def analyze_pe_pb_dis(symbol, alias, start_date, end_date):
"""分析PE\PB分布"""
datas = database_manager.load_finance_data(symbol + '.' + alias, start_date, end_date)
df_finance = pd.DataFrame(columns=('code', 'datetime', 'pe', 'pb'))
for data in datas:
df_finance = df_finance.append({'code': data.code, 'datetime': data.datetime, 'pe': data.pe, 'pb': data.pb},
ignore_index=True)
_, axs = plt.subplots(nrows=2, ncols=2, figsize=(14, 7))
sns.distplot(df_finance.pe, ax=axs[0][0])
sns.boxplot(df_finance.pe, ax=axs[0][1])
sns.distplot(df_finance.pb, ax=axs[1][0])
sns.boxplot(df_finance.pb, ax=axs[1][1])
# 展示PE/PB关系
sns.jointplot(x='pb', y='pe', data=df_finance, height=7)
# 将PE分成十个分位，查看各个分位PE数量
pe_array = df_finance.pe.values
value_counts = pd.cut(pe_array, 10).value_counts()
print(value_counts)
low = value_counts[0:4].sum()
medin = value_counts[4:6].sum()
high = value_counts[6:10].sum()
print('比值({}：{}：{})'.format(low, medin, high))
plt.figure(figsize=(14, 4))
sns.barplot(x=np.arange(0, len(value_counts)), y=value_counts.values)
plt.show()
def draw_pe_pb(code, start_date, end_date):
"""画出pe、pb的趋势图"""
datas = database_manager.load_finance_data(code, start_date, end_date)
df = pd.DataFrame(columns=('code', 'datetime', 'pe', 'pb'))
for data in datas:
df = df.append({'code': data.code, 'datetime': data.datetime, 'pe': data.pe, 'pb': data.pb}, ignore_index=True)
# 设置索引
df['datetime'] = pd.to_datetime(df['datetime'])
df.set_index(['datetime'], inplace=True)
df[['pe', 'pb']].plot(figsize=(14, 7), subplots=True)
# TODO: PE和close对比
# df[['pe', 'pb']].plot(secondary_y=['pb'], alpha=.8)
plt.show()
def draw_price_pe(symbol, alias, start_date, end_date):
"""画出价格与pe关系"""
df = dp.load_bar_data(symbol, alias, start_date=start_date, end_data=end_date)
datas = database_manager.load_finance_data(symbol + '.' + alias, start_date, end_date)
df_finance = pd.DataFrame(columns=('code', 'datetime', 'pe', 'pb'))
for data in datas:
df_finance = df_finance.append({'code': data.code, 'datetime': data.datetime, 'pe': data.pe, 'pb': data.pb},
ignore_index=True)
if len(df) == len(df_finance):
df['pe'] = df_finance['pe'].copy()
df['pb'] = df_finance['pb'].copy()
df['date'] = pd.to_datetime(df['date'])
df.set_index(['date'], inplace=True)
_, axs = plt.subplots(ncols=2, figsize=(14, 5))
df[['close', 'pe']].plot(secondary_y=['pe'], ax=axs[0], alpha=.8)
df[['close', 'pb']].plot(secondary_y=['pb'], ax=axs[1], alpha=.8)
plt.show()
def load_finance_data(code,
start_date: datetime = None,
end_date: datetime = None):
datas = database_manager.load_finance_data(code, start_date, end_date)
df_finance = pd.DataFrame(columns=('code', 'datetime', 'pe', 'pb',
'pe_mid', 'pb_mid'))
for data in datas:
df_finance = df_finance.append(
{
'code': data.code,
'datetime': data.datetime,
'pe': data.pe,
'pb': data.pb,
'pe_mid': data.pe_mid,
'pb_mid': data.pb_mid
},
ignore_index=True)
return df_finance
def show_quantile(symbol, alias, start_date, end_date):
"""展示分位区间"""
datas = database_manager.load_finance_data(symbol + '.' + alias, start_date, end_date)
df_finance = pd.DataFrame(columns=('code', 'datetime', 'pe', 'pb'))
for data in datas:
df_finance = df_finance.append({'code': data.code, 'datetime': data.datetime, 'pe': data.pe, 'pb': data.pb},
ignore_index=True)
_df = pd.DataFrame()
df = df_finance.copy()
df.index.name = None
_df['pe'] = df.pe
_df = _df
p_high = [_df.pe.quantile(i / 10) for i in [4, 5, 6]]
for p_h, i in zip(p_high, [4, 5, 6]):
_df[str(i / 10 * 100) + '%'] = p_h
print(_df.head(10))
_df['datetime'] = df_finance.datetime.copy()
_df['datetime'] = pd.to_datetime(_df['datetime']) # 转换时间类型
_df.set_index(['datetime'], inplace=True)
_df.plot(figsize=(14, 7))
plt.show()
def model2(symbol, alias, start_date, end_date):
"""
定投模型2：
1、PE处于适中估值，不做任何操作，每月定投资金放入现金池
2、PE处于低估，持续定投
3、PE高于适中估值，卖出并且放入现金池中
"""
# 获取交易日价格和金融数据
df = dp.load_bar_data(symbol, alias, start_date=start_date, end_data=end_date)
datas = database_manager.load_finance_data(symbol + '.' + alias, start_date, end_date)
df_finance = pd.DataFrame(columns=('code', 'datetime', 'pe', 'pb'))
for data in datas:
df_finance = df_finance.append({'code': data.code, 'datetime': data.datetime, 'pe': data.pe, 'pb': data.pb},
ignore_index=True)
if len(df) == len(df_finance):
df['pe'] = df_finance['pe'].copy()
df['pb'] = df_finance['pb'].copy()
df['date'] = pd.to_datetime(df['date']) # 转换时间类型
df.set_index(['date'], inplace=True)
df.index.name = None # 去掉索引列名
# 获取每个月第一个交易日
pre_year = 0
pre_month = 0
first_day = []
for index, row in df.iterrows():
if index.year != pre_year or index.month != pre_month:
first_day.append(index)
pre_year = index.year
pre_month = index.month
index_df = df.loc[first_day]
day_df = index_df.copy()
day_df['pct_change'] = day_df.close.pct_change()
# day_df = day_df[['close', 'pb', 'pe' 'pct_change']]
# print(day_df.head(10))
miden_estimation = (12.17798, 12.93906)
save_money = [] # 每月定存
back_money = [] # 回收资金
hold_money = [] # 持仓资金
base_money = 1000 # 定投基准
for i in range(len(day_df)):
pe = day_df['pe'][i] # 估值位
print(pe)
if i == 0: # 初始买入
# 1.计算买入金额
save_money.append(base_money)
# 2. 计算回收金额
back_money.append(0)
# 3. 计算持仓变化
hold_money.append(base_money)
continue
if pe <= miden_estimation[0]: # 执行买入计算
# 1.计算买入金额
save_money.append(base_money)
# 2. 计算回收金额
back_money.append(0)
# 3. 计算持仓变化
hold_money.append(hold_money[-1] * (1 + day_df['pct_change'][i]) + base_money)
elif pe >= miden_estimation[-1]: # 执行卖出计算
# 1. 计算买入金额
save_money.append(0)
# 2. 计算回收金额
back_money.append(base_money)
# 3. 计算持仓变化
hold_money.append(hold_money[-1] * (1 + day_df['pct_change'][i]) - base_money)
else:
# 1.计算买入金额
save_money.append(0)
# 2. 计算回收金额
back_money.append(0)
# 3. 计算持仓变化
hold_money.append(hold_money[-1] * (1 + day_df['pct_change'][i]))
day_df['save_money'] = save_money # 定投金额
day_df['save_money_cumsum'] = day_df['save_money'].cumsum() # 定投累计金额
day_df['hold_money'] = hold_money # 持仓金额
day_df['back_money'] = back_money # 回收金额
day_df['back_money_cumsum'] = day_df['back_money'].cumsum() # 累计回收金额
day_df['total_money'] = day_df['hold_money'] + day_df['back_money_cumsum'] # 总资金
day_df['return_money'] = day_df['total_money'] - day_df['save_money_cumsum'] # 持续收益
day_df['return_rate'] = (day_df['total_money'] / day_df['save_money_cumsum']) - 1 # 持续收益率
day_df[['save_money_cumsum', 'total_money', 'back_money_cumsum', 'return_money']].plot(figsize=(14, 7))
plt.legend(['save_money_cumsum', 'total_money', 'back_money_cumsum', 'return_money'])
plt.show()
print('累计投入: {}元'.format(day_df['save_money_cumsum'][-1]))
print('累计收益: {}元'.format(day_df['return_money'][-1]))
print('最终本息累积: {}元'.format(day_df['total_money'][-1]))
print('绝对收益率为: {}%'.format((day_df['return_money'][-1] / day_df['save_money_cumsum'][-1]) * 100))