繁簡切換您正在訪問的是FX168財經網,本網站所提供的內容及信息均遵守中華人民共和國香港特別行政區當地法律法規。

FX168财经网>人物频道>帖子

银行股及其可转债配对交易策略

作者/jedfsjfjsdf 2019-08-06 18:00 0 来源: FX168财经网人物频道

本文为集思录帖子“3A银行转债的增强策略”的复现实验。

基本介绍

本文回测实验是一种股债轮动增强策略。操作对象是银行股及其3A级可转债。

因为大行PB一般都小于1,其可转债很难下调转股价。所以银行可转债受下调转股价影响较小。另一方面,从历史来看,3A转债无一不强赎成功,也就是一定会转股,或者可转债价格一定会大于130。

基于上述经验,可以采用“下跌靠债,上涨靠股”的轮动策略针对含可转债的银行股进行投资。

策略描述

策略的基本思想类似于配对交易,当股票净值偏离可转债净值达一定程度,则进行轮动换仓。具体策略如下:

(1)回测首日净值为1;
(2)转债净值-股票净值>5%,买入股票,卖出债券;
(3)股票净值-转债净值>0, 卖出股票,买入债券;

策略回测

实验数据在下方研究板块中,可转债行情数据可从Wind等终端获得。

光大银行股债轮动

光大银行股票及可转债收盘价选取2017-4-5到2019-8-1日线数据(股票价格前复权)。首先看看股票和可转债的净值曲线。

gd_stock_nets = [1]
for i in range(1, len(gd_stock_prices)):
    gd_stock_nets.append(gd_stock_nets[i-1]*gd_stock_prices[i]/gd_stock_prices[i-1])

gd_kzz_nets = [1]
for i in range(1, len(gd_kzz_prices)):
    gd_kzz_nets.append(gd_kzz_nets[i-1]*gd_kzz_prices[i]/gd_kzz_prices[i-1])

import matplotlib.pyplot as plt

fig = plt.figure(figsize=(12, 6))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])

offsets = [i for i in range(len(gd_dates))]
ax.plot(offsets, gd_stock_nets, color="blue", linewidth=1.5, linestyle="-", label="股票净值")
ax.plot(offsets, gd_kzz_nets, color="green", linewidth=1.5, linestyle="-", label="转债净值")
plt.legend(loc='upper left', frameon=False)

Img

下面我们采用上述策略进行配对交易:

gd_position = [0]  # 0代表持有可转债,1代表持有银行股票,初始持有可转债
gd_stgy_nets = [1]
gd_chng_count = 0
for i in range(1, len(gd_dates)):
    if gd_kzz_nets[i] - gd_stock_nets[i] > 0.05:
        gd_position.append(1)
        if gd_position[i-1] != 1:
            gd_chng_count  = 1
    elif gd_stock_nets[i] - gd_kzz_nets[i] > 0:
        gd_position.append(0)
        if gd_position[i-1] != 0:
            gd_chng_count  = 1
    else:
        gd_position.append(gd_position[i-1])

    if gd_position[i-1] == 0:
        gd_stgy_nets.append(gd_stgy_nets[i-1]*(gd_kzz_prices[i]/gd_kzz_prices[i-1]))
    elif gd_position[i-1] == 1:
        gd_stgy_nets.append(gd_stgy_nets[i-1]*(gd_stock_prices[i]/gd_stock_prices[i-1]))

print("调仓次数:%d" % gd_chng_count)
print("股票净值:%f" % gd_stock_nets[-1])
print("可转债净值:%f" % gd_kzz_nets[-1])
print("策略净值:%f" % gd_stgy_nets[-1])

fig = plt.figure(figsize=(12, 6))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])

offsets = [i for i in range(len(gd_dates))]
ax.plot(offsets, gd_stock_nets, color="blue", linewidth=1.5, linestyle="-", label="股票净值")
ax.plot(offsets, gd_kzz_nets, color="green", linewidth=1.5, linestyle="-", label="转债净值")
ax.plot(offsets, gd_stgy_nets, color="red", linewidth=1.5, linestyle="-", label="策略净值")
plt.legend(loc='upper left', frameon=False)
调仓次数:11
股票净值:1.069369
可转债净值:1.096699
策略净值:1.512695

Img

可以看出明显的超额收益。

宁波银行股债轮动

宁波银行股票及可转债收盘价选取2018-1-12到2019-8-1日线数据(股票价格前复权)。实验代码如下:

nb_stock_nets = [1]
for i in range(1, len(nb_stock_prices)):
    nb_stock_nets.append(nb_stock_nets[i-1]*nb_stock_prices[i]/nb_stock_prices[i-1])

nb_kzz_nets = [1]
for i in range(1, len(nb_kzz_prices)):
    nb_kzz_nets.append(nb_kzz_nets[i-1]*nb_kzz_prices[i]/nb_kzz_prices[i-1])

nb_position = [0]  # 0代表持有可转债,1代表持有银行股票,初始持有可转债
nb_stgy_nets = [1]
nb_chng_count = 0
for i in range(1, len(nb_dates)):
    if nb_kzz_nets[i] - nb_stock_nets[i] > 0.05:
        nb_position.append(1)
        if nb_position[i-1] != 1:
            nb_chng_count  = 1
    elif nb_stock_nets[i] - nb_kzz_nets[i] > 0:
        nb_position.append(0)
        if nb_position[i-1] != 0:
            nb_chng_count  = 1
    else:
        nb_position.append(nb_position[i-1])

    if nb_position[i-1] == 0:
        nb_stgy_nets.append(nb_stgy_nets[i-1]*(nb_kzz_prices[i]/nb_kzz_prices[i-1]))
    elif nb_position[i-1] == 1:
        nb_stgy_nets.append(nb_stgy_nets[i-1]*(nb_stock_prices[i]/nb_stock_prices[i-1]))

print("调仓次数:%d" % nb_chng_count)
print("股票净值:%f" % nb_stock_nets[-1])
print("可转债净值:%f" % nb_kzz_nets[-1])
print("策略净值:%f" % nb_stgy_nets[-1])

fig = plt.figure(figsize=(12, 6))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])

offsets = [i for i in range(len(nb_dates))]
ax.plot(offsets, nb_stock_nets, color="blue", linewidth=1.5, linestyle="-", label="股票净值")
ax.plot(offsets, nb_kzz_nets, color="green", linewidth=1.5, linestyle="-", label="转债净值")
ax.plot(offsets, nb_stgy_nets, color="red", linewidth=1.5, linestyle="-", label="策略净值")
plt.legend(loc='upper left', frameon=False)
调仓次数:4
股票净值:1.315276
可转债净值:1.171215
策略净值:1.335564

Img

上述回测结果中,光大银行的股债配对交易有明显的超额收益,而宁波银行不太明显。原贴也分析了这个原因。其主要由于光大银行的股价波动周期明显,因而股债切换次数较为频繁,每一次股切债相当于避免了大回撤;而每一次债切股又抓股了高涨幅。相反,宁波银行股价偏向单边走势,切换次数也不多,所以超额收益不明显。

一个小优化

上面交易中的5%区间是一个主观值。我们可以通过统计股票净值与可转债净值的差值分布来获得一个统计值。

import numpy as np

gd_nets_diff_fit = np.array(gd_stock_nets) - np.array(gd_kzz_nets)
gd_diff_fit_mean = gd_nets_diff_fit.mean()
gd_diff_fit_std = np.std(gd_nets_diff_fit)

print(gd_diff_fit_mean, gd_diff_fit_std)

-0.03743553771100191 0.028274851087685723

光大银行的股债净值差的均值为-0.037,标准差为0.028。股票净值向下偏离一个标准差,净值差大概是-0.065,向上偏离一个标准差大概是-0.009。这个与主观值差不多。

再看看宁波银行:

nb_nets_diff_fit = np.array(nb_stock_nets) - np.array(nb_kzz_nets)
nb_diff_fit_mean = nb_nets_diff_fit.mean()
nb_diff_fit_std = np.std(nb_nets_diff_fit)

print(nb_diff_fit_mean, nb_diff_fit_std)

0.004643115736568061 0.07745156370857084

宁波银行的股债净值差的均值为0.0046,标准差为0.0775。股票净值向下偏离一个标准差,净值差大概是-0.0729,向上偏离一个标准差大概是0.0821。这个与主观值差的比较多。

下面我们用上面的统计值代入策略看看效果:

nb_position = [0]  # 0代表持有可转债,1代表持有银行股票,初始持有可转债
nb_stgy_nets = [1]
nb_chng_count = 0
for i in range(1, len(nb_dates)):
    if nb_kzz_nets[i] - nb_stock_nets[i] > 0.0729:
        nb_position.append(1)
        if nb_position[i-1] != 1:
            nb_chng_count  = 1
    elif nb_stock_nets[i] - nb_kzz_nets[i] > 0.0821:
        nb_position.append(0)
        if nb_position[i-1] != 0:
            nb_chng_count  = 1
    else:
        nb_position.append(nb_position[i-1])

    if nb_position[i-1] == 0:
        nb_stgy_nets.append(nb_stgy_nets[i-1]*(nb_kzz_prices[i]/nb_kzz_prices[i-1]))
    elif nb_position[i-1] == 1:
        nb_stgy_nets.append(nb_stgy_nets[i-1]*(nb_stock_prices[i]/nb_stock_prices[i-1]))

print("调仓次数:%d" % nb_chng_count)
print("股票净值:%f" % nb_stock_nets[-1])
print("可转债净值:%f" % nb_kzz_nets[-1])
print("策略净值:%f" % nb_stgy_nets[-1])

fig = plt.figure(figsize=(12, 6))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])

offsets = [i for i in range(len(nb_dates))]
ax.plot(offsets, nb_stock_nets, color="blue", linewidth=1.5, linestyle="-", label="股票净值")
ax.plot(offsets, nb_kzz_nets, color="green", linewidth=1.5, linestyle="-", label="转债净值")
ax.plot(offsets, nb_stgy_nets, color="red", linewidth=1.5, linestyle="-", label="策略净值")
plt.legend(loc='upper left', frameon=False)
调仓次数:2
股票净值:1.315276
可转债净值:1.171215
策略净值:1.374715

Img

可以看到针对宁波银行的优化策略收益增加了4%,但是调仓频率反而降低了。因为这里用了一个标准差,使得调仓阈值更大。另外,这里其实用到了未来函数。在实盘中,我们应该针对历史数据计算统计值。

后期优化

上述策略其实是一种配对轮动交易,原始策略中的5%区间是人为定的,第二部分通过计算最简单的统计量确定交易阈值。

另一种优化方法是以“转股溢价率”为锚来找交易阈值。转股溢价率可以看作投资者对于某只可转债的估值。那么统计历史估值,找到溢价率分布的均值,然后用一个区间(比如上下一个标准差)做配对策略。这个以后有机会研究一下。

# 光大银行日期、股票收盘价、可转债数据
gd_dates = ['2017-4-5', '2017-4-6', '2017-4-7', '2017-4-10', '2017-4-11', '2017-4-12', '2017-4-13', '2017-4-14', '2017-4-17', '2017-4-18', '2017-4-19', '2017-4-20', '2017-4-21', '2017-4-24', '2017-4-25', '2017-4-26', '2017-4-27', '2017-4-28', '2017-5-2', '2017-5-3', '2017-5-4', '2017-5-5', '2017-5-8', '2017-5-9', '2017-5-10', '2017-5-11', '2017-5-12', '2017-5-15', '2017-5-16', '2017-5-17', '2017-5-18', '2017-5-19', '2017-5-22', '2017-5-23', '2017-5-24', '2017-5-25', '2017-5-26', '2017-5-31', '2017-6-1', '2017-6-2', '2017-6-5', '2017-6-6', '2017-6-7', '2017-6-8', '2017-6-9', '2017-6-12', '2017-6-13', '2017-6-14', '2017-6-15', '2017-6-16', '2017-6-19', '2017-6-20', '2017-6-21', '2017-6-22', '2017-6-23', '2017-6-26', '2017-6-27', '2017-6-28', '2017-6-29', '2017-6-30', '2017-7-3', '2017-7-4', '2017-7-5', '2017-7-6', '2017-7-7', '2017-7-10', '2017-7-11', '2017-7-12', '2017-7-13', '2017-7-14', '2017-7-17', '2017-7-18', '2017-7-19', '2017-7-20', '2017-7-21', '2017-7-24', '2017-7-25', '2017-7-26', '2017-7-27', '2017-7-28', '2017-7-31', '2017-8-1', '2017-8-2', '2017-8-3', '2017-8-4', '2017-8-7', '2017-8-8', '2017-8-9', '2017-8-10', '2017-8-11', '2017-8-14', '2017-8-15', '2017-8-16', '2017-8-17', '2017-8-18', '2017-8-21', '2017-8-22', '2017-8-23', '2017-8-24', '2017-8-25', '2017-8-28', '2017-8-29', '2017-8-30', '2017-8-31', '2017-9-1', '2017-9-4', '2017-9-5', '2017-9-6', '2017-9-7', '2017-9-8', '2017-9-11', '2017-9-12', '2017-9-13', '2017-9-14', '2017-9-15', '2017-9-18', '2017-9-19', '2017-9-20', '2017-9-21', '2017-9-22', '2017-9-25', '2017-9-26', '2017-9-27', '2017-9-28', '2017-9-29', '2017-10-9', '2017-10-10', '2017-10-11', '2017-10-12', '2017-10-13', '2017-10-16', '2017-10-17', '2017-10-18', '2017-10-19', '2017-10-20', '2017-10-23', '2017-10-24', '2017-10-25', '2017-10-26', '2017-10-27', '2017-10-30', '2017-10-31', '2017-11-1', '2017-11-2', '2017-11-3', '2017-11-6', '2017-11-7', '2017-11-8', '2017-11-9', '2017-11-10', '2017-11-13', '2017-11-14', '2017-11-15', '2017-11-16', '2017-11-17', '2017-11-20', '2017-11-21', '2017-11-22', '2017-11-23', '2017-11-24', '2017-11-27', '2017-11-28', '2017-11-29', '2017-11-30', '2017-12-1', '2017-12-4', '2017-12-5', '2017-12-6', '2017-12-7', '2017-12-8', '2017-12-11', '2017-12-12', '2017-12-13', '2017-12-14', '2017-12-15', '2017-12-18', '2017-12-19', '2017-12-20', '2017-12-21', '2017-12-22', '2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28', '2017-12-29', '2018-1-2', '2018-1-3', '2018-1-4', '2018-1-5', '2018-1-8', '2018-1-9', '2018-1-10', '2018-1-11', '2018-1-12', '2018-1-15', '2018-1-16', '2018-1-17', '2018-1-18', '2018-1-19', '2018-1-22', '2018-1-23', '2018-1-24', '2018-1-25', '2018-1-26', '2018-1-29', '2018-1-30', '2018-1-31', '2018-2-1', '2018-2-2', '2018-2-5', '2018-2-6', '2018-2-7', '2018-2-8', '2018-2-9', '2018-2-12', '2018-2-13', '2018-2-14', '2018-2-22', '2018-2-23', '2018-2-26', '2018-2-27', '2018-2-28', '2018-3-1', '2018-3-2', '2018-3-5', '2018-3-6', '2018-3-7', '2018-3-8', '2018-3-9', '2018-3-12', '2018-3-13', '2018-3-14', '2018-3-15', '2018-3-16', '2018-3-19', '2018-3-20', '2018-3-21', '2018-3-22', '2018-3-23', '2018-3-26', '2018-3-27', '2018-3-28', '2018-3-29', '2018-3-30', '2018-4-2', '2018-4-3', '2018-4-4', '2018-4-9', '2018-4-10', '2018-4-11', '2018-4-12', '2018-4-13', '2018-4-16', '2018-4-17', '2018-4-18', '2018-4-19', '2018-4-20', '2018-4-23', '2018-4-24', '2018-4-25', '2018-4-26', '2018-4-27', '2018-5-2', '2018-5-3', '2018-5-4', '2018-5-7', '2018-5-8', '2018-5-9', '2018-5-10', '2018-5-11', '2018-5-14', '2018-5-15', '2018-5-16', '2018-5-17', '2018-5-18', '2018-5-21', '2018-5-22', '2018-5-23', '2018-5-24', '2018-5-25', '2018-5-28', '2018-5-29', '2018-5-30', '2018-5-31', '2018-6-1', '2018-6-4', '2018-6-5', '2018-6-6', '2018-6-7', '2018-6-8', '2018-6-11', '2018-6-12', '2018-6-13', '2018-6-14', '2018-6-15', '2018-6-19', '2018-6-20', '2018-6-21', '2018-6-22', '2018-6-25', '2018-6-26', '2018-6-27', '2018-6-28', '2018-6-29', '2018-7-2', '2018-7-3', '2018-7-4', '2018-7-5', '2018-7-6', '2018-7-9', '2018-7-10', '2018-7-11', '2018-7-12', '2018-7-13', '2018-7-16', '2018-7-17', '2018-7-18', '2018-7-19', '2018-7-20', '2018-7-23', '2018-7-24', '2018-7-25', '2018-7-26', '2018-7-27', '2018-7-30', '2018-7-31', '2018-8-1', '2018-8-2', '2018-8-3', '2018-8-6', '2018-8-7', '2018-8-8', '2018-8-9', '2018-8-10', '2018-8-13', '2018-8-14', '2018-8-15', '2018-8-16', '2018-8-17', '2018-8-20', '2018-8-21', '2018-8-22', '2018-8-23', '2018-8-24', '2018-8-27', '2018-8-28', '2018-8-29', '2018-8-30', '2018-8-31', '2018-9-3', '2018-9-4', '2018-9-5', '2018-9-6', '2018-9-7', '2018-9-10', '2018-9-11', '2018-9-12', '2018-9-13', '2018-9-14', '2018-9-17', '2018-9-18', '2018-9-19', '2018-9-20', '2018-9-21', '2018-9-25', '2018-9-26', '2018-9-27', '2018-9-28', '2018-10-8', '2018-10-9', '2018-10-10', '2018-10-11', '2018-10-12', '2018-10-15', '2018-10-16', '2018-10-17', '2018-10-18', '2018-10-19', '2018-10-22', '2018-10-23', '2018-10-24', '2018-10-25', '2018-10-26', '2018-10-29', '2018-10-30', '2018-10-31', '2018-11-1', '2018-11-2', '2018-11-5', '2018-11-6', '2018-11-7', '2018-11-8', '2018-11-9', '2018-11-12', '2018-11-13', '2018-11-14', '2018-11-15', '2018-11-16', '2018-11-19', '2018-11-20', '2018-11-21', '2018-11-22', '2018-11-23', '2018-11-26', '2018-11-27', '2018-11-28', '2018-11-29', '2018-11-30', '2018-12-3', '2018-12-4', '2018-12-5', '2018-12-6', '2018-12-7', '2018-12-10', '2018-12-11', '2018-12-12', '2018-12-13', '2018-12-14', '2018-12-17', '2018-12-18', '2018-12-19', '2018-12-20', '2018-12-21', '2018-12-24', '2018-12-25', '2018-12-26', '2018-12-27', '2018-12-28', '2019-1-2', '2019-1-3', '2019-1-4', '2019-1-7', '2019-1-8', '2019-1-9', '2019-1-10', '2019-1-11', '2019-1-14', '2019-1-15', '2019-1-16', '2019-1-17', '2019-1-18', '2019-1-21', '2019-1-22', '2019-1-23', '2019-1-24', '2019-1-25', '2019-1-28', '2019-1-29', '2019-1-30', '2019-1-31', '2019-2-1', '2019-2-11', '2019-2-12', '2019-2-13', '2019-2-14', '2019-2-15', '2019-2-18', '2019-2-19', '2019-2-20', '2019-2-21', '2019-2-22', '2019-2-25', '2019-2-26', '2019-2-27', '2019-2-28', '2019-3-1', '2019-3-4', '2019-3-5', '2019-3-6', '2019-3-7', '2019-3-8', '2019-3-11', '2019-3-12', '2019-3-13', '2019-3-14', '2019-3-15', '2019-3-18', '2019-3-19', '2019-3-20', '2019-3-21', '2019-3-22', '2019-3-25', '2019-3-26', '2019-3-27', '2019-3-28', '2019-3-29', '2019-4-1', '2019-4-2', '2019-4-3', '2019-4-4', '2019-4-8', '2019-4-9', '2019-4-10', '2019-4-11', '2019-4-12', '2019-4-15', '2019-4-16', '2019-4-17', '2019-4-18', '2019-4-19', '2019-4-22', '2019-4-23', '2019-4-24', '2019-4-25', '2019-4-26', '2019-4-29', '2019-4-30', '2019-5-6', '2019-5-7', '2019-5-8', '2019-5-9', '2019-5-10', '2019-5-13', '2019-5-14', '2019-5-15', '2019-5-16', '2019-5-17', '2019-5-20', '2019-5-21', '2019-5-22', '2019-5-23', '2019-5-24', '2019-5-27', '2019-5-28', '2019-5-29', '2019-5-30', '2019-5-31', '2019-6-3', '2019-6-4', '2019-6-5', '2019-6-6', '2019-6-10', '2019-6-11', '2019-6-12', '2019-6-13', '2019-6-14', '2019-6-17', '2019-6-18', '2019-6-19', '2019-6-20', '2019-6-21', '2019-6-24', '2019-6-25', '2019-6-26', '2019-6-27', '2019-6-28', '2019-7-1', '2019-7-2', '2019-7-3', '2019-7-4', '2019-7-5', '2019-7-8', '2019-7-9', '2019-7-10', '2019-7-11', '2019-7-12', '2019-7-15', '2019-7-16', '2019-7-17', '2019-7-18', '2019-7-19', '2019-7-22', '2019-7-23', '2019-7-24', '2019-7-25', '2019-7-26', '2019-7-29', '2019-7-30', '2019-7-31', '2019-8-1']
gd_stock_prices = [3.6657135535152015, 3.6389565202778638, 3.630037509198752, 3.6121994870405265, 3.603280475961414, 3.5943614648823017, 3.576523442724077, 3.5676044316449644, 3.5497664094867396, 3.5051713540911775, 3.4605762986956154, 3.451657287616503, 3.469495309774728, 3.4605762986956154, 3.469495309774728, 3.4605762986956154, 3.4427382765373906, 3.4427382765373906, 3.433819265458278, 3.451657287616503, 3.4427382765373906, 3.4427382765373906, 3.4427382765373906, 3.451657287616503, 3.469495309774728, 3.4784143208538403, 3.540847398407627, 3.5319283873285148, 3.51409036517029, 3.4784143208538403, 3.4427382765373906, 3.433819265458278, 3.433819265458278, 3.4873333319329527, 3.451657287616503, 3.5497664094867396, 3.540847398407627, 3.558685420565852, 3.576523442724077, 3.5943614648823017, 3.540847398407627, 3.5319283873285148, 3.540847398407627, 3.540847398407627, 3.558685420565852, 3.540847398407627, 3.5319283873285148, 3.5230093762494024, 3.5051713540911775, 3.5051713540911775, 3.5230093762494024, 3.5230093762494024, 3.5319283873285148, 3.558685420565852, 3.5676044316449644, 3.558685420565852, 3.585442453803189, 3.603280475961414, 3.6121994870405265, 3.6121994870405265, 3.603280475961414, 3.5943614648823017, 3.6126529303317576, 3.6309448439030576, 3.6126529303317576, 3.6217988871174076, 3.676674627831307, 3.722404411759558, 3.768134195687808, 3.8230099364017076, 3.9601992881864585, 3.932761417829508, 3.932761417829508, 3.9510533314008085, 3.896177590686908, 3.9053235474725576, 3.9053235474725576, 3.923615461043858, 3.877885677115608, 3.8687397203299585, 3.8687397203299585, 3.896177590686908, 3.932761417829508, 3.887031633901258, 3.8321558931873585, 3.8230099364017076, 3.8230099364017076, 3.7772801524734576, 3.758988238902158, 3.7498422821165076, 3.722404411759558, 3.7406963253308576, 3.713258454973907, 3.7498422821165076, 3.7498422821165076, 3.7315503685452076, 3.7406963253308576, 3.758988238902158, 3.722404411759558, 3.850447806758658, 3.877885677115608, 3.9144695042582085, 3.8687397203299585, 3.841301849973008, 3.795572066044758, 3.850447806758658, 3.8687397203299585, 3.841301849973008, 3.813863979616058, 3.795572066044758, 3.804718022830408, 3.813863979616058, 3.7864261092591076, 3.7772801524734576, 3.758988238902158, 3.7406963253308576, 3.7498422821165076, 3.7406963253308576, 3.768134195687808, 3.768134195687808, 3.768134195687808, 3.7498422821165076, 3.7315503685452076, 3.722404411759558, 3.7041124981882576, 3.7498422821165076, 3.7498422821165076, 3.758988238902158, 3.7498422821165076, 3.7498422821165076, 3.7772801524734576, 3.758988238902158, 3.7772801524734576, 3.7864261092591076, 3.758988238902158, 3.713258454973907, 3.722404411759558, 3.722404411759558, 3.713258454973907, 3.722404411759558, 3.7041124981882576, 3.6949665414026076, 3.685820584616958, 3.685820584616958, 3.685820584616958, 3.6583827142600076, 3.7041124981882576, 3.7041124981882576, 3.676674627831307, 3.6583827142600076, 3.713258454973907, 3.685820584616958, 3.676674627831307, 3.6675286710456576, 3.7406963253308576, 3.758988238902158, 3.758988238902158, 3.8687397203299585, 3.8230099364017076, 3.8230099364017076, 3.8230099364017076, 3.7864261092591076, 3.7772801524734576, 3.8230099364017076, 3.8230099364017076, 3.8230099364017076, 3.887031633901258, 3.850447806758658, 3.841301849973008, 3.813863979616058, 3.813863979616058, 3.768134195687808, 3.795572066044758, 3.768134195687808, 3.713258454973907, 3.7041124981882576, 3.7406963253308576, 3.7315503685452076, 3.7315503685452076, 3.713258454973907, 3.713258454973907, 3.7406963253308576, 3.722404411759558, 3.7041124981882576, 3.7041124981882576, 3.7315503685452076, 3.758988238902158, 3.7498422821165076, 3.768134195687808, 3.7772801524734576, 3.7498422821165076, 3.7772801524734576, 3.795572066044758, 3.804718022830408, 3.850447806758658, 3.850447806758658, 3.9053235474725576, 3.9601992881864585, 4.060804812828609, 4.097388639971209, 4.390059257112009, 4.390059257112009, 4.371767343540709, 4.399205213897659, 4.344329473183759, 4.207140121399008, 4.271161818898559, 4.326037559612459, 4.280307775684209, 4.56383243603936, 4.499810738539809, 4.344329473183759, 4.143118423899459, 4.024220985686009, 3.923615461043858, 3.9510533314008085, 3.9510533314008085, 4.033366942471659, 4.097388639971209, 4.088242683185558, 4.005929072114708, 3.941907374615158, 3.9510533314008085, 3.9053235474725576, 3.9144695042582085, 3.9510533314008085, 3.932761417829508, 3.941907374615158, 3.932761417829508, 3.923615461043858, 3.923615461043858, 3.887031633901258, 3.877885677115608, 3.8321558931873585, 3.8321558931873585, 3.841301849973008, 3.841301849973008, 3.841301849973008, 3.8321558931873585, 3.7315503685452076, 3.713258454973907, 3.713258454973907, 3.7498422821165076, 3.7315503685452076, 3.713258454973907, 3.685820584616958, 3.685820584616958, 3.6949665414026076, 3.768134195687808, 3.804718022830408, 3.768134195687808, 3.722404411759558, 3.6492367574743576, 3.6309448439030576, 3.6492367574743576, 3.6583827142600076, 3.6400908006887076, 3.6400908006887076, 3.7041124981882576, 3.6675286710456576, 3.6675286710456576, 3.6949665414026076, 3.676674627831307, 3.685820584616958, 3.676674627831307, 3.685820584616958, 3.7315503685452076, 3.7315503685452076, 3.7406963253308576, 3.7498422821165076, 3.768134195687808, 3.7498422821165076, 3.7041124981882576, 3.6949665414026076, 3.7315503685452076, 3.7406963253308576, 3.722404411759558, 3.685820584616958, 3.676674627831307, 3.676674627831307, 3.6583827142600076, 3.676674627831307, 3.6126529303317576, 3.6309448439030576, 3.6309448439030576, 3.6492367574743576, 3.6400908006887076, 3.6217988871174076, 3.6217988871174076, 3.566923146403507, 3.5577771896178576, 3.5577771896178576, 3.5394852760465576, 3.5394852760465576, 3.5577771896178576, 3.566923146403507, 3.5211933624752576, 3.502901448903957, 3.493755492118307, 3.448025708190057, 3.384004010690507, 3.329128269976607, 3.310836356405307, 3.347420183547907, 3.2102308317631563, 3.2285227453344567, 3.2285227453344567, 3.2742525292627067, 3.2742525292627067, 3.365712097119207, 3.347420183547907, 3.310836356405307, 3.365712097119207, 3.3565661403335567, 3.3016903996196567, 3.3016903996196567, 3.3016903996196567, 3.2925444428340067, 3.411441881047457, 3.466317621761357, 3.502901448903957, 3.512047405689607, 3.466317621761357, 3.4855226536095163, 3.514328625953397, 3.52393061673469, 3.4663186720469294, 3.389502745796582, 3.4183087181404623, 3.4183087181404623, 3.4663186720469294, 3.4279107089217558, 3.4663186720469294, 3.4471146904843426, 3.4183087181404623, 3.408706727359169, 3.341492791890115, 3.3510947826714084, 3.3126868195462347, 3.360696773452702, 3.389502745796582, 3.389502745796582, 3.408706727359169, 3.4855226536095163, 3.5527365890785707, 3.5527365890785707, 3.533532607515984, 3.52393061673469, 3.5815425614224505, 3.5527365890785707, 3.610348533766331, 3.5527365890785707, 3.5719405706411576, 3.5815425614224505, 3.5719405706411576, 3.5527365890785707, 3.5815425614224505, 3.6295525153289177, 3.6199505245476247, 3.6199505245476247, 3.6679604784540913, 3.6679604784540913, 3.725572423141852, 3.7735823770483194, 3.744776404704439, 3.7351744139231458, 3.7351744139231458, 3.7543783954857326, 3.6583584876727984, 
                   3.6295525153289177, 3.687164460016678, 3.5623385798598637, 3.6487564968915045, 3.6487564968915045, 3.6487564968915045, 3.7351744139231458, 3.6199505245476247, 3.763980386267026, 3.86000029407996, 3.783184367829613, 3.8407963125173734, 3.8888062664238405, 3.8407963125173734, 3.8023883493921997, 3.8984082572051335, 3.850398303298667, 3.8407963125173734, 3.869602284861254, 3.811990340173493, 3.8023883493921997, 3.7927863586109063, 3.811990340173493, 3.687164460016678, 3.687164460016678, 3.696766450797972, 3.6679604784540913, 3.696766450797972, 3.706368441579265, 3.7735823770483194, 3.725572423141852, 3.725572423141852, 3.696766450797972, 3.6487564968915045, 3.677562469235385, 3.677562469235385, 3.687164460016678, 3.6583584876727984, 3.696766450797972, 3.7543783954857326, 3.783184367829613, 3.7543783954857326, 3.715970432360559, 3.696766450797972, 3.6583584876727984, 3.6583584876727984, 3.6679604784540913, 3.696766450797972, 3.6583584876727984, 3.677562469235385, 3.6391545061102115, 3.6295525153289177, 3.5527365890785707, 3.504726635172103, 3.4951246443908097, 3.4855226536095163, 3.4951246443908097, 3.504726635172103, 3.5527365890785707, 3.504726635172103, 3.6007465429850374, 3.687164460016678, 3.687164460016678, 3.6679604784540913, 3.715970432360559, 3.687164460016678, 3.715970432360559, 3.706368441579265, 3.744776404704439, 3.763980386267026, 3.744776404704439, 3.7927863586109063, 3.811990340173493, 3.7351744139231458, 3.7543783954857326, 3.7351744139231458, 3.8215923309547866, 3.783184367829613, 3.8407963125173734, 3.83119432173608, 3.9272142295490142, 3.908010247986428, 3.9272142295490142, 3.879204275642547, 3.908010247986428, 3.8888062664238405, 3.83119432173608, 3.86000029407996, 3.869602284861254, 3.8888062664238405, 3.86000029407996, 3.8984082572051335, 4.119254045174883, 4.004030155799362, 4.090448072831003, 4.061642100487123, 4.224875943769111, 4.311293860800752, 4.292089879238165, 4.301691870019459, 4.186467980643937, 4.004030155799362, 4.004030155799362, 4.013632146580655, 3.9944281650180686, 3.965622192674188, 3.965622192674188, 4.052040109705828, 4.013632146580655, 4.013632146580655, 3.9752241834554813, 3.9464182111116015, 3.86000029407996, 3.8407963125173734, 3.86000029407996, 3.8407963125173734, 3.936816220330307, 3.9944281650180686, 3.984826174236775, 4.004030155799362, 4.061642100487123, 4.090448072831003, 4.071244091268416, 4.004030155799362, 3.965622192674188, 3.9752241834554813, 4.004030155799362, 4.16726399908135, 4.205671962206524, 4.138458026737469, 4.148060017518763, 4.052040109705828, 4.071244091268416, 4.061642100487123, 4.023234137361949, 3.9272142295490142, 4.004030155799362, 4.004030155799362, 3.879204275642547, 3.879204275642547, 3.8215923309547866, 3.763980386267026, 3.8023883493921997, 3.7543783954857326, 3.725572423141852, 3.744776404704439, 3.7543783954857326, 3.715970432360559, 3.725572423141852, 3.7543783954857326, 3.744776404704439, 3.715970432360559, 3.706368441579265, 3.7351744139231458, 3.763980386267026, 3.715970432360559, 3.725572423141852, 3.687164460016678, 3.7351744139231458, 3.744776404704439, 3.763980386267026, 3.763980386267026, 3.783184367829613, 3.811990340173493, 3.8215923309547866, 3.8023883493921997, 3.783184367829613, 3.8023883493921997, 3.811990340173493, 3.8407963125173734, 3.879204275642547, 3.869602284861254, 3.8888062664238405, 3.86000029407996, 3.83, 3.83, 3.81, 3.85, 3.85, 3.87, 3.87, 3.86, 3.79, 3.77, 3.78, 3.77, 3.8, 3.8, 3.8, 3.78, 3.76, 3.78, 3.79, 3.8, 3.81, 3.85, 3.87, 3.94, 3.95, 3.93, 3.92]
gd_kzz_prices = [103.0, 103.61, 103.0, 103.44, 103.29, 103.12, 103.01, 102.31, 101.78, 101.7, 101.54, 101.53, 101.71, 101.48, 101.85, 101.6, 101.72, 101.5, 101.0, 100.95, 100.91, 100.6, 100.11, 100.19, 100.16, 100.29, 100.94, 100.94, 100.63, 100.38, 100.38, 100.49, 100.5, 100.57, 100.4, 101.5, 101.15, 101.5, 101.75, 101.93, 101.37, 101.61, 101.97, 102.0, 102.4, 102.59, 102.8, 102.35, 102.0, 102.15, 103.22, 103.89, 103.8, 104.0, 103.9, 105.4, 106.0, 105.71, 105.6, 105.12, 105.09, 104.8, 105.17, 105.2, 105.16, 104.96, 105.87, 106.3, 107.81, 109.23, 110.6, 110.04, 111.8, 112.79, 112.16, 113.29, 111.35, 111.99, 111.9, 112.14, 112.39, 113.71, 115.81, 115.2, 114.35, 113.87, 113.95, 112.8, 112.26, 111.19, 111.72, 112.65, 112.65, 113.16, 113.34, 112.63, 112.64, 112.7, 112.03, 114.29, 115.91, 116.22, 115.94, 115.29, 115.4, 115.85, 116.49, 116.32, 115.85, 116.3, 115.67, 113.95, 113.31, 113.0, 112.87, 112.73, 112.85, 112.75, 112.54, 112.3, 112.07, 110.7, 110.87, 110.69, 111.51, 111.83, 112.49, 112.93, 113.06, 112.95, 112.65, 112.79, 112.53, 111.39, 111.57, 110.73, 111.44, 110.88, 110.38, 111.3, 109.61, 109.74, 109.4, 109.32, 109.09, 108.34, 109.9, 110.01, 110.16, 110.12, 110.97, 110.3, 108.9, 108.62, 109.21, 108.74, 109.3, 109.5, 108.33, 108.66, 108.14, 107.63, 106.69, 106.5, 105.53, 105.47, 106.03, 106.04, 106.03, 107.5, 108.2, 106.6, 107.26, 106.1, 106.14, 105.6, 106.24, 105.7, 105.9, 105.78, 105.94, 105.7, 104.9, 104.41, 104.5, 105.8, 107.17, 107.2, 107.99, 107.84, 107.43, 108.0, 107.55, 107.8, 108.37, 109.1, 109.89, 111.6, 113.79, 115.25, 119.23, 120.63, 119.89, 120.14, 119.03, 117.1, 118.84, 118.2, 120.36, 123.78, 121.98, 119.9, 114.2, 111.92, 112.6, 113.25, 113.5, 115.68, 116.43, 116.06, 114.03, 113.51, 113.05, 112.5, 110.88, 112.9, 112.5, 113.24, 113.7, 113.0, 113.0, 112.3, 112.21, 112.18, 111.49, 111.09, 110.59, 110.21, 109.0, 108.7, 108.39, 107.91, 109.17, 108.5, 108.54, 108.4, 109.46, 109.8, 112.31, 112.89, 111.53, 110.45, 109.34, 109.08, 110.81, 110.12, 108.95, 108.26, 109.92, 109.5, 108.77, 109.8, 109.16, 109.04, 109.3, 109.99, 110.61, 110.6, 110.05, 109.97, 109.97, 110.0, 109.1, 109.3, 109.71, 110.3, 110.0, 109.1, 108.03, 107.8, 107.31, 105.91, 105.68, 106.12, 106.11, 106.6, 106.9, 106.3, 107.06, 105.76, 105.0, 105.15, 105.1, 105.0, 104.86, 103.13, 103.14, 103.25, 103.2, 102.82, 101.8, 100.31, 100.12, 101.6, 100.18, 100.86, 100.95, 101.41, 101.78, 103.48, 104.11, 103.79, 104.02, 104.01, 103.66, 103.7, 103.74, 104.65, 107.09, 108.29, 107.53, 106.91, 105.55, 105.89, 106.26, 106.9, 105.76, 105.41, 105.4, 105.4, 106.9, 106.13, 106.41, 105.99, 105.33, 105.05, 104.88, 104.78, 104.3, 104.36, 105.0, 105.2, 105.42, 105.8, 106.39, 106.05, 105.84, 105.51, 105.43, 105.26, 105.82, 105.37, 105.66, 105.58, 105.45, 105.1, 105.11, 105.51, 105.73, 105.46, 106.09, 106.67, 107.37, 108.5, 108.25, 108.34, 107.9, 108.57, 107.2, 107.17, 107.75, 106.52, 107.3, 106.64, 106.6, 107.44, 106.65, 107.86, 108.48, 107.53, 108.71, 109.01, 109.46, 109.27, 108.95, 108.68, 108.65, 109.2, 108.86, 108.67, 108.47, 108.57, 107.14, 107.56, 107.9, 107.76, 108.45, 108.89, 108.91, 108.0, 108.01, 107.7, 107.37, 107.94, 107.92, 108.38, 107.8, 107.91, 108.3, 107.92, 108.02, 107.86, 107.8, 107.41, 107.16, 107.3, 107.84, 107.6, 107.23, 105.8, 105.65, 104.47, 103.91, 104.8, 105.05, 104.88, 104.7, 105.43, 103.78, 104.8, 106.55, 106.83, 106.79, 107.38, 107.85, 108.03, 107.51, 108.32, 108.61, 108.0, 109.01, 109.06, 108.57, 108.2, 109.0, 111.53, 111.02, 111.26, 111.79, 112.24, 113.5, 113.5, 113.12, 113.4, 113.5, 112.66, 114.07, 113.95, 113.44, 113.35, 113.56, 118.0, 115.5, 115.18, 114.12, 116.81, 118.17, 119.0, 118.93, 116.31, 113.98, 113.87, 114.7, 113.77, 113.73, 113.64, 114.96, 115.25, 115.3, 115.74, 115.7, 114.26, 113.59, 113.5, 112.81, 114.9, 117.31, 117.21, 117.28, 117.71, 117.58, 116.36, 114.35, 113.68, 113.23, 114.09, 114.88, 116.1, 115.03, 114.7, 113.39, 112.28, 111.75, 110.97, 110.24, 111.29, 112.05, 108.73, 109.3, 109.2, 109.1, 110.02, 109.91, 110.04, 110.6, 110.9, 110.88, 110.99, 110.57, 109.79, 109.06, 109.0, 108.4, 108.78, 108.66, 107.77, 107.48, 107.22, 106.55, 106.85, 107.1, 107.7, 109.3, 108.9, 108.66, 108.07, 108.07, 108.05, 108.75, 110.85, 110.95, 110.52, 109.49, 109.31, 109.43, 108.63, 108.88, 109.15, 109.27, 109.04, 109.1, 108.88, 108.8, 108.8, 108.9, 109.89, 109.86, 109.9, 110.02, 109.68, 110.53, 110.55, 110.4, 110.39, 110.85, 112.24, 112.96, 113.0, 112.5, 112.96]

# 宁波银行日期、股票收盘价、可转债数据
nb_dates = ['2018-1-12', '2018-1-15', '2018-1-16', '2018-1-17', '2018-1-18', '2018-1-19', '2018-1-22', '2018-1-23', '2018-1-24', '2018-1-25', '2018-1-26', '2018-1-29', '2018-1-30', '2018-1-31', '2018-2-1', '2018-2-2', '2018-2-5', '2018-2-6', '2018-2-7', '2018-2-8', '2018-2-9', '2018-2-12', '2018-2-13', '2018-2-14', '2018-2-22', '2018-2-23', '2018-2-26', '2018-2-27', '2018-2-28', '2018-3-1', '2018-3-2', '2018-3-5', '2018-3-6', '2018-3-7', '2018-3-8', '2018-3-9', '2018-3-12', '2018-3-13', '2018-3-14', '2018-3-15', '2018-3-16', '2018-3-19', '2018-3-20', '2018-3-21', '2018-3-22', '2018-3-23', '2018-3-26', '2018-3-27', '2018-3-28', '2018-3-29', '2018-3-30', '2018-4-2', '2018-4-3', '2018-4-4', '2018-4-9', '2018-4-10', '2018-4-11', '2018-4-12', '2018-4-13', '2018-4-16', '2018-4-17', '2018-4-18', '2018-4-19', '2018-4-20', '2018-4-23', '2018-4-24', '2018-4-25', '2018-4-26', '2018-4-27', '2018-5-2', '2018-5-3', '2018-5-4', '2018-5-7', '2018-5-8', '2018-5-9', '2018-5-10', '2018-5-11', '2018-5-14', '2018-5-15', '2018-5-16', '2018-5-17', '2018-5-18', '2018-5-21', '2018-5-22', '2018-5-23', '2018-5-24', '2018-5-25', '2018-5-28', '2018-5-29', '2018-5-30', '2018-5-31', '2018-6-1', '2018-6-4', '2018-6-5', '2018-6-6', '2018-6-7', '2018-6-8', '2018-6-11', '2018-6-12', '2018-6-13', '2018-6-14', '2018-6-15', '2018-6-19', '2018-6-20', '2018-6-21', '2018-6-22', '2018-6-25', '2018-6-26', '2018-6-27', '2018-6-28', '2018-6-29', '2018-7-2', '2018-7-3', '2018-7-4', '2018-7-5', '2018-7-6', '2018-7-9', '2018-7-10', '2018-7-11', '2018-7-12', '2018-7-13', '2018-7-16', '2018-7-17', '2018-7-18', '2018-7-19', '2018-7-20', '2018-7-23', '2018-7-24', '2018-7-25', '2018-7-26', '2018-7-27', '2018-7-30', '2018-7-31', '2018-8-1', '2018-8-2', '2018-8-3', '2018-8-6', '2018-8-7', '2018-8-8', '2018-8-9', '2018-8-10', '2018-8-13', '2018-8-14', '2018-8-15', '2018-8-16', '2018-8-17', '2018-8-20', '2018-8-21', '2018-8-22', '2018-8-23', '2018-8-24', '2018-8-27', '2018-8-28', '2018-8-29', '2018-8-30', '2018-8-31', '2018-9-3', '2018-9-4', '2018-9-5', '2018-9-6', '2018-9-7', '2018-9-10', '2018-9-11', '2018-9-12', '2018-9-13', '2018-9-14', '2018-9-17', '2018-9-18', '2018-9-19', '2018-9-20', '2018-9-21', '2018-9-25', '2018-9-26', '2018-9-27', '2018-9-28', '2018-10-8', '2018-10-9', '2018-10-10', '2018-10-11', '2018-10-12', '2018-10-15', '2018-10-16', '2018-10-17', '2018-10-18', '2018-10-19', '2018-10-22', '2018-10-23', '2018-10-24', '2018-10-25', '2018-10-26', '2018-10-29', '2018-10-30', '2018-10-31', '2018-11-1', '2018-11-2', '2018-11-5', '2018-11-6', '2018-11-7', '2018-11-8', '2018-11-9', '2018-11-12', '2018-11-13', '2018-11-14', '2018-11-15', '2018-11-16', '2018-11-19', '2018-11-20', '2018-11-21', '2018-11-22', '2018-11-23', '2018-11-26', '2018-11-27', '2018-11-28', '2018-11-29', '2018-11-30', '2018-12-3', '2018-12-4', '2018-12-5', '2018-12-6', '2018-12-7', '2018-12-10', '2018-12-11', '2018-12-12', '2018-12-13', '2018-12-14', '2018-12-17', '2018-12-18', '2018-12-19', '2018-12-20', '2018-12-21', '2018-12-24', '2018-12-25', '2018-12-26', '2018-12-27', '2018-12-28', '2019-1-2', '2019-1-3', '2019-1-4', '2019-1-7', '2019-1-8', '2019-1-9', '2019-1-10', '2019-1-11', '2019-1-14', '2019-1-15', '2019-1-16', '2019-1-17', '2019-1-18', '2019-1-21', '2019-1-22', '2019-1-23', '2019-1-24', '2019-1-25', '2019-1-28', '2019-1-29', '2019-1-30', '2019-1-31', '2019-2-1', '2019-2-11', '2019-2-12', '2019-2-13', '2019-2-14', '2019-2-15', '2019-2-18', '2019-2-19', '2019-2-20', '2019-2-21', '2019-2-22', '2019-2-25', '2019-2-26', '2019-2-27', '2019-2-28', '2019-3-1', '2019-3-4', '2019-3-5', '2019-3-6', '2019-3-7', '2019-3-8', '2019-3-11', '2019-3-12', '2019-3-13', '2019-3-14', '2019-3-15', '2019-3-18', '2019-3-19', '2019-3-20', '2019-3-21', '2019-3-22', '2019-3-25', '2019-3-26', '2019-3-27', '2019-3-28', '2019-3-29', '2019-4-1', '2019-4-2', '2019-4-3', '2019-4-4', '2019-4-8', '2019-4-9', '2019-4-10', '2019-4-11', '2019-4-12', '2019-4-15', '2019-4-16', '2019-4-17', '2019-4-18', '2019-4-19', '2019-4-22', '2019-4-23', '2019-4-24', '2019-4-25', '2019-4-26', '2019-4-29', '2019-4-30', '2019-5-6', '2019-5-7', '2019-5-8', '2019-5-9', '2019-5-10', '2019-5-13', '2019-5-14', '2019-5-15', '2019-5-16', '2019-5-17', '2019-5-20', '2019-5-21', '2019-5-22', '2019-5-23', '2019-5-24', '2019-5-27', '2019-5-28', '2019-5-29', '2019-5-30', '2019-5-31', '2019-6-3', '2019-6-4', '2019-6-5', '2019-6-6', '2019-6-10', '2019-6-11', '2019-6-12', '2019-6-13', '2019-6-14', '2019-6-17', '2019-6-18', '2019-6-19', '2019-6-20', '2019-6-21', '2019-6-24', '2019-6-25', '2019-6-26', '2019-6-27', '2019-6-28', '2019-7-1', '2019-7-2', '2019-7-3', '2019-7-4', '2019-7-5', '2019-7-8', '2019-7-9', '2019-7-10', '2019-7-11', '2019-7-12', '2019-7-15', '2019-7-16', '2019-7-17', '2019-7-18', '2019-7-19', '2019-7-22', '2019-7-23', '2019-7-24', '2019-7-25', '2019-7-26', '2019-7-29', '2019-7-30', '2019-7-31', '2019-8-1']
nb_stock_prices = [17.752929230544783, 18.385934810888358, 18.712028594701714, 18.990167410307222, 19.181987283138607, 19.16280529585547, 18.99975840394879, 19.814992863482182, 19.373807155969995, 19.1532143022139, 18.980576416665652, 18.702437601060144, 18.4338897790962, 19.229942251346454, 19.32585218776215, 19.25871523227116, 20.735728253072836, 20.591863348449294, 19.34503417504529, 19.172396289497037, 18.06943202071657, 18.472253753662482, 19.229942251346454, 19.095668340364483, 19.834174850765322, 19.834174850765322, 20.131495653653968, 19.33544318140372, 19.45053510510255, 19.795810876199045, 19.383398149611565, 18.89425747389153, 19.076486353081346, 19.642354977933934, 19.738264914349628, 19.45053510510255, 19.114850327647623, 19.105259334006057, 18.89425747389153, 18.9038484675331, 18.86548449296682, 19.047713372156636, 19.047713372156636, 19.28748821319587, 18.836711512042115, 18.62570965192759, 18.002295065225585, 17.407653459448287, 17.62824631320438, 18.558572696436606, 18.251660899906387, 18.280433880831094, 17.935158109734598, 17.877612147885184, 17.50356339586398, 18.33797984268051, 18.61611865828602, 18.33797984268051, 18.155750963490693, 17.13910563748435, 16.860966821878836, 17.225424580258473, 17.23501557390004, 17.158287624767485, 16.995240732860807, 17.599473332279675, 17.35010749759887, 17.292561535749456, 17.36928948488201, 17.0911506692765, 16.736283904538436, 16.506100057140774, 16.82260284731256, 17.0911506692765, 17.2158335866169, 17.2158335866169, 17.417244453089857, 17.359698491240444, 17.417244453089857, 17.11033265655964, 17.024013713785514, 17.263788554824746, 17.311743523032593, 16.995240732860807, 16.784238872746283, 16.54446403170705, 16.467736082574497, 16.582828006273328, 16.362235152517233, 15.882685470438767, 16.323871177950956, 16.563646018990188, 16.717101917255295, 16.678737942689022, 16.458145088932927, 16.573237012631758, 16.458145088932927, 16.55405502534862, 16.669146949047448, 16.68832893633059, 16.669146949047448, 16.880148809161977, 16.51569105078234, 16.602009993556464, 16.669146949047448, 16.68832893633059, 16.343053165234092, 15.825139508589352, 15.633319635757967, 15.479863737492858, 15.623728642116395, 15.393544794718734, 15.575773673908548, 15.777184540381505, 15.911458451363476, 15.767593546739937, 16.266325216101542, 16.227961241535265, 15.921049445005046, 15.980017954360326, 15.970190145040299, 15.744150530679729, 15.803117386599876, 15.68518367475958, 15.783461767959826, 16.35347470852127, 16.628653369481963, 16.55003089492176, 16.608997750841908, 16.785898318602356, 16.55003089492176, 16.805553937242408, 17.129871644803224, 16.707275844042158, 16.68762022540211, 16.40261375512139, 16.432097183081463, 17.228149738003474, 17.139699454123253, 17.169182882083323, 16.864520793162555, 16.608997750841908, 16.55985870424179, 16.186401950080842, 16.009501382320398, 15.577077772239306, 15.262587873998513, 15.753978339999755, 15.714667102719655, 15.911223289120151, 16.225713187360945, 16.628653369481963, 16.569686513561813, 16.58934213220186, 16.265024424641044, 16.491064039001614, 16.45175280172151, 16.58934213220186, 16.07829604756057, 15.87191205184005, 15.95053452640025, 15.87191205184005, 15.803117386599876, 15.734322721359705, 15.793289577279854, 15.862084242520027, 15.655700246799505, 15.989845763680348, 16.284680043281092, 16.46158061104154, 17.149527263443275, 16.99228231432288, 17.34608344984377, 17.267460975283573, 17.454189352364047, 16.95297107704278, 16.667964606762062, 16.923487649082702, 16.25519661532102, 16.746587081322257, 16.392785945801364, 16.45175280172151, 16.500891848321636, 16.107779475520648, 16.608997750841908, 17.208494119363426, 16.628653369481963, 16.874348602482584, 16.91365983976268, 17.011937932962926, 16.510719657641665, 17.29694440324365, 17.306772212563672, 17.2379775473235, 17.93575200904526, 17.473844971004095, 17.247805356643525, 17.208494119363426, 17.454189352364047, 16.864520793162555, 16.795726127922382, 16.795726127922382, 16.55985870424179, 16.677792416082085, 16.805553937242408, 17.090560407523128, 16.805553937242408, 16.91365983976268, 16.85469298384253, 16.55003089492176, 16.667964606762062, 16.491064039001614, 16.746587081322257, 16.42226937376144, 16.618825560161937, 16.93331545840273, 16.884176411802606, 16.72693146268221, 16.471408420361566, 16.48123622968159, 16.06846823824055, 15.980017954360326, 16.048812619600497, 16.25519661532102, 16.11760728484067, 16.058640428920523, 16.048812619600497, 15.999673573000376, 15.606561200199382, 15.390349395158836, 15.66552805611953, 15.842428623879979, 15.921051098440175, 15.8522564332, 15.940706717080223, 15.714667102719655, 15.82277300523993, 16.166746331440795, 16.265024424641044, 15.999673573000376, 16.206057568720894, 16.107779475520648, 16.225713187360945, 16.07829604756057, 16.274852233961067, 16.294507852601114, 16.284680043281092, 16.55985870424179, 16.58934213220186, 16.520547466961688, 16.618825560161937, 16.85469298384253, 17.070904788883077, 16.864520793162555, 16.99228231432288, 17.011937932962926, 17.149527263443275, 17.277288784603595, 17.277288784603595, 17.63108992012449, 17.680228966724613, 17.581950873524367, 17.306772212563672, 17.866957343805087, 17.758851441284815, 17.817818297204962, 17.65074553876454, 17.83747391584501, 19.06595008084811, 18.417314665726472, 18.82025484784749, 18.967671987647865, 19.341128741808806, 19.5769961654894, 19.488545881609177, 19.75389673324985, 19.567168356169375, 18.82025484784749, 19.164228174048358, 19.164228174048358, 19.233022839288534, 19.18388379268841, 19.134744746088284, 19.50820150024923, 19.75389673324985, 20.50081024157173, 20.333737483131312, 20.323909673811286, 20.097870059450713, 19.488545881609177, 19.832519207810044, 19.86200263577012, 20.874266995732672, 21.817736690455053, 21.97498163957545, 22.407405249656545, 22.898795715657783, 23.380358372338996, 22.84965666905766, 22.741550766537387, 22.741550766537387, 22.564650198776942, 22.820173241097585, 23.19362999525853, 22.71206733857731, 22.72189514789734, 22.987245999538008, 22.22067687257607, 22.377921821696468, 22.377921821696468, 22.328782775096343, 21.817736690455053, 22.309127156456295, 22.50568334285679, 21.041339754173094, 21.778425453174957, 21.424624317654064, 20.893922614372727, 21.532730220174336, 21.76859764385493, 21.532730220174336, 22.201021253936023, 22.299299347136273, 22.053604114135652, 21.925842592975325, 22.515511152176817, 22.71206733857731, 22.269815919176196, 22.21084906325605, 22.45654429625667, 23.105179711378305, 23.046212855458155, 22.859484478377684, 22.800517622457534, 22.377921821696468, 21.778425453174957, 21.680147359974708, 21.58186926677446, 22.269815919176196, 22.79068981313751, 22.761206385177434, 22.45654429625667, 22.299299347136273, 22.328782775096343, 22.368094012376446, 22.771034194497464, 24.019165978140613, 23.380358372338996, 23.73415950785989, 23.262424660498702, 23.20345780457855, 23.586742368059518, 23.822609791740113, 24.372967113661502, 24.422106160261627, 23.970026931540488, 23.370530563018974, 23.370530563018974, 22.83000105041761, 22.83000105041761, 22.27, 22.03, 22.84, 22.62, 22.4, 22.81, 22.72, 23.41, 23.65, 23.36, 23.57, 24.01, 23.89, 23.89, 23.81, 23.59, 23.35]
nb_kzz_prices = [112.379, 113.003, 114.92, 116.231, 118.05, 120.23, 119.99, 121.4, 121.4, 119.22, 119.55, 116.0, 116.101, 118.402, 117.65, 119.52, 123.649, 122.7, 120.4, 116.9, 113.84, 115.95, 117.91, 118.32, 121.18, 121.35, 121.4, 119.1, 118.861, 119.4, 118.1, 117.4, 119.08, 119.11, 119.5, 119.015, 118.33, 118.304, 118.333, 117.8, 117.7, 117.0, 117.5, 117.6, 116.65, 115.0, 114.83, 114.25, 113.299, 115.301, 114.974, 115.55, 115.0, 116.033, 116.068, 118.36, 119.51, 118.05, 117.03, 115.851, 115.2, 116.26, 116.166, 115.6, 114.2, 116.029, 115.45, 114.29, 114.71, 114.313, 114.425, 114.36, 114.8, 116.003, 115.8, 115.31, 115.45, 115.562, 115.3, 114.77, 114.808, 115.009, 115.5, 114.721, 113.294, 112.479, 111.192, 111.66, 109.96, 107.7, 107.7, 108.11, 108.214, 108.315, 108.12, 109.06, 107.93, 107.99, 108.701, 108.872, 108.4, 108.091, 105.1, 105.477, 105.72, 105.552, 106.499, 104.92, 103.11, 102.884, 104.7, 104.007, 105.511, 105.81, 105.52, 105.9, 108.3, 108.179, 108.1, 108.551, 108.501, 108.005, 107.9, 107.9, 108.47, 111.9, 113.6, 112.301, 112.448, 111.15, 110.85, 110.85, 112.0, 111.9, 111.01, 111.13, 111.168, 114.0, 113.57, 114.051, 113.21, 112.109, 112.52, 111.835, 111.41, 110.551, 110.0, 111.01, 111.6, 112.03, 112.31, 112.96, 112.866, 112.501, 111.39, 111.315, 111.307, 112.01, 110.65, 110.31, 111.2, 110.99, 110.78, 109.6, 109.5, 109.25, 108.61, 108.92, 110.68, 110.5, 112.5, 112.0, 112.702, 112.321, 113.699, 111.863, 110.691, 111.298, 110.2, 112.5, 110.5, 110.196, 110.4, 110.0, 111.99, 112.55, 112.7, 112.5, 112.597, 113.3, 111.33, 112.398, 112.39, 112.445, 113.9, 113.3, 112.652, 112.9, 112.63, 111.15, 111.095, 111.95, 111.56, 112.099, 113.2, 113.4, 112.3, 112.49, 111.88, 111.17, 111.05, 110.94, 111.396, 110.53, 110.65, 112.0, 112.001, 112.06, 111.8, 111.6, 111.2, 110.909, 110.62, 110.998, 110.06, 109.5, 107.83, 106.2, 105.23, 105.1, 105.977, 106.2, 105.878, 106.09, 106.0, 104.36, 104.55, 106.848, 107.7, 107.71, 107.8, 108.79, 109.545, 108.3, 108.538, 108.95, 108.665, 109.2, 109.61, 109.898, 110.1, 110.899, 113.598, 113.0, 113.3, 113.705, 114.39, 115.5, 115.9, 116.15, 116.4, 116.4, 115.145, 118.15, 117.6, 116.8, 116.798, 116.8, 120.8, 118.199, 118.2, 118.65, 120.02, 120.2, 120.85, 120.102, 118.71, 116.206, 117.47, 117.31, 116.99, 117.011, 117.16, 118.989, 120.401, 121.9, 122.502, 122.8, 122.0, 118.38, 119.201, 119.05, 122.93, 127.0, 128.8, 129.9, 131.9, 132.0, 129.0, 128.3, 128.5, 127.5, 127.58, 130.0, 129.101, 128.6, 129.71, 126.27, 125.95, 126.0, 124.9, 123.301, 125.9, 127.85, 125.0, 124.9, 124.489, 123.8, 126.99, 127.39, 127.48, 128.4, 127.8, 126.8, 125.81, 128.0, 128.85, 127.8, 127.45, 126.6, 129.3, 130.0, 129.4, 128.6, 126.1, 122.91, 122.25, 121.76, 125.301, 128.5, 128.5, 127.035, 125.807, 125.801, 126.0, 128.45, 133.0, 132.486, 132.8, 131.25, 131.1, 133.03, 134.08, 137.41, 137.76, 135.299, 131.69, 131.98, 129.02, 129.15, 127.52, 127.42, 129.98, 130.001, 129.613, 130.069, 129.71, 131.95, 132.999, 131.605, 132.891, 135.199, 134.75, 134.678, 134.301, 133.098, 131.62]
分享到:
举报财经168客户端下载

全部回复

0/140

投稿 您想发表你的观点和看法?

更多人气分析师

  • 李冉晴

    人气2096文章3821粉丝34

    李冉晴,专业现贷实盘分析师。

  • 张迎妤

    人气1752文章3305粉丝34

    个人专注于行情技术分析,消息面解读剖析,给予您第一时间方向...

  • 刘钥钥1

    人气1944文章3119粉丝34

    专业从事现货黄金、现货白银模似实盘操作分析指导

  • 梁孟梵

    人气1960文章3177粉丝39

    qq:2294906466 了解群指导添加微信mfmacd

  • 金帝财神

    人气4520文章8329粉丝117

    本文由资深分析师金帝财神微信:934295330,指导黄金,白银,...

  • 金泰铬J

    人气2160文章3925粉丝51

    投资问答解咨询金泰铬V/信tgtg67即可获取每日的实时资讯、行情...

  • 指导老师

    人气1752文章4423粉丝52

    暂无个人简介信息

  • 金算盘

    人气2520文章7761粉丝124

    高级分析师,混过名校,厮杀于股市和期货、证券市场多年,专注...

  • 张亦巧

    人气2024文章4145粉丝45

    暂无个人简介信息