def _add_heat_map_grid(self, tickers: Sequence[Ticker], parameters_list: Sequence[tuple], third_param=None):
grid = GridElement(mode=PlottingMode.PDF, figsize=self.image_size)
result_df = QFDataFrame()
for param_tuple in parameters_list:
row = param_tuple[0]
column = param_tuple[1]
trades_eval_result = self.backtest_evaluator.evaluate_params_for_tickers(param_tuple, tickers)
result_df.loc[row, column] = trades_eval_result
result_df.sort_index(axis=0, inplace=True, ascending=False)
result_df.sort_index(axis=1, inplace=True)
sqn_avg_nr_trades = result_df.applymap(lambda x: x.sqn_per_avg_nr_trades)
sqn_per100trades = result_df.applymap(lambda x: x.sqn_per100trades)
avg_nr_of_trades = result_df.applymap(lambda x: x.avg_nr_of_trades_1Y)
annualised_return = result_df.applymap(lambda x: x.annualised_return)
drawdown = result_df.applymap(lambda x: x.drawdown)
grid.add_chart(self._create_single_heat_map("SQN / AVG #trades", sqn_avg_nr_trades, tickers, 0, 1, third_param))
grid.add_chart(self._create_single_heat_map("SQN / 100 trades", sqn_per100trades, tickers, 0, 2, third_param))
grid.add_chart(self._create_single_heat_map("Avg # trades 1Y", avg_nr_of_trades, tickers, 3, 30, third_param))
grid.add_chart(self._create_single_heat_map("An Return", annualised_return, tickers, -0.1, 0.2, third_param))
grid.add_chart(self._create_single_heat_map("Drawdown", drawdown, tickers, -0.5, -0.1, third_param))
self.document.add_element(grid)
def _add_heat_maps(self, tickers: Sequence[Ticker]):
parameters_list = sorted(
self.backtest_evaluator.params_backtest_summary_elem_dict.keys())
# Group plots by type, so that they appear in the given logical order
title_to_grid = defaultdict(lambda: GridElement(
mode=PlottingMode.PDF, figsize=self.image_size))
for start_time, end_time in [
(self.backtest_summary.start_date, self.out_of_sample_start_date),
(self.out_of_sample_start_date, self.backtest_summary.end_date)
]:
results = QFDataFrame()
for param_tuple in parameters_list:
trades_eval_result = self.backtest_evaluator.evaluate_params_for_tickers(
param_tuple, tickers, start_time, end_time)
row, column = param_tuple
results.loc[row, column] = trades_eval_result
results.sort_index(axis=0, inplace=True, ascending=False)
results.sort_index(axis=1, inplace=True)
results.fillna(TradesEvaluationResult(), inplace=True)
sqn_avg_nr_trades = results.applymap(
lambda x: x.sqn_per_avg_nr_trades).fillna(0)
avg_nr_of_trades = results.applymap(
lambda x: x.avg_nr_of_trades_1Y).fillna(0)
annualised_return = results.applymap(
lambda x: x.annualised_return).fillna(0)
adjusted_start_time = results.applymap(
lambda x: x.start_date).min().min()
adjusted_end_time = results.applymap(
lambda x: x.end_date).max().max()
if adjusted_start_time >= adjusted_end_time:
adjusted_end_time = adjusted_start_time if adjusted_start_time <= self.backtest_summary.end_date \
else end_time
adjusted_start_time = start_time
title = "{} - {} ".format(adjusted_start_time.strftime("%Y-%m-%d"),
adjusted_end_time.strftime("%Y-%m-%d"))
title_to_grid["SQN (Arithmetic return) per year"].add_chart(
self._create_single_heat_map(title, sqn_avg_nr_trades, 0, 0.5))
title_to_grid["Avg # trades 1Y"].add_chart(
self._create_single_heat_map(title, avg_nr_of_trades, 2, 15))
if len(tickers) == 1:
title_to_grid["Annualised return"].add_chart(
self._create_single_heat_map(title, annualised_return, 0.0,
0.3))
tickers_used = "Many tickers" if len(tickers) > 1 else (
tickers[0].name if isinstance(
tickers[0], FutureTicker) else tickers[0].as_string())
for description, grid in title_to_grid.items():
self.document.add_element(
HeadingElement(3, "{} - {}".format(description, tickers_used)))
self.document.add_element(grid)