Esempio n. 1
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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)
Esempio n. 2
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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
Esempio n. 3
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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 = []