FEATURE: adding tests

pdesolvers/optionspricing/monte_carlo.py

@@ -159,7 +159,7 @@ def plot_price_paths(self, closing_prices=None, export=False):
             raise RuntimeError("Plots cannot be generated because the simulation has not been run yet.")
 
         plt.rcParams['font.family'] = 'monospace'
-        plt.rcParams['font.size'] = 10
+        plt.rcParams['font.size'] = 18
 
         t = self.__generate_grid()
 
@@ -193,7 +193,7 @@ def plot_distribution_of_final_prices(self, export=False):
             raise RuntimeError("Plots cannot be generated because the simulation has not been run yet.")
 
         plt.rcParams['font.family'] = 'monospace'
-        plt.rcParams['font.size'] = 10
+        plt.rcParams['font.size'] = 18
 
         final_prices = self.__S[:, -1]
 
@@ -205,7 +205,8 @@ def plot_distribution_of_final_prices(self, export=False):
 
         if export:
             plt.savefig("monte_carlo_prices.pdf", format="pdf", bbox_inches="tight", pad_inches=0.2)
-            plt.show()
+
+        plt.show()
 
     def plot_distribution_of_payoff(self, export=False):
         """
@@ -217,7 +218,7 @@ def plot_distribution_of_payoff(self, export=False):
             raise RuntimeError("Plots cannot be generated because the simulation has not been run yet.")
 
         plt.rcParams['font.family'] = 'monospace'
-        plt.rcParams['font.size'] = 10
+        plt.rcParams['font.size'] = 18
 
         plt.figure(figsize=(10, 6))
         plt.hist(self.__payoff, bins=50, edgecolor='darkblue', alpha=0.5, color='blue', density=True)
@@ -244,7 +245,7 @@ def plot_convergence_analysis(self, analytical_solution, num_simulations_list=No
         errors = self.get_benchmark_errors(analytical_solution, num_simulations_list, export=export)
 
         plt.rcParams['font.family'] = 'monospace'
-        plt.rcParams['font.size'] = 10
+        plt.rcParams['font.size'] = 18
 
         fig, ax = plt.subplots(figsize=(10,6))
         ax.plot(num_simulations_list, errors, 'bo-', linewidth=2)

pdesolvers/tests/test_options_pricing.py

@@ -3,6 +3,7 @@
 from unittest.mock import patch
 
 from pdesolvers.optionspricing.monte_carlo import MonteCarloPricing
+from pdesolvers.optionspricing.black_scholes_formula import BlackScholesFormula
 from pdesolvers.enums.enums import OptionType, Greeks
 
 @pytest.fixture
@@ -17,6 +18,16 @@ def mc_pricing_params():
         'sim': 5
     }
 
+@pytest.fixture
+def bs_pricing_params():
+    return {
+        'S0': 100.0,
+        'strike_price': 100.0,
+        'r': 0.05,
+        'sigma': 0.2,
+        'expiry': 1.0
+    }
+
 class TestMonteCarlo:
 
     @patch('pdesolvers.optionspricing.monte_carlo.MonteCarloPricing.simulate_gbm')
@@ -83,11 +94,51 @@ def test_check_simulate_gbm_initial_values(self, mc_pricing_params):
 
         assert (results[:,0] == mc_pricing_params['S0']).all()
 
-    def test_check_get_execution_time_raises_exception_when_no_simulation_is_run(self, mc_pricing_params):
+    def test_check_get_execution_time_raises_error_when_no_simulation_is_run(self, mc_pricing_params):
 
         test_mc = MonteCarloPricing(
             OptionType.EUROPEAN_CALL, **mc_pricing_params
         )
 
         with pytest.raises(RuntimeError, match="Execution time is not available because the simulation has not been run yet."):
             test_mc.get_execution_time()
+
+    def test_check_plot_payoff_raises_error_when_no_simulation_is_run(self, mc_pricing_params):
+
+        test_mc = MonteCarloPricing(
+            OptionType.EUROPEAN_CALL, **mc_pricing_params
+        )
+
+        with pytest.raises(RuntimeError, match="Plots cannot be generated because the simulation has not been run yet."):
+            test_mc.plot_distribution_of_payoff()
+
+    def test_check_plot_price_distribution_raises_error_when_no_simulation_is_run(self, mc_pricing_params):
+
+        test_mc = MonteCarloPricing(
+            OptionType.EUROPEAN_CALL, **mc_pricing_params
+        )
+
+        with pytest.raises(RuntimeError, match="Plots cannot be generated because the simulation has not been run yet."):
+            test_mc.plot_distribution_of_final_prices()
+
+    def test_check_plot_price_paths_raises_error_when_no_simulation_is_run(self, mc_pricing_params):
+
+        test_mc = MonteCarloPricing(
+            OptionType.EUROPEAN_CALL, **mc_pricing_params
+        )
+
+        with pytest.raises(RuntimeError, match="Plots cannot be generated because the simulation has not been run yet."):
+            test_mc.plot_price_paths()
+
+    def test_check_plot_convergence_raises_error_when_no_list_supplied(self, mc_pricing_params, bs_pricing_params):
+
+        test_mc = MonteCarloPricing(
+            OptionType.EUROPEAN_CALL, **mc_pricing_params
+        )
+
+        results = test_mc.simulate_gbm()
+
+        price = BlackScholesFormula(OptionType.EUROPEAN_CALL, **bs_pricing_params)
+
+        with pytest.raises(ValueError, match="Number of simulations need to be defined."):
+            test_mc.plot_convergence_analysis(price)

playground/brownian_motion.py

@@ -43,6 +43,9 @@ def plot(self):
         t = self.__generate_grid()
         B = self.__get_paths()
 
+        plt.rcParams['font.family'] = 'monospace'
+        plt.rcParams['font.size'] = 18
+
         plt.figure(figsize=(10,6))
         for i in range(self.__sim):
             plt.plot(t, B[i], color='grey', alpha=0.3)
@@ -55,7 +58,7 @@ def plot(self):
 
 
 def main():
-    BrownianMotion(T=1, time_steps=365, sim=200).simulate_bm().plot()
+    BrownianMotion(T=1, time_steps=365, sim=100).simulate_bm().plot()
 
 if __name__ == "__main__":
     main()

playground/export.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+
+mv /Users/chemardes/Documents/repos/PDESolvers/playground/brownian_plot.pdf /Users/chemardes/Documents/school/gpu-project/report/GPU-Accelerated\ Solution\ for\ BSE/figures/

playground/plot_results_csv.py

@@ -0,0 +1,38 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import os
+
+def plot_option_surface(file_path):
+    # Expand file path if using ~
+    file_path = os.path.expanduser(file_path)
+
+    # Load data
+    df = pd.read_csv(file_path, header=None)
+    print(f"Data shape: {df.shape}")
+
+    # Process data
+    grid_data = df.values.T
+
+    # Create coordinate grids
+    price_grid = np.linspace(0, 300, grid_data.shape[0])
+    time_grid = np.linspace(0, 1, grid_data.shape[1])
+    X, Y = np.meshgrid(time_grid, price_grid)
+
+    # Create plot
+    fig = plt.figure(figsize=(10, 6))
+    ax = fig.add_subplot(111, projection='3d')
+    surf = ax.plot_surface(X, Y, grid_data, cmap='viridis')
+
+    # Add labels and colorbar
+    ax.set_xlabel('Time')
+    ax.set_ylabel('Asset Price')
+    ax.set_zlabel('Option Value')
+    ax.set_title('Option Value Surface Plot')
+    fig.colorbar(surf, shrink=0.5, aspect=5)
+
+    return fig, ax
+
+if __name__ == "__main__":
+    fig, ax = plot_option_surface('~/Downloads/out.csv')
+    plt.show()