8,17 and plots

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
 
@@ -176,8 +176,8 @@ class QDCA(DCA):
 
 class SmaCross(Investing):
     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):
@@ -187,12 +187,10 @@ 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
+        if self.crossover > 0:  # if fast crosses slow to the upside
+            self.order_target_value(
+                target=self.broker.get_value() - reserve
+            )  # enter long
 
 
 class SmaVA(SmaCross):

year_intervals.py

@@ -6,21 +6,29 @@ 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=1000, 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(
-            (monthly_params["coef"] * 100) - 100,
-            monthly_params["sum"],
-        )
+    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.DCA,
+        st.QDCA,
+        st.SmaCross,
+    )
+
+    df_comp = pd.DataFrame(index=list(st.__name__ for st in strategies))
+    df_vwr = pd.DataFrame(index=list(st.__name__ for st in strategies))
     for start_year in range(start_date.year, datetime.datetime.today().year, year_step):
         start_date = datetime.date(start_year, 1, 1)
         end_date = datetime.date(start_year + year_step - 1, 12, 31)
@@ -38,9 +46,18 @@ 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_comp[start_year] = newdf[plot_param].values
         # print(df[["annual%", "froi%", "cost", "total_value", "max_dd%", "max_md"]])
         # print(newdf["annual%"])
+
+    print(df_comp.to_string())
+    df_comp.T.plot()
+    plt.grid()
+    plt.title(label_string)
+    plt.ylabel(plot_param)
+    plt.xlabel("year")
+    plt.show()