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supertrend indicator plot python

Wongsakorn Sanwises
5 min readAug 22, 2021

ตัวอย่างใช้โบรค FTX เชื่อมด้วย library CCXT

Parabolic SAR Python Trading

ccxt เป็น library ที่ทำให้เราเชื่อมต่อโบรค Crypto ได้กว่า 130 โบรคในที่นี้จะใช้ ccxt เชื่อมเข้ากับ FTX…

paohuee.medium.com

วิธีดึงข้อมูล Historical Data ตลาดไทย ด้วย Yahoo Finance

สร้างระบบเทรด MACD ตลาดหุ้นไทยด้วย python

กลยุทธ์ซื้อ-ขายตลาดหุ้นไทยด้วย MACD โดยการใช้ python

paohuee.medium.com

เรียกใช้ library

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport requestsfrom math import floorfrom termcolor import colored as climport ccxtplt.style.use('fivethirtyeight')plt.rcParams['figure.figsize'] = (20,10)

สร้าง functions เรียกราคาจาก FTX

ftx = ccxt.ftx({‘api_key’: ‘************************’,
‘secret’: ‘*************************’})
symbol = 'SOL/USD'
tf = '1h'
n = 500def get_historical_data(n,timeframe,product):
    ohlc = ftx.fetch_ohlcv(product,timeframe=timeframe,limit=n)
    ohlc_df = pd.DataFrame(ohlc, columns =['datetime', 'open','high','low','close','volume'])
    ohlc_df['datetime'] = pd.to_datetime(ohlc_df['datetime'], unit='ms')
    return ohlc_df.set_index('datetime')

สร้าง function คำนวณ Supertrend Indicator จาก historical data

def get_supertrend(high, low, close, lookback, multiplier):
    
    # ATR
    
    tr1 = pd.DataFrame(high - low)
    tr2 = pd.DataFrame(abs(high - close.shift(1)))
    tr3 = pd.DataFrame(abs(low - close.shift(1)))
    frames = [tr1, tr2, tr3]
    tr = pd.concat(frames, axis = 1, join = 'inner').max(axis = 1)
    atr = tr.ewm(lookback).mean()
    
    # H/L AVG AND BASIC UPPER & LOWER BAND
    
    hl_avg = (high + low) / 2
    upper_band = (hl_avg + multiplier * atr).dropna()
    lower_band = (hl_avg - multiplier * atr).dropna()
    
    # FINAL UPPER BAND
    final_bands = pd.DataFrame(columns = ['upper', 'lower'])
    final_bands.iloc[:,0] = [x for x in upper_band - upper_band]
    final_bands.iloc[:,1] = final_bands.iloc[:,0]
    for i in range(len(final_bands)):
        if i == 0:
            final_bands.iloc[i,0] = 0
        else:
            if (upper_band[i] < final_bands.iloc[i-1,0]) | (close[i-1] > final_bands.iloc[i-1,0]):
                final_bands.iloc[i,0] = upper_band[i]
            else:
                final_bands.iloc[i,0] = final_bands.iloc[i-1,0]
    
    # FINAL LOWER BAND
    
    for i in range(len(final_bands)):
        if i == 0:
            final_bands.iloc[i, 1] = 0
        else:
            if (lower_band[i] > final_bands.iloc[i-1,1]) | (close[i-1] < final_bands.iloc[i-1,1]):
                final_bands.iloc[i,1] = lower_band[i]
            else:
                final_bands.iloc[i,1] = final_bands.iloc[i-1,1]
    
    # SUPERTREND
    
    supertrend = pd.DataFrame(columns = [f'supertrend_{lookback}'])
    supertrend.iloc[:,0] = [x for x in final_bands['upper'] - final_bands['upper']]
    
    for i in range(len(supertrend)):
        if i == 0:
            supertrend.iloc[i, 0] = 0
        elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 0] and close[i] < final_bands.iloc[i, 0]:
            supertrend.iloc[i, 0] = final_bands.iloc[i, 0]
        elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 0] and close[i] > final_bands.iloc[i, 0]:
            supertrend.iloc[i, 0] = final_bands.iloc[i, 1]
        elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 1] and close[i] > final_bands.iloc[i, 1]:
            supertrend.iloc[i, 0] = final_bands.iloc[i, 1]
        elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 1] and close[i] < final_bands.iloc[i, 1]:
            supertrend.iloc[i, 0] = final_bands.iloc[i, 0]
    
    supertrend = supertrend.set_index(upper_band.index)
    supertrend = supertrend.dropna()[1:]
    
    # ST UPTREND/DOWNTREND
    
    upt = []
    dt = []
    close = close.iloc[len(close) - len(supertrend):]for i in range(len(supertrend)):
        if close[i] > supertrend.iloc[i, 0]:
            upt.append(supertrend.iloc[i, 0])
            dt.append(np.nan)
        elif close[i] < supertrend.iloc[i, 0]:
            upt.append(np.nan)
            dt.append(supertrend.iloc[i, 0])
        else:
            upt.append(np.nan)
            dt.append(np.nan)
            
    st, upt, dt = pd.Series(supertrend.iloc[:, 0]), pd.Series(upt), pd.Series(dt)
    upt.index, dt.index = supertrend.index, supertrend.index
    
    return st, upt, dtdef implement_st_strategy(prices, st):
    buy_price = []
    sell_price = []
    st_signal = []
    signal = 0
    
    for i in range(len(st)):
        if st[i-1] > prices[i-1] and st[i] < prices[i]:
            if signal != 1:
                buy_price.append(prices[i])
                sell_price.append(np.nan)
                signal = 1
                st_signal.append(signal)
            else:
                buy_price.append(np.nan)
                sell_price.append(np.nan)
                st_signal.append(0)
        elif st[i-1] < prices[i-1] and st[i] > prices[i]:
            if signal != -1:
                buy_price.append(np.nan)
                sell_price.append(prices[i])
                signal = -1
                st_signal.append(signal)
            else:
                buy_price.append(np.nan)
                sell_price.append(np.nan)
                st_signal.append(0)
        else:
            buy_price.append(np.nan)
            sell_price.append(np.nan)
            st_signal.append(0)
            
    return buy_price, sell_price, st_signal

เรียก plot

Full Code

Google Colaboratory

Edit description

colab.research.google.com

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import requests
from math import floor
from termcolor import colored as cl
import ccxtplt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (20,10)ftx = ccxt.ftx({
 'api_key': '',
 'secret': ''})def get_historical_data(n,timeframe,product):
    ohlc = ftx.fetch_ohlcv(product,timeframe=timeframe,limit=n)
    ohlc_df = pd.DataFrame(ohlc, columns =['datetime', 'open','high','low','close','volume'])
    ohlc_df['datetime'] = pd.to_datetime(ohlc_df['datetime'], unit='ms')
    return ohlc_df.set_index('datetime')def get_supertrend(high, low, close, lookback, multiplier):
    
    # ATR
    
    tr1 = pd.DataFrame(high - low)
    tr2 = pd.DataFrame(abs(high - close.shift(1)))
    tr3 = pd.DataFrame(abs(low - close.shift(1)))
    frames = [tr1, tr2, tr3]
    tr = pd.concat(frames, axis = 1, join = 'inner').max(axis = 1)
    atr = tr.ewm(lookback).mean()
    
    # H/L AVG AND BASIC UPPER & LOWER BAND
    
    hl_avg = (high + low) / 2
    upper_band = (hl_avg + multiplier * atr).dropna()
    lower_band = (hl_avg - multiplier * atr).dropna()
    
    # FINAL UPPER BAND
    final_bands = pd.DataFrame(columns = ['upper', 'lower'])
    final_bands.iloc[:,0] = [x for x in upper_band - upper_band]
    final_bands.iloc[:,1] = final_bands.iloc[:,0]
    for i in range(len(final_bands)):
        if i == 0:
            final_bands.iloc[i,0] = 0
        else:
            if (upper_band[i] < final_bands.iloc[i-1,0]) | (close[i-1] > final_bands.iloc[i-1,0]):
                final_bands.iloc[i,0] = upper_band[i]
            else:
                final_bands.iloc[i,0] = final_bands.iloc[i-1,0]
    
    # FINAL LOWER BAND
    
    for i in range(len(final_bands)):
        if i == 0:
            final_bands.iloc[i, 1] = 0
        else:
            if (lower_band[i] > final_bands.iloc[i-1,1]) | (close[i-1] < final_bands.iloc[i-1,1]):
                final_bands.iloc[i,1] = lower_band[i]
            else:
                final_bands.iloc[i,1] = final_bands.iloc[i-1,1]
    
    # SUPERTREND
    
    supertrend = pd.DataFrame(columns = [f'supertrend_{lookback}'])
    supertrend.iloc[:,0] = [x for x in final_bands['upper'] - final_bands['upper']]
    
    for i in range(len(supertrend)):
        if i == 0:
            supertrend.iloc[i, 0] = 0
        elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 0] and close[i] < final_bands.iloc[i, 0]:
            supertrend.iloc[i, 0] = final_bands.iloc[i, 0]
        elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 0] and close[i] > final_bands.iloc[i, 0]:
            supertrend.iloc[i, 0] = final_bands.iloc[i, 1]
        elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 1] and close[i] > final_bands.iloc[i, 1]:
            supertrend.iloc[i, 0] = final_bands.iloc[i, 1]
        elif supertrend.iloc[i-1, 0] == final_bands.iloc[i-1, 1] and close[i] < final_bands.iloc[i, 1]:
            supertrend.iloc[i, 0] = final_bands.iloc[i, 0]
    
    supertrend = supertrend.set_index(upper_band.index)
    supertrend = supertrend.dropna()[1:]
    
    # ST UPTREND/DOWNTREND
    
    upt = []
    dt = []
    close = close.iloc[len(close) - len(supertrend):]for i in range(len(supertrend)):
        if close[i] > supertrend.iloc[i, 0]:
            upt.append(supertrend.iloc[i, 0])
            dt.append(np.nan)
        elif close[i] < supertrend.iloc[i, 0]:
            upt.append(np.nan)
            dt.append(supertrend.iloc[i, 0])
        else:
            upt.append(np.nan)
            dt.append(np.nan)
            
    st, upt, dt = pd.Series(supertrend.iloc[:, 0]), pd.Series(upt), pd.Series(dt)
    upt.index, dt.index = supertrend.index, supertrend.index
    
    return st, upt, dtdef implement_st_strategy(prices, st):
    buy_price = []
    sell_price = []
    st_signal = []
    signal = 0
    
    for i in range(len(st)):
        if st[i-1] > prices[i-1] and st[i] < prices[i]:
            if signal != 1:
                buy_price.append(prices[i])
                sell_price.append(np.nan)
                signal = 1
                st_signal.append(signal)
            else:
                buy_price.append(np.nan)
                sell_price.append(np.nan)
                st_signal.append(0)
        elif st[i-1] < prices[i-1] and st[i] > prices[i]:
            if signal != -1:
                buy_price.append(np.nan)
                sell_price.append(prices[i])
                signal = -1
                st_signal.append(signal)
            else:
                buy_price.append(np.nan)
                sell_price.append(np.nan)
                st_signal.append(0)
        else:
            buy_price.append(np.nan)
            sell_price.append(np.nan)
            st_signal.append(0)
            
    return buy_price, sell_price, st_signalsymbol = 'SOL/USD'
tf = '1h'
n = 500OHLC = get_historical_data(n,tf,symbol)
OHLCOHLC['st'], OHLC['s_upt'], OHLC['st_dt'] = get_supertrend(OHLC['high'], OHLC['low'], OHLC['close'], 10, 3)
OHLC = OHLC[1:]
# print(BTC.head())buy_price, sell_price, st_signal = implement_st_strategy(OHLC['close'], OHLC['st'])plt.plot(OHLC['close'], linewidth = 2)
plt.plot(OHLC['st'], color = 'green', linewidth = 2, label = 'ST UPTREND')
plt.plot(OHLC['st_dt'], color = 'r', linewidth = 2, label = 'ST DOWNTREND')plt.plot(OHLC.index, buy_price, marker = '^', color = 'green', markersize = 12, linewidth = 0, label = 'BUY SIGNAL')
plt.plot(OHLC.index, sell_price, marker = 'v', color = 'r', markersize = 12, linewidth = 0, label = 'SELL SIGNAL')plt.title(f'{symbol} ST TRADING SIGNALS')
plt.legend(loc = 'upper left')
plt.show()

A Step-By-Step Guide to Implementing the SuperTrend Indicator in Python

Learn to build a powerful trading strategy with the SuperTrend indicator in python

medium.com

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Wongsakorn Sanwises

Written by Wongsakorn Sanwises

574 Followers

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