This strategy generates trading signals using the RSI momentum indicator Delta-RSI based on polynomial interpolation. Delta-RSI smooths RSI through local polynomial regression to obtain its first order time derivative as a momentum indicator. This strategy incorporates additional filters based on ATR, volume and RSI to filter out some “false” signals.
The core indicator of this strategy is Delta-RSI. Its calculation steps are:
The strategy filters signals using ATR, volume and RSI filters:
The advantages of this strategy include:
The risks of this strategy include:
These can be controlled via parameter optimization, filter adjustment and tighter stops.
This strategy can be further improved by:
By exploiting Delta-RSI’s high sensitivity and strict filtering mechanisms, this strategy can improve quality while controlling risks. Further parameter and model optimization may expand positive profit rate. It is an effective quantitative trading strategy.
/*backtest start: 2024-01-04 00:00:00 end: 2024-01-11 00:00:00 period: 1h basePeriod: 15m 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 With Filters // // This is a version of the Delta-RSI Oscillator Strategy compatible with // the Strategy Tester. // // This version also allows filtering the trade signals generated by Delts-RSI // by means of volatility (defined by ATR), relative volume and RSI(14). // // Delta-RSI (© tbiktag) is a smoothed time derivative of the RSI designed // as a momentum indicator. For the original publication, see link below: // https://www.tradingview.com/script/OXQVFTQD-Delta-RSI-Oscillator/ // // D-RSI model parameters: // RSI Length: The timeframe of the RSI that serves as an input to D-RSI. // Frame 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. // 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) // //@version=4 strategy(title="Delta-RSI Strategy with Filters", shorttitle = "D-RSI with filters", 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 for j = 0 to _ncolumns-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 for j = 0 to _ncolumnsB-1 elementC := 0 for k = 0 to _ncolumnsA-1 elementC := elementC + matrix_get(_A,i,k,_nrowsA)*matrix_get(_B,k,j,_nrowsB) 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 _norm := _norm + pow(array.get(_X,i),2) 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 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 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 for i = 0 to _nrows-1 array.set(_a,i,matrix_get(_A,i,k,_nrows)) for j = 0 to k-1 //get R_jk as scalar product of Q_j column and A_k column: _r := 0 for i = 0 to _nrows-1 _r := _r + matrix_get(_Q,i,j,_nrows)*array.get(_a,i) matrix_set(_R,_r,j,k,_ncolumns) //update vector _a for i = 0 to _nrows-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 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 for i = 0 to j-1 _r := 0.0 for k = i to j-1 _r := _r + matrix_get(_Rinv,i,k,_ncolumns)*matrix_get(_R,k,j,_ncolumns) matrix_set(_Rinv,_r,i,j,_ncolumns) for k = 0 to j-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 else int _N = array.size(_x) float _mse = 0.0 for i = 0 to _N-1 _mse := _mse + pow(array.get(_x,i) - array.get(_xhat,i),2)/_N _xmean = array.sum(_x)/_N _nrmse := sqrt(_mse) /_xmean _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 for j = 0 to _degree matrix_set(_J,pow(i,j),i,j,_window) // Vector of raw datapoints: var _Y_raw = array.new_float(_window,na) for j = 0 to _window-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 _diff := _diff + i*array.get(_a_coef,i)*pow(_window-1,i-1) // 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(title="Polynomial Order", group = "Model Parameters:", inline = "linepar1", type = input.integer, defval=3, minval = 1) rsi_l = input(title = "RSI Length", group = "Model Parameters:", inline = "linepar1", type = input.integer, defval = 21, minval = 1, tooltip="The period length of RSI that is used as input.") window = input(title="Length ( > Order)", group = "Model Parameters:", inline = "linepar2", type = input.integer, defval=50, minval = 2) signalLength = input(title="Signal Length", group = "Model Parameters:", inline = "linepar2", type=input.integer, defval=9, tooltip="The signal line is a EMA of the D-RSI time series.") islong = input(title = "Long", group = "Allowed Entries:", inline = "lineent",type = input.bool, defval = true) isshort = input(title = "Short", group = "Allowed Entries:", inline = "lineent", type = input.bool, defval= true) buycond = input(title="Buy", group = "Entry and Exit Conditions:", inline = "linecond",type = input.string, defval="Signal Line Crossing", options=["Zero-Crossing", "Signal Line Crossing","Direction Change"]) sellcond = input(title="Sell", group = "Entry and Exit Conditions:", inline = "linecond",type = input.string, defval="Signal Line Crossing", options=["Zero-Crossing", "Signal Line Crossing","Direction Change"]) endcond = input(title="Exit", group = "Entry and Exit Conditions:", inline = "linecond",type = input.string, defval="Signal Line Crossing", options=["Zero-Crossing", "Signal Line Crossing","Direction Change"]) filterlong =input(title = "Long Entries", inline = 'linefilt', group = 'Apply Filters to', type = input.bool, defval = true) filtershort =input(title = "Short Enties", inline = 'linefilt', group = 'Apply Filters to', type = input.bool, defval = true) filterend =input(title = "Exits", inline = 'linefilt', group = 'Apply Filters to', type = input.bool, defval = true) usevol =input(title = "", inline = 'linefiltvol', group = 'Relative Volume Filter:', type = input.bool, defval = false) rvol = input(title = "Volume >", inline = 'linefiltvol', group = 'Relative Volume Filter:', type = input.integer, defval = 1) len_vol = input(title = "Avg. Volume Over Period", inline = 'linefiltvol', group = 'Relative Volume Filter:', type = input.integer, defval = 30, minval = 1, tooltip="The current volume must be greater than N times the M-period average volume.") useatr =input(title = "", inline = 'linefiltatr', group = 'Volatility Filter:', type = input.bool, defval = false) len_atr1 = input(title = "ATR", inline = 'linefiltatr', group = 'Volatility Filter:', type = input.integer, defval = 5, minval = 1) len_atr2 = input(title = "> ATR", inline = 'linefiltatr', group = 'Volatility Filter:', type = input.integer, defval = 30, minval = 1, tooltip="The N-period ATR must be greater than the M-period ATR.") usersi =input(title = "", inline = 'linersi', group = 'Overbought/Oversold Filter:', type = input.bool, defval = false) rsitrhs1 = input(title = "", inline = 'linersi', group = 'Overbought/Oversold Filter:', type = input.integer, defval = 0, minval=0, maxval=100) rsitrhs2 = input(title = "< RSI (14) >", inline = 'linersi', group = 'Overbought/Oversold Filter:', type = input.integer, defval = 100, minval=0, maxval=100, tooltip="RSI(14) must be in the range between N and M.") issl = input(title = "SL", inline = 'linesl1', group = 'Stop Loss / Take Profit:', type = input.bool, defval = false) slpercent = input(title = ", %", inline = 'linesl1', group = 'Stop Loss / Take Profit:', type = input.float, defval = 10, minval=0.0) istrailing = input(title = "Trailing", inline = 'linesl1', group = 'Stop Loss / Take Profit:', type = input.bool, defval = false) istp = input(title = "TP", inline = 'linetp1', group = 'Stop Loss / Take Profit:', type = input.bool, defval = false) tppercent = input(title = ", %", inline = 'linetp1', group = 'Stop Loss / Take Profit:', type = input.float, defval = 20) fixedstart =input(title="", group = "Fixed Backtest Period Start/End Dates:", inline = "linebac1", type = input.bool, defval = true) backtest_start=input(title = "", type = input.time, inline = "linebac1", group = "Fixed Backtest Period Start/End Dates:", defval = timestamp("01 Jan 2017 13:30 +0000"), tooltip="If deactivated, backtest staring from the first available price bar.") fixedend = input(title="", group = "Fixed Backtest Period Start/End Dates:", inline = "linebac2", type = input.bool, defval = false) backtest_end =input(title = "", type = input.time, inline = "linebac2", group = "Fixed Backtest Period Start/End Dates:", defval = timestamp("30 Dec 2080 23:30 +0000"), tooltip="If deactivated, backtesting ends at the last available price bar.") if window < degree window := degree+1 src = rsi(close,rsi_l) [drsi,nrmse] = diff(src,window,degree) signalline = ema(drsi, signalLength) // Conditions for D-RSI 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 = crossover(drsi,0.0) crossdw = crossunder(drsi,0.0) crosssignalup = crossover(drsi,signalline) crosssignaldw = crossunder(drsi,signalline) // D-RSI signals drsilong = (buycond=="Direction Change"?dirchangeup:(buycond=="Zero-Crossing"?crossup:crosssignalup)) drsishort= (sellcond=="Direction Change"?dirchangedw:(sellcond=="Zero-Crossing"?crossdw:crosssignaldw)) drisendlong = (endcond=="Direction Change"?dirchangedw:(endcond=="Zero-Crossing"?crossdw:crosssignaldw)) drisendshort= (endcond=="Direction Change"?dirchangeup:(endcond=="Zero-Crossing"?crossup:crosssignalup)) // Filters rsifilter = usersi?(rsi(close,14) > rsitrhs1 and rsi(close,14) < rsitrhs2):true volatilityfilter = useatr?(atr(len_atr1) > atr(len_atr2)):true volumefilter = usevol?(volume > rvol*sma(volume,len_vol)):true totalfilter = volatilityfilter and volumefilter and rsifilter //Filtered signals golong = drsilong and islong and (filterlong?totalfilter:true) goshort = drsishort and isshort and (filtershort?totalfilter:true) endlong = drisendlong and (filterend?totalfilter:true) endshort = drisendlong and (filterend?totalfilter:true) // Backtest period //backtest_start = timestamp(syminfo.timezone, startYear, startMonth, startDate, 0, 0) //backtest_end = timestamp(syminfo.timezone, endYear, endMonth, endDate, 0, 0) isinrange = true // Entry price / Take profit / Stop Loss startprice = valuewhen(condition=golong or goshort, source=close, occurrence=0) pm = golong?1:goshort?-1:1/sign(strategy.position_size) takeprofit = startprice*(1+pm*tppercent*0.01) // fixed stop loss stoploss = startprice * (1-pm*slpercent*0.01) // trailing stop loss if istrailing and strategy.position_size>0 stoploss := max(close*(1 - slpercent*0.01),stoploss[1]) else if istrailing and strategy.position_size<0 stoploss := min(close*(1 + slpercent*0.01),stoploss[1]) tpline = plot(takeprofit,color=color.blue,transp=100, title="TP") slline = plot(stoploss, color=color.red, transp=100, title="SL") p1 = plot(close,transp=100,color=color.white, title="Dummy Close") fill(p1, tpline, color=color.green, transp=istp?70:100, title="TP") fill(p1, slline, color=color.red, transp=issl?70:100, title="SL") // Backtest: Basic Entry and Exit Conditions if golong and isinrange and islong strategy.entry("long", true ) alert("D-RSI Long " + syminfo.tickerid, alert.freq_once_per_bar_close) if goshort and isinrange and isshort strategy.entry("short", false) alert("D-RSI Short " + syminfo.tickerid, alert.freq_once_per_bar_close) if endlong strategy.close("long", alert_message="Close Long") alert("D-RSI Exit Long " + syminfo.tickerid, alert.freq_once_per_bar_close) if endshort strategy.close("short", alert_message="Close Short") alert("D-RSI Exit Short " + syminfo.tickerid, alert.freq_once_per_bar_close) // Exit via SL or TP strategy.exit(id="sl/tp long", from_entry="long", stop=issl?stoploss:na, limit=istp?takeprofit:na, alert_message="Close Long") strategy.exit(id="sl/tp short",from_entry="short",stop=issl?stoploss:na, limit=istp?takeprofit:na, alert_message="Stop Loss Short") // Close if outside the range if (not isinrange) strategy.close_all()template: strategy.tpl:40:21: executing "strategy.tpl" at <.api.GetStrategyListByName>: wrong number of args for GetStrategyListByName: want 7 got 6