def main():
'''Main Function'''
# List of symbols
ls_symbols = ["AAPL", "GOOG"]
# Start and End date of the charts
dt_start = dt.datetime(2008, 1, 1)
dt_end = dt.datetime(2010, 12, 31)
# We need closing prices so the timestamp should be hours=16.
dt_timeofday = dt.timedelta(hours=16)
# Get a list of trading days between the start and the end.
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
# Creating an object of the dataaccess class with Yahoo as the source.
c_dataobj = da.DataAccess('Yahoo')
# Reading just the close prices
df_close = c_dataobj.get_data(ldt_timestamps, ls_symbols, "close")
# Creating the allocation dataframe
# We offset the time for the simulator to have atleast one
# datavalue before the allocation.
df_alloc = pd.DataFrame(np.array([[0.5, 0.5]]),
index=[ldt_timestamps[0] + dt.timedelta(hours=5)],
columns=ls_symbols)
dt_last_date = ldt_timestamps[0]
# Looping through all dates and creating monthly allocations
for dt_date in ldt_timestamps[1:]:
if dt_last_date.month != dt_date.month:
# Create allocation
na_vals = np.random.randint(0, 1000, len(ls_symbols))
na_vals = na_vals / float(sum(na_vals))
na_vals = na_vals.reshape(1, -1)
# Append to the dataframe
df_new_row = pd.DataFrame(na_vals, index=[dt_date],
columns=ls_symbols)
df_alloc = df_alloc.append(df_new_row)
dt_last_date = dt_date
# Adding cash to the allocation matrix
df_alloc['_CASH'] = 0.0
# Running the simulator on the allocation frame
(ts_funds, ts_leverage, f_commission, f_slippage, f_borrow_cost) = qstksim.tradesim(df_alloc,
df_close, f_start_cash=10000.0, i_leastcount=1, b_followleastcount=True,
f_slippage=0.0005, f_minimumcommision=5.0, f_commision_share=0.0035,
i_target_leverage=1, f_rate_borrow=3.5, log="transaction.csv")
print "Simulated Fund Time Series : "
print ts_funds
print "Transaction Costs : "
print "Commissions : ", f_commission
print "Slippage : ", f_slippage
print "Borrowing Cost : ", f_borrow_cost
print df_alloc
def main():
'''Main Function'''
# List of symbols
ls_symbols = ["AAPL", "GOOG"]
# Start and End date of the charts
dt_start = dt.datetime(2008, 1, 1)
dt_end = dt.datetime(2010, 12, 31)
# We need closing prices so the timestamp should be hours=16.
dt_timeofday = dt.timedelta(hours=16)
# Get a list of trading days between the start and the end.
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
# Creating an object of the dataaccess class with Yahoo as the source.
c_dataobj = da.DataAccess('Yahoo')
# Reading just the close prices
df_close = c_dataobj.get_data(ldt_timestamps, ls_symbols, "close")
# Creating the allocation dataframe
# We offset the time for the simulator to have atleast one
# datavalue before the allocation.
df_alloc = pd.DataFrame(np.array([[0.5, 0.5]]),
index=[ldt_timestamps[0] + dt.timedelta(hours=5)],
columns=ls_symbols)
dt_last_date = ldt_timestamps[0]
# Looping through all dates and creating monthly allocations
for dt_date in ldt_timestamps[1:]:
if dt_last_date.month != dt_date.month:
# Create allocation
na_vals = np.random.randint(0, 1000, len(ls_symbols))
na_vals = na_vals / float(sum(na_vals))
na_vals = na_vals.reshape(1, -1)
# Append to the dataframe
df_new_row = pd.DataFrame(na_vals, index=[dt_date],
columns=ls_symbols)
df_alloc = df_alloc.append(df_new_row)
dt_last_date = dt_date
# Adding cash to the allocation matrix
df_alloc['_CASH'] = 0.0
# Running the simulator on the allocation frame
(ts_funds, ts_leverage, f_commission, f_slippage, f_borrow_cost) = qstksim.tradesim(df_alloc,
df_close, f_start_cash=10000.0, i_leastcount=1, b_followleastcount=True,
f_slippage=0.0005, f_minimumcommision=5.0, f_commision_share=0.0035,
i_target_leverage=1, f_rate_borrow=3.5, log="transaction.csv")
print "Simulated Fund Time Series : "
print ts_funds
print "Transaction Costs : "
print "Commissions : ", f_commission
print "Slippage : ", f_slippage
print "Borrowing Cost : ", f_borrow_cost
def test_buy_close(self):
""" Tests tradesim buy-on-open functionality """
(df_funds, ts_leverage, f_commision, f_slippage, f_borrow) = qstksim.tradesim(
self.df_alloc, self.df_close, 10000, 1, True, 0.02, 5, 0.02
)
print "Commision Costs : " + str(f_commision)
print "Slippage : " + str(f_slippage)
print "Short Borrowing Cost : " + str(f_borrow)
print "Leverage : "
print ts_leverage
np.testing.assert_approx_equal(df_funds[-1], 10000 * self.i_open_result, significant=3)
self.assertTrue(True)
def test_buy_close(self):
''' Tests tradesim buy-on-open functionality '''
(df_funds, ts_leverage, f_commision, f_slippage, f_borrow) = \
qstksim.tradesim( self.df_alloc, self.df_close, 10000, 1, True,
0.02, 5, 0.02)
print 'Commision Costs : ' + str(f_commision)
print 'Slippage : ' + str(f_slippage)
print 'Short Borrowing Cost : ' + str(f_borrow)
print 'Leverage : '
print ts_leverage
np.testing.assert_approx_equal(df_funds[-1], \
10000 * self.i_open_result, significant = 3)
self.assertTrue(True)
def test_buy_close(self):
''' Tests tradesim buy-on-open functionality '''
(df_funds, ts_leverage, f_commision, f_slippage, f_borrow) = \
qstksim.tradesim( self.df_alloc, self.df_close, 10000, 1, True,
0.02, 5, 0.02)
print('Commision Costs : ' + str(f_commision))
print('Slippage : ' + str(f_slippage))
print('Short Borrowing Cost : ' + str(f_borrow))
print('Leverage : ')
print(ts_leverage)
np.testing.assert_approx_equal(df_funds[-1], \
10000 * self.i_open_result, significant = 3)
self.assertTrue(True)
dataobj = da.DataAccess('Yahoo')
ls_symbols = dataobj.get_symbols_from_list('sp5002012')
#ls_symbols = ['GOOG','IBM']
ls_symbols.append('SPY')
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
ldf_data = dataobj.get_data(ldt_timestamps, ls_symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
for s_key in ls_keys:
d_data[s_key] = d_data[s_key].fillna(method='ffill')
d_data[s_key] = d_data[s_key].fillna(method='bfill')
d_data[s_key] = d_data[s_key].fillna(1.0)
df_events = find_events(ls_symbols, d_data)
print "Simulating Study"
(ts_funds, ts_leverage, f_commission, f_slippage,
f_borrow_cost) = qstksim.tradesim(df_alloc,
df_close,
f_start_cash=1000000,
i_leastcount=1,
b_followleastcount=True,
f_slippage=0.0005,
f_minimumcommision=5.0,
f_commision_share=0.0035,
i_target_leverage=1,
f_rate_borrow=3.5,
log='simbb.csv')
'''
http://wiki.quantsoftware.org/index.php?title=CompInvesti_Homework_7
'''
total_return_factor = pd.DataFrame(np.ones(shape=(8, noa)), \
index=dates, columns=symbols)
trade_volume = pd.DataFrame(np.random.randint(100, 500, size=(8, noa)), \
index=dates, columns=symbols)
exec_price_return = pd.DataFrame(np.random.randint(low=5,size=(8, noa)), \
index=dates, columns=symbols)
initial_holding = pd.DataFrame([[100, 100, 500, 600, 800, 10000],[100, 100, 500, 600, 800, 10000]], \
index=dates[:2], columns=symbols_and_cash)
#initial_holding = 1000
target_portfolio_wgt = pd.DataFrame()
for i in range(8):
weights = np.random.random(noa)
weights /= np.sum(weights)
target_portfolio_wgt = target_portfolio_wgt.append(pd.DataFrame(weights).T)
target_portfolio_wgt.index = dates
target_portfolio_wgt.columns = symbols
target_portfolio_wgt['Cash'] = 0
# List of symbols
ls_symbols = ['A', 'B', 'C', 'D', 'E']
# Start and End date of the charts
dt_start = dt.datetime(2006, 1, 1)
dt_end = dt.datetime(2010, 12, 31)
df_close = market_to_market_price
(ts_funds, ts_leverage, f_commission, f_slippage, f_borrow_cost) = qstksim.tradesim(target_portfolio_wgt,
market_to_market_price, f_start_cash=10000.0, i_leastcount=1, b_followleastcount=True,
f_slippage=0.0005, f_minimumcommision=5.0, f_commision_share=0.0035,
i_target_leverage=1, f_rate_borrow=3.5, log="transaction.csv")