def trendBreak(pdDataFrame):
"""
trendBreak based on provdied market data
"""
from abupy import pd_rolling_max
from abupy import pd_expanding_max
# 当天收盘价格超过N1天内最高价格作为买入信号
N1 = 20
# 当天收盘价格超过N2天内最低价格作为卖出信号
N2 = 15
kl_pd = pdDataFrame
# 通过rolling_max方法计算最近N1个交易日的最高价
# kl_pd['n1_high'] = pd.rolling_max(kl_pd['high'], window=N1)
kl_pd['n1_high'] = pd_rolling_max(kl_pd['high'], window=N1)
# 表7-4所示
# expanding_max
# expan_max = pd.expanding_max(kl_pd['close'])
expan_max = pd_expanding_max(kl_pd['close'])
# fillna使用序列对应的expan_max
kl_pd['n1_high'].fillna(value=expan_max, inplace=True)
# 表7-5所示
#print('kl_pd[0:5]:\n', kl_pd[0:5])
from abupy import pd_rolling_min, pd_expanding_min
# 通过rolling_min方法计算最近N2个交易日的最低价格
# rolling_min与rolling_max类似
# kl_pd['n2_low'] = pd.rolling_min(kl_pd['low'], window=N2)
kl_pd['n2_low'] = pd_rolling_min(kl_pd['low'], window=N2)
# expanding_min与expanding_max类似
# expan_min = pd.expanding_min(kl_pd['close'])
expan_min = pd_expanding_min(kl_pd['close'])
# fillna使用序列对应的eexpan_min
kl_pd['n2_low'].fillna(value=expan_min, inplace=True)
# 当天收盘价格超过N天内的最高价或最低价, 超过最高价格作为买入信号买入股票持有
buy_index = kl_pd[kl_pd['close'] > kl_pd['n1_high'].shift(1)].index
kl_pd.loc[buy_index, 'signal'] = 1
# 当天收盘价格超过N天内的最高价或最低价, 超过最低价格作为卖出信号
sell_index = kl_pd[kl_pd['close'] < kl_pd['n2_low'].shift(1)].index
kl_pd.loc[sell_index, 'signal'] = 0
#kl_pd.signal.value_counts().plot(kind='pie', figsize=(5, 5))
#plt.show()
"""
将信号操作序列移动一个单位，代表第二天再将操作信号执行，转换得到持股状态
这里不shift(1)也可以，代表信号产生当天执行，但是由于收盘价格是在收盘后
才确定的，计算突破使用了收盘价格，所以使用shift(1)更接近真实情况
"""
kl_pd['keep'] = kl_pd['signal'].shift(1)
kl_pd['keep'].fillna(method='ffill', inplace=True)
return kl_pd
def MINMACACalculate(sample):
rate = 0.015
sample['EMA12'] = QA.EMA(sample.close, 12)
sample['EMA5'] = QA.EMA(sample.close, 5)
sample['MA64'] = QA.MA(sample.close, 64)
sample['MA256'] = QA.MA(sample.close, 256)
sample['EMA20'] = QA.EMA(sample.close, 20)
sample['k1'] = 0.618 * QA.HHV(sample.high, 256) + 0.382 * QA.LLV(
sample.low, 256)
sample['k2'] = 0.5 * QA.HHV(sample.high, 256) + 0.5 * QA.LLV(
sample.low, 256)
sample['k3'] = 0.382 * QA.HHV(sample.high, 256) + 0.618 * QA.LLV(
sample.low, 256)
sample['EMA30'] = QA.EMA(sample.close, 30)
sample['EMA13'] = QA.EMA(sample.close, 13)
sample['optimism'] = sample.high - sample.EMA13
sample['pessmist'] = sample.low - sample.EMA13
sample['up'] = sample.EMA13 * (1 + rate)
sample['down'] = sample.EMA13 * (1 - rate)
sample['EMA26'] = QA.EMA(sample.close, 26)
sample['MACDQ'] = sample['EMA12'] - sample['EMA26']
sample['MACDSIG'] = QA.EMA(sample['MACDQ'], 9)
sample['MACDBlock'] = sample['MACDQ'] - sample['MACDSIG']
sample['VolumeEMA'] = QA.EMA(sample.volume, 5)
#sample['VolumeEMA'] = QA.EMA(sample.vol, 5)
#trend block
from abupy import pd_rolling_max
from abupy import pd_expanding_max
N = 15
sample['nhigh'] = pd_rolling_max(sample.high, window=N)
expanmax = pd_expanding_max(sample.close)
sample['nhigh'].fillna(value=expanmax, inplace=True)
from abupy import pd_rolling_min, pd_expanding_min
sample['nlow'] = pd_rolling_min(sample.low, window=N)
expanmin = pd_expanding_min(sample.close)
sample['nlow'].fillna(value=expanmin, inplace=True)
sroc = []
for i in range(sample.shape[0]):
if (i - 21 > 0 and sample.iloc[i].EMA13 != None
and sample.iloc[i - 21].EMA13 != None):
# print(sample.iloc[i].EMA13/sample.iloc[i-21].EMA13)
sroc.append(
(sample.iloc[i].EMA13 / sample.iloc[i - 21].EMA13) * 100)
else:
sroc.append(100)
sample['SROC'] = sroc
return sample
def TrendFinder(day, short=20, mid=60, long=120):
#20, 30 , 60 5.3/10
# 20, 60, 120 5.0/10
#10, 20, 60 6.7/10
#10, 30, 60 5.7/10
day['long'] = QA.EMA(day.close, long)
day['mid'] = QA.EMA(day.close, mid)
day['short'] = QA.EMA(day.close, short)
day['BIAS'] = (day.close - day.long) * 100 / day.long
day['BMA'] = QA.EMA(day.BIAS, short)
day['CS'] = (day.close - day.short) * 100 / day.short
day['SM'] = (day.short - day.mid) * 100 / day.mid
day['ML'] = (day.mid - day.long) * 100 / day.long
day['threshold'] = pd_rolling_max(day.BIAS, window=2 * long)
day['nthreshold'] = pd_rolling_min(day.BIAS, window=2 * long)
sig = []
buy = 0
sell = 0
for i in range(day.shape[0]):
if (day.BIAS[i] >= 0):
flag = day.BIAS[i] / day.threshold[i]
else:
flag = day.BIAS[i] / day.nthreshold[i]
if (day.CS[i] > 0 and day.SM[i] > 0 and buy == 0):
sig.append(1)
buy = 1
sell = 0
elif (day.CS[i] < 0 and day.SM[i] < 0 and sell == 1):
sig.append(3)
sell = 1
buy = 0
elif (day.CS[i] < 0 and day.BIAS[i] < day.BMA[i] and sell == 0):
sig.append(5)
buy = 0
sell = 1
else:
sig.append(0)
day['single'] = sig
# day['single'] = [0]+sig[:-1]
return day
def trendBreak(pdDataFrame):
from abupy import pd_rolling_max
from abupy import pd_expanding_max
# 当天收盘价格超过N1天内最高价格作为买入信号
N1 = 40
# 当天收盘价格超过N2天内最低价格作为卖出信号
N2 = 20
kl_pd = pdDataFrame
# 通过rolling_max方法计算最近N1个交易日的最高价
# kl_pd['n1_high'] = pd.rolling_max(kl_pd['high'], window=N1)
kl_pd['n1_high'] = pd_rolling_max(kl_pd['high'], window=N1)
# 表7-4所示
# expanding_max
# expan_max = pd.expanding_max(kl_pd['close'])
expan_max = pd_expanding_max(kl_pd['close'])
# fillna使用序列对应的expan_max
kl_pd['n1_high'].fillna(value=expan_max, inplace=True)
# 表7-5所示
# print('kl_pd[0:5]:\n', kl_pd[0:5])
from abupy import pd_rolling_min, pd_expanding_min
# 通过rolling_min方法计算最近N2个交易日的最低价格
# rolling_min与rolling_max类似
# kl_pd['n2_low'] = pd.rolling_min(kl_pd['low'], window=N2)
kl_pd['n2_low'] = pd_rolling_min(kl_pd['low'], window=N2)
# expanding_min与expanding_max类似
# expan_min = pd.expanding_min(kl_pd['close'])
expan_min = pd_expanding_min(kl_pd['close'])
# fillna使用序列对应的eexpan_min
kl_pd['n2_low'].fillna(value=expan_min, inplace=True)
cb = QA.CROSS(kl_pd.close,kl_pd.n1_high)
cs = QA.CROSS(kl_pd.n2_low,kl_pd.close)
ss = np.where(cs==1,3,0)
bs = np.where(cb==1,1,ss)
sig = [0]+bs[:-1].tolist()
kl_pd['single'] = sig
return kl_pd
# plt.axhline(close_mean,color='black',lw=1) # plt.axhline(sell_signal,color='g',lw=3) # plt.legend(['train close','buy_signal','close_mean','sell_signal'], loc='best') # # buy_index = train_kl[train_kl['close']< buy_signal].index # train_kl.loc[buy_index,'signal'] =1 #趋势跟踪策略 #当天收盘价超过N1天内最高价格作为买入信号 N1 = 42 #当天收盘价超过N2天内最低价格作为卖出信号 N2 = 21 kl_pd['n1_high'] = pd_rolling_max(kl_pd['high'], window=N1) kl_pd['n2_low'] = pd_rolling_min(kl_pd['low'], window=N2) expan_min = pd_expanding_min(kl_pd['close']) kl_pd['n2_low'].fillna(value=expan_min, inplace=True) buy_index = kl_pd[kl_pd['close'] > kl_pd['n1_high'].shift(1)].index kl_pd.loc[buy_index, 'signal'] = 1 sell_index = kl_pd[kl_pd['close'] < kl_pd['n2_low'].shift(1)].index kl_pd.loc[sell_index, 'signal'] = 0 kl_pd['keep'] = kl_pd['signal'].shift(1) kl_pd['keep'].fillna(method='ffill', inplace=True) #计算基准收益 kl_pd['benchmark_profit'] = np.log(kl_pd['close'] / kl_pd['close'].shift(1)) #计算使用趋势突破策略的收益
def sample_713():
"""
7.1.3 趋势跟踪策略
:return:
"""
# rolling_max示例序列
demo_list = np.array([1, 2, 1, 1, 100, 1000])
# 对示例序列以3个为一组，寻找每一组中的最大值
from abupy import pd_rolling_max
# print('pd.rolling_max(demo_list, window=3):', pd.rolling_max(demo_list, window=3))
print('pd.rolling_max(demo_list, window=3):', pd_rolling_max(demo_list, window=3))
from abupy import pd_expanding_max
# expanding_max示例序列
demo_list = np.array([1, 2, 1, 1, 100, 1000])
# print('pd.expanding_max(demo_list):', pd.expanding_max(demo_list))
print('pd.expanding_max(demo_list):', pd_expanding_max(demo_list))
# 当天收盘价格超过N1天内最高价格作为买入信号
N1 = 42
# 当天收盘价格超过N2天内最低价格作为卖出信号
N2 = 21
# 通过rolling_max方法计算最近N1个交易日的最高价
# kl_pd['n1_high'] = pd.rolling_max(kl_pd['high'], window=N1)
kl_pd['n1_high'] = pd_rolling_max(kl_pd['high'], window=N1)
# 表7-4所示
print('kl_pd[0:5]:\n', kl_pd[0:5])
# expanding_max
# expan_max = pd.expanding_max(kl_pd['close'])
expan_max = pd_expanding_max(kl_pd['close'])
# fillna使用序列对应的expan_max
kl_pd['n1_high'].fillna(value=expan_max, inplace=True)
# 表7-5所示
print('kl_pd[0:5]:\n', kl_pd[0:5])
from abupy import pd_rolling_min, pd_expanding_min
# 通过rolling_min方法计算最近N2个交易日的最低价格
# rolling_min与rolling_max类似
# kl_pd['n2_low'] = pd.rolling_min(kl_pd['low'], window=N2)
kl_pd['n2_low'] = pd_rolling_min(kl_pd['low'], window=N2)
# expanding_min与expanding_max类似
# expan_min = pd.expanding_min(kl_pd['close'])
expan_min = pd_expanding_min(kl_pd['close'])
# fillna使用序列对应的eexpan_min
kl_pd['n2_low'].fillna(value=expan_min, inplace=True)
# 当天收盘价格超过N天内的最高价或最低价, 超过最高价格作为买入信号买入股票持有
buy_index = kl_pd[kl_pd['close'] > kl_pd['n1_high'].shift(1)].index
kl_pd.loc[buy_index, 'signal'] = 1
# 当天收盘价格超过N天内的最高价或最低价, 超过最低价格作为卖出信号
sell_index = kl_pd[kl_pd['close'] < kl_pd['n2_low'].shift(1)].index
kl_pd.loc[sell_index, 'signal'] = 0
kl_pd.signal.value_counts().plot(kind='pie', figsize=(5, 5))
plt.show()
"""
将信号操作序列移动一个单位，代表第二天再将操作信号执行，转换得到持股状态
这里不shift(1)也可以，代表信号产生当天执行，但是由于收盘价格是在收盘后
才确定的，计算突破使用了收盘价格，所以使用shift(1)更接近真实情况
"""
kl_pd['keep'] = kl_pd['signal'].shift(1)
kl_pd['keep'].fillna(method='ffill', inplace=True)
# 计算基准收益
kl_pd['benchmark_profit'] = np.log(
kl_pd['close'] / kl_pd['close'].shift(1))
# 计算使用趋势突破策略的收益
kl_pd['trend_profit'] = kl_pd['keep'] * kl_pd['benchmark_profit']
# 可视化收益的情况对比
kl_pd[['benchmark_profit', 'trend_profit']].cumsum().plot(grid=True,
figsize=(
14, 7))
plt.show()