diff --git a/strategies.py b/strategies.py index a376079..930a9ab 100644 --- a/strategies.py +++ b/strategies.py @@ -111,7 +111,7 @@ class Investing(bt.Strategy): elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log("Order Canceled/Margin/Rejected") - print(order.status, [order.Canceled, order.Margin, order.Rejected]) + print(order, order.status, [order.Canceled, order.Margin, order.Rejected]) self.order = None @@ -141,9 +141,15 @@ class FormulaInvesting(Investing): def notify_timer(self, timer, when, *args): super().notify_timer(timer, when, *args) self.formula() + self.prev_value = self.broker.get_value() class VA(FormulaInvesting): + """ + When market is down, buy + When up, sell. Shitty strategy: https://en.wikipedia.org/wiki/Value_averaging + """ + def formula(self): target_value = ( min( @@ -156,28 +162,68 @@ class VA(FormulaInvesting): # self.broker.set_cash(self.broker.get_cash() * self.monthly_params["t_rate"]) +class QVA(VA): + """ + Same but quarterly + """ + + def formula(self): + if not self.months % 3: + super().formula() + + class DCA(FormulaInvesting): + """ + Buy everything monthly + """ + def formula(self): target_value = self.broker.get_value() - reserve self.order_target_value(target=target_value) -class QVA(VA): +class QDCA(DCA): + """ + Buy everything quarterly + """ + def formula(self): if not self.months % 3: super().formula() -class QDCA(DCA): - def formula(self): - if not self.months % 3: - super().formula() +class EDCA(Investing): + """ + When market is down, BUY THE DIP + When up, leave some cash + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.prev_value = 0 + + def notify_timer(self, timer, when, *args): + self.months += 1 + + if self.prev_value >= self.broker.get_value(): + self.broker.add_cash(1.1 * self.monthly_cash) + else: + self.broker.add_cash(0.9 * self.monthly_cash) + + target_value = self.broker.get_value() + self.order_target_value(target=target_value - reserve) + + self.prev_value = self.broker.get_value() class SmaCross(Investing): + """ + Buy when fast moving average crosses slow upwards + """ + params = dict( - pfast=50, # period for the fast moving average - pslow=200, # period for the slow moving average + pfast=8, # period for the fast moving average + pslow=17, # period for the slow moving average ) # list of parameters which are configurable for the strategy def __init__(self, *args, **kwargs): @@ -187,19 +233,7 @@ class SmaCross(Investing): self.crossover = bt.ind.CrossOver(sma1, sma2) # crossover signal def next(self): - if not self.position: # not in the market - if self.crossover > 0: # if fast crosses slow to the upside - self.order_target_value(target=self.broker.get_cash()) # enter long - - elif self.crossover < 0: # in the market & cross to the downside - self.close() # close long position - - -class SmaVA(SmaCross): - def next(self): - if not self.position: # not in the market - if self.crossover > 0: # if fast crosses slow to the upside - self.order_target_value(target=self.broker.get_cash()) # enter long - - elif self.crossover < 0: # in the market & cross to the downside - self.order_target_value(target=self.broker.get_value() / 2) # close half + if self.crossover > 0: # if fast crosses slow to the upside + self.order_target_value( + target=self.broker.get_value() - reserve + ) # enter long diff --git a/year_intervals.py b/year_intervals.py index 44da8b4..75cfa76 100644 --- a/year_intervals.py +++ b/year_intervals.py @@ -6,22 +6,35 @@ import pandas as pd if __name__ == "__main__": stockList = ["^GSPC"] - monthly_params = dict(sum=100000, coef=1.005, t_rate=1 + 0.02 / 12) + monthly_params = dict(sum=100000, coef=1, t_rate=1 + 0.02 / 12) # startDate = datetime.datetime.fromisoformat("2000-01-01") # endDate = datetime.datetime.fromisoformat("2022-01-01") print(stockList[0]) - stockData = lib.get_data( + stockData = lib.get_data(stockList[0]) + start_date = stockData.index[0] + year_step = 1 + label_string = "Investing to {} monthly over {} years, increasing {:.2f}%, starting from ${}".format( stockList[0], + year_step, + (monthly_params["coef"] * 100) - 100, + monthly_params["sum"], ) - start_date = stockData.index[0] - year_step = 5 - print( - "Investing monthly, increasing {:.2f}%, starting from ${}".format( - (monthly_params["coef"] * 100) - 100, - monthly_params["sum"], - ) + print(label_string) + plot_param = "annual%" + + _strategies = ( + st.QDCA, + st.EDCA, + st.SmaCross, ) - for start_year in range(start_date.year, datetime.datetime.today().year, year_step): + strategies = (st.DCA,) + _strategies + + df_comp = pd.DataFrame(index=list(st.__name__ for st in strategies)) + df_vwr = pd.DataFrame(index=list(st.__name__ for st in strategies)) + df_rel_to_dca = pd.DataFrame(index=list(st.__name__ for st in _strategies)) + for start_year in range( + start_date.year, datetime.datetime.today().year - year_step + 1, year_step + ): start_date = datetime.date(start_year, 1, 1) end_date = datetime.date(start_year + year_step - 1, 12, 31) if (today := datetime.date.today()) < end_date: @@ -38,9 +51,25 @@ if __name__ == "__main__": newdf = pd.concat( [ lib.test_strategy(stockData, strategy, monthly_params) - for strategy in (st.DCA, st.QDCA) + for strategy in strategies ] ) print(newdf.to_string()) + df_rel_to_dca[start_year] = list( + newdf.loc[s.__name__][plot_param] - newdf.loc[st.DCA.__name__][plot_param] + for s in _strategies + ) + df_comp[start_year] = newdf[plot_param].values # print(df[["annual%", "froi%", "cost", "total_value", "max_dd%", "max_md"]]) # print(newdf["annual%"]) + + print(df_rel_to_dca.to_string()) + df_comp.T.plot() + for index, s in df_rel_to_dca.iterrows(): + print("{}: avg {:.3f} dev {:.3f}".format(index, s.mean(), s.std())) + df_rel_to_dca.T.hist(bins=20) + plt.grid() + plt.title(label_string) + plt.ylabel(plot_param) + plt.xlabel("year") + plt.show()