A Bollinger Bands Breakout Strategy é uma estratégia de brecha de tendência de curto prazo otimizada para a negociação de criptomoedas.
A estratégia apresenta um elevado nível de configurabilidade, incluindo os parâmetros de Bollinger Bands, vários filtros, configurações de take profit/stop loss e limite máximo de perda intradiária.
A estratégia gira em torno do indicador Bollinger Bands, que calcula uma faixa média, uma faixa superior e uma faixa inferior que servem como substitutos para as médias de preços e limites de volatilidade.
Além disso, são implementados vários filtros para evitar falsos sinais:
Filtro de tendência: longo acima da média móvel, curto abaixo da média móvel
Filtro de volatilidade: negociar apenas quando a volatilidade aumentar
Filtro de direção: configurável apenas para direções longas, curtas ou ambas
Filtro de taxa de variação: movimento de preço suficiente do fechamento anterior necessário
Filtro de data: para especificação do quadro de tempo de backtesting
As saídas são tratadas através de mecanismos de take profit, stop loss e trailing stop para bloquear ganhos e limitar perdas.
As principais vantagens desta estratégia incluem:
Indicador Bollinger Bands confiável como sinal principal
Filtros personalizáveis impedem transações indesejadas
Projeto abrangente de stop loss/take profit
Previsão de perda intradiária máxima
Prospera em mercados de tendências com potencial de lucro
Apesar das vantagens, continuam a existir alguns riscos:
Os problemas em torno das bandas de Bollinger podem levar a perdas.
Os filtros demasiado rígidos reduzem as transacções nos mercados limitados ao intervalo
As lacunas podem parar as posições de forma preventiva
Movimentos extremos não podem ser totalmente evitados
As atenuações incluem ajustes de filtros, intervenção manual e paradas ajustadas.
Optimizações possíveis para esta estratégia:
Procurar combinações de parâmetros ideais
Introduzir aprendizado de máquina para otimização adaptativa
Pesquisar melhores métodos de stop loss, por exemplo, paradas de volatilidade
Incorporar sentimentos para orientar ações discricionárias
Utilização de instrumentos correlacionados para arbitragem estatística
A Bollinger Bands Breakout Strategy é um sistema testado no tempo para negociação de tendências de curto prazo. Combinando os méritos do sinal Bollinger Bands e filtros prudentes, ele gera entradas de qualidade para tendências, evitando sinais falsos. Mecanismos abrangentes de gerenciamento de risco também contêm drawdowns efetivamente. Com melhorias contínuas, essa estratégia tem o potencial de se tornar um formidável sistema de negociação automatizado.
/*backtest start: 2022-11-22 00:00:00 end: 2023-11-04 05:20:00 period: 1d basePeriod: 1h exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}] */ //@version=5 strategy("Bollinger Bands - Breakout Strategy",overlay=true ) // Define the length of the Bollinger Bands bbLengthInput = input.int (15,title="Length", group="Bollinger Bands", inline="BB") bbDevInput = input.float (2.0,title="StdDev", group="Bollinger Bands", inline="BB") // Define the settings for the Trend Filter trendFilterInput = input.bool(false, title="Above/Below", group = "Trend Filter", inline="Trend") trendFilterPeriodInput = input(223,title="", group = "Trend Filter", inline="Trend") trendFilterType = input.string (title="", defval="EMA",options=["EMA","SMA","RMA", "WMA"], group = "Trend Filter", inline="Trend") volatilityFilterInput = input.bool(true,title="StdDev", group = "Volatility Filter", inline="Vol") volatilityFilterStDevLength = input(15,title="",group = "Volatility Filter", inline="Vol") volatilityStDevMaLength = input(15,title=">MA",group = "Volatility Filter", inline="Vol") // ROC Filter // f_security function by LucF for PineCoders available here: https://www.tradingview.com/script/cyPWY96u-How-to-avoid-repainting-when-using-security-PineCoders-FAQ/ f_security(_sym, _res, _src, _rep) => request.security(_sym, _res, _src[not _rep and barstate.isrealtime ? 1 : 0])[_rep or barstate.isrealtime ? 0 : 1] high_daily = f_security(syminfo.tickerid, "D", high, false) roc_enable = input.bool(false, "", group="ROC Filter from CloseD", inline="roc") roc_threshold = input.float(1, "Treshold", step=0.5, group="ROC Filter from CloseD", inline="roc") closed = f_security(syminfo.tickerid,"1D",close, false) roc_filter= roc_enable ? (close-closed)/closed*100 > roc_threshold : true // Trade Direction Filter // tradeDirectionInput = input.string("Auto",options=["Auto", "Long&Short","Long Only", "Short Only"], title="Trade", group="Direction Filter", tooltip="Auto: if a PERP is detected (in the symbol description), trade long and short\n Otherwise as per user-input") // tradeDirection = switch tradeDirectionInput // "Auto" => str.contains(str.lower(syminfo.description), "perp") or str.contains(str.lower(syminfo.description), ".p") ? strategy.direction.all : strategy.direction.long // "Long&Short" => strategy.direction.all // "Long Only" => strategy.direction.long // "Short Only" => strategy.direction.short // => strategy.direction.all // strategy.risk.allow_entry_in(tradeDirection) // Calculate and plot the Bollinger Bands [bbMiddle, bbUpper, bbLower] = ta.bb (close, bbLengthInput, bbDevInput) plot(bbMiddle, "Basis", color=color.orange) bbUpperPlot = plot(bbUpper, "Upper", color=color.blue) bbLowerrPlot = plot(bbLower, "Lower", color=color.blue) fill(bbUpperPlot, bbLowerrPlot, title = "Background", color=color.new(color.blue, 95)) // Calculate and view Trend Filter float tradeConditionMa = switch trendFilterType "EMA" => ta.ema(close, trendFilterPeriodInput) "SMA" => ta.sma(close, trendFilterPeriodInput) "RMA" => ta.rma(close, trendFilterPeriodInput) "WMA" => ta.wma(close, trendFilterPeriodInput) // Default used when the three first cases do not match. => ta.wma(close, trendFilterPeriodInput) trendConditionLong = trendFilterInput ? close > tradeConditionMa : true trendConditionShort = trendFilterInput ? close < tradeConditionMa : true plot(trendFilterInput ? tradeConditionMa : na, color=color.yellow) // Calculate and view Volatility Filter stdDevClose = ta.stdev(close,volatilityFilterStDevLength) volatilityCondition = volatilityFilterInput ? stdDevClose > ta.sma(stdDevClose,volatilityStDevMaLength) : true bbLowerCrossUnder = ta.crossunder(close, bbLower) bbUpperCrossOver = ta.crossover(close, bbUpper) bgcolor(volatilityCondition ? na : color.new(color.red, 95)) // Date Filter start = input(timestamp("2017-01-01"), "Start", group="Date Filter") finish = input(timestamp("2050-01-01"), "End", group="Date Filter") date_filter = true // Entry and Exit Conditions entryLongCondition = bbUpperCrossOver and trendConditionLong and volatilityCondition and date_filter and roc_filter entryShortCondition = bbLowerCrossUnder and trendConditionShort and volatilityCondition and date_filter and roc_filter exitLongCondition = bbLowerCrossUnder exitShortCondition = bbUpperCrossOver // Orders if entryLongCondition strategy.entry("EL", strategy.long) if entryShortCondition strategy.entry("ES", strategy.short) if exitLongCondition strategy.close("EL") if exitShortCondition strategy.close("ES") // Long SL/TP/TS xl_ts_percent = input.float(2,step=0.5, title= "TS", group="Exit Long", inline="LTS", tooltip="Trailing Treshold %") xl_to_percent = input.float(0.5, step=0.5, title= "TO", group="Exit Long", inline="LTS", tooltip="Trailing Offset %") xl_ts_tick = xl_ts_percent * close/syminfo.mintick/100 xl_to_tick = xl_to_percent * close/syminfo.mintick/100 xl_sl_percent = input.float (2, step=0.5, title="SL",group="Exit Long", inline="LSLTP") xl_tp_percent = input.float(9, step=0.5, title="TP",group="Exit Long", inline="LSLTP") xl_sl_price = strategy.position_avg_price * (1-xl_sl_percent/100) xl_tp_price = strategy.position_avg_price * (1+xl_tp_percent/100) strategy.exit("XL+SL/TP", "EL", stop=xl_sl_price, limit=xl_tp_price, trail_points=xl_ts_tick, trail_offset=xl_to_tick,comment_loss= "XL-SL", comment_profit = "XL-TP",comment_trailing = "XL-TS") // Short SL/TP/TS xs_ts_percent = input.float(2,step=0.5, title= "TS",group="Exit Short", inline ="STS", tooltip="Trailing Treshold %") xs_to_percent = input.float(0.5, step=0.5, title= "TO",group="Exit Short", inline ="STS", tooltip="Trailing Offset %") xs_ts_tick = xs_ts_percent * close/syminfo.mintick/100 xs_to_tick = xs_to_percent * close/syminfo.mintick/100 xs_sl_percent = input.float (2, step=0.5, title="SL",group="Exit Short", inline="ESSLTP", tooltip="Stop Loss %") xs_tp_percent = input.float(9, step=0.5, title="TP",group="Exit Short", inline="ESSLTP", tooltip="Take Profit %") xs_sl_price = strategy.position_avg_price * (1+xs_sl_percent/100) xs_tp_price = strategy.position_avg_price * (1-xs_tp_percent/100) strategy.exit("XS+SL/TP", "ES", stop=xs_sl_price, limit=xs_tp_price, trail_points=xs_ts_tick, trail_offset=xs_to_tick,comment_loss= "XS-SL", comment_profit = "XS-TP",comment_trailing = "XS-TS") max_intraday_loss = input.int(10, title="Max Intraday Loss (Percent)", group="Risk Management") //strategy.risk.max_intraday_loss(max_intraday_loss, strategy.percent_of_equity) // Monthly Returns table, modified from QuantNomad. Please put calc_on_every_tick = true to plot it. monthly_table(int results_prec, bool results_dark) => new_month = month(time) != month(time[1]) new_year = year(time) != year(time[1]) eq = strategy.equity bar_pnl = eq / eq[1] - 1 cur_month_pnl = 0.0 cur_year_pnl = 0.0 // Current Monthly P&L cur_month_pnl := new_month ? 0.0 : (1 + cur_month_pnl[1]) * (1 + bar_pnl) - 1 // Current Yearly P&L cur_year_pnl := new_year ? 0.0 : (1 + cur_year_pnl[1]) * (1 + bar_pnl) - 1 // Arrays to store Yearly and Monthly P&Ls var month_pnl = array.new_float(0) var month_time = array.new_int(0) var year_pnl = array.new_float(0) var year_time = array.new_int(0) last_computed = false if (not na(cur_month_pnl[1]) and (new_month or barstate.islast)) if (last_computed[1]) array.pop(month_pnl) array.pop(month_time) array.push(month_pnl , cur_month_pnl[1]) array.push(month_time, time[1]) if (not na(cur_year_pnl[1]) and (new_year or barstate.islast)) if (last_computed[1]) array.pop(year_pnl) array.pop(year_time) array.push(year_pnl , cur_year_pnl[1]) array.push(year_time, time[1]) last_computed := barstate.islast ? true : nz(last_computed[1]) // Monthly P&L Table var monthly_table = table(na) cell_hr_bg_color = results_dark ? #0F0F0F : #F5F5F5 cell_hr_text_color = results_dark ? #D3D3D3 : #555555 cell_border_color = results_dark ? #000000 : #FFFFFF // ell_hr_bg_color = results_dark ? #0F0F0F : #F5F5F5 // cell_hr_text_color = results_dark ? #D3D3D3 : #555555 // cell_border_color = results_dark ? #000000 : #FFFFFF if (barstate.islast) monthly_table := table.new(position.bottom_right, columns = 14, rows = array.size(year_pnl) + 1, bgcolor=cell_hr_bg_color,border_width=1,border_color=cell_border_color) table.cell(monthly_table, 0, 0, syminfo.tickerid + " " + timeframe.period, text_color=cell_hr_text_color, bgcolor=cell_hr_bg_color) table.cell(monthly_table, 1, 0, "Jan", text_color=cell_hr_text_color, bgcolor=cell_hr_bg_color) table.cell(monthly_table, 2, 0, "Feb", text_color=cell_hr_text_color, bgcolor=cell_hr_bg_color) table.cell(monthly_table, 3, 0, "Mar", text_color=cell_hr_text_color, bgcolor=cell_hr_bg_color) table.cell(monthly_table, 4, 0, "Apr", text_color=cell_hr_text_color, bgcolor=cell_hr_bg_color) table.cell(monthly_table, 5, 0, "May", text_color=cell_hr_text_color, bgcolor=cell_hr_bg_color) table.cell(monthly_table, 6, 0, "Jun", text_color=cell_hr_text_color, bgcolor=cell_hr_bg_color) table.cell(monthly_table, 7, 0, "Jul", text_color=cell_hr_text_color, bgcolor=cell_hr_bg_color) table.cell(monthly_table, 8, 0, "Aug", text_color=cell_hr_text_color, bgcolor=cell_hr_bg_color) table.cell(monthly_table, 9, 0, "Sep", text_color=cell_hr_text_color, bgcolor=cell_hr_bg_color) table.cell(monthly_table, 10, 0, "Oct", text_color=cell_hr_text_color, bgcolor=cell_hr_bg_color) table.cell(monthly_table, 11, 0, "Nov", text_color=cell_hr_text_color, bgcolor=cell_hr_bg_color) table.cell(monthly_table, 12, 0, "Dec", text_color=cell_hr_text_color, bgcolor=cell_hr_bg_color) table.cell(monthly_table, 13, 0, "Year", text_color=cell_hr_text_color, bgcolor=cell_hr_bg_color) for yi = 0 to array.size(year_pnl) - 1 table.cell(monthly_table, 0, yi + 1, str.tostring(year(array.get(year_time, yi))), text_color=cell_hr_text_color, bgcolor=cell_hr_bg_color) y_color = array.get(year_pnl, yi) > 0 ? color.lime : array.get(year_pnl, yi) < 0 ? color.red : color.gray table.cell(monthly_table, 13, yi + 1, str.tostring(math.round(array.get(year_pnl, yi) * 100, results_prec)), bgcolor = y_color) for mi = 0 to array.size(month_time) - 1 m_row = year(array.get(month_time, mi)) - year(array.get(year_time, 0)) + 1 m_col = month(array.get(month_time, mi)) m_color = array.get(month_pnl, mi) > 0 ? color.lime : array.get(month_pnl, mi) < 0 ? color.red : color.gray table.cell(monthly_table, m_col, m_row, str.tostring(math.round(array.get(month_pnl, mi) * 100, results_prec)), bgcolor = m_color) results_prec = input(2, title = "Precision", group="Results Table") results_dark = input.bool(defval=true, title="Dark Mode", group="Results Table") monthly_table(results_prec, results_dark)