def testStrategy(symbol, sd, ed, sv=100000):
# Set the symbol and the time period
sd = sd
ed = ed
# Get the orders
#############################################################
sleanrer = sl.StrategyLearner()
sleanrer.addEvidence(symbol=symbol, sd=sd, ed=ed, sv=sv)
order_index = sleanrer.testPolicy(symbol=symbol, sd=sd, ed=ed, sv=sv)
order_index = order_index[order_index.iloc[:, 0] != 0]
order_index['Order'] = ''
order_index.loc[order_index['Trade'] > 0, 'Order'] = 'BUY'
order_index.loc[order_index['Trade'] < 0, 'Order'] = 'SELL'
order_index['Shares'] = order_index.loc[:, 'Trade'].abs()
order_index['Symbol'] = symbol
order_index.reset_index(inplace=True)
order_index = order_index.iloc[:, [0, 4, 2, 3]]
order_index.columns = ['Date', 'Symbol', 'Order', 'Shares']
df_trades=order_index
######################################################################
# Get the portfolio value and the benchmark value
matrix_v_port = mktsim.compute_portvals(df_trades, start_val=sv, commission=9.95, impact=0.005, sd=sd, ed=ed)[0]
data_bench = df_trades.iloc[:1]
data_bench.iloc[0, 2] = 'BUY'
matrix_v_bench = mktsim.compute_portvals(data_bench, start_val=sv, commission=9.95, impact=0.005, sd=sd, ed=ed)[
0]
# Get the orders data for vertical lines
orders = mktsim.compute_portvals(df_trades, start_val=sv, commission=9.95, impact=0.005, sd=sd, ed=ed)[1]
# Get the statistics for the portfolio
daily_ret = matrix_v_port.pct_change()
adr = matrix_v_port.pct_change().mean()
sddr = matrix_v_port.pct_change().std()
cr = matrix_v_port.iloc[-1] / matrix_v_port.iloc[0] - 1
# Get the statistics for the benchmark
daily_ret_bench = matrix_v_bench.pct_change()
adr_bench = matrix_v_bench.pct_change().mean()
sddr_bench = matrix_v_bench.pct_change().std()
cr_bench = matrix_v_bench.iloc[-1] / matrix_v_port.iloc[0] - 1
# Get the normalized data for the two portfolios
port_norm = matrix_v_port / matrix_v_port.iloc[0]
bench_norm = matrix_v_bench / matrix_v_bench.iloc[0]
print 'Cumulative return of the benchmark is ', cr_bench
print 'Cumulative return of the portfolio is ', cr
print 'Stdev of the daily return of the benchmark is ', sddr_bench
print 'Stdev of the daily return of the portfolio is ', sddr
print 'Mean of the daily return of the benchmark is ', adr_bench
print 'Mean of the daily return of the portfolio is ', adr
print 'The trading strategy is \n', df_trades
pl_data = pd.concat([port_norm, bench_norm], axis=1)
pl_data.columns = ['Manual Strategy', 'Benchmark']
ms.plot_data(pl_data, orders=orders)
def ms_testStrategy(symbol, sd, ed, sv=100000):
# Set the symbol and the time period
# Get the orders
#############################################################
sd = sd
ed = ed
# Get the orders
df_trades = ms.testPolicy(symbol=symbol, sd=sd, ed=ed)
######################################################################
# Get the portfolio value and the benchmark value
matrix_v_port = mktsim.compute_portvals(df_trades, start_val=sv, commission=9.95, impact=0.05, sd=sd, ed=ed)[0]
data_bench = df_trades.iloc[:1]
data_bench.iloc[0, 2] = 'BUY'
matrix_v_bench = mktsim.compute_portvals(data_bench, start_val=sv, commission=9.95, impact=0.05, sd=sd, ed=ed)[
0]
# Get the orders data for vertical lines
orders = mktsim.compute_portvals(df_trades, start_val=sv, commission=9.95, impact=0.05, sd=sd, ed=ed)[1]
# Get the statistics for the portfolio
daily_ret = matrix_v_port.pct_change()
adr = matrix_v_port.pct_change().mean()
sddr = matrix_v_port.pct_change().std()
cr = matrix_v_port.iloc[-1] / matrix_v_port.iloc[0] - 1
# Get the statistics for the benchmark
daily_ret_bench = matrix_v_bench.pct_change()
adr_bench = matrix_v_bench.pct_change().mean()
sddr_bench = matrix_v_bench.pct_change().std()
cr_bench = matrix_v_bench.iloc[-1] / matrix_v_port.iloc[0] - 1
# Get the normalized data for the two portfolios
port_norm = matrix_v_port / matrix_v_port.iloc[0]
bench_norm = matrix_v_bench / matrix_v_bench.iloc[0]
print 'Cumulative return of the benchmark is ', cr_bench
print 'Cumulative return of the portfolio is ', cr
print 'Stdev of the daily return of the benchmark is ', sddr_bench
print 'Stdev of the daily return of the portfolio is ', sddr
print 'Mean of the daily return of the benchmark is ', adr_bench
print 'Mean of the daily return of the portfolio is ', adr
print 'The trading strategy is \n', df_trades
pl_data = pd.concat([port_norm, bench_norm], axis=1)
pl_data.columns = ['Manual Strategy', 'Benchmark']
ms.plot_data(pl_data, orders=orders)
def calls():
ms.plot_data()
ms.plot_data(sd=dt.datetime(2010, 1, 1), ed=dt.datetime(2011, 12, 31), n=2)
ex1.compare()
ex2.impact_effect()