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Make zip naming consistent and start marketsim project

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Felix Martin
2020-10-08 20:24:49 -04:00
parent 4e2a7a8e77
commit 0429c497c7
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"""MC2-P1: Market simulator.
Copyright 2018, Georgia Institute of Technology (Georgia Tech)
Atlanta, Georgia 30332
All Rights Reserved
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.
-----do not edit anything above this line---
Student Name: Tucker Balch (replace with your name)
GT User ID: tb34 (replace with your User ID)
GT ID: 900897987 (replace with your GT ID)
"""
import pandas as pd
import numpy as np
import datetime as dt
import os
from util import get_data, plot_data
def compute_portvals(orders_file = "./orders/orders.csv", start_val = 1000000, commission=9.95, impact=0.005):
# this is the function the autograder will call to test your code
# NOTE: orders_file may be a string, or it may be a file object. Your
# code should work correctly with either input
# TODO: Your code here
# In the template, instead of computing the value of the portfolio, we just
# read in the value of IBM over 6 months
start_date = dt.datetime(2008,1,1)
end_date = dt.datetime(2008,6,1)
portvals = get_data(['IBM'], pd.date_range(start_date, end_date))
portvals = portvals[['IBM']] # remove SPY
rv = pd.DataFrame(index=portvals.index, data=portvals.values)
return rv
return portvals
def test_code():
# this is a helper function you can use to test your code
# note that during autograding his function will not be called.
# Define input parameters
of = "./orders/orders2.csv"
sv = 1000000
# Process orders
portvals = compute_portvals(orders_file = of, start_val = sv)
if isinstance(portvals, pd.DataFrame):
portvals = portvals[portvals.columns[0]] # just get the first column
else:
"warning, code did not return a DataFrame"
# Get portfolio stats
# Here we just fake the data. you should use your code from previous assignments.
start_date = dt.datetime(2008,1,1)
end_date = dt.datetime(2008,6,1)
cum_ret, avg_daily_ret, std_daily_ret, sharpe_ratio = [0.2,0.01,0.02,1.5]
cum_ret_SPY, avg_daily_ret_SPY, std_daily_ret_SPY, sharpe_ratio_SPY = [0.2,0.01,0.02,1.5]
# Compare portfolio against $SPX
print(f"Date Range: {start_date} to {end_date}")
print()
print(f"Sharpe Ratio of Fund: {sharpe_ratio}")
print(f"Sharpe Ratio of SPY : {sharpe_ratio_SPY}")
print()
print(f"Cumulative Return of Fund: {cum_ret}")
print(f"Cumulative Return of SPY : {cum_ret_SPY}")
print()
print(f"Standard Deviation of Fund: {std_daily_ret}")
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()