Стратегия покупки и продажи Bullish Engulfing - это количественная торговая стратегия, основанная на моделях свечей.
Эта стратегия определяет перевороты цен на основе
Когда акция находится в нисходящем тренде, если за свечами с небольшим реальным телом следует свеча, чье реальное тело полностью поглощает предыдущее реальное тело, и цена закрытия выше предыдущей высокой цены, это образует модель бычьего поглощения, сигнализирующую о надвигающемся перевороте тренда, где цена начнет расти.
Эта стратегия позволит открыть длинную позицию, когда будет выявлена тенденция бычьего поглощения, с целевой прибылью 1% и стоп-лосом 1%, чтобы закрепить прибыль.
Преимущества этой стратегии:
Эта стратегия сопряжена с некоторыми рисками:
Для решения этих рисков мы можем:
Эта стратегия также может быть усилена путем:
Стратегия покупки и продажи Bullish Engulfing является зрелой количественной торговой стратегией, основанной на техническом анализе, с преимуществами простых и ясных торговых сигналов, которые легко внедряются.
/*backtest start: 2022-12-20 00:00:00 end: 2023-12-26 00:00:00 period: 1d basePeriod: 1h 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/ // © thequantscience // ██████╗ ██╗ ██╗██╗ ██╗ ██╗███████╗██╗ ██╗ ███████╗███╗ ██╗ ██████╗ ██╗ ██╗██╗ ███████╗██╗███╗ ██╗ ██████╗ // ██╔══██╗██║ ██║██║ ██║ ██║██╔════╝██║ ██║ ██╔════╝████╗ ██║██╔════╝ ██║ ██║██║ ██╔════╝██║████╗ ██║██╔════╝ // ██████╔╝██║ ██║██║ ██║ ██║███████╗███████║ █████╗ ██╔██╗ ██║██║ ███╗██║ ██║██║ █████╗ ██║██╔██╗ ██║██║ ███╗ // ██╔══██╗██║ ██║██║ ██║ ██║╚════██║██╔══██║ ██╔══╝ ██║╚██╗██║██║ ██║██║ ██║██║ ██╔══╝ ██║██║╚██╗██║██║ ██║ // ██████╔╝╚██████╔╝███████╗███████╗██║███████║██║ ██║ ███████╗██║ ╚████║╚██████╔╝╚██████╔╝███████╗██║ ██║██║ ╚████║╚██████╔╝ // ╚═════╝ ╚═════╝ ╚══════╝╚══════╝╚═╝╚══════╝╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═══╝ ╚═════╝ ╚═════╝ ╚══════╝╚═╝ ╚═╝╚═╝ ╚═══╝ ╚═════╝ //@version=5 strategy( "Buy&Sell Bullish Engulfing - The Quant Science", overlay = true, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, pyramiding = 1, currency = currency.EUR, initial_capital = 10000, commission_type = strategy.commission.percent, commission_value = 0.07, process_orders_on_close = true, close_entries_rule = "ANY" ) startDate = input.int(title="D: ", defval=1, minval=1, maxval=31, inline = 'Start', group = "START DATE BACKTESTING", tooltip = "D is Day, M is Month, Y is Year.") startMonth = input.int(title="M: ", defval=1, minval=1, maxval=12, inline = 'Start', group = "START DATE BACKTESTING", tooltip = "D is Day, M is Month, Y is Year.") startYear = input.int(title="Y: ", defval=2022, minval=1800, maxval=2100, inline = 'Start', group = "START DATE BACKTESTING", tooltip = "D is Day, M is Month, Y is Year.") endDate = input.int(title="D: ", defval=31, minval=1, maxval=31, inline = 'End', group = "END DATE BACKTESTING", tooltip = "D is Day, M is Month, Y is Year.") endMonth = input.int(title="M: ", defval=12, minval=1, maxval=12, inline = 'End', group = "END DATE BACKTESTING", tooltip = "D is Day, M is Month, Y is Year.") endYear = input.int(title="Y: ", defval=2023, minval=1800, maxval=2100, inline = 'End', group = "END DATE BACKTESTING", tooltip = "D is Day, M is Month, Y is Year.") inDateRange = (time >= timestamp(syminfo.timezone, startYear, startMonth, startDate, 0, 0)) and (time < timestamp(syminfo.timezone, endYear, endMonth, endDate, 0, 0)) PROFIT = input.float(defval = 1, minval = 0, title = "Target profit (%): ", step = 0.10, group = "TAKE PROFIT-STOP LOSS") STOPLOSS = input.float(defval = 1, minval = 0, title = "Stop Loss (%): ", step = 0.10, group = "TAKE PROFIT-STOP LOSS") var float equity_trades = 0 strategy.initial_capital = 50000 equity_trades := strategy.initial_capital var float equity = 0 var float qty_order = 0 t_ordersize = "Percentage size of each new order. With 'Reinvestment Profit' activate, the size will be calculate on the equity, with 'Reinvestment Profit' deactivate the size will be calculate on the initial capital." orders_size = input.float(defval = 2, title = "Orders size (%): ", minval = 0.10, step = 0.10, maxval = 100, group = "RISK MANAGEMENT", tooltip = t_ordersize) qty_order := ((equity_trades * orders_size) / 100 ) / close C_DownTrend = true C_UpTrend = true var trendRule1 = "SMA50" var trendRule2 = "SMA50, SMA200" var trendRule = input.string(trendRule1, "Detect Trend Based On", options=[trendRule1, trendRule2, "No detection"], group = "BULLISH ENGULFING") if trendRule == trendRule1 priceAvg = ta.sma(close, 50) C_DownTrend := close < priceAvg C_UpTrend := close > priceAvg if trendRule == trendRule2 sma200 = ta.sma(close, 200) sma50 = ta.sma(close, 50) C_DownTrend := close < sma50 and sma50 < sma200 C_UpTrend := close > sma50 and sma50 > sma200 C_Len = 14 C_ShadowPercent = 5.0 C_ShadowEqualsPercent = 100.0 C_DojiBodyPercent = 5.0 C_Factor = 2.0 C_BodyHi = math.max(close, open) C_BodyLo = math.min(close, open) C_Body = C_BodyHi - C_BodyLo C_BodyAvg = ta.ema(C_Body, C_Len) C_SmallBody = C_Body < C_BodyAvg C_LongBody = C_Body > C_BodyAvg C_UpShadow = high - C_BodyHi C_DnShadow = C_BodyLo - low C_HasUpShadow = C_UpShadow > C_ShadowPercent / 100 * C_Body C_HasDnShadow = C_DnShadow > C_ShadowPercent / 100 * C_Body C_WhiteBody = open < close C_BlackBody = open > close C_Range = high-low C_IsInsideBar = C_BodyHi[1] > C_BodyHi and C_BodyLo[1] < C_BodyLo C_BodyMiddle = C_Body / 2 + C_BodyLo C_ShadowEquals = C_UpShadow == C_DnShadow or (math.abs(C_UpShadow - C_DnShadow) / C_DnShadow * 100) < C_ShadowEqualsPercent and (math.abs(C_DnShadow - C_UpShadow) / C_UpShadow * 100) < C_ShadowEqualsPercent C_IsDojiBody = C_Range > 0 and C_Body <= C_Range * C_DojiBodyPercent / 100 C_Doji = C_IsDojiBody and C_ShadowEquals patternLabelPosLow = low - (ta.atr(30) * 0.6) patternLabelPosHigh = high + (ta.atr(30) * 0.6) label_color_bullish = input.color(color.rgb(43, 255, 0), title = "Label Color Bullish", group = "BULLISH ENGULFING") C_EngulfingBullishNumberOfCandles = 2 C_EngulfingBullish = C_DownTrend and C_WhiteBody and C_LongBody and C_BlackBody[1] and C_SmallBody[1] and close >= open[1] and open <= close[1] and ( close > open[1] or open < close[1] ) if C_EngulfingBullish var ttBullishEngulfing = "Engulfing\nAt the end of a given downward trend, there will most likely be a reversal pattern. To distinguish the first day, this candlestick pattern uses a small body, followed by a day where the candle body fully overtakes the body from the day before, and closes in the trend’s opposite direction. Although similar to the outside reversal chart pattern, it is not essential for this pattern to completely overtake the range (high to low), rather only the open and the close." label.new(bar_index, patternLabelPosLow, text="BE", style=label.style_label_up, color = label_color_bullish, textcolor=color.white, tooltip = ttBullishEngulfing) bgcolor(ta.highest(C_EngulfingBullish?1:0, C_EngulfingBullishNumberOfCandles)!=0 ? color.new(#21f321, 90) : na, offset=-(C_EngulfingBullishNumberOfCandles-1)) var float c = 0 var float o = 0 var float c_exit = 0 var float c_stopl = 0 if C_EngulfingBullish and strategy.opentrades==0 and inDateRange c := strategy.equity o := close c_exit := c + (c * PROFIT / 100) c_stopl := c - (c * STOPLOSS / 100) strategy.entry(id = "LONG", direction = strategy.long, qty = qty_order, limit = o) if ta.crossover(strategy.equity, c_exit) strategy.exit(id = "CLOSE-LONG", from_entry = "LONG", limit = close) if ta.crossunder(strategy.equity, c_stopl) strategy.exit(id = "CLOSE-LONG", from_entry = "LONG", limit = close)