forked from steveyx/PortfolioOptimization
-
Notifications
You must be signed in to change notification settings - Fork 0
/
portfolio_visualize.py
179 lines (170 loc) · 8.68 KB
/
portfolio_visualize.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
plt.rcParams['font.family'] = 'monospace'
class PortfolioVisualize:
@staticmethod
def visualize(df_results, best_indices_sim, gd_results, max_sharpe_port, stocks):
fig = plt.figure(figsize=(5, 6), dpi=300)
ax2 = fig.add_axes([0.12, 0.55, 0.8, 0.4])
ax3 = fig.add_axes([0.12, 0.07, 0.8, 0.4])
ax2.sharex(ax3)
_indices = best_indices_sim
_x_sim = df_results.loc[_indices, 'stdev'].values.flatten()
_y_sim = df_results.loc[_indices, 'ret'].values.flatten()
ax2.scatter(_x_sim, _y_sim, color="blue", marker=".", label="Monte Carlo Simulation")
ax2.scatter(_x_sim[-1:], _y_sim[-1:], color="r", marker=(5, 1, 0), label="Max")
ax2.quiver(_x_sim[:-1], _y_sim[:-1], _x_sim[1:] - _x_sim[:-1], _y_sim[1:] - _y_sim[:-1],
scale_units='xy', angles='xy', width=0.005, scale=1, color="blue", joinstyle="round")
_x_gd, _y_gd = gd_results[:, 1], gd_results[:, 0]
ax3.scatter(_x_gd, _y_gd, color="purple", marker=".", label="Gradient Descent Solution")
ax3.scatter(_x_gd[-1:], _y_gd[-1:], color="r", marker=(5, 1, 0), label="Max")
ax3.quiver(_x_gd[:-1], _y_gd[:-1], _x_gd[1:] - _x_gd[:-1], _y_gd[1:] - _y_gd[:-1],
scale_units='xy', angles='xy', width=0.005, scale=1, color="purple", joinstyle="round")
ax2.set_xlim(0.23, 0.28)
ax2.set_ylim(0.3, 0.4)
ax3.set_ylim(0.3, 0.4)
ax2.set_ylabel('Return', fontsize=10)
ax2.set_title('Maximize Sharpe Ratio: Monte Carlo Simulation', fontsize=11)
ax3.set_title('Maximize Sharpe Ratio: Gradient Descent Solution', fontsize=11)
ax3.set_ylabel('Return', fontsize=10)
ax3.set_xlabel('Volatility', fontsize=10)
ax2.tick_params(axis='x', labelsize=8)
ax2.tick_params(axis='y', labelsize=8)
ax3.tick_params(axis='x', labelsize=8)
ax3.tick_params(axis='y', labelsize=8)
plt.setp(ax2.get_xticklabels(), visible=False)
weights_sim = ["{0}{1:.3f}".format(stk.ljust(18), max_sharpe_port[stk]) for j, stk in enumerate(stocks)]
weights_gd_ = gd_results[-1, 3:]
weights_gd = ["{0}{1:.3f}".format(stk.ljust(18), weights_gd_[j]) for j, stk in enumerate(stocks)]
_max_sharpe_sim = "{0}{1:.3f}".format("Max Sharpe Ratio".ljust(18), max_sharpe_port["sharpe"])
_max_sharpe_gd = "{0}{1:.3f}".format("Max Sharpe Ratio".ljust(18), gd_results[-1, 2])
_sharpe_sim = "\n".join([_max_sharpe_sim] + weights_sim)
_text_sharpe_sim = ax2.text(0.26, 0.34, _sharpe_sim, fontsize=8, color='blue',
horizontalalignment='left', verticalalignment='top')
_sharpe_gd = "\n".join([_max_sharpe_gd] + weights_gd)
_text_sharpe_gd = ax3.text(0.26, 0.34, _sharpe_gd, fontsize=8, color='blue',
horizontalalignment='left', verticalalignment='top')
plt.savefig("figure_benchmark.png")
fig_manager = plt.get_current_fig_manager()
fig_manager.window.showMaximized()
plt.show()
@staticmethod
def visualize_simulation(df_results, max_sharpe_port, min_vol_port):
fig = plt.figure(figsize=(5, 5), dpi=300)
ax = fig.add_axes([0.12, 0.1, 0.8, 0.8])
# create scatter plot coloured by Sharpe Ratio
data_points = ax.scatter(df_results.stdev,
df_results.ret,
s=20,
label="Portfolios",
c=df_results.sharpe, cmap='RdYlBu')
ax.set_title('Portfolio Optimization by Monte Carlo Simulation', fontsize=10)
ax.set_xlabel('Volatility', fontsize=10)
ax.set_ylabel('Return', fontsize=10)
ax.set_xlim(df_results['stdev'].min()-0.02, df_results['stdev'].max()+0.02)
ax.set_ylim(df_results['ret'].min()-0.01, df_results['ret'].max()+0.01)
ax.tick_params(axis='x', labelsize=8)
ax.tick_params(axis='y', labelsize=8)
ax.set_aspect(aspect="equal")
# plot red star to highlight position of portfolio with highest Sharpe Ratio
max_sharpe = ax.scatter(max_sharpe_port[1], max_sharpe_port[0], marker=(5, 1, 0), color='r', s=100,
label="Max Sharpe Ratio")
# plot green star to highlight position of minimum variance portfolio
min_vol = ax.scatter(min_vol_port[1], min_vol_port[0], marker=(5, 1, 0), color='g', s=100,
label="Min Volatility")
ax.legend(loc="upper left", fontsize=8)
plt.savefig("simulation.png")
fig_manager = plt.get_current_fig_manager()
fig_manager.window.showMaximized()
plt.show()
@staticmethod
def plot_benchmark_table(data=None):
if data is None:
data = [
{
"Assets": 10,
"Portfolios Simulation": 100000,
"Learning Rate Gradient Descent": 0.05,
"SR Simulation": 1.55,
"SR Gradient Descent": 1.56,
"Time Simulation": 39.9,
"Time Gradient Descent": 0.03
},
{
"Assets": 20,
"Portfolios Simulation": 200000,
"Learning Rate Gradient Descent": 0.05,
"SR Simulation": 1.70,
"SR Gradient Descent": 1.79,
"Time Simulation": 67.1,
"Time Gradient Descent": 0.03
},
{
"Assets": 50,
"Portfolios Simulation": 500000,
"Learning Rate Gradient Descent": 0.05,
"SR Simulation": 1.54,
"SR Gradient Descent": 1.87,
"Time Simulation": 204.1,
"Time Gradient Descent": 0.12
},
{
"Assets": 100,
"Portfolios Simulation": 1000000,
"Learning Rate Gradient Descent": 0.05,
"SR Simulation": 1.42,
"SR Gradient Descent": 1.93,
"Time Simulation": 394.7,
"Time Gradient Descent": 0.26,
}
]
df = pd.DataFrame(data)
# df.rename(columns={
# "Portfolios Simulation": "Portfolios\nSimulation",
# "Learning Rate Gradient Descent": "Learning Rate\nGradient Descent",
# "SR Gradient Descent": "SR\nGradient Descent",
# "Time Gradient Descent": "Time\nGradient Descent"
# }, inplace=True)
col_labels = ["Assets", "Portfolios\nSimulation", "Learning Rate\nGradient Descent",
"SR\nSimulation",
"SR\nGradient Descent",
"Time\nSimulation",
"Time\nGradient Descent"]
df["Assets"] = df["Assets"].astype(int)
df["Portfolios Simulation"] = df["Portfolios Simulation"].astype(int)
fig = plt.figure(figsize=(8, 1.4), dpi=200)
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
tables = []
ax.axis('off')
_values = df.values.tolist()
for i in range(len(df)):
_values[i][0], _values[i][1] = int(_values[i][0]), int(_values[i][1])
tab = ax.table(cellText=_values,
cellLoc='center', rowLoc='center',
colWidths=[0.07, 0.1, 0.15, 0.1, 0.15, 0.1, 0.15],
colLabels=col_labels,
# colColours=[None, None, None, "lightgreen", "lightgreen", "lightblue", "lightblue"],
# rowLabels=df.index[_s:_e].tolist(),
loc="left",
bbox=[0.05, 0.02, .9, 0.95])
tables.append(tab)
for tab in tables:
# scalex, scaley = 1, 1
# tab.scale(scalex, scaley)
tab.auto_set_font_size(False)
tab.set_fontsize(7.5)
for key, cell in tab.get_celld().items():
cell.set_linewidth(0)
for row in range(len(df)+1):
tables[0][(row, 0)].set_facecolor("lightgray")
tables[0][(row, 1)].set_facecolor("lightgray")
tables[0][(row, 2)].set_facecolor("lightgray")
tables[0][(row, 3)].set_facecolor("lightgreen")
tables[0][(row, 4)].set_facecolor("lightgreen")
tables[0][(row, 5)].set_facecolor("lightblue")
tables[0][(row, 6)].set_facecolor("lightblue")
plt.subplots_adjust(wspace=0.4)
plt.savefig("data/benchmark_performance.png", dpi=300)
if __name__ == "__main__":
PortfolioVisualize.plot_benchmark_table()