def main(symbol="JPM",
start_date=dt.datetime(2008, 1, 1),
end_date=dt.datetime(2009, 12, 31)):
np.random.seed(3**9)
random.seed(3**9)
print "Random seed is: ", 3**9, "\n\n"
# Manual Strategy
(df_trades, indicators_manual) = testPolicy(symbol=symbol,
sd=start_date,
ed=end_date,
sv=100000)
#df_trades.to_csv("manual.csv")
Manual_trade_dates = df_trades[symbol].nonzero()
#print SL_trade_dates[0][0]
print "\nFirst trading date of Manual Strategy: ", df_trades.index.values[
Manual_trade_dates[0][0]]
print "Trading times of Manual Strategy: ", len(
Manual_trade_dates[0]), "\n"
Manual_portval = compute_portvals(df_trades,
start_val=100000,
commission=0.0,
impact=0.0,
symbol=symbol)
Manual_portval_stats = compute_portvals_stats(Manual_portval)
# Learning Strategy
strategy_learner = sl.StrategyLearner(verbose=False, impact=0.0)
strategy_learner.addEvidence(symbol=symbol,
sd=start_date,
ed=end_date,
sv=100000)
df_SL = strategy_learner.testPolicy(symbol=symbol,
sd=start_date,
ed=end_date,
sv=100000)
SL_trade_dates = df_SL[symbol].nonzero()
#print SL_trade_dates[0][0]
print "\nFirst trading date of Strategy Learner: ", df_SL.index.values[
SL_trade_dates[0][0]]
print "Trading times of Strategy Learner: ", len(SL_trade_dates[0]), "\n"
SL_portval = compute_portvals(df_SL,
start_val=100000,
commission=0.0,
impact=0.0,
symbol=symbol)
SL_portval_stats = compute_portvals_stats(SL_portval)
# Benchmark
df_benchmark = Benchmark(symbol=symbol,
sd=start_date,
ed=end_date,
sv=100000)
BM_trade_dates = df_benchmark[symbol].nonzero()
#print df_benchmark
print "\nFirst trading date of Benchmark: ", df_benchmark.index.values[
BM_trade_dates[0][0]]
print "Trading times of Benchmark: ", len(BM_trade_dates[0]), "\n"
Benchmark_portval = compute_portvals(df_benchmark,
start_val=100000,
commission=0.0,
impact=0.0,
symbol=symbol)
Benchmark_portval_stats = compute_portvals_stats(Benchmark_portval)
############################
#
# Print Stats
#
############################
print "Date Range: {} to {}".format(start_date, end_date)
print
print "Sharpe Ratio of Manual: {}".format(Manual_portval_stats[0])
print "Sharpe Ratio of SL: {}".format(SL_portval_stats[0])
print "Sharpe Ratio of Benchmark: {}".format(Benchmark_portval_stats[0])
print
print "Cumulative Return of Manual: {}".format(Manual_portval_stats[1])
print "Cumulative Return of SL: {}".format(SL_portval_stats[1])
print "Cumulative Return of Benchmark: {}".format(
Benchmark_portval_stats[1])
print
print "Average Daily Return of Manual: {}".format(Manual_portval_stats[2])
print "Average Daily Return of SL: {}".format(SL_portval_stats[2])
print "Average Daily Return of Benchmark: {}".format(
Benchmark_portval_stats[2])
print
print "Standard Deviation of Manual: {}".format(Manual_portval_stats[3])
print "Standard Deviation of SL: {}".format(SL_portval_stats[3])
print "Standard Deviation of Benchmark: {}".format(
Benchmark_portval_stats[3])
print
print "Final Portfolio Value Manual: {}".format(Manual_portval[-1])
print "Final Portfolio Value SL: {}".format(SL_portval[-1])
print "Final Portfolio Value Benchmark: {}".format(Benchmark_portval[-1])
portfolio_df = pd.DataFrame(
index=Benchmark_portval.index,
columns=['Manual Strategy', 'Strategy Learner', 'Benchmark'])
portfolio_df['Manual Strategy'] = Manual_portval / Manual_portval[0]
portfolio_df['Strategy Learner'] = SL_portval / SL_portval[0]
portfolio_df['Benchmark'] = Benchmark_portval / Benchmark_portval[0]
#manual_buy = indicators_manual[indicators_manual['Final_Signal'] == 1]
#print manual_buy.index
#manual_sell = indicators_manual[indicators_manual['Final_Signal'] == -1]
#print manual_sell.index
ax = portfolio_df.plot(fontsize=16, color=["blue", "red", "black"])
ax.set_xlabel("Dates", fontsize=16)
ax.set_ylabel("Normalized Portfolio Values", fontsize=16)
#ymin, ymax = ax.get_ylim()
#plt.vlines(manual_buy.index,ymin,ymax,color='g')
#plt.vlines(manual_sell.index,ymin,ymax,color='r')
#filename = "Figure2.png"
#plt.savefig(filename)
figure = plt.gcf() # get current figure
figure.set_size_inches(8, 6)
figure.suptitle("Manual Strategy Vs. Strategy Learner Vs. Benchmark",
fontsize=16)
# when saving, specify the DPI
figure.savefig("Figure1.png")
def main(symbol="JPM", start_date=dt.datetime(2008,1,1), end_date=dt.datetime(2009,12,31)):
np.random.seed(3 ** 9)
random.seed(3 ** 9)
print "Random seed is: ", 3**9, "\n\n"
# Learning Strategy: (impact = 0.0)
strategy_learner1 = sl.StrategyLearner(verbose = False, impact=0.0)
strategy_learner1.addEvidence(symbol=symbol, sd=start_date, ed=end_date, sv=100000)
df_SL1 = strategy_learner1.testPolicy(symbol=symbol, sd=start_date, ed=end_date, sv=100000)
SL1_portval = compute_portvals(df_SL1, start_val = 100000, commission=0.0, impact=0.0, symbol=symbol)
SL1_portval_stats = compute_portvals_stats(SL1_portval)
SL_trade_dates = df_SL1[symbol].nonzero()
#print SL_trade_dates[0][0]
if len(SL_trade_dates[0]):
print "\nFirst trading date of Strategy Learner (impact=0.0): ", df_SL1.index.values[SL_trade_dates[0][0]]
print "Trading times of Strategy Learner (impact=0.0): ", len(SL_trade_dates[0]), "\n"
else:
print "\nFirst trading date of Strategy Learner (impact=0.0): ", 0
print "Trading times of Strategy Learner (impact=0.0): ", "No trading"
# Learning Strategy: (impact = 0.005)
strategy_learner2 = sl.StrategyLearner(verbose = False, impact=0.005)
strategy_learner2.addEvidence(symbol=symbol, sd=start_date, ed=end_date, sv=100000)
df_SL2 = strategy_learner2.testPolicy(symbol=symbol, sd=start_date, ed=end_date, sv=100000)
SL2_portval = compute_portvals(df_SL2, start_val = 100000, commission=0.0, impact=0.005, symbol=symbol)
SL2_portval_stats = compute_portvals_stats(SL2_portval)
SL_trade_dates = df_SL2[symbol].nonzero()
#print SL_trade_dates[0][0]
#print len(SL_trade_dates[0])
if len(SL_trade_dates[0]):
print "\nFirst trading date of Strategy Learner (impact=0.005): ", df_SL2.index.values[SL_trade_dates[0][0]]
print "Trading times of Strategy Learner (impact=0.005): ", len(SL_trade_dates[0]), "\n"
else:
print "\nFirst trading date of Strategy Learner (impact=0.005): ", 0
print "Trading times of Strategy Learner (impact=0.005): ", "No trading"
# Learning Strategy: (impact = 0.05)
strategy_learner3 = sl.StrategyLearner(verbose = False, impact=0.05)
strategy_learner3.addEvidence(symbol=symbol, sd=start_date, ed=end_date, sv=100000)
df_SL3 = strategy_learner3.testPolicy(symbol=symbol, sd=start_date, ed=end_date, sv=100000)
SL3_portval = compute_portvals(df_SL3, start_val = 100000, commission=0.0, impact=0.05, symbol=symbol)
SL3_portval_stats = compute_portvals_stats(SL3_portval)
SL_trade_dates = df_SL3[symbol].nonzero()
if len(SL_trade_dates[0]):
print "\nFirst trading date of Strategy Learner (impact=0.05): ", df_SL3.index.values[SL_trade_dates[0][0]]
print "Trading times of Strategy Learner (impact=0.05): ", len(SL_trade_dates[0]), "\n"
else:
print "\nFirst trading date of Strategy Learner (impact=0.05): ", 0
print "Trading times of Strategy Learner (impact=0.05): ", "No trading"
# Learning Strategy: (impact = 0.5)
strategy_learner4 = sl.StrategyLearner(verbose = False, impact=0.5)
strategy_learner4.addEvidence(symbol=symbol, sd=start_date, ed=end_date, sv=100000)
df_SL4 = strategy_learner4.testPolicy(symbol=symbol, sd=start_date, ed=end_date, sv=100000)
SL4_portval = compute_portvals(df_SL4, start_val = 100000, commission=0.0, impact=0.5, symbol=symbol)
SL4_portval_stats = compute_portvals_stats(SL4_portval)
SL_trade_dates = df_SL4[symbol].nonzero()
#print SL_trade_dates[0][0]
if len(SL_trade_dates[0]):
print "\nFirst trading date of Strategy Learner (impact=0.5): ", df_SL4.index.values[SL_trade_dates[0][0]]
print "Trading times of Strategy Learner (impact=0.5): ", len(SL_trade_dates[0]), "\n"
else:
print "\nFirst trading date of Strategy Learner (impact=0.5): ", 0
print "Trading times of Strategy Learner (impact=0.5): ", "No trading"
# Benchmark
df_benchmark = Benchmark(symbol=symbol, sd=start_date, ed=end_date, sv=100000)
Benchmark_portval = compute_portvals(df_benchmark, start_val = 100000, commission=0.0, impact=0.0, symbol=symbol)
Benchmark_portval_stats = compute_portvals_stats(Benchmark_portval)
BM_trade_dates = df_benchmark[symbol].nonzero()
#print df_benchmark
print "\nFirst trading date of Benchmark: ", df_benchmark.index.values[BM_trade_dates[0][0]]
print "Trading times of Benchmark: ", len(BM_trade_dates[0]), "\n"
############################
#
# Print Stats
#
############################
print "Date Range: {} to {}".format(start_date, end_date)
print
print "Sharpe Ratio of SL1: {}".format(SL1_portval_stats[0])
print "Sharpe Ratio of SL2: {}".format(SL2_portval_stats[0])
print "Sharpe Ratio of SL3: {}".format(SL3_portval_stats[0])
print "Sharpe Ratio of SL4: {}".format(SL4_portval_stats[0])
print "Sharpe Ratio of Benchmark: {}".format(Benchmark_portval_stats[0])
print
print "Cumulative Return of SL1: {}".format(SL1_portval_stats[1])
print "Cumulative Return of SL2: {}".format(SL2_portval_stats[1])
print "Cumulative Return of SL3: {}".format(SL3_portval_stats[1])
print "Cumulative Return of SL4: {}".format(SL4_portval_stats[1])
print "Cumulative Return of Benchmark: {}".format(Benchmark_portval_stats[1])
print
print "Average Daily Return of SL1: {}".format(SL1_portval_stats[2])
print "Average Daily Return of SL2: {}".format(SL2_portval_stats[2])
print "Average Daily Return of SL3: {}".format(SL3_portval_stats[2])
print "Average Daily Return of SL4: {}".format(SL4_portval_stats[2])
print "Average Daily Return of Benchmark: {}".format(Benchmark_portval_stats[2])
print
print "Standard Deviation of SL1: {}".format(SL1_portval_stats[3])
print "Standard Deviation of SL2: {}".format(SL2_portval_stats[3])
print "Standard Deviation of SL3: {}".format(SL3_portval_stats[3])
print "Standard Deviation of SL4: {}".format(SL4_portval_stats[3])
print "Standard Deviation of Benchmark: {}".format(Benchmark_portval_stats[3])
print
print "Final Portfolio Value SL1: {}".format(SL1_portval[-1])
print "Final Portfolio Value SL2: {}".format(SL2_portval[-1])
print "Final Portfolio Value SL3: {}".format(SL3_portval[-1])
print "Final Portfolio Value SL4: {}".format(SL4_portval[-1])
print "Final Portfolio Value Benchmark: {}".format(Benchmark_portval[-1])
portfolio_df = pd.DataFrame(index=Benchmark_portval.index, columns=['Strategy Learner (impact=0.0)', \
'Strategy Learner (impact=0.005)', \
'Strategy Learner (impact=0.05)', \
'Strategy Learner (impact=0.5)', 'Benchmark'])
portfolio_df['Strategy Learner (impact=0.0)'] = SL1_portval / SL1_portval[0]
portfolio_df['Strategy Learner (impact=0.005)'] = SL2_portval / SL2_portval[0]
portfolio_df['Strategy Learner (impact=0.05)'] = SL3_portval / SL3_portval[0]
portfolio_df['Strategy Learner (impact=0.5)'] = SL4_portval / SL4_portval[0]
portfolio_df['Benchmark'] = Benchmark_portval / Benchmark_portval[0]
#manual_buy = indicators_manual[indicators_manual['Final_Signal'] == 1]
#print manual_buy.index
#manual_sell = indicators_manual[indicators_manual['Final_Signal'] == -1]
#print manual_sell.index
ax = portfolio_df.plot(fontsize=16, color = ["red", "orange", "green", "navy", "black"])
ax.set_xlabel("Dates", fontsize=16)
ax.set_ylabel("Normalized Portfolio Values", fontsize=16)
#ymin, ymax = ax.get_ylim()
#plt.vlines(manual_buy.index,ymin,ymax,color='g')
#plt.vlines(manual_sell.index,ymin,ymax,color='r')
#filename = "Figure2.png"
#plt.savefig(filename)
figure = plt.gcf() # get current figure
figure.set_size_inches(9, 6)
figure.suptitle("Strategy Learner with various impact factors Vs. Benchmark", fontsize=16)
# when saving, specify the DPI
figure.savefig("Figure2.png")
def main(start_date=dt.datetime(2008, 1, 1),
end_date=dt.datetime(2009, 12, 31)):
(df_trades, indicators_manual) = testPolicy(symbol="JPM",
sd=start_date,
ed=end_date,
sv=100000)
#print df_trades.head(10)
Manual_portval = compute_portvals(df_trades,
start_val=100000,
commission=9.95,
impact=0.005,
symbol="JPM")
Manual_portval_stats = compute_portvals_stats(Manual_portval)
df_benchmark = Benchmark(symbol="JPM",
sd=start_date,
ed=end_date,
sv=100000)
Benchmark_portval = compute_portvals(df_benchmark,
start_val=100000,
commission=9.95,
impact=0.005,
symbol="JPM")
#print Benchmark_portval
Benchmark_portval_stats = compute_portvals_stats(Benchmark_portval)
#print df_benchmark
############################
#
# Print Stats
#
############################
print "Date Range: {} to {}".format(start_date, end_date)
print
print "Sharpe Ratio of Manual: {}".format(Manual_portval_stats[0])
print "Sharpe Ratio of Benchmark: {}".format(Benchmark_portval_stats[0])
print
print "Cumulative Return of Manual: {}".format(Manual_portval_stats[1])
print "Cumulative Return of Benchmark: {}".format(
Benchmark_portval_stats[1])
print
print "Average Daily Return of Manual: {}".format(Manual_portval_stats[2])
print "Average Daily Return of Benchmark: {}".format(
Benchmark_portval_stats[2])
print
print "Standard Deviation of Manual: {}".format(Manual_portval_stats[3])
print "Standard Deviation of Benchmark: {}".format(
Benchmark_portval_stats[3])
print
print "Final Portfolio Value Manual: {}".format(Manual_portval[-1])
print "Final Portfolio Value Benchmark: {}".format(Benchmark_portval[-1])
portfolio_df = pd.DataFrame(index=Manual_portval.index,
columns=['Manual Strategy', 'Benchmark'])
portfolio_df['Manual Strategy'] = Manual_portval / Manual_portval[0]
portfolio_df['Benchmark'] = Benchmark_portval / Benchmark_portval[0]
manual_buy = indicators_manual[indicators_manual['Final_Signal'] == 1]
#print manual_buy.index
manual_sell = indicators_manual[indicators_manual['Final_Signal'] == -1]
#print manual_sell.index
plt.figure(figsize=(12, 8))
ax = portfolio_df.plot(title="Manual Strategy Vs. Benchmark",
fontsize=12,
color=["black", "blue"])
ax.set_xlabel("Dates")
ax.set_ylabel("Normalized Portfolio Values")
ymin, ymax = ax.get_ylim()
plt.vlines(manual_buy.index, ymin, ymax, color='g')
plt.vlines(manual_sell.index, ymin, ymax, color='r')
filename = "Manual.Vs.Benchmark" + "_" + str(start_date) + "_" + str(
end_date) + ".png"
plt.savefig(filename)