nice table over years

compare.py

@@ -6,27 +6,11 @@ import matplotlib.pyplot as plt
 from pandas_datareader import data as pdr
 import backtrader as bt
 
-stockList = ["VOO"]
-total_days_in_market = 365 * 10
+stockList = ["SWPPX"]
 month_sum = 500  # usd
 reserve = 5  # usd for comms etc
-startDate = datetime.datetime.fromisoformat("2000-01-01")
-endDate = datetime.datetime.fromisoformat("2022-01-30")
-
-
-# import data
-def get_data(stocks, start, end):
-    stockData = pdr.get_data_yahoo(stocks, start, end)
-    return stockData
-
-
-# endDate = datetime.datetime.now()
-# startDate = endDate - datetime.timedelta(days=total_days_in_market)
-stockData = get_data(stockList[0], startDate, endDate)
-
-actualStart: datetime.datetime = stockData.index[0]
-
-data = bt.feeds.PandasData(dataname=stockData)
+# startDate = datetime.datetime.fromisoformat("2000-01-01")
+# endDate = datetime.datetime.fromisoformat("2022-01-01")
 
 
 class LSI(bt.Strategy):
@@ -53,12 +37,15 @@ class LSI(bt.Strategy):
 
 
 class PercentageCommisionScheme(bt.CommInfoBase):
-    paras = (
+    params = (
         ("commission", 0.004),
         ("stocklike", True),
         ("commtype", bt.CommInfoBase.COMM_PERC),
     )
 
+    def _getcommission(self, size, price, pseudoexec):
+        return size * price * self.p.commission + 4  # 290rub/month
+
 
 class FormulaInvesting(bt.Strategy):
     def __init__(self):
@@ -71,10 +58,10 @@ class FormulaInvesting(bt.Strategy):
 
     def log(self, txt, dt=None):
         dt = dt or self.datas[0].datetime.date(0)
-        print("%s, %s" % (dt.isoformat(), txt))
+        # print("%s, %s" % (dt.isoformat(), txt))
 
     def start(self):
-        # self.broker.set_fundmode(fundmode=True, fundstartval=100.0)
+        self.broker.set_fundmode(fundmode=True, fundstartval=100.0)
 
         self.cash_start = self.broker.get_cash()
         self.val_start = 100.0
@@ -93,31 +80,22 @@ class FormulaInvesting(bt.Strategy):
 
         if order.status in [order.Completed]:
             if order.isbuy():
-                self.log(
-                    "BUY EXECUTED, Price %.2f, Cost %.2f, Comm %.2f, Size %.0f"
-                    % (
-                        order.executed.price,
-                        order.executed.value,
-                        order.executed.comm,
-                        order.executed.size,
-                    )
-                )
-
                 self.units += order.executed.size
                 self.cost += order.executed.value
-                self.comms += order.executed.comm
 
             elif order.issell():
+                self.units -= order.executed.size
+
             self.log(
-                    "SELL EXECUTED, Price %.2f, Cost %.2f, Comm %.2f, Size %.0f"
+                "%s Price %.2f, Units %.0f, Value %.2f, Comm %.2f, "
                 % (
+                    "BUY" if order.isbuy else "SELL",
                     order.executed.price,
+                    order.executed.size,
                     order.executed.value,
                     order.executed.comm,
-                        order.executed.size,
                 )
             )
-                self.units -= order.executed.size
 
             self.comms += order.executed.comm
             self.times += 1
@@ -128,19 +106,15 @@ class FormulaInvesting(bt.Strategy):
 
         self.order = None
 
-    def stop(self):
-        self.calc_params()
-
     def calc_params(self):
         # calculate actual returns
         self.froi = self.broker.get_fundvalue() - self.val_start
-        value = self.datas[0].close * self.units + self.broker.get_cash()
         return (
             # Annual
             100 * ((1 + self.froi / 100) ** (365 / (endDate - actualStart).days) - 1),
             self.froi,
             self.cost,
-            value,
+            self.broker.get_value() + self.broker.get_cash(),
             self.times,
             self.units,
             self.comms,
@@ -179,6 +153,7 @@ class QDCA(DCA):
 def run(strategy, data, fund_mode=False):
     if fund_mode:
         cerebro = bt.Cerebro()
+        cerebro.addobserver(bt.observers.FundShares)
     else:
         cerebro = bt.Cerebro(stdstats=False)
         cerebro.addobserver(bt.observers.Broker)
@@ -186,8 +161,6 @@ def run(strategy, data, fund_mode=False):
 
     cerebro.adddata(data)
     cerebro.addstrategy(strategy)
-    print("-" * 50)
-    print(strategy.__name__)
 
     # Broker Information
     broker_args = dict(coc=True)
@@ -206,7 +179,7 @@ def run(strategy, data, fund_mode=False):
     )
     thestrats = cerebro.run()
     thestrat = thestrats[0]
-    cerebro.plot(iplot=False, style="candlestick")
+    # cerebro.plot(iplot=False, style="candlestick")
     return thestrat
 
 
@@ -226,7 +199,19 @@ if __name__ == "__main__":
             "twr",
         ]
     )
-    for i, strategy in enumerate((DCA, QDCA, VA, QVA)):
+    # import data
+    def get_data(stocks, start, end):
+        stockData = pdr.get_data_yahoo(stocks, start, end)
+        return stockData
+
+    for period_years in (1, 2, 5, 10, 15, 20, 25):
+        endDate = datetime.datetime.now()
+        startDate = endDate - datetime.timedelta(days=period_years * 365)
+        stockData = get_data(stockList[0], startDate, endDate)
+        actualStart: datetime.datetime = stockData.index[0]
+        data = bt.feeds.PandasData(dataname=stockData)
+
+        for i, strategy in enumerate((DCA, QDCA)):  # , VA, QVA)):
             therun = run(strategy, data)
             dd = therun.analyzers.drawdown
             ret = therun.analyzers.returns
@@ -241,4 +226,5 @@ if __name__ == "__main__":
             )
         print("Starting from:", actualStart)
         print("Time in Market: {:.1f} years".format((endDate - actualStart).days / 365))
-    print(df)
+        print(df[["annual%", "froi", "cost", "total_value"]])
+        # print(df)