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alpha-mind
Commit 8a4af8cd authored by wegamekinglc's avatar wegamekinglc
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using more options as optimal

parent 1214d5ec
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alphamind/strategy/strategy.py
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...@@ -271,6 +271,7 @@ class Strategy(object): ...@@ -271,6 +271,7 @@ class Strategy(object):
if __name__ == '__main__': if __name__ == '__main__':
import os
from matplotlib import pyplot as plt from matplotlib import pyplot as plt
from dask.distributed import Client from dask.distributed import Client
from PyFin.api import CSQuantiles from PyFin.api import CSQuantiles
...@@ -290,82 +291,133 @@ if __name__ == '__main__': ...@@ -290,82 +291,133 @@ if __name__ == '__main__':
mpl.rcParams['font.sans-serif'] = ['SimHei'] mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False mpl.rcParams['axes.unicode_minus'] = False
start_date = '2010-05-01' # Back test parameter settings
end_date = '2018-05-17' start_date = '2016-01-01'
end_date = '2018-06-11'
freq = '10b' freq = '10b'
neutralized_risk = None industry_name = 'sw_adj'
industry_level = 1
turn_over_target = 0.4
batch = 1
horizon = map_freq(freq)
weights_bandwidth = 0.02
universe = Universe('zz800') universe = Universe('zz800')
dask_client = Client('10.63.6.176:8786') data_source = os.environ['DB_URI']
benchmark_code = 300
method = 'risk_neutral'
# Model settings
alpha_factors = { alpha_factors = {
'f1': CSQuantiles(LAST('ILLIQUIDITY') * LAST('NegMktValue'), groups='sw1_adj'), 'ep_q_cs': CSQuantiles(LAST('ep_q'), groups='sw1_adj'),
'f2': CSQuantiles('con_pe', groups='sw1_adj') 'roe_q_cs': CSQuantiles(LAST('roe_q'), groups='sw1_adj'),
'SGRO_cs': CSQuantiles(LAST('SGRO'), groups='sw1_adj'),
'GREV_cs': CSQuantiles(LAST('GREV'), groups='sw1_adj'),
'con_peg_rolling_cs': CSQuantiles(LAST('con_peg_rolling'), groups='sw1_adj'),
'con_pe_rolling_order_cs': CSQuantiles(LAST('con_pe_rolling_order'), groups='sw1_adj'),
'IVR_cs': CSQuantiles(LAST('IVR'), groups='sw1_adj'),
'ILLIQUIDITY_cs': CSQuantiles(LAST('ILLIQUIDITY') * LAST('NegMktValue'), groups='sw1_adj'),
'DividendPaidRatio_cs': CSQuantiles(LAST('DividendPaidRatio'), groups='sw1_adj'),
} }
weights = {'f1': 1., 'f2': 0.} weights = dict(ep_q_cs=1.,
roe_q_cs=1.,
# alpha_model = XGBTrainer(objective='reg:linear', SGRO_cs=0.0,
# booster='gbtree', GREV_cs=0.0,
# n_estimators=300, con_peg_rolling_cs=-0.25,
# eval_sample=0.25, con_pe_rolling_order_cs=-0.25,
# features=alpha_factors) IVR_cs=0.5,
ILLIQUIDITY_cs=0.5,
DividendPaidRatio_cs=0.5)
alpha_model = ConstLinearModel(features=alpha_factors, weights=weights) alpha_model = ConstLinearModel(features=alpha_factors, weights=weights)
data_meta = DataMeta(freq=freq, data_meta = DataMeta(freq=freq,
universe=universe, universe=universe,
batch=1, batch=1,
neutralized_risk=neutralized_risk, neutralized_risk=None,
pre_process=None, pre_process=None,
post_process=None, post_process=None,
warm_start=1) data_source=data_source)
industries = industry_list('sw_adj', 1) # Constraintes settings
total_risk_names = ['total', 'benchmark'] + industries industry_names = industry_list(industry_name, industry_level)
constraint_risk = ['SIZE', 'BETA']
total_risk_names = constraint_risk + ['benchmark', 'total']
all_styles = risk_styles + industry_names + macro_styles
b_type = [] b_type = []
l_val = [] l_val = []
u_val = [] u_val = []
previous_pos = pd.DataFrame()
rets = []
turn_overs = []
leverags = []
for name in total_risk_names: for name in total_risk_names:
if name == 'total': if name == 'benchmark':
b_type.append(BoundaryType.RELATIVE)
l_val.append(0.8)
u_val.append(1.0)
elif name == 'total':
b_type.append(BoundaryType.ABSOLUTE) b_type.append(BoundaryType.ABSOLUTE)
l_val.append(.0) l_val.append(.0)
u_val.append(.0) u_val.append(.0)
elif name == 'benchmark': elif name == 'EARNYILD':
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(0.00)
u_val.append(0.60)
elif name == 'GROWTH':
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(-0.05)
u_val.append(0.05)
elif name == 'MOMENTUM':
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(0.20)
u_val.append(0.20)
elif name == 'SIZE':
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(-0.05)
u_val.append(0.05)
elif name == 'LIQUIDTY':
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(-0.40)
u_val.append(-0.0)
elif benchmark_code == 905 and name not in ["计算机", "医药生物", "国防军工", "信息服务", "机械设备"] and name in industry_names:
b_type.append(BoundaryType.RELATIVE) b_type.append(BoundaryType.RELATIVE)
l_val.append(0.8) l_val.append(0.8)
u_val.append(1.0) u_val.append(1.0)
elif benchmark_code == 300 and name in ["银行", "保险", "证券", "多元金融"]:
b_type.append(BoundaryType.RELATIVE)
l_val.append(0.70)
u_val.append(0.90)
elif name in ["计算机", "医药生物", "国防军工", "信息服务", "机械设备"]:
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(0.0)
u_val.append(0.05)
else: else:
b_type.append(BoundaryType.MAXABSREL) b_type.append(BoundaryType.ABSOLUTE)
l_val.append((0.00, 0.0)) l_val.append(-0.002)
u_val.append((0.00, 0.0)) u_val.append(0.002)
bounds = create_box_bounds(total_risk_names, b_type, l_val, u_val) bounds = create_box_bounds(total_risk_names, b_type, l_val, u_val)
running_setting = RunningSetting(lbound=None, # Running settings
ubound=None, running_setting = RunningSetting(weights_bandwidth=weights_bandwidth,
weights_bandwidth=0.01, rebalance_method=method,
rebalance_method='risk_neutral',
bounds=bounds, bounds=bounds,
target_vol=0.05, turn_over_target=turn_over_target)
turn_over_target=0.4)
# Strategy
strategy = Strategy(alpha_model, strategy = Strategy(alpha_model,
data_meta, data_meta,
universe=universe, universe=universe,
start_date=start_date, start_date=start_date,
end_date=end_date, end_date=end_date,
freq=freq, freq=freq,
benchmark=906, benchmark=benchmark_code)
dask_client=dask_client)
strategy.prepare_backtest_data()
ret_df, positions = strategy.run(running_setting) strategy.prepare_backtest_data()
ret_df.rename(columns={'excess_return': '超额收益', 'turn_over': '换手率'}, inplace=True) ret_df, positions = strategy.run(running_setting=running_setting)
ret_df[['超额收益', '换手率']].cumsum().plot(secondary_y='换手率') \ No newline at end of file
plt.title("原始ILLIQUIDITY因子")
plt.show()
positions.to_csv('d:/positions.csv', encoding='gbk')
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