def create_stock_figure_column(code, start_date, end_date):
obj = CStock(code)
delta_days = (end_date - start_date).days
if delta_days <= 0: return None
start_date = start_date.strftime('%Y-%m-%d')
end_date = end_date.strftime('%Y-%m-%d')
df = obj.get_k_data_in_range(start_date, end_date)
if df is None: return None
df['date'] = df['date'].apply(
lambda x: str_to_datetime(x, dformat="%Y-%m-%d"))
source = ColumnDataSource(df)
mapper = linear_cmap(field_name='pchange',
palette=['red', 'green'],
low=0,
high=0,
low_color='green',
high_color='red')
p = figure(plot_height=500,
plot_width=1300,
tools="",
toolbar_location=None,
sizing_mode="scale_both",
x_range=(0, len(df)))
p.xaxis.axis_label = "时间"
p.yaxis.axis_label = "点数"
p.segment(x0='index',
y0='low',
x1='index',
y1='high',
line_width=2,
color='black',
source=source)
p.vbar(x='index',
bottom='open',
top='close',
width=50 / delta_days,
color=mapper,
source=source)
p.xaxis.major_label_overrides = {
i: mdate.strftime('%Y-%m-%d')
for i, mdate in enumerate(df["date"])
}
volume_p = figure(plot_height=150,
plot_width=1300,
tools="",
toolbar_location=None,
sizing_mode="scale_both")
volume_p.x_range = p.x_range
volume_p.vbar(x='index',
top='volume',
width=50 / delta_days,
color=mapper,
source=source)
volume_p.xaxis.major_label_overrides = {
i: mdate.strftime('%Y-%m-%d')
for i, mdate in enumerate(df["date"])
}
return column(p, volume_p)
def choose_stock(code_list, start_date, end_date):
state_dict = dict()
state_dict[ct.FL] = list()
state_dict[ct.JL] = list()
state_dict[ct.QL] = list()
state_dict[ct.KL] = list()
good_code_list = list()
#stock_info = DataFrame(columns={'code', 'ppercent', 'npercent', 'sai', 'sri', 'pchange'})
stock_info = DataFrame()
for code in code_list:
cstock_obj = CStock(code, redis_host = '127.0.0.1')
df_profit = cstock_obj.get_base_floating_profit_in_range(start_date, end_date)
if df_profit.profit.mean() > 2:
state_dict[ct.FL].append(code)
elif df_profit.profit.mean() < -2:
state_dict[ct.KL].append(code)
elif df_profit.profit.mean() >= -2 and df_profit.profit.mean() <= 0:
state_dict[ct.QL].append(code)
else:
state_dict[ct.JL].append(code)
df = cstock_obj.get_k_data_in_range(start_date, end_date)
if df is None or df.empty or len(df) < 55: continue
if average_amount_volume(df) and large_down_time(df) and max_turnover(df):
good_code_list.append(code)
return state_dict, good_code_list
from os.path import abspath, dirname
sys.path.insert(0, dirname(dirname(abspath(__file__))))
import const as ct
import numpy as np
import pandas as pd
from cstock import CStock
import matplotlib.pyplot as plt
start_date = '2016-01-01'
end_date = '2019-01-01'
selected = ['601318', '600547', '002153', '600584', '601933', '600600', '000063', '000837', '000543', '000423']
df = pd.DataFrame()
for code in selected:
obj = CStock(code, dbinfo = ct.OUT_DB_INFO, redis_host = '127.0.0.1')
data = obj.get_k_data_in_range(start_date, end_date)
data['code'] = code
data = data[['code', 'date', 'close']]
df = df.append(data)
df = df.set_index('date')
table = df.pivot(columns='code')
table = table[~table.index.isin(table[table.isnull().any(axis=1)].index.tolist())]
returns_daily = table.pct_change()
returns_annual = returns_daily.mean() * 250
cov_daily = returns_daily.cov()
cov_annual = cov_daily * 250
port_returns = []