Hi Nicolas, a request to convert this tradingsystem. I hope there’s interest in this! Thank you. The first 2 pics are probably from tradestation. //@version=4 study("Dual Thrust Trading Algorithm (ps4)", overlay=true) // author: capissimo // This is an PS4 update to the Dual Thrust trading algorithm developed by Michael Chalek. // It has been commonly used in futures, forex and equity markets. // The idea of Dual Thrust is similar to a typical breakout system, // however dual thrust uses the historical price to construct update the look back period - // theoretically making it more stable in any given period. // see: https://www.quantconnect.com/tutorials/strategy-library/dual-thrust-trading-algorithm //*** Inputs p = input(20, "Lookback Window", minval=1) mult = input(2.0, "Multiplier", minval=0.001, maxval=50) rule = input("Original", "Trend Identification Rule", options=["Original","SMA3","EMA10","SMA5/SMA10"]) algo = input("Algo #1", "Algorithm used:", options=["Algo #1", "Algo #2"]) mlen = input(5, "Lookback Window M") // 4 nlen = input(14, "Lookback Window N") // 20 k = input(0.9, "Coeff", step=0.01) // .7, .9 disc = input(0.55, "Trending discount", step=0.01) // .6 use_bb = input(false, "Bollinger? (alt. Standard Error) Bands") pbb = input(20, "Lookback Window", minval=1) sdeg = input(3, "Smoothing Factor", minval=1) multbb = input(2.0, "Bands Multiplier", minval=0.001, maxval=50) repnt = input(true, "Repaint?") //*** Main O = security(syminfo.tickerid, tostring(timeframe.multiplier), repnt ? open : open[1], barmerge.gaps_off, barmerge.lookahead_on) H = security(syminfo.tickerid, tostring(timeframe.multiplier), repnt ? high : high[1], barmerge.gaps_off, barmerge.lookahead_on) L = security(syminfo.tickerid, tostring(timeframe.multiplier), repnt ? low : low[1], barmerge.gaps_off, barmerge.lookahead_on) C = security(syminfo.tickerid, tostring(timeframe.multiplier), repnt ? close : close[1], barmerge.gaps_off, barmerge.lookahead_on) // ==Bands== // // Standard Error of the Estimate (SEE) Bands are constructed around a linear regression curve and // based on two standard errors above and below this regression line. // The error bands measure the standard error of the estimate around the linear re-gression line. // Therefore, as a price series follows the course of the regression line the bands will narrow, // showing little error in the estimate. As the market gets noisy and random, // the error will be greater resulting in wider bands. beta(array,per) => val1 = sum(bar_index*array,per)-(per*sma(bar_index,per)*sma(array,per)) val2 = sum(pow(bar_index,2),per)-(per*pow(sma(bar_index,per),2)) calcB = val1/val2 alpha(array,per) => calcA = sma(array,per)-(beta(array,per)*sma(bar_index,per)) see(array,per,mult,dir,type) => lr = linreg(array,per,0) val1 = (sum(pow(array,2),per))-((alpha(array,per)*sum(array,per)))-((beta(array,per)*sum(bar_index*array,per))) val2 = per - 2 narrow = sqrt(val1/val2) est = sum(pow(lr-array,2),per) / (per - 2 ) wide = sqrt(est) d = dir ? 1 : -1 band = type ? narrow : wide seb = lr + d * mult * band // SEE Bands UWB = plot(use_bb ? na: sma(see(close, pbb, 2.1, true, false), sdeg), color=color.blue, transp=90) UNB = plot(use_bb ? na: sma(see(close, pbb, 2, true, true), sdeg), color=color.blue, transp=90) plot(use_bb ? na: sma(linreg(close, pbb, 0), sdeg), color=color.orange, transp=0) BNB = plot(use_bb ? na: sma(see(close, pbb, 2, false, true), sdeg), color=color.blue, transp=90) BWB = plot(use_bb ? na: sma(see(close, pbb, 2.1, false, false), sdeg), color=color.blue, transp=90) fill(UWB, BWB, title="WideSEE", color=color.blue) fill(UNB, BNB, title="NarrowSEE", color=color.blue) // Bollinger Bands basis = sma(close, pbb) dev = multbb * stdev(close, pbb) upper = basis + dev lower = basis - dev plot(use_bb ? basis : na, color=color.orange, linewidth=2, transp=0) fill(plot(use_bb ? upper : na, transp=65), plot(use_bb ? lower : na, transp=65), color=color.blue, transp=90) // ==Dual Thrust Trading Algorithm== // At the close of the day, calculate two values: // the highest price - the closing price, and // the closing price - the lowest price. // Then take the two larger ones, multiply the k values. The results are called trig-ger values. // On the second day, the opening price is recorded, // and then immediately after the price exceeds (opening + trigger value), // or the price is lower than the (opening - trigger value), the short selling immedi-ately. // This is an inversion system without a single stop? i.e. the reverse signal is also the unwinding signal. // K1 and K2 are the parameters. // When K1 is greater than K2, it is much easier to trigger the long signal and vice versa. // For demonstration, here we choose K1 = K2 = 0.5. // In live trading, we can still use historical data to optimize those parameters or // adjust the parameters according to the market trend. // K1 should be small than k2 if you are bullish on the market and k1 should be much bigger if you are bearish on the market. // Trend Identification - Bullish or Bearish uptrend = false, dntrend = false if rule=="Original" rng = C - O doji = rng == 0 uptrend := rng > 0 or doji and rng[1] > 0 dntrend := rng < 0 or doji and rng[1] < 0 else sm = sma(C, 3) // #1 em = ema(C, 10) // #2 ma5 = sma(C, 5), ma10 = sma(C, 10) // #3 uptrend := rule=="SMA3" ? C > sm : rule=="EMA10" ? C > em : ma5 > ma10 dntrend := rule=="SMA3" ? C < sm : rule=="EMA10" ? C < em : ma5 < ma10 k1 = k, k2 = k if uptrend // Assigned empirically. Should be optimized separately k1 := k1 * disc //.2 k2 := k2 * (1 + disc) if dntrend k1 := k1 * (1 + disc) k2 := k2 * disc //.2 dtta1_algo(k1, k2, len) => hh = highest(H, len)[1] hc = highest(C, len)[1] lc = lowest(C, len)[1] ll = lowest(L, len)[1] // The range is calculated based on the close, high and low over the most recent N-periods. // A position is opened when the market moves a certain range from the opening price. range = max(hh - lc, hc - ll) [O + k1 * range, O - k2 * range] dtta2_algo(k1, k2, ml, nl) => hh = 0.0, ll = 0.0, hc = 0.0, lc = 0.0 hh := highest(H, ml)[1] hc := highest(C, ml)[1] lc := lowest(C, ml)[1] ll := lowest(L, ml)[1] sellRange = (hh - lc) >= (hc - ll) ? hh - lc : hc - ll hh := highest(H, nl)[1] hc := highest(C, nl)[1] lc := lowest(C, nl)[1] ll := lowest(L, nl)[1] buyRange = (hh - lc) >= (hc - ll) ? hh - lc : hc - ll [O + k1 * buyRange, O - k2 * sellRange] [bt1, st1] = dtta1_algo(k1, k2, mlen) [bt2, st2] = dtta2_algo(k1, k2, mlen, nlen) buyTrig = 0.0, sellTrig = 0.0 if algo == "Algo #1" buyTrig := bt1, sellTrig := st1 else buyTrig := bt2, sellTrig := st2 longCondition = C >= buyTrig shortCondition = C <= sellTrig state = 0 state := longCondition ? 1 : shortCondition ? -1 : nz(state[1]) long = change(state) and state[1]==-1 short = change(state) and state[1]==1 plotshape(uptrend and long ? low : na, location=location.belowbar, style=shape.labelup, color=color.green, size=size.tiny, text="B", textcolor=color.white, transp=0) plotshape(dntrend and short ? high : na, location=location.abovebar, style=shape.labeldown, color=color.red, size=size.tiny, text="S", textcolor=color.white, transp=0) alertcondition(long, title='Buy', message='go long') alertcondition(short, title='Sell', message='go short')
.
.
