def backtest(agent, env):
global PATH_prefix
print("starting to backtest......")
from agents.UCRP import UCRP
from agents.Winner import WINNER
from agents.Losser import LOSSER
agents = []
agents.extend(agent)
agents.append(WINNER())
#agents.append(UCRP())
agents.append(LOSSER())
labels = ['PG', 'Winner', 'Losser']
wealths_result = []
rs_result = []
for i, agent in enumerate(agents):
stocktrader = StockTrader()
info = env.step(None, None, 'False')
r, contin, s, w1, p, risk = parse_info(info)
contin = 1
wealth = 10000
wealths = [wealth]
rs = [1]
while contin:
w2 = agent.predict(s, w1)
env_info = env.step(w1, w2, 'False')
r, contin, s_next, w1, p, risk = parse_info(env_info)
wealth = wealth * math.exp(r)
rs.append(math.exp(r) - 1)
wealths.append(wealth)
s = s_next
stocktrader.update_summary(0, r, 0, 0, w2, p)
#stocktrader.write(map(lambda x: str(x), env.get_codes()),labels[i])
print('finish one agent')
wealths_result.append(wealths)
rs_result.append(rs)
print('agent', ' ', 'cumulative return', ' ', 'average daily return',
' ', 'sharpe ratio', ' ', 'maximum drawback')
plt.figure(figsize=(8, 6), dpi=100)
for i in range(len(agents)):
plt.plot(wealths_result[i], label=labels[i])
cumr = float((wealths_result[i][-1] - 10000) / 10000 * 100)
mrr = float(np.mean(rs_result[i]) * 100)
sharpe = float(
np.mean(rs_result[i]) / np.std(rs_result[i]) * np.sqrt(252))
maxdrawdown = float(
max(1 - min(wealths_result[i]) /
np.maximum.accumulate(wealths_result[i])))
print(labels[i], ' ', round(cumr, 3), '%', ' ', round(mrr, 3), '%',
' ', round(sharpe, 3), ' ', round(maxdrawdown, 3), '%')
plt.legend()
plt.xlabel('time')
plt.ylabel('wealth')
plt.savefig(PATH_prefix + 'backtest.png')
plt.show()
def backtest(agent, env, market):
global PATH_prefix
print("starting to backtest......")
from agents.UCRP import UCRP
from agents.Winner import WINNER
from agents.Losser import LOSSER
agents = []
agents.extend(agent)
agents.append(WINNER())
agents.append(UCRP())
agents.append(LOSSER())
labels = ['PG', 'Winner', 'UCRP', 'Losser']
wealths_result = []
rs_result = []
for i, agent in enumerate(agents):
stocktrader = StockTrader()
info = env.step(None, None, 'False')
r, contin, s, w1, p, risk = parse_info(info)
contin = 1
wealth = 10000
wealths = [wealth]
rs = [1]
while contin:
w2 = agent.predict(s, w1)
env_info = env.step(w1, w2, 'False')
r, contin, s_next, w1, p, risk = parse_info(env_info)
wealth = wealth * math.exp(r)
rs.append(math.exp(r) - 1)
wealths.append(wealth)
s = s_next
stocktrader.update_summary(0, r, 0, 0, w2, p)
stocktrader.write(map(lambda x: str(x), env.get_codes()), labels[i])
print('finish one agent')
wealths_result.append(wealths)
rs_result.append(rs)
# print('资产名称',' ','平均日收益率',' ','夏普率',' ','最大回撤')
print('Asset Name', ' ', 'Average daily rate of return', ' ',
'Sharp rate', ' ', 'Maximum withdrawal')
plt.figure(figsize=(8, 6), dpi=100)
for i in range(len(agents)):
plt.plot(wealths_result[i], label=labels[i])
plt.title('RL - PM in the {} stock market'.format(market))
mrr = float(np.mean(rs_result[i]) * 100)
sharpe = float(
np.mean(rs_result[i]) / np.std(rs_result[i]) * np.sqrt(252))
maxdrawdown = float(
max(1 - min(wealths_result[i]) /
np.maximum.accumulate(wealths_result[i])))
print(labels[i], ' ', round(mrr, 3), '%', ' ', round(sharpe, 3),
' ', round(maxdrawdown, 3))
plt.legend()
plt.savefig(PATH_prefix + 'backtest.png')
plt.show()
def backtest(agent, env):
global PATH_prefix
print("starting to backtest......")
from agents.Winner import WINNER
from agents.Losser import LOSSER
agents = []
agents.extend(agent)
agents.append(WINNER())
# agents.append(UCRP())
agents.append(LOSSER())
labels = ['DDPG', 'Winner', 'Losser']
wealths_result = []
rs_result = []
for i, agent in enumerate(agents):
stocktrader = StockTrader()
info = env.step(None, None, 'False')
r, contin, s, w1, p, risk = parse_info(info)
contin = 1
wealth = 10000
wealths = [wealth]
rs = [1]
while contin:
w2 = agent.predict(s, w1)
env_info = env.step(w1, w2, 'False')
r, contin, s_next, w1, p, risk = parse_info(env_info)
wealth = wealth * math.exp(r)
rs.append(math.exp(r) - 1)
wealths.append(wealth)
s = s_next
stocktrader.update_summary(0, r, 0, 0, w2, p)
stocktrader.write(map(lambda x: str(x), env.get_codes()), labels[i])
print('finish one agent')
wealths_result.append(wealths)
rs_result.append(rs)
plt.figure(figsize=(8, 6), dpi=100)
for i in range(len(agents)):
plt.plot(wealths_result[i], label=labels[i])
plt.legend()
plt.savefig(PATH_prefix + 'backtest.png')
plt.show()
print(PATH_prefix)
def backtest(agent,env):
print("starting to backtest......")
from agents.UCRP import UCRP
from agents.Winner import WINNER
from agents.Losser import LOSSER
agents=[]
agents.append(agent)
agents.append(WINNER())
agents.append(UCRP())
agents.append(LOSSER())
labels=['PG','Winner','UCRP','Losser']
wealths_result=[]
rs_result=[]
for i,agent in enumerate(agents):
info = env.step(None, None)
r, contin, s, w1, p, risk = parse_info(info)
contin = 1
wealth=10000
wealths = [wealth]
rs=[1]
while contin:
w2 = agent.predict(s, w1)
if i==0:
print(w2)
env_info = env.step(w1, w2)
r, contin, s_next, w1, p, risk = parse_info(env_info)
wealth=wealth*math.exp(r)
rs.append(math.exp(r)-1)
wealths.append(wealth)
s=s_next
print('finish one agent')
wealths_result.append(wealths)
rs_result.append(rs)
for i in range(len(agents)):
plt.plot(wealths_result[i],label=labels[i])
print(labels[i],' ',np.mean(rs_result[i]),' ',np.std(rs_result[i]))
plt.legend()
plt.show()
def backtest(agent, env):
global PATH_prefix
print("starting to backtest......")
from agents.UCRP import UCRP
from agents.Winner import WINNER
from agents.Losser import LOSSER
agents = []
agents.extend(agent)
agents.append(WINNER())
agents.append(UCRP())
agents.append(LOSSER())
labels = ['PG', 'Winner', 'UCRP', 'Losser']
wealths_result = []
rs_result = []
for i, agent in enumerate(agents):
stocktrader = StockTrader()
info = env.step(None, None, 'False')
r, contin, s, w1, p, risk = parse_info(info)
contin = 1
wealth = 10000
wealths = [wealth]
rs = [1]
while contin:
w2 = agent.predict(s, w1)
env_info = env.step(w1, w2, 'False')
r, contin, s_next, w1, p, risk = parse_info(env_info)
wealth = wealth * math.exp(r)
rs.append(math.exp(r) - 1)
wealths.append(wealth)
s = s_next
stocktrader.update_summary(0, r, 0, 0, w2, p)
stocktrader.write(map(lambda x: str(x), env.get_codes()), labels[i])
print('finish one agent')
wealths_result.append(wealths)
rs_result.append(rs)
print('资产名称', ' ', '平均日收益率', ' ', '夏普率', ' ', '最大回撤')
plt.figure(figsize=(8, 6), dpi=100)
for i in range(len(agents)):
# if labels[i] == 'UCRP' or labels[i] == 'Losser':
# continue
plt.plot(wealths_result[i], label=labels[i])
fileObject = open(labels[i] + '.txt', 'w')
for ip in wealths_result[i]:
fileObject.write(str(ip))
fileObject.write(', ')
fileObject.close()
mrr = float(np.mean(rs_result[i]) * 100)
sharpe = float(
np.mean(rs_result[i]) / np.std(rs_result[i]) * np.sqrt(252))
maxdrawdown = float(
max(1 - min(wealths_result[i]) /
np.maximum.accumulate(wealths_result[i])))
print(labels[i], ' ', round(mrr, 3), '%', ' ', round(sharpe, 3),
' ', round(maxdrawdown, 3))
# new_data = []
# for i in LSTM:
# for j in range(8):
# new_data.append(i)
# inter_op = interpolate.interp1d(range(len(hand_data)), hand_data, kind='linear')
# hand_data = inter_op(7*range(len(hand_data)))
# def longer(i):
# k = np.zeros(180)
# j = 0
# p = 0
# while j + 1 < len(i):
# print([p, j])
# k[p] = i[j]
# k[p + 1] = i[j]
# k[p + 2] = i[j + 1]
# p += 3
# j += 2
# return i
# LSTM = longer(LSTM)
# Random = longer(Random)
# Uniform = longer(Uniform)
# plt.plot(LSTM, label='LSTM')
# plt.plot(Random, label='Random')
# plt.plot(Uniform, label='Uniform')
plt.legend()
plt.savefig(PATH_prefix + 'backtest_with_hand.png')
plt.show()