Implement first version of strategy learner
This version does not pass the automatic test.
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"""
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Template for implementing StrategyLearner (c) 2016 Tucker Balch
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Copyright 2018, Georgia Institute of Technology (Georgia Tech)
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Atlanta, Georgia 30332
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All Rights Reserved
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Template code for CS 4646/7646
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Georgia Tech asserts copyright ownership of this template and all derivative
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works, including solutions to the projects assigned in this course. Students
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and other users of this template code are advised not to share it with others
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or to make it available on publicly viewable websites including repositories
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such as github and gitlab. This copyright statement should not be removed
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or edited.
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We do grant permission to share solutions privately with non-students such
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as potential employers. However, sharing with other current or future
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students of CS 7646 is prohibited and subject to being investigated as a
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GT honor code violation.
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-----do not edit anything above this line---
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Student Name: Tucker Balch (replace with your name)
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GT User ID: tb34 (replace with your User ID)
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GT ID: 900897987 (replace with your GT ID)
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"""
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import datetime as dt
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import pandas as pd
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import util as ut
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import util
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import indicators
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from BagLearner import BagLearner
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from RTLearner import RTLearner
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class StrategyLearner(object):
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# constructor
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def __init__(self, verbose = False, impact=0.0, commission=0.0):
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def __init__(self, verbose=False, impact=0.0, commission=0.0, testing=False):
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self.verbose = verbose
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self.impact = impact
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self.commission = commission
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self.testing = testing
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# this method should create a QLearner, and train it for trading
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def addEvidence(self, symbol = "IBM", \
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sd=dt.datetime(2008,1,1), \
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ed=dt.datetime(2009,1,1), \
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sv = 10000):
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# add your code to do learning here
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# example usage of the old backward compatible util function
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syms=[symbol]
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dates = pd.date_range(sd, ed)
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prices_all = ut.get_data(syms, dates) # automatically adds SPY
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prices = prices_all[syms] # only portfolio symbols
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# prices_SPY = prices_all['SPY'] # only SPY, for comparison later
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if self.verbose: print(prices)
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# example use with new colname
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volume_all = ut.get_data(syms, dates, colname = "Volume") # automatically adds SPY
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def _get_volume(self):
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"""For reference."""
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volume_all = ut.get_data(syms, dates, colname="Volume")
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volume = volume_all[syms] # only portfolio symbols
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# volume_SPY = volume_all['SPY'] # only SPY, for comparison later
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if self.verbose: print(volume)
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if self.verbose:
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print(volume)
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# this method should use the existing policy and test it against new data
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def testPolicy(self, symbol = "IBM", \
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sd=dt.datetime(2009,1,1), \
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ed=dt.datetime(2010,1,1), \
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sv = 10000):
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def _add_indicators(self, df, symbol):
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"""Add indicators for learning to DataFrame."""
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df.drop(columns=["SPY"], inplace=True)
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indicators.macd(df, symbol)
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indicators.rsi(df, symbol)
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indicators.price_sma(df, symbol, [8])
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indicators.price_delta(df, symbol, 3)
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df.dropna(inplace=True)
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# here we build a fake set of trades
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# your code should return the same sort of data
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dates = pd.date_range(sd, ed)
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prices_all = ut.get_data([symbol], dates) # automatically adds SPY
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trades = prices_all[[symbol,]] # only portfolio symbols
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# trades_SPY = prices_all['SPY'] # only SPY, for comparison later
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trades.values[:,:] = 0 # set them all to nothing
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trades.values[0,:] = 1000 # add a BUY at the start
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trades.values[40,:] = -1000 # add a SELL
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trades.values[41,:] = 1000 # add a BUY
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trades.values[60,:] = -2000 # go short from long
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trades.values[61,:] = 2000 # go long from short
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trades.values[-1,:] = -1000 #exit on the last day
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if self.verbose: print(type(trades)) # it better be a DataFrame!
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if self.verbose: print(trades)
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if self.verbose: print(prices_all)
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return trades
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def addEvidence(self, symbol="IBM",
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sd=dt.datetime(2008, 1, 1),
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ed=dt.datetime(2009, 1, 1),
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sv=10000):
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self.indicators = ['macd_diff', 'rsi', 'price_sma_8']
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df = util.get_data([symbol], pd.date_range(sd, ed))
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self._add_indicators(df, symbol)
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def classify_y(row):
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if row > 0.1:
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return 1
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elif row < -0.1:
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return -1
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return 0
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self.learner = RTLearner(leaf_size = 7)
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# self.learner = BagLearner(RTLearner, 5, {'leaf_size': 5})
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data_x = df[self.indicators].to_numpy()
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y = df['pct_3'].apply(classify_y)
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self.learner.addEvidence(data_x, y.to_numpy())
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return y
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def strat(self, data_y, orders):
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self.holding = 0
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def strat(row):
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y = int(data_y.loc[row.name][0])
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shares = 0
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if self.holding == 0 and y == 1:
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shares = 1000
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elif self.holding == -1000 and y == 1:
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shares = 2000
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elif self.holding == 0 and y == -1:
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shares = -1000
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elif self.holding == 1000 and y == -1:
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shares = -2000
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self.holding += shares
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return shares
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orders["Shares"] = orders.apply(strat, axis=1)
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def testPolicy(self, symbol="IBM",
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sd=dt.datetime(2009, 1, 1),
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ed=dt.datetime(2010, 1, 1),
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sv=10000):
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df = util.get_data([symbol], pd.date_range(sd, ed))
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self._add_indicators(df, symbol)
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data_x = df[self.indicators].to_numpy()
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data_y = pd.DataFrame(index=df.index, data=self.learner.query(data_x))
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orders = pd.DataFrame(index=df.index)
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orders["Symbol"] = symbol
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orders["Order"] = ""
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orders["Shares"] = 0
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self.strat(data_y, orders)
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if self.testing:
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return orders
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else:
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return orders[["Shares"]]
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if __name__=="__main__":
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print("One does not simply think up a strategy")
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@@ -93,29 +93,29 @@ def compare_manual_strategies(symbol, sv, sd, ed):
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def experiment1():
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symbol = "JPM"
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start_value = 10000
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sv = 10000
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sd = dt.datetime(2008, 1, 1) # in-sample
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ed = dt.datetime(2009, 12, 31) # in-sample
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# sd = dt.datetime(2010, 1, 1) # out-sample
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# ed = dt.datetime(2011, 12, 31) # out-sample
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sd_out = dt.datetime(2010, 1, 1) # out-sample
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ed_out = dt.datetime(2011, 12, 31) # out-sample
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df = util.get_data([symbol], pd.date_range(sd, ed_out))
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df.drop(columns=["SPY"], inplace=True)
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# visualize_correlations(symbol, df)
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# plot_indicators(symbol, df)
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# compare_manual_strategies(symbol, start_value, sd, ed)
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df = util.get_data([symbol], pd.date_range(sd, ed))
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df.drop(columns=["SPY"], inplace=True)
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# compare_manual_strategies(symbol, sv, sd, ed)
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bs = BenchmarkStrategy()
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orders = bs.testPolicy(symbol, sd, ed, start_value)
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df["Benchmark"] = marketsim.compute_portvals(orders, start_value)
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orders = bs.testPolicy(symbol, sd_out, ed_out, sv)
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df["Benchmark"] = marketsim.compute_portvals(orders, sv)
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df["Orders Benchmark"] = orders["Shares"]
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sl = StrategyLearner()
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orders = ms.testPolicy(symbol, sd, ed, start_value)
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df["SL"] = marketsim.compute_portvals(orders, start_value)
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sl = StrategyLearner(testing=True)
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sl.addEvidence(symbol, sd, ed, sv)
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orders = sl.testPolicy(symbol, sd_out, ed_out, sv)
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df["SL"] = marketsim.compute_portvals(orders, sv)
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df["Orders SL"] = orders["Shares"]
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# df["Holding Manual"] = orders["Shares"].cumsum()
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fig, ax = plt.subplots(3, sharex=True)
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df[[symbol]].plot(ax=ax[0])
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@@ -127,6 +127,12 @@ def experiment1():
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MultiCursor(fig.canvas, ax, color='r', lw=0.5)
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plt.show()
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# For debugging the classification learner:
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# df["y_train"] = sl.addEvidence(symbol, sd, ed, sv)
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# df["y_query"] = sl.testPolicy(symbol, sd, ed, sv)
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# df[["y_train", "y_query"]].plot(ax=ax[1])
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if __name__ == "__main__":
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experiment1()
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