Add template for project 8
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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 random
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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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self.verbose = verbose
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self.impact = impact
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self.commission = commission
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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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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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# 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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# 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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if __name__=="__main__":
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print("One does not simply think up a strategy")
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@ -0,0 +1,339 @@
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"""MC3-P3: Strategy Learner - grading script.
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Usage:
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- Switch to a student feedback directory first (will write "points.txt" and "comments.txt" in pwd).
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- Run this script with both ml4t/ and student solution in PYTHONPATH, e.g.:
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PYTHONPATH=ml4t:MC1-P2/jdoe7 python ml4t/mc2_p1_grading/grade_marketsim.py
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Copyright 2017, Georgia Tech Research Corporation
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Atlanta, Georgia 30332-0415
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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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|
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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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|
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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 pytest
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from grading.grading import grader, GradeResult, run_with_timeout, IncorrectOutput
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import os
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import sys
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import traceback as tb
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import datetime as dt
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import numpy as np
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import pandas as pd
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from collections import namedtuple
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import time
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import util
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import random
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# Test cases
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StrategyTestCase = namedtuple('Strategy', ['description','insample_args','outsample_args','benchmark_type','benchmark','impact','train_time','test_time','max_time','seed'])
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strategy_test_cases = [
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StrategyTestCase(
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description="ML4T-220",
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insample_args=dict(symbol="ML4T-220",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000),
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outsample_args=dict(symbol="ML4T-220",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000),
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benchmark_type='clean',
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benchmark=1.0, #benchmark updated Apr 24 2017
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impact=0.0,
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train_time=25,
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test_time=5,
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max_time=60,
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seed=1481090000
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),
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StrategyTestCase(
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description="AAPL",
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insample_args=dict(symbol="AAPL",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000),
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outsample_args=dict(symbol="AAPL",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000),
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benchmark_type='stock',
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benchmark=0.1581999999999999, #benchmark computed Nov 22 2017
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impact=0.0,
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train_time=25,
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test_time=5,
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max_time=60,
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seed=1481090000
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),
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StrategyTestCase(
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description="SINE_FAST_NOISE",
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insample_args=dict(symbol="SINE_FAST_NOISE",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000),
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outsample_args=dict(symbol="SINE_FAST_NOISE",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000),
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benchmark_type='noisy',
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benchmark=2.0, #benchmark updated Apr 24 2017
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impact=0.0,
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train_time=25,
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test_time=5,
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max_time=60,
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seed=1481090000
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),
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StrategyTestCase(
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description="UNH - In sample",
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insample_args=dict(symbol="UNH",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000),
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outsample_args=dict(symbol="UNH",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000),
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benchmark_type='stock',
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benchmark= -0.25239999999999996, #benchmark computed Nov 22 2017
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impact=0.0,
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train_time=25,
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test_time=5,
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max_time=60,
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seed=1481090000
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),
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]
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max_points = 60.0
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html_pre_block = True # surround comments with HTML <pre> tag (for T-Square comments field)
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MAX_HOLDINGS = 1000
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# Test functon(s)
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@pytest.mark.parametrize("description, insample_args, outsample_args, benchmark_type, benchmark, impact, train_time, test_time, max_time, seed", strategy_test_cases)
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def test_strategy(description, insample_args, outsample_args, benchmark_type, benchmark, impact, train_time, test_time, max_time, seed, grader):
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"""Test StrategyLearner.
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Requires test description, insample args (dict), outsample args (dict), benchmark_type (str), benchmark (float)
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max time (seconds), points for this test case (int), random seed (long), and a grader fixture.
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"""
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points_earned = 0.0 # initialize points for this test case
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try:
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incorrect = True
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if not 'StrategyLearner' in globals():
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import importlib
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m = importlib.import_module('StrategyLearner')
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globals()['StrategyLearner'] = m
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outsample_cr_to_beat = None
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if benchmark_type == 'clean':
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outsample_cr_to_beat = benchmark
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def timeoutwrapper_strategylearner():
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#Set fixed seed for repetability
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np.random.seed(seed)
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random.seed(seed)
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learner = StrategyLearner.StrategyLearner(verbose=False,impact=impact)
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tmp = time.time()
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learner.addEvidence(**insample_args)
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train_t = time.time()-tmp
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tmp = time.time()
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insample_trades_1 = learner.testPolicy(**insample_args)
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test_t = time.time()-tmp
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insample_trades_2 = learner.testPolicy(**insample_args)
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tmp = time.time()
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outsample_trades = learner.testPolicy(**outsample_args)
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out_test_t = time.time()-tmp
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return insample_trades_1, insample_trades_2, outsample_trades, train_t, test_t, out_test_t
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msgs = []
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in_trades_1, in_trades_2, out_trades, train_t, test_t, out_test_t = run_with_timeout(timeoutwrapper_strategylearner,max_time,(),{})
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incorrect = False
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if len(in_trades_1.shape)!=2 or in_trades_1.shape[1]!=1:
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incorrect=True
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msgs.append(" First insample trades DF has invalid shape: {}".format(in_trades_1.shape))
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elif len(in_trades_2.shape)!=2 or in_trades_2.shape[1]!=1:
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incorrect=True
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msgs.append(" Second insample trades DF has invalid shape: {}".format(in_trades_2.shape))
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elif len(out_trades.shape)!=2 or out_trades.shape[1]!=1:
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incorrect=True
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msgs.append(" Out-of-sample trades DF has invalid shape: {}".format(out_trades.shape))
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else:
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tmp_csum=0.0
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for date,trade in in_trades_1.iterrows():
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tmp_csum+= trade.iloc[0]
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if (trade.iloc[0]!=0) and\
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(trade.abs().iloc[0]!=MAX_HOLDINGS) and\
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(trade.abs().iloc[0]!=2*MAX_HOLDINGS):
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incorrect=True
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msgs.append(" illegal trade in first insample DF. abs(trade) not one of ({},{},{}).\n Date {}, Trade {}".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS,date,trade))
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break
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elif abs(tmp_csum)>MAX_HOLDINGS:
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incorrect=True
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msgs.append(" holdings more than {} long or short in first insample DF. Date {}, Trade {}".format(MAX_HOLDINGS,date,trade))
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break
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tmp_csum=0.0
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for date,trade in in_trades_2.iterrows():
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tmp_csum+= trade.iloc[0]
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if (trade.iloc[0]!=0) and\
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(trade.abs().iloc[0]!=MAX_HOLDINGS) and\
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(trade.abs().iloc[0]!=2*MAX_HOLDINGS):
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incorrect=True
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msgs.append(" illegal trade in second insample DF. abs(trade) not one of ({},{},{}).\n Date {}, Trade {}".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS,date,trade))
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break
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elif abs(tmp_csum)>MAX_HOLDINGS:
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incorrect=True
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msgs.append(" holdings more than {} long or short in second insample DF. Date {}, Trade {}".format(MAX_HOLDINGS,date,trade))
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break
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tmp_csum=0.0
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for date,trade in out_trades.iterrows():
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tmp_csum+= trade.iloc[0]
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if (trade.iloc[0]!=0) and\
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(trade.abs().iloc[0]!=MAX_HOLDINGS) and\
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(trade.abs().iloc[0]!=2*MAX_HOLDINGS):
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incorrect=True
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msgs.append(" illegal trade in out-of-sample DF. abs(trade) not one of ({},{},{}).\n Date {}, Trade {}".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS,date,trade))
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break
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elif abs(tmp_csum)>MAX_HOLDINGS:
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incorrect=True
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msgs.append(" holdings more than {} long or short in out-of-sample DF. Date {}, Trade {}".format(MAX_HOLDINGS,date,trade))
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break
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# if (((in_trades_1.abs()!=0) & (in_trades_1.abs()!=MAX_HOLDINGS) & (in_trades_1.abs()!=2*MAX_HOLDINGS)).any().any() or\
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# ((in_trades_2.abs()!=0) & (in_trades_2.abs()!=MAX_HOLDINGS) & (in_trades_2.abs()!=2*MAX_HOLDINGS)).any().any() or\
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# ((out_trades.abs()!=0) & (out_trades.abs()!=MAX_HOLDINGS) & (out_trades.abs()!=2*MAX_HOLDINGS)).any().any()):
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# incorrect = True
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# msgs.append(" illegal trade. abs(trades) not one of ({},{},{})".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS))
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# if ((in_trades_1.cumsum().abs()>MAX_HOLDINGS).any()[0]) or ((in_trades_2.cumsum().abs()>MAX_HOLDINGS).any()[0]) or ((out_trades.cumsum().abs()>MAX_HOLDINGS).any()[0]):
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# incorrect = True
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# msgs.append(" holdings more than {} long or short".format(MAX_HOLDINGS))
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if not(incorrect):
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if train_t>train_time:
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incorrect=True
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msgs.append(" addEvidence() took {} seconds, max allowed {}".format(train_t,train_time))
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else:
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points_earned += 1.0
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if test_t > test_time:
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incorrect = True
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msgs.append(" testPolicy() took {} seconds, max allowed {}".format(test_t,test_time))
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else:
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points_earned += 2.0
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if not((in_trades_1 == in_trades_2).all()[0]):
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incorrect = True
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mismatches = in_trades_1.join(in_trades_2,how='outer',lsuffix='1',rsuffix='2')
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mismatches = mismatches[mismatches.iloc[:,0]!=mismatches.iloc[:,1]]
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msgs.append(" consecutive calls to testPolicy() with same input did not produce same output:")
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msgs.append(" Mismatched trades:\n {}".format(mismatches))
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else:
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points_earned += 2.0
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student_insample_cr = evalPolicy2(insample_args['symbol'],in_trades_1,insample_args['sv'],insample_args['sd'],insample_args['ed'],market_impact=impact,commission_cost=0.0)
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student_outsample_cr = evalPolicy2(outsample_args['symbol'],out_trades, outsample_args['sv'],outsample_args['sd'],outsample_args['ed'],market_impact=impact,commission_cost=0.0)
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if student_insample_cr <= benchmark:
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incorrect = True
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msgs.append(" in-sample return ({}) did not beat benchmark ({})".format(student_insample_cr,benchmark))
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else:
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points_earned += 5.0
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if outsample_cr_to_beat is None:
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if out_test_t > test_time:
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incorrect = True
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msgs.append(" out-sample took {} seconds, max of {}".format(out_test_t,test_time))
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else:
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points_earned += 5.0
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else:
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if student_outsample_cr < outsample_cr_to_beat:
|
||||||
|
incorrect = True
|
||||||
|
msgs.append(" out-sample return ({}) did not beat benchmark ({})".format(student_outsample_cr,outsample_cr_to_beat))
|
||||||
|
else:
|
||||||
|
points_earned += 5.0
|
||||||
|
if incorrect:
|
||||||
|
inputs_str = " insample_args: {}\n" \
|
||||||
|
" outsample_args: {}\n" \
|
||||||
|
" benchmark_type: {}\n" \
|
||||||
|
" benchmark: {}\n" \
|
||||||
|
" train_time: {}\n" \
|
||||||
|
" test_time: {}\n" \
|
||||||
|
" max_time: {}\n" \
|
||||||
|
" seed: {}\n".format(insample_args, outsample_args, benchmark_type, benchmark, train_time, test_time, max_time,seed)
|
||||||
|
raise IncorrectOutput("Test failed on one or more output criteria.\n Inputs:\n{}\n Failures:\n{}".format(inputs_str, "\n".join(msgs)))
|
||||||
|
except Exception as e:
|
||||||
|
# Test result: failed
|
||||||
|
msg = "Test case description: {}\n".format(description)
|
||||||
|
|
||||||
|
# Generate a filtered stacktrace, only showing erroneous lines in student file(s)
|
||||||
|
tb_list = tb.extract_tb(sys.exc_info()[2])
|
||||||
|
for i in range(len(tb_list)):
|
||||||
|
row = tb_list[i]
|
||||||
|
tb_list[i] = (os.path.basename(row[0]), row[1], row[2], row[3]) # show only filename instead of long absolute path
|
||||||
|
# tb_list = [row for row in tb_list if row[0] in ['QLearner.py','StrategyLearner.py']]
|
||||||
|
if tb_list:
|
||||||
|
msg += "Traceback:\n"
|
||||||
|
msg += ''.join(tb.format_list(tb_list)) # contains newlines
|
||||||
|
elif 'grading_traceback' in dir(e):
|
||||||
|
msg += "Traceback:\n"
|
||||||
|
msg += ''.join(tb.format_list(e.grading_traceback))
|
||||||
|
msg += "{}: {}".format(e.__class__.__name__, str(e))
|
||||||
|
|
||||||
|
# Report failure result to grader, with stacktrace
|
||||||
|
grader.add_result(GradeResult(outcome='failed', points=points_earned, msg=msg))
|
||||||
|
raise
|
||||||
|
else:
|
||||||
|
# Test result: passed (no exceptions)
|
||||||
|
grader.add_result(GradeResult(outcome='passed', points=points_earned, msg=None))
|
||||||
|
|
||||||
|
def compute_benchmark(sd,ed,sv,symbol,market_impact,commission_cost,max_holdings):
|
||||||
|
date_idx = util.get_data([symbol,],pd.date_range(sd,ed)).index
|
||||||
|
orders = pd.DataFrame(index=date_idx)
|
||||||
|
orders['orders'] = 0; orders['orders'][0] = max_holdings; orders['orders'][-1] = -max_holdings
|
||||||
|
return evalPolicy2(symbol,orders,sv,sd,ed,market_impact,commission_cost)
|
||||||
|
|
||||||
|
def evalPolicy(student_trades,sym_prices,startval):
|
||||||
|
ending_cash = startval - student_trades.mul(sym_prices,axis=0).sum()
|
||||||
|
ending_stocks = student_trades.sum()*sym_prices.iloc[-1]
|
||||||
|
return float((ending_cash+ending_stocks)/startval)-1.0
|
||||||
|
|
||||||
|
def evalPolicy2(symbol, student_trades, startval, sd, ed, market_impact,commission_cost):
|
||||||
|
orders_df = pd.DataFrame(columns=['Shares','Order','Symbol'])
|
||||||
|
for row_idx in student_trades.index:
|
||||||
|
nshares = student_trades.loc[row_idx][0]
|
||||||
|
if nshares == 0:
|
||||||
|
continue
|
||||||
|
order = 'sell' if nshares < 0 else 'buy'
|
||||||
|
new_row = pd.DataFrame([[abs(nshares),order,symbol],],columns=['Shares','Order','Symbol'],index=[row_idx,])
|
||||||
|
orders_df = orders_df.append(new_row)
|
||||||
|
portvals = compute_portvals(orders_df, sd, ed, startval,market_impact,commission_cost)
|
||||||
|
return float(portvals[-1]/portvals[0])-1
|
||||||
|
|
||||||
|
def compute_portvals(orders_df, start_date, end_date, startval, market_impact=0.0, commission_cost=0.0):
|
||||||
|
"""Simulate the market for the given date range and orders file."""
|
||||||
|
symbols = []
|
||||||
|
orders = []
|
||||||
|
orders_df = orders_df.sort_index()
|
||||||
|
for date, order in orders_df.iterrows():
|
||||||
|
shares = order['Shares']
|
||||||
|
action = order['Order']
|
||||||
|
symbol = order['Symbol']
|
||||||
|
if action.lower() == 'sell':
|
||||||
|
shares *= -1
|
||||||
|
order = (date, symbol, shares)
|
||||||
|
orders.append(order)
|
||||||
|
symbols.append(symbol)
|
||||||
|
symbols = list(set(symbols))
|
||||||
|
dates = pd.date_range(start_date, end_date)
|
||||||
|
prices_all = util.get_data(symbols, dates)
|
||||||
|
prices = prices_all[symbols]
|
||||||
|
prices = prices.fillna(method='ffill').fillna(method='bfill')
|
||||||
|
prices['_CASH'] = 1.0
|
||||||
|
trades = pd.DataFrame(index=prices.index, columns=symbols)
|
||||||
|
trades = trades.fillna(0)
|
||||||
|
cash = pd.Series(index=prices.index)
|
||||||
|
cash = cash.fillna(0)
|
||||||
|
cash.iloc[0] = startval
|
||||||
|
for date, symbol, shares in orders:
|
||||||
|
price = prices[symbol][date]
|
||||||
|
val = shares * price
|
||||||
|
# transaction cost model
|
||||||
|
val += commission_cost + (pd.np.abs(shares)*price*market_impact)
|
||||||
|
positions = prices.loc[date] * trades.sum()
|
||||||
|
totalcash = cash.sum()
|
||||||
|
if (date < prices.index.min()) or (date > prices.index.max()):
|
||||||
|
continue
|
||||||
|
trades[symbol][date] += shares
|
||||||
|
cash[date] -= val
|
||||||
|
trades['_CASH'] = cash
|
||||||
|
holdings = trades.cumsum()
|
||||||
|
df_portvals = (prices * holdings).sum(axis=1)
|
||||||
|
return df_portvals
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
pytest.main(["-s", __file__])
|
||||||
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Loading…
Reference in New Issue