def __init__(self,
account,
benchmark_code='000300',
benchmark_type=MARKET_TYPE.INDEX_CN,
if_fq=True,
market_data=None,
auto_reload=False):
"""
account: QA_Account类/QA_PortfolioView类
benchmark_code: [str]对照参数代码
benchmark_type: [QA.PARAM]对照参数的市场
if_fq: [Bool]原account是否使用复权数据
if_fq选项是@尧提出的,关于回测的时候成交价格问题(如果按不复权撮合 应该按不复权价格计算assets)
"""
self.account = account
self.benchmark_code = benchmark_code # 默认沪深300
self.benchmark_type = benchmark_type
self.client = DATABASE.risk
self.client.create_index([("account_cookie", ASCENDING),
("user_cookie", ASCENDING),
("portfolio_cookie", ASCENDING)],
unique=True)
if auto_reload:
pass
else:
self.fetch = {
MARKET_TYPE.STOCK_CN: QA_fetch_stock_day_adv,
MARKET_TYPE.INDEX_CN: QA_fetch_index_day_adv
}
if market_data == None:
if self.account.market_type == MARKET_TYPE.STOCK_CN:
self.market_data = QA_fetch_stock_day_adv(
self.account.code, self.account.start_date,
self.account.end_date)
elif self.account.market_type == MARKET_TYPE.FUTURE_CN:
self.market_data = QA_fetch_future_day_adv(
self.account.code, self.account.start_date,
self.account.end_date)
else:
self.market_data = market_data
self.if_fq = if_fq
if self.account.market_type == MARKET_TYPE.FUTURE_CN:
self.if_fq = False # 如果是期货， 默认设为FALSE
if self.market_value is not None:
self._assets = (
self.market_value.sum(axis=1) +
self.account.daily_cash.set_index('date').cash).fillna(
method='pad')
else:
self._assets = self.account.daily_cash.set_index(
'date').cash.fillna(method='pad')
self.time_gap = QA_util_get_trade_gap(self.account.start_date,
self.account.end_date)
self.init_cash = self.account.init_cash
self.init_assets = self.account.init_assets
class QA_Risk():
"""QARISK 是一个风险插件
需要加载一个account/portfolio类进来:
需要有
code,start_date,end_date,daily_cash,daily_hold
TODO:
资金利用率 反应资金的利用程度
股票周转率 反应股票的持仓天数
预期PNL/统计学PNL
"""
def __init__(self,
account,
benchmark_code='000300',
benchmark_type=MARKET_TYPE.INDEX_CN,
if_fq=True,
market_data=None,
auto_reload=False):
"""
account: QA_Account类/QA_PortfolioView类
benchmark_code: [str]对照参数代码
benchmark_type: [QA.PARAM]对照参数的市场
if_fq: [Bool]原account是否使用复权数据
if_fq选项是@尧提出的,关于回测的时候成交价格问题(如果按不复权撮合 应该按不复权价格计算assets)
"""
self.account = account
self.benchmark_code = benchmark_code # 默认沪深300
self.benchmark_type = benchmark_type
self.client = DATABASE.risk
self.client.create_index([("account_cookie", ASCENDING),
("user_cookie", ASCENDING),
("portfolio_cookie", ASCENDING)],
unique=True)
if auto_reload:
pass
else:
self.fetch = {
MARKET_TYPE.STOCK_CN: QA_fetch_stock_day_adv,
MARKET_TYPE.INDEX_CN: QA_fetch_index_day_adv
}
if market_data == None:
if self.account.market_type == MARKET_TYPE.STOCK_CN:
self.market_data = QA_fetch_stock_day_adv(
self.account.code, self.account.start_date,
self.account.end_date)
elif self.account.market_type == MARKET_TYPE.FUTURE_CN:
self.market_data = QA_fetch_future_day_adv(
self.account.code, self.account.start_date,
self.account.end_date)
else:
self.market_data = market_data
self.if_fq = if_fq
if self.account.market_type == MARKET_TYPE.FUTURE_CN:
self.if_fq = False # 如果是期货， 默认设为FALSE
if self.market_value is not None:
if self.account.market_type == MARKET_TYPE.FUTURE_CN and self.account.allow_margin == True:
print('margin!')
self._assets = (
self.account.daily_frozen +
self.account.daily_cash.set_index('date').cash
).dropna()
else:
self._assets = (
self.market_value.sum(axis=1) +
self.account.daily_cash.set_index('date').cash).fillna(
method='pad')
else:
self._assets = self.account.daily_cash.set_index(
'date').cash.fillna(method='pad')
self.time_gap = QA_util_get_trade_gap(self.account.start_date,
self.account.end_date)
self.init_cash = self.account.init_cash
self.init_assets = self.account.init_assets
def __repr__(self):
return '< QA_RISK ANALYSIS ACCOUNT/PORTFOLIO >'
def __call__(self):
return pd.DataFrame([self.message])
@property
def total_timeindex(self):
return self.account.trade_range
@property
def market_value(self):
"""每日每个股票持仓市值表
Returns:
pd.DataFrame -- 市值表
"""
if self.account.daily_hold is not None:
if self.if_fq:
return (self.market_data.to_qfq().pivot('close').fillna(
method='ffill') *
self.account.daily_hold.apply(abs)).fillna(
method='ffill')
else:
return (
self.market_data.pivot('close').fillna(method='ffill') *
self.account.daily_hold.apply(abs)).fillna(method='ffill')
else:
return None
@property
def daily_market_value(self):
"""每日持仓总市值表
Returns:
pd.DataFrame -- 市值表
"""
if self.market_value is not None:
return self.market_value.sum(axis=1)
else:
return None
@property
def assets(self):
x1 = self._assets.reset_index()
return x1.assign(date=pd.to_datetime(x1.date)).set_index('date')[0]
@property
def max_dropback(self):
"""最大回撤
"""
return round(
float(
max([(self.assets.iloc[idx] - self.assets.iloc[idx::].min()) /
self.assets.iloc[idx]
for idx in range(len(self.assets))])), 2)
@property
def total_commission(self):
"""总手续费
"""
return float(
-abs(round(self.account.history_table.commission.sum(), 2)))
@property
def total_tax(self):
"""总印花税
"""
return float(-abs(round(self.account.history_table.tax.sum(), 2)))
@property
def profit_construct(self):
"""利润构成
Returns:
dict -- 利润构成表
"""
return {
'total_buyandsell':
round(self.profit_money - self.total_commission - self.total_tax,
2),
'total_tax':
self.total_tax,
'total_commission':
self.total_commission,
'total_profit':
self.profit_money
}
@property
def profit_money(self):
"""盈利额
Returns:
[type] -- [description]
"""
return float(round(self.assets.iloc[-1] - self.assets.iloc[0], 2))
@property
def profit(self):
"""盈利率(百分比)
Returns:
[type] -- [description]
"""
return round(float(self.calc_profit(self.assets)), 2)
@property
def profit_pct(self):
"""利润
"""
return self.calc_profitpctchange(self.assets)
@property
def annualize_return(self):
"""年化收益
Returns:
[type] -- [description]
"""
return round(
float(self.calc_annualize_return(self.assets, self.time_gap)), 2)
@property
def volatility(self):
"""波动率
Returns:
[type] -- [description]
"""
return round(float(self.profit_pct.std() * math.sqrt(250)), 2)
@property
def ir(self):
return round(self.calc_IR(), 2)
@property
@lru_cache()
def message(self):
return {
'account_cookie': self.account.account_cookie,
'portfolio_cookie': self.account.portfolio_cookie,
'user_cookie': self.account.user_cookie,
'annualize_return': round(self.annualize_return, 2),
'profit': round(self.profit, 2),
'max_dropback': self.max_dropback,
'time_gap': self.time_gap,
'volatility': self.volatility,
'benchmark_code': self.benchmark_code,
'bm_annualizereturn': self.benchmark_annualize_return,
'bm_profit': self.benchmark_profit,
'beta': self.beta,
'alpha': self.alpha,
'sharpe': self.sharpe,
'init_cash': "%0.2f" % (float(self.assets[0])),
'last_assets': "%0.2f" % (float(self.assets.iloc[-1])),
'total_tax': self.total_tax,
'total_commission': self.total_commission,
'profit_money': self.profit_money,
'assets': list(self.assets),
'benchmark_assets': list(self.benchmark_assets),
'timeindex': self.account.trade_day,
'totaltimeindex': self.total_timeindex,
'ir': self.ir,
'month_profit': self.month_assets_profit.to_dict()
# 'init_assets': round(float(self.init_assets), 2),
# 'last_assets': round(float(self.assets.iloc[-1]), 2)
}
@property
def benchmark_data(self):
"""
基准组合的行情数据(一般是组合,可以调整)
"""
return self.fetch[self.benchmark_type](self.benchmark_code,
self.account.start_date,
self.account.end_date)
@property
def benchmark_assets(self):
"""
基准组合的账户资产队列
"""
return (self.benchmark_data.close /
float(self.benchmark_data.close.iloc[0]) *
float(self.assets[0]))
@property
def benchmark_profit(self):
"""
基准组合的收益
"""
return round(float(self.calc_profit(self.benchmark_assets)), 2)
@property
def benchmark_annualize_return(self):
"""基准组合的年化收益
Returns:
[type] -- [description]
"""
return round(
float(
self.calc_annualize_return(self.benchmark_assets,
self.time_gap)), 2)
@property
def benchmark_profitpct(self):
"""
benchmark 基准组合的收益百分比计算
"""
return self.calc_profitpctchange(self.benchmark_assets)
@property
def beta(self):
"""
beta比率 组合的系统性风险
"""
try:
res = round(
float(
self.calc_beta(self.profit_pct.dropna(),
self.benchmark_profitpct.dropna())), 2)
except:
print('贝塔计算错误。。')
res = 0
return res
@property
def alpha(self):
"""
alpha比率 与市场基准收益无关的超额收益率
"""
return round(
float(
self.calc_alpha(self.annualize_return,
self.benchmark_annualize_return, self.beta,
0.05)), 2)
@property
def sharpe(self):
"""
夏普比率
"""
return round(
float(
self.calc_sharpe(self.annualize_return, self.volatility,
0.05)), 2)
@property
def sortino(self):
"""
索提诺比率 投资组合收益和下行风险比值
"""
pass
@property
def calmar(self):
"""
卡玛比率
"""
pass
def set_benchmark(self, code, market_type):
self.benchmark_code = code
self.benchmark_type = market_type
def calc_annualize_return(self, assets, days):
return round((float(assets.iloc[-1]) / float(assets.iloc[0]) - 1) /
(float(days) / 250), 2)
def calc_profitpctchange(self, assets):
return assets[::-1].pct_change()[::-1]
def calc_beta(self, assest_profit, benchmark_profit):
calc_cov = np.cov(assest_profit, benchmark_profit)
beta = calc_cov[0, 1] / calc_cov[1, 1]
return beta
def calc_alpha(self,
annualized_returns,
benchmark_annualized_returns,
beta,
r=0.05):
alpha = (annualized_returns - r) - (beta) *\
(benchmark_annualized_returns - r)
return alpha
def calc_IR(self):
"""计算信息比率
Returns:
[type] -- [description]
"""
if self.volatility == 0:
return 0
else:
return self.annualize_return / self.volatility
def calc_profit(self, assets):
"""
计算账户收益
期末资产/期初资产 -1
"""
return (float(assets.iloc[-1]) / float(assets.iloc[0])) - 1
def calc_sharpe(self, annualized_returns, volatility_year, r=0.05):
"""
计算夏普比率
r是无风险收益
"""
# 会出现0
if volatility_year == 0:
return 0
return (annualized_returns - r) / volatility_year
@property
def max_holdmarketvalue(self):
"""最大持仓市值
Returns:
[type] -- [description]
"""
if self.daily_market_value is not None:
return self.daily_market_value.max()
else:
return 0
@property
def min_holdmarketvalue(self):
"""最小持仓市值
Returns:
[type] -- [description]
"""
if self.daily_market_value is not None:
return self.daily_market_value.min()
else:
return 0
@property
def average_holdmarketvalue(self):
"""平均持仓市值
Returns:
[type] -- [description]
"""
if self.daily_market_value is not None:
return self.daily_market_value.mean()
else:
return 0
@property
def max_cashhold(self):
"""最大闲置资金
"""
return self.account.daily_cash.cash.max()
@property
def min_cashhold(self):
"""最小闲置资金
"""
return self.account.daily_cash.cash.min()
@property
def average_cashhold(self):
"""平均闲置资金
Returns:
[type] -- [description]
"""
return self.account.daily_cash.cash.mean()
def save(self):
"""save to mongodb
"""
save_riskanalysis(self.message)
def plot_assets_curve(self, length=14, height=12):
"""
资金曲线叠加图
@Roy T.Burns 2018/05/29 修改百分比显示错误
"""
plt.style.use('ggplot')
plt.figure(figsize=(length, height))
plt.subplot(211)
plt.title('BASIC INFO', fontsize=12)
plt.axis([0, length, 0, 0.6])
plt.axis('off')
i = 0
for item in ['account_cookie', 'portfolio_cookie', 'user_cookie']:
plt.text(i,
0.5,
'{} : {}'.format(item, self.message[item]),
fontsize=10,
rotation=0,
wrap=True)
i += (length / 2.8)
i = 0
for item in ['benchmark_code', 'time_gap', 'max_dropback']:
plt.text(i,
0.4,
'{} : {}'.format(item, self.message[item]),
fontsize=10,
ha='left',
rotation=0,
wrap=True)
i += (length / 2.8)
i = 0
for item in ['annualize_return', 'bm_annualizereturn', 'profit']:
plt.text(i,
0.3,
'{} : {} %'.format(item,
self.message.get(item, 0) * 100),
fontsize=10,
ha='left',
rotation=0,
wrap=True)
i += length / 2.8
i = 0
for item in ['init_cash', 'last_assets', 'volatility']:
plt.text(i,
0.2,
'{} : {} '.format(item, self.message[item]),
fontsize=10,
ha='left',
rotation=0,
wrap=True)
i += length / 2.8
i = 0
for item in ['alpha', 'beta', 'sharpe']:
plt.text(i,
0.1,
'{} : {}'.format(item, self.message[item]),
ha='left',
fontsize=10,
rotation=0,
wrap=True)
i += length / 2.8
plt.subplot(212)
self.assets.plot()
self.benchmark_assets.xs(self.benchmark_code, level=1).plot()
asset_p = mpatches.Patch(color='red',
label='{}'.format(
self.account.account_cookie))
asset_b = mpatches.Patch(
label='benchmark {}'.format(self.benchmark_code))
plt.legend(handles=[asset_p, asset_b], loc=1)
plt.title('ASSET AND BENCKMARK')
return plt
@property
def month_assets(self):
return self.assets.resample('M').last()
@property
def month_assets_profit(self):
res = pd.concat([pd.Series(self.assets.iloc[0]),
self.month_assets]).diff().dropna()
res.index = res.index.map(str)
return res
@property
def daily_assets_profit(self):
return self.assets.diff()
def plot_dailyhold(self, start=None, end=None):
"""
使用热力图画出每日持仓
"""
start = self.account.start_date if start is None else start
end = self.account.end_date if end is None else end
_, ax = plt.subplots(figsize=(20, 8))
sns.heatmap(self.account.daily_hold.reset_index().drop(
'account_cookie', axis=1).set_index('date').loc[start:end],
cmap="YlGnBu",
linewidths=0.05,
ax=ax)
ax.set_title('HOLD TABLE --ACCOUNT: {}'.format(
self.account.account_cookie))
ax.set_xlabel('Code')
ax.set_ylabel('DATETIME')
return plt
def plot_signal(self, start=None, end=None):
"""
使用热力图画出买卖信号
"""
start = self.account.start_date if start is None else start
end = self.account.end_date if end is None else end
_, ax = plt.subplots(figsize=(20, 18))
sns.heatmap(self.account.trade.reset_index().drop(
'account_cookie', axis=1).set_index('datetime').loc[start:end],
cmap="YlGnBu",
linewidths=0.05,
ax=ax)
ax.set_title('SIGNAL TABLE --ACCOUNT: {}'.format(
self.account.account_cookie))
ax.set_xlabel('Code')
ax.set_ylabel('DATETIME')
return plt
def generate_plots(self):
"""
生成图像
"""
self.plot_assets_curve()
self.plot_dailyhold()
self.plot_signal()
def __init__(
self,
account,
benchmark_code='000300',
benchmark_type=MARKET_TYPE.INDEX_CN,
if_fq=True,
market_data=None,
auto_reload=False
):
"""
account: QA_Account类/QA_PortfolioView类
benchmark_code: [str]对照参数代码
benchmark_type: [QA.PARAM]对照参数的市场
if_fq: [Bool]原account是否使用复权数据
if_fq选项是@尧提出的,关于回测的时候成交价格问题(如果按不复权撮合 应该按不复权价格计算assets)
"""
self.account = account
self.benchmark_code = benchmark_code # 默认沪深300
self.benchmark_type = benchmark_type
self.client = DATABASE.risk
self.client.create_index(
[
("account_cookie",
ASCENDING),
("user_cookie",
ASCENDING),
("portfolio_cookie",
ASCENDING)
],
unique=True
)
if auto_reload:
pass
else:
self.fetch = {
MARKET_TYPE.STOCK_CN: QA_fetch_stock_day_adv,
MARKET_TYPE.INDEX_CN: QA_fetch_index_day_adv
}
if market_data == None:
if self.account.market_type == MARKET_TYPE.STOCK_CN:
self.market_data = QA_fetch_stock_day_adv(
self.account.code,
self.account.start_date,
self.account.end_date
)
elif self.account.market_type == MARKET_TYPE.FUTURE_CN:
self.market_data = QA_fetch_future_day_adv(
self.account.code,
self.account.start_date,
self.account.end_date
)
else:
self.market_data = market_data
self.if_fq = if_fq
if self.account.market_type == MARKET_TYPE.FUTURE_CN:
self.if_fq = False # 如果是期货， 默认设为FALSE
if self.market_value is not None:
if self.account.market_type == MARKET_TYPE.FUTURE_CN and self.account.allow_margin == True:
print('margin!')
self._assets = (
self.account.daily_frozen +
self.account.daily_cash.set_index('date').cash
).dropna()
else:
self._assets = (
self.market_value.sum(axis=1) +
self.account.daily_cash.set_index('date').cash
).fillna(method='pad')
else:
self._assets = self.account.daily_cash.set_index('date'
).cash.fillna(
method='pad'
)
self.time_gap = QA_util_get_trade_gap(
self.account.start_date,
self.account.end_date
)
self.init_cash = self.account.init_cash
self.init_assets = self.account.init_assets
class QA_Risk():
"""QARISK 是一个风险插件
需要加载一个account/portfolio类进来:
需要有
code,start_date,end_date,daily_cash,daily_hold
TODO:
资金利用率 反应资金的利用程度
股票周转率 反应股票的持仓天数
预期PNL/统计学PNL
"""
def __init__(
self,
account,
benchmark_code='000300',
benchmark_type=MARKET_TYPE.INDEX_CN,
if_fq=True,
market_data=None,
auto_reload=False
):
"""
account: QA_Account类/QA_PortfolioView类
benchmark_code: [str]对照参数代码
benchmark_type: [QA.PARAM]对照参数的市场
if_fq: [Bool]原account是否使用复权数据
if_fq选项是@尧提出的,关于回测的时候成交价格问题(如果按不复权撮合 应该按不复权价格计算assets)
"""
self.account = account
self.benchmark_code = benchmark_code # 默认沪深300
self.benchmark_type = benchmark_type
self.client = DATABASE.risk
self.client.create_index(
[
("account_cookie",
ASCENDING),
("user_cookie",
ASCENDING),
("portfolio_cookie",
ASCENDING)
],
unique=True
)
if auto_reload:
pass
else:
self.fetch = {
MARKET_TYPE.STOCK_CN: QA_fetch_stock_day_adv,
MARKET_TYPE.INDEX_CN: QA_fetch_index_day_adv
}
if market_data == None:
if self.account.market_type == MARKET_TYPE.STOCK_CN:
self.market_data = QA_fetch_stock_day_adv(
self.account.code,
self.account.start_date,
self.account.end_date
)
elif self.account.market_type == MARKET_TYPE.FUTURE_CN:
self.market_data = QA_fetch_future_day_adv(
self.account.code,
self.account.start_date,
self.account.end_date
)
else:
self.market_data = market_data
self.if_fq = if_fq
if self.account.market_type == MARKET_TYPE.FUTURE_CN:
self.if_fq = False # 如果是期货， 默认设为FALSE
if self.market_value is not None:
if self.account.market_type == MARKET_TYPE.FUTURE_CN and self.account.allow_margin == True:
print('margin!')
self._assets = (
self.account.daily_frozen +
self.account.daily_cash.set_index('date').cash
).dropna()
else:
self._assets = (
self.market_value.sum(axis=1) +
self.account.daily_cash.set_index('date').cash
).fillna(method='pad')
else:
self._assets = self.account.daily_cash.set_index('date'
).cash.fillna(
method='pad'
)
self.time_gap = QA_util_get_trade_gap(
self.account.start_date,
self.account.end_date
)
self.init_cash = self.account.init_cash
self.init_assets = self.account.init_assets
def __repr__(self):
return '< QA_RISK ANALYSIS ACCOUNT/PORTFOLIO >'
def __call__(self):
return pd.DataFrame([self.message])
@property
def total_timeindex(self):
return self.account.trade_range
@property
def market_value(self):
"""每日每个股票持仓市值表
Returns:
pd.DataFrame -- 市值表
"""
if self.account.daily_hold is not None:
if self.if_fq:
return (
self.market_data.to_qfq().pivot('close').fillna(
method='ffill'
) * self.account.daily_hold.apply(abs)
).fillna(method='ffill')
else:
return (
self.market_data.pivot('close').fillna(method='ffill') *
self.account.daily_hold.apply(abs)
).fillna(method='ffill')
else:
return None
@property
def daily_market_value(self):
"""每日持仓总市值表
Returns:
pd.DataFrame -- 市值表
"""
if self.market_value is not None:
return self.market_value.sum(axis=1)
else:
return None
@property
def assets(self):
x1 = self._assets.reset_index()
return x1.assign(date=pd.to_datetime(x1.date)).set_index('date')[0]
@property
def max_dropback(self):
"""最大回撤
"""
return round(
float(
max(
[
(self.assets.iloc[idx] - self.assets.iloc[idx::].min())
/ self.assets.iloc[idx]
for idx in range(len(self.assets))
]
)
),
2
)
@property
def total_commission(self):
"""总手续费
"""
return float(
-abs(round(self.account.history_table.commission.sum(),
2))
)
@property
def total_tax(self):
"""总印花税
"""
return float(-abs(round(self.account.history_table.tax.sum(), 2)))
@property
def profit_construct(self):
"""利润构成
Returns:
dict -- 利润构成表
"""
return {
'total_buyandsell':
round(
self.profit_money - self.total_commission - self.total_tax,
2
),
'total_tax':
self.total_tax,
'total_commission':
self.total_commission,
'total_profit':
self.profit_money
}
@property
def profit_money(self):
"""盈利额
Returns:
[type] -- [description]
"""
return float(round(self.assets.iloc[-1] - self.assets.iloc[0], 2))
@property
def profit(self):
"""盈利率(百分比)
Returns:
[type] -- [description]
"""
return round(float(self.calc_profit(self.assets)), 2)
@property
def profit_pct(self):
"""利润
"""
return self.calc_profitpctchange(self.assets)
@property
def annualize_return(self):
"""年化收益
Returns:
[type] -- [description]
"""
return round(
float(self.calc_annualize_return(self.assets,
self.time_gap)),
2
)
@property
def volatility(self):
"""波动率
Returns:
[type] -- [description]
"""
return round(float(self.profit_pct.std() * math.sqrt(250)), 2)
@property
def ir(self):
return round(self.calc_IR(), 2)
@property
@lru_cache()
def message(self):
return {
'account_cookie': self.account.account_cookie,
'portfolio_cookie': self.account.portfolio_cookie,
'user_cookie': self.account.user_cookie,
'annualize_return': round(self.annualize_return,
2),
'profit': round(self.profit,
2),
'max_dropback': self.max_dropback,
'time_gap': self.time_gap,
'volatility': self.volatility,
'benchmark_code': self.benchmark_code,
'bm_annualizereturn': self.benchmark_annualize_return,
'bm_profit': self.benchmark_profit,
'beta': self.beta,
'alpha': self.alpha,
'sharpe': self.sharpe,
'init_cash': "%0.2f" % (float(self.assets[0])),
'last_assets': "%0.2f" % (float(self.assets.iloc[-1])),
'total_tax': self.total_tax,
'total_commission': self.total_commission,
'profit_money': self.profit_money,
'assets': list(self.assets),
'benchmark_assets': list(self.benchmark_assets),
'timeindex': self.account.trade_day,
'totaltimeindex': self.total_timeindex,
'ir': self.ir,
'month_profit': self.month_assets_profit.to_dict()
# 'init_assets': round(float(self.init_assets), 2),
# 'last_assets': round(float(self.assets.iloc[-1]), 2)
}
@property
def benchmark_data(self):
"""
基准组合的行情数据(一般是组合,可以调整)
"""
return self.fetch[self.benchmark_type](
self.benchmark_code,
self.account.start_date,
self.account.end_date
)
@property
def benchmark_assets(self):
"""
基准组合的账户资产队列
"""
return (
self.benchmark_data.close /
float(self.benchmark_data.close.iloc[0])
* float(self.assets[0])
)
@property
def benchmark_profit(self):
"""
基准组合的收益
"""
return round(float(self.calc_profit(self.benchmark_assets)), 2)
@property
def benchmark_annualize_return(self):
"""基准组合的年化收益
Returns:
[type] -- [description]
"""
return round(
float(
self.calc_annualize_return(
self.benchmark_assets,
self.time_gap
)
),
2
)
@property
def benchmark_profitpct(self):
"""
benchmark 基准组合的收益百分比计算
"""
return self.calc_profitpctchange(self.benchmark_assets)
@property
def beta(self):
"""
beta比率 组合的系统性风险
"""
try:
res = round(
float(
self.calc_beta(
self.profit_pct.dropna(),
self.benchmark_profitpct.dropna()
)
),
2
)
except:
print('贝塔计算错误。。')
res = 0
return res
@property
def alpha(self):
"""
alpha比率 与市场基准收益无关的超额收益率
"""
return round(
float(
self.calc_alpha(
self.annualize_return,
self.benchmark_annualize_return,
self.beta,
0.05
)
),
2
)
@property
def sharpe(self):
"""
夏普比率
"""
return round(
float(
self.calc_sharpe(self.annualize_return,
self.volatility,
0.05)
),
2
)
@property
def sortino(self):
"""
索提诺比率 投资组合收益和下行风险比值
"""
pass
@property
def calmar(self):
"""
卡玛比率
"""
pass
def set_benchmark(self, code, market_type):
self.benchmark_code = code
self.benchmark_type = market_type
def calc_annualize_return(self, assets, days):
return round(
(float(assets.iloc[-1]) / float(assets.iloc[0]) - 1) /
(float(days) / 250),
2
)
def calc_profitpctchange(self, assets):
return assets[::-1].pct_change()[::-1]
def calc_beta(self, assest_profit, benchmark_profit):
calc_cov = np.cov(assest_profit, benchmark_profit)
beta = calc_cov[0, 1] / calc_cov[1, 1]
return beta
def calc_alpha(
self,
annualized_returns,
benchmark_annualized_returns,
beta,
r=0.05
):
alpha = (annualized_returns - r) - (beta) *\
(benchmark_annualized_returns - r)
return alpha
def calc_IR(self):
"""计算信息比率
Returns:
[type] -- [description]
"""
if self.volatility == 0:
return 0
else:
return self.annualize_return / self.volatility
def calc_profit(self, assets):
"""
计算账户收益
期末资产/期初资产 -1
"""
return (float(assets.iloc[-1]) / float(assets.iloc[0])) - 1
def calc_sharpe(self, annualized_returns, volatility_year, r=0.05):
"""
计算夏普比率
r是无风险收益
"""
# 会出现0
if volatility_year == 0:
return 0
return (annualized_returns - r) / volatility_year
@property
def max_holdmarketvalue(self):
"""最大持仓市值
Returns:
[type] -- [description]
"""
if self.daily_market_value is not None:
return self.daily_market_value.max()
else:
return 0
@property
def min_holdmarketvalue(self):
"""最小持仓市值
Returns:
[type] -- [description]
"""
if self.daily_market_value is not None:
return self.daily_market_value.min()
else:
return 0
@property
def average_holdmarketvalue(self):
"""平均持仓市值
Returns:
[type] -- [description]
"""
if self.daily_market_value is not None:
return self.daily_market_value.mean()
else:
return 0
@property
def max_cashhold(self):
"""最大闲置资金
"""
return self.account.daily_cash.cash.max()
@property
def min_cashhold(self):
"""最小闲置资金
"""
return self.account.daily_cash.cash.min()
@property
def average_cashhold(self):
"""平均闲置资金
Returns:
[type] -- [description]
"""
return self.account.daily_cash.cash.mean()
def save(self):
"""save to mongodb
"""
save_riskanalysis(self.message)
def plot_assets_curve(self, length=14, height=12):
"""
资金曲线叠加图
@Roy T.Burns 2018/05/29 修改百分比显示错误
"""
plt.style.use('ggplot')
plt.figure(figsize=(length, height))
plt.subplot(211)
plt.title('BASIC INFO', fontsize=12)
plt.axis([0, length, 0, 0.6])
plt.axis('off')
i = 0
for item in ['account_cookie', 'portfolio_cookie', 'user_cookie']:
plt.text(
i,
0.5,
'{} : {}'.format(item,
self.message[item]),
fontsize=10,
rotation=0,
wrap=True
)
i += (length / 2.8)
i = 0
for item in ['benchmark_code', 'time_gap', 'max_dropback']:
plt.text(
i,
0.4,
'{} : {}'.format(item,
self.message[item]),
fontsize=10,
ha='left',
rotation=0,
wrap=True
)
i += (length / 2.8)
i = 0
for item in ['annualize_return', 'bm_annualizereturn', 'profit']:
plt.text(
i,
0.3,
'{} : {} %'.format(item,
self.message.get(item,
0) * 100),
fontsize=10,
ha='left',
rotation=0,
wrap=True
)
i += length / 2.8
i = 0
for item in ['init_cash', 'last_assets', 'volatility']:
plt.text(
i,
0.2,
'{} : {} '.format(item,
self.message[item]),
fontsize=10,
ha='left',
rotation=0,
wrap=True
)
i += length / 2.8
i = 0
for item in ['alpha', 'beta', 'sharpe']:
plt.text(
i,
0.1,
'{} : {}'.format(item,
self.message[item]),
ha='left',
fontsize=10,
rotation=0,
wrap=True
)
i += length / 2.8
plt.subplot(212)
self.assets.plot()
self.benchmark_assets.xs(self.benchmark_code, level=1).plot()
asset_p = mpatches.Patch(
color='red',
label='{}'.format(self.account.account_cookie)
)
asset_b = mpatches.Patch(
label='benchmark {}'.format(self.benchmark_code)
)
plt.legend(handles=[asset_p, asset_b], loc=0)
plt.title('ASSET AND BENCKMARK')
return plt
@property
def month_assets(self):
return self.assets.resample('M').last()
@property
def month_assets_profit(self):
res = pd.concat([pd.Series(self.assets.iloc[0]),
self.month_assets]).diff().dropna()
res.index = res.index.map(str)
return res
@property
def daily_assets_profit(self):
return self.assets.diff()
def plot_dailyhold(self, start=None, end=None):
"""
使用热力图画出每日持仓
"""
start = self.account.start_date if start is None else start
end = self.account.end_date if end is None else end
_, ax = plt.subplots(figsize=(20, 8))
sns.heatmap(
self.account.daily_hold.reset_index().drop(
'account_cookie',
axis=1
).set_index('date').loc[start:end],
cmap="YlGnBu",
linewidths=0.05,
ax=ax
)
ax.set_title(
'HOLD TABLE --ACCOUNT: {}'.format(self.account.account_cookie)
)
ax.set_xlabel('Code')
ax.set_ylabel('DATETIME')
return plt
def plot_signal(self, start=None, end=None):
"""
使用热力图画出买卖信号
"""
start = self.account.start_date if start is None else start
end = self.account.end_date if end is None else end
_, ax = plt.subplots(figsize=(20, 18))
sns.heatmap(
self.account.trade.reset_index().drop(
'account_cookie',
axis=1
).set_index('datetime').loc[start:end],
cmap="YlGnBu",
linewidths=0.05,
ax=ax
)
ax.set_title(
'SIGNAL TABLE --ACCOUNT: {}'.format(self.account.account_cookie)
)
ax.set_xlabel('Code')
ax.set_ylabel('DATETIME')
return plt
def generate_plots(self):
"""
生成图像
"""
self.plot_assets_curve()
self.plot_dailyhold()
self.plot_signal()
