def experiment2():
sl = StrategyLearner()
ms = ManualStrategy()
start_date = dt.datetime(2008, 1, 1)
end_date = dt.datetime(2009, 12, 31)
symbol = 'JPM'
starting_value = 100000
commission = 0
impact = 0.002
sl.addEvidence(symbol=symbol,
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=starting_value)
qlearn_trades = sl.testPolicy(symbol="JPM",
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=starting_value)
manual_trades = ms.testPolicy(symbol="JPM",
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=starting_value)
benchmark_trades = ms.get_benchmark_order(symbol="JPM",
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=starting_value)
qlearn_portfolio = (mm.compute_portvals(
qlearn_trades, starting_value, commission, impact)) / starting_value
manual_potfolio = (mm.compute_portvals(
manual_trades, starting_value, commission, impact)) / starting_value
benchmark_portfolio = (mm.compute_portvals(
benchmark_trades, starting_value, commission, impact)) / starting_value
plt.clf()
plt.figure(figsize=(15, 5))
plt.xlim(start_date, end_date)
plt.ylim(0.5, 2.0)
ax = qlearn_portfolio.plot(
title=
"Figure 1. QLearning Strategy vs. Manual Rule-based vs. Benchmark with 0.002 Impact",
fontsize=12,
color="green",
label="QLearner-based")
benchmark_portfolio.plot(ax=ax, color="black", label="Benchmark")
manual_potfolio.plot(ax=ax, color="blue", label="Manual Rule-based")
ax.set_ylabel('Normalized Value')
ax.set_xlabel('Dates')
plt.legend(['QLearner', 'Benchmark', "Manual Rule"], loc="lower right")
plt.savefig("Impact1.png")
#plt.show()
#======================================================================================================================================================
impact_2 = 0.007
sl_2 = StrategyLearner()
ms_2 = ManualStrategy()
sl_2.addEvidence(symbol=symbol,
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=starting_value)
qlearn_trades_2 = sl_2.testPolicy(symbol="JPM",
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=starting_value)
manual_trades_2 = ms.testPolicy(symbol="JPM",
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=starting_value)
benchmark_trades_2 = ms.get_benchmark_order(symbol="JPM",
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=starting_value)
qlearn_portfolio_2 = (mm.compute_portvals(qlearn_trades_2, starting_value,
commission,
impact_2)) / starting_value
manual_potfolio_2 = (mm.compute_portvals(manual_trades_2, starting_value,
commission,
impact_2)) / starting_value
benchmark_portfolio_2 = (mm.compute_portvals(benchmark_trades_2,
starting_value, commission,
impact_2)) / starting_value
plt.clf()
plt.figure(figsize=(15, 5))
plt.xlim(start_date, end_date)
plt.ylim(0.5, 2.0)
ax = qlearn_portfolio_2.plot(
title=
"Figure 1. QLearning Strategy vs. Manual Rule-based vs. Benchmark with 0.007 Impact",
fontsize=12,
color="green",
label="QLearner-based")
benchmark_portfolio_2.plot(ax=ax, color="black", label="Benchmark")
manual_potfolio_2.plot(ax=ax, color="blue", label="Manual Rule-based")
ax.set_ylabel('Normalized Value')
ax.set_xlabel('Dates')
plt.legend(['QLearner', 'Benchmark', "Manual Rule"], loc="lower right")
plt.savefig("Impact2.png")
def experiment1():
sl = StrategyLearner()
ms = ManualStrategy()
start_date = dt.datetime(2008, 1, 1)
end_date = dt.datetime(2009, 12, 31)
symbol = 'JPM'
starting_value = 100000
commission = 0
impact = 0
sl.addEvidence(symbol=symbol,
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=starting_value)
qlearn_trades = sl.testPolicy(symbol="JPM",
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=starting_value)
manual_trades = ms.testPolicy(symbol="JPM",
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=starting_value)
benchmark_trades = ms.get_benchmark_order(symbol="JPM",
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=starting_value)
qlearn_portfolio = (mm.compute_portvals(
qlearn_trades, starting_value, commission, impact)) / starting_value
manual_potfolio = (mm.compute_portvals(
manual_trades, starting_value, commission, impact)) / starting_value
benchmark_portfolio = (mm.compute_portvals(
benchmark_trades, starting_value, commission, impact)) / starting_value
plt.clf()
plt.figure(figsize=(15, 5))
plt.xlim(start_date, end_date)
ax = qlearn_portfolio.plot(
title=
"Figure 1. QLearning Strategy vs. Manual Rule-based vs. Benchmark",
fontsize=12,
color="green",
label="QLearner-based")
benchmark_portfolio.plot(ax=ax, color="black", label="Benchmark")
manual_potfolio.plot(ax=ax, color="blue", label="Manual Rule-based")
ax.set_ylabel('Normalized Value')
ax.set_xlabel('Dates')
plt.legend(['QLearner', 'Benchmark', "Manual Rule"], loc="lower right")
plt.savefig("In-Sample.png")
#plt.show()
q_cr, q_adr, q_sddr, q_sr = get_portfolio_stats(qlearn_portfolio)
m_cr, m_adr, m_sddr, m_sr = get_portfolio_stats(manual_potfolio)
b_cr, b_adr, b_sddr, b_sr = get_portfolio_stats(benchmark_portfolio)
print "Cumulative Return of {}: {}".format("QLearner", q_cr)
print "Cumulative Return of {}: {}".format("ManualStrategy", m_cr)
print "Cumulative Return of {}: {}".format("Benchmark", b_cr)
print "Mean Daily Return of {}: {}".format("QLearner", q_adr)
print "Mean Daily Return of {}: {}".format("ManualStrategy", m_adr)
print "Mean Daily Return of {}: {}".format("Benchmark", b_adr)
print "Standard Deviation of daily return of {}: {}".format(
"QLearner", q_sddr)
print "Standard Deviation of daily return of {}: {}".format(
"ManualStrategy", m_sddr)
print "Standard Deviation of daily return of {}: {}".format(
"Benchmark", b_sddr)