Show both MACD and indicator strat on figure

Prepare for strategy learner.

strategy_evaluation/ManualStrategy.py

@@ -36,18 +36,21 @@ class ManualStrategy:
             print(volume)
 
     def macd_strat(self, macd, orders):
+        """Strategy based on MACD cross."""
 
         def strat(ser):
             m = macd.loc[ser.index]
-            prev_macd, prev_signal = m.iloc[0]
+            prev_macd, prev_signal, _ = m.iloc[0]
-            cur_macd, cur_signal = m.iloc[1]
+            cur_macd, cur_signal, _ = m.iloc[1]
             shares = 0
-            if cur_macd < -1 and prev_macd < prev_signal and cur_macd > cur_signal:
+            if cur_macd < -1 and prev_macd < prev_signal \
+                             and cur_macd > cur_signal:
                 if self.holding == 0:
                     shares = 1000
                 elif self.holding == -1000:
                     shares = 2000
-            elif cur_macd > 1 and prev_macd > prev_signal and cur_macd < cur_signal:
+            elif cur_macd > 1 and prev_macd > prev_signal \
+                              and cur_macd < cur_signal:
                 if self.holding == 0:
                     shares = -1000
                 elif self.holding == 1000:
@@ -58,6 +61,8 @@ class ManualStrategy:
         orders['Shares'] = orders['Shares'].rolling(2).apply(strat)
 
     def three_indicator_strat(self, macd, rsi, price_sma, orders):
+        """Strategy based on three indicators. Thresholds selected based on
+        scatter plots."""
         def strat(row):
             shares = 0
             _, _, macd_diff = macd.loc[row.name]
@@ -87,7 +92,7 @@ class ManualStrategy:
     def testPolicy(self, symbol="IBM",
                    sd=dt.datetime(2009, 1, 1),
                    ed=dt.datetime(2010, 1, 1),
-                   sv=10000):
+                   sv=10000, macd_strat=False):
 
         self.holding = 0
         df = util.get_data([symbol], pd.date_range(sd, ed))
@@ -102,7 +107,8 @@ class ManualStrategy:
         rsi = indicators.rsi(df, symbol)
         price_sma = indicators.price_sma(df, symbol, [8])
 
-        # self.macd_strat(macd, orders)
+        if macd_strat:
+            self.macd_strat(macd, orders)
+        else:
             self.three_indicator_strat(macd, rsi, price_sma, orders)
         return orders
-

strategy_evaluation/experiment1.py

@@ -9,6 +9,7 @@ import matplotlib.pyplot as plt
 from matplotlib.widgets import MultiCursor
 from BenchmarkStrategy import BenchmarkStrategy
 from ManualStrategy import ManualStrategy
+from StrategyLearner import StrategyLearner
 
 
 def plot_indicators(symbol, df):
@@ -16,7 +17,6 @@ def plot_indicators(symbol, df):
 
     price_sma = indicators.price_sma(df, symbol, [8])
     bb = indicators.bollinger_band(df, symbol)
-    sma = indicators.sma(df, symbol, [8])
     rsi = indicators.rsi(df, symbol)
     macd = indicators.macd(df, symbol).copy()
 
@@ -57,6 +57,40 @@ def visualize_correlations(symbol, df):
     sys.exit(0)
 
 
+def compare_manual_strategies(symbol, sv, sd, ed):
+
+    df = util.get_data([symbol], pd.date_range(sd, ed))
+    df.drop(columns=["SPY"], inplace=True)
+
+    bs = BenchmarkStrategy()
+    orders = bs.testPolicy(symbol, sd, ed, sv)
+    df["Benchmark"] = marketsim.compute_portvals(orders, sv)
+    df["Orders Benchmark"] = orders["Shares"]
+
+    ms = ManualStrategy()
+    orders = ms.testPolicy(symbol, sd, ed, sv, macd_strat=True)
+    df["MACD Strat"] = marketsim.compute_portvals(orders, sv)
+    df["Orders MACD"] = orders["Shares"]
+    # df["Holding Manual"] = orders["Shares"].cumsum()
+
+    orders = ms.testPolicy(symbol, sd, ed, sv)
+    df["Three Strat"] = marketsim.compute_portvals(orders, sv)
+    df["Orders Three"] = orders["Shares"]
+
+    fig, ax = plt.subplots(3, sharex=True)
+    df[[symbol]].plot(ax=ax[0])
+    df[["Benchmark", "MACD Strat", "Three Strat"]].plot(ax=ax[1])
+    df[["Orders Benchmark", "Orders MACD", "Orders Three"]].plot(ax=ax[2])
+
+    for a in ax:
+        a.grid()
+    MultiCursor(fig.canvas, ax, color='r', lw=0.5)
+
+    # plt.show()
+    fig.set_size_inches(10, 8, forward=True)
+    plt.savefig('figure_1.png', dpi=fig.dpi)
+
+
 def experiment1():
     symbol = "JPM"
     start_value = 10000
@@ -65,33 +99,33 @@ def experiment1():
     # sd = dt.datetime(2010, 1, 1)  # out-sample
     # ed = dt.datetime(2011, 12, 31)  # out-sample
 
-    df = util.get_data([symbol], pd.date_range(sd, ed))
-    df.drop(columns=["SPY"], inplace=True)
-
     # visualize_correlations(symbol, df)
     # plot_indicators(symbol, df)
+    # compare_manual_strategies(symbol, start_value, sd, ed)
+
+    df = util.get_data([symbol], pd.date_range(sd, ed))
+    df.drop(columns=["SPY"], inplace=True)
 
     bs = BenchmarkStrategy()
     orders = bs.testPolicy(symbol, sd, ed, start_value)
     df["Benchmark"] = marketsim.compute_portvals(orders, start_value)
     df["Orders Benchmark"] = orders["Shares"]
 
-    ms = ManualStrategy()
+    sl = StrategyLearner()
     orders = ms.testPolicy(symbol, sd, ed, start_value)
-    df["Manual"] = marketsim.compute_portvals(orders, start_value)
+    df["SL"] = marketsim.compute_portvals(orders, start_value)
-    df["Orders Manual"] = orders["Shares"]
+    df["Orders SL"] = orders["Shares"]
-    df["Holding Manual"] = orders["Shares"].cumsum()
+    # df["Holding Manual"] = orders["Shares"].cumsum()
 
     fig, ax = plt.subplots(3, sharex=True)
     df[[symbol]].plot(ax=ax[0])
-    df[["Benchmark", "Manual"]].plot(ax=ax[1])
+    df[["Benchmark", "SL"]].plot(ax=ax[1])
-    df[["Orders Benchmark", "Orders Manual"]].plot(ax=ax[2])
+    df[["Orders Benchmark", "Orders SL"]].plot(ax=ax[2])
 
     for a in ax:
         a.grid()
-    multi = MultiCursor(fig.canvas, ax, color='r', lw=0.5)
+    MultiCursor(fig.canvas, ax, color='r', lw=0.5)
     plt.show()
-    # plt.savefig('figure_1.png')
 
 
 if __name__ == "__main__":

strategy_evaluation/indicators.py

@@ -73,7 +73,7 @@ def rsi(df, symbol, period=14):
                            (avg_loss / period))))
         return rsi
 
-    key = f"rsi"
+    key = "rsi"
     # Add one to get 'period' price changes (first change is nan).
     period += 1
     df[key] = df[symbol].rolling(period).apply(rsi)
@@ -91,13 +91,6 @@ def macd(df, symbol):
     return df[[k1, k2, k3]]
 
 
-def price_delta(df, symbol, period=1):
-    """Calculate delta between previous day and today."""
-    k = f"diff_{period}"
-    df[k] = df[symbol].diff(periods=period)
-    return df[k]
-
-
 def price_delta(df, symbol, period=1):
     """Calculate percentage change for period."""
     k = f"pct_{period}"