择时买卖

未分类

---

1. Second Order Breakout(Confirmation)
   突破20天最高，同时是在40天内第一次破20天新高。

import numpy as np
import talib as ta
from fxdayu_data.data import MongoHandler, StockData
import pandas as pd
import matplotlib.pyplot as plt
mh = MongoHandler(db='Global_index')
hs300 = mh.read('hs300.D')

x = np.array(range(10, 81, 10))
def Second_Breakout(s):
    high = ta.MAX(hs300.high.values, s)
    low = ta.MIN(hs300.low.values, s)
    return {s: high, -s: low}
def day_range(data):
    low = 0
    high = 0
    is_high = 1
    is_low = 1
    for i in x:
        if is_low:
            if data['low'] <= data[-i]:
                low = -i
            else:
                is_low = 0
                if is_high == 0:
                    return low
        if is_high:
            if data['high'] >= data[i]:
                high = i
            else:
                is_high = 0
                if is_low == 0:
                    return high
    if is_high:
        return high
    elif is_low:
        return low
if __name__ == '__main__':
    dct = {'high': hs300.high, 'low': hs300.low}
    for i in x:
        dct.update(Second_Breakout(i))
    df = pd.DataFrame(dct).dropna()
    BreakDay = []
    for row in df.iterrows():
        BreakDay.append(day_range(row[1]))
    df['BreakDay'] = BreakDay
    fig,(ax,ax1) = plt.subplots(2,1, sharex=True)
    ax.plot(hs300.close)
    ax1.bar(df.index, df.BreakDay)
    plt.show()

1. MACD Histogram Replacement(Accelarating)
   加速度短期均线在长期上金叉确认时进场，加速度短期均线在长期下方死叉确认时出场。

macd = ta.abstract.MACD(hs)
macd = macd.dropna()
macd['sma_hist'] = ta.abstract.MA(macd, 40, price='macdhist').dropna()
macd['lma_hist'] = ta.abstract.MA(macd, 60, price='macdhist').dropna()
hist = macd.iterrows()
t0,d0 = next(hist)
x_b = []
y_b = []
x_s = []
y_s = []
pos = 0
for t1,d1 in hist:
    if pos == 0 and d1.sma_hist > d1.lma_hist and d0.sma_hist < d0.lma_hist:
        x_b.append(t1)
        y_b.append(d1.sma_hist)
        pos = 1
    elif pos == 1 and d1.sma_hist < d1.lma_hist and d0.sma_hist > d0.lma_hist:
        x_s.append(t1)
        y_s.append(d1.sma_hist)
        pos = 0
    t0, d0 = t1, d1
import matplotlib as mpl
mpl.rc('xtick', labelsize=8)
fig,(ax, ax1) = plt.subplots(2,1,sharex=True)
ax.plot(hs['close'])
ax1.plot(macd.sma_hist, 'y')
ax1.plot(macd.lma_hist, 'b')
ax1.scatter(x_b, y_b, c='r', marker='^', linewidths=3)
ax1.scatter(x_s, y_s, c='g', marker='v', linewidths=3)
plt.show()

3. Divergence Index(Divergence)
长期动能上涨，短期动能下跌。

$$\text{Divergence Index} = \frac{10_{day} momentum *40_{day}momentum}{(40_{day}\text{ standard deviation of price changes)}^2}$$
Enter when Divergence Index falls below –10 and mom40>0

import talib as ta
from fxdayu_data.data import MongoHandler
import matplotlib.pyplot as plt
mh = MongoHandler(db='Global_index')
hs300 = mh.read('hs300.D')
hs300['mom10'] = ta.abstract.MOM(hs300, 10, price='close')
hs300['mom40'] = ta.abstract.MOM(hs300, 40, price='close')
hs300['price_change'] = hs300.close.diff()
hs300['Var40'] = ta.abstract.VAR(hs300, 40, price='price_change')
hs300 = hs300.dropna()
Div_index = hs300.mom10*hs300.mom40/hs300.Var40
plt.subplot(3,1,1)
plt.plot(hs300.close)
plt.subplot(3,1,2)
plt.plot(Div_index)
plt.hlines(-10, hs300.index[0], hs300.index[-1], linestyles='dashed', alpha=0.5)
plt.subplot(3,1,3)
plt.plot(hs300.mom40)
plt.hlines(0, hs300.index[0], hs300.index[-1], linestyles='dashed', alpha=0.5)
plt.show()

1. Normalized Envelope Indicator(Ranking)
   用排序来确定阻力与支撑位。

import talib as ta
from fxdayu_data.data import MongoHandler
import matplotlib.pyplot as plt
import pandas as pd
mh = MongoHandler(db='Global_index')
hs300 = mh.read('hs300.D')
A = 25
hs300['MA50'] = ta.abstract.MA(hs300,50)
hs300['price_change'] = hs300.close.diff()
hs300['NEI'] = A * (hs300.close-hs300.MA50)/ta.abstract.STDDEV(hs300, 50, price='price_change')
hs300 = hs300.dropna()
def rank(df, price='NEI', gap=50, rank1=10, rank2=40):
    s = df[price]
    rank1 -= 1
    rank2 -= 1
    def resort(index):
        series = s.iloc[index-gap:index]
        value = sorted(series.values)
        return series.index[-1], value[rank1], value[rank2]
    df[['lowerband', 'upperband']] = pd.DataFrame(map(resort, range(gap, len(s)+1))).set_index(0)
    return df
hs300 = rank(hs300)
middleband = ta.abstract.MA(hs300, timeperiod=50)
upperband = middleband + hs300['upperband']
lowerband = middleband + hs300['lowerband']
data_B = pd.concat([middleband, upperband , lowerband], axis=1)
data_B.columns = ['middleband', 'upperband', 'lowerband']
print(data_B)
plt.plot(hs300['close'])
plt.plot(data_B['upperband'], 'g')
plt.plot(data_B['lowerband'], 'r')
plt.show()

1. Volume Reversal Strategy(Comparison)
   用成交量减少作为反转信号。