Finish experiment 1 and start with Q trader

strategy_evaluation/QLearner.py

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+import datetime as dt
+import pandas as pd
+import util
+import indicators
+from qlearning_robot.QLearner import QLearner as Learner
+
+
+class QLearner(object):
+
+    def __init__(self, verbose=False, impact=0.0, commission=0.0, testing=False):
+        self.verbose = verbose
+        self.impact = impact
+        self.commission = commission
+        self.testing = testing
+
+    def _get_volume(self):
+        """For reference."""
+        volume_all = ut.get_data(syms, dates, colname="Volume")
+        volume = volume_all[syms]  # only portfolio symbols
+        # volume_SPY = volume_all['SPY']  # only SPY, for comparison later
+        if self.verbose:
+            print(volume)
+
+    def _add_indicators(self, df, symbol):
+        """Add indicators for learning to DataFrame."""
+        df.drop(columns=["SPY"], inplace=True)
+        indicators.macd(df, symbol)
+        indicators.rsi(df, symbol)
+        indicators.price_sma(df, symbol, [8])
+        indicators.price_delta(df, symbol, 3)
+        df.dropna(inplace=True)
+
+    def addEvidence(self, symbol="IBM",
+                    sd=dt.datetime(2008, 1, 1),
+                    ed=dt.datetime(2009, 1, 1),
+                    sv=10000):
+
+        self.indicators = ['macd_diff', 'rsi', 'price_sma_8']
+        df = util.get_data([symbol], pd.date_range(sd, ed))
+        self._add_indicators(df, symbol)
+
+        self.learner = Learner()
+        # self.learner.query(data_x, y.to_numpy())
+        # data_x = df[self.indicators].to_numpy()
+
+    def strat(self, data_y, orders):
+        self.holding = 0
+
+        def strat(row):
+            y = int(data_y.loc[row.name][0])
+            shares = 0
+            if self.holding == 0 and y == 1:
+                shares = 1000
+            elif self.holding == -1000 and y == 1:
+                shares = 2000
+            elif self.holding == 0 and y == -1:
+                shares = -1000
+            elif self.holding == 1000 and y == -1:
+                shares = -2000
+            self.holding += shares
+            return shares
+
+        orders["Shares"] = orders.apply(strat, axis=1)
+
+    def testPolicy(self, symbol="IBM",
+                   sd=dt.datetime(2009, 1, 1),
+                   ed=dt.datetime(2010, 1, 1),
+                   sv=10000):
+        df = util.get_data([symbol], pd.date_range(sd, ed))
+        self._add_indicators(df, symbol)
+        # data_x = df[self.indicators].to_numpy()
+        # data_y = pd.DataFrame(index=df.index, data=self.learner.query(data_x))
+
+        orders = pd.DataFrame(index=df.index)
+        orders["Symbol"] = symbol
+        orders["Order"] = ""
+        orders["Shares"] = 0
+        return orders
+
+        if self.testing:
+            return orders
+        else:
+            return orders[["Shares"]]
+