This is a quantitative trading strategy that combines the use of Bill Williams’ fractal theory and the ZZ indicator. It judges market trends through the calculation of Williams fractals and identifies potential breakout points by drawing support/resistance lines using the ZZ indicator to implement trend-following trades.
The strategy first calculates the Williams fractals to determine whether the current fractal is rising or falling. If it is a rising fractal, it is believed that the current trend is upward. If it is a falling fractal, it is believed that the current trend is downward.
It then draws ZZ indicator’s support and resistance lines based on the fractal points. If the price breaks through the resistance line corresponding to the rising fractal, go long. If the price breaks through the support line corresponding to the falling fractal, go short.
Through such a combination, it is possible to capture changes in trends in a timely manner and implement trend-following trades.
This strategy combines two different technical analysis methods - Williams fractals and ZZ indicators - to uncover more trading opportunities.
It can timely judge the turning point of market trends and has good stop loss/take profit criteria to capture the main trend direction. In addition, the ZZ indicator can filter out some false breakouts to avoid unnecessary losses.
In general, this strategy considers both trend judgment and specific entry point selections to balance risks and returns.
The biggest risk of this strategy is that fractal judgments and ZZ indicator may issue wrong trading signals, leading to unnecessary losses. For example, after breaking through the resistance line, prices may quickly fall back, unable to sustain the uptrend.
In addition, the way fractals are calculated can lead to misjudgments if the timeframe is set improperly. Setting the timeframe too short increases the probability of false breakouts.
To reduce these risks, appropriately adjust the calculation parameters of fractals and increase filtering conditions to reduce erroneous signals. Besides, set wider stop loss to control single trade loss size.
This strategy can be further optimized in the following aspects:
Add momentum indicator filters such as MACD or Bollinger Bands to avoid some false breakouts.
Optimize fractal parameter settings and adjust the calculation of highs and lows and shorten the timeframe to obtain more accurate trend judgments.
Increase machine learning algorithms to judge trend accuracy and avoid human limitations.
Add adaptive stop loss mechanism based on market volatility.
Use deep learning algorithms to optimize overall parameter settings.
By skillfully combining Williams’ fractal theory and the ZZ indicator, this strategy achieves timely detection and capturing of changes in market trends. It maintains high win rate and expects to obtain long-term excess returns. Next step by introducing more filters and AI capabilities, it is expected to further improve strategy stability and return rate.
/*backtest start: 2023-12-01 00:00:00 end: 2023-12-31 23:59:59 period: 1h basePeriod: 15m exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}] */ //@version=4 strategy(title = "robotrading ZZ-8 fractals", shorttitle = "ZZ-8", overlay = true, default_qty_type = strategy.percent_of_equity, initial_capital = 100, default_qty_value = 100, commission_value = 0.1) //Settings needlong = input(true, defval = true, title = "Long") needshort = input(false, defval = true, title = "Short") filterBW = input(false, title="filter Bill Williams Fractals") showll = input(true, title = "Show levels") showff = input(true, title = "Show fractals (repaint!)") showdd = input(true, title = "Show dots (repaint!)") showbg = input(false, title = "Show background") showlb = input(false, title = "Show drawdown") startTime = input(defval = timestamp("01 Jan 2000 00:00 +0000"), title = "Start Time", type = input.time, inline = "time1") finalTime = input(defval = timestamp("31 Dec 2099 23:59 +0000"), title = "Final Time", type = input.time, inline = "time1") //Variables loss = 0.0 maxloss = 0.0 equity = 0.0 truetime = true //Fractals isRegularFractal(mode) => ret = mode == 1 ? high[4] < high[3] and high[3] < high[2] and high[2] > high[1] and high[1] > high[0] : mode == -1 ? low[4] > low[3] and low[3] > low[2] and low[2] < low[1] and low[1] < low[0] : false isBWFractal(mode) => ret = mode == 1 ? high[4] < high[2] and high[3] <= high[2] and high[2] >= high[1] and high[2] > high[0] : mode == -1 ? low[4] > low[2] and low[3] >= low[2] and low[2] <= low[1] and low[2] < low[0] : false filteredtopf = filterBW ? isRegularFractal(1) : isBWFractal(1) filteredbotf = filterBW ? isRegularFractal(-1) : isBWFractal(-1) //Triangles plotshape(filteredtopf and showff, title='Filtered Top Fractals', style=shape.triangledown, location=location.abovebar, color= color.red, offset=-2) plotshape(filteredbotf and showff, title='Filtered Bottom Fractals', style=shape.triangleup, location=location.belowbar, color= color.lime, offset=-2) //Levels hh = 0.0 ll = 0.0 hh := filteredtopf ? high[2] : hh[1] ll := filteredbotf ? low[2] : ll[1] //Trend trend = 0 trend := high >= hh[1] ? 1 : low <= ll[1] ? -1 : trend[1] //Lines hcol = showll and hh == hh[1] and close < hh ? color.lime : na lcol = showll and ll == ll[1] and close > ll ? color.red : na plot(hh, color = hcol) plot(ll, color = lcol) //Dots // var line hline = na // if hh != hh[1] and showdd // hline := line.new(bar_index - 0, hh[0], bar_index - 2, hh[0], xloc = xloc.bar_index, extend = extend.none, style = line.style_dotted, color = color.lime, width = 1) // var line lline = na // if ll != ll[1] and showdd // lline := line.new(bar_index - 0, ll[0] - syminfo.mintick, bar_index - 2, ll[0] - syminfo.mintick, xloc = xloc.bar_index, extend = extend.none, style = line.style_dotted, color = color.red, width = 1) //Background bgcol = showbg == false ? na : trend == 1 ? color.lime : trend == -1 ? color.red : na bgcolor(bgcol, transp = 80) //Orders if hh > 0 and needlong strategy.entry("Long", strategy.long, na, stop = hh, when = needlong and truetime) strategy.exit("Exit Long", "Long", stop = ll, when = needshort == false) if ll > 0 and startTime strategy.entry("Short", strategy.short, na, stop = ll, when = needshort and truetime) strategy.exit("Exit Short", "Short", stop = hh, when = needlong == false) if time > finalTime strategy.close_all() strategy.cancel("Long") strategy.cancel("Short") if showlb //Drawdown max = 0.0 max := max(strategy.equity, nz(max[1])) dd = (strategy.equity / max - 1) * 100 min = 100.0 min := min(dd, nz(min[1])) //Max loss size equity := strategy.position_size != strategy.position_size[1] ? strategy.equity : equity[1] loss := equity < equity[1] ? ((equity / equity[1]) - 1) * 100 : 0 maxloss := min(nz(maxloss[1]), loss) //Label min := round(min * 100) / 100 maxloss := round(maxloss * 100) / 100 labeltext = "Drawdown: " + tostring(min) + "%" + "\nMax.loss " + tostring(maxloss) + "%" var label la = na label.delete(la) tc = min > -100 ? color.white : color.red osx = timenow + round(change(time)*50) osy = highest(100) la := label.new(x = osx, y = osy, text = labeltext, xloc = xloc.bar_time, yloc = yloc.price, color = color.black, style = label.style_labelup, textcolor = tc)template: strategy.tpl:40:21: executing "strategy.tpl" at <.api.GetStrategyListByName>: wrong number of args for GetStrategyListByName: want 7 got 6