Python之字形算法函数未返回预期结果
概述 我试图在财务数据上使用这个Python之字形烛台指示器（使用高、低、收盘值），但下面的代码似乎有一个bug 如果您能提供任何帮助来解决此问题，或者有其他Python模块提供此功能，请告知 什么是之字形指示器 “当价格反转的百分比大于预先选择的变量时，Z字形指示器在图表上绘制点。” 我试过什么 在为烛台图表搜索Python之字形指示器时，我能找到的唯一代码就是来自此Python之字形算法函数未返回预期结果,python,pandas,algorithm,numpy,Python,Pandas,Algorithm,Numpy,概述 我试图在财务数据上使用这个Python之字形烛台指示器（使用高、低、收盘值），但下面的代码似乎有一个bug 如果您能提供任何帮助来解决此问题，或者有其他Python模块提供此功能，请告知 什么是之字形指示器 “当价格反转的百分比大于预先选择的变量时，Z字形指示器在图表上绘制点。” 我试过什么 在为烛台图表搜索Python之字形指示器时，我能找到的唯一代码就是来自此 peak\u valley\u pivots\u candlestick函数几乎可以按预期工作，但使用以下数据，在如何计算支点
peak\u valley\u pivots\u candlestickdict1 = {'Date': {77: '2018-12-19',
78: '2018-12-20',
79: '2018-12-21',
80: '2018-12-24',
81: '2018-12-25',
82: '2018-12-26',
83: '2018-12-27',
84: '2018-12-28',
85: '2018-12-31',
86: '2019-01-01',
87: '2019-01-02',
88: '2019-01-03',
89: '2019-01-04',
90: '2019-01-07',
91: '2019-01-08',
92: '2019-01-09',
93: '2019-01-10',
94: '2019-01-11',
95: '2019-01-14',
96: '2019-01-15',
97: '2019-01-16',
98: '2019-01-17',
99: '2019-01-18',
100: '2019-01-21',
101: '2019-01-22',
102: '2019-01-23',
103: '2019-01-24',
104: '2019-01-25',
105: '2019-01-28',
106: '2019-01-29',
107: '2019-01-30',
108: '2019-01-31',
109: '2019-02-01',
110: '2019-02-04',
111: '2019-02-05'},
'Open': {77: 1.2654544115066528,
78: 1.2625147104263306,
79: 1.266993522644043,
80: 1.2650061845779421,
81: 1.2712942361831665,
82: 1.2689388990402222,
83: 1.2648460865020752,
84: 1.264606237411499,
85: 1.2689228057861328,
86: 1.275022268295288,
87: 1.2752337455749512,
88: 1.2518777847290041,
89: 1.2628973722457886,
90: 1.2732852697372437,
91: 1.2786905765533447,
92: 1.2738852500915527,
93: 1.2799508571624756,
94: 1.275835633277893,
95: 1.2849836349487305,
96: 1.2876144647598269,
97: 1.287282943725586,
98: 1.2884771823883057,
99: 1.298296570777893,
100: 1.2853471040725708,
101: 1.2892745733261108,
102: 1.2956725358963013,
103: 1.308318257331848,
104: 1.3112174272537231,
105: 1.3207770586013794,
106: 1.3159972429275513,
107: 1.308061599731445,
108: 1.311681866645813,
109: 1.3109252452850342,
110: 1.3078563213348389,
111: 1.3030844926834106},
'High': {77: 1.267909288406372,
78: 1.2705351114273071,
79: 1.269728422164917,
80: 1.273658275604248,
81: 1.277791976928711,
82: 1.2719732522964478,
83: 1.2671220302581787,
84: 1.2700024843215942,
85: 1.2813942432403564,
86: 1.2756729125976562,
87: 1.2773349285125732,
88: 1.2638230323791504,
89: 1.2739664316177368,
90: 1.2787723541259766,
91: 1.2792304754257202,
92: 1.2802950143814087,
93: 1.2801146507263184,
94: 1.2837464809417725,
95: 1.292774677276611,
96: 1.2916558980941772,
97: 1.2895737886428833,
98: 1.2939958572387695,
99: 1.299376368522644,
100: 1.2910722494125366,
101: 1.296714186668396,
102: 1.3080273866653442,
103: 1.3095861673355105,
104: 1.3176618814468384,
105: 1.3210039138793943,
106: 1.3196616172790527,
107: 1.311991572380066,
108: 1.3160665035247805,
109: 1.311475396156311,
110: 1.3098777532577517,
111: 1.3051422834396362},
'Low': {77: 1.2608431577682495,
78: 1.2615113258361816,
79: 1.2633600234985352,
80: 1.2636953592300415,
81: 1.266784906387329,
82: 1.266512155532837,
83: 1.261877417564392,
84: 1.2636473178863523,
85: 1.268182635307312,
86: 1.2714558839797974,
87: 1.2584631443023682,
88: 1.2518777847290041,
89: 1.261781930923462,
90: 1.2724264860153198,
91: 1.2714881896972656,
92: 1.271779179573059,
93: 1.273058295249939,
94: 1.2716660499572754,
95: 1.2821005582809448,
96: 1.2756240367889404,
97: 1.2827255725860596,
98: 1.2836146354675293,
99: 1.2892080545425415,
100: 1.2831699848175049,
101: 1.2855949401855469,
102: 1.2945822477340698,
103: 1.301371693611145,
104: 1.3063528537750244,
105: 1.313870549201965,
106: 1.313145875930786,
107: 1.3058068752288818,
108: 1.3101180791854858,
109: 1.3045804500579834,
110: 1.3042230606079102,
111: 1.2929919958114624},
'Close': {77: 1.2655024528503418,
78: 1.262785792350769,
79: 1.2669775485992432,
80: 1.2648941278457642,
81: 1.2710840702056885,
82: 1.2688745260238647,
83: 1.2648781538009644,
84: 1.2646220922470093,
85: 1.269357681274414,
86: 1.2738043069839478,
87: 1.2754288911819458,
88: 1.2521913051605225,
89: 1.2628813982009888,
90: 1.2734960317611694,
91: 1.278608798980713,
92: 1.2737879753112793,
93: 1.279967188835144,
94: 1.2753963470458984,
95: 1.2849836349487305,
96: 1.2874983549118042,
97: 1.2872166633605957,
98: 1.28857684135437,
99: 1.2983977794647217,
100: 1.2853471040725708,
101: 1.2891747951507568,
102: 1.295773148536682,
103: 1.308215618133545,
104: 1.3121638298034668,
105: 1.3208470344543457,
106: 1.3160146474838257,
107: 1.30804443359375,
108: 1.3117163181304932,
109: 1.3109424114227295,
110: 1.3077365159988403,
111: 1.3031013011932373},
'Pivots': {77: 0,
78: 0,
79: 0,
80: 0,
81: 0,
82: 0,
83: 0,
84: 0,
85: 1,
86: 0,
87: 0,
88: 0,
89: -1,
90: 0,
91: 0,
92: 0,
93: 0,
94: 0,
95: 0,
96: 0,
97: 0,
98: 0,
99: 0,
100: 0,
101: 0,
102: 0,
103: 0,
104: 0,
105: 1,
106: 0,
107: 0,
108: 0,
109: 0,
110: 0,
111: 0},
'Pivot Price': {77: nan,
78: nan,
79: nan,
80: nan,
81: nan,
82: nan,
83: nan,
84: nan,
85: 1.2813942432403564,
86: nan,
87: nan,
88: nan,
89: 1.261781930923462,
90: nan,
91: nan,
92: nan,
93: nan,
94: nan,
95: nan,
96: nan,
97: nan,
98: nan,
99: nan,
100: nan,
101: nan,
102: nan,
103: nan,
104: nan,
105: 1.3210039138793943,
106: nan,
107: nan,
108: nan,
109: nan,
110: nan,
111: nan}}
2019-01-032019-01-04pivots = peak_valley_pivots_candlestick(df.Close, df.High, df.Low ,.01,-.01)
import numpy as np
import plotly.graph_objects as go
import pandas as pd
from datetime import datetime
df = pd.DataFrame(dict1)
fig = go.Figure(data=[go.Candlestick(x=df['Date'],
open=df['Open'],
high=df['High'],
low=df['Low'],
close=df['Close'])])
df_diff = df['Pivot Price'].dropna().diff().copy()
fig.add_trace(
go.Scatter(mode = "lines+markers",
x=df['Date'],
y=df["Pivot Price"]
))
fig.update_layout(
autosize=False,
width=1000,
height=800,)
fig.add_trace(go.Scatter(x=df['Date'], y=df['Pivot Price'].interpolate(),
mode = 'lines',
line = dict(color='black')))
def annot(value):
if np.isnan(value):
return ''
else:
return value
j = 0
for i, p in enumerate(df['Pivot Price']):
if not np.isnan(p):
fig.add_annotation(dict(font=dict(color='rgba(0,0,200,0.8)',size=12),
x=df['Date'].iloc[i],
y=p,
showarrow=False,
text=annot(round(abs(df_diff.iloc[j]),3)),
textangle=0,
xanchor='right',
xref="x",
yref="y"))
j = j + 1
fig.update_xaxes(type='category')
fig.show()
编辑。这是我用来创建
PivotsPivot Pricedf['Pivots']=Pivots-df.loc[df['Pivots']==1，“Pivot-Price']=df.High-df.loc[df['Pivots']==1，“Pivot-Price']=df.Lowdf的
Pivot Price已经包含Pivot Price
列，因此您需要删除df['Pivot Price']
中的值，并根据Pivot将其设置为新值
我已使用以下代码创建了正确的'Pivots'
和'Pivot Price'
列：
pivots = peak_valley_pivots_candlestick(df.Close, df.High, df.Low ,.01,-.01)
df['Pivots'] = pivots
df['Pivot Price'] = np.nan # This line clears old pivot prices
df.loc[df['Pivots'] == 1, 'Pivot Price'] = df.High
df.loc[df['Pivots'] == -1, 'Pivot Price'] = df.Low
主要问题在于锯齿形代码。函数peak\u valley\u pivots\u candlestick
有两个小错误。在for循环中，当条件如果r>=up\u thresh:
为真时，则最后一个轴x
设置为x
，但应设置为高[t]
if r >= up_thresh:
pivots[last_pivot_t] = trend#
trend = 1
#last_pivot_x = x
last_pivot_x = high[t]
last_pivot_t = t
如果r 0:
raise VALUE ERROR（'下降阈值必须为负值'）
初始_轴=_识别_初始_轴（关闭、上_脱粒、下_脱粒）
t_n=len（关闭）
pivots=np.zero（t_n，dtype='i1'）
支点[0]=初始支点
#将一个添加到相对更改阈值可以保存操作。相反
#用x_j/x_i-1计算每个点的相对变化，它是
#计算为x_j/x_1。然后，将该值与阈值+1进行比较。
#这节省了（t_n-1）减法。
向上_阈值+=1
向下_阈值+=1
趋势=-初始趋势
最后一个轴t=0
最后一个轴=关闭[0]
对于范围（1，len（close））内的t：
如果趋势==-1：
x=低[t]
r=x/最后一个轴
如果r>=向上脱粒：
支点[最后支点]=趋势#
趋势=1
#最后一个轴x=x
最后一个轴=高[t]
最后一个轴t=t
elif x<最后一个轴：
最后一个轴x=x
最后一个轴t=t
其他：
x=高[t]
r=x/最后一个轴
如果r最后一个轴x：
最后一个轴x=x
最后一个轴t=t
如果最后一个轴t==t\u n-1：
支点[最后支点]=趋势
elif枢轴[t_n-1]==0：
支点[t_n-1]=趋势
返回枢轴
df=pd.read\u csv（'for\u so.csv'）
支点=峰谷支点(df.Close,df.High,df.Low,df.01,df.01)
df['Pivots']=支点
df['Pivot Price']=np.nan#此行清除旧的Pivot Price
df.loc[df['Pivots']=1，“Pivot Price']=df.High
df.loc[df['Pivots']=-1，'Pivot Price']=df.Low
fig=go.Figure（数据=[go.Candlestick（x=df['Date']），图，
打开=df[‘打开’]，
高=df[‘高’]，
低=df[‘低’]，
close=df['close']））
df_diff=df['Pivot Price'].dropna（）.diff（）.copy（）
图1添加_轨迹(
go.Scatter（mode=“行+标记”，
x=df[“日期”]，
y=df[“核心价格”]
))
图1.2.2.1.1.1.1.1.1.2.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.2.2.2.2.2.2.2.1.1.1(
autosize=False，
宽度=1000，
高度=800，）
图添加轨迹（去散点（x=df['Date']），
y=df[‘枢轴价格’]。插值（），
模式='行'，
line=dict（color='black'））
def annot（值）：
如果np.isnan（值）：
返回“”
其他：
返回值
j=0
对于枚举中的i，p（df['Pivot Price']）：
如果不是np.isnan（p）：
图添加注释（dict（font=dict（color='rgba（0,0200,0.8）），大小=12），
x=df['Date'].iloc[i]，
y=p，
showarrow=False，
text=annot（圆形（abs（df_diff.iloc[j]），3）），
textangle=0，
xanchor='right'，
xref=“x”，
yref=“y”））
j=j+1
图更新_xaxes（type='category'）
图2（图3）
上面的代码生成此图表：
谢谢你的帮助。我看到你已经将这部分数据的索引设置为Datetime
，这是我的一个子集。当我在完整数据上尝试你的建议答案时，问题仍然存在，2019-01-03
应该是低轴，而不是2019-01-04
。这是我用来创建Pivots
和pivot Price
的代码它返回的结果与Lambdadf['Pivots']=Pivots-df.loc[df['Pivots']==1，“Pivot-Price']=df.High-df.loc[df['Pivots']=-1，“Pivot-Price']=df.Lowif r >= up_thresh:
pivots[last_pivot_t] = trend#
trend = 1
#last_pivot_x = x
last_pivot_x = high[t]
last_pivot_t = t
if r <= down_thresh:
pivots[last_pivot_t] = trend
trend = -1
#last_pivot_x = x
last_pivot_x = low[t]
last_pivot_t = t
import numpy as np
import plotly.graph_objects as go
import pandas as pd
PEAK, VALLEY = 1, -1
def _identify_initial_pivot(X, up_thresh, down_thresh):
"""Quickly identify the X[0] as a peak or valley."""
x_0 = X[0]
max_x = x_0
max_t = 0
min_x = x_0
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
def peak_valley_pivots_candlestick(close, high, low, up_thresh, down_thresh):
"""
Finds the peaks and valleys of a series of HLC (open is not necessary).
TR: This is modified peak_valley_pivots function in order to find peaks and valleys for OHLC.
Parameters
----------
close : This is series with closes prices.
high : This is series with highs prices.
low : This is series with lows prices.
up_thresh : The minimum relative change necessary to define a peak.
down_thesh : The minimum relative change necessary to define a valley.
Returns
-------
an array with 0 indicating no pivot and -1 and 1 indicating valley and peak
respectively
Using Pandas
------------
For the most part, close, high and low may be a pandas series. However, the index must
either be [0,n) or a DateTimeIndex. Why? This function does X[t] to access
each element where t is in [0,n).
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.')
initial_pivot = _identify_initial_pivot(close, up_thresh, down_thresh)
t_n = len(close)
pivots = np.zeros(t_n, dtype='i1')
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
trend = -initial_pivot
last_pivot_t = 0
last_pivot_x = close[0]
for t in range(1, len(close)):
if trend == -1:
x = low[t]
r = x / last_pivot_x
if r >= up_thresh:
pivots[last_pivot_t] = trend#
trend = 1
#last_pivot_x = x
last_pivot_x = high[t]
last_pivot_t = t
elif x < last_pivot_x:
last_pivot_x = x
last_pivot_t = t
else:
x = high[t]
r = x / last_pivot_x
if r <= down_thresh:
pivots[last_pivot_t] = trend
trend = -1
#last_pivot_x = x
last_pivot_x = low[t]
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
df = pd.read_csv('for_so.csv')
pivots = peak_valley_pivots_candlestick(df.Close, df.High, df.Low ,.01,-.01)
df['Pivots'] = pivots
df['Pivot Price'] = np.nan # This line clears old pivot prices
df.loc[df['Pivots'] == 1, 'Pivot Price'] = df.High
df.loc[df['Pivots'] == -1, 'Pivot Price'] = df.Low
fig = go.Figure(data=[go.Candlestick(x=df['Date'],
open=df['Open'],
high=df['High'],
low=df['Low'],
close=df['Close'])])
df_diff = df['Pivot Price'].dropna().diff().copy()
fig.add_trace(
go.Scatter(mode = "lines+markers",
x=df['Date'],
y=df["Pivot Price"]
))
fig.update_layout(
autosize=False,
width=1000,
height=800,)
fig.add_trace(go.Scatter(x=df['Date'],
y=df['Pivot Price'].interpolate(),
mode = 'lines',
line = dict(color='black')))
def annot(value):
if np.isnan(value):
return ''
else:
return value
j = 0
for i, p in enumerate(df['Pivot Price']):
if not np.isnan(p):
fig.add_annotation(dict(font=dict(color='rgba(0,0,200,0.8)',size=12),
x=df['Date'].iloc[i],
y=p,
showarrow=False,
text=annot(round(abs(df_diff.iloc[j]),3)),
textangle=0,
xanchor='right',
xref="x",
yref="y"))
j = j + 1
fig.update_xaxes(type='category')
fig.show()