def get_all_code_price(self, date, holding_stock_dict, to_buy, to_sell,
to_add):
all_code_set = set()
for code in holding_stock_dict.keys():
all_code_set.add(code)
all_code_set = all_code_set.union(to_sell).union(to_buy).union(to_add)
if len(all_code_set) == 0:
return None
df_price = rqd.get_price(all_code_set,
date,
date,
fields=['open', 'close'],
expect_df=True).droplevel(1)
is_suspend = rqd.is_suspended(all_code_set, date, date).squeeze()
df_price['is_suspended'] = is_suspend
return df_price
# 数据导入
df_buy_sell = pd.read_csv(inputPath + "汇总个股买卖时点.csv",
index_col=0,
engine='python')
df_buy_sell.sort_values(by='buy_date', axis=0, ascending=True, inplace=True)
df_buy_sell = df_buy_sell.reset_index(drop=True)
# 剔除停牌及建仓日涨跌停个股
list_suspended_index = pd.DataFrame(index=df_buy_sell.index, columns=['index'])
for ind in df_buy_sell.index:
ind_code = df_buy_sell.loc[ind, 'code']
ind_date = df_buy_sell.loc[ind, 'buy_date']
try:
suspended_index = rq.is_suspended(ind_code,
start_date=ind_date,
end_date=ind_date)
except:
list_suspended_index.loc[ind, 'index'] = True
else:
if suspended_index is None:
list_suspended_index.loc[ind, 'index'] = True
else:
list_suspended_index.loc[ind,
'index'] = suspended_index.loc[ind_date,
ind_code]
list_limit_index = pd.DataFrame(index=df_buy_sell.index, columns=['index'])
for ind in df_buy_sell.index:
ind_code = df_buy_sell.loc[ind, 'code']
ind_date = df_buy_sell.loc[ind, 'buy_date']
def drop_suspended(universe, date):
# squeeze(axis=0):DataFrames with a single column or a single row are squeezed to a Series
is_suspended = rqd.is_suspended(universe, date, date).squeeze()
assert isinstance(is_suspended, pd.Series), 'is_suspended is not series'
return is_suspended.index[~is_suspended]
def analyze(self):
dates = rqd.get_trading_dates(self.begin_date, self.end_date)
top_group, bottom_group = [], []
last_date = None
df_profit = pd.DataFrame(columns=['top', 'bottom', 'benchmark'])
# 间隔一段时间遍历交易日
for index in range(0, len(dates), self.interval):
date = dates[index]
print("当前日期:", date)
if last_date is None:
df_profit.loc[date] = {'top': 0, 'bottom': 0, 'benchmark': 0}
else:
# 股票收盘价
close = rqd.get_price(top_group + bottom_group,
last_date,
date,
fields='close')
# 基准收盘价
bench_close = rqd.get_price(self.benchmark,
last_date,
date,
fields='close')
# 股票收益
profit_series = close.iloc[-1] / close.iloc[0] - 1
top_profit = profit_series[top_group].mean()
bottom_profit = profit_series[bottom_group].mean()
# 基准收益
bench_profit = bench_close[-1] / bench_close[0] - 1
df_profit.loc[date] = {
'top': top_profit,
'bottom': bottom_profit,
'benchmark': bench_profit
}
last_date = date
# 选出当天可交易的股票
universe = filter_stock_pool(date)
factor_df = rqd.get_factor(universe, self.factor, date,
date)[lambda x: x > 0]
# 每档数量
position_size = int(len(factor_df) / self.group)
# 清空首档没有停牌的
if len(top_group) > 0:
top_suspend = []
for code in top_group:
if rqd.is_suspended(code, date, date).squeeze():
top_suspend.append(code)
top_group = top_suspend
# 清空末档没有停牌的
if len(bottom_group) > 0:
bottom_suspend = []
for code in bottom_group:
if rqd.is_suspended(code, date, date).squeeze():
bottom_suspend.append(code)
bottom_group = bottom_suspend
# 添加首档
for code in factor_df.sort_values().index:
if len(top_group) >= position_size:
break
if code not in top_group:
top_group.append(code)
# 添加末档
for code in factor_df.sort_values(ascending=False).index:
if len(bottom_group) >= position_size:
break
if code not in bottom_group:
bottom_group.append(code)
df_profit['top-bottom'] = df_profit['top'] - df_profit['bottom']
df_cumprod_profit = (df_profit + 1).cumprod()
df_cumprod_profit.plot()
plt.show()