Tags:

The Bullish Engulfing buy and sell strategy is a quantitative trading strategy based on candlestick patterns. It captures opportunities to profit from price reversals by identifying the “Bullish Engulfing” candlestick pattern. The main advantages of this strategy are:

- It is based on mature technical analysis theories to identify high probability price reversal opportunities.
- It has simple and intuitive trading signals.
- The risks are controllable.

This strategy identifies price reversals based on the “Bullish Engulfing” candlestick pattern.

When a stock is in a downtrend, if a candlestick with a small real body is followed by a candlestick whose real body completely engulfs the previous real body, and the closing price is higher than the previous high price, this forms a Bullish Engulfing pattern, signaling an imminent trend reversal, where the price will start rising.

This strategy will open a long position when a Bullish Engulfing pattern is identified, with a profit target of 1% and a stop loss of 1%, to lock in profits.

The advantages of this strategy are:

- It is based on mature technical analysis theories. The Bullish Engulfing pattern signals a high probability price reversal.
- The trading signals are simple and intuitive, easy to understand and automate for quantitative trading.
- Trading high liquidity products like index futures allows efficient entries and exits.
- The profit target and stop loss exits effectively control the risk/reward ratio of each trade, ensuring profitability and avoiding huge losses.
- Flexible parameter adjustments suit different products and market environments.

There are some risks to this strategy:

- False signals risks exist as it is based on technical analysis theories.
- Market regime changes may invalidate parameters which need adjustment.
- Stop loss values that are too tight may result in premature exiting, while values too wide may produce large losses.

To address these risks, we can:

- Optimize parameters and verify performance across market conditions.
- Widen stop loss levels to control single trade loss at acceptable levels.
- Trade high liquidity products with suitable volatility like index and futures ETFs.

This strategy can also be enhanced by:

- Adding filters like moving averages to avoid trading against trends.
- Increasing profit target to expand profit potential.
- Optimizing stop loss mechanisms, like trailing stops to reduce probability of stopping out.
- Using combinations of other candlestick patterns similar to “Bullish Engulfing” to create a trading system.

The Bullish Engulfing buy and sell strategy is a mature quantitative trading strategy based on technical analysis, with the advantages of simple and clear trading signals that are easy to implement. With optimized parameters and good risk control measures, it can produce steady profits and is highly recommendable.

/*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)

- Kana Candle Breakout Strategy Based on Moving Average and Support Resistance
- Triple Supertrend Ichimoku Cloud Quantitative Trading Strategy
- Double Strategy Combination – Stochastic Slow and Relative Strength Index
- JBravo Quantitative Trend Strategy
- Keltner Channel Pullback Strategy
- Trending Darvas Box Quantitative Trading Strategy
- MFI and MA Based Quantitative Reversal Strategy
- Up versus Down Close Candles Strategy with EMA filter and Session Timeframes
- Dual RSI Breakthrough Strategy
- Cross-Pair Bollinger Band Crossover Quantitative Strategy
- Quant Bitcoin Trading Strategy Combining MACD, RSI and FIB
- Dual Moving Average Golden Cross Quantitative Strategy
- Leveraged Position Sizing with Margin Call Risk Management Strategy
- Ichimoku Balance Line Strategy
- Ichimoku Cloud with Dual Moving Average Crossover Strategy
- An ATR and Breakout based ETF Trading Strategy
- Tracking Supertrend Strategy
- Moving Average Envelopes Trading Strategy
- Dual Moving Average Golden Cross Quantitative Strategy
- Quantitative Reversal Index Strategy Integrating Dual Trend Signals