Python中的ZigZag指示符
Python中的ZigZag指示符
提问于 2020-06-18 23:56:05
我想为股票创建一个Z字形指标。我正在使用python，我的英语很差，所以我为此道歉。我的部分代码取自：Pandas: Zigzag segmentation of data based on local minima-maxima
问题是我想要的zigzag是这个(Metastock zigzag indicator)：
我的zigzag代码看起来像这样(请注意，您可以使用过滤器更改百分比)：
from pandas_datareader import data
import pandas as pd
from datetime import date
from pandas_datareader.nasdaq_trader import get_nasdaq_symbols
from scipy import signal
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
np.random.seed(0)
def filter(values, percentage):
previous = values[0]
mask = [True]
for value in values[1:]:
relative_difference = np.abs(value - previous)/previous
if relative_difference > percentage:
previous = value
mask.append(True)
else:
mask.append(False)
return mask
def main(stock=None, start_date=None, end_date=None):
df = data.DataReader(
stock,
start=start_date, end=end_date,
data_source='yahoo'
)
return df
if __name__ == '__main__':
today = '{}'.format(date.today())
stocks = ['BLL']
cont = 0
for stock in stocks:
cont += 1
try:
serie = main(stock=stock, start_date='2018-1-1', end_date=today)
serie.insert(loc=0, column='Date', value=serie.index)
serie = serie.reset_index(drop=True)
# Create zigzag trendline.
########################################
# Find peaks(max).
data_x = serie.index.values
data_y = serie['Close'].values
peak_indexes = signal.argrelextrema(data_y, np.greater)
peak_indexes = peak_indexes[0]
# Find valleys(min).
valley_indexes = signal.argrelextrema(data_y, np.less)
valley_indexes = valley_indexes[0]
# Merge peaks and valleys data points using pandas.
df_peaks = pd.DataFrame({'date': data_x[peak_indexes], 'zigzag_y': data_y[peak_indexes]})
df_valleys = pd.DataFrame({'date': data_x[valley_indexes], 'zigzag_y': data_y[valley_indexes]})
df_peaks_valleys = pd.concat([df_peaks, df_valleys], axis=0, ignore_index=True, sort=True)
# Sort peak and valley datapoints by date.
df_peaks_valleys = df_peaks_valleys.sort_values(by=['date'])
p = 0.1 # 20%
filter_mask = filter(df_peaks_valleys.zigzag_y, p)
filtered = df_peaks_valleys[filter_mask]
# Instantiate axes.
(fig, ax) = plt.subplots(figsize=(10,10))
# Plot zigzag trendline.
ax.plot(df_peaks_valleys['date'].values, df_peaks_valleys['zigzag_y'].values,
color='red', label="Extrema")
# Plot zigzag trendline.
ax.plot(filtered['date'].values, filtered['zigzag_y'].values,
color='blue', label="ZigZag")
# Plot original line.
ax.plot(data_x, data_y, linestyle='dashed', color='black', label="Org. line", linewidth=1)
plt.show()
print('{} - {}| success'.format(cont, stock))
except Exception:
print('{} - {}| ERROR'.format(cont, stock))回答 1
Stack Overflow用户
发布于 2020-06-24 15:16:26
以下是github上的一个示例：zigzag
cimport cython
import numpy as np
from numpy cimport ndarray, int_t
DEF PEAK = 1
DEF VALLEY = -1
@cython.boundscheck(False)
@cython.wraparound(False)
cpdef int_t identify_initial_pivot(double [:] X,
double up_thresh,
double down_thresh):
cdef:
double x_0 = X[0]
double x_t = x_0
double max_x = x_0
double min_x = x_0
int_t max_t = 0
int_t min_t = 0
up_thresh += 1
down_thresh += 1
for t in range(1, len(X)):
x_t = X[t]
if x_t / min_x >= up_thresh:
return VALLEY if min_t == 0 else PEAK
if x_t / max_x <= down_thresh:
return PEAK if max_t == 0 else VALLEY
if x_t > max_x:
max_x = x_t
max_t = t
if x_t < min_x:
min_x = x_t
min_t = t
t_n = len(X)-1
return VALLEY if x_0 < X[t_n] else PEAK
@cython.boundscheck(False)
@cython.wraparound(False)
cpdef peak_valley_pivots(double [:] X,
double up_thresh,
double down_thresh):
"""
Find the peaks and valleys of a series.
:param X: the series to analyze
:param up_thresh: minimum relative change necessary to define a peak
:param down_thesh: minimum relative change necessary to define a valley
:return: an array with 0 indicating no pivot and -1 and 1 indicating
valley and peak
The First and Last Elements
---------------------------
The first and last elements are guaranteed to be annotated as peak or
valley even if the segments formed do not have the necessary relative
changes. This is a tradeoff between technical correctness and the
propensity to make mistakes in data analysis. The possible mistake is
ignoring data outside the fully realized segments, which may bias
analysis.
"""
if down_thresh > 0:
raise ValueError('The down_thresh must be negative.')
cdef:
int_t initial_pivot = identify_initial_pivot(X,
up_thresh,
down_thresh)
int_t t_n = len(X)
ndarray[int_t, ndim=1] pivots = np.zeros(t_n, dtype=np.int_)
int_t trend = -initial_pivot
int_t last_pivot_t = 0
double last_pivot_x = X[0]
double x, r
pivots[0] = initial_pivot
# Adding one to the relative change thresholds saves operations. Instead
# of computing relative change at each point as x_j / x_i - 1, it is
# computed as x_j / x_1. Then, this value is compared to the threshold + 1.
# This saves (t_n - 1) subtractions.
up_thresh += 1
down_thresh += 1
for t in range(1, t_n):
x = X[t]
r = x / last_pivot_x
if trend == -1:
if r >= up_thresh:
pivots[last_pivot_t] = trend
trend = PEAK
last_pivot_x = x
last_pivot_t = t
elif x < last_pivot_x:
last_pivot_x = x
last_pivot_t = t
else:
if r <= down_thresh:
pivots[last_pivot_t] = trend
trend = VALLEY
last_pivot_x = x
last_pivot_t = t
elif x > last_pivot_x:
last_pivot_x = x
last_pivot_t = t
if last_pivot_t == t_n-1:
pivots[last_pivot_t] = trend
elif pivots[t_n-1] == 0:
pivots[t_n-1] = -trend
return pivots
@cython.boundscheck(False)
@cython.wraparound(False)
cpdef double max_drawdown(ndarray[double, ndim=1] X):
"""
Compute the maximum drawdown of some sequence.
:return: 0 if the sequence is strictly increasing.
otherwise the abs value of the maximum drawdown
of sequence X
"""
cdef:
double mdd = 0
double peak = X[0]
double x, dd
for x in X:
if x > peak:
peak = x
dd = (peak - x) / peak
if dd > mdd:
mdd = dd
return mdd if mdd != 0.0 else 0.0
@cython.boundscheck(False)
@cython.wraparound(False)
def pivots_to_modes(int_t [:] pivots):
"""
Translate pivots into trend modes.
:param pivots: the result of calling ``peak_valley_pivots``
:return: numpy array of trend modes. That is, between (VALLEY, PEAK] it
is 1 and between (PEAK, VALLEY] it is -1.
"""
cdef:
int_t x, t
ndarray[int_t, ndim=1] modes = np.zeros(len(pivots),
dtype=np.int_)
int_t mode = -pivots[0]
modes[0] = pivots[0]
for t in range(1, len(pivots)):
x = pivots[t]
if x != 0:
modes[t] = mode
mode = -x
else:
modes[t] = mode
return modes
def compute_segment_returns(X, pivots):
"""
:return: numpy array of the pivot-to-pivot returns for each segment."""
pivot_points = X[pivots != 0]
return pivot_points[1:] / pivot_points[:-1] - 1.0页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/62454181
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