def compute_portvals(orders, start_val=1000000):
# this is the function the autograder will call to test your code
# Import Orders into DataFrame
# orders = pd.read_csv(orders_file, index_col=0, parse_dates=True, sep=',')
orders = orders.sort_index()
start_date = orders.index[0].to_datetime()
end_date = orders.index[-1].to_datetime()
orders.index = orders.index
dates = pd.date_range(start_date, end_date)
dates = get_data(['SPY'], dates).index.get_values() #fix dates so you only get when SPY is trading
symbols = orders.get('Symbol').unique().tolist() # Get a LIST of symbols
# Read in adjusted closing prices for given symbols, date range
prices_all = get_data(symbols, dates) # automatically adds SPY
prices = prices_all[symbols] # only portfolio symbols
prices = pd.concat([prices, pd.DataFrame(index=dates)], axis=1) # all dates in prices
prices = prices.fillna(method='ffill') # fillna
prices_SPY = prices_all['SPY'] # only SPY, for comparison later
#establish holder for shares
share_transactions = pd.DataFrame(columns = symbols, index = [dates])
share_transactions = share_transactions.fillna(value=0)
# Establish Cash Values
# columns = ['cash', 'buying_power'] + symbols # Headers of DataFrame is Cash and Buying Power
columns = ['cash'] # TODO Buying power needs to be its own DataFrame
positions = pd.DataFrame(columns=columns, index=[
dates]) # initialize portfolio values, buying power = -999 until I incorporate leverage
positions.ix[0] = 0
# Calculate Cash Transactions
cash_transactions = pd.DataFrame(columns=['cash_transaction'], index=[dates])
cash_transactions.ix[0, ['cash_transaction']] = start_val
cash_transactions = cash_transactions.fillna(value=0)
# Calculate Leverage
leverage = pd.DataFrame(columns = ['leverage'], index=[dates])
leverage.ix[:,['leverage']] = 0
#positions.ix[:,symbols].abs().sum(axis=1)
# Iterate through the rows in orders
for i in range(len(orders)):
if orders.ix[i]['Order'] == 'BUY':
mult = 1.0
else:
mult = -1.0
share_transactions.ix[orders.index[i].to_datetime(), orders.ix[i]['Symbol']] = share_transactions.ix[orders.index[i].to_datetime(), orders.ix[i]['Symbol']] + mult * orders.ix[i]['Shares']
order_cost = mult * orders.ix[i]['Shares'] * prices.ix[orders.index[i].to_datetime()][
orders.ix[i]['Symbol']]
# print orders.ix[i]['Symbol']
# print orders.ix[i]['Order']
# print order_cost
# print orders.index[i].to_datetime()
# print
cash_transactions.ix[orders.index[i].to_datetime(), ['cash_transaction']] = cash_transactions.ix[orders.index[i].to_datetime(), ['cash_transaction']] - order_cost
positions.cash = cash_transactions.cash_transaction.cumsum() #Calculate total amt of cash in the portfolio at a given time (sum up the cash transactions)
shares = share_transactions.cumsum() #Calculate total num of shares in the portfolio at a given time (sum up the share orders)
positions = pd.concat([positions,shares * prices],axis=1)
portfolio_value = positions.sum(axis=1)
#The trick to getting leverage in there is to concatenate the length of the positions dataframe every time an order is executed. That seems like a lot of work that I don't want to do right now.
# I'll take the 4% hit
# In the template, instead of computing the value of the portfolio, we just
# read in the value of IBM over 6 months
start_dates = dt.datetime(2008, 1, 1)
end_dates = dt.datetime(2008, 6, 1)
# positions = get_data(['IBM'], pd.date_range(start_dates, end_dates))
# positions = positions[['IBM']] # remove SPY
return portfolio_value
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/orders_test.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_dates = dt.datetime(2008, 1, 1)
#end_dates = 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]
cum_ret, avg_daily_ret, std_daily_ret, sharpe_ratio, portfolio_value, start_dates, end_dates = compute_portfolio_stats(portvals)
cum_ret_SPY, avg_daily_ret_SPY, std_daily_ret_SPY, sharpe_ratio_SPY, spy_value, start_dates, end_dates = compute_portfolio_stats(get_data(['SPY'], dates = pd.date_range(start_dates, end_dates)))
# Compare portfolio against $SPX
print
print "Date Range: {} to {}".format(start_dates, end_dates)
print
print "Sharpe Ratio of Fund: {}".format(sharpe_ratio)
print "Sharpe Ratio of SPY : {}".format(sharpe_ratio_SPY)
print
print "Cumulative Return of Fund: {}".format(cum_ret)
print "Cumulative Return of SPY : {}".format(cum_ret_SPY)
print
print "Standard Deviation of Fund: {}".format(std_daily_ret)
print "Standard Deviation of SPY : {}".format(std_daily_ret_SPY)
print
print "Average Daily Return of Fund: {}".format(avg_daily_ret)
print "Average Daily Return of SPY : {}".format(avg_daily_ret_SPY)
print
print "Final Portfolio Value: {}".format(portvals[-1])
