市场宽度

对市场内股票数据进行统计，观察样本变化。

目录

1. 什么是市场宽度？
2. 数据预处理
3. 市场宽度计算公式
4. 如何用图形展示市场宽度？

1_什么是市场宽度？

市场宽度指标指某一日收盘时，显示某指数组合的股票的上涨数量与下跌数量的比率，有比较准确预测大盘行情的功能。

怎么计算市场宽度？

2_数据预处理

from jaqs.data import DataView
from jaqs.data import RemoteDataService
import os
import numpy as np
import talib as ta
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
dataview_folder = 'JAQS_Data/hs300'
dv = DataView()
dv.load_dataview(dataview_folder)

Dataview loaded successfully.

def change_index(df):
    df.index = pd.Index(map(lambda x: datetime.strptime(str(x),"%Y%m%d") , df.index))
    return df

def formula(positive, negative, total):
    return (positive-negative)/total
def sumrows(frame):
    return frame.sum(axis=1)

mask = dv.get_ts('index_member')

PN = pd.Panel({'high' : change_index(dv.get_ts('high_adj').loc[20150105:][mask==1].dropna(how='all',axis=1)),
 'low' : change_index(dv.get_ts('low_adj').loc[20150105:][mask==1].dropna(how='all',axis=1)),
 'close' : change_index(dv.get_ts('close_adj').loc[20150105:][mask==1].dropna(how='all',axis=1)),
 'volume' : change_index(dv.get_ts('volume').loc[20150105:])[mask==1].dropna(how='all',axis=1)})

print(PN)

<class 'pandas.core.panel.Panel'>
Dimensions: 436 (items) x 727 (major_axis) x 4 (minor_axis)
Items axis: 000001.SZ to 603993.SH
Major_axis axis: 2015-01-05 00:00:00 to 2017-12-22 00:00:00
Minor_axis axis: close to volume

PN = PN.transpose(2,1,0)

3_市场宽度计算公式

# cpc = close price 相对于前一天的涨跌幅
cpc = PN.minor_xs('close').pct_change()
participation = formula(
    sumrows(cpc>0),
    sumrows(cpc<0),
    300)
print(participation.tail())

2017-12-18   -0.066667
2017-12-19    0.493333
2017-12-20   -0.296667
2017-12-21    0.233333
2017-12-22   -0.336667
dtype: float64

计算新高新低的量

dh = PN.minor_xs('high').rolling(5).max().pct_change()
dl = PN.minor_xs('low').rolling(5).min().pct_change()
leadership = formula(
    sumrows(dh>0),
    sumrows(dl<0),
    300
)
print(leadership.tail())

2017-12-18   -0.303333
2017-12-19    0.086667
2017-12-20   -0.126667
2017-12-21   -0.116667
2017-12-22    0.080000
dtype: float64

计算均线上下的量

#MA50
import talib as ta
close = PN.minor_xs('close')
def cal_ma(s, timeperiod=50):
    s = s.dropna()
    try:
        return pd.Series(ta.MA(s.values, timeperiod), s.index)
    except:
        return pd.Series()
ma = close.apply(cal_ma)
trend = formula(
    sumrows(close>ma),
    sumrows(close<ma),
    300
)
print(trend.tail())

2017-12-18   -0.436667
2017-12-19   -0.370000
2017-12-20   -0.410000
2017-12-21   -0.356667
2017-12-22   -0.323333
dtype: float64

平滑数据（smooth）

def smooth(series, fast=20, slow=50):
    f = ta.MA(series.values, fast)
    s = ta.MA(series.values, slow)
    return pd.DataFrame({"fast": f, "slow": s}, series.index)
def plot_smooth(ax, smoothed, origin):
    ax.set_title(origin.name)
    ax.bar(origin.index, origin.values)
    ax.plot(smoothed.slow, c='r', label = 'slow')
    ax.plot(smoothed.fast, c='g', label = 'fast')
    ax.legend()
    return ax

4_如何用图形展示市场宽度？

读取数据

import numpy as np
hs300 = change_index(dv.data_benchmark)
hs300["trend"] = trend
hs300["participation"] = participation
hs300["leadership"] = leadership

import matplotlib.pyplot as plt
f, axs = plt.subplots(4, 1, sharex=True, figsize=(15, 15))
axs[0].set_title("hs300")
axs[0].plot(hs300['close'].loc['2015-03-05':])
to_smooth = ["trend", "participation", "leadership"]
for ax, (name, item) in zip(axs[1:], hs300[to_smooth].iteritems()):
    plot_smooth(ax, smooth(item), item)
plt.show()