def _add_table(self, tickers_eval_list):
table = Table(column_names=["Ticker", "Max SQN per 100 trades", "Avg #trades per 1Y for Max SQN"],
css_class="table stats-table")
sorted_tickers_eval_list = sorted(tickers_eval_list, key=lambda x: x.SQN)
for ticker_eval in sorted_tickers_eval_list:
table.add_row([ticker_eval.ticker.as_string(), ticker_eval.SQN, ticker_eval.avg_nr_of_trades_1Y])
self.document.add_element(table)
def _add_rolling_table(self):
dtos = RollingAnalysisFactory.calculate_analysis(self.strategy_series, self.benchmark_series)
column_names = [
Table.ColumnCell("Rolling Return Period", css_class="left-align"),
"Strategy Average",
"Strategy Worst",
Table.ColumnCell("Strategy Best", css_class="right-align"),
"Benchmark Average",
"Benchmark Worst",
Table.ColumnCell("Benchmark Best", css_class="right-align"),
Table.ColumnCell("% Strategy outperform Benchmark")]
result = Table(column_names, grid_proportion=GridProportion.Sixteen, css_class="table rolling-table")
for dto in dtos:
result.add_row([Table.Cell(dto.period, css_class="right-align"),
Table.Cell(dto.strategy_average, "{:.2%}"),
Table.Cell(dto.strategy_worst, "{:.2%}"),
Table.Cell(dto.strategy_best, "{:.2%}"),
Table.Cell(dto.benchmark_average, "{:.2%}"),
Table.Cell(dto.benchmark_worst, "{:.2%}"),
Table.Cell(dto.benchmark_best, "{:.2%}"),
Table.Cell(dto.percentage_difference, "{:.2%}")])
self.document.add_element(result)
def _add_statistics_table(self, ta_list: List[TimeseriesAnalysis]):
table = Table(css_class="table stats-table")
for ta in ta_list:
ta.populate_table(table)
self.document.add_element(table)
def _add_statistics_table(self):
table = Table(column_names=["Measure", "Value"], css_class="table stats-table")
number_of_trades = self.returns_of_trades.count()
table.add_row(["Number of trades", number_of_trades])
period_length = self.end_date - self.start_date
period_length_in_years = to_days(period_length) / DAYS_PER_YEAR_AVG
avg_number_of_trades = number_of_trades / period_length_in_years / self.nr_of_assets_traded
table.add_row(["Avg number of trades per year per asset", avg_number_of_trades])
positive_trades = self.returns_of_trades[self.returns_of_trades > 0]
negative_trades = self.returns_of_trades[self.returns_of_trades < 0]
percentage_of_positive = positive_trades.count() / number_of_trades
percentage_of_negative = negative_trades.count() / number_of_trades
table.add_row(["% of positive trades", percentage_of_positive * 100])
table.add_row(["% of negative trades", percentage_of_negative * 100])
avg_positive = positive_trades.mean()
avg_negative = negative_trades.mean()
table.add_row(["Avg positive trade [%]", avg_positive * 100])
table.add_row(["Avg negative trade [%]", avg_negative * 100])
best_return = max(self.returns_of_trades)
worst_return = min(self.returns_of_trades)
table.add_row(["Best trade [%]", best_return * 100])
table.add_row(["Worst trade [%]", worst_return * 100])
max_dd = max_drawdown(self.returns_of_trades)
table.add_row(["Max drawdown [%]", max_dd * 100])
prices_tms = self.returns_of_trades.to_prices()
total_return = prices_tms.iloc[-1] / prices_tms.iloc[0] - 1
table.add_row(["Total return [%]", total_return * 100])
annualised_ret = annualise_total_return(total_return, period_length_in_years, SimpleReturnsSeries)
table.add_row(["Annualised return [%]", annualised_ret * 100])
avg_return = self.returns_of_trades.mean()
table.add_row(["Avg return of trade [%]", avg_return * 100])
std_of_returns = self.returns_of_trades.std()
table.add_row(["Std of return of trades [%]", std_of_returns * 100])
# System Quality Number
sqn = avg_return / std_of_returns
table.add_row(["SQN", sqn])
table.add_row(["SQN for 100 trades", sqn * 10]) # SQN * sqrt(100)
table.add_row(["SQN * Sqrt(avg number of trades per year)", sqn * sqrt(avg_number_of_trades)])
self.document.add_element(table)
def populate_table(self, table: Table, name=None) -> None:
"""
Adds the data calculated in this analysis to the specified table. The table may be brand new or contain other
analyses of the same kind.
Parameters
----------
table
The table to add the data to.
name
Name to give this analysis in the columns.
"""
new_table = Table()
if name is None:
name = self.returns_tms.name
new_table.set_column_names(["Statistic", name])
for item in self._get_results_list():
row_name = item[1] + " [" + item[3] + "]"
if item[3] == '':
row_name = item[1]
new_table.add_row([row_name, Table.Cell(item[2])])
if len(table.rows) != 0:
new_table = table.combine(new_table)
table.set_column_names(new_table.get_column_names())
table.rows = new_table.rows
def _insert_table_with_overall_measures(self, prices_df: PricesDataFrame, ticker: Ticker):
table = Table(column_names=["Measure", "Value"], css_class="table stats-table")
table.add_row(["Instrument", ticker.as_string()])
series = prices_df[PriceField.Close]
table.add_row(["Start date", date_to_str(series.index[0])])
table.add_row(["End date", date_to_str(series.index[-1])])
trend_strength_overall = trend_strength(prices_df, self.use_next_open_instead_of_close)
table.add_row(["Overall strength of the day trends", trend_strength_overall])
trend_strength_1y = trend_strength(prices_df.tail(252), self.use_next_open_instead_of_close)
table.add_row(["Strength of the day trends in last 1Y", trend_strength_1y])
self.ticker_to_trend_dict[ticker] = (trend_strength_1y, trend_strength_overall)
table.add_row(["Up trends strength", up_trend_strength(prices_df, self.use_next_open_instead_of_close)])
table.add_row(["Down trends strength", down_trend_strength(prices_df, self.use_next_open_instead_of_close)])
self.document.add_element(table)
def _pdf_usage(ta):
table = Table()
ta.populate_table(table)