Merge branch 'master' into windoza

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
+            self.order_target_value(
+                target=self.broker.get_value() - reserve
+            )  # enter long

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(
-        stockList[0],
-    )
+    stockData = lib.get_data(stockList[0])
     start_date = stockData.index[0]
-    year_step = 5
-    print(
-        "Investing monthly, increasing {:.2f}%, starting from ${}".format(
+    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"],
     )
+    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()