안녕하세요 예스스탁입니다. 1번,2번을 사용자함수로 만드신 후에 3번 지표식 작성해 적용하시면 됩니다. 1 함수명 : GetAverage 반환값형 : 숫자형 input : _data(Numeric), _len(Numeric), MAOption(Numeric); var : alpha(0),RMA(0); if MAOption == 1 Then GetAverage = ma(_data, _len); Else if MAOption == 2 Then GetAverage = ema(_data, _len); Else if MAOption == 3 Then GetAverage = wma(2 * wma(_data, _len / 2) - wma(_data, _len), round(sqrt(_len),0)); Else if MAOption == 4 Then { alpha = 1/_len; RMA = iff(Isnan(RMA[1]) == true , ma(_data, _len) , alpha * _data + (1 - alpha) * iff(isnan(RMA[1]) == true,0,RMA[1])); GetAverage = Rma; } Else GetAverage = wma(_data, _len); 2 함수명 : 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(5);#1: SMA, 2: EMA 3 : HMA, 4: RMA', 5:WMA input : smooth(3); input : sig_length(5); input : S_Type(5);#1: SMA, 2: EMA 3 : HMA, 4: RMA', 5:WMA input : lookback(20); input : lkbk_Calc(1); #1:Simple, #2:MA_Type var : lkbk_MA_Type(0),Vola(0),Vola_n_pre(0),Vola_n(0); var : BarSpread(0),BarRange(0),R2(0),SrcShift(0); var : sign_shift(0),sign_spread(0),barclosing(0); var : s2r(0),BarSpread_abs(0),BarSpread_avg(0); var : BarSpread_ratio_n(0),barclosing_2(0),Shift2Bar_toR2(0); var : SrcShift_abs(0),srcshift_avg(0),srcshift_ratio_n(0); var : Pricea_n(0),bar_flow(0),bulls(0),bears(0),bulls_avg(0),bears_avg(0); var : dx(0),RROF(0),RROF_s(0),Signal(0); lkbk_MA_Type = iff(lkbk_Calc == 1 , 1 , MA_Type); Vola = GetAverage(v, lookback, lkbk_MA_Type); Vola_n_pre = Normalize(v, Vola) * 100; Vola_n = Vola_n_pre; BarSpread = close - open; BarRange = high - low; R2 = highest(h,2) - lowest(l,2); SrcShift = close-close[1]; sign_shift = iff(SrcShift> 0,1,IFf(SrcShift<0,-1,0)); sign_spread = iff(BarSpread> 0,1,IFf(BarSpread<0,-1,0)); barclosing = 2 * (close - low) / BarRange * 100 - 100; s2r = BarSpread / BarRange * 100; 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); plot2(RROF_s, "RROF Smooth"); plot3(Signal, "Signal Line"); 즐거운 하루되세요 > 레전드 님이 쓴 글입니다. > 제목 : 문의 > 2선만나오독부탁드립니다 / This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/ // &#169; RedKTrader - March 2023 //@version=5 // ****************************** // EVEREX v2.0 adds markers for key patterns based on nPrice:nVol ratios // starting with EoM and Compression - maybe add "Balanced" and "Mega" ?? // to-do list // inspecting the effort vs result concept by plotting volume vs. price change // this is like looking at distance versus fuel consumption - but comparing a normalized average of each // to help reveal areas of volume & price action anomalies, contraction & expansion indicator('RedK Effort Versus Results Explorer v2.0', 'RedK EVEREX v2.0', precision = 0, timeframe = '', timeframe_gaps = false, explicit_plot_zorder = true) // *********************************************************************************************************** // This function calcualtes a xable 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) fuction이라는 글자 옆에 금지어가 있어서 XX라고 표현햇습니다