Please add OTT (Optimized trend tracker) to Pandas_TA

[mhgutier]

Hi Kevin,

Please help to add OTT to pandas_ta by KivancOzbilgicin trading view.

i converted the pinescript to python, it has correct output though my logic seems to be not efficient as it takes 10 seconds or so to calculate. i think it has almost similar logic with QQE. Thanks in advance

================= my version

   #OTT variables
    pds = 2
   percent = 1.4
    alpha = 2 / (pds + 1)
    df['atr'] = talib.ATR(df['high'], df['low'], df['close'], timeperiod=14)
    np_atr = np.array(df['atr'])
    ratr = format(np_atr[-2], '.4f')

    df['ud1'] = np.where(df['close'] > df['close'].shift(1), (df['close'] - df['close'].shift()) , 0)
    df['dd1'] = np.where(df['close'] < df['close'].shift(1), (df['close'].shift() - df['close']) , 0)
    df['UD'] = talib.SUM(df['ud1'], timeperiod=9)
    df['DD'] = talib.SUM(df['dd1'], timeperiod=9)
    df['CMO'] = ((df['UD'] - df['DD']) / (df['UD'] + df['DD'])).fillna(0).abs()

    # df['Var'] = talib.EMA(df['close'], timeperiod=5)
    df['Var'] = 0.0
    for i in range(pds, len(df)):
        df['Var'].iat[i] = (alpha * df['CMO'].iat[i] * df['close'].iat[i]) + (1 - alpha * df['CMO'].iat[i]) * df['Var'].iat[i-1]

    df['fark'] = df['Var'] * percent * 0.01
    df['newlongstop'] = df['Var'] - df['fark']
    df['newshortstop'] = df['Var'] + df['fark']
    df['longstop'] = 0.0
    df['shortstop'] = 999999999999999999
    # df['dir'] = 1
    for i in df['RSI_MA']:

        def maxlongstop():
            df.loc[(df['newlongstop'] > df['longstop'].shift(1)) , 'longstop'] = df['newlongstop']
            df.loc[(df['longstop'].shift(1) > df['newlongstop']), 'longstop'] = df['longstop'].shift(1) 

            return df['longstop']

        def minshortstop():
            df.loc[(df['newshortstop'] < df['shortstop'].shift(1)), 'shortstop'] = df['newshortstop']
            df.loc[(df['shortstop'].shift(1) < df['newshortstop']), 'shortstop'] = df['shortstop'].shift(1)

            return df['shortstop']

        df['longstop']= np.where (
            (
                (df['Var'] > df['longstop'].shift(1))
            ),maxlongstop(),df['newlongstop']
        )

        df['shortstop'] = np.where(
            (
                (df['Var'] < df['shortstop'].shift(1))
            ), minshortstop(), df['newshortstop'])

    #get xover

    df['xlongstop'] = np.where (
        (
            (df['Var'].shift(1) > df['longstop'].shift(1)) & 
            (df['Var'] < df['longstop'].shift(1))
        ), 1,0)

    df['xshortstop'] =np.where(
        (
            (df['Var'].shift(1) < df['shortstop'].shift(1)) & 
            (df['Var'] > df['shortstop'].shift(1))
        ), 1,0)

    df['trend']=0
    df['dir'] = 0
    for i in df['RSI_MA']:
            df['trend'] = np.where(
            (
                (df['xshortstop'] == 1)
            ),1, (np.where((df['xlongstop'] == 1),-1,df['trend'].shift(1)))
        )

            df['dir'] = np.where(
            (
                (df['xshortstop'] == 1)
            ),1, (np.where((df['xlongstop'] == 1),-1,df['dir'].shift(1).fillna(1)))
        )

    #get OTT

    df['MT'] = np.where(df['dir'] == 1, df['longstop'], df['shortstop'])
    df['OTT'] = np.where(df['Var'] > df['MT'], (df['MT'] * (200 + percent) / 200), (df['MT'] * (200 - percent) / 200))
    df['OTT'] = df['OTT'].shift(2) 

============
Pine version

//@version=4
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © KivancOzbilgic

//created by: @Anil_Ozeksi
//developer: ANIL ÖZEKŞİ
//author: @kivancozbilgic

study("Optimized Trend Tracker","OTT", overlay=true)
src = input(close, title="Source")
length=input(2, "OTT Period", minval=1)
percent=input(1.4, "OTT Percent", type=input.float, step=0.1, minval=0)
showsupport = input(title="Show Support Line?", type=input.bool, defval=true)
showsignalsk = input(title="Show Support Line Crossing Signals?", type=input.bool, defval=true)
showsignalsc = input(title="Show Price/OTT Crossing Signals?", type=input.bool, defval=false)
highlight = input(title="Show OTT Color Changes?", type=input.bool, defval=false)
showsignalsr = input(title="Show OTT Color Change Signals?", type=input.bool, defval=false)
highlighting = input(title="Highlighter On/Off ?", type=input.bool, defval=true)
mav = input(title="Moving Average Type", defval="VAR", options=["SMA", "EMA", "WMA", "TMA", "VAR", "WWMA", "ZLEMA", "TSF"])
Var_Func(src,length)=>
    valpha=2/(length+1)
    vud1=src>src[1] ? src-src[1] : 0
    vdd1=src<src[1] ? src[1]-src : 0
    vUD=sum(vud1,9)
    vDD=sum(vdd1,9)
    vCMO=nz((vUD-vDD)/(vUD+vDD))
    VAR=0.0
    VAR:=nz(valpha*abs(vCMO)*src)+(1-valpha*abs(vCMO))*nz(VAR[1])
VAR=Var_Func(src,length)
Wwma_Func(src,length)=>
    wwalpha = 1/ length
    WWMA = 0.0
    WWMA := wwalpha*src + (1-wwalpha)*nz(WWMA[1])
WWMA=Wwma_Func(src,length)
Zlema_Func(src,length)=>
    zxLag = length/2==round(length/2) ? length/2 : (length - 1) / 2
    zxEMAData = (src + (src - src[zxLag]))
    ZLEMA = ema(zxEMAData, length)
ZLEMA=Zlema_Func(src,length)
Tsf_Func(src,length)=>
    lrc = linreg(src, length, 0)
    lrc1 = linreg(src,length,1)
    lrs = (lrc-lrc1)
    TSF = linreg(src, length, 0)+lrs
TSF=Tsf_Func(src,length)
getMA(src, length) =>
    ma = 0.0
    if mav == "SMA"
        ma := sma(src, length)
        ma

    if mav == "EMA"
        ma := ema(src, length)
        ma

    if mav == "WMA"
        ma := wma(src, length)
        ma

    if mav == "TMA"
        ma := sma(sma(src, ceil(length / 2)), floor(length / 2) + 1)
        ma

    if mav == "VAR"
        ma := VAR
        ma

    if mav == "WWMA"
        ma := WWMA
        ma

    if mav == "ZLEMA"
        ma := ZLEMA
        ma

    if mav == "TSF"
        ma := TSF
        ma
    ma

MAvg=getMA(src, length)
fark=MAvg*percent*0.01
longStop = MAvg - fark
longStopPrev = nz(longStop[1], longStop)
longStop := MAvg > longStopPrev ? max(longStop, longStopPrev) : longStop
shortStop =  MAvg + fark
shortStopPrev = nz(shortStop[1], shortStop)
shortStop := MAvg < shortStopPrev ? min(shortStop, shortStopPrev) : shortStop
dir = 1
dir := nz(dir[1], dir)
dir := dir == -1 and MAvg > shortStopPrev ? 1 : dir == 1 and MAvg < longStopPrev ? -1 : dir
MT = dir==1 ? longStop: shortStop
OTT=MAvg>MT ? MT*(200+percent)/200 : MT*(200-percent)/200

[twopirllc]

Hello @mhgutier,

Added it to the list. ✅

I appreciate that you shared both the source and your Python implementation. 😎

If you are feeling venturous, try the following guide: Creating a Custom Indicator: The Big 4. If not, hopefully someone can help implement this sooner. I'll keep you posted when it's ready on the development branch for testing.

Kind Regards, KJ

[bibinvargheset]

@mhgutier I was trying to run your code but df['RSI_MA']: is not done can share the the complete code?

[mhgutier]

Hi @twopirllc ,

Please see updated code below. please free to modify if needed. i am not a developer, just learned python in youtube , for me the code is slow unlike the way pandas_ta compute for super trend/qqe (super fast)

import pandas as pd
import numpy as np
import talib
import pandas_ta
import schedule
import ccxt

exchange = ccxt.binance()

pair='ETH/USDT'
ohlc = exchange.fetch_ohlcv(pair, timeframe='5m')
df = pd.DataFrame(ohlc, columns = ['time', 'open', 'high', 'low', 'close', 'volume'])

#OTT variables
pds = 2
percent = 1.4
alpha = 2 / (pds + 1)
df['atr'] = talib.ATR(df['high'], df['low'], df['close'], timeperiod=14)

df['ud1'] = np.where(df['close'] > df['close'].shift(1), (df['close'] - df['close'].shift()) , 0)
df['dd1'] = np.where(df['close'] < df['close'].shift(1), (df['close'].shift() - df['close']) , 0)
df['UD'] = talib.SUM(df['ud1'], timeperiod=9)
df['DD'] = talib.SUM(df['dd1'], timeperiod=9)
df['CMO'] = ((df['UD'] - df['DD']) / (df['UD'] + df['DD'])).fillna(0).abs()

df['Var'] = 0.0
for i in range(pds, len(df)):
    df['Var'].iat[i] = (alpha * df['CMO'].iat[i] * df['close'].iat[i]) + (1 - alpha * df['CMO'].iat[i]) * df['Var'].iat[i-1]

df['fark'] = df['Var'] * percent * 0.01
df['newlongstop'] = df['Var'] - df['fark']
df['newshortstop'] = df['Var'] + df['fark']
df['longstop'] = 0.0
df['shortstop'] = 999999999999999999

for i in (df['close']):

    def maxlongstop():
        df.loc[(df['newlongstop'] > df['longstop'].shift(1)) , 'longstop'] = df['newlongstop']
        df.loc[(df['longstop'].shift(1) > df['newlongstop']), 'longstop'] = df['longstop'].shift(1) 

        return df['longstop']

    def minshortstop():
        df.loc[(df['newshortstop'] < df['shortstop'].shift(1)), 'shortstop'] = df['newshortstop']
        df.loc[(df['shortstop'].shift(1) < df['newshortstop']), 'shortstop'] = df['shortstop'].shift(1)

        return df['shortstop']

    df['longstop']= np.where (
        (
            (df['Var'] > df['longstop'].shift(1))
        ),maxlongstop(),df['newlongstop']
    )

    df['shortstop'] = np.where(
        (
            (df['Var'] < df['shortstop'].shift(1))
        ), minshortstop(), df['newshortstop'])

#get xover

df['xlongstop'] = np.where (
    (
        (df['Var'].shift(1) > df['longstop'].shift(1)) & 
        (df['Var'] < df['longstop'].shift(1))
    ), 1,0)

df['xshortstop'] =np.where(
    (
        (df['Var'].shift(1) < df['shortstop'].shift(1)) & 
        (df['Var'] > df['shortstop'].shift(1))
    ), 1,0)

df['trend']=0
df['dir'] = 0
for i in df['close']:
        df['trend'] = np.where(
        (
            (df['xshortstop'] == 1)
        ),1, (np.where((df['xlongstop'] == 1),-1,df['trend'].shift(1)))
    )

        df['dir'] = np.where(
        (
            (df['xshortstop'] == 1)
        ),1, (np.where((df['xlongstop'] == 1),-1,df['dir'].shift(1).fillna(1)))
    )

#get OTT

df['MT'] = np.where(df['dir'] == 1, df['longstop'], df['shortstop'])
df['OTT'] = np.where(df['Var'] > df['MT'], (df['MT'] * (200 + percent) / 200), (df['MT'] * (200 - percent) / 200))
df['OTT'] = df['OTT'].shift(2) 

#print OTT
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
print(df[['close','Var','OTT']].round(2))

[twopirllc]

Hello @mhgutier,

I will look into it as soon as I can. I will let you know when it is available to testing on the development branch.

Kind Regards, KJ