def cagr(qf_series: QFSeries, frequency=None):
"""
Returns the Compound Annual Growth Rate (CAGR) calculated for the given series.
Parameters
----------
qf_series: QFSeries
series of returns of an asset
frequency: Frequency
Frequency of the timeseries of returns;
if it is None (by default) it is inferred from the timeseries of returns
Returns
-------
float
annual compound return for the given series of prices
"""
prices_tms = qf_series.to_prices(frequency=frequency, initial_price=1.0)
last_date = prices_tms.index[-1]
first_date = prices_tms.index[0]
period_length = last_date - first_date
period_length_in_years = to_days(period_length) / DAYS_PER_YEAR_AVG
total_return = prices_tms[-1] / prices_tms[0] - 1
return annualise_total_return(
total_return=total_return,
period_length_in_years=period_length_in_years,
returns_type=SimpleReturnsSeries)
def avg_nr_of_trades_per1y(trades_returns: QFSeries, start_date: datetime,
end_date: datetime):
"""
Calculates average number of trades per year for a given data-frame of trades.
"""
period_length = end_date - start_date
period_length_in_years = to_days(period_length) / DAYS_PER_YEAR_AVG
avg_number_of_trades_1y = len(trades_returns) / period_length_in_years
return avg_number_of_trades_1y
def avg_nr_of_trades_per1y(trades: QFDataFrame, start_date: datetime,
end_date: datetime):
"""
Calculates average number of trades per year for a given data-frame of trades.
"""
returns = trades[TradeField.Return]
period_length = end_date - start_date
period_length_in_years = to_days(period_length) / DAYS_PER_YEAR_AVG
avg_number_of_trades_1y = returns.count() / period_length_in_years
return avg_number_of_trades_1y
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 _objective_function(self, trades: QFDataFrame):
"""
Calculates the simple SQN * sqrt(average number of trades per year)
"""
number_of_instruments_traded = len(self.all_tickers_tested)
returns = trades[TradeField.Return]
period_length = self.backtest_summary.end_date - self.backtest_summary.start_date
period_length_in_years = to_days(period_length) / DAYS_PER_YEAR_AVG
avg_number_of_trades_1y = returns.count() / period_length_in_years / number_of_instruments_traded
sqn = returns.mean() / returns.std()
sqn = sqn * np.sqrt(avg_number_of_trades_1y)
return sqn
def test_to_days(self):
expected = 3
actual = to_days(Timedelta('3 days'))
self.assertEqual(expected, actual)
