{"id":15,"date":"2024-06-22T13:57:00","date_gmt":"2024-06-22T13:57:00","guid":{"rendered":""},"modified":"2025-06-21T15:30:05","modified_gmt":"2025-06-21T15:30:05","slug":"new-pine-script-cosine-kernel-regression","status":"publish","type":"post","link":"https:\/\/kriptos.my\/index.php\/2024\/06\/22\/new-pine-script-cosine-kernel-regression\/","title":{"rendered":"New Pine Script: Cosine Kernel Regression"},"content":{"rendered":"
<\/a><\/div>\n

<\/p>\n

 This pine script is adapted from Cosine Kernel Regression by QuantraSystems<\/a>. Credit goes to this brilliant script writer. <\/p>\n

The Cosine Kernel Regressions indicator (CKR) uses mathematical concepts to offer a unique approach to market analysis. This indicator employs Kernel Regressions using bespoke tunable Cosine functions in order to smoothly interpret a variety of market data, providing traders with incredibly clean insights into market trends. Details here..<\/a> <\/p>\n

Strategy Description: This stratgey uses many indicators to combine into an indicator that minimizes noises to get as accurate as possible. Order Buy is placed on Oversold signal<\/b>, and exits the order when Overbought signal<\/b>.<\/p>\n

Recommended Settings:<\/u><\/h3>\n

Pair: RNDRUSDT (Binance)<\/p>\n

Time frame: 1 hour<\/p>\n

Initial Capital: 100<\/p>\n

Base Currency: Default<\/p>\n

Order Size: 100% equity<\/p>\n

Pyramiding: 1 order<\/p>\n

Commission: 0.1 % <\/p>\n

<\/p>\n

\n
\/\/ <\/span>@version=<\/span>5<\/span><\/div>\n
strategy<\/span>(“CKR: capayam.com credit to QuantraSystems”<\/span>,<\/span> overlay<\/span>=<\/span>false<\/span>,<\/span> initial_capital<\/span> =<\/span> 10<\/span>,<\/span> default_qty_type<\/span>=<\/span>strategy.percent_of_equity<\/span>,<\/span> default_qty_value<\/span>=<\/span>100<\/span>,<\/span> commission_type<\/span>=<\/span>strategy.commission.percent<\/span> ,<\/span> commission_value<\/span>=<\/span>0.1<\/span>)<\/div>\n
import<\/span> QuantraAI\/QuantraMAs\/2<\/span> as<\/span> Dynamic<\/span><\/div>\n

<\/p>\n

\/\/ Desc: refer to Cosine Kernel Regression by QuantraSystems for original pine script.<\/span><\/div>\n
\/\/ Default setting is best with TF1hour<\/span><\/div>\n

<\/p>\n

\/\/ Input Groups<\/span><\/div>\n

<\/p>\n

GRP1<\/span>          =<\/span> “Display:”<\/span><\/div>\n
GRP2<\/span>          =<\/span> “Indicator Setup:”<\/span><\/div>\n
GRP3<\/span>          =<\/span> “Kernel Calibration:”<\/span> <\/div>\n

<\/p>\n

\/\/ Indicator Calibrations<\/span><\/div>\n
src<\/span>           =<\/span> input.source<\/span>(close<\/span>,<\/span>          “Source”<\/span>,<\/span>                                          group<\/span> =<\/span> GRP2<\/span>            )<\/div>\n

<\/p>\n

bool_STOCH<\/span>    =<\/span> input.bool<\/span>  (true<\/span>,<\/span>           “”<\/span>,<\/span>                              inline<\/span> =<\/span> “STOCH”<\/span>,<\/span> group<\/span> =<\/span> GRP2<\/span>            )<\/div>\n
bool_RSI<\/span>      =<\/span> input.bool<\/span>  (true<\/span>,<\/span>           “”<\/span>,<\/span>                              inline<\/span> =<\/span> “RSI”<\/span>,<\/span>   group<\/span> =<\/span> GRP2<\/span>            )       <\/div>\n
bool_BBPCT<\/span>    =<\/span> input.bool<\/span>  (true<\/span>,<\/span>           “”<\/span>,<\/span>                              inline<\/span> =<\/span> “BBPCT”<\/span>,<\/span> group<\/span> =<\/span> GRP2<\/span>            )<\/div>\n
bool_CMO<\/span>      =<\/span> input.bool<\/span>  (true<\/span>,<\/span>           “”<\/span>,<\/span>                              inline<\/span> =<\/span> “CMO”<\/span>,<\/span>   group<\/span> =<\/span> GRP2<\/span>            )<\/div>\n
bool_CCI<\/span>      =<\/span> input.bool<\/span>  (true<\/span>,<\/span>           “”<\/span>,<\/span>                              inline<\/span> =<\/span> “CCI”<\/span>,<\/span>   group<\/span> =<\/span> GRP2<\/span>            )<\/div>\n
bool_FISH<\/span>     =<\/span> input.bool<\/span>  (true<\/span>,<\/span>           “”<\/span>,<\/span>                              inline<\/span> =<\/span> “FISH”<\/span>,<\/span>  group<\/span> =<\/span> GRP2<\/span>            )<\/div>\n
bool_VZO<\/span>      =<\/span> input.bool<\/span>  (true<\/span>,<\/span>           “”<\/span>,<\/span>                              inline<\/span> =<\/span> “VZO”<\/span>,<\/span>   group<\/span> =<\/span> GRP2<\/span>            )<\/div>\n

<\/p>\n

length_STOCH<\/span>  =<\/span> input.int<\/span>   (14<\/span>,<\/span>             “Stochastic Length”<\/span>,<\/span>             inline<\/span> =<\/span> “STOCH”<\/span>,<\/span> group<\/span> =<\/span> GRP2<\/span>            )<\/div>\n
length_RSI<\/span>    =<\/span> input.int<\/span>   (14<\/span>,<\/span>             “RSI Length”<\/span>,<\/span>                    inline<\/span> =<\/span> “RSI”<\/span>,<\/span>   group<\/span> =<\/span> GRP2<\/span>            )<\/div>\n
length_BBPCT<\/span>  =<\/span> input.int<\/span>   (20<\/span>,<\/span>             “BBPCT Length”<\/span>,<\/span>                  inline<\/span> =<\/span> “BBPCT”<\/span>,<\/span> group<\/span> =<\/span> GRP2<\/span>            )<\/div>\n
length_CMO<\/span>    =<\/span> input.int<\/span>   (14<\/span>,<\/span>             “Chande Momentum Length”<\/span>,<\/span>        inline<\/span> =<\/span> “CMO”<\/span>,<\/span>   group<\/span> =<\/span> GRP2<\/span>            )<\/div>\n
length_CCI<\/span>    =<\/span> input.int<\/span>   (20<\/span>,<\/span>             “CCI Length”<\/span>,<\/span>                    inline<\/span> =<\/span> “CCI”<\/span>,<\/span>   group<\/span> =<\/span> GRP2<\/span>            )<\/div>\n
length_FISH<\/span>   =<\/span> input.int<\/span>   (9<\/span>,<\/span>              “Fisher Transform Length”<\/span>,<\/span>       inline<\/span> =<\/span> “FISH”<\/span>,<\/span>  group<\/span> =<\/span> GRP2<\/span>            )<\/div>\n
length_VZO<\/span>    =<\/span> input.int<\/span>   (21<\/span>,<\/span>             “VZO Length”<\/span>,<\/span>                    inline<\/span> =<\/span> “VZO”<\/span>,<\/span>   group<\/span> =<\/span> GRP2<\/span>            )<\/div>\n
\/\/ Cosine Kernel Setup<\/span><\/div>\n
varient<\/span>       =<\/span> input.string<\/span>(“Tuneable”<\/span>,<\/span>     “Cosine Kernel Regression Type”<\/span>,<\/span>                   group<\/span> =<\/span> GRP3<\/span>,<\/span> options<\/span> =<\/span> [“Tuneable”<\/span>,<\/span> “Stepped”<\/span>])<\/div>\n
lookbackR<\/span>     =<\/span> input.int<\/span>   (60<\/span>,<\/span>             “Regression Lookback”<\/span>,<\/span>                             group<\/span> =<\/span> GRP3<\/span>            )<\/div>\n
tuning<\/span>        =<\/span> input.float<\/span> (15.<\/span>,<\/span>            “Tuning Coefficient”<\/span>,<\/span>                              group<\/span> =<\/span> GRP3<\/span>            )<\/div>\n
\/\/ Display<\/span><\/div>\n
ColType<\/span>       =<\/span> input.string<\/span>(“Fast Trend”<\/span>,<\/span>   “Choose Mode”<\/span>,<\/span>                                     group<\/span> =<\/span> GRP1<\/span>,<\/span> options<\/span> =<\/span> [“None”<\/span>,<\/span>    “Fast Trend”<\/span>,<\/span> “Slow Trend”<\/span>],<\/span> tooltip<\/span> =<\/span> “Signal Mode – Barcolor”<\/span>)<\/div>\n
ColMode<\/span>       =<\/span> input.string<\/span>(“Modern”<\/span>,<\/span>       “Color Palette Choice”<\/span>,<\/span>          inline<\/span> =<\/span> “Drop”<\/span>,<\/span>  group<\/span> =<\/span> GRP1<\/span>,<\/span> options<\/span> =<\/span> [“Classic”<\/span>,<\/span> “Modern”<\/span>,<\/span> “Robust”<\/span>,<\/span> “Accented”<\/span>,<\/span> “Monochrome”<\/span>])<\/div>\n
man<\/span>           =<\/span> input.bool<\/span>  (false<\/span>,<\/span>          “Custom Palette”<\/span>,<\/span>                inline<\/span> =<\/span> “Drop”<\/span>,<\/span>  group<\/span> =<\/span> GRP1<\/span>            )<\/div>\n
manUpC<\/span>        =<\/span> input.color<\/span> (#00ff00<\/span>,<\/span>      “Custom Up”<\/span>,<\/span>                     inline<\/span> =<\/span> “Man”<\/span>,<\/span>   group<\/span> =<\/span> GRP1<\/span>            ) <\/div>\n
manDnC<\/span>        =<\/span> input.color<\/span> (#ff0000<\/span>,<\/span>      “Custom Down”<\/span>,<\/span>                   inline<\/span> =<\/span> “Man”<\/span>,<\/span>   group<\/span> =<\/span> GRP1<\/span>            )<\/div>\n
\/\/ Color assignment based on the selected palette<\/span><\/div>\n
[UpC<\/span>,<\/span> DnC<\/span>]       =<\/span>   switch<\/span>     ColMode<\/span><\/div>\n
    “Classic”<\/span>    =><\/span> [#00E676<\/span>,<\/span> #880E4F<\/span>]<\/div>\n
    “Modern”<\/span>     =><\/span> [#5ffae0<\/span>,<\/span> #c22ed0<\/span>]<\/div>\n
    “Robust”<\/span>     =><\/span> [#ffbb00<\/span>,<\/span> #770737<\/span>]<\/div>\n
    “Accented”<\/span>   =><\/span> [#9618f7<\/span>,<\/span> #ff0078<\/span>]<\/div>\n
    “Monochrome”<\/span> =><\/span> [#dee2e6<\/span>,<\/span> #495057<\/span>]<\/div>\n
\/\/ Override with custom colors if manual palette is selected<\/span><\/div>\n
[UpCol<\/span>,<\/span> DnCol<\/span>]   =<\/span>   switch<\/span>      man<\/span><\/div>\n
    false<\/span>        =><\/span> [UpC<\/span>,<\/span>        DnC<\/span>     ]<\/div>\n
    true<\/span>         =><\/span> [manUpC<\/span>,<\/span>     manDnC<\/span>  ]<\/div>\n

<\/p>\n

\/\/ Define a function to compute the cosine of an input scaled by a frequency tuner<\/span><\/div>\n
cosine<\/span>(x<\/span>,<\/span> z<\/span>) =><\/span><\/div>\n
    \/\/ Where x = source input<\/span><\/div>\n
    \/\/       y = function output<\/span><\/div>\n
    \/\/       z = frequency tuner<\/span><\/div>\n
    var<\/span> y<\/span> =<\/span> 0.<\/span><\/div>\n
    y<\/span> :=<\/span> math.cos<\/span>(z<\/span> *<\/span> x<\/span>)<\/div>\n
    y<\/span><\/div>\n
\/\/ Define a kernel that utilizes the cosine function<\/span><\/div>\n
kernel<\/span>(x<\/span>,<\/span> z<\/span>) =><\/span><\/div>\n
    var<\/span> y<\/span> =<\/span> 0.<\/span><\/div>\n
    y<\/span> :=<\/span> cosine<\/span>(x<\/span>,<\/span> z<\/span>)<\/div>\n
    math.abs<\/span>(x<\/span>) <=<\/span> math.pi<\/span>\/<\/span>(2<\/span> *<\/span> z<\/span>) ?<\/span> math.abs<\/span>(y<\/span>) :<\/span> 0.<\/span>    \/\/    cos(zx) = 0<\/span><\/div>\n
\/\/  The above restricts the wave to positive values      \/\/     when x = \u03c0 \/ 2z<\/span><\/div>\n
\/\/ Kernel Regression                                                       <\/span><\/div>\n
kernelRegression<\/span>(src<\/span>,<\/span> lookback<\/span>,<\/span> tuning<\/span>) =><\/span>                                <\/div>\n
                                                                          <\/div>\n
    currentWeight<\/span> =<\/span> 0.<\/span>                                                    <\/div>\n
    totalWeight<\/span>   =<\/span> 0.<\/span>                                                    <\/div>\n
    for<\/span> i<\/span> =<\/span> 0<\/span> to<\/span> math.min<\/span>(lookback<\/span> –<\/span> 1<\/span>,<\/span> bar_index<\/span>)                        <\/div>\n
        y<\/span> =<\/span> src<\/span>[i<\/span>]                                                        <\/div>\n
        w<\/span> =<\/span> kernel<\/span>(i<\/span> \/<\/span> lookback<\/span>,<\/span> tuning<\/span>)                                  <\/div>\n
        currentWeight<\/span> +=<\/span> y<\/span> *<\/span> w<\/span>                                            <\/div>\n
        totalWeight<\/span>   +=<\/span> w<\/span>                                                <\/div>\n
    currentWeight<\/span> \/<\/span> totalWeight<\/span>                                           <\/div>\n
                                                                          <\/div>\n

<\/p>\n

\/\/ Multi Cosine                                                           <\/span><\/div>\n
multicosine<\/span>(src<\/span>,<\/span> lookback<\/span>,<\/span> steps<\/span>) =><\/span>                                      <\/div>\n
                                                                          <\/div>\n
    regression<\/span> =<\/span> 0.<\/span>                                                       <\/div>\n
    for<\/span> i<\/span> =<\/span> 1<\/span> to<\/span> math.min<\/span>(steps<\/span> –<\/span> 1<\/span>,<\/span> bar_index<\/span>)                           <\/div>\n
        regression<\/span> +=<\/span> kernelRegression<\/span>(src<\/span>,<\/span> lookback<\/span>,<\/span> i<\/span>)                  <\/div>\n
    regression<\/span> \/<\/span> steps<\/span>                                                    <\/div>\n
                                                                          <\/div>\n
RSI_ReScale<\/span>   (_res<\/span>      ) =><\/span> (      _res<\/span> –<\/span> 50<\/span>   ) *<\/span> 2.8<\/span><\/div>\n
STOCH_ReScale<\/span> (_stoch<\/span>    ) =><\/span> (    _stoch<\/span> –<\/span> 50<\/span>   ) *<\/span> 2<\/span><\/div>\n
BBPCT_ReScale<\/span> (_bbpct<\/span>    ) =><\/span> (    _bbpct<\/span> –<\/span> 0.5<\/span>  ) *<\/span> 120<\/span><\/div>\n
CMO_ReScale<\/span>   (_chandeMO<\/span> ) =><\/span> ( _chandeMO<\/span> *<\/span> 1.15<\/span> )<\/div>\n
CCI_ReScale<\/span>   (_cci<\/span>      ) =><\/span> (      _cci<\/span> \/<\/span> 2<\/span>    )<\/div>\n
FISH_ReScale<\/span>  (_fish1<\/span>    ) =><\/span> (    _fish1<\/span> *<\/span> 30<\/span>   )<\/div>\n
VZO_ReScale<\/span>   (_VP<\/span>,<\/span> _TV<\/span>  ) =><\/span> (_VP<\/span> \/<\/span> _TV<\/span>) *<\/span> 110<\/span><\/div>\n
DynamicStdev<\/span>(src<\/span>,<\/span> length<\/span>) =><\/span><\/div>\n
    actualLength<\/span> =<\/span>    math.min<\/span>(length<\/span>,<\/span> bar_index<\/span> +<\/span> 1<\/span>)<\/div>\n
    mean<\/span>         =<\/span> Dynamic<\/span>.<\/span>SMA<\/span>(src<\/span>,<\/span>     actualLength<\/span>)<\/div>\n
    sumSquares<\/span>   =<\/span> 0.0<\/span><\/div>\n
    for<\/span> i<\/span> =<\/span> 0<\/span> to<\/span> actualLength<\/span> –<\/span> 1<\/span><\/div>\n
        sumSquares<\/span> :=<\/span> sumSquares<\/span> +<\/span> math.pow<\/span>(nz<\/span>(src<\/span>[i<\/span>]) –<\/span> mean<\/span>,<\/span> 2<\/span>)<\/div>\n
    stdev<\/span> =<\/span> math.sqrt<\/span>(sumSquares<\/span> \/<\/span> actualLength<\/span>)<\/div>\n
    stdev<\/span><\/div>\n
DynamicDev<\/span>(source<\/span>,<\/span> length<\/span>) =><\/span><\/div>\n
    mean<\/span>  =<\/span> Dynamic<\/span>.<\/span>SMA<\/span>(source<\/span>,<\/span> length<\/span>)<\/div>\n
    sum<\/span>   =<\/span> 0.0<\/span><\/div>\n
    for<\/span> i<\/span> =<\/span> 0<\/span> to<\/span> length<\/span> –<\/span> 1<\/span><\/div>\n
        val<\/span>  =<\/span> source<\/span>[i<\/span>]<\/div>\n
        sum<\/span> :=<\/span> sum<\/span> +<\/span> math.abs<\/span>(val<\/span> –<\/span> mean<\/span>)<\/div>\n
    dev<\/span> =<\/span> sum<\/span> \/<\/span> length<\/span><\/div>\n
    <\/div>\n

<\/p>\n

DynamicRMA<\/span>(src<\/span>,<\/span> length<\/span>) =><\/span><\/div>\n
    srcx<\/span> =<\/span> src<\/span> *<\/span> 100<\/span><\/div>\n
    var<\/span> float<\/span> rma<\/span> =<\/span> na<\/span><\/div>\n
    rma<\/span> :=<\/span> length<\/span> ==<\/span> 1<\/span> ?<\/span> <\/div>\n
           Dynamic<\/span>.<\/span>SMA<\/span>(srcx<\/span>,<\/span> length<\/span>) :<\/span> <\/div>\n
           ((rma<\/span>[1<\/span>] *<\/span> (length<\/span> –<\/span> 1<\/span>) ) +<\/span> srcx<\/span>) \/<\/span> length<\/span> <\/div>\n
    rma<\/span><\/div>\n
DynamicRSI<\/span>(src<\/span>,<\/span> length<\/span>) =><\/span><\/div>\n
    var<\/span> float<\/span> res<\/span> =<\/span> na<\/span><\/div>\n
    len<\/span>  =<\/span>     math.min<\/span>(length<\/span>,<\/span>   bar_index<\/span>        )<\/div>\n
    u<\/span>    =<\/span>     math.max<\/span>(src<\/span>     –<\/span> src<\/span>[1<\/span>],<\/span> 0<\/span>        ) <\/div>\n
    d<\/span>    =<\/span>     math.max<\/span>(src<\/span>[1<\/span>]  –<\/span> src<\/span>,<\/span>     0<\/span>       )<\/div>\n
    rs<\/span>   =<\/span>   DynamicRMA<\/span>(u<\/span>,<\/span> len<\/span>) \/<\/span> DynamicRMA<\/span>(d<\/span>,<\/span> len<\/span>)<\/div>\n
    res<\/span> :=<\/span> 100<\/span> –<\/span> 100<\/span> \/<\/span> (1<\/span> +<\/span> rs<\/span>)<\/div>\n
    RSI_ReScale<\/span>(res<\/span>)<\/div>\n
DynamicSTOCH<\/span>(source<\/span>,<\/span> hi<\/span>,<\/span> lo<\/span>,<\/span> length<\/span>) =><\/span><\/div>\n
    len<\/span>         =<\/span>    math.min<\/span>(length<\/span>,<\/span>   1<\/span> +<\/span> bar_index<\/span>)<\/div>\n
    lowestLow<\/span>   =<\/span>   ta.lowest<\/span>(lo<\/span>,<\/span>           len<\/span>      )<\/div>\n
    highestHigh<\/span> =<\/span>  ta.highest<\/span>(hi<\/span>,<\/span>           len<\/span>      )<\/div>\n
    stochastic<\/span>  =<\/span>      100<\/span> *<\/span> (source<\/span>      –<\/span> lowestLow<\/span>) \/<\/span> <\/div>\n
                             (highestHigh<\/span> –<\/span> lowestLow<\/span>)<\/div>\n
    STOCH_ReScale<\/span>(stochastic<\/span>)<\/div>\n
DynamicBBPCT<\/span>(length<\/span>,<\/span> multi<\/span>) =><\/span><\/div>\n
    basis<\/span> =<\/span>   Dynamic<\/span>.<\/span>SMA<\/span>(src<\/span>,<\/span> length<\/span>)<\/div>\n
    dev<\/span>   =<\/span>   multi<\/span> *<\/span> DynamicStdev<\/span>(src<\/span>,<\/span> length<\/span>)<\/div>\n
    upper<\/span> =<\/span>   basis<\/span> +<\/span> dev<\/span><\/div>\n
    lower<\/span> =<\/span>   basis<\/span> –<\/span> dev<\/span><\/div>\n
    bbpct<\/span> =<\/span>  (src<\/span>   –<\/span> lower<\/span>) \/<\/span> <\/div>\n
             (upper<\/span> –<\/span> lower<\/span>)<\/div>\n
    BBPCT_ReScale<\/span>(bbpct<\/span>)<\/div>\n

<\/p>\n

 <\/div>\n
DynamicCMO<\/span>(length<\/span>) =><\/span><\/div>\n
    len<\/span>  =<\/span> math.min<\/span>(length<\/span>,<\/span> bar_index<\/span> +<\/span> 1<\/span>)            <\/div>\n
    momm<\/span> =<\/span> ta.change<\/span>(src<\/span>)                             <\/div>\n
    m1<\/span>   =<\/span> momm<\/span> >=<\/span> 0<\/span> ?<\/span>  momm<\/span> :<\/span> 0.0<\/span>                     <\/div>\n
    m2<\/span>   =<\/span> momm<\/span> <<\/span>  0<\/span> ?<\/span> –<\/span>momm<\/span> :<\/span> 0.0<\/span>                     <\/div>\n
    sm1<\/span>  =<\/span> math.sum<\/span>(m1<\/span>,<\/span> len<\/span>)                          <\/div>\n
    sm2<\/span>  =<\/span> math.sum<\/span>(m2<\/span>,<\/span> len<\/span>)                          <\/div>\n
    div<\/span>  =<\/span> sm1<\/span> +<\/span> sm2<\/span>                                  <\/div>\n
    chandeMO<\/span> =<\/span> div<\/span> !=<\/span> 0<\/span> ?<\/span> 100<\/span> *<\/span> (sm1<\/span> –<\/span> sm2<\/span>) \/<\/span> div<\/span> :<\/span> 0<\/span> <\/div>\n
    CMO_ReScale<\/span>(chandeMO<\/span>)                             <\/div>\n
DynamicCCI<\/span>(length<\/span>) =><\/span><\/div>\n
    len<\/span> =<\/span> math.min<\/span>(length<\/span>,<\/span> bar_index<\/span> +<\/span> 1<\/span>)<\/div>\n
    ma<\/span>  =<\/span> Dynamic<\/span>.<\/span>SMA<\/span>(src<\/span>,<\/span> len<\/span>) <\/div>\n
    cci<\/span> =<\/span> (src<\/span> –<\/span> ma<\/span>) \/<\/span> (0.015<\/span> *<\/span> DynamicDev<\/span>(src<\/span>,<\/span> len<\/span>))<\/div>\n
    CCI_ReScale<\/span>(cci<\/span>)<\/div>\n
DynamicFisher<\/span>(length<\/span>) =><\/span><\/div>\n
    len<\/span>     =<\/span>   math.min<\/span>(length<\/span>,<\/span> 1<\/span> +<\/span> bar_index<\/span>)<\/div>\n
    high_<\/span>   =<\/span> ta.highest<\/span>(hl2<\/span>,<\/span>        len<\/span>      )<\/div>\n
    low_<\/span>    =<\/span>  ta.lowest<\/span>(hl2<\/span>,<\/span>        len<\/span>      )<\/div>\n
    value1<\/span>  =<\/span> 0.0<\/span><\/div>\n
    value2<\/span>  =<\/span> 0.0<\/span><\/div>\n
    value1<\/span> :=<\/span> 0.66<\/span> *<\/span> ((hl2<\/span> –<\/span> low_<\/span>)  \/<\/span> (high_<\/span> –<\/span> low_<\/span>) –<\/span> 0.5<\/span>)  +<\/span> 0.67<\/span> *<\/span> nz<\/span>(value1<\/span>[1<\/span>])<\/div>\n
    value2<\/span> :=<\/span> value1<\/span> ><\/span> 0.99<\/span> ?<\/span> 0.999<\/span> :<\/span> value1<\/span> <<\/span> -.99<\/span>  ?<\/span> -.999<\/span> :<\/span> value1<\/span><\/div>\n
    fish1<\/span>   =<\/span> 0.0<\/span><\/div>\n
    fish1<\/span>  :=<\/span> 0.5<\/span> *<\/span> math.log<\/span>((1<\/span> +<\/span> value2<\/span>) \/<\/span> (1<\/span> –<\/span> value2<\/span>)) +<\/span> 0.5<\/span> *<\/span> nz<\/span>(fish1<\/span>[1<\/span>]) <\/div>\n
    FISH_ReScale<\/span>(fish1<\/span>)<\/div>\n
DynamicVZO<\/span>(length<\/span>) =><\/span><\/div>\n
    VP<\/span> =<\/span> Dynamic<\/span>.<\/span>EMA<\/span>(math.sign<\/span>(ta.change<\/span>(hlc3<\/span>)) *<\/span> volume<\/span>,<\/span> length<\/span> \/<\/span> 3<\/span>)<\/div>\n
    TV<\/span> =<\/span> Dynamic<\/span>.<\/span>EMA<\/span>(volume<\/span>,<\/span> length<\/span> \/<\/span> 3<\/span>)<\/div>\n
    VZO_ReScale<\/span>(VP<\/span>,<\/span> TV<\/span>)<\/div>\n
\/\/ Function to check the status of user-bools<\/span><\/div>\n
booleanCheck<\/span>(condition<\/span>,<\/span> func<\/span>) =><\/span> condition<\/span> ?<\/span> func<\/span> :<\/span> na<\/span><\/div>\n
\/\/ Function to count active indicators<\/span><\/div>\n
countCondition<\/span>(condition<\/span>)     =><\/span> condition<\/span> ?<\/span> 1<\/span> :<\/span> 0<\/span><\/div>\n
\/\/ Pull all standardized base indicator values<\/span><\/div>\n
val_RSI<\/span>   =<\/span> booleanCheck<\/span>(bool_RSI<\/span>,<\/span>   DynamicRSI<\/span>    (src<\/span>,<\/span> length_RSI<\/span>)              )<\/div>\n
val_STOCH<\/span> =<\/span> booleanCheck<\/span>(bool_STOCH<\/span>,<\/span> DynamicSTOCH<\/span>  (src<\/span>,<\/span> high<\/span>,<\/span> low<\/span>,<\/span> length_STOCH<\/span>) )<\/div>\n
val_BBPCT<\/span> =<\/span> booleanCheck<\/span>(bool_BBPCT<\/span>,<\/span> DynamicBBPCT<\/span>  (length_BBPCT<\/span>,<\/span> 2<\/span>)              )<\/div>\n
val_CMO<\/span>   =<\/span> booleanCheck<\/span>(bool_CMO<\/span>,<\/span>   DynamicCMO<\/span>    (length_CMO<\/span>     )              )<\/div>\n
val_CCI<\/span>   =<\/span> booleanCheck<\/span>(bool_CCI<\/span>,<\/span>   DynamicCCI<\/span>    (length_CCI<\/span>     )              )<\/div>\n
val_FISH<\/span>  =<\/span> booleanCheck<\/span>(bool_FISH<\/span>,<\/span>  DynamicFisher<\/span> (length_FISH<\/span>    )              )<\/div>\n
val_VZO<\/span>   =<\/span> booleanCheck<\/span>(bool_VZO<\/span>,<\/span>   DynamicVZO<\/span>    (length_VZO<\/span>     )              )<\/div>\n
\/\/ Count the number of active indicators<\/span><\/div>\n
activeIndicators<\/span> =<\/span> <\/div>\n
          countCondition<\/span>(bool_RSI<\/span>  ) +<\/span><\/div>\n
          countCondition<\/span>(bool_STOCH<\/span>) +<\/span><\/div>\n
          countCondition<\/span>(bool_BBPCT<\/span>) +<\/span><\/div>\n
          countCondition<\/span>(bool_CMO<\/span>  ) +<\/span><\/div>\n
          countCondition<\/span>(bool_CCI<\/span>  ) +<\/span><\/div>\n
          countCondition<\/span>(bool_FISH<\/span> ) +<\/span><\/div>\n
          countCondition<\/span>(bool_VZO<\/span>  )<\/div>\n
\/\/ Calculate the average only with active indicators<\/span><\/div>\n
value<\/span> =<\/span> activeIndicators<\/span> ><\/span> 0<\/span> ?<\/span> (<\/div>\n
          nz<\/span>(val_RSI<\/span>,<\/span>   0<\/span>) +<\/span><\/div>\n
          nz<\/span>(val_STOCH<\/span>,<\/span> 0<\/span>) +<\/span><\/div>\n
          nz<\/span>(val_BBPCT<\/span>,<\/span> 0<\/span>) +<\/span><\/div>\n
          nz<\/span>(val_CMO<\/span>,<\/span>   0<\/span>) +<\/span><\/div>\n
          nz<\/span>(val_CCI<\/span>,<\/span>   0<\/span>) +<\/span><\/div>\n
          nz<\/span>(val_FISH<\/span>,<\/span>  0<\/span>) +<\/span><\/div>\n
          nz<\/span>(val_VZO<\/span>,<\/span>   0<\/span>)<\/div>\n
          ) \/<\/span> activeIndicators<\/span> :<\/span> na<\/span><\/div>\n

<\/p>\n

\/\/ Gentle ALMA smoothing<\/span><\/div>\n
value<\/span> :=<\/span> Dynamic<\/span>.<\/span>ALMA<\/span>(value<\/span>,<\/span> 9<\/span>,<\/span> 0<\/span>,<\/span> 6<\/span>)<\/div>\n
\/\/ Calulate the Output – Depending on the method of Cosine Regression Selected<\/span><\/div>\n
out<\/span>  =<\/span> switch<\/span> varient<\/span> <\/div>\n
    “Tuneable”<\/span> =><\/span> kernelRegression<\/span>(value<\/span>,<\/span> lookbackR<\/span>,<\/span> tuning<\/span>)<\/div>\n
    “Stepped”<\/span>  =><\/span>      multicosine<\/span>(value<\/span>,<\/span> lookbackR<\/span>,<\/span> tuning<\/span>)<\/div>\n

<\/p>\n

out2<\/span> =<\/span> switch<\/span> varient<\/span> <\/div>\n
    “Tuneable”<\/span> =><\/span> kernelRegression<\/span>(value<\/span>,<\/span> lookbackR<\/span>,<\/span> math.round<\/span>(tuning<\/span> \/<\/span> 5<\/span>))<\/div>\n
    “Stepped”<\/span>  =><\/span>      multicosine<\/span>(value<\/span>,<\/span> lookbackR<\/span>,<\/span> math.round<\/span>(tuning<\/span> \/<\/span> 5<\/span>))<\/div>\n
\/\/ Define Alert Conditions<\/span><\/div>\n
fastTrend_up<\/span>  =<\/span> out<\/span>  ><\/span> out<\/span>[1<\/span>]  and<\/span> not<\/span> (out<\/span>[1<\/span>]  ><\/span> out<\/span>[2<\/span>])<\/div>\n
fastTrend_dn<\/span>  =<\/span> out<\/span>  <<\/span> out<\/span>[1<\/span>]  and<\/span> not<\/span> (out<\/span>[1<\/span>]  <<\/span> out<\/span>[2<\/span>])<\/div>\n
slowTrend_up<\/span>  =<\/span> out2<\/span> ><\/span>     0<\/span>   and<\/span> not<\/span> (out2<\/span>[1<\/span>] ><\/span>     0<\/span> )<\/div>\n
slowTrend_dn<\/span>  =<\/span> out2<\/span> <<\/span>     0<\/span>   and<\/span> not<\/span> (out2<\/span>[1<\/span>] <<\/span>     0<\/span> )<\/div>\n
overbought<\/span>    =<\/span> out<\/span>  ><\/span>    50<\/span>   and<\/span> not<\/span> (out<\/span>[1<\/span>]  ><\/span>    50<\/span> )<\/div>\n
oversold<\/span>      =<\/span> out<\/span>  <<\/span>   -50<\/span>   and<\/span> not<\/span> (out<\/span>[1<\/span>]  <<\/span>   -50<\/span> )<\/div>\n

<\/p>\n

fastTrend<\/span>     =<\/span> fastTrend_up<\/span> or<\/span> fastTrend_dn<\/span><\/div>\n
slowTrend<\/span>     =<\/span> slowTrend_up<\/span> or<\/span> slowTrend_dn<\/span><\/div>\n
                                                                                                           <\/div>\n
                                                                                                           <\/div>\n
barcol<\/span> =<\/span> switch<\/span> ColType<\/span>                                                                                    <\/div>\n
    “None”<\/span>                   =><\/span> na<\/span>                                                                         <\/div>\n
    “Fast Trend”<\/span>             =><\/span> out<\/span>  ><\/span> out<\/span>[1<\/span>]       ?<\/span>      UpCol<\/span> :<\/span> DnCol<\/span>                                   <\/div>\n
    “Slow Trend”<\/span>             =><\/span> out2<\/span> ><\/span> 0<\/span>            ?<\/span>      UpCol<\/span> :<\/span> DnCol<\/span>                                   <\/div>\n
col<\/span>                          =<\/span>  out<\/span>  ><\/span> out<\/span>[1<\/span>]       ?<\/span>      UpCol<\/span> :<\/span> DnCol<\/span>                                   <\/div>\n
col2<\/span>                         =<\/span>  color.new<\/span>((out2<\/span> ><\/span> 0<\/span> ?<\/span>      UpCol<\/span> :<\/span> DnCol<\/span>),<\/span> 80<\/span>)                             <\/div>\n
                                                                                                           <\/div>\n
mid<\/span>  =<\/span> plot<\/span>(  0<\/span>,<\/span> “Midline”<\/span>,<\/span>   color.gray<\/span> ,<\/span>               display<\/span> =<\/span> display.all<\/span>         )                 <\/div>\n
                                                                                                           <\/div>\n
                                                                                                           <\/div>\n
sig<\/span>  =<\/span> plot<\/span>(out<\/span>,<\/span>  “Fast Signal”<\/span>,<\/span>          col<\/span>,<\/span>       3<\/span>,<\/span>    display<\/span> =<\/span> display.all<\/span>         ),<\/span>                <\/div>\n
       plot<\/span>(out<\/span>,<\/span>  “”<\/span>,<\/span>           color.new<\/span>(col<\/span>,<\/span> 85<\/span>),<\/span> 10<\/span>,<\/span>    display<\/span> =<\/span> display.pane<\/span>        ),<\/span>                <\/div>\n
       plot<\/span>(out<\/span>,<\/span>  “”<\/span>,<\/span>           color.new<\/span>(col<\/span>,<\/span> 98<\/span>),<\/span> 25<\/span>,<\/span>    display<\/span> =<\/span> display.pane<\/span>        )                 <\/div>\n
sig2<\/span> =<\/span> plot<\/span>(out2<\/span>,<\/span> “Slow Signal”<\/span>,<\/span>          col2<\/span>,<\/span>      3<\/span>,<\/span>    display<\/span> =<\/span> display.all<\/span>,<\/span> style<\/span> =<\/span> plot.style_area<\/span>) <\/div>\n
                                                                                                           <\/div>\n
                                                                                                           <\/div>\n
l1<\/span>   =<\/span> plot<\/span>( 100<\/span>,<\/span> “”<\/span>,<\/span>                     na<\/span>,<\/span>              display<\/span> =<\/span> display.data_window<\/span> )                 <\/div>\n
l2<\/span>   =<\/span> plot<\/span>(  50<\/span>,<\/span> “”<\/span>,<\/span>           color.new<\/span>(DnCol<\/span>,<\/span> 50<\/span>),<\/span>      display<\/span> =<\/span> display.data_window<\/span> )                 <\/div>\n
l3<\/span>   =<\/span> plot<\/span>( -50<\/span>,<\/span> “”<\/span>,<\/span>           color.new<\/span>(UpCol<\/span>,<\/span> 50<\/span>),<\/span>      display<\/span> =<\/span> display.data_window<\/span> )                 <\/div>\n
l4<\/span>   =<\/span> plot<\/span>(-100<\/span>,<\/span> “”<\/span>,<\/span>                     na<\/span>,<\/span>              display<\/span> =<\/span> display.data_window<\/span> )                 <\/div>\n
                                                                                                           <\/div>\n
                                                                                                           <\/div>\n
plotshape<\/span>  (out<\/span>,<\/span> “”<\/span>,<\/span>  shape.xcross<\/span>,<\/span>      color<\/span> =<\/span> fastTrend_dn<\/span> ?<\/span> color.new<\/span>(DnCol<\/span>,<\/span> 15<\/span>) :<\/span> na<\/span>,<\/span>                 <\/div>\n
           location<\/span> =<\/span> location.absolute<\/span>,<\/span> size<\/span>  =<\/span> size.small<\/span>                              )                 <\/div>\n
plotshape<\/span>  (out<\/span>,<\/span> “”<\/span>,<\/span>  shape.circle<\/span>,<\/span>      color<\/span> =<\/span> fastTrend_up<\/span> ?<\/span> color.new<\/span>(UpCol<\/span>,<\/span> 15<\/span>) :<\/span> na<\/span>,<\/span>                 <\/div>\n
           location<\/span> =<\/span> location.absolute<\/span>,<\/span> size<\/span>  =<\/span> size.small<\/span>                              )                 <\/div>\n
                                                                                                           <\/div>\n
fill<\/span>(l1<\/span>,<\/span> l2<\/span>,<\/span> 100<\/span>,<\/span>   50<\/span>,<\/span>         color.new<\/span>(DnCol<\/span>,<\/span> 60<\/span>),<\/span>           color.new<\/span>(chart.bg_color<\/span>,<\/span> 30<\/span>))             <\/div>\n
fill<\/span>(l4<\/span>,<\/span> l3<\/span>,<\/span> -50<\/span>,<\/span> -100<\/span>,<\/span>         color.new<\/span>(chart.bg_color<\/span>,<\/span> 30<\/span>),<\/span>  color.new<\/span>(UpCol<\/span>,<\/span> 60<\/span>)     )                 <\/div>\n
                                                                                                           <\/div>\n
barcolor<\/span>(barcol<\/span>)                                                                                           <\/div>\n
                                                                                                           <\/div>\n

<\/p>\n

symbol<\/span> =<\/span> “CKR [QuantraSystems] >>> {{exchange}}:{{ticker}}”<\/span><\/div>\n

<\/p>\n

\/\/ Fast Trend Alerts<\/span><\/div>\n
alertcondition<\/span>(fastTrend_up<\/span>,<\/span> “Fast Positive Trend”<\/span>,<\/span>  symbol<\/span> +<\/span> ” – Fast Positive Trend!”<\/span> )<\/div>\n
alertcondition<\/span>(fastTrend_dn<\/span>,<\/span> “Fast Negative Trend”<\/span>,<\/span>  symbol<\/span> +<\/span> ” – Fast Negative Trend!”<\/span> )<\/div>\n

<\/p>\n

\/\/ Slow Trend Alerts  <\/span><\/div>\n
alertcondition<\/span>(slowTrend_up<\/span>,<\/span> “Slow Positive Trend”<\/span>,<\/span>  symbol<\/span> +<\/span> ” – Slow Positive Trend!”<\/span> )<\/div>\n
alertcondition<\/span>(slowTrend_dn<\/span>,<\/span> “Slow Negative Trend”<\/span>,<\/span>  symbol<\/span> +<\/span> ” – Slow Negative Trend!”<\/span> )<\/div>\n

<\/p>\n

\/\/ Combined Alerts<\/span><\/div>\n
alertcondition<\/span>(fastTrend<\/span>,<\/span>    “Fast Trend Shift”<\/span>,<\/span>     symbol<\/span> +<\/span> ” – Fast Trend Shift!”<\/span>    )<\/div>\n
alertcondition<\/span>(slowTrend<\/span>,<\/span>    “Slow Trend Shift”<\/span>,<\/span>     symbol<\/span> +<\/span> ” – Slow Trend Shift!”<\/span>    )<\/div>\n

<\/p>\n

    \/\/ Define Buy Exit condition<\/span><\/div>\n
uptrend_condition<\/span> =<\/span> oversold<\/span><\/div>\n
downtrend_condition<\/span> =<\/span> overbought<\/span><\/div>\n
\/\/ Strategy entry and exit conditions<\/span><\/div>\n
if<\/span> (uptrend_condition<\/span>)<\/div>\n
    strategy.entry<\/span>(“Buy”<\/span>,<\/span> strategy.long<\/span>)<\/div>\n
    <\/div>\n
if<\/span> (downtrend_condition<\/span>)<\/div>\n
    strategy.close<\/span>(“Buy”<\/span>)<\/div>\n

<\/div>\n

<\/p>\n

*Feel free to share your different settings in the comment section<\/p>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

 This pine script is adapted from Cosine Kernel Regression by QuantraSystems. Credit goes to this brilliant script writer.  The Cosine Kernel Regressions indicator (CKR) uses mathematical concepts to offer a<\/p>\n

Continue ReadingNew Pine Script: Cosine Kernel Regression<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[3],"class_list":["post-15","post","type-post","status-publish","format-standard","hentry","category-pine-script-strategy","tag-pine-script-strategy"],"yoast_head":"\nNew Pine Script: Cosine Kernel Regression - kriptos.my<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/kriptos.my\/index.php\/2024\/06\/22\/new-pine-script-cosine-kernel-regression\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"New Pine Script: Cosine Kernel Regression - kriptos.my\" \/>\n<meta property=\"og:description\" content=\" This pine script is adapted from Cosine Kernel Regression by QuantraSystems. 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