def _test_account():
# 建立相关数据
n_step = 60
ohlcav_col_name_list = ["open", "high", "low", "close", "amount", "volume"]
from ibats_common.example.data import load_data
md_df = load_data('RB.csv').set_index('trade_date')[ohlcav_col_name_list]
md_df.index = pd.DatetimeIndex(md_df.index)
from ibats_common.backend.factor import get_factor, transfer_2_batch
factors_df = get_factor(md_df, dropna=True)
df_index, df_columns, data_arr_batch = transfer_2_batch(factors_df,
n_step=n_step)
md_df = md_df.loc[df_index, :]
shape = [
data_arr_batch.shape[0], 5,
int(n_step / 5), data_arr_batch.shape[2]
]
data_factors = np.transpose(data_arr_batch.reshape(shape), [0, 2, 3, 1])
print(data_arr_batch.shape, '->', shape, '->', data_factors.shape)
# 建立 Account
env = Account(md_df, data_factors)
next_observation = env.reset()
print('next_observation.shape:', next_observation.shape)
assert next_observation.shape == (1, 12, 78, 5)
next_state, reward, done = env.step(1)
assert next_observation.shape == (1, 12, 78, 5)
assert not done
def _test_quote_market():
n_step = 60
ohlcav_col_name_list = ["open", "high", "low", "close", "amount", "volume"]
from ibats_common.example.data import load_data
md_df = load_data('RB.csv').set_index('trade_date')[ohlcav_col_name_list]
md_df.index = pd.DatetimeIndex(md_df.index)
from ibats_common.backend.factor import get_factor, transfer_2_batch
factors_df = get_factor(md_df, dropna=True)
df_index, df_columns, data_arr_batch = transfer_2_batch(factors_df,
n_step=n_step)
md_df = md_df.loc[df_index, :]
# 建立 QuotesMarket
qm = QuotesMarket(md_df=md_df[['close', 'open']],
data_factors=data_arr_batch,
state_with_flag=True)
next_observation = qm.reset()
assert len(next_observation) == 2
assert next_observation[0].shape[0] == n_step
assert next_observation[1] == 0
next_observation, reward, done = qm.step(1)
assert len(next_observation) == 2
assert next_observation[1] == 1
assert not done
next_observation, reward, done = qm.step(0)
assert next_observation[1] == 0
assert reward != 0
next_observation, reward, done = qm.step(0)
assert next_observation[1] == 0
assert reward == 0
next_observation, reward, done = qm.step(3)
assert next_observation[1] == 0
assert reward == 0
next_observation, reward, done = qm.step(2)
assert next_observation[1] == -1
assert not done
next_observation, reward, done = qm.step(3)
assert next_observation[1] == -1
assert reward != 0
try:
qm.step(4)
except ValueError:
print('is ok for not supporting action>3')
def _test_quote_market():
import os
n_step = 60
from ibats_common.example.data import load_data
md_df = load_data(
'RB.csv',
folder_path=os.path.join(os.pardir, os.pardir, os.pardir, 'example', 'data') # r'..\..\..\example\data'
).set_index('trade_date')[DEFAULT_MD_OHLCVA_LABELS]
md_df.index = pd.DatetimeIndex(md_df.index)
from ibats_common.backend.factor import get_factor, transfer_2_batch
factors_df = get_factor(md_df, dropna=True)
df_index, df_columns, data_arr_batch = transfer_2_batch(factors_df, n_step=n_step)
md_df = md_df.loc[df_index, :]
# 建立 QuotesMarket
qm = QuotesMarket(md_df=md_df[['close', 'open']], data_factors=data_arr_batch, state_with_flag=True)
next_observation = qm.reset()
assert len(next_observation) == 3
assert next_observation[0].shape[0] == n_step
assert next_observation[1] == FLAG_EMPTY
next_observation, reward, done = qm.step(ACTION_LONG)
assert len(next_observation) == 3
assert next_observation[1] == FLAG_LONG
assert not done
next_observation, reward, done = qm.step(ACTION_CLOSE)
assert next_observation[1] == FLAG_EMPTY
assert reward != 0
next_observation, reward, done = qm.step(ACTION_CLOSE)
assert next_observation[1] == FLAG_EMPTY
assert reward == 0
next_observation, reward, done = qm.step(ACTION_KEEP)
assert next_observation[1] == FLAG_EMPTY
assert reward == 0
next_observation, reward, done = qm.step(ACTION_SHORT)
assert next_observation[1] == FLAG_SHORT
assert not done
next_observation, reward, done = qm.step(ACTION_KEEP)
assert next_observation[1] == FLAG_SHORT
assert reward != 0
try:
qm.step(4)
except ValueError:
print('is ok for not supporting action>3')
def _test_account2():
"""测试 plot_data 返回数据是否符合预期"""
n_step = 60
ohlcav_col_name_list = ["open", "high", "low", "close", "amount", "volume"]
from ibats_common.example.data import load_data
md_df = load_data('RB.csv').set_index('trade_date')[ohlcav_col_name_list]
md_df.index = pd.DatetimeIndex(md_df.index)
from ibats_common.backend.factor import get_factor, transfer_2_batch
factors_df = get_factor(md_df, dropna=True)
df_index, df_columns, data_arr_batch = transfer_2_batch(factors_df,
n_step=n_step)
md_df = md_df.loc[df_index, :]
shape = [
data_arr_batch.shape[0], 5,
int(n_step / 5), data_arr_batch.shape[2]
]
data_factors = np.transpose(data_arr_batch.reshape(shape), [0, 2, 3, 1])
print(data_arr_batch.shape, '->', shape, '->', data_factors.shape)
# 建立 Account
env = Account(md_df, data_factors)
next_observation = env.reset()
# 做空
env.step(2)
for n in range(int(md_df.shape[0] / 2)):
env.step(3)
# 做多
next_observation, reward, done = env.step(1)
while not done:
next_observation, reward, done = env.step(3)
# 展示结果
reward_df = env.plot_data()
value_s = reward_df.iloc[:, 0]
from ibats_utils.mess import datetime_2_str
from datetime import datetime
dt_str = datetime_2_str(datetime.now(), '%Y-%m-%d %H_%M_%S')
title = f'test_account_{dt_str}'
from ibats_common.analysis.plot import plot_twin
plot_twin(value_s, md_df["close"], name=title)