Merge branch 'pr/174'

.gitignore

@@ -55,3 +55,9 @@ docs/_build/
 
 # PyBuilder
 target/
+
+# VIM
+*.sw?
+
+# IPython notebook checkpoints
+.ipynb_checkpoints/

pyfolio/tears.py

@@ -37,6 +37,16 @@
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
 import seaborn as sns
+from time import time
+
+def timer(msg_body, previous_time):
+
+    current_time = time()
+    run_time = current_time - previous_time
+    message = "\nFinished " + msg_body + " (required {:.2f} seconds)."
+    print(message.format(run_time))
+
+    return current_time
 
 
 def create_full_tear_sheet(returns, positions=None, transactions=None,
@@ -534,7 +544,7 @@ def create_interesting_times_tear_sheet(
 @plotting_context
 def create_bayesian_tear_sheet(returns, benchmark_rets=None,
                                live_start_date=None, samples=2000,
-                               return_fig=False):
+                               return_fig=False, stoch_vol=False):
     """
     Generate a number of Bayesian distributions and a Bayesian
     cone plot of returns.
@@ -560,6 +570,8 @@ def create_bayesian_tear_sheet(returns, benchmark_rets=None,
         If True, returns the figure that was plotted on.
     set_context : boolean, optional
         If True, set default plotting style context.
+    stoch_vol : boolean, optional
+        If True, run and plot the stochastic volatility model
     """
 
     if live_start_date is None:
@@ -577,13 +589,21 @@ def create_bayesian_tear_sheet(returns, benchmark_rets=None,
     df_test = returns.loc[returns.index >= live_start_date]
 
     # Run T model with missing data
+    print("Running T model")
+    previous_time = time()
+    # track the total run time of the Bayesian tear sheet
+    start_time = previous_time
+
     trace_t = bayesian.run_model('t', df_train, returns_test=df_test,
                                  samples=samples)
+    previous_time = timer("T model", previous_time)
 
     # Compute BEST model
+    print("\nRunning BEST model")
     trace_best = bayesian.run_model('best', df_train,
                                     returns_test=df_test,
                                     samples=samples)
+    previous_time = timer("BEST model", previous_time)
 
     # Plot results
 
@@ -598,6 +618,7 @@ def create_bayesian_tear_sheet(returns, benchmark_rets=None,
     bayesian.plot_bayes_cone(df_train, df_test,
                              trace=trace_t,
                              ax=ax_cone)
+    previous_time = timer("plotting Bayesian cone", previous_time)
 
     # Plot BEST results
     row += 1
@@ -614,6 +635,7 @@ def create_bayesian_tear_sheet(returns, benchmark_rets=None,
     axs.append(plt.subplot(gs[row, :]))
 
     bayesian.plot_best(trace=trace_best, axs=axs)
+    previous_time = timer("plotting BEST results", previous_time)
 
     # Compute Bayesian predictions
     row += 1
@@ -631,6 +653,8 @@ def create_bayesian_tear_sheet(returns, benchmark_rets=None,
                          verticalalignment='bottom',
                          horizontalalignment='right',
                          transform=ax_ret_pred_day.transAxes)
+    previous_time = timer("computing Bayesian predictions", previous_time)
+
     # Plot Bayesian VaRs
     week_pred = (
         np.cumprod(trace_t['returns_missing'][:, :5] + 1, 1) - 1)[:, -1]
@@ -645,12 +669,15 @@ def create_bayesian_tear_sheet(returns, benchmark_rets=None,
                           verticalalignment='bottom',
                           horizontalalignment='right',
                           transform=ax_ret_pred_week.transAxes)
+    previous_time = timer("plotting Bayesian VaRs estimate", previous_time)
 
     # Run alpha beta model
+    print("\nRunning alpha beta model")
     benchmark_rets = benchmark_rets.loc[df_train.index]
     trace_alpha_beta = bayesian.run_model('alpha_beta', df_train,
                                           bmark=benchmark_rets,
                                           samples=samples)
+    previous_time = timer("running alpha beta model", previous_time)
 
     # Plot alpha and beta
     row += 1
@@ -662,6 +689,39 @@ def create_bayesian_tear_sheet(returns, benchmark_rets=None,
     sns.distplot(trace_alpha_beta['beta'][100:], ax=ax_beta)
     ax_beta.set_xlabel('Beta')
     ax_beta.set_ylabel('Belief')
+    previous_time = timer("plotting alpha beta model", previous_time)
+
+    if stoch_vol:
+        # run stochastic volatility model
+        print("\nRunning stochastic volatility model on most recent 400 days of returns")
+        returns_cutoff = 400
+        if df_train.size > returns_cutoff:
+            df_train_truncated = df_train[-returns_cutoff:]
+        trace_stoch_vol = bayesian.model_stoch_vol(df_train_truncated)
+        previous_time = timer("running stochastic volatility model", previous_time)
+
+        # plot log(sigma) and log(nu)
+        print("\nPlotting stochastic volatility model")
+        row += 1
+        ax_sigma_log = plt.subplot(gs[row, 0])
+        ax_nu_log = plt.subplot(gs[row, 1])
+        sigma_log = trace_stoch_vol['sigma_log']
+        sns.distplot(sigma_log, ax=ax_sigma_log)
+        ax_sigma_log.set_xlabel('log(Sigma)')
+        ax_sigma_log.set_ylabel('Belief')
+        nu_log = trace_stoch_vol['nu_log']
+        sns.distplot(nu_log, ax=ax_nu_log)
+        ax_nu_log.set_xlabel('log(nu)')
+        ax_nu_log.set_ylabel('Belief')
+
+        # plot latent volatility
+        row += 1
+        ax_volatility = plt.subplot(gs[row, :])
+        bayesian.plot_stoch_vol(df_train_truncated, trace=trace_stoch_vol, ax=ax_volatility)
+        previous_time = timer("plotting stochastic volatility model", previous_time)
+
+    total_time = time() - start_time
+    print("\nTotal runtime was {:.2f} seconds.").format(total_time)
 
     gs.tight_layout(fig)