def main(argv):
from quotation.quotation import Quotation
q = Quotation()
d = q.get_daily_data('000001')
d = d.reset_index()
# d = pd.read_csv('/Users/zhangyunsheng/Dev/sonata/data/AAPL.csv')
d = d.sort_values('date')
e = StockTradingEnv(d)
e.reset()
print(e._nextObservation())
e.render()
def predict(self, code):
if self.model == None:
self.load()
q = Quotation()
d = q.get_daily_data(code)
d = d.reset_index()
n = len(d.loc[:, 'open'].values)
last_five_d = d.loc[n - 6:, :]
obs = StockTradingEnv.observation(last_five_d)
action, _states = self.model.predict(obs)
print(action)
print(_states)
return
def load(self):
try:
q = Quotation()
d = q.get_daily_data('000001')
d = d.reset_index()
# d = pd.read_csv('/Users/zhangyunsheng/Dev/sonata/data/AAPL.csv')
d = d.sort_values('date')
env = DummyVecEnv([lambda: StockTradingEnv(d)])
self.model = PPO2(MlpPolicy, env, verbose=1)
self.model = PPO2.load(self.model_dir)
except Exception as e:
Logger.warn(e)
return False
return True
def train(self):
q = Quotation()
d = q.get_daily_data('000001')
#d = q.get_daily_data('000651')
d = d.reset_index()
# d = pd.read_csv('/Users/zhangyunsheng/Dev/sonata/data/AAPL.csv')
d = d.sort_values('date')
#print(d.head(10).loc[0: 6, 'open'].values)
#return True
train_data = d.head(3718)
test_data = d.tail(1500).reset_index()
# The algorithms require a vectorized environment to run
#env = DummyVecEnv([lambda: StockTradingEnv(d)])
#env = StockTradingEnv(d)
env = StockTradingEnv(train_data)
m = PPO2(MlpPolicy, env, verbose=1)
m.learn(total_timesteps=200000)
m.save(self.model_dir)
env = StockTradingEnv(test_data)
m = PPO2.load(self.model_dir)
#m = PPO2.load('/Users/zhangyunsheng/Dev/sonata/data/model/demo.2M.zip')
obs = env.reset()
for i in range(1500):
action, _states = m.predict(obs)
#print(action) # TODO
act = ''
#if action[0][0] < 1:
# if action[0][1] != 0:
if action[0] < 1:
if action[1] != 0:
act = 'BUY'
print('BUY') # TODO
#elif action[0][0] < 2:
# if action[0][1] != 0:
elif action[0] < 2:
if action[1] != 0:
act = 'SELL'
print('SELL') # TODO
obs, rewards, done, info = env.step(action)
if act != '':
env.render()
env.render()
return True
#strats = cerebo.optstrategy(
# TestStrategy,
# maperiod=range(10, 31))
# modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
# datapath = os.path.join(modpath, 'orcl-1995-2014.txt')
# data = bt.feeds.YahooFinanceCSVData(
# dataname=datapath,
# fromdate=datetime.datetime(2000, 1, 1),
# todate=datetime.datetime(2000, 12, 31),
# reverse=False
# )
from quotation.quotation import Quotation
q = Quotation()
d = q.get_daily_data('000001')
print(d)
# import pandas
# #d = pandas.read_csv(datapath, skiprows=0, parse_dates=True, header=0, index_col=0)
# datapath = "/Users/zhangyunsheng/Dev/sonata/data/daily/000001"
# d = pandas.read_csv(datapath, sep='\t', skiprows=0, parse_dates=True, header=0, index_col=0)
# print(d)
# exit()
#data = bt.feeds.PandasData(
# dataname=d,
# fromdate=datetime.datetime(2001, 1, 1),
# todate=datetime.datetime(2001, 12, 31),
# )
data = MyPandasData(