Merge pull request #63 from cvxgrp/final-touches

Some final touches

README.md

@@ -13,5 +13,11 @@ Please run all experiments using
 make experiments
 ```
 
-Before the experiments start the virtual environment defined in 'requirements.txt'
-is replicated locally.
+This first replicates the virtual environment defined in 'requirements.txt'
+locally and then runs the experiments defined in 'experiments.py'.
+
+## Reproducibility
+
+The main packages used are specified in 'requirements.txt', with a frozen
+version of all packages and their sub-dependencies in 'requirements_frozen.txt'.
+We used Python 3.10.13 to run the experiments.

experiments/markowitz.py

@@ -69,7 +69,7 @@ def markowitz(data: Data, param: Parameters) -> tuple[np.ndarray, float, cp.Prob
     w, c = cp.Variable(data.n_assets), cp.Variable()
 
     z = w - data.w_prev
-    T = cp.norm1(z)
+    T = cp.norm1(z) / 2
     L = cp.norm1(w)
 
     # worst-case (robust) return

experiments/scaling_small.py

@@ -149,7 +149,10 @@ def plot_timings(timings: list[Timing]) -> None:
     plt.ylabel("Time (s)")
     plt.legend()
     plt.savefig(figures_path() / "timing_parametrized.pdf")
-    plt.show()
+
+    show_plot = False
+    if show_plot:
+        plt.show()
 
 
 def initialize_problem(n_assets: int, sigma_target: float) -> None:

experiments/taming.py

@@ -150,14 +150,16 @@ def main(from_checkpoint: bool = False) -> None:
         robust_result,
     )
 
-    generate_per_year_tables(
-        equal_weights_results,
-        basic_result,
-        weight_limited_result,
-        leverage_limit_result,
-        turnover_limit_result,
-        robust_result,
-    )
+    show_yearly_metrics = False
+    if show_yearly_metrics:
+        generate_per_year_tables(
+            equal_weights_results,
+            basic_result,
+            weight_limited_result,
+            leverage_limit_result,
+            turnover_limit_result,
+            robust_result,
+        )
 
 
 def run_all_strategies(annualized_target: float) -> None:

experiments/tuning_example.py

@@ -162,6 +162,8 @@ def plot_results(
     turnovers_train: np.ndarray,
     turnovers_test: np.ndarray,
 ) -> None:
+    show_plot = False
+
     # sharpe
     plt.plot(backtests, sharpe_ratios_train, label="in-sample", marker="o")
     plt.plot(backtests, sharpe_ratios_test, label="out-of-sample", marker="o")
@@ -172,7 +174,8 @@ def plot_results(
     plt.legend()
     plt.ylim(3, 7)
     plt.savefig(experiment_path() / "tuning_results/tuning_SR.pdf", bbox_inches="tight")
-    plt.show()
+    if show_plot:
+        plt.show()
 
     # vola
     plt.plot(backtests, volas_train, label="train", marker="o")
@@ -183,7 +186,8 @@ def plot_results(
     plt.gca().xaxis.set_major_locator(plt.MultipleLocator(5))
     plt.ylim(0.0, 0.12)
     plt.savefig(experiment_path() / "tuning_results/tuning_vola.pdf", bbox_inches="tight")
-    plt.show()
+    if show_plot:
+        plt.show()
 
     # leverage
     plt.plot(backtests, leverages_train, label="train", marker="o")
@@ -194,7 +198,8 @@ def plot_results(
     plt.gca().xaxis.set_major_locator(plt.MultipleLocator(5))
     plt.ylim(1, 2)
     plt.savefig(experiment_path() / "tuning_results/tuning_lev.pdf", bbox_inches="tight")
-    plt.show()
+    if show_plot:
+        plt.show()
 
     # turnover
     plt.plot(backtests, turnovers_train, label="train", marker="o")
@@ -205,7 +210,8 @@ def plot_results(
     plt.gca().xaxis.set_major_locator(plt.MultipleLocator(5))
     plt.ylim(20, 50)
     plt.savefig(experiment_path() / "tuning_results/tuning_turn.pdf", bbox_inches="tight")
-    plt.show()
+    if show_plot:
+        plt.show()
 
 
 if __name__ == "__main__":