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alpha-mind
Commit 32227589 authored by Dr.李's avatar Dr.李
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update strategy

parent d66ca648
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Showing with 48 additions and 42 deletions
alphamind/strategy/strategy.py
View file @ 32227589
......@@ -73,7 +73,7 @@ class Strategy(object):
self.engine = SqlEngine(self.data_meta.data_source)
self.dask_client = dask_client
def _create_lu_bounds(self, codes, benchmark_w, current_position, is_tradable):
def _create_lu_bounds(self, codes, benchmark_w):
codes = np.array(codes)
......@@ -107,14 +107,6 @@ class Strategy(object):
for i, c in enumerate(codes):
if c not in ub:
ubound[i] = ub['other']
if current_position is None:
lbound[~is_tradable] = 0.
ubound[~is_tradable] = 0.
else:
lbound[~is_tradable] = current_position[~is_tradable]
ubound[~is_tradable] = current_position[~is_tradable]
return lbound, ubound
def run(self):
......@@ -154,7 +146,7 @@ class Strategy(object):
total_data = pd.merge(total_data, total_returns, on=['trade_date', 'code'])
total_data = pd.merge(total_data, total_risk_exposure, on=['trade_date', 'code'])
is_in_benchmark = (total_data.weight > 0.).astype(float).reshape((-1, 1))
is_in_benchmark = (total_data.weight > 0.).astype(float).values.reshape((-1, 1))
total_data.loc[:, 'benchmark'] = is_in_benchmark
total_data.loc[:, 'total'] = np.ones_like(is_in_benchmark)
total_data.sort_values(['trade_date', 'code'], inplace=True)
......@@ -181,16 +173,9 @@ class Strategy(object):
models = dict(results)
for ref_date, this_data in total_data_groups:
new_model = models[ref_date]
this_data = this_data.fillna(this_data[new_model.features].median())
codes = this_data.code.values.tolist()
# fast path optimization to disable trading on codes touch price limit
# should be refined later
is_tradable = (this_data.isOpen) & (this_data.chgPct <= 0.099) & (this_data.chgPct >= -0.099)
if previous_pos.empty:
current_position = None
else:
......@@ -211,16 +196,23 @@ class Strategy(object):
this_data,
benchmark_w)
lbound, ubound = self._create_lu_bounds(codes, benchmark_w, current_position, is_tradable)
lbound, ubound = self._create_lu_bounds(codes, benchmark_w)
features = new_model.features
raw_factors = this_data[features].values
dfs = []
for name in features:
data_cleaned = this_data.dropna(subset=[name])
raw_factors = data_cleaned[[name]].values
new_factors = factor_processing(raw_factors,
pre_process=self.data_meta.pre_process,
risk_factors=this_data[self.data_meta.neutralized_risk].values.astype(float) if self.data_meta.neutralized_risk else None,
risk_factors=data_cleaned[self.data_meta.neutralized_risk].values.astype(float) if self.data_meta.neutralized_risk else None,
post_process=self.data_meta.post_process)
df = pd.DataFrame(new_factors, columns=[name], index=data_cleaned.code)
dfs.append(df)
er = new_model.predict(pd.DataFrame(new_factors, columns=features)).astype(float)
new_factors = pd.concat(dfs, axis=1)
new_factors = new_factors.loc[codes].fillna(new_factors.median())
er = new_model.predict(new_factors).astype(float)
alpha_logger.info('{0} re-balance: {1} codes'.format(ref_date, len(er)))
target_pos = self._calculate_pos(er,
......@@ -294,6 +286,7 @@ if __name__ == '__main__':
from matplotlib import pyplot as plt
from dask.distributed import Client
from PyFin.api import CSQuantiles
from PyFin.api import CSMeanAdjusted
from PyFin.api import LAST
from alphamind.api import Universe
from alphamind.api import ConstLinearModel
......@@ -309,20 +302,20 @@ if __name__ == '__main__':
mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False
start_date = '2010-01-01'
end_date = '2018-05-14'
start_date = '2017-01-01'
end_date = '2018-05-17'
freq = '10b'
neutralized_risk = None
universe = Universe("custom", ['zz800'])
universe = Universe('zz800')
dask_client = Client('10.63.6.176:8786')
factor = CSQuantiles(LAST('ILLIQUIDITY'),
groups='sw1_adj')
alpha_factors = {
str(factor): factor,
'f1': CSQuantiles(LAST('ILLIQUIDITY') * LAST('NegMktValue'), groups='sw1_adj'),
'f2': CSQuantiles('con_pe', groups='sw1_adj')
}
weights = {str(factor): 1.}
weights = {'f1': 1., 'f2': 0.}
# alpha_model = XGBTrainer(objective='reg:linear',
# booster='gbtree',
......@@ -335,23 +328,32 @@ if __name__ == '__main__':
data_meta = DataMeta(freq=freq,
universe=universe,
batch=1,
neutralized_risk=None, # industry_styles,
pre_process=None, # [winsorize_normal, standardize],
post_process=None,
warm_start=1) # [standardize])
neutralized_risk=neutralized_risk,
pre_process=None, #[winsorize_normal, standardize],
post_process=None, #[standardize],
warm_start=1)
industries = industry_list('sw_adj', 1)
total_risk_names = ['total'] + industries
total_risk_names = ['total', 'benchmark'] + industries
b_type = []
l_val = []
u_val = []
for name in total_risk_names:
if name == 'total':
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(.0)
u_val.append(.0)
elif name == 'benchmark':
b_type.append(BoundaryType.RELATIVE)
l_val.append(0.8)
u_val.append(1.0)
else:
b_type.append(BoundaryType.MAXABSREL)
l_val.append((0.00, 0.0))
u_val.append((0.00, 0.0))
bounds = create_box_bounds(total_risk_names, b_type, l_val, u_val)
......@@ -360,8 +362,10 @@ if __name__ == '__main__':
end_date,
freq,
benchmark=906,
lbound=None,
ubound=None,
weights_bandwidth=0.01,
rebalance_method='tv',
rebalance_method='risk_neutral',
bounds=bounds,
target_vol=0.05,
turn_over_target=0.4)
......@@ -369,6 +373,8 @@ if __name__ == '__main__':
strategy = Strategy(alpha_model, data_meta, running_setting, dask_client=dask_client)
ret_df, positions = strategy.run()
ret_df.rename(columns={'excess_return': '超额收益', 'turn_over': '换手率'}, inplace=True)
ret_df[['超额收益', '换手率']].cumsum().plot(secondary_y='换手率', figsize=(14, 7))
ret_df[['超额收益', '换手率']].cumsum().plot(secondary_y='换手率')
plt.title("原始ILLIQUIDITY因子")
plt.show()
positions.to_csv('d:/positions.csv', encoding='gbk')
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