Add tests for genetic algorithm optimizer

diogomatoschaves · Mar 1, 2024 · d26e2ca · d26e2ca
1 parent 5ad6354
commit d26e2ca
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Showing 7 changed files with 119 additions and 26 deletions.
diff --git a/stratestic/backtesting/_mixin.py b/stratestic/backtesting/_mixin.py
@@ -280,7 +280,7 @@ def optimize(
         self,
         params,
         optimization_metric="Return",
-        optimizer: Literal["brute", "gen_alg"] = 'brute',
+        optimizer: Literal["brute_force", "gen_alg"] = 'brute_force',
         **kwargs
     ):
         """Optimizes the trading strategy using brute force.
@@ -321,7 +321,7 @@ def optimize(
         opt_params, strategy_params_mapping, optimization_steps = adapt_optimization_input(self.strategy, params)
 
         self.bar = progressbar.ProgressBar(
-            max_value=optimization_steps if self._optimizer == 'brute' else progressbar.UnknownLength,
+            max_value=optimization_steps if self._optimizer == 'brute_force' else progressbar.UnknownLength,
             redirect_stdout=True
         )
         self.optimization_steps = 0
@@ -340,8 +340,11 @@ def optimize(
         self.bar.finish()
         print()
 
-        return (get_params_mapping(self.strategy, opt, strategy_params_mapping, params),
-                -self._update_and_run(opt, True, True, strategy_params_mapping, params))
+        optimized_params = get_params_mapping(self.strategy, opt, strategy_params_mapping, params)
+        optimized_result = self._update_and_run(opt, True, True, strategy_params_mapping, params)
+        optimized_result = optimized_result if self._optimizer == 'gen_alg' else -optimized_result
+
+        return optimized_params, optimized_result
 
     def maximum_leverage(self, margin_threshold=None):
         """
@@ -427,7 +430,7 @@ def _check_metric_input(self, optimization_metric):
             self.optimization_metric = optimization_options[optimization_metric]
 
     def _check_optimizer_input(self, optimizer):
-        optimizer_options = ["brute", "gen_alg"]
+        optimizer_options = ["brute_force", "gen_alg"]
         if optimizer not in optimizer_options:
             raise ValueError(f"The selected optimizer is not supported. "
                              f"Choose one of: {', '.join(optimizer_options)}")

diff --git a/stratestic/backtesting/optimization/_optimization.py b/stratestic/backtesting/optimization/_optimization.py
@@ -47,7 +47,7 @@ def strategy_optimizer(
     strategy_runner,
     opt_params: tuple,
     runner_args: tuple,
-    optimizer: Literal["brute", "gen_alg"] = 'brute',
+    optimizer: Literal["brute_force", "gen_alg"] = 'brute',
     **kwargs
 ):
     """
@@ -63,7 +63,7 @@ def strategy_optimizer(
         (start2, stop2, step2), ...).
     runner_args : tuple
         Additional arguments required by the `strategy_runner` function.
-    optimizer : Literal["brute", "gen_alg"]
+    optimizer : Literal["brute_force", "gen_alg"]
         Choice of algorithm for the optimization.
     **kwargs : dict
         Additional keyword arguments passed to the `brute` function from scipy.optimize.
@@ -79,7 +79,7 @@ def strategy_optimizer(
     defined by `opt_params`. The objective function to be optimized is defined by `strategy_runner`.
     """
 
-    if optimizer == "brute":
+    if optimizer == "brute_force":
 
         return brute(
             strategy_runner,

diff --git a/tests/backtesting/iterative/out/bollinger_bands_trading_costs.py b/tests/backtesting/iterative/out/bollinger_bands_trading_costs.py
@@ -3,7 +3,10 @@
 expected_performance = 0.985337
 expected_outperformance = -0.030928
 
-expected_optimization_results = ({'ma': 2.0, 'sd': 1.0}, 0)
+expected_optimization_results = {
+    "brute_force": ({'ma': 2.0, 'sd': 1.0}, 0),
+    "gen_alg": ({'ma': 5.0, 'sd': 5.0}, 0),
+}
 
 expected_results = [
     {

diff --git a/tests/backtesting/iterative/out/machine_learning_no_trading_costs.py b/tests/backtesting/iterative/out/machine_learning_no_trading_costs.py
@@ -1,6 +1,10 @@
+import numpy as np
 from pandas import Timestamp
 
-expected_optimization_results = ({'nr_lags': 2.0}, 13.175614657220974)
+expected_optimization_results = {
+    "brute_force": ({'nr_lags': 2.0}, 13.175614657220974),
+    "gen_alg": ({'nr_lags': 5.0}, np.inf),
+}
 
 expected_results = [
     {

diff --git a/tests/backtesting/iterative/out/momentum_no_trading_costs.py b/tests/backtesting/iterative/out/momentum_no_trading_costs.py
@@ -3,7 +3,10 @@
 expected_performance = 1.009399
 expected_outperformance = -0.006867
 
-expected_optimization_results = ({'window': 5.0}, -0.2131523135281635)
+expected_optimization_results = {
+    "brute_force": ({'window': 5.0}, -0.2131523135281635),
+    "gen_alg": ({'window': 5.0}, -0.2131523135281635),
+}
 
 expected_results = [
     {

diff --git a/tests/backtesting/iterative/out/moving_average_crossover_trading_costs.py b/tests/backtesting/iterative/out/moving_average_crossover_trading_costs.py
@@ -3,7 +3,10 @@
 expected_performance = 1.003375
 expected_outperformance = -0.003015
 
-expected_optimization_results = ({'sma_l': 6.0, 'sma_s': 1.0}, 100.0)
+expected_optimization_results = {
+    "brute_force": ({'sma_l': 6.0, 'sma_s': 1.0}, 100.0),
+    "gen_alg": ({'sma_s': 5.0, 'sma_l': 7.0}, 100.0)
+}
 
 expected_results = [
     {

diff --git a/tests/backtesting/iterative/test_iterative.py b/tests/backtesting/iterative/test_iterative.py
@@ -162,14 +162,29 @@ def test_optimize_parameters_input_validation(
             backtester.load_data(data=test_data)
             backtester.optimize(optimization_params)
 
+    @pytest.mark.parametrize(
+        "optimizer,extra_args",
+        [
+            pytest.param(
+                "brute_force",
+                {},
+                id="BruteForce",
+            ),
+            pytest.param(
+                "gen_alg",
+                {"pop_size": 4, "max_gen": 1, "random_state": 42},
+                id="GenAlg",
+            )
+        ]
+    )
     @pytest.mark.parametrize(
         "fixture",
         [
             pytest.param(fixture, id=fixture_name)
             for fixture_name, fixture in fixtures.items()
         ],
     )
-    def test_optimize_parameters(self, fixture, common_fixture):
+    def test_optimize_parameters(self, fixture, optimizer, extra_args, common_fixture):
         strategy = fixture["in"]["strategy"]
         params = fixture["in"]["params"]
         optimization_params = fixture["in"]["optimization_params"]
@@ -181,11 +196,25 @@ def test_optimize_parameters(self, fixture, common_fixture):
 
         ite = IterativeBacktester(strategy_instance, trading_costs=trading_costs)
 
-        optimization_results = ite.optimize(*optimization_params)
+        optimization_results = ite.optimize(*optimization_params, optimizer=optimizer, **extra_args)
 
-        assert optimization_results == fixture["out"]["expected_optimization_results"]
+        assert optimization_results == fixture["out"]["expected_optimization_results"][optimizer]
 
-    @pytest.mark.slow
+    @pytest.mark.parametrize(
+        "optimizer,extra_args",
+        [
+            pytest.param(
+                "brute_force",
+                {},
+                id="BruteForce",
+            ),
+            pytest.param(
+                "gen_alg",
+                {"pop_size": 4, "max_gen": 1, "random_state": 42},
+                id="GenAlg",
+            )
+        ]
+    )
     @pytest.mark.parametrize(
         "input_params,optimization_params,expected_results",
         [
@@ -195,7 +224,10 @@ def test_optimize_parameters(self, fixture, common_fixture):
                     "method": "Unanimous"
                 },
                 [{"window": (2, 4)}, {"ma": (1, 3)}],
-                [{'window': 3.0}, {'ma': 2.0}],
+                {
+                    "brute_force": [{'window': 3.0}, {'ma': 2.0}],
+                    "gen_alg": [{'window': 4.0}, {'ma': 3.0}]
+                },
                 id='2_strategies-unanimous'
             ),
             pytest.param(
@@ -204,7 +236,10 @@ def test_optimize_parameters(self, fixture, common_fixture):
                     "method": "Majority"
                 },
                 [{"window": (2, 4)}, {"ma": (1, 3)}],
-                [{'window': 3.0}, {'ma': 1.0}],
+                {
+                    "brute_force": [{'window': 3.0}, {'ma': 1.0}],
+                    "gen_alg": [{'window': 4.0}, {'ma': 3.0}]
+                },
                 id='2_strategies-majority'
             ),
             pytest.param(
@@ -213,7 +248,10 @@ def test_optimize_parameters(self, fixture, common_fixture):
                     "method": "Majority"
                 },
                 [{"window": (2, 4)}, {"ma": (1, 3)}, {}],
-                [{'window': 3.0}, {'ma': 2.0}, {}],
+                {
+                    "brute_force": [{'window': 3.0}, {'ma': 2.0}, {}],
+                    "gen_alg": [{'window': 4.0}, {'ma': 3.0}, {}]
+                },
                 id='3_strategies-majority'
             ),
             pytest.param(
@@ -222,7 +260,10 @@ def test_optimize_parameters(self, fixture, common_fixture):
                     "method": "Unanimous"
                 },
                 [{"sma_s": (2, 4), "sma_l": (4, 6)}],
-                [{'sma_s': 3.0, 'sma_l': 4.0}],
+                {
+                    "brute_force": [{'sma_s': 3.0, 'sma_l': 4.0}],
+                    "gen_alg": [{'sma_l': 6.0, 'sma_s': 4.0}]
+                },
                 id='1_strategies-unanimous'
             ),
         ],
@@ -232,6 +273,8 @@ def test_optimize_parameters_combined_strategies(
         input_params,
         optimization_params,
         expected_results,
+        optimizer,
+        extra_args,
         common_fixture
     ):
         test_data = data.set_index("open_time")
@@ -240,10 +283,25 @@ def test_optimize_parameters_combined_strategies(
 
         ite = IterativeBacktester(strategy_instance)
 
-        optimization_results, perf = ite.optimize(optimization_params)
+        optimization_results, perf = ite.optimize(optimization_params, optimizer=optimizer, **extra_args)
 
-        assert optimization_results == expected_results
+        assert optimization_results == expected_results[optimizer]
 
+    @pytest.mark.parametrize(
+        "optimizer,extra_args",
+        [
+            pytest.param(
+                "brute_force",
+                {},
+                id="BruteForce",
+            ),
+            pytest.param(
+                "gen_alg",
+                {"pop_size": 4, "max_gen": 1, "random_state": 42},
+                id="GenAlg",
+            )
+        ]
+    )
     @pytest.mark.parametrize(
         "input_params,optimization_params",
         [
@@ -269,6 +327,8 @@ def test_load_data_optimize_parameters_combined_strategies(
         self,
         input_params,
         optimization_params,
+        optimizer,
+        extra_args,
         common_fixture
     ):
         test_data = data.set_index("open_time")
@@ -280,12 +340,27 @@ def test_load_data_optimize_parameters_combined_strategies(
         ite_2 = IterativeBacktester(strategy_instance_2)
         ite_2.load_data(data=test_data)
 
-        optimization_results_1, perf_1 = ite_1.optimize(optimization_params)
-        optimization_results_2, perf_2 = ite_2.optimize(optimization_params)
+        optimization_results_1, perf_1 = ite_1.optimize(optimization_params, optimizer=optimizer, **extra_args)
+        optimization_results_2, perf_2 = ite_2.optimize(optimization_params, optimizer=optimizer, **extra_args)
 
         assert optimization_results_1 == optimization_results_2
         assert perf_1 == perf_2
 
+    @pytest.mark.parametrize(
+        "optimizer,extra_args",
+        [
+            pytest.param(
+                "brute_force",
+                {},
+                id="BruteForce",
+            ),
+            pytest.param(
+                "gen_alg",
+                {"pop_size": 4, "max_gen": 1, "random_state": 42},
+                id="GenAlg",
+            )
+        ]
+    )
     @pytest.mark.parametrize(
         "input_params,optimization_params",
         [
@@ -311,6 +386,8 @@ def test_optimize_parameters_combined_strategies_equal_to_vectorized(
         self,
         input_params,
         optimization_params,
+        optimizer,
+        extra_args,
         common_fixture
     ):
         test_data = data.set_index("open_time")
@@ -321,8 +398,8 @@ def test_optimize_parameters_combined_strategies_equal_to_vectorized(
         ite = IterativeBacktester(strategy_instance_1)
         vect = IterativeBacktester(strategy_instance_2)
 
-        optimization_results_1, perf_1 = ite.optimize(optimization_params)
-        optimization_results_2, perf_2 = vect.optimize(optimization_params)
+        optimization_results_1, perf_1 = ite.optimize(optimization_params, optimizer=optimizer, **extra_args)
+        optimization_results_2, perf_2 = vect.optimize(optimization_params, optimizer=optimizer, **extra_args)
 
         assert optimization_results_1 == optimization_results_2
         assert perf_1 == perf_2