커뮤니티
수식 변경 부탁드립니다
2023-11-22 20:45:08
1760
글번호 174288
// This function calcualtes a se lectable average type
GetAverage(_data, _len, MAOption) =>
value = switch MAOption
'SMA' => ta.sma(_data, _len)
'EMA' => ta.ema(_data, _len)
'HMA' => ta.hma(_data, _len)
'RMA' => ta.rma(_data, _len)
=>
ta.wma(_data, _len)
// ***********************************************************************************************************
// ========================================================================================
// Normalization function - Normalizes values that are not restricted within a zero to 100 range
// This technique provides a scale that is closer to a "human" estimation of value in "bands"
// as in: low, below average, average, above average, high, super high
// this also avoids the issue of extreme values when using the stoch() -based technique
// these values are subjective, and can be changed - but slight changes here won't lead to major changes in outcome
// since all is relative to the same data series.
//
Normalize(_Value, _Avg) =>
_X = _Value / _Avg
_Nor =
_X > 1.50 ? 1.00 :
_X > 1.20 ? 0.90 :
_X > 1.00 ? 0.80 :
_X > 0.80 ? 0.70 :
_X > 0.60 ? 0.60 :
_X > 0.40 ? 0.50 :
_X > 0.20 ? 0.25 :
0.1
// ===================================================================================
// ===========================================================================================================
// Inputs
// ===========================================================================================================
grp_1 = 'Rate of FLow (RoF)'
grp_2 = 'Lookback Parameters'
grp_3 = 'Bias / Sentiment'
grp_4 = 'EVEREX Bands'
length = input.int(10, minval = 1, inline = 'ROF', group = grp_1)
MA_Type = input.string(defval = 'WMA', title = 'MA type',
options = ['WMA', 'EMA', 'SMA', 'HMA', 'RMA'], inline = 'ROF', group = grp_1)
smooth = input.int(defval = 3, title = 'Smooth', minval = 1, inline = 'ROF', group = grp_1)
//src = input.source(close, title = "Source (for 2-Bar Shift)", group = grp_1)
sig_length = input.int(5, 'Signal Length', minval = 1, inline = 'Signal', group = grp_1)
S_Type = input.string(defval = 'WMA', title = 'Signal Type',
options = ['WMA', 'EMA', 'SMA', 'HMA', 'RMA'], inline = 'Signal', group = grp_1)
lookback = input.int(defval = 20, title = 'Length', minval = 1, inline = 'Lookback', group = grp_2)
lkbk_Calc = input.string(defval = 'Simple', title = 'Averaging',
options = ['Simple', 'Same as RRoF'], inline='Lookback', group = grp_2 )
showBias = input.bool(defval = false, title = 'Bias Plot ? -- ', inline = 'Bias', group = grp_3)
B_Length = input.int(defval = 30, title = 'Length', minval = 1, inline = 'Bias', group = grp_3)
B_Type = input.string(defval = 'WMA', title = 'MA type',
options = ['WMA', 'EMA', 'SMA', 'HMA', 'RMA'], inline = 'Bias', group = grp_3)
showEVEREX = input.bool(true, 'Show EVEREX Bands ? -- ', inline = 'EVEREX', group = grp_4)
// a simple mechanism to control/change the strength band scale for improving visualization
// applies only to the "bands" and the level hlines
bandscale = str.tonumber(input.string("100", title = "Band Scale",
options = ['100', '200', '400'], inline = 'EVEREX', group = grp_4))
DispBias = showBias ? display.pane : display.none
DispBands = showEVEREX ? display.pane : display.none
showhlines = showEVEREX ? display.all : display.none
Disp_vals = display.status_line + display.data_window
// ===========================================================================================================
// Calculations
// ===========================================================================================================
// Volume "effort" Calculation -- will revert to no volume acceleration for instruments with no volume data
v = na(volume) ? 1 : volume // this part ensures we're not hit with calc issues due to NaN's
NoVol_Flag = na(volume) ? true : false // this is a flag to use later
lkbk_MA_Type = lkbk_Calc == 'Simple' ? 'SMA' : MA_Type
Vola = GetAverage(v, lookback, lkbk_MA_Type)
Vola_n_pre = Normalize(v, Vola) * 100
//Now trap the case of no volume data - ensure final calculation not impacted
Vola_n = NoVol_Flag ? 100 : Vola_n_pre
//plot(Vola_n , "Volume Normalized", color = color.white, display = display.none)
// ===============================================================================================================
// Price "result" calculation
// we'll consider "result" (strength or weakness) to be the outcome (average) of 6 elements:
// Same (in-)Bar strength elements:
// 1 - Bar Closing: the closing within the bar --> this will be a direct +100 / -100 value
// 2 - Spread to range: the spread to range ratio (that's BoP formula) --> direct +100 / -100 value
// 3 - Relative Spread: spread relative to average spread during lookback period --> normalized
// 2-bar strength elements:
// 4 - 2-bar closing: the closing within 2-bar range (that accomodates open gap effect)
// 5 - 2-bar Closing Shift to Range: Change in close relative to the 2-bar range
// 6 - 2-bar Relative Shift: the 2-bar Close (or source price) shift - relative to the average 2-bar shift during lookback period --> normalized
BarSpread = close - open
BarRange = high - low
R2 = ta.highest(2) - ta.lowest(2)
SrcShift = ta.change(close)
//TR = ta.tr(true)
sign_shift = math.sign(SrcShift)
sign_spread = math.sign(BarSpread)
// =========================================================================================================
// in-bar assessments
// =========================================================================================================
// 1. Calculate closing within bar - should be max value at either ends of the bar range
barclosing = 2 * (close - low) / BarRange * 100 - 100
//plot(barclosing, "Bar Closing %" , color=color.fuchsia, display = display.none)
// 2. caluclate spread to range ratio
s2r = BarSpread / BarRange * 100
//plot(s2r, "Spread:Range", color = color.lime, display = display.none)
// 3. Calculate relative spread compared to average spread during lookback
BarSpread_abs = math.abs(BarSpread)
BarSpread_avg = GetAverage(BarSpread_abs, lookback, lkbk_MA_Type)
BarSpread_ratio_n = Normalize(BarSpread_abs, BarSpread_avg) * 100 * sign_spread
//plot(BarSpread_ratio_n, "Bar Spread Ratio", color=color.orange, display=display.none)
// =========================================================================================================
// 2-bar assessments
// =========================================================================================================
// 4. Calculate closing within 2 bar range - should be max value at either ends of the 2-bar range
barclosing_2 = 2 * (close - ta.lowest(2)) / R2 * 100 - 100
//plot(barclosing_2, "2-Bar Closing %" , color=color.navy, display = display.none)
// 5. calculate 2-bar shift to range ratio
Shift2Bar_toR2 = SrcShift / R2 * 100
//plot(Shift2Bar_toR2, "2-bar Shift vs 2R", color=color.yellow, display = display.none)
// 6. Calculate 2-bar Relative Shift
SrcShift_abs = math.abs(SrcShift)
srcshift_avg = GetAverage(SrcShift_abs, lookback, lkbk_MA_Type)
srcshift_ratio_n = Normalize(SrcShift_abs, srcshift_avg) * 100 * sign_shift
//plot(srcshift_ratio_n, "2-bar Shift vs Avg", color=color.white, display = display.none)
// ===============================================================================
// =========================================================================================
// Relative Price Strength combining all strength elements
Pricea_n = (barclosing + s2r + BarSpread_ratio_n + barclosing_2 + Shift2Bar_toR2 + srcshift_ratio_n) / 6
//plot(Pricea_n, "Price Normalized", color=color.orange, display = display.none)
//Let's take Bar Flow as the combined price strength * the volume:avg ratio
// this works in a similar way to a volume-weighted RSI
bar_flow = Pricea_n * Vola_n / 100
//plot(bar_flow, 'bar_flow', color=color.green, display = display.none)
// calc avergae relative rate of flow, then smooth the resulting average
// classic formula would be this
//RROF = f_ma(bar_flow, length, MA_Type)
//
// or we can create a relative index by separating bulls from bears, like in an RSI - my preferred method
// here we have an added benefit of plotting the (average) bulls vs bears separately - as an option
bulls = math.max(bar_flow, 0)
bears = -1 * math.min(bar_flow, 0)
bulls_avg = GetAverage(bulls, length, MA_Type)
bears_avg = GetAverage(bears, length, MA_Type)
dx = bulls_avg / bears_avg
RROF = 2 * (100 - 100 / (1 + dx)) - 100
RROF_s = ta.wma(RROF, smooth)
Signal = GetAverage(RROF_s, sig_length, S_Type)
// Calculate Bias / sentiment on longer length
dx_b = GetAverage(bulls, B_Length, B_Type) / GetAverage(bears, B_Length, B_Type)
RROF_b = 2 * (100 - 100 / (1 + dx_b)) - 100
RROF_bs = ta.wma(RROF_b, smooth)
// ===========================================================================================================
// Colors & plots
// ===========================================================================================================
c_zero = color.new(#1163f6, 25)
c_band = color.new(color.yellow, 40)
c_up = color.aqua
c_dn = color.orange
c_sup = color.new(#00aa00, 70)
c_sdn = color.new(#ff180b, 70)
up = RROF_s >= 0
s_up = RROF_bs >=0
// ==================================== Plots ==========================================================
// // Display the ATR & VOl Ratio values only on the indicator status line & in the Data Window
// plotchar(shift, title = "Shift", char = "", color = color.white, editable=false, display=display.status_line + display.data_window)
// plotchar(lbk_tr, title = "Avg Shift", char = "", color = color.aqua, editable=false, display=display.status_line + display.data_window)
// plotchar(vola/lbk_vola, title = "Vol Ratio", char = "", color = color.yellow, editable=false, display=display.status_line + display.data_window)
hline(0, 'Zero Line', c_zero, linestyle = hline.style_solid)
// plot the band scale guide lines -- these lines will show/hide along with the EVEREX "Equalizer Bands Plot"
hline(0.25 * bandscale, title = '1/4 Level', color=c_band, linestyle = hline.style_dotted, display = showhlines)
hline(0.50 * bandscale, title = '2/4 Level', color=c_band, linestyle = hline.style_dotted, display = showhlines)
hline(0.75 * bandscale, title = '3/4 Level', color=c_band, linestyle = hline.style_dotted, display = showhlines)
hline(bandscale, title = '4/4 Level', color=c_band, linestyle = hline.style_dotted, display = showhlines)
// Plot Bulls & Bears - these are optional plots and hidden by default - adjust this section later
plot(ta.wma(bulls_avg, smooth), "Bulls", color = #11ff20, linewidth = 2, display = display.none)
plot(ta.wma(bears_avg, smooth), "Bears", color = #d5180b, linewidth = 2, display = display.none)
// =============================================================================
// Plot Bias / Sentiment
plot (RROF_bs, "Bias / Sentiment", style=plot.style_area,
color = s_up ? c_sup : c_sdn, linewidth = 4, display = DispBias )
// =============================================================================
// Plot Price Strength & Relative Volume as stacked "equalizer bands"
// adding visualization option to make the bands joint or separate at the mid-scale mark
Eq_band_option = input.string("Joint", title = 'Band Option', options = ["Joint", "Separate"], group = grp_4)
nPrice = math.max(math.min(Pricea_n, 100), -100)
nVol = math.max(math.min(Vola_n, 100), -100)
bar = bar_flow
c_vol_grn = color.new(#26a69a, 75)
c_vol_red = color.new(#ef5350, 75)
cb_vol_grn = color.new(#26a69a, 20)
cb_vol_red = color.new(#ef5350, 20)
c_vol = bar > 0 ? c_vol_grn : c_vol_red
cb_vol = bar > 0 ? cb_vol_grn : cb_vol_red
vc_lo = 0
vc_hi = nVol * bandscale / 100 / 2
plotcandle(vc_lo, vc_hi, vc_lo, vc_hi , "Volume Band", c_vol, c_vol, bordercolor = cb_vol, display = DispBands)
c_pri_grn = color.new(#3ed73e, 75)
c_pri_red = color.new(#ff870a, 75)
cb_pri_grn = color.new(#3ed73e, 20)
cb_pri_red = color.new(#ff870a, 20)
c_pri = bar > 0 ? c_pri_grn : c_pri_red
cb_pri = bar > 0 ? cb_pri_grn : cb_pri_red
pc_lo_base = Eq_band_option == "Joint" ? vc_hi : 0.50 * bandscale
pc_lo = pc_lo_base
pc_hi = pc_lo_base + math.abs(nPrice) * bandscale / 100 / 2
plotcandle(pc_lo, pc_hi, pc_lo ,pc_hi , "Price Band", c_pri, c_pri, bordercolor = cb_pri, display = DispBands)
// print the normalized volume and price values - only on statys line and in the data window
// these values are independant of the band scale or visualization options
plotchar(nVol, "Normalized Vol", char = "", color = c_vol, editable = false, display = Disp_vals)
plotchar(nPrice, "Normalized Price", char = "", color = c_pri, editable = false, display = Disp_vals)
// =============================================================================
// =============================================================================
// Plot main plot, smoothed plot and signal line
plot(RROF, 'RROF Raw', color.new(#2470f0, 9), display=display.none)
plot(RROF_s, 'RROF Smooth', color = color.new(#b2b5be,40), linewidth = 2)
plot(Signal, "Signal Line", up ? c_up : c_dn, 3)
// ===========================================================================================================
// basic alerts
// ===========================================================================================================
Alert_up = ta.crossover(RROF_s,0)
Alert_dn = ta.crossunder(RROF_s,0)
Alert_swing = ta.cross(RROF_s,0)
// "." in alert title for the alerts to show in the right order up/down/swing
alertcondition(Alert_up, ". RROF Crossing 0 Up", "RROF Up - Buying Action Detected!")
alertcondition(Alert_dn, ".. RROF Crossing 0 Down", "RROF Down - Selling Action Detected!")
alertcondition(Alert_swing, "... RROF Crossing 0", "RROF Swing - Possible Reversal")
// ===========================================================================================================
// v2.0 Adding Markers for Key Patterns
// ===========================================================================================================
// we can re-utilize the Normailize() function here too - but it's cleaner to have a separate ratio calc
nPrice_abs = math.abs(nPrice)
//EV_Ratio = 100 * Normalize(nPrice_abs, nVol)
EV_Ratio = 100 * nPrice_abs / nVol
// initial mapping of return ratios (to be revised)
// -------------------------------------------------------
// Case (1): Price > Vol => ratio > 120 = Ease of Move (EoM)
// Case (2): Price close to Vol => ratio between 80 - 120 = Reasonable Balance
// Case (3): Price less than Vol but reasonable => ratio between 80 - 50 = Drift / "nothing much to see here" bar
// Case (4): Price a lot less than Vol => 50 or less = Compression / Squat
// we're most interested in cases 1 & 4
//plot (EV_Ratio) // for validation only
is_positive = nPrice > 0
is_Compression = EV_Ratio <= 50
is_EoM = EV_Ratio >= 120
//Provide option to show/hide those EVEREX Markers - and an option for Compression bar
// - some folks would prefer a cross, others may prefer a circle - can adjust based on feedback
// no option for Ease of Move, guessing the triangle has the right significance
var showMarkers = input.bool(true, 'Show EVEREX Markers ?')
var Mshape = input.string("Circles", "Compression Marker", options = ['Circles','Crosses'])
SetShape(_x) =>
switch _x
'Circles' => shape.circle
'Crosses' => shape.cross
// Plot markers
plotshape(showMarkers and is_EoM and is_positive ? 0 : na, "EoM +ve", shape.triangleup, color=color.green,
location=location.absolute, size=size.auto, editable = false, display = display.pane)
plotshape(showMarkers and is_EoM and not(is_positive) ? 0 : na, "EoM -ve", shape.triangledown, color=color.red,
location=location.absolute, size=size.auto, editable = false, display = display.pane)
plotshape(showMarkers and is_Compression and is_positive ? 0 : na, "Compression +ve", style = SetShape(Mshape),
color=color.green, location=location.absolute, size = size.auto, editable = false, display = display.pane)
plotshape(showMarkers and is_Compression and not(is_positive) ? 0 : na, "Compression -ve", style = SetShape(Mshape),
color=color.red, location=location.absolute, size=size.auto, editable = false, display = display.pane)
답변 1
예스스탁 예스스탁 답변
2023-11-27 10:05:04
안녕하세요
예스스탁입니다.
1번,2번은 사용자함수, 3번이 지표입니다.
1번과 2번을 먼저 사용자함수로 만들 후 3번을 지표식으로 만드시면 됩니다.
그래프의 모양은 지표속성창에서 지정하셔야 합니다.
수식 작성후에 문법검증(f4) 후 f5키를 누르면 지표속성창이 나타납니다.
지표속성 차트표시탭에서
Price Band, Volume Band는 막대
EoM +ve, EoM -ve, Compression +ve, Compression -ve는 점그래프로 지정하고 적용하셔야 합니다.
1
사용자함수명 : GetAverage
반환값형 : 숫자형
input : _data(Numeric),_len(Numeric),MAOption(String);
var : alpha(0);
if MAOption == "SMA" Then
GetAverage = ma(_data,_len);
if MAOption == "EMA" Then
GetAverage = ema(_data,_len);
if MAOption == "SMA" Then
GetAverage = ma(_data,_len);
if MAOption == "WMA" Then
GetAverage = wma(_data,_len);
if MAOption == "HMA" Then
{
GetAverage = wma( 2 * wma(_data, IntPortion( _len * 0.5 ) ) - wma(_data, _len ), IntPortion( SquareRoot( _len ) ) );
}
if MAOption == "RMA" Then
{
alpha = 1/_len;
GetAverage = 0.0;
GetAverage = iff(isNaN(GetAverage[1]) == true, ma(_data, _len) , alpha * _data + (1 - alpha) * iff(isnan(GetAverage[1])==true,0,GetAverage[1]));
}
2
사용자함수명 : Normalize
반환값형 : 숫자형
input : _value(Numeric),_Avg(Numeric);
var : _X(0);
_X = _Value / _Avg;
Normalize =
IFF(_X > 1.50 , 1.00 ,
IFF(_X > 1.20 , 0.90 ,
IFF(_X > 1.00 , 0.80 ,
IFF(_X > 0.80 , 0.70 ,
IFF(_X > 0.60 , 0.60 ,
IFF( _X > 0.40 , 0.50 ,
IFF(_X > 0.20 , 0.25 ,0.1)))))));
3
input : length(10);
input : MA_Type("WMA");
input : smooth(3);
input : sig_length(5);
input : S_Type("WMA");
input : lookback(20);
input : lkbk_Calc("Simple");
input : showBias(false);
input : B_Length(30);
input : B_Type("WMA");
input : showEVEREX(true);
input : bandscale(100);
var : vv(0),NoVol_Flag(0),lkbk_MA_Type("");
var : Vola(0),Vola_n_pre(0),Vola_n(0);
var : BarSpread(0),BarRange(0),R2(0),SrcShift(0),barclosing(0);
var : sign_shift(0),sign_spread(0),s2r(0);
vv = iff(IsNan(volume)==true , 1 , volume);
NoVol_Flag = iff(IsNan(volume)==true , true , False);
if lkbk_Calc == "Simple" Then
lkbk_MA_Type = "SMA";
Else
lkbk_MA_Type = MA_Type;
Vola = GetAverage(vv, lookback, lkbk_MA_Type);
Vola_n_pre = Normalize(vv, Vola) * 100;
Vola_n = iff(NoVol_Flag , 100 , Vola_n_pre);
BarSpread = close - open;
BarRange = high - low;
R2 = highest(H,2) - lowest(L,2);
SrcShift = close-close[1];
sign_shift = sin(SrcShift) ;
sign_spread = sin(BarSpread);
barclosing = 2 * (close - low) / BarRange * 100 - 100;
s2r = BarSpread / BarRange * 100;
var : BarSpread_abs(0),BarSpread_avg(0),BarSpread_ratio_n(0);
var : barclosing_2(0),Shift2Bar_toR2(0),SrcShift_abs(0),srcshift_avg(0),srcshift_ratio_n(0);
var : Pricea_n(0),bar_flow(0),bulls(0),bears(0);
var : bulls_avg(0),bears_avg(0);
var : dx(0),RROF(0),RROF_s(0),Signal(0);
var : dx_b(0),RROF_b(0),RROF_bs(0);
BarSpread_abs = abs(BarSpread);
BarSpread_avg = GetAverage(BarSpread_abs, lookback, lkbk_MA_Type);
BarSpread_ratio_n = Normalize(BarSpread_abs, BarSpread_avg) * 100 * sign_spread ;
barclosing_2 = 2 * (close - lowest(L,2)) / R2 * 100 - 100;
Shift2Bar_toR2 = SrcShift / R2 * 100 ;
SrcShift_abs = abs(SrcShift);
srcshift_avg = GetAverage(SrcShift_abs, lookback, lkbk_MA_Type) ;
srcshift_ratio_n = Normalize(SrcShift_abs, srcshift_avg) * 100 * sign_shift;
Pricea_n = (barclosing + s2r + BarSpread_ratio_n + barclosing_2 + Shift2Bar_toR2 + srcshift_ratio_n) / 6;
bar_flow = Pricea_n * Vola_n / 100 ;
bulls = max(bar_flow, 0);
bears = -1 * min(bar_flow, 0);
bulls_avg = GetAverage(bulls, length, MA_Type);
bears_avg = GetAverage(bears, length, MA_Type);
dx = bulls_avg / bears_avg;
RROF = 2 * (100 - 100 / (1 + dx)) - 100;
RROF_s = wma(RROF, smooth);
Signal = GetAverage(RROF_s, sig_length, S_Type);
dx_b = GetAverage(bulls, B_Length, B_Type) / GetAverage(bears, B_Length, B_Type);
RROF_b = 2 * (100 - 100 / (1 + dx_b)) - 100;
RROF_bs = wma(RROF_b, smooth);
var : up(False),s_up(False);
up = RROF_s >= 0;
s_up = RROF_bs >=0 ;
PlotBaseLine1(0, "Zero Line",Blue);
PlotBaseLine2(0.25 * bandscale,"1/4 Level",Yellow);
PlotBaseLine3(0.50 * bandscale,"2/4 Level",Yellow);
PlotBaseLine4(0.75 * bandscale,"3/4 Level",Yellow);
PlotBaseLine5(bandscale, "4/4 Level",Yellow);
plot1(wma(bulls_avg, smooth), "Bulls",Lime);
plot2(wma(bears_avg, smooth), "Bears",Red);
plot3(RROF_bs, "Bias / Sentiment",IFf(s_up==true,Green,Orange));
input : Eq_band_option("Joint");
var : nPrice(0),nVol(0),bar(0);
var : c_vol(0),cb_vol(0),vc_lo(0),vc_hi(0);
var : c_pri(0),cb_pri(0),pc_lo_base(0),pc_lo(0),pc_hi(0);
nPrice = max(min(Pricea_n, 100), -100);
nVol = max(min(Vola_n, 100), -100);
bar = bar_flow;
c_vol = iff(bar > 0 , Green , red);
cb_vol = iff(bar > 0 , Green , red);
vc_lo = 0;
vc_hi = nVol * bandscale / 100 / 2;
c_pri = iff(bar > 0 , Lime , Magenta);
cb_pri = iff(bar > 0 , Lime , Magenta);
pc_lo_base = iff(Eq_band_option == "Joint" , vc_hi , 0.50 * bandscale);
pc_lo = pc_lo_base;
pc_hi = pc_lo_base + abs(nPrice) * bandscale / 100 / 2;
plot4(pc_hi,"Price Band",C_pri); //막대
plot5(vc_hi, "Volume Band",c_vol); //막대
plot6(RROF, "RROF Raw",Blue);
plot7(RROF_s, "RROF Smooth",Gray);
plot8(Signal, "Signal Line",IFf(up ,Cyan ,Orange));
input : showMarkers(true);
var : nPrice_abs(0),EV_Ratio(0);
var : is_positive(False),is_Compression(False),is_EoM(False);
nPrice_abs = abs(nPrice);
EV_Ratio = 100 * nPrice_abs / nVol;
is_positive = nPrice > 0;
is_Compression = EV_Ratio <= 50;
is_EoM = EV_Ratio >= 120;
if showMarkers and is_EoM and is_positive Then
plot9(0,"EoM +ve"); //점
if showMarkers and is_EoM and is_positive == false Then
plot10(0,"EoM -ve"); //점
if showMarkers and is_Compression and is_positive Then
plot11(0,"Compression +ve"); //점
if showMarkers and is_Compression and is_positive == False Then
plot12(0,"Compression -ve"); //점
즐거운 하루되세요
> 센스짱 님이 쓴 글입니다.
> 제목 : 수식 변경 부탁드립니다
>
// This function calcualtes a se lectable average type
GetAverage(_data, _len, MAOption) =>
value = switch MAOption
'SMA' => ta.sma(_data, _len)
'EMA' => ta.ema(_data, _len)
'HMA' => ta.hma(_data, _len)
'RMA' => ta.rma(_data, _len)
=>
ta.wma(_data, _len)
// ***********************************************************************************************************
// ========================================================================================
// Normalization function - Normalizes values that are not restricted within a zero to 100 range
// This technique provides a scale that is closer to a "human" estimation of value in "bands"
// as in: low, below average, average, above average, high, super high
// this also avoids the issue of extreme values when using the stoch() -based technique
// these values are subjective, and can be changed - but slight changes here won't lead to major changes in outcome
// since all is relative to the same data series.
//
Normalize(_Value, _Avg) =>
_X = _Value / _Avg
_Nor =
_X > 1.50 ? 1.00 :
_X > 1.20 ? 0.90 :
_X > 1.00 ? 0.80 :
_X > 0.80 ? 0.70 :
_X > 0.60 ? 0.60 :
_X > 0.40 ? 0.50 :
_X > 0.20 ? 0.25 :
0.1
// ===================================================================================
// ===========================================================================================================
// Inputs
// ===========================================================================================================
grp_1 = 'Rate of FLow (RoF)'
grp_2 = 'Lookback Parameters'
grp_3 = 'Bias / Sentiment'
grp_4 = 'EVEREX Bands'
length = input.int(10, minval = 1, inline = 'ROF', group = grp_1)
MA_Type = input.string(defval = 'WMA', title = 'MA type',
options = ['WMA', 'EMA', 'SMA', 'HMA', 'RMA'], inline = 'ROF', group = grp_1)
smooth = input.int(defval = 3, title = 'Smooth', minval = 1, inline = 'ROF', group = grp_1)
//src = input.source(close, title = "Source (for 2-Bar Shift)", group = grp_1)
sig_length = input.int(5, 'Signal Length', minval = 1, inline = 'Signal', group = grp_1)
S_Type = input.string(defval = 'WMA', title = 'Signal Type',
options = ['WMA', 'EMA', 'SMA', 'HMA', 'RMA'], inline = 'Signal', group = grp_1)
lookback = input.int(defval = 20, title = 'Length', minval = 1, inline = 'Lookback', group = grp_2)
lkbk_Calc = input.string(defval = 'Simple', title = 'Averaging',
options = ['Simple', 'Same as RRoF'], inline='Lookback', group = grp_2 )
showBias = input.bool(defval = false, title = 'Bias Plot ? -- ', inline = 'Bias', group = grp_3)
B_Length = input.int(defval = 30, title = 'Length', minval = 1, inline = 'Bias', group = grp_3)
B_Type = input.string(defval = 'WMA', title = 'MA type',
options = ['WMA', 'EMA', 'SMA', 'HMA', 'RMA'], inline = 'Bias', group = grp_3)
showEVEREX = input.bool(true, 'Show EVEREX Bands ? -- ', inline = 'EVEREX', group = grp_4)
// a simple mechanism to control/change the strength band scale for improving visualization
// applies only to the "bands" and the level hlines
bandscale = str.tonumber(input.string("100", title = "Band Scale",
options = ['100', '200', '400'], inline = 'EVEREX', group = grp_4))
DispBias = showBias ? display.pane : display.none
DispBands = showEVEREX ? display.pane : display.none
showhlines = showEVEREX ? display.all : display.none
Disp_vals = display.status_line + display.data_window
// ===========================================================================================================
// Calculations
// ===========================================================================================================
// Volume "effort" Calculation -- will revert to no volume acceleration for instruments with no volume data
v = na(volume) ? 1 : volume // this part ensures we're not hit with calc issues due to NaN's
NoVol_Flag = na(volume) ? true : false // this is a flag to use later
lkbk_MA_Type = lkbk_Calc == 'Simple' ? 'SMA' : MA_Type
Vola = GetAverage(v, lookback, lkbk_MA_Type)
Vola_n_pre = Normalize(v, Vola) * 100
//Now trap the case of no volume data - ensure final calculation not impacted
Vola_n = NoVol_Flag ? 100 : Vola_n_pre
//plot(Vola_n , "Volume Normalized", color = color.white, display = display.none)
// ===============================================================================================================
// Price "result" calculation
// we'll consider "result" (strength or weakness) to be the outcome (average) of 6 elements:
// Same (in-)Bar strength elements:
// 1 - Bar Closing: the closing within the bar --> this will be a direct +100 / -100 value
// 2 - Spread to range: the spread to range ratio (that's BoP formula) --> direct +100 / -100 value
// 3 - Relative Spread: spread relative to average spread during lookback period --> normalized
// 2-bar strength elements:
// 4 - 2-bar closing: the closing within 2-bar range (that accomodates open gap effect)
// 5 - 2-bar Closing Shift to Range: Change in close relative to the 2-bar range
// 6 - 2-bar Relative Shift: the 2-bar Close (or source price) shift - relative to the average 2-bar shift during lookback period --> normalized
BarSpread = close - open
BarRange = high - low
R2 = ta.highest(2) - ta.lowest(2)
SrcShift = ta.change(close)
//TR = ta.tr(true)
sign_shift = math.sign(SrcShift)
sign_spread = math.sign(BarSpread)
// =========================================================================================================
// in-bar assessments
// =========================================================================================================
// 1. Calculate closing within bar - should be max value at either ends of the bar range
barclosing = 2 * (close - low) / BarRange * 100 - 100
//plot(barclosing, "Bar Closing %" , color=color.fuchsia, display = display.none)
// 2. caluclate spread to range ratio
s2r = BarSpread / BarRange * 100
//plot(s2r, "Spread:Range", color = color.lime, display = display.none)
// 3. Calculate relative spread compared to average spread during lookback
BarSpread_abs = math.abs(BarSpread)
BarSpread_avg = GetAverage(BarSpread_abs, lookback, lkbk_MA_Type)
BarSpread_ratio_n = Normalize(BarSpread_abs, BarSpread_avg) * 100 * sign_spread
//plot(BarSpread_ratio_n, "Bar Spread Ratio", color=color.orange, display=display.none)
// =========================================================================================================
// 2-bar assessments
// =========================================================================================================
// 4. Calculate closing within 2 bar range - should be max value at either ends of the 2-bar range
barclosing_2 = 2 * (close - ta.lowest(2)) / R2 * 100 - 100
//plot(barclosing_2, "2-Bar Closing %" , color=color.navy, display = display.none)
// 5. calculate 2-bar shift to range ratio
Shift2Bar_toR2 = SrcShift / R2 * 100
//plot(Shift2Bar_toR2, "2-bar Shift vs 2R", color=color.yellow, display = display.none)
// 6. Calculate 2-bar Relative Shift
SrcShift_abs = math.abs(SrcShift)
srcshift_avg = GetAverage(SrcShift_abs, lookback, lkbk_MA_Type)
srcshift_ratio_n = Normalize(SrcShift_abs, srcshift_avg) * 100 * sign_shift
//plot(srcshift_ratio_n, "2-bar Shift vs Avg", color=color.white, display = display.none)
// ===============================================================================
// =========================================================================================
// Relative Price Strength combining all strength elements
Pricea_n = (barclosing + s2r + BarSpread_ratio_n + barclosing_2 + Shift2Bar_toR2 + srcshift_ratio_n) / 6
//plot(Pricea_n, "Price Normalized", color=color.orange, display = display.none)
//Let's take Bar Flow as the combined price strength * the volume:avg ratio
// this works in a similar way to a volume-weighted RSI
bar_flow = Pricea_n * Vola_n / 100
//plot(bar_flow, 'bar_flow', color=color.green, display = display.none)
// calc avergae relative rate of flow, then smooth the resulting average
// classic formula would be this
//RROF = f_ma(bar_flow, length, MA_Type)
//
// or we can create a relative index by separating bulls from bears, like in an RSI - my preferred method
// here we have an added benefit of plotting the (average) bulls vs bears separately - as an option
bulls = math.max(bar_flow, 0)
bears = -1 * math.min(bar_flow, 0)
bulls_avg = GetAverage(bulls, length, MA_Type)
bears_avg = GetAverage(bears, length, MA_Type)
dx = bulls_avg / bears_avg
RROF = 2 * (100 - 100 / (1 + dx)) - 100
RROF_s = ta.wma(RROF, smooth)
Signal = GetAverage(RROF_s, sig_length, S_Type)
// Calculate Bias / sentiment on longer length
dx_b = GetAverage(bulls, B_Length, B_Type) / GetAverage(bears, B_Length, B_Type)
RROF_b = 2 * (100 - 100 / (1 + dx_b)) - 100
RROF_bs = ta.wma(RROF_b, smooth)
// ===========================================================================================================
// Colors & plots
// ===========================================================================================================
c_zero = color.new(#1163f6, 25)
c_band = color.new(color.yellow, 40)
c_up = color.aqua
c_dn = color.orange
c_sup = color.new(#00aa00, 70)
c_sdn = color.new(#ff180b, 70)
up = RROF_s >= 0
s_up = RROF_bs >=0
// ==================================== Plots ==========================================================
// // Display the ATR & VOl Ratio values only on the indicator status line & in the Data Window
// plotchar(shift, title = "Shift", char = "", color = color.white, editable=false, display=display.status_line + display.data_window)
// plotchar(lbk_tr, title = "Avg Shift", char = "", color = color.aqua, editable=false, display=display.status_line + display.data_window)
// plotchar(vola/lbk_vola, title = "Vol Ratio", char = "", color = color.yellow, editable=false, display=display.status_line + display.data_window)
hline(0, 'Zero Line', c_zero, linestyle = hline.style_solid)
// plot the band scale guide lines -- these lines will show/hide along with the EVEREX "Equalizer Bands Plot"
hline(0.25 * bandscale, title = '1/4 Level', color=c_band, linestyle = hline.style_dotted, display = showhlines)
hline(0.50 * bandscale, title = '2/4 Level', color=c_band, linestyle = hline.style_dotted, display = showhlines)
hline(0.75 * bandscale, title = '3/4 Level', color=c_band, linestyle = hline.style_dotted, display = showhlines)
hline(bandscale, title = '4/4 Level', color=c_band, linestyle = hline.style_dotted, display = showhlines)
// Plot Bulls & Bears - these are optional plots and hidden by default - adjust this section later
plot(ta.wma(bulls_avg, smooth), "Bulls", color = #11ff20, linewidth = 2, display = display.none)
plot(ta.wma(bears_avg, smooth), "Bears", color = #d5180b, linewidth = 2, display = display.none)
// =============================================================================
// Plot Bias / Sentiment
plot (RROF_bs, "Bias / Sentiment", style=plot.style_area,
color = s_up ? c_sup : c_sdn, linewidth = 4, display = DispBias )
// =============================================================================
// Plot Price Strength & Relative Volume as stacked "equalizer bands"
// adding visualization option to make the bands joint or separate at the mid-scale mark
Eq_band_option = input.string("Joint", title = 'Band Option', options = ["Joint", "Separate"], group = grp_4)
nPrice = math.max(math.min(Pricea_n, 100), -100)
nVol = math.max(math.min(Vola_n, 100), -100)
bar = bar_flow
c_vol_grn = color.new(#26a69a, 75)
c_vol_red = color.new(#ef5350, 75)
cb_vol_grn = color.new(#26a69a, 20)
cb_vol_red = color.new(#ef5350, 20)
c_vol = bar > 0 ? c_vol_grn : c_vol_red
cb_vol = bar > 0 ? cb_vol_grn : cb_vol_red
vc_lo = 0
vc_hi = nVol * bandscale / 100 / 2
plotcandle(vc_lo, vc_hi, vc_lo, vc_hi , "Volume Band", c_vol, c_vol, bordercolor = cb_vol, display = DispBands)
c_pri_grn = color.new(#3ed73e, 75)
c_pri_red = color.new(#ff870a, 75)
cb_pri_grn = color.new(#3ed73e, 20)
cb_pri_red = color.new(#ff870a, 20)
c_pri = bar > 0 ? c_pri_grn : c_pri_red
cb_pri = bar > 0 ? cb_pri_grn : cb_pri_red
pc_lo_base = Eq_band_option == "Joint" ? vc_hi : 0.50 * bandscale
pc_lo = pc_lo_base
pc_hi = pc_lo_base + math.abs(nPrice) * bandscale / 100 / 2
plotcandle(pc_lo, pc_hi, pc_lo ,pc_hi , "Price Band", c_pri, c_pri, bordercolor = cb_pri, display = DispBands)
// print the normalized volume and price values - only on statys line and in the data window
// these values are independant of the band scale or visualization options
plotchar(nVol, "Normalized Vol", char = "", color = c_vol, editable = false, display = Disp_vals)
plotchar(nPrice, "Normalized Price", char = "", color = c_pri, editable = false, display = Disp_vals)
// =============================================================================
// =============================================================================
// Plot main plot, smoothed plot and signal line
plot(RROF, 'RROF Raw', color.new(#2470f0, 9), display=display.none)
plot(RROF_s, 'RROF Smooth', color = color.new(#b2b5be,40), linewidth = 2)
plot(Signal, "Signal Line", up ? c_up : c_dn, 3)
// ===========================================================================================================
// basic alerts
// ===========================================================================================================
Alert_up = ta.crossover(RROF_s,0)
Alert_dn = ta.crossunder(RROF_s,0)
Alert_swing = ta.cross(RROF_s,0)
// "." in alert title for the alerts to show in the right order up/down/swing
alertcondition(Alert_up, ". RROF Crossing 0 Up", "RROF Up - Buying Action Detected!")
alertcondition(Alert_dn, ".. RROF Crossing 0 Down", "RROF Down - Selling Action Detected!")
alertcondition(Alert_swing, "... RROF Crossing 0", "RROF Swing - Possible Reversal")
// ===========================================================================================================
// v2.0 Adding Markers for Key Patterns
// ===========================================================================================================
// we can re-utilize the Normailize() function here too - but it's cleaner to have a separate ratio calc
nPrice_abs = math.abs(nPrice)
//EV_Ratio = 100 * Normalize(nPrice_abs, nVol)
EV_Ratio = 100 * nPrice_abs / nVol
// initial mapping of return ratios (to be revised)
// -------------------------------------------------------
// Case (1): Price > Vol => ratio > 120 = Ease of Move (EoM)
// Case (2): Price close to Vol => ratio between 80 - 120 = Reasonable Balance
// Case (3): Price less than Vol but reasonable => ratio between 80 - 50 = Drift / "nothing much to see here" bar
// Case (4): Price a lot less than Vol => 50 or less = Compression / Squat
// we're most interested in cases 1 & 4
//plot (EV_Ratio) // for validation only
is_positive = nPrice > 0
is_Compression = EV_Ratio <= 50
is_EoM = EV_Ratio >= 120
//Provide option to show/hide those EVEREX Markers - and an option for Compression bar
// - some folks would prefer a cross, others may prefer a circle - can adjust based on feedback
// no option for Ease of Move, guessing the triangle has the right significance
var showMarkers = input.bool(true, 'Show EVEREX Markers ?')
var Mshape = input.string("Circles", "Compression Marker", options = ['Circles','Crosses'])
SetShape(_x) =>
switch _x
'Circles' => shape.circle
'Crosses' => shape.cross
// Plot markers
plotshape(showMarkers and is_EoM and is_positive ? 0 : na, "EoM +ve", shape.triangleup, color=color.green,
location=location.absolute, size=size.auto, editable = false, display = display.pane)
plotshape(showMarkers and is_EoM and not(is_positive) ? 0 : na, "EoM -ve", shape.triangledown, color=color.red,
location=location.absolute, size=size.auto, editable = false, display = display.pane)
plotshape(showMarkers and is_Compression and is_positive ? 0 : na, "Compression +ve", style = SetShape(Mshape),
color=color.green, location=location.absolute, size = size.auto, editable = false, display = display.pane)
plotshape(showMarkers and is_Compression and not(is_positive) ? 0 : na, "Compression -ve", style = SetShape(Mshape),
color=color.red, location=location.absolute, size=size.auto, editable = false, display = display.pane)
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