnull().sum(axis=1)
number_of_contracts_decorator = DataElementDecorator(
number_of_contracts)
chart.add_decorator(number_of_contracts_decorator)
legend.add_entry(number_of_contracts_decorator, "Contracts")
# Count number of assets in the portfolio (if on two contracts from same future family exist in the same time,
# e.g. during rolling day, in the portfolio, they are counted as one asset)
def contract_to_ticker(c: Contract):
return self.backtest_result.portfolio.contract_ticker_mapper. \
contract_to_ticker(c, strictly_to_specific_ticker=False)
assets_history = positions_history.rename(columns=contract_to_ticker)
assets_history = assets_history.groupby(level=0, axis=1).apply(func=(
# For each asset, group all of the corresponding columns (each of which corresponds to one contract),
# and check if at any given timestamp the "value" of any of the contracts was different than None - this
# indicates that at this point of time a position concerning the given asset was open in the portfolio
# (which in the resulting series will be denoted as 1, otherwise - 0, so that it will be possible to
# sum positions of all open assets at any given point of time)
lambda x: x.notna().any(axis=1).astype(int)))
number_of_assets = assets_history.sum(axis=1)
number_of_assets_decorator = DataElementDecorator(number_of_assets)
chart.add_decorator(number_of_assets_decorator)
legend.add_entry(number_of_assets_decorator, "Assets")
# Add title
title_decorator = TitleDecorator("Number of assets in the portfolio")
chart.add_decorator(title_decorator)
# Add legend
chart.add_decorator(legend)
# Add y label
label_decorator = AxesLabelDecorator(
y_label="Number of contracts / assets")
chart.add_decorator(label_decorator)
self.document.add_element(
ChartElement(chart, figsize=self.full_image_size, dpi=self.dpi))
def _get_perf_chart(self):
strategy_tms = self.returns_of_trades.to_prices(1)
chart = LineChart()
line_decorator = HorizontalLineDecorator(1, key="h_line", linewidth=1)
chart.add_decorator(line_decorator)
series_elem = DataElementDecorator(strategy_tms)
chart.add_decorator(series_elem)
title_decorator = TitleDecorator("Alpha Model Performance", key="title")
chart.add_decorator(title_decorator)
return chart
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 _get_line_chart(self,
series: QFSeries,
title: str,
rotate_x_axis: bool = False):
chart = LineChart(rotate_x_axis=rotate_x_axis)
series_elem = DataElementDecorator(series)
chart.add_decorator(series_elem)
title_decorator = TitleDecorator(title, key="title")
chart.add_decorator(title_decorator)
return chart
def create_return_quantiles(returns: QFSeries, live_start_date: datetime = None, x_axis_labels_rotation: int = 20) \
-> BoxplotChart:
"""
Creates a new return quantiles boxplot chart based on the returns specified.
A swarm plot is also rendered on the chart if the ``live_start_date`` is specified.
Parameters
----------
returns
The returns series to plot on the chart.
live_start_date
The live start date that will determine whether a swarm plot should be rendered.
x_axis_labels_rotation
Returns
-------
A new ``BoxplotChart`` instance.
"""
simple_returns = returns.to_simple_returns()
# case when we can plot IS together with OOS
if live_start_date is not None:
oos_returns = simple_returns.loc[simple_returns.index >= live_start_date]
if len(oos_returns) > 0:
in_sample_returns = simple_returns.loc[simple_returns.index < live_start_date]
in_sample_weekly = get_aggregate_returns(in_sample_returns, Frequency.WEEKLY, multi_index=True)
in_sample_monthly = get_aggregate_returns(in_sample_returns, Frequency.MONTHLY, multi_index=True)
oos_weekly = get_aggregate_returns(oos_returns, Frequency.WEEKLY, multi_index=True)
oos_monthly = get_aggregate_returns(oos_returns, Frequency.MONTHLY, multi_index=True)
chart = BoxplotChart([in_sample_returns, oos_returns, in_sample_weekly,
oos_weekly, in_sample_monthly, oos_monthly], linewidth=1)
x_labels = ["daily IS", "daily OOS", "weekly IS", "weekly OOS", "monthly IS", "monthly OOS"]
tick_decorator = AxisTickLabelsDecorator(labels=x_labels, axis=Axis.X, rotation=x_axis_labels_rotation)
else:
chart, tick_decorator = _get_simple_quantile_chart(simple_returns)
else: # case where there is only one set of data
chart, tick_decorator = _get_simple_quantile_chart(simple_returns)
# fixed_format_decorator = AxesFormatterDecorator(x_major=fixed_formatter)
chart.add_decorator(tick_decorator)
# Set title.
title = TitleDecorator("Return Quantiles")
chart.add_decorator(title)
chart.add_decorator(AxesLabelDecorator(y_label="Returns"))
return chart
def create_returns_bar_chart(
returns: QFSeries,
frequency: Frequency = Frequency.YEARLY) -> BarChart:
"""
Constructs a new returns bar chart based on the returns specified. By default a new annual returns bar chart will
be created.
"""
colors = Chart.get_axes_colors()
# Calculate data.
aggregate_returns = get_aggregate_returns(returns,
frequency,
multi_index=True)
data_series = QFSeries(aggregate_returns.sort_index(ascending=True))
chart = BarChart(Orientation.Horizontal, align="center")
chart.add_decorator(DataElementDecorator(data_series))
chart.add_decorator(BarValuesDecorator(data_series))
# Format the x-axis so that its labels are shown as a percentage.
chart.add_decorator(AxesFormatterDecorator(x_major=PercentageFormatter()))
# Format Y axis to make sure we have a tick for each year or 2 years
if len(data_series) > 10:
y_labels = data_series[data_series.index % 2 == 1].index
else:
y_labels = data_series.index
chart.add_decorator(
AxisTickLabelsDecorator(labels=y_labels,
axis=Axis.Y,
tick_values=y_labels))
# Add an average line.
avg_line = VerticalLineDecorator(aggregate_returns.values.mean(),
color=colors[1],
key="avg_line",
linestyle="--",
alpha=0.8)
chart.add_decorator(avg_line)
# Add a legend.
legend = LegendDecorator(key="legend_decorator")
legend.add_entry(avg_line, "Mean")
chart.add_decorator(legend)
# Add a title.
title = TitleDecorator(str(frequency).capitalize() + " Returns",
key="title_decorator")
chart.add_decorator(title)
chart.add_decorator(AxesLabelDecorator("Returns", "Year"))
return chart
def _create_single_heat_map(self, measure_name, result_df, tickers, min_v, max_v, third_param):
chart = HeatMapChart(data=result_df, color_map=plt.get_cmap("coolwarm"), min_value=min_v, max_value=max_v)
chart.add_decorator(AxisTickLabelsDecorator(labels=list(result_df.columns), axis=Axis.X))
chart.add_decorator(AxisTickLabelsDecorator(labels=list(reversed(result_df.index)), axis=Axis.Y))
chart.add_decorator(ValuesAnnotations())
param_names = self._get_param_names()
chart.add_decorator(AxesLabelDecorator(x_label=param_names[1], y_label=param_names[0]))
if third_param is None:
title = self._get_chart_title(tickers, measure_name)
else:
title = "{}, {} = {:0.2f}".format(self._get_chart_title(tickers, measure_name), param_names[2], third_param)
chart.add_decorator(TitleDecorator(title))
self._resize_chart(chart)
return chart
def create_holdings_chart(positions: QFDataFrame) -> LineChart:
"""
Creates a line chart showing holdings per day based on the specified positions.
Parameters
----------
positions: QFDataFrame
Positions as returned by the extract_rets_pos_txn_from_zipline function.
Returns
-------
LineChart
Created line chart
"""
# Based on:
# https://github.com/quantopian/pyfolio/blob/5d63df4ca6e0ead83f4bebf9860732d37f532026/pyfolio/plotting.py#L323
result = LineChart()
# Perform some calculations.
positions = positions.copy().drop("cash", axis="columns")
holdings = positions.apply(lambda x: np.sum(x != 0), axis="columns")
holdings_by_month = holdings.resample("1M").mean()
holdings_decorator = DataElementDecorator(holdings,
color="steelblue",
linewidth=1.5)
result.add_decorator(holdings_decorator)
holdings_by_month_decorator = DataElementDecorator(holdings_by_month,
color="orangered",
alpha=0.5,
linewidth=2)
result.add_decorator(holdings_by_month_decorator)
hline_decorator = HorizontalLineDecorator(holdings.values.mean(),
linestyle="--")
result.add_decorator(hline_decorator)
legend = LegendDecorator()
legend.add_entry(holdings_decorator, "Daily Holdings")
legend.add_entry(holdings_by_month_decorator,
"Average Daily Holdings, by month")
legend.add_entry(hline_decorator, "Average Daily Holdings, net")
result.add_decorator(legend)
result.add_decorator(TitleDecorator("Holdings per Day"))
result.add_decorator(
AxesLabelDecorator(y_label="Amount of holdings per Day"))
return result
def _add_price_chart(self, prices_df: QFDataFrame):
close_tms = prices_df[PriceField.Close]
price_tms = close_tms.to_prices(1)
chart = LineChart(start_x=price_tms.index[0], end_x=price_tms.index[-1])
price_elem = DataElementDecorator(price_tms)
chart.add_decorator(price_elem)
line_decorator = HorizontalLineDecorator(1, key="h_line", linewidth=1)
chart.add_decorator(line_decorator)
legend = LegendDecorator()
legend.add_entry(price_elem, "Close Price")
chart.add_decorator(legend)
title_decorator = TitleDecorator("Price of the instrument", key="title")
chart.add_decorator(title_decorator)
self._add_axes_position_decorator(chart)
self.document.add_element(ChartElement(chart, figsize=self.image_size, dpi=self.dpi))
def _add_trend_strength_chart(self, prices_df: QFDataFrame):
def _fun(df):
return trend_strength(df, self.use_next_open_instead_of_close)
trend_strength_tms = prices_df.rolling_time_window(window_length=self.window_len, step=1, func=_fun)
chart = LineChart()
trend_elem = DataElementDecorator(trend_strength_tms, color='black')
chart.add_decorator(trend_elem)
legend = LegendDecorator(legend_placement=Location.BEST, key='legend')
legend.add_entry(trend_elem, 'Trend strength')
chart.add_decorator(legend)
title_decorator = TitleDecorator("Strength of the trend - rolling {} days".format(self.window_len), key="title")
chart.add_decorator(title_decorator)
self._add_axes_position_decorator(chart)
self.document.add_element(ChartElement(chart, figsize=self.image_size, dpi=self.dpi))
def correlation_matrix_chart(self) -> HeatMapChart:
data = self.model.correlation_matrix
names = [
self._get_security_name(ticker)
for ticker in self.model.correlation_matrix.columns.values
]
heatmap_chart = HeatMapChart(data, min_value=-1, max_value=1)
heatmap_chart.add_decorator(
AxisTickLabelsDecorator(axis=Axis.X, labels=names))
heatmap_chart.add_decorator(
AxisTickLabelsDecorator(axis=Axis.Y, labels=reversed(names)))
heatmap_chart.add_decorator(ValuesAnnotations())
heatmap_chart.add_decorator(ColorBar())
heatmap_chart.add_decorator(TitleDecorator("Correlation matrix"))
return heatmap_chart
def beta_and_alpha_chart(self,
benchmark_coefficients: Sequence[float] = None
) -> BarChart:
colors_palette = Chart.get_axes_colors()
coeff_names = [
self._get_security_name(ticker)
for ticker in self.model.coefficients.index.values
]
coeff_values = self.model.coefficients.values
bars_colors = [colors_palette[0]] * len(self.model.coefficients)
title = 'Coefficients of regressors'
if self.model.input_data.is_fit_intercept:
coeff_names = np.insert(coeff_names, 0, "intercept")
coeff_values = np.insert(coeff_values, 0, self.model.intercept)
bars_colors = ['gold'] + bars_colors
if benchmark_coefficients is not None:
raise ValueError(
"Benchmark coefficients aren't used when model contains a bias value (constant)"
)
elif benchmark_coefficients is not None:
coeff_values -= benchmark_coefficients
title = 'Relative coefficients of regressors'
index_translator = self._get_index_translator(coeff_names)
coefficients = QFSeries(index=pd.Index(coeff_names), data=coeff_values)
bar_chart = BarChart(orientation=Orientation.Horizontal,
index_translator=index_translator,
thickness=self._bars_width,
align='center')
bar_chart.add_decorator(
DataElementDecorator(coefficients, color=bars_colors))
bar_chart.add_decorator(TitleDecorator(title))
bar_chart.add_decorator(AxesLabelDecorator(x_label="sensitivity"))
labels = ['{:.2f}'.format(value) for value in coeff_values]
self._add_labels_for_bars(bar_chart, coefficients, labels)
return bar_chart
