Implement first version of strategy learner

This version does not pass the automatic test.

strategy_evaluation/experiment1.py

@@ -93,29 +93,29 @@ def compare_manual_strategies(symbol, sv, sd, ed):
 
 def experiment1():
     symbol = "JPM"
-    start_value = 10000
+    sv = 10000
     sd = dt.datetime(2008, 1, 1)  # in-sample
     ed = dt.datetime(2009, 12, 31)  # in-sample
-    # sd = dt.datetime(2010, 1, 1)  # out-sample
-    # ed = dt.datetime(2011, 12, 31)  # out-sample
+    sd_out = dt.datetime(2010, 1, 1)  # out-sample
+    ed_out = dt.datetime(2011, 12, 31)  # out-sample
+
+    df = util.get_data([symbol], pd.date_range(sd, ed_out))
+    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)
+    # compare_manual_strategies(symbol, sv, sd, ed)
 
     bs = BenchmarkStrategy()
-    orders = bs.testPolicy(symbol, sd, ed, start_value)
-    df["Benchmark"] = marketsim.compute_portvals(orders, start_value)
+    orders = bs.testPolicy(symbol, sd_out, ed_out, sv)
+    df["Benchmark"] = marketsim.compute_portvals(orders, sv)
     df["Orders Benchmark"] = orders["Shares"]
 
-    sl = StrategyLearner()
-    orders = ms.testPolicy(symbol, sd, ed, start_value)
-    df["SL"] = marketsim.compute_portvals(orders, start_value)
+    sl = StrategyLearner(testing=True)
+    sl.addEvidence(symbol, sd, ed, sv)
+    orders = sl.testPolicy(symbol, sd_out, ed_out, sv)
+    df["SL"] = marketsim.compute_portvals(orders, sv)
     df["Orders SL"] = orders["Shares"]
-    # df["Holding Manual"] = orders["Shares"].cumsum()
 
     fig, ax = plt.subplots(3, sharex=True)
     df[[symbol]].plot(ax=ax[0])
@@ -127,6 +127,12 @@ def experiment1():
     MultiCursor(fig.canvas, ax, color='r', lw=0.5)
     plt.show()
 
+    # For debugging the classification learner:
+    # df["y_train"] = sl.addEvidence(symbol, sd, ed, sv)
+    # df["y_query"] = sl.testPolicy(symbol, sd, ed, sv)
+    # df[["y_train", "y_query"]].plot(ax=ax[1])
+
+
 
 if __name__ == "__main__":
     experiment1()