Parse orders and extract information (start/end date and symbols)
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0429c497c7
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117d97b8e9
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@ -21,8 +21,8 @@ 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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GT User ID: felixm (replace with your User ID)
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GT ID: 1337 (replace with your GT ID)
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"""
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import pandas as pd
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@ -30,47 +30,81 @@ 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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from optimize_something.optimization import calculate_stats
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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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def read_orders(orders_file):
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"""
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Parser orders into the form:
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Date datetime64[ns]
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Symbol object
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Order object
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Shares int32
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This is how the order book looks like:
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Date,Symbol,Order,Shares
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2011-01-10,AAPL,BUY,1500
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2011-01-10,AAPL,SELL,1500
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"""
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orders = pd.read_csv(orders_file,
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dtype='|str, str, str, i4',
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parse_dates=["Date"])
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orders.sort_values(by="Date", inplace=True)
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return orders
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def get_order_book_info(orders):
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"""Return start_date, end_date, and symbols (as a list)."""
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start_date = orders.iloc[0].Date
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end_date = orders.iloc[0].Date
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symbols = sorted(list((set(orders.Symbol.tolist()))))
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return start_date, end_date, symbols
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def compute_portvals(orders_file="./orders/orders-01.csv", start_val=1000000, commission=9.95, impact=0.005):
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orders = read_orders(orders_file)
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start_date, end_date, symbols = get_order_book_info(orders)
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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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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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# Don't know why this was in template. Keep for now.
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# rv = pd.DataFrame(index=portvals.index, data=portvals.values)
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# return rv
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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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of = "./orders/orders-02.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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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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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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raise Exception("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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# One way of getting the portfolio dates
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# print(portvals.index[0])
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# Compare portfolio against $SPX
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# Get portfolio stats.
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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, \
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std_daily_ret, sharpe_ratio = calculate_stats(portvals.to_frame(), [1])
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spy = get_data(['SPY'], pd.date_range(start_date, end_date))
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cum_ret_SPY, avg_daily_ret_SPY, \
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std_daily_ret_SPY, sharpe_ratio_SPY = calculate_stats(spy, [1])
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# Compare portfolio against $SPY
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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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@ -87,5 +121,6 @@ def test_code():
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print()
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print(f"Final Portfolio Value: {portvals[-1]}")
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if __name__ == "__main__":
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test_code()
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