یہ حکمت عملی آر ایس آئی متحرک اوسکیلیٹر پر مبنی ایک مقداری تجارتی نظام ہے۔ آر ایس آئی اشارے پر کثیر الثانی فٹنگ اور ٹائم سیریز تجزیہ انجام دے کر ، یہ مارکیٹ کی رفتار کو حاصل کرنے کے لئے آر ایس آئی کی تبدیلی کی شرح کا حساب لگاتا ہے۔ یہ حکمت عملی سگنل پروسیسنگ کے لئے کیو آر تحلیل جیسے جدید ریاضیاتی طریقوں کا استعمال کرتی ہے اور تجارتی فیصلوں کے لئے اوسط حرکت پذیر نظام کے ساتھ مل جاتی ہے۔
اسٹریٹیجی کا مرکز ڈیلٹا-آر ایس آئی اوسیلیٹر ہے، جسے مندرجہ ذیل مراحل کے ذریعے لاگو کیا جاتا ہے:
تجارتی سگنل تین طریقوں سے تیار کیے جا سکتے ہیں:
یہ ایک مکمل مقداری تجارتی حکمت عملی ہے جس کی ٹھوس نظریاتی بنیاد ہے۔ سگنل پروسیسنگ کے لئے جدید ریاضیاتی طریقوں کے ساتھ مل کر آر ایس آئی کی متحرک خصوصیات کے تجزیے کے ذریعے ، یہ مارکیٹ کے رجحانات کو مؤثر طریقے سے پکڑ سکتا ہے۔ اگرچہ پیرامیٹر حساسیت اور کمپیوٹیشنل پیچیدگی کے ساتھ کچھ مسائل ہیں ، لیکن مناسب پیرامیٹر انتخاب اور اصلاح کی بہتری کے ذریعے اس حکمت عملی کی عملی قدر ہے۔ براہ راست تجارت پر لاگو کرتے وقت ، یہ مشورہ دیا جاتا ہے کہ رسک کنٹرول پر توجہ دیں ، مناسب پوزیشن سائز طے کریں ، اور حکمت عملی کی کارکردگی کی مسلسل نگرانی کریں۔
/*backtest start: 2024-11-10 00:00:00 end: 2024-12-09 08:00:00 period: 4h basePeriod: 4h exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}] */ // This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/ // © tbiktag // // Delta-RSI Oscillator Strategy // // A strategy that uses Delta-RSI Oscillator (© tbiktag) as a stand-alone indicator: // https://www.tradingview.com/script/OXQVFTQD-Delta-RSI-Oscillator/ // // Delta-RSI is a smoothed time derivative of the RSI, plotted as a histogram // and serving as a momentum indicator. // // Input parameters: // RSI Length: The timeframe of the RSI that serves as an input to D-RSI. // Length: The length of the lookback frame used for local regression. // Polynomial Order: The order of the local polynomial function used to interpolate the RSI. // Signal Length: The length of a EMA of the D-RSI series that is used as a signal line. // Trade signals are generated based on three optional conditions: // - Zero-crossing: bullish when D-RSI crosses zero from negative to positive values (bearish otherwise) // - Signal Line Crossing: bullish when D-RSI crosses from below to above the signal line (bearish otherwise) // - Direction Change: bullish when D-RSI was negative and starts ascending (bearish otherwise) // // Since D-RSI oscillator is based on polynomial fitting of the RSI curve, there is also an option // to filter trade signal by means of the root mean-square error of the fit (normalized by the sample average). // //@version=5 strategy(title='Delta-RSI Oscillator Strategy-QuangVersion', shorttitle='D-RSI-Q', overlay=true) // ---Subroutines--- matrix_get(_A, _i, _j, _nrows) => // Get the value of the element of an implied 2d matrix //input: // _A :: array: pseudo 2d matrix _A = [[column_0],[column_1],...,[column_(n-1)]] // _i :: integer: row number // _j :: integer: column number // _nrows :: integer: number of rows in the implied 2d matrix array.get(_A, _i + _nrows * _j) matrix_set(_A, _value, _i, _j, _nrows) => // Set a value to the element of an implied 2d matrix //input: // _A :: array, changed on output: pseudo 2d matrix _A = [[column_0],[column_1],...,[column_(n-1)]] // _value :: float: the new value to be set // _i :: integer: row number // _j :: integer: column number // _nrows :: integer: number of rows in the implied 2d matrix array.set(_A, _i + _nrows * _j, _value) transpose(_A, _nrows, _ncolumns) => // Transpose an implied 2d matrix // input: // _A :: array: pseudo 2d matrix _A = [[column_0],[column_1],...,[column_(n-1)]] // _nrows :: integer: number of rows in _A // _ncolumns :: integer: number of columns in _A // output: // _AT :: array: pseudo 2d matrix with implied dimensions: _ncolums x _nrows var _AT = array.new_float(_nrows * _ncolumns, 0) for i = 0 to _nrows - 1 by 1 for j = 0 to _ncolumns - 1 by 1 matrix_set(_AT, matrix_get(_A, i, j, _nrows), j, i, _ncolumns) _AT multiply(_A, _B, _nrowsA, _ncolumnsA, _ncolumnsB) => // Calculate scalar product of two matrices // input: // _A :: array: pseudo 2d matrix // _B :: array: pseudo 2d matrix // _nrowsA :: integer: number of rows in _A // _ncolumnsA :: integer: number of columns in _A // _ncolumnsB :: integer: number of columns in _B // output: // _C:: array: pseudo 2d matrix with implied dimensions _nrowsA x _ncolumnsB var _C = array.new_float(_nrowsA * _ncolumnsB, 0) int _nrowsB = _ncolumnsA float elementC = 0.0 for i = 0 to _nrowsA - 1 by 1 for j = 0 to _ncolumnsB - 1 by 1 elementC := 0 for k = 0 to _ncolumnsA - 1 by 1 elementC += matrix_get(_A, i, k, _nrowsA) * matrix_get(_B, k, j, _nrowsB) elementC matrix_set(_C, elementC, i, j, _nrowsA) _C vnorm(_X, _n) => //Square norm of vector _X with size _n float _norm = 0.0 for i = 0 to _n - 1 by 1 _norm += math.pow(array.get(_X, i), 2) _norm math.sqrt(_norm) qr_diag(_A, _nrows, _ncolumns) => //QR Decomposition with Modified Gram-Schmidt Algorithm (Column-Oriented) // input: // _A :: array: pseudo 2d matrix _A = [[column_0],[column_1],...,[column_(n-1)]] // _nrows :: integer: number of rows in _A // _ncolumns :: integer: number of columns in _A // output: // _Q: unitary matrix, implied dimenstions _nrows x _ncolumns // _R: upper triangular matrix, implied dimansions _ncolumns x _ncolumns var _Q = array.new_float(_nrows * _ncolumns, 0) var _R = array.new_float(_ncolumns * _ncolumns, 0) var _a = array.new_float(_nrows, 0) var _q = array.new_float(_nrows, 0) float _r = 0.0 float _aux = 0.0 //get first column of _A and its norm: for i = 0 to _nrows - 1 by 1 array.set(_a, i, matrix_get(_A, i, 0, _nrows)) _r := vnorm(_a, _nrows) //assign first diagonal element of R and first column of Q matrix_set(_R, _r, 0, 0, _ncolumns) for i = 0 to _nrows - 1 by 1 matrix_set(_Q, array.get(_a, i) / _r, i, 0, _nrows) if _ncolumns != 1 //repeat for the rest of the columns for k = 1 to _ncolumns - 1 by 1 for i = 0 to _nrows - 1 by 1 array.set(_a, i, matrix_get(_A, i, k, _nrows)) for j = 0 to k - 1 by 1 //get R_jk as scalar product of Q_j column and A_k column: _r := 0 for i = 0 to _nrows - 1 by 1 _r += matrix_get(_Q, i, j, _nrows) * array.get(_a, i) _r matrix_set(_R, _r, j, k, _ncolumns) //update vector _a for i = 0 to _nrows - 1 by 1 _aux := array.get(_a, i) - _r * matrix_get(_Q, i, j, _nrows) array.set(_a, i, _aux) //get diagonal R_kk and Q_k column _r := vnorm(_a, _nrows) matrix_set(_R, _r, k, k, _ncolumns) for i = 0 to _nrows - 1 by 1 matrix_set(_Q, array.get(_a, i) / _r, i, k, _nrows) [_Q, _R] pinv(_A, _nrows, _ncolumns) => //Pseudoinverse of matrix _A calculated using QR decomposition // Input: // _A:: array: implied as a (_nrows x _ncolumns) matrix _A = [[column_0],[column_1],...,[column_(_ncolumns-1)]] // Output: // _Ainv:: array implied as a (_ncolumns x _nrows) matrix _A = [[row_0],[row_1],...,[row_(_nrows-1)]] // ---- // First find the QR factorization of A: A = QR, // where R is upper triangular matrix. // Then _Ainv = R^-1*Q^T. // ---- [_Q, _R] = qr_diag(_A, _nrows, _ncolumns) _QT = transpose(_Q, _nrows, _ncolumns) // Calculate Rinv: var _Rinv = array.new_float(_ncolumns * _ncolumns, 0) float _r = 0.0 matrix_set(_Rinv, 1 / matrix_get(_R, 0, 0, _ncolumns), 0, 0, _ncolumns) if _ncolumns != 1 for j = 1 to _ncolumns - 1 by 1 for i = 0 to j - 1 by 1 _r := 0.0 for k = i to j - 1 by 1 _r += matrix_get(_Rinv, i, k, _ncolumns) * matrix_get(_R, k, j, _ncolumns) _r matrix_set(_Rinv, _r, i, j, _ncolumns) for k = 0 to j - 1 by 1 matrix_set(_Rinv, -matrix_get(_Rinv, k, j, _ncolumns) / matrix_get(_R, j, j, _ncolumns), k, j, _ncolumns) matrix_set(_Rinv, 1 / matrix_get(_R, j, j, _ncolumns), j, j, _ncolumns) // _Ainv = multiply(_Rinv, _QT, _ncolumns, _ncolumns, _nrows) _Ainv norm_rmse(_x, _xhat) => // Root Mean Square Error normalized to the sample mean // _x. :: array float, original data // _xhat :: array float, model estimate // output // _nrmse:: float float _nrmse = 0.0 if array.size(_x) != array.size(_xhat) _nrmse := na _nrmse else int _N = array.size(_x) float _mse = 0.0 for i = 0 to _N - 1 by 1 _mse += math.pow(array.get(_x, i) - array.get(_xhat, i), 2) / _N _mse _xmean = array.sum(_x) / _N _nrmse := math.sqrt(_mse) / _xmean _nrmse _nrmse diff(_src, _window, _degree) => // Polynomial differentiator // input: // _src:: input series // _window:: integer: wigth of the moving lookback window // _degree:: integer: degree of fitting polynomial // output: // _diff :: series: time derivative // _nrmse:: float: normalized root mean square error // // Vandermonde matrix with implied dimensions (window x degree+1) // Linear form: J = [ [z]^0, [z]^1, ... [z]^degree], with z = [ (1-window)/2 to (window-1)/2 ] var _J = array.new_float(_window * (_degree + 1), 0) for i = 0 to _window - 1 by 1 for j = 0 to _degree by 1 matrix_set(_J, math.pow(i, j), i, j, _window) // Vector of raw datapoints: var _Y_raw = array.new_float(_window, na) for j = 0 to _window - 1 by 1 array.set(_Y_raw, j, _src[_window - 1 - j]) // Calculate polynomial coefficients which minimize the loss function _C = pinv(_J, _window, _degree + 1) _a_coef = multiply(_C, _Y_raw, _degree + 1, _window, 1) // For first derivative, approximate the last point (i.e. z=window-1) by float _diff = 0.0 for i = 1 to _degree by 1 _diff += i * array.get(_a_coef, i) * math.pow(_window - 1, i - 1) _diff // Calculates data estimate (needed for rmse) _Y_hat = multiply(_J, _a_coef, _window, _degree + 1, 1) float _nrmse = norm_rmse(_Y_raw, _Y_hat) [_diff, _nrmse] /// --- main --- degree = input.int(title='Polynomial Order', group='Model Parameters:', inline='linepar1', defval=2, minval=1) rsi_l = input.int(title='RSI Length', group='Model Parameters:', inline='linepar1', defval=21, minval=1, tooltip='The period length of RSI that is used as input.') window = input.int(title='Length ( > Order)', group='Model Parameters:', inline='linepar2', defval=21, minval=2) signalLength = input.int(title='Signal Length', group='Model Parameters:', inline='linepar2', defval=9, tooltip='The signal line is a EMA of the D-RSI time series.') islong = input.bool(title='Buy', group='Show Signals:', inline='lineent', defval=true) isshort = input.bool(title='Sell', group='Show Signals:', inline='lineent', defval=true) showendlabels = input.bool(title='Exit', group='Show Signals:', inline='lineent', defval=true) buycond = input.string(title='Buy', group='Entry and Exit Conditions:', inline='linecond', defval='Zero-Crossing', options=['Zero-Crossing', 'Signal Line Crossing', 'Direction Change']) sellcond = input.string(title='Sell', group='Entry and Exit Conditions:', inline='linecond', defval='Zero-Crossing', options=['Zero-Crossing', 'Signal Line Crossing', 'Direction Change']) endcond = input.string(title='Exit', group='Entry and Exit Conditions:', inline='linecond', defval='Zero-Crossing', options=['Zero-Crossing', 'Signal Line Crossing', 'Direction Change']) usenrmse = input.bool(title='', group='Filter by Means of Root-Mean-Square Error of RSI Fitting:', inline='linermse', defval=false) rmse_thrs = input.float(title='RSI fitting Error Threshold, %', group='Filter by Means of Root-Mean-Square Error of RSI Fitting:', inline='linermse', defval=10, minval=0.0) / 100 src = ta.rsi(close, rsi_l) [drsi, nrmse] = diff(src, window, degree) signalline = ta.ema(drsi, signalLength) // Conditions and filters filter_rmse = usenrmse ? nrmse < rmse_thrs : true dirchangeup = drsi > drsi[1] and drsi[1] < drsi[2] and drsi[1] < 0.0 dirchangedw = drsi < drsi[1] and drsi[1] > drsi[2] and drsi[1] > 0.0 crossup = ta.crossover(drsi, 0.0) crossdw = ta.crossunder(drsi, 0.0) crosssignalup = ta.crossover(drsi, signalline) crosssignaldw = ta.crossunder(drsi, signalline) //Signals golong = (buycond == 'Direction Change' ? dirchangeup : buycond == 'Zero-Crossing' ? crossup : crosssignalup) and filter_rmse goshort = (sellcond == 'Direction Change' ? dirchangedw : sellcond == 'Zero-Crossing' ? crossdw : crosssignaldw) and filter_rmse endlong = (endcond == 'Direction Change' ? dirchangedw : endcond == 'Zero-Crossing' ? crossdw : crosssignaldw) and filter_rmse endshort = (endcond == 'Direction Change' ? dirchangeup : endcond == 'Zero-Crossing' ? crossup : crosssignalup) and filter_rmse plotshape(golong and islong ? low : na, location=location.belowbar, style=shape.labelup, color=color.new(#2E7C13, 0), size=size.small, title='Buy') plotshape(goshort and isshort ? high : na, location=location.abovebar, style=shape.labeldown, color=color.new(#BF217C, 0), size=size.small, title='Sell') plotshape(showendlabels and endlong and islong ? high : na, location=location.abovebar, style=shape.xcross, color=color.new(#2E7C13, 0), size=size.tiny, title='Exit Long') plotshape(showendlabels and endshort and isshort ? low : na, location=location.belowbar, style=shape.xcross, color=color.new(#BF217C, 0), size=size.tiny, title='Exit Short') alertcondition(golong, title='Long Signal', message='D-RSI: Long Signal') alertcondition(goshort, title='Short Signal', message='D-RSI: Short Signal') alertcondition(endlong, title='Exit Long Signal', message='D-RSI: Exit Long') alertcondition(endshort, title='Exit Short Signal', message='D-RSI: Exit Short') strategy.entry('long', strategy.long, when=golong and islong) strategy.entry('short', strategy.short, when=goshort and isshort) strategy.close('long', when=endlong and islong) strategy.close('short', when=endshort and isshort)