Make zip naming consistent and start marketsim project
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"""MC2-P1: Market simulator - 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 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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||||
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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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"""
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import pytest
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from grading.grading import grader, GradeResult, time_limit, 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 numpy as np
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import pandas as pd
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from collections import namedtuple
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from util import get_data
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from util import get_orders_data_file
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# Student code
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main_code = "marketsim" # module name to import
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# Test cases
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MarketsimTestCase = namedtuple('MarketsimTestCase', ['description', 'group', 'inputs', 'outputs'])
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marketsim_test_cases = [
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MarketsimTestCase(
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description="Orders 1",
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group='basic',
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inputs=dict(
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orders_file='orders-01.csv',
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start_val=1000000,
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commission=0.,
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impact=0.
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),
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outputs=dict(
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num_days = 245 ,
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last_day_portval = 1115569.2,
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sharpe_ratio = 0.612340613407 ,
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avg_daily_ret = 0.00055037432146
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)
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),
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MarketsimTestCase(
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description="Orders 2",
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group='basic',
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inputs=dict(
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orders_file='orders-02.csv',
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start_val=1000000,
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commission=0.,
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impact=0.
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),
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outputs=dict(
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num_days = 245 ,
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last_day_portval = 1095003.35,
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sharpe_ratio = 1.01613520942,
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avg_daily_ret = 0.000390534819609
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)
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),
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MarketsimTestCase(
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description="Orders 3",
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group='basic',
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inputs=dict(
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orders_file='orders-03.csv',
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start_val=1000000,
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commission=0.,
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impact=0.
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),
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outputs=dict(
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num_days = 240,
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last_day_portval = 857616.0,
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sharpe_ratio = -0.759896272199,
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avg_daily_ret = -0.000571326189931
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)
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),
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MarketsimTestCase(
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description="Orders 4",
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group='basic',
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inputs=dict(
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orders_file='orders-04.csv',
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start_val=1000000,
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commission=0.,
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impact=0.
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),
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outputs=dict(
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num_days = 233,
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last_day_portval = 923545.4,
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sharpe_ratio = -0.266030146916,
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avg_daily_ret = -0.000240200768212
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)
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),
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MarketsimTestCase(
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description="Orders 5",
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group='basic',
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inputs=dict(
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orders_file='orders-05.csv',
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start_val=1000000,
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commission=0.,
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impact=0.
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),
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outputs=dict(
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num_days = 296,
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last_day_portval = 1415563.0,
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sharpe_ratio = 2.19591520826,
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avg_daily_ret = 0.00121733290744
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)
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),
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MarketsimTestCase(
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description="Orders 6",
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group='basic',
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inputs=dict(
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orders_file='orders-06.csv',
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start_val=1000000,
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commission=0.,
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impact=0.
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),
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outputs=dict(
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num_days = 210,
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last_day_portval = 894604.3,
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sharpe_ratio = -1.23463930987,
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avg_daily_ret = -0.000511281541086
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)
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),
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MarketsimTestCase(
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description="Orders 7",
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group='basic',
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inputs=dict(
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orders_file='orders-07.csv',
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start_val=1000000,
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commission=0.,
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impact=0.
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),
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outputs=dict(
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num_days = 237,
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last_day_portval = 1106563.3,
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sharpe_ratio = 2.10356512897,
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avg_daily_ret = 0.0004345040621
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)
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),
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MarketsimTestCase(
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description="Orders 8",
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group='basic',
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inputs=dict(
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orders_file='orders-08.csv',
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start_val=1000000,
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commission=0.,
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impact=0.
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),
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outputs=dict(
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num_days = 229,
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last_day_portval = 1074884.1,
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sharpe_ratio = 0.941858298061,
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avg_daily_ret = 0.000332404156893
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)
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),
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MarketsimTestCase(
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description="Orders 9",
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group='basic',
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inputs=dict(
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orders_file='orders-09.csv',
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start_val=1000000,
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commission=0.,
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impact=0.
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),
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outputs=dict(
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num_days = 37,
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last_day_portval = 1067710.0,
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sharpe_ratio = 2.90848480553,
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avg_daily_ret = 0.00187252252117
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)
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),
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#########################
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# Commission and impact #
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#########################
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MarketsimTestCase(
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description="Orders 11, commission",
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group='commission',
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inputs=dict(
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orders_file='orders-11.csv',
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start_val=1000000,
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commission=9.95,
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impact=0.
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),
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outputs=dict(
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num_days = 37,
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last_day_portval = 1045700.45,
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sharpe_ratio = 1.80327835983,
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avg_daily_ret = 0.00130745837411
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)
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),
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MarketsimTestCase(
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description="Orders 12, impact",
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group='impact',
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inputs=dict(
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orders_file='orders-12.csv',
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start_val=1000000,
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commission=0.,
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impact=0.005
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),
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outputs=dict(
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num_days = 240,
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last_day_portval = 1705686.6665,
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sharpe_ratio = 1.26427802107,
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avg_daily_ret = 0.00290246139389
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)
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),
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MarketsimTestCase(
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description="Orders 10, impact and commission",
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group='both',
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inputs=dict(
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orders_file='orders-10.csv',
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start_val=1000000,
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commission=9.95,
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impact=0.005
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),
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outputs=dict(
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num_days = 141,
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last_day_portval = 1026658.3265,
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sharpe_ratio = 0.627643575702,
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avg_daily_ret = 0.000222013722594
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)
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),
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MarketsimTestCase(
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description="author() test",
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group='author',
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inputs=None,
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outputs=None
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),
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#######################
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# Withheld test cases #
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#######################
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]
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seconds_per_test_case = 10 # execution time limit
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# Grading parameters (picked up by module-level grading fixtures)
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max_points = 100.0 # 9.5 * 10 + 2.5 * 2 + 1 secret point
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html_pre_block = True # surround comments with HTML <pre> tag (for T-Square comments field)
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# Test functon(s)
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@pytest.mark.parametrize("description,group,inputs,outputs", marketsim_test_cases)
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def test_marketsim(description, group, inputs, outputs, grader):
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"""Test compute_portvals() returns correct daily portfolio values.
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Requires test description, test case group, inputs, expected outputs, 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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# Try to import student code (only once)
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if not main_code in globals():
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import importlib
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# * Import module
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mod = importlib.import_module(main_code)
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globals()[main_code] = mod
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# * Import methods to test
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for m in ['compute_portvals']:
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globals()[m] = getattr(mod, m)
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incorrect = False
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msgs = []
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if group == 'author':
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try:
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auth_string = run_with_timeout(marketsim.author,seconds_per_test_case,(),{})
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if auth_string == 'tb34':
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incorrect = True
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msgs.append(" Incorrect author name (tb34)")
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points_earned = -10
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elif auth_string == '':
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incorrect = True
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msgs.append(" Empty author name")
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points_earned = -10
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except Exception as e:
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incorrect = True
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msgs.append(" Exception occured when calling author() method: {}".format(e))
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points_earned = -10
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else:
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# Unpack test case
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orders_file = inputs['orders_file']
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start_val = inputs['start_val']
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impct = inputs['impact']
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commish = inputs['commission']
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portvals = None
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fullpath_orders_file = get_orders_data_file(orders_file)
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portvals = run_with_timeout(compute_portvals,seconds_per_test_case,(),{'orders_file':fullpath_orders_file,'start_val':start_val,'commission':commish,'impact':impct})
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# * Check return type is correct, coax into Series
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assert (type(portvals) == pd.Series) or (type(portvals) == pd.DataFrame and len(portvals.columns) == 1), "You must return a Series or single-column DataFrame!"
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if type(portvals) == pd.DataFrame:
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portvals = portvals[portvals.columns[0]] # convert single-column DataFrame to Series
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if portvals.isnull().values.any():
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incorrect=True
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msgs.append("Portfolio values cannot be NaNs!")
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else:
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if group == 'basic':
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if len(portvals) != outputs['num_days']:
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incorrect=True
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msgs.append(" Incorrect number of days: {}, expected {}".format(len(portvals), outputs['num_days']))
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else:
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points_earned += 2.0
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if abs(portvals[-1]-outputs['last_day_portval']) > (0.001*outputs['last_day_portval']):
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incorrect=True
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msgs.append(" Incorrect final value: {}, expected {}".format(portvals[-1],outputs['last_day_portval']))
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else:
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points_earned += 5.0
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adr,sr = get_stats(portvals)
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if abs(sr-outputs['sharpe_ratio']) > abs(0.001*outputs['sharpe_ratio']):
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incorrect=True
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msgs.append(" Incorrect sharpe ratio: {}, expected {}".format(sr,outputs['sharpe_ratio']))
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else:
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points_earned += 1.0
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if abs(adr-outputs['avg_daily_ret']) > abs(0.001*outputs['avg_daily_ret']):
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incorrect=True
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msgs.append(" Incorrect avg daily return: {}, expected {}".format(adr,outputs['avg_daily_ret']))
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else:
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points_earned += 1.0
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elif group=='commission' or group=='impact' or group=='both':
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if abs(portvals[-1]-outputs['last_day_portval']) > 0.001:#(0.001*outputs['last_day_portval']):
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incorrect = True
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msgs.append(" Incorrect final value: {}, expected {}".format(portvals[-1],outputs['last_day_portval']))
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else:
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points_earned += 2.0
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if incorrect:
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# inputs_str = " orders_file: {}\n" \
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# " start_val: {}\n".format(orders_file, start_val)
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raise IncorrectOutput("Test failed on one or more output criteria.\n Inputs:\n{}\n Failures:\n{}".format(inputs, "\n".join(msgs)))
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except Exception as e:
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# Test result: failed
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msg = "Test case description: {}\n".format(description)
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# Generate a filtered stacktrace, only showing erroneous lines in student file(s)
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tb_list = tb.extract_tb(sys.exc_info()[2])
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if 'grading_traceback' in dir(e):
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tb_list = e.grading_traceback
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for i in range(len(tb_list)):
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row = tb_list[i]
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tb_list[i] = (os.path.basename(row[0]), row[1], row[2], row[3]) # show only filename instead of long absolute path
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tb_list = [row for row in tb_list if row[0] == 'marketsim.py']
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if tb_list:
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msg += "Traceback:\n"
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msg += ''.join(tb.format_list(tb_list)) # contains newlines
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msg += "{}: {}".format(e.__class__.__name__, str(e))
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# Report failure result to grader, with stacktrace
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grader.add_result(GradeResult(outcome='failed', points=max(points_earned,0), msg=msg))
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raise
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else:
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# Test result: passed (no exceptions)
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grader.add_result(GradeResult(outcome='passed', points=points_earned, msg=None))
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def get_stats(port_val):
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daily_rets = (port_val / port_val.shift(1)) - 1
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daily_rets = daily_rets[1:]
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avg_daily_ret = daily_rets.mean()
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std_daily_ret = daily_rets.std()
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sharpe_ratio = np.sqrt(252) * daily_rets.mean() / std_daily_ret
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return avg_daily_ret, sharpe_ratio
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if __name__ == "__main__":
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pytest.main(["-s", __file__])
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@ -0,0 +1,91 @@
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"""MC2-P1: Market simulator.
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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
|
||||
|
||||
Georgia Tech asserts copyright ownership of this template and all derivative
|
||||
works, including solutions to the projects assigned in this course. Students
|
||||
and other users of this template code are advised not to share it with others
|
||||
or to make it available on publicly viewable websites including repositories
|
||||
such as github and gitlab. This copyright statement should not be removed
|
||||
or edited.
|
||||
|
||||
We do grant permission to share solutions privately with non-students such
|
||||
as potential employers. However, sharing with other current or future
|
||||
students of CS 7646 is prohibited and subject to being investigated as a
|
||||
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 pandas as pd
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import numpy as np
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import datetime as dt
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import os
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from util import get_data, plot_data
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def compute_portvals(orders_file = "./orders/orders.csv", start_val = 1000000, commission=9.95, impact=0.005):
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# this is the function the autograder will call to test your code
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# NOTE: orders_file may be a string, or it may be a file object. Your
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# code should work correctly with either input
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# TODO: Your code here
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# In the template, instead of computing the value of the portfolio, we just
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# read in the value of IBM over 6 months
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start_date = dt.datetime(2008,1,1)
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end_date = dt.datetime(2008,6,1)
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portvals = get_data(['IBM'], pd.date_range(start_date, end_date))
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portvals = portvals[['IBM']] # remove SPY
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rv = pd.DataFrame(index=portvals.index, data=portvals.values)
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return rv
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return portvals
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def test_code():
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# this is a helper function you can use to test your code
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# note that during autograding his function will not be called.
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# Define input parameters
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of = "./orders/orders2.csv"
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sv = 1000000
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# Process orders
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portvals = compute_portvals(orders_file = of, start_val = sv)
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if isinstance(portvals, pd.DataFrame):
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portvals = portvals[portvals.columns[0]] # just get the first column
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else:
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"warning, code did not return a DataFrame"
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# Get portfolio stats
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# Here we just fake the data. you should use your code from previous assignments.
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start_date = dt.datetime(2008,1,1)
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end_date = dt.datetime(2008,6,1)
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cum_ret, avg_daily_ret, std_daily_ret, sharpe_ratio = [0.2,0.01,0.02,1.5]
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cum_ret_SPY, avg_daily_ret_SPY, std_daily_ret_SPY, sharpe_ratio_SPY = [0.2,0.01,0.02,1.5]
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# Compare portfolio against $SPX
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print(f"Date Range: {start_date} to {end_date}")
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print()
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print(f"Sharpe Ratio of Fund: {sharpe_ratio}")
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print(f"Sharpe Ratio of SPY : {sharpe_ratio_SPY}")
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print()
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print(f"Cumulative Return of Fund: {cum_ret}")
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print(f"Cumulative Return of SPY : {cum_ret_SPY}")
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print()
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print(f"Standard Deviation of Fund: {std_daily_ret}")
|
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print(f"Standard Deviation of SPY : {std_daily_ret_SPY}")
|
||||
print()
|
||||
print(f"Average Daily Return of Fund: {avg_daily_ret}")
|
||||
print(f"Average Daily Return of SPY : {avg_daily_ret_SPY}")
|
||||
print()
|
||||
print(f"Final Portfolio Value: {portvals[-1]}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_code()
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
Date,Symbol,Order,Shares
|
||||
2011-01-10,AAPL,BUY,1500
|
||||
2011-01-13,AAPL,SELL,1500
|
||||
2011-01-13,IBM,BUY,4000
|
||||
2011-01-26,GOOG,BUY,1000
|
||||
2011-02-02,XOM,SELL,4000
|
||||
2011-02-10,XOM,BUY,4000
|
||||
2011-03-03,GOOG,SELL,1000
|
||||
2011-03-03,GOOG,SELL,2200
|
||||
2011-06-03,IBM,SELL,3300
|
||||
2011-05-03,IBM,BUY,1500
|
||||
2011-06-10,AAPL,BUY,1200
|
||||
2011-08-01,GOOG,BUY,55
|
||||
2011-08-01,GOOG,SELL,55
|
||||
2011-12-20,AAPL,SELL,1200
|
||||
2011-12-21,AAPL,BUY,20
|
||||
2011-12-27,GOOG,BUY,2200
|
||||
2011-12-28,IBM,SELL,2200
|
||||
|
|
|
@ -0,0 +1,18 @@
|
|||
Date,Symbol,Order,Shares
|
||||
2011-01-10,AAPL,BUY,1500
|
||||
2011-01-10,AAPL,SELL,1500
|
||||
2011-01-13,AAPL,SELL,1500
|
||||
2011-01-13,IBM,BUY,4000
|
||||
2011-01-26,GOOG,BUY,1000
|
||||
2011-02-02,XOM,SELL,4000
|
||||
2011-02-10,XOM,BUY,4000
|
||||
2011-03-03,GOOG,SELL,1000
|
||||
2011-06-03,IBM,SELL,3300
|
||||
2011-05-03,IBM,BUY,1500
|
||||
2011-06-10,AAPL,BUY,1200
|
||||
2011-08-01,GOOG,BUY,55
|
||||
2011-08-01,GOOG,SELL,55
|
||||
2011-08-01,GOOG,BUY,55
|
||||
2011-12-20,AAPL,SELL,1200
|
||||
2011-12-21,AAPL,BUY,20
|
||||
2011-12-28,IBM,SELL,2200
|
||||
|
|
|
@ -0,0 +1,14 @@
|
|||
Date,Symbol,Order,Shares
|
||||
2011-01-10,AAPL,BUY,1500
|
||||
2011-01-13,AAPL,BUY,1500
|
||||
2011-01-26,GOOG,BUY,1000
|
||||
2011-02-02,XOM,SELL,4000
|
||||
2011-02-10,XOM,BUY,4000
|
||||
2011-03-03,GOOG,SELL,1000
|
||||
2011-03-03,IBM,SELL,1200
|
||||
2011-06-03,IBM,SELL,3300
|
||||
2011-05-03,IBM,BUY,1500
|
||||
2011-06-10,AAPL,SELL,3000
|
||||
2011-08-01,GOOG,BUY,55
|
||||
2011-08-01,GOOG,BUY,55
|
||||
2011-12-20,GOOG,SELL,110
|
||||
|
|
|
@ -0,0 +1,15 @@
|
|||
Date,Symbol,Order,Shares
|
||||
2008-01-14,GOOG,SELL,1000
|
||||
2008-01-17,GOOG,BUY,1000
|
||||
2008-03-19,AAPL,SELL,3000
|
||||
2008-03-31,IBM,SELL,2000
|
||||
2008-05-02,BAC,BUY,5000
|
||||
2008-05-09,BAC,SELL,5000
|
||||
2008-06-02,IBM,BUY,2000
|
||||
2008-06-02,AAPL,BUY,3100
|
||||
2008-06-02,AAPL,BUY,4300
|
||||
2008-07-23,AAPL,SELL,3500
|
||||
2008-07-10,GOOG,SELL,1000
|
||||
2008-08-07,IBM,SELL,55
|
||||
2008-08-11,IBM,BUY,55
|
||||
2008-12-12,GOOG,BUY,1000
|
||||
|
|
|
@ -0,0 +1,15 @@
|
|||
Date,Symbol,Order,Shares
|
||||
2009-05-04,GLD,BUY,3000
|
||||
2009-06-08,AAPL,BUY,1500
|
||||
2009-07-07,IBM,BUY,4000
|
||||
2009-07-07,GOOG,BUY,1000
|
||||
2009-10-19,IBM,SELL,4000
|
||||
2010-01-13,XOM,BUY,3000
|
||||
2010-02-26,GOOG,SELL,1000
|
||||
2010-03-09,XOM,SELL,900
|
||||
2010-04-20,GLD,SELL,3000
|
||||
2010-05-03,BAC,BUY,1500
|
||||
2010-05-27,BAC,SELL,1500
|
||||
2010-06-29,XOM,SELL,2100
|
||||
2010-07-02,AAPL,SELL,300
|
||||
2010-07-06,AAPL,SELL,1200
|
||||
|
|
|
@ -0,0 +1,15 @@
|
|||
Date,Symbol,Order,Shares
|
||||
2007-01-10,AAPL,BUY,2500
|
||||
2007-01-17,AAPL,SELL,1500
|
||||
2007-01-19,IBM,BUY,400
|
||||
2007-01-26,GOOG,BUY,1000
|
||||
2007-02-02,XOM,SELL,4000
|
||||
2007-02-16,XOM,BUY,700
|
||||
2007-03-05,GOOG,SELL,550
|
||||
2007-03-05,IBM,SELL,2200
|
||||
2007-05-14,XOM,SELL,880
|
||||
2007-05-15,XOM,BUY,550
|
||||
2007-09-13,GOOG,SELL,1000
|
||||
2007-10-10,XOM,BUY,100
|
||||
2007-10-12,XOM,SELL,3000
|
||||
2007-11-07,XOM,BUY,400
|
||||
|
|
|
@ -0,0 +1,15 @@
|
|||
Date,Symbol,Order,Shares
|
||||
2009-01-14,AAPL,BUY,150
|
||||
2009-01-21,AAPL,SELL,150
|
||||
2009-01-21,IBM,BUY,400
|
||||
2009-01-30,GOOG,BUY,100
|
||||
2009-02-02,XOM,SELL,400
|
||||
2009-02-10,XOM,BUY,400
|
||||
2009-03-03,GOOG,SELL,100
|
||||
2009-03-03,IBM,SELL,220
|
||||
2009-06-03,IBM,SELL,330
|
||||
2009-05-08,IBM,BUY,1500
|
||||
2009-06-10,AAPL,BUY,1200
|
||||
2009-08-03,GOOG,BUY,550
|
||||
2009-08-03,GOOG,SELL,550
|
||||
2009-12-21,AAPL,SELL,1200
|
||||
|
|
|
@ -0,0 +1,15 @@
|
|||
Date,Symbol,Order,Shares
|
||||
2010-01-12,AAPL,BUY,150
|
||||
2010-01-21,IBM,BUY,400
|
||||
2010-01-21,GOOG,BUY,100
|
||||
2010-01-27,AAPL,SELL,150
|
||||
2010-02-05,XOM,SELL,400
|
||||
2010-02-11,GOOG,SELL,100
|
||||
2010-03-10,IBM,SELL,220
|
||||
2010-04-15,XOM,BUY,400
|
||||
2010-05-20,GOOG,BUY,550
|
||||
2010-06-03,IBM,SELL,330
|
||||
2010-06-24,AAPL,BUY,1200
|
||||
2010-08-18,IBM,BUY,1500
|
||||
2010-09-15,GOOG,SELL,550
|
||||
2010-12-07,AAPL,SELL,1200
|
||||
|
|
|
@ -0,0 +1,10 @@
|
|||
Date,Symbol,Order,Shares
|
||||
2011-01-10,AAPL,BUY,2500
|
||||
2011-01-10,AAPL,BUY,2500
|
||||
2011-01-13,AAPL,SELL,5000
|
||||
2011-01-13,IBM,BUY,4000
|
||||
2011-01-26,GOOG,BUY,1000
|
||||
2011-02-02,XOM,SELL,4000
|
||||
2011-02-10,XOM,BUY,4000
|
||||
2011-03-03,GOOG,SELL,1000
|
||||
2011-03-03,IBM,SELL,4000
|
||||
|
|
|
@ -0,0 +1,13 @@
|
|||
Date,Symbol,Order,Shares
|
||||
2011-01-10,AAPL,BUY,1500
|
||||
2011-01-13,AAPL,SELL,1500
|
||||
2011-01-13,IBM,BUY,4000
|
||||
2011-01-26,GOOG,BUY,1000
|
||||
2011-02-02,XOM,SELL,4000
|
||||
2011-02-10,XOM,BUY,4000
|
||||
2011-03-03,GOOG,SELL,1000
|
||||
2011-03-03,IBM,SELL,2200
|
||||
2011-06-03,IBM,SELL,3300
|
||||
2011-05-03,IBM,BUY,1500
|
||||
2011-08-01,GOOG,BUY,55
|
||||
2011-08-01,GOOG,SELL,55
|
||||
|
|
|
@ -0,0 +1,10 @@
|
|||
Date,Symbol,Order,Shares
|
||||
2011-01-10,AAPL,BUY,2500
|
||||
2011-01-10,AAPL,BUY,2500
|
||||
2011-01-13,AAPL,SELL,5000
|
||||
2011-01-13,IBM,BUY,4000
|
||||
2011-01-26,GOOG,BUY,1000
|
||||
2011-02-02,XOM,SELL,12000
|
||||
2011-02-10,XOM,BUY,4000
|
||||
2011-03-03,GOOG,SELL,1000
|
||||
2011-03-03,IBM,SELL,4000
|
||||
|
|
|
@ -0,0 +1,15 @@
|
|||
Date,Symbol,Order,Shares
|
||||
2011-01-10,AAPL,BUY,1500
|
||||
2011-01-13,AAPL,SELL,1500
|
||||
2011-01-13,IBM,BUY,4000
|
||||
2011-01-26,GOOG,BUY,1000
|
||||
2011-02-02,XOM,SELL,4000
|
||||
2011-02-10,XOM,BUY,4000
|
||||
2011-03-03,GOOG,SELL,1000
|
||||
2011-03-03,IBM,SELL,2200
|
||||
2011-06-03,IBM,SELL,3300
|
||||
2011-05-03,IBM,BUY,1500
|
||||
2011-06-10,AAPL,BUY,10000
|
||||
2011-08-01,GOOG,BUY,55
|
||||
2011-08-01,GOOG,SELL,55
|
||||
2011-12-20,AAPL,SELL,1200
|
||||
|
Loading…
Reference in New Issue