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robo-dividend
Commit 2c487c1a authored by jichao's avatar jichao
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依赖注入实现中

parent 3052be21
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Showing with 354 additions and 281 deletions
api.py
View file @ 2c487c1a
...@@ -223,6 +223,68 @@ class PortfoliosBuilder(ABC): ...@@ -223,6 +223,68 @@ class PortfoliosBuilder(ABC):
pass pass
class Solver(ABC):
'''
解算器
'''
def solve_max_rtn(self):
'''
:return: max_rtn, max_var, minCVaR_whenMaxR
'''
pass
def solve_min_rtn(self):
'''
:return: min_rtn, min_var, maxCVaR_whenMinR
'''
pass
def solve_mpt(self, min_rtn, max_rtn):
'''
常规mpt计算
:param min_rtn: 最小回报率
:param max_rtn: 最大回报率
:return: 投组，cvar
'''
pass
def solve_poem(self, min_rtn, max_rtn, base_cvar, max_cvar):
'''
poem方式的mpt计算
:param min_rtn: 最小回报率
:param max_rtn: 最大回报率
:param base_cvar: 基础cvar
:param max_cvar: 最大cvar
:return: 投组，cvar
'''
pass
def reset_navs(self, day):
'''
根据指定的日期，重置当前解算器，其他计算，全部依赖这里重置后的基金净值数据
:param day: 指定的日期
:return: 根据指定日期获取的，基金净值数据
'''
pass
@property
@abstractmethod
def navs(self):
'''
:return: 当前解算器需要的基金净值
'''
pass
class SolverFactory(ABC):
'''
解算器工厂
'''
@abstractmethod
def create_solver(self, risk: PortfoliosRisk, type: PortfoliosType = PortfoliosType.NORMAL) -> Solver:
pass
class SignalBuilder(ABC): class SignalBuilder(ABC):
''' '''
控制信号，发起是否调仓服务 控制信号，发起是否调仓服务
......
basic/datum.py
View file @ 2c487c1a
import json import json
from api import DatumType, Datum from api import DatumType, Datum, PortfoliosRisk
from basic.dao import robo_base_datum as rbd from basic.dao import robo_base_datum as rbd
from framework import component, parse_date from framework import component, parse_date
...@@ -17,3 +17,6 @@ class DefaultDatum(Datum): ...@@ -17,3 +17,6 @@ class DefaultDatum(Datum):
result = rbd.get_base_datums(type=DatumType.INDEX, ticker=ticker, datum_ids=index_ids) result = rbd.get_base_datums(type=DatumType.INDEX, ticker=ticker, datum_ids=index_ids)
return [{**json.loads(x['datas']), 'id': x['id']} for x in result] return [{**json.loads(x['datas']), 'id': x['id']} for x in result]
def get_high_risk_datums(self, risk: PortfoliosRisk):
pass
config.yml
View file @ 2c487c1a
...@@ -74,7 +74,7 @@ asset-pool: ...@@ -74,7 +74,7 @@ asset-pool:
threshold: -0.03 threshold: -0.03
coef: 0.95 coef: 0.95
portfolios: portfolios:
builder: solver:
tol: 1E-10 tol: 1E-10
navs: navs:
months: 3 months: 3
......
portfolios/builder.py
View file @ 2c487c1a
import datetime
import json import json
import os
import sys
from logging import DEBUG
import pandas as pd from api import PortfoliosBuilder, PortfoliosRisk, AssetPool, Navs, PortfoliosType, Datum, SolveType, SolverFactory
from dateutil.relativedelta import relativedelta from framework import component, autowired, format_date, get_logger
from numpy import NAN
from pyomo.environ import *
from api import PortfoliosBuilder, PortfoliosRisk, AssetPool, Navs, PortfoliosType, Datum, SolveType
from framework import component, autowired, get_config, format_date, get_logger, parse_date
from portfolios.dao import robo_mpt_portfolios as rmp from portfolios.dao import robo_mpt_portfolios as rmp
logger = get_logger(__name__) logger = get_logger(__name__)
def create_solver():
if sys.platform.find('win') == 0:
executor = 'bonmin.exe'
elif sys.platform == 'linux':
executor = 'bonmin_linux'
else:
executor = 'bonmin_mac'
return SolverFactory('Bonmin', executable=os.path.join(os.path.dirname(__file__), executor))
class MptSolver:
@autowired
def __init__(self, risk: PortfoliosRisk, type: PortfoliosType, assets: AssetPool = None, navs: Navs = None,
datum: Datum = None):
self.__navs = None
self.risk = risk
self.type = type or PortfoliosType.NORMAL
self._assets = assets
self._navs = navs
self._datum = datum
self._config = get_config(__name__)
self._solver = create_solver()
self._solver.options['tol'] = float(self.get_config('tol') or 1E-10)
@property
def navs(self):
return self.__navs
@property
def rtn_matrix(self):
result = self.navs / self.navs.shift(self.get_config('matrix-rtn-days')) - 1
result.dropna(inplace=True)
return result
@property
def rtn_annualized(self):
return list(self.rtn_matrix.mean() * 12)
@property
def sigma(self):
rtn = (self.navs / self.navs.shift(1) - 1)[1:]
return rtn.cov() * 252
@property
def rtn_history(self):
result = self.rtn_matrix * 12
return result.values
@property
def beta(self):
return self.get_config('mpt.cvar-beta')
@property
def k_beta(self):
return round(len(self.rtn_history) * self.beta + 0.499999)
@property
def quantile(self):
return self.get_config('mpt.quantile')
def solve_max_rtn(self):
model = self.create_model()
model.objective = Objective(expr=sum([model.w[i] * self.rtn_annualized[i] for i in model.indices]),
sense=maximize)
self._solver.solve(model)
self.debug_solve_result(model)
max_rtn = self.calc_port_rtn(model)
max_var = self.calc_port_var(model)
minCVaR_whenMaxR = self.calc_port_cvar(model)
logger.debug({
'max_rtn': max_rtn,
'max_var': max_var,
'minCVaR_whenMaxR': minCVaR_whenMaxR,
})
return max_rtn, max_var, minCVaR_whenMaxR
def solve_min_rtn(self):
model = self.create_model()
model.objective = Objective(
expr=sum([model.w[i] * model.w[j] * self.sigma.iloc[i, j] for i in model.indices for j in model.indices]),
sense=minimize)
self._solver.solve(model)
self.debug_solve_result(model)
min_rtn = self.calc_port_rtn(model)
min_var = self.calc_port_var(model)
maxCVaR_whenMinV = self.calc_port_cvar(model)
logger.debug({
'min_rtn': min_rtn,
'min_var': min_var,
'maxCVaR_whenMinV': maxCVaR_whenMinV,
})
return min_rtn, min_var, maxCVaR_whenMinV
def solve_mpt(self, min_rtn, max_rtn):
logger.debug(f'...... ...... ...... ...... ...... ...... ...... ...... '
f'MPT ... sub risk : pct_value = {self.quantile}')
big_y = min_rtn + self.quantile * (max_rtn - min_rtn)
logger.debug(f'big_Y = target_Return = {big_y}')
model = self.create_model()
model.cons_rtn = Constraint(expr=sum([model.w[i] * self.rtn_annualized[i] for i in model.indices]) >= big_y)
model.objective = Objective(
expr=sum([model.w[i] * model.w[j] * self.sigma.iloc[i, j] for i in model.indices for j in model.indices]),
sense=minimize)
result = self._solver.solve(model)
if result.solver.termination_condition == TerminationCondition.infeasible:
logger.debug('...... MPT: Infeasible Optimization Problem.')
return None, None
logger.debug('...... MPT: Has solution.')
self.debug_solve_result(model)
return self.calc_port_weight(model), self.calc_port_cvar(model)
def solve_poem(self, min_rtn, max_rtn, base_cvar, max_cvar):
k_history = len(self.rtn_history)
quantile = self.quantile
logger.debug(f'...... ...... ...... ...... ...... ...... ...... ...... '
f'POEM With CVaR constraints ... sub risk : pct_value = {quantile}')
big_y = min_rtn + quantile * (max_rtn - min_rtn)
small_y = base_cvar + (max_cvar - base_cvar) * self.get_config('poem.cvar-scale-factor') * quantile
logger.debug(f'big_Y = target_Return = {big_y} | small_y = target_cvar = {small_y}')
model = self.create_model()
model.alpha = Var(domain=Reals)
model.x = Var(range(k_history), domain=NonNegativeReals)
model.cons_cvar_aux = Constraint(range(k_history), rule=lambda m, k: m.x[k] >= m.alpha - sum(
[m.w[i] * self.rtn_history[k][i] for i in m.indices]))
model.cons_rtn = Constraint(expr=sum([model.w[i] * self.rtn_annualized[i] for i in model.indices]) >= big_y)
model.cons_cvar = Constraint(
expr=model.alpha - (1 / self.k_beta) * sum([model.x[k] for k in range(k_history)]) >= small_y)
result = self._solver.solve(model)
if result.solver.termination_condition == TerminationCondition.infeasible:
logger.debug('...... POEM: Infeasible Optimization Problem.')
return None, None
logger.debug('...... POEM: Has solution.')
self.debug_solve_result(model)
return self.calc_port_weight(model), self.calc_port_cvar(model)
def calc_port_weight(self, model):
id_list = self.navs.columns
weight_list = []
for i in model.indices:
weight_list.append(model.w[i]._value * model.z[i]._value)
df_w = pd.DataFrame(data=weight_list, index=id_list, columns=['weight'])
df_w.replace(0, NAN, inplace=True)
df_w.dropna(axis=0, inplace=True)
df_w['weight'] = self.format_weight(df_w['weight'])
dict_w = df_w.to_dict()['weight']
return dict_w
@staticmethod
def format_weight(weight_series):
weight_series = weight_series.fillna(0)
minidx = weight_series[weight_series > 0].idxmin()
maxidx = weight_series.idxmax()
weight_series = weight_series.apply(lambda x: round(x, 2))
if weight_series.sum() < 1:
weight_series[minidx] += 1 - weight_series.sum()
elif weight_series.sum() > 1:
weight_series[maxidx] += 1 - weight_series.sum()
return weight_series.apply(lambda x: round(float(x), 2))
def calc_port_rtn(self, model):
return sum([model.w[i]._value * self.rtn_annualized[i] for i in model.indices])
def calc_port_var(self, model):
return sum([model.w[i]._value * model.w[j]._value * self.sigma.iloc[i, j] for i in model.indices for j in
model.indices])
def calc_port_cvar(self, model):
port_r_hist = []
for k in range(len(self.rtn_history)):
port_r_hist.append(
sum([model.w[i]._value * model.z[i]._value * self.rtn_history[k][i] for i in model.indices]))
port_r_hist.sort()
return sum(port_r_hist[0: self.k_beta]) / self.k_beta
def create_model(self):
count = self.get_config('asset-count')
min_count = count[0] if isinstance(count, list) else count
max_count = count[1] if isinstance(count, list) else count
low_weight = self.get_config('mpt.low-weight')
high_weight = self.get_config('mpt.high-weight')
if isinstance(high_weight, list):
high_weight = high_weight[min(len(self.navs.columns), min_count, len(high_weight)) - 1]
model = ConcreteModel()
model.indices = range(0, len(self.navs.columns))
model.w = Var(model.indices, domain=NonNegativeReals)
model.z = Var(model.indices, domain=Binary)
model.cons_sum_weight = Constraint(expr=sum([model.w[i] for i in model.indices]) == 1)
model.cons_num_asset = Constraint(
expr=inequality(min_count, sum([model.z[i] for i in model.indices]), max_count, strict=False))
model.cons_bounds_low = Constraint(model.indices, rule=lambda m, i: m.z[i] * low_weight <= m.w[i])
model.cons_bounds_up = Constraint(model.indices, rule=lambda m, i: m.z[i] * high_weight >= m.w[i])
return model
def reset_navs(self, day):
asset_ids = self._assets.get_pool(day)
asset_risk = self.get_config('navs.risk')
datum = self._datum.get_fund_datums(fund_ids=asset_ids, risk=asset_risk)
exclude = self.get_config('navs.exclude-asset-type') or []
asset_ids = list(set(asset_ids) & set([x['id'] for x in datum if x['assetType'] not in exclude]))
min_date = day - relativedelta(months=self.get_config('navs.months'))
navs = pd.DataFrame(self._navs.get_fund_navs(fund_ids=asset_ids, max_date=day, min_date=min_date))
navs['nav_date'] = pd.to_datetime(navs['nav_date'])
navs = navs.pivot_table(index='nav_date', columns='fund_id', values='nav_cal')
navs = navs.sort_index()
navs_nan = navs.isna().sum()
navs.drop(columns=[x for x in navs_nan.index if navs_nan.loc[x] >= self.get_config('navs.max-nan.asset')],
inplace=True)
navs_nan = navs.apply(lambda r: r.isna().sum() / len(r), axis=1)
navs.drop(index=[x for x in navs_nan.index if navs_nan.loc[x] >= self.get_config('navs.max-nan.day')],
inplace=True)
navs.fillna(method='ffill', inplace=True)
self.__navs = navs
def get_config(self, name):
def load_config(config):
for key in name.split('.'):
if key in config:
config = config[key]
else:
return None
return config
value = load_config(self._config[self.type.value] if self.type is not PortfoliosType.NORMAL else self._config)
if value is None:
value = load_config(self._config)
return value[f'ft{self.risk.value}'] if value and isinstance(value, dict) else value
def debug_solve_result(self, model):
if logger.isEnabledFor(DEBUG):
logger.debug('===============================')
logger.debug('solution: id | w(id)')
w_sum = 0
for i in model.indices:
if model.z[i]._value == 1:
logger.debug(f'{self.navs.columns[i]} | {model.w[i]._value}')
w_sum += model.w[i]._value
logger.debug(f'w_sum = {w_sum}')
logger.debug({
'beta': self.beta,
'kbeta': self.k_beta,
'port_R': self.calc_port_rtn(model),
'port_V': self.calc_port_cvar(model),
'port_CVaR': self.calc_port_cvar(model)
})
logger.debug('-------------------------------')
@component(bean_name='mpt') @component(bean_name='mpt')
class MptPortfoliosBuilder(PortfoliosBuilder): class MptPortfoliosBuilder(PortfoliosBuilder):
@autowired @autowired
def __init__(self, assets: AssetPool = None, navs: Navs = None, datum: Datum = None): def __init__(self, assets: AssetPool = None, navs: Navs = None, datum: Datum = None, factory: SolverFactory = None):
self._assets = assets self._assets = assets
self._navs = navs self._navs = navs
self._datum = datum self._datum = datum
self._factory = factory
def get_portfolios(self, day, risk: PortfoliosRisk, type: PortfoliosType = PortfoliosType.NORMAL): def get_portfolios(self, day, risk: PortfoliosRisk, type: PortfoliosType = PortfoliosType.NORMAL):
portfolio = rmp.get_one(day, type, risk) portfolio = rmp.get_one(day, type, risk)
...@@ -297,7 +37,7 @@ class MptPortfoliosBuilder(PortfoliosBuilder): ...@@ -297,7 +37,7 @@ class MptPortfoliosBuilder(PortfoliosBuilder):
for risk in PortfoliosRisk: for risk in PortfoliosRisk:
logger.info( logger.info(
f"start build protfolio of type[{type.name}] and risk[{risk.name}] with date[{format_date(day)}]") f"start build protfolio of type[{type.name}] and risk[{risk.name}] with date[{format_date(day)}]")
solver = MptSolver(risk, type) solver = self._factory.create_solver(risk, type)
solver.reset_navs(day) solver.reset_navs(day)
logger.debug({ logger.debug({
'Khist': len(solver.rtn_history), 'Khist': len(solver.rtn_history),
...@@ -333,7 +73,7 @@ class PoemPortfoliosBuilder(MptPortfoliosBuilder): ...@@ -333,7 +73,7 @@ class PoemPortfoliosBuilder(MptPortfoliosBuilder):
for risk in PortfoliosRisk: for risk in PortfoliosRisk:
if result[risk]['solve'] is SolveType.INFEASIBLE: if result[risk]['solve'] is SolveType.INFEASIBLE:
continue continue
solver = MptSolver(risk, type) solver = self._factory.create_solver(risk, type)
solver.reset_navs(day) solver.reset_navs(day)
min_rtn = detail[risk]['min_rtn'] min_rtn = detail[risk]['min_rtn']
max_rtn = detail[risk]['max_rtn'] max_rtn = detail[risk]['max_rtn']
......
portfolios/solver.py 0 → 100644
View file @ 2c487c1a
import os
import sys
from logging import DEBUG
import pandas as pd
from dateutil.relativedelta import relativedelta
from numpy import NAN
from pyomo.environ import *
from api import SolverFactory as Factory, PortfoliosRisk, PortfoliosType, AssetPool, Navs, Solver, Datum
from framework import component, autowired, get_config, get_logger
logger = get_logger(__name__)
def create_solver():
if sys.platform.find('win') == 0:
executor = 'bonmin.exe'
elif sys.platform == 'linux':
executor = 'bonmin_linux'
else:
executor = 'bonmin_mac'
return SolverFactory('Bonmin', executable=os.path.join(os.path.dirname(__file__), executor))
@component
class DefaultFactory(Factory):
def create_solver(self, risk: PortfoliosRisk, type: PortfoliosType = PortfoliosType.NORMAL) -> Solver:
return DefaultSolver(risk, type)
class DefaultSolver(Solver):
@autowired
def __init__(self, risk: PortfoliosRisk, type: PortfoliosType, assets: AssetPool = None, navs: Navs = None,
datum: Datum = None):
self.__navs = None
self.risk = risk
self.type = type or PortfoliosType.NORMAL
self._assets = assets
self._navs = navs
self._datum = datum
self._config = get_config(__name__)
self._solver = create_solver()
self._solver.options['tol'] = float(self.get_config('tol') or 1E-10)
@property
def navs(self):
return self.__navs
@property
def rtn_matrix(self):
result = self.navs / self.navs.shift(self.get_config('matrix-rtn-days')) - 1
result.dropna(inplace=True)
return result
@property
def rtn_annualized(self):
return list(self.rtn_matrix.mean() * 12)
@property
def sigma(self):
rtn = (self.navs / self.navs.shift(1) - 1)[1:]
return rtn.cov() * 252
@property
def rtn_history(self):
result = self.rtn_matrix * 12
return result.values
@property
def beta(self):
return self.get_config('mpt.cvar-beta')
@property
def k_beta(self):
return round(len(self.rtn_history) * self.beta + 0.499999)
@property
def quantile(self):
return self.get_config('mpt.quantile')
def solve_max_rtn(self):
model = self.create_model()
model.objective = Objective(expr=sum([model.w[i] * self.rtn_annualized[i] for i in model.indices]),
sense=maximize)
self._solver.solve(model)
self.debug_solve_result(model)
max_rtn = self.calc_port_rtn(model)
max_var = self.calc_port_var(model)
minCVaR_whenMaxR = self.calc_port_cvar(model)
logger.debug({
'max_rtn': max_rtn,
'max_var': max_var,
'minCVaR_whenMaxR': minCVaR_whenMaxR,
})
return max_rtn, max_var, minCVaR_whenMaxR
def solve_min_rtn(self):
model = self.create_model()
model.objective = Objective(
expr=sum([model.w[i] * model.w[j] * self.sigma.iloc[i, j] for i in model.indices for j in model.indices]),
sense=minimize)
self._solver.solve(model)
self.debug_solve_result(model)
min_rtn = self.calc_port_rtn(model)
min_var = self.calc_port_var(model)
maxCVaR_whenMinV = self.calc_port_cvar(model)
logger.debug({
'min_rtn': min_rtn,
'min_var': min_var,
'maxCVaR_whenMinV': maxCVaR_whenMinV,
})
return min_rtn, min_var, maxCVaR_whenMinV
def solve_mpt(self, min_rtn, max_rtn):
logger.debug(f'...... ...... ...... ...... ...... ...... ...... ...... '
f'MPT ... sub risk : pct_value = {self.quantile}')
big_y = min_rtn + self.quantile * (max_rtn - min_rtn)
logger.debug(f'big_Y = target_Return = {big_y}')
model = self.create_model()
model.cons_rtn = Constraint(expr=sum([model.w[i] * self.rtn_annualized[i] for i in model.indices]) >= big_y)
model.objective = Objective(
expr=sum([model.w[i] * model.w[j] * self.sigma.iloc[i, j] for i in model.indices for j in model.indices]),
sense=minimize)
result = self._solver.solve(model)
if result.solver.termination_condition == TerminationCondition.infeasible:
logger.debug('...... MPT: Infeasible Optimization Problem.')
return None, None
logger.debug('...... MPT: Has solution.')
self.debug_solve_result(model)
return self.calc_port_weight(model), self.calc_port_cvar(model)
def solve_poem(self, min_rtn, max_rtn, base_cvar, max_cvar):
k_history = len(self.rtn_history)
quantile = self.quantile
logger.debug(f'...... ...... ...... ...... ...... ...... ...... ...... '
f'POEM With CVaR constraints ... sub risk : pct_value = {quantile}')
big_y = min_rtn + quantile * (max_rtn - min_rtn)
small_y = base_cvar + (max_cvar - base_cvar) * self.get_config('poem.cvar-scale-factor') * quantile
logger.debug(f'big_Y = target_Return = {big_y} | small_y = target_cvar = {small_y}')
model = self.create_model()
model.alpha = Var(domain=Reals)
model.x = Var(range(k_history), domain=NonNegativeReals)
model.cons_cvar_aux = Constraint(range(k_history), rule=lambda m, k: m.x[k] >= m.alpha - sum(
[m.w[i] * self.rtn_history[k][i] for i in m.indices]))
model.cons_rtn = Constraint(expr=sum([model.w[i] * self.rtn_annualized[i] for i in model.indices]) >= big_y)
model.cons_cvar = Constraint(
expr=model.alpha - (1 / self.k_beta) * sum([model.x[k] for k in range(k_history)]) >= small_y)
result = self._solver.solve(model)
if result.solver.termination_condition == TerminationCondition.infeasible:
logger.debug('...... POEM: Infeasible Optimization Problem.')
return None, None
logger.debug('...... POEM: Has solution.')
self.debug_solve_result(model)
return self.calc_port_weight(model), self.calc_port_cvar(model)
def calc_port_weight(self, model):
id_list = self.navs.columns
weight_list = []
for i in model.indices:
weight_list.append(model.w[i]._value * model.z[i]._value)
df_w = pd.DataFrame(data=weight_list, index=id_list, columns=['weight'])
df_w.replace(0, NAN, inplace=True)
df_w.dropna(axis=0, inplace=True)
df_w['weight'] = self.format_weight(df_w['weight'])
dict_w = df_w.to_dict()['weight']
return dict_w
@staticmethod
def format_weight(weight_series):
weight_series = weight_series.fillna(0)
minidx = weight_series[weight_series > 0].idxmin()
maxidx = weight_series.idxmax()
weight_series = weight_series.apply(lambda x: round(x, 2))
if weight_series.sum() < 1:
weight_series[minidx] += 1 - weight_series.sum()
elif weight_series.sum() > 1:
weight_series[maxidx] += 1 - weight_series.sum()
return weight_series.apply(lambda x: round(float(x), 2))
def calc_port_rtn(self, model):
return sum([model.w[i]._value * self.rtn_annualized[i] for i in model.indices])
def calc_port_var(self, model):
return sum([model.w[i]._value * model.w[j]._value * self.sigma.iloc[i, j] for i in model.indices for j in
model.indices])
def calc_port_cvar(self, model):
port_r_hist = []
for k in range(len(self.rtn_history)):
port_r_hist.append(
sum([model.w[i]._value * model.z[i]._value * self.rtn_history[k][i] for i in model.indices]))
port_r_hist.sort()
return sum(port_r_hist[0: self.k_beta]) / self.k_beta
def create_model(self):
count = self.get_config('asset-count')
min_count = count[0] if isinstance(count, list) else count
max_count = count[1] if isinstance(count, list) else count
low_weight = self.get_config('mpt.low-weight')
high_weight = self.get_config('mpt.high-weight')
if isinstance(high_weight, list):
high_weight = high_weight[min(len(self.navs.columns), min_count, len(high_weight)) - 1]
model = ConcreteModel()
model.indices = range(0, len(self.navs.columns))
model.w = Var(model.indices, domain=NonNegativeReals)
model.z = Var(model.indices, domain=Binary)
model.cons_sum_weight = Constraint(expr=sum([model.w[i] for i in model.indices]) == 1)
model.cons_num_asset = Constraint(
expr=inequality(min_count, sum([model.z[i] for i in model.indices]), max_count, strict=False))
model.cons_bounds_low = Constraint(model.indices, rule=lambda m, i: m.z[i] * low_weight <= m.w[i])
model.cons_bounds_up = Constraint(model.indices, rule=lambda m, i: m.z[i] * high_weight >= m.w[i])
return model
def reset_navs(self, day):
asset_ids = self._assets.get_pool(day)
asset_risk = self.get_config('navs.risk')
datum = self._datum.get_fund_datums(fund_ids=asset_ids, risk=asset_risk)
exclude = self.get_config('navs.exclude-asset-type') or []
asset_ids = list(set(asset_ids) & set([x['id'] for x in datum if x['assetType'] not in exclude]))
min_date = day - relativedelta(months=self.get_config('navs.months'))
navs = pd.DataFrame(self._navs.get_fund_navs(fund_ids=asset_ids, max_date=day, min_date=min_date))
navs['nav_date'] = pd.to_datetime(navs['nav_date'])
navs = navs.pivot_table(index='nav_date', columns='fund_id', values='nav_cal')
navs = navs.sort_index()
navs_nan = navs.isna().sum()
navs.drop(columns=[x for x in navs_nan.index if navs_nan.loc[x] >= self.get_config('navs.max-nan.asset')],
inplace=True)
navs_nan = navs.apply(lambda r: r.isna().sum() / len(r), axis=1)
navs.drop(index=[x for x in navs_nan.index if navs_nan.loc[x] >= self.get_config('navs.max-nan.day')],
inplace=True)
navs.fillna(method='ffill', inplace=True)
self.__navs = navs
def get_config(self, name):
def load_config(config):
for key in name.split('.'):
if key in config:
config = config[key]
else:
return None
return config
value = load_config(self._config[self.type.value] if self.type is not PortfoliosType.NORMAL else self._config)
if value is None:
value = load_config(self._config)
return value[f'ft{self.risk.value}'] if value and isinstance(value, dict) else value
def debug_solve_result(self, model):
if logger.isEnabledFor(DEBUG):
logger.debug('===============================')
logger.debug('solution: id | w(id)')
w_sum = 0
for i in model.indices:
if model.z[i]._value == 1:
logger.debug(f'{self.navs.columns[i]} | {model.w[i]._value}')
w_sum += model.w[i]._value
logger.debug(f'w_sum = {w_sum}')
logger.debug({
'beta': self.beta,
'kbeta': self.k_beta,
'port_R': self.calc_port_rtn(model),
'port_V': self.calc_port_cvar(model),
'port_CVaR': self.calc_port_cvar(model)
})
logger.debug('-------------------------------')
portfolios/test_case.py
View file @ 2c487c1a
import unittest import unittest
from framework import autowired, parse_date, get_logger from framework import autowired, parse_date, get_logger
from api import PortfoliosBuilder, PortfoliosType from api import PortfoliosBuilder, PortfoliosType, PortfoliosRisk
class PortfoliosTest(unittest.TestCase): class PortfoliosTest(unittest.TestCase):
...@@ -8,14 +8,19 @@ class PortfoliosTest(unittest.TestCase): ...@@ -8,14 +8,19 @@ class PortfoliosTest(unittest.TestCase):
logger = get_logger(__name__) logger = get_logger(__name__)
@autowired(names={'builder': 'poem'}) @autowired(names={'builder': 'poem'})
def test_poem_portfolio_builder(self, builder: PortfoliosBuilder = None): def test_poem_build_portfolio(self, builder: PortfoliosBuilder = None):
result, detail = builder.build_portfolio(parse_date('2011-11-07'), PortfoliosType.NORMAL) result, detail = builder.build_portfolio(parse_date('2022-11-07'), PortfoliosType.NORMAL)
self.logger.info("portfolios: ") self.logger.info("portfolios: ")
for risk, portfolio in result.items(): for risk, portfolio in result.items():
self.logger.info(risk.name) self.logger.info(risk.name)
self.logger.info(portfolio) self.logger.info(portfolio)
self.logger.info(detail[risk]) self.logger.info(detail[risk])
@autowired(names={'builder': 'poem'})
def test_poem_get_portfolio(self, builder: PortfoliosBuilder = None):
portfolio = builder.get_portfolios(parse_date('2022-11-07'), PortfoliosRisk.FT9)
self.logger.info(portfolio)
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()
test.py deleted 100644 → 0
View file @ 3052be21
import unittest
class MyTestCase(unittest.TestCase):
def test_something(self):
self.assertEqual(True, True) # add assertion here
if __name__ == '__main__':
unittest.main()
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