marketsim_test.py

from __future__ import division
import timeit
import unittest
import datetime as dt
import marketsim as sim
from marketsim import compute_portfolio_stats

class Mc2P1Test(unittest.TestCase):
    pass

def t_generator(expected, computed):
    def t(self):
        self.assertAlmostEqual(expected, computed)
    return t

if __name__ == '__main__':
    tests = {
        "orders_short": {
            "final_value": 998035.0,
            "file": "orders-short.csv",
            "sharpe": -0.446948390642,
            "sharpe_spy": 0.882168679776,
            "cumulative_return": -0.001965,
            "cumulative_return_spy": 0.00289841448894,
            "stdev": 0.00634128215394,
            "stdev_spy": 0.00544933521991,
            "average_daily": -0.000178539446839,
            "average_daily_spy": 0.000302827205547,

        },
        "orders": {
            "final_value": 1133860.0,
            "file": "orders.csv",
            "sharpe": 1.21540888742,
            "sharpe_spy": 0.0183389807443,
            "cumulative_return": 0.13386,
            "cumulative_return_spy": -0.0224059854302,
            "stdev": 0.00720514136323,
            "stdev_spy": 0.0149716091522,
            "average_daily": 0.000551651296638,
            "average_daily_spy": 1.7295909534e-05,

        },
        "orders2": {
            "final_value": 1078752.6,
            "file": "orders2.csv",
            "sharpe": 0.788982285751,
            "sharpe_spy": -0.177203019906,
            "cumulative_return": 0.0787526,
            "cumulative_return_spy": -0.0629581516192,
            "stdev": 0.00711102080156,
            "stdev_spy": 0.0150564855724,
            "average_daily": 0.000353426354584,
            "average_daily_spy": -0.000168071648902,

        },
        "orders3": {
            "final_value": 1050160.0,
            "file": "orders3.csv",
            "sharpe": 1.03455887842,
            "sharpe_spy": 0.247809335326,
            "cumulative_return": 0.05016,
            "cumulative_return_spy": 0.0135380980508,
            "stdev": 0.00560508094997,
            "stdev_spy": 0.00840618502785,
            "average_daily": 0.000365289198877,
            "average_daily_spy": 0.000131224926273,

        },
        "orders_leverage_1": {
            "final_value": 1050160.0,
            "file": "orders-leverage-1.csv",
            "sharpe": 1.19402406143,
            "sharpe_spy": 0.0814792462178,
            "cumulative_return": 0.05016,
            "cumulative_return_spy": 0.000968694624926,
            "stdev": 0.00647534272091,
            "stdev_spy": 0.00801527501158,
            "average_daily": 0.000487052265169,
            "average_daily_spy": 4.11400826828e-05,

        },
        "orders_leverage_2": {
            "final_value": 1074650.0,
            "file": "orders-leverage-2.csv",
            "sharpe": 4.92529481246,
            "sharpe_spy": 2.65553881849,
            "cumulative_return": 0.07465,
            "cumulative_return_spy": 0.0482142153967,
            "stdev": 0.00651837064888,
            "stdev_spy": 0.00801128120646,
            "average_daily": 0.00202241842159,
            "average_daily_spy": 0.00134015293001,
        },
        "orders_leverage_3": {
            "final_value": 1050160.0,
            "file": "orders-leverage-3.csv",
            "sharpe": 1.03455887842,
            "sharpe_spy": 0.247809335326,
            "cumulative_return": 0.05016,
            "cumulative_return_spy": 0.0135380980508,
            "stdev": 0.00560508094997,
            "stdev_spy": 0.00840618502785,
            "average_daily": 0.000365289198877,
            "average_daily_spy": 0.000131224926273,

        },
    }

    for k, values in tests.iteritems():
        filename = "orders/{0}".format(values['file'])
        portvals = sim.compute_portvals(filename, 1000000)
        results = compute_portfolio_stats(portvals, [1])

        # results = (final value, ...
        # cumulative return, average daily return, stdev, sharpe)

        name = "test_{0}_{1}"

        for idx, key in enumerate(('final_value',
                                   'cumulative_return',
                                   'average_daily',
                                   'stdev',
                                   'sharpe',
        )):
            expected, computed = values[key], results[idx]
            t = t_generator(expected, computed)
            setattr(Mc2P1Test, name.format(k, key), t)

        # time constraint test
        def wrapper():
            portvals = sim.compute_portvals(filename, 1000000)
        number = 10
        timer = timeit.Timer(wrapper)
        result = timer.timeit(number=number)
        print("Ran 10 trials with mean time: {0}".format(result / number))
        t = lambda self: self.assertLessEqual(result / number, 10)
        setattr(Mc2P1Test, "test_time_limit_{0}".format(k), t)
    unittest.main()