Python Zig Zag algorithm function not returning expected results
Question
Overview
I am trying to use this Python Zig Zag candlestick indicator (utilises High,Low,Close values) on financial data but the code below appears to have a bug.
Is there another working Python module that provides this functionality?
What is a Zig Zag indicator
The Zig Zag indicator plots points on the chart whenever prices reverse by a percentage greater than a pre-chosen variable.
What have I tried
While searching for a Python zigzag indicator for candlestick charts the only code I could find was from this pull request .
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_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_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
It can be utilised as follows:
pivots = peak_valley_pivots_candlestick(df.Close, df.High, df.Low ,.01,-.01)
The peak_valley_pivots_candlestick function is almost working as expected but with the following data there appears to be a bug in how the Pivots are calculated.
Data
The data below is a slice from the complete data set.
dict1 = {'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}}
Chart showing the issue
2019-01-03 should be the low pivot not 2019-01-04
Code to show the issue in a chart:
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()
Generally the function works as can be seen in this chart.
Edit. This is the code I used to create the Pivots and Pivot Price cols. Updating as per comment from @ands
df['Pivots'] = pivots df.loc[df['Pivots'] == 1, 'Pivot Price'] = df.High df.loc[df['Pivots'] == -1, 'Pivot Price'] = df.Low
1 answers
solution1
4 ACCPTED 2021-01-24 00:44:46
There is a small problem with Pivot Price column of df , your data set for_so.csv already contains column Pivot Price so you need to delete values in df['Pivot Price'] and set it to new values based on pivots .
I have used the following code to create the correct 'Pivots' and 'Pivot Price' columns:
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
The main problem is with the zigzag code. Function peak_valley_pivots_candlestick has two small error. In for loop when condition if r >= up_thresh: is true then last_pivot_x is set to x , but it should be set to high[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
It is the same with code in condition if r <= down_thresh: where last_pivot_x should be is set to low[t] instead of 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
And here is complete code:
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()
The code above produces this chart:
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