Start working on strategy evaluation

strategy_evaluation/ManualStrategy.py

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+import datetime as dt
+import pandas as pd
+import util as ut
+
+
+class ManualStrategy:
+    def __init__(self, verbose=False, impact=0.0, commission=0.0):
+        self.verbose = verbose
+        self.impact = impact
+        self.commission = commission
+
+    # this method should create a QLearner, and train it for trading
+    def addEvidence(self, symbol="IBM",
+                    sd=dt.datetime(2008, 1, 1),
+                    ed=dt.datetime(2009, 1, 1),
+                    sv=10000):
+
+        # add your code to do learning here
+
+        # example usage of the old backward compatible util function
+        syms = [symbol]
+        dates = pd.date_range(sd, ed)
+        prices_all = ut.get_data(syms, dates)  # automatically adds SPY
+        prices = prices_all[syms]  # only portfolio symbols
+        # prices_SPY = prices_all['SPY']  # only SPY, for comparison later
+        if self.verbose:
+            print(prices)
+
+        # example use with new colname
+        # automatically adds SPY
+        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)
+
+    # this method should use the existing policy and test it against new data
+    def testPolicy(self, symbol="IBM",
+                   sd=dt.datetime(2009, 1, 1),
+                   ed=dt.datetime(2010, 1, 1),
+                   sv=10000):
+        dates = pd.date_range(sd, ed)
+        prices = ut.get_data([symbol], dates)  # automatically adds SPY
+        orders = pd.DataFrame(index=prices.index)
+        orders["Symbol"] = symbol
+        orders["Order"] = ""
+        orders["Shares"] = 0
+
+        # here we build a fake set of trades
+        orders.iloc[0] = [symbol, "BUY", 1000]
+        orders.iloc[40] = [symbol, "SELL", 1000]
+        orders.iloc[41] = [symbol, "BUY", 1000]
+        orders.iloc[60] = [symbol, "SELL", 2000]
+        orders.iloc[61] = [symbol, "BUY", 2000]
+        orders.iloc[-1] = [symbol, "SELL", 1000]
+        orders = orders[orders["Shares"] != 0]
+        return orders
+