Optimized Trend Tracker OTT indicator

[tarantula3535]

hi i want to write this indicator in python but i dont have much time.. here is the tradingview's url
https://tr.tradingview.com/script/zVhoDQME/
this indicator is very helpfull the filtering in the buy signals conditions..
I would be glad if you could write this indicator in Python

[TomPer]

Hi ! I would also be very interested to have this OTT indicator integrated into Freqtrade.

[tarantula3535]

and also this indicator..
https://tr.tradingview.com/script/sU9molfV/
Pmax profit maximizer
can anyone write this code and implement in it the freqtrade?

[tarantula3535]

If I can take a little time out, it's like I figured out the logic. It can be solved by something like adding a moving average to Supertrend.

[tarantula3535]

hi... i use basic PMAX indicator.. here is the code.. anyone want to use it..

def PMAX(dataframe, period = 10, multiplier = 3, length=12, MAtype=1 ):
    """
    Function to compute SuperTrend
    
    Args :
        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns
        period : Integer indicates the period of computation in terms of number of candles
        multiplier : Integer indicates value to multiply the ATR
        length: moving averages length
        MAtype: type of the moving averafe 1 EMA 2 DEMA 3 T3 4 SMA 5 VIDYA
        
    Returns :
        df : Pandas DataFrame with new columns added for 
            True Range (TR), ATR (ATR_$period)
            PMAX (pm_$period_$multiplier_$length_$Matypeint)
            PMAX Direction (pmX_$period_$multiplier_$length_$Matypeint)
    """
    import talib.abstract as ta
    df = dataframe.copy()
    mavalue = 'MA_' + str(length)
    atr = 'ATR_' + str(period)
    df[atr]=ta.ATR(df , timeperiod = period)
    pm = 'pm_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype)
    pmx = 'pmX_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype)   
    """
    Pmax Algorithm :

        BASIC UPPERBAND = MA + Multiplier * ATR
        BASIC LOWERBAND = MA - Multiplier * ATR
        
        FINAL UPPERBAND = IF( (Current BASICUPPERBAND < Previous FINAL UPPERBAND) or (Previous Close > Previous FINAL UPPERBAND))
                            THEN (Current BASIC UPPERBAND) ELSE Previous FINALUPPERBAND)
        FINAL LOWERBAND = IF( (Current BASIC LOWERBAND > Previous FINAL LOWERBAND) or (Previous Close < Previous FINAL LOWERBAND)) 
                            THEN (Current BASIC LOWERBAND) ELSE Previous FINAL LOWERBAND)
        
        PMAX = IF((Previous PMAX = Previous FINAL UPPERBAND) and (Current Close <= Current FINAL UPPERBAND)) THEN
                        Current FINAL UPPERBAND
                    ELSE
                        IF((Previous PMAX = Previous FINAL UPPERBAND) and (Current Close > Current FINAL UPPERBAND)) THEN
                            Current FINAL LOWERBAND
                        ELSE
                            IF((Previous PMAX = Previous FINAL LOWERBAND) and (Current Close >= Current FINAL LOWERBAND)) THEN
                                Current FINAL LOWERBAND
                            ELSE
                                IF((Previous PMAX = Previous FINAL LOWERBAND) and (Current Close < Current FINAL LOWERBAND)) THEN
                                    Current FINAL UPPERBAND
    
    """
    # MAtype==1 --> EMA
    # MAtype==2 --> DEMA
    # MAtype==3 --> T3
    # MAtype==4 --> SMA
    # MAtype==5 --> VIDYA
    # MAtype==6 --> TEMA
    # MAtype==7 --> WMA
    # MAtype==8 --> VWMA
    # Compute basic upper and lower bands
    if MAtype==1:
        df[mavalue]=ta.EMA(df , timeperiod = length)
    elif MAtype==2:
        df[mavalue]=ta.DEMA(df , timeperiod = length)
    elif MAtype==3:
        df[mavalue]=ta.T3(df , timeperiod = length)
    elif MAtype==4:
        df[mavalue]=ta.SMA(df , timeperiod = length)
    elif MAtype==5:
        df[mavalue]= VIDYA(df , length= length)
    elif MAtype==6:
        df[mavalue]= ta.TEMA(df , timeperiod = length)
    elif MAtype==7:
        df[mavalue]= ta.WMA(df , timeperiod = length)
    elif MAtype==8:
        df[mavalue]= vwma(df , length)                        
    # Compute basic upper and lower bands
    df['basic_ub'] = df[mavalue] + multiplier * df[atr]
    df['basic_lb'] = df[mavalue] - multiplier * df[atr]
    # Compute final upper and lower bands
    df['final_ub'] = 0.00
    df['final_lb'] = 0.00
    for i in range(period, len(df)):
        df['final_ub'].iat[i] = df['basic_ub'].iat[i] if df['basic_ub'].iat[i] < df['final_ub'].iat[i - 1] or df[mavalue].iat[i - 1] > df['final_ub'].iat[i - 1] else df['final_ub'].iat[i - 1]
        df['final_lb'].iat[i] = df['basic_lb'].iat[i] if df['basic_lb'].iat[i] > df['final_lb'].iat[i - 1] or df[mavalue].iat[i - 1] < df['final_lb'].iat[i - 1] else df['final_lb'].iat[i - 1]
       
    # Set the Pmax value
    df[pm] = 0.00
    for i in range(period, len(df)):
        df[pm].iat[i] = df['final_ub'].iat[i] if df[pm].iat[i - 1] == df['final_ub'].iat[i - 1] and df[mavalue].iat[i] <= df['final_ub'].iat[i] else \
                        df['final_lb'].iat[i] if df[pm].iat[i - 1] == df['final_ub'].iat[i - 1] and df[mavalue].iat[i] >  df['final_ub'].iat[i] else \
                        df['final_lb'].iat[i] if df[pm].iat[i - 1] == df['final_lb'].iat[i - 1] and df[mavalue].iat[i] >= df['final_lb'].iat[i] else \
                        df['final_ub'].iat[i] if df[pm].iat[i - 1] == df['final_lb'].iat[i - 1] and df[mavalue].iat[i] <  df['final_lb'].iat[i] else 0.00 
                 
    # Mark the trend direction up/down
    df[pmx] = np.where((df[pm] > 0.00), np.where((df['close'] < df[pm]), 'down',  'up'), np.NaN)

    # Remove basic and final bands from the columns
    df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1)
    
    df.fillna(0, inplace=True)

    return df

[xmatthias]

This should work for OTT:

Would be great if you could have a look at it too - i'm no pinescript expert - but would be great if we can include this in the technical module.

def OTT(dataframe, *, pds = 2, percent = 1.4):
    """
    Source: https://www.tradingview.com/script/zVhoDQME/
    Author: Anıl Özekşi
    
    Pinescript Developer: KivancOzbilgic
    
    Idea: 
        Buy when Signal line crosses above OTT
        Sell when signal crosses below OTT
        
    usage:
      dataframe['OTT'], dataframe['OTTSignal'] = OTT(dataframe)
    """
    df = dataframe.copy()
    alpha = 2 / (pds + 1)
    
    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'] = df['ud1'].rolling(9).sum()
    df['DD'] = df['dd1'].rolling(9).sum()
    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['longStop'] = df['Var'] - df['fark']
    df['longStopPrev'] = df['longStop'].shift(1).ffill(limit=1)
    
    df['longStop'] = np.where(df['Var'] > df['longStopPrev'], df[['longStop', 'longStopPrev']].max(axis=1), df['longStop'])
    
    df['shortStop'] = df['Var'] + df['fark']
    df['shortStopPrev'] = df['shortStop'].shift(1).ffill(limit=1)
    df['shortStop'] = np.where(df['Var'] < df['shortStopPrev'], df[['shortStop', 'shortStopPrev']].max(axis=1), df['shortStop'])

    df['dir'] = 1
    # dir = 1
    # dir := nz(dir[1], dir)
    # dir := dir == -1 and Var > shortStopPrev ? 1 : dir == 1 and Var < longStopPrev ? -1 : dir
    df['dir'] = np.where(df['Var'] > df['shortStopPrev'], 1, np.where(df['Var'] < df['longStopPrev'], -1, df['dir']))
    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)
    
    
    return df['OTT'], df['Var']
    

[tarantula3535]

in the OTT
If the calculations compare correctly with tradingview, different results come out..
I could not find the reason for this.. e.g. ARDR USDT

[xmatthias]

Interresting
the point i'm not confident is the dir calculation.

    # dir = 1
    # dir := nz(dir[1], dir)
    # dir := dir == -1 and Var > shortStopPrev ? 1 : dir == 1 and Var < longStopPrev ? -1 : dir

In my understanding, dir will be assigned 1 - will be assigned to all rows with 1 by using the previous row - making the comparisons to dir below (if it's 1 or -1) irrelevant.

Might be this should be calculated in a loop - but i'm not sure if that's how pinescript does it.

[tarantula3535]

i couldnt find the bug where is... some pairs work correctly..some pairs not..

[xmatthias]

One big problem with these indicators is always the starting point (this is fully based on the result of the previous row - so a change 2000 lines ago will (very slightly) change the result in the last candle.
It's usually not visible as it's in the 10th decimal place (or wherever) - but slight change nonetheless.

Obviously, this error will then depend on the price of the coin (a coin priced 0.00005 is more likely to have a "visible" error than one priced 200.05)...

It should however be similar with other indicators as well (like VIDYA) - which also has a similar calculation

[tarantula3535]

@xmatthias you right that... Pmax is also an indicator that works well.... you should take a look at it too

[Cuzeppe]

Hi @tarantula3535 I'm a newbie and don't know how to add this indicator. but my favorite indicator on tradingview is pmax. Is there any documentation that explains how to do this?

[tarantula3535]

def PMAX(dataframe, period = 10, multiplier = 3, length=12, MAtype=1, src=1):
    """
    Function to compute PMAX
    
    Args :
        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns
        period : Integer indicates the period of computation in terms of number of candles
        multiplier : Integer indicates value to multiply the ATR
        length: moving averages length
        MAtype: type of the moving averafe 1 EMA 2 DEMA 3 T3 4 SMA 5 VIDYA
        
    Returns :
        df : Pandas DataFrame with new columns added for 
            True Range (TR), ATR (ATR_$period)
            PMAX (pm_$period_$multiplier_$length_$Matypeint)
            PMAX Direction (pmX_$period_$multiplier_$length_$Matypeint)
    """
    import talib.abstract as ta
    df = dataframe.copy()
    mavalue = 'MA_' + str(MAtype) + '_' + str(length)
    atr = 'ATR_' + str(period)
    df[atr]=ta.ATR(df , timeperiod = period)
    pm = 'pm_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype)
    pmx = 'pmX_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype)
    """
    Pmax Algorithm :

        BASIC UPPERBAND = MA + Multiplier * ATR
        BASIC LOWERBAND = MA - Multiplier * ATR
        
        FINAL UPPERBAND = IF( (Current BASICUPPERBAND < Previous FINAL UPPERBAND) or (Previous Close > Previous FINAL UPPERBAND))
                            THEN (Current BASIC UPPERBAND) ELSE Previous FINALUPPERBAND)
        FINAL LOWERBAND = IF( (Current BASIC LOWERBAND > Previous FINAL LOWERBAND) or (Previous Close < Previous FINAL LOWERBAND)) 
                            THEN (Current BASIC LOWERBAND) ELSE Previous FINAL LOWERBAND)
        
        PMAX = IF((Previous PMAX = Previous FINAL UPPERBAND) and (Current Close <= Current FINAL UPPERBAND)) THEN
                        Current FINAL UPPERBAND
                    ELSE
                        IF((Previous PMAX = Previous FINAL UPPERBAND) and (Current Close > Current FINAL UPPERBAND)) THEN
                            Current FINAL LOWERBAND
                        ELSE
                            IF((Previous PMAX = Previous FINAL LOWERBAND) and (Current Close >= Current FINAL LOWERBAND)) THEN
                                Current FINAL LOWERBAND
                            ELSE
                                IF((Previous PMAX = Previous FINAL LOWERBAND) and (Current Close < Current FINAL LOWERBAND)) THEN
                                    Current FINAL UPPERBAND
    
    """
    # MAtype==1 --> EMA
    # MAtype==2 --> DEMA
    # MAtype==3 --> T3
    # MAtype==4 --> SMA
    # MAtype==5 --> VIDYA
    # MAtype==6 --> TEMA
    # MAtype==7 --> WMA
    # MAtype==8 --> VWMA
    # Compute basic upper and lower bands
    if src == 1:
        masrc=df["close"]
    elif src == 2:
        masrc = (df["high"] + df["low"]) / 2
    elif src == 3:
        masrc = (df["high"] + df["low"]+ df["close"] + df["open"]) / 4
    if MAtype==1:
        df[mavalue]= ta.EMA(masrc , timeperiod = length)
    elif MAtype==2:
        df[mavalue]= ta.DEMA(masrc , timeperiod = length)
    elif MAtype==3:
        df[mavalue]= ta.T3(masrc , timeperiod = length)
    elif MAtype==4:
        df[mavalue]= ta.SMA(masrc , timeperiod = length)
    elif MAtype==5:
        df[mavalue]= VIDYA(df , length= length)
    elif MAtype==6:
        df[mavalue]= ta.TEMA(masrc , timeperiod = length)
    elif MAtype==7:
        df[mavalue]= ta.WMA(df , timeperiod = length)
    elif MAtype==8:
        df[mavalue]= vwma(df , length)
    elif MAtype==9:
        df[mavalue]= zema(df , period=length)
    # Compute basic upper and lower bands
    df['basic_ub'] = df[mavalue] + (multiplier * df[atr])
    df['basic_lb'] = df[mavalue] - (multiplier * df[atr])
    # Compute final upper and lower bands
    df['final_ub'] = 0.00
    df['final_lb'] = 0.00
    for i in range(period, len(df)):
        df['final_ub'].iat[i] = df['basic_ub'].iat[i] if df['basic_ub'].iat[i] < df['final_ub'].iat[i - 1] or df[mavalue].iat[i - 1] > df['final_ub'].iat[i - 1] else df['final_ub'].iat[i - 1]
        df['final_lb'].iat[i] = df['basic_lb'].iat[i] if df['basic_lb'].iat[i] > df['final_lb'].iat[i - 1] or df[mavalue].iat[i - 1] < df['final_lb'].iat[i - 1] else df['final_lb'].iat[i - 1]
       
    # Set the Pmax value
    df[pm] = 0.00
    for i in range(period, len(df)):
        df[pm].iat[i] = df['final_ub'].iat[i] if df[pm].iat[i - 1] == df['final_ub'].iat[i - 1] and df[mavalue].iat[i] <= df['final_ub'].iat[i] else \
                        df['final_lb'].iat[i] if df[pm].iat[i - 1] == df['final_ub'].iat[i - 1] and df[mavalue].iat[i] >  df['final_ub'].iat[i] else \
                        df['final_lb'].iat[i] if df[pm].iat[i - 1] == df['final_lb'].iat[i - 1] and df[mavalue].iat[i] >= df['final_lb'].iat[i] else \
                        df['final_ub'].iat[i] if df[pm].iat[i - 1] == df['final_lb'].iat[i - 1] and df[mavalue].iat[i] <  df['final_lb'].iat[i] else 0.00 
                 
    # Mark the trend direction up/down
    df[pmx] = np.where((df[pm] > 0.00), np.where((df[mavalue] < df[pm]), 'down',  'up'), np.NaN)

    # Remove basic and final bands from the columns
    df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1)
    
    df.fillna(0, inplace=True)

    return df

This is the function i upgrade it..

        pmdf2 = PMAX(dataframe, period=10, multiplier=3, length=9, MAtype=2, src=3)
        dataframe['pmX_10_3_9_2'] = pmdf2['pmX_10_3_9_2']

i use in the strategy just like that...i hopefully helped you..
e.g. trend is "up" look other indicators...
e.g. ema crossover pmax value..

[Cuzeppe]

Actually i couldnt find where to add the indicator codes and which part of its and how to implement indicator to my strategy file. can u share ur strategy file and indicators file pls? cryptoying@gmail.com I would be grateful