def get_port_val_sl(impact, symbol, start_date, end_date, sv, commission=9.95):
sl = StrategyLearner.StrategyLearner(impact=impact, commission=commission)
sl.addEvidence(symbol=symbol,
sd=start_date,
ed=end_date,
sv=100000,
n_bins=5)
df_trades_sl = sl.testPolicy(symbol=symbol,
sd=start_date,
ed=end_date,
sv=100000)
df_orders_sl, _ = ManualStrategy.generate_orders(df_trades_sl, symbol)
port_vals_sl = ManualStrategy.compute_portvals(df_orders_sl,
start_val=100000,
sd=start_date,
ed=end_date,
commission=commission,
impact=impact)
normed_port_sl = port_vals_sl / port_vals_sl.ix[0]
return normed_port_sl
def experiment1():
ms = ManualStrategy.ManualStrategy()
sl = StrategyLearner.StrategyLearner()
commission = 9.95
impact = 0.005
# in sample
start_date = dt.datetime(2008, 1, 1)
end_date = dt.datetime(2009, 12, 31)
# dates = pd.date_range(start_date, end_date)
symbol = 'JPM'
sl.addEvidence(symbol=symbol,
sd=start_date,
ed=end_date,
sv=100000,
n_bins=5)
df_trades_ms = ms.testPolicy(symbol=symbol,
sd=start_date,
ed=end_date,
sv=100000)
df_trades_sl = sl.testPolicy(symbol=symbol,
sd=start_date,
ed=end_date,
sv=100000)
# generate orders based on trades
df_orders_ms, benchmark_orders = ManualStrategy.generate_orders(
df_trades_ms, symbol)
df_orders_sl, _ = ManualStrategy.generate_orders(df_trades_sl, symbol)
port_vals_ms = ManualStrategy.compute_portvals(df_orders_ms,
start_val=100000,
sd=start_date,
ed=end_date,
commission=commission,
impact=impact)
port_vals_sl = ManualStrategy.compute_portvals(df_orders_sl,
start_val=100000,
sd=start_date,
ed=end_date,
commission=commission,
impact=impact)
#benchmark_orders.loc[benchmark_orders.index[1], 'Shares'] = 0
benchmark_vals = ManualStrategy.compute_portvals(benchmark_orders,
sd=start_date,
ed=end_date,
start_val=100000,
commission=commission,
impact=impact)
normed_port_ms = port_vals_ms / port_vals_ms.ix[0]
normed_port_sl = port_vals_sl / port_vals_sl.ix[0]
normed_bench = benchmark_vals / benchmark_vals.ix[0]
dates = pd.date_range(start_date, end_date)
prices_all = get_data([symbol], dates, addSPY=True, colname='Adj Close')
prices = prices_all[symbol] # only portfolio symbols
# get indicators
lookback = 14
_, PSR = id.get_SMA(prices, lookback)
_, _, bb_indicator = id.get_BB(prices, lookback)
momentum = id.get_momentum(prices, lookback)
# figure 5.
plt.figure(figsize=(12, 6.5))
top = plt.subplot2grid((5, 1), (0, 0), rowspan=3, colspan=1)
bottom = plt.subplot2grid((5, 1), (3, 0), rowspan=2, colspan=1, sharex=top)
# plot the Long or short action
for index, marks in df_trades_sl.iterrows():
if marks['Trades'] > 0:
plt.axvline(x=index, color='blue', linestyle='dashed', alpha=.9)
elif marks['Trades'] < 0:
plt.axvline(x=index, color='black', linestyle='dashed', alpha=.9)
else:
pass
top.xaxis_date()
top.grid(True)
top.plot(normed_port_sl, lw=2, color='red', label='Q-Learning Strategy')
top.plot(normed_port_ms, lw=1.5, color='black', label='Manual Strategy')
top.plot(normed_bench, lw=1.2, color='green', label='Benchmark')
top.set_title(
'Machine Learning Strategy (MLS), Manual Strategy (MS) - In Sample Analysis'
)
top.set_ylabel('Normalized Value')
for index, marks in df_trades_sl.iterrows():
if marks['Trades'] > 0:
top.axvline(x=index, color='blue', linestyle='dashed', alpha=.9)
elif marks['Trades'] < 0:
top.axvline(x=index, color='black', linestyle='dashed', alpha=.9)
else:
pass
bottom.plot(momentum, color='olive', lw=1, label="momentum")
bottom.plot(PSR, color='purple', lw=1, label="PSR")
#bottom.plot(bb_indicator, color='blue', lw=1, label="Bollinger")
bottom.set_title('Indicators')
bottom.axhline(y=-0.2, color='grey', linestyle='--', alpha=0.5)
bottom.axhline(y=0, color='grey', linestyle='--', alpha=0.5)
bottom.axhline(y=0.2, color='grey', linestyle='--', alpha=0.5)
bottom.legend()
top.legend()
top.axes.get_xaxis().set_visible(False)
plt.xlim(start_date, end_date)
filename = '01_MLS_insample.png'
plt.savefig(filename)
plt.close()
port_cr_sl, port_adr_sl, port_stddr_sl, port_sr_sl = ManualStrategy.get_portfolio_stats(
port_vals_sl)
port_cr_ms, port_adr_ms, port_stddr_ms, port_sr_ms = ManualStrategy.get_portfolio_stats(
port_vals_ms)
bench_cr, bench_adr, bench_stddr, bench_sr = ManualStrategy.get_portfolio_stats(
benchmark_vals)
# Compare portfolio against benchmark
print "=== Machine Learning Strategy (MLS) V.S. Manual Strategy (MS) In Sample ==="
print "Date Range: {} to {}".format(start_date, end_date)
print
print "Sharpe Ratio of MLS: {}".format(port_sr_sl)
print "Sharpe Ratio of MS: {}".format(port_sr_ms)
print "Sharpe Ratio of BenchMark : {}".format(bench_sr)
print
print "Cumulative Return of MLS: {}".format(port_cr_sl)
print "Cumulative Return of MS: {}".format(port_cr_ms)
print "Cumulative Return of Benchmark : {}".format(bench_cr)
print
print "Standard Deviation of MLS: {}".format(port_stddr_sl)
print "Standard Deviation of MS: {}".format(port_stddr_ms)
print "Standard Deviation of Benchmark : {}".format(bench_stddr)
print
print "Average Daily Return of MLS: {}".format(port_adr_sl)
print "Average Daily Return of MS: {}".format(port_adr_ms)
print "Average Daily Return of BenchMark : {}".format(bench_adr)
print
print "Final MLS Portfolio Value: {}".format(port_vals_sl[-1])
print "Final MS Portfolio Value: {}".format(port_vals_ms[-1])
print "Final Benchmark Portfolio Value: {}".format(benchmark_vals[-1])
print
# ========================
# OUT OF SAMPLE Analysis
# ========================
start_date = dt.datetime(2010, 1, 1)
end_date = dt.datetime(2011, 12, 31)
# dates = pd.date_range(start_date, end_date)
symbol = 'JPM'
df_trades_ms = ms.testPolicy(symbol=symbol,
sd=start_date,
ed=end_date,
sv=100000)
df_trades_sl = sl.testPolicy(symbol=symbol,
sd=start_date,
ed=end_date,
sv=100000)
# generate orders based on trades
df_orders_ms, benchmark_orders = ManualStrategy.generate_orders(
df_trades_ms, symbol)
df_orders_sl, _ = ManualStrategy.generate_orders(df_trades_sl, symbol)
port_vals_ms = ManualStrategy.compute_portvals(df_orders_ms,
start_val=100000,
sd=start_date,
ed=end_date,
commission=commission,
impact=impact)
port_vals_sl = ManualStrategy.compute_portvals(df_orders_sl,
start_val=100000,
sd=start_date,
ed=end_date,
commission=commission,
impact=impact)
#benchmark_orders.loc[benchmark_orders.index[1], 'Shares'] = 0
benchmark_vals = ManualStrategy.compute_portvals(benchmark_orders,
sd=start_date,
ed=end_date,
start_val=100000,
commission=commission,
impact=impact)
normed_port_ms = port_vals_ms / port_vals_ms.ix[0]
normed_port_sl = port_vals_sl / port_vals_sl.ix[0]
normed_bench = benchmark_vals / benchmark_vals.ix[0]
dates = pd.date_range(start_date, end_date)
prices_all = get_data([symbol], dates, addSPY=True, colname='Adj Close')
prices = prices_all[symbol] # only portfolio symbols
# get indicators
lookback = 14
_, PSR = id.get_SMA(prices, lookback)
_, _, bb_indicator = id.get_BB(prices, lookback)
momentum = id.get_momentum(prices, lookback)
# figure 5.
plt.figure(figsize=(12, 6.5))
top = plt.subplot2grid((5, 1), (0, 0), rowspan=3, colspan=1)
bottom = plt.subplot2grid((5, 1), (3, 0), rowspan=2, colspan=1, sharex=top)
# plot the Long or short action
for index, marks in df_trades_sl.iterrows():
if marks['Trades'] > 0:
plt.axvline(x=index, color='blue', linestyle='dashed', alpha=.9)
elif marks['Trades'] < 0:
plt.axvline(x=index, color='black', linestyle='dashed', alpha=.9)
else:
pass
top.xaxis_date()
top.grid(True)
top.plot(normed_port_sl, lw=2, color='red', label='Q-Learning Strategy')
top.plot(normed_port_ms, lw=1.5, color='black', label='Manual Strategy')
top.plot(normed_bench, lw=1.2, color='green', label='Benchmark')
top.set_title(
'Machine Learning Strategy (MLS) V.S. Manual Strategy (MS) - Out Sample Analysis'
)
top.set_ylabel('Normalized Value')
for index, marks in df_trades_sl.iterrows():
if marks['Trades'] > 0:
top.axvline(x=index, color='blue', linestyle='dashed', alpha=.9)
elif marks['Trades'] < 0:
top.axvline(x=index, color='black', linestyle='dashed', alpha=.9)
else:
pass
bottom.plot(momentum, color='olive', lw=1, label="momentum")
bottom.plot(PSR, color='purple', lw=1, label="PSR")
#bottom.plot(bb_indicator, color='blue', lw=1, label="Bollinger")
bottom.set_title('Indicators')
bottom.axhline(y=-0.2, color='grey', linestyle='--', alpha=0.5)
bottom.axhline(y=0, color='grey', linestyle='--', alpha=0.5)
bottom.axhline(y=0.2, color='grey', linestyle='--', alpha=0.5)
bottom.legend()
top.legend()
top.axes.get_xaxis().set_visible(False)
plt.xlim(start_date, end_date)
filename = '02_MLS_outsample.png'
plt.savefig(filename)
plt.close()
port_cr_sl, port_adr_sl, port_stddr_sl, port_sr_sl = ManualStrategy.get_portfolio_stats(
port_vals_sl)
port_cr_ms, port_adr_ms, port_stddr_ms, port_sr_ms = ManualStrategy.get_portfolio_stats(
port_vals_ms)
bench_cr, bench_adr, bench_stddr, bench_sr = ManualStrategy.get_portfolio_stats(
benchmark_vals)
# Compare portfolio against benchmark
print "=== Machine Learning Strategy (MLS) V.S. Manual Strategy (MS) OUT Sample ==="
print "Date Range: {} to {}".format(start_date, end_date)
print
print "Sharpe Ratio of MLS: {}".format(port_sr_sl)
print "Sharpe Ratio of MS: {}".format(port_sr_ms)
print "Sharpe Ratio of BenchMark : {}".format(bench_sr)
print
print "Cumulative Return of MLS: {}".format(port_cr_sl)
print "Cumulative Return of MS: {}".format(port_cr_ms)
print "Cumulative Return of Benchmark : {}".format(bench_cr)
print
print "Standard Deviation of MLS: {}".format(port_stddr_sl)
print "Standard Deviation of MS: {}".format(port_stddr_ms)
print "Standard Deviation of Benchmark : {}".format(bench_stddr)
print
print "Average Daily Return of MLS: {}".format(port_adr_sl)
print "Average Daily Return of MS: {}".format(port_adr_ms)
print "Average Daily Return of BenchMark : {}".format(bench_adr)
print
print "Final MLS Portfolio Value: {}".format(port_vals_sl[-1])
print "Final MS Portfolio Value: {}".format(port_vals_ms[-1])
print "Final Benchmark Portfolio Value: {}".format(benchmark_vals[-1])
print