Name
动量突破与吞噬模型算法交易策略Momentum-Breakout-and-Engulfing-Pattern-Algorithmic-Trading-Strategy
Author
ChaoZhang
Strategy Description
本文介绍一种以吞噬模式识别获利机会和价格与移动平均线交叉做为入场信号的算法交易策略。该策略结合价格技术分析与趋势跟踪方法,旨在在盘整和趋势反转点位获利。
该策略的核心逻辑基于两个非相关指标的融合:
-
吞噬形态:一种两根K线的反转模式,第二根K线的实体完全“吞噬”了第一根K线的实体,用以识别反转机会。
-
价格与移动平均线交叉:当价格从移动平均线下方突破向上交叉移动平均线时,产生买入信号;当价格从移动平均线上方突破向下交叉移动平均线时,产生卖出信号。
通过吞噬形态判断市场可能反转的时机,再结合价格与移动平均线的穿越作为确定反转的过滤信号,可以提高获利概率。
具体来说,该策略通过追踪三种吞噬形态:多头吞噬、空头吞噬和无影线吞噬,判断盘整和反转的可能。再结合价格与移动平均线的金叉和死叉信号过滤,最终决定开仓方向。
该策略最大的优势是利用非相关指标的融合提高决策效果。吞噬形态判断市场反转时机和概率;而价格与移动平均线交叉验证反转方向和力度。两者相互验证,可以有效降低假信号导致的交易损失。
另一个优势是参数设置灵活。用户可以自行设定移动平均线周期、止损幅度等参数,对策略进行优化。
尽管利用多种指标提高判断效果,该策略依然存在一定的假信号风险。吞噬形态并不是百分之百可靠的反转信号,价格与移动平均线的交叉也会有失效情况。这些都可能导致超前开仓亏损。
此外,像大多数技术分析策略一样,该策略对冲突行情如价格震荡盘整的市场效果也较差。持续震荡可能触发止损或缩短获利空间。
为控制风险,可以适当调整移动平均线参数,优化止损幅度。也可以考虑结合其他指标识别趋势和震荡行情,动态调整策略参与程度。
该策略可以通过以下几个方面进行优化:
-
测试更多类型的移动平均线,寻找最佳参数组合。例如加权移动平均线、移动平均线的顺次平滑等。
-
增加趋势判断指标,避免震荡行情开仓。例如ADX,布林带等。
-
优化止损方式,提高止损效果。可考虑追踪止损、Chandelier Exit等止损策略。
-
增加机器学习方法判断K线形态,提高吞噬识别准确度。
-
添加自动参数优化功能,实现参数自适应。
本策略以吞噬形态判断反转时机,以价格与移动平均线交叉验证反转方向。通过指标融合提高决策效果,是一种技术分析策略。优点是指标互补,参数灵活;缺点是仍有假信号风险,对震荡行情弱。可通过优化移动平均线参数、止损方式、加入趋势判断等方法进一步增强策略效果。
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This article introduces an algorithmic trading strategy that identifies profitable opportunities through engulfing patterns and uses price crossover with moving average as entry signals. Combining technical analysis with trend following methods, this strategy aims to profit at consolidation and trend reversal points.
The core logic of this strategy is based on the convergence of two unrelated indicators:
-
Engulfing Pattern: A two-candlestick reversal pattern in which the body of the second candle completely "engulfs" the body of the first one, used to identify reversal opportunities.
-
Price Crossover with Moving Average: A buy signal is generated when the price crosses above the moving average line from below; A sell signal is when the price crosses below the moving average line from above.
By judging the timing of potential market reversal with engulfing patterns and using price crossover with moving average as confirmation signals, the probability of profiting can be improved.
Specifically, this strategy tracks three types of engulfing patterns - bullish, bearish and no shadow engulfing to determine possible consolidation and reversals. Together with the golden cross and dead cross signals of price and moving average crossover, the direction of opening positions is finally determined.
The biggest advantage of this strategy is utilizing the convergence of unrelated indicators to improve decision efficacy. Engulfing patterns judge the timing and probability of market reversal; while price crossover with moving average verifies the direction and momentum of the reversal. The two validate each other and can effectively reduce trading losses caused by false signals.
Another advantage is the flexibility of parameter settings. Users can set parameters like moving average period and stop loss range to optimize the strategy themselves.
Although using multiple indicators improves judgement, there are still some risks of false signals in this strategy. Engulfing patterns are not 100% reliable reversal signals, and failure scenarios also exist in price crossover with moving average. These can all lead to losses from premature opening positions.
Moreover, like most technical analysis strategies, it also performs poorly in conflicting trending markets like ranging and consolidation. Prolonged sideways price action may trigger stop loss or limit profit capturing space.
To control risks, parameters like moving average period and stop loss range can be adjusted accordingly. Other indicators can also be considered to identify trending and sideways markets, so that strategy participation can be managed dynamically.
The following areas can be optimized for this strategy:
-
Test more moving average types to find optimal parameter sets, like weighted moving average, double smoothed moving average etc.
-
Add trend judging indicators to avoid opening positions in sideways markets. Examples are ADX, Bollinger Bands etc.
-
Optimize stop loss methods to improve efficacy. Trailing stop loss, Chandelier Exit can be considered.
-
Increase machine learning methods to judge candlestick patterns and improve engulfing recognition accuracy.
-
Add parameter optimization functions for adaptive adjustment.
This strategy identifies reversal timing with engulfing patterns and verifies the direction using price crossover with moving average. By improving decision efficacy through indicator convergence, it is a technical analysis approach. Advantages include complementary indicators and flexible parameters. Disadvantages are risks of false signals and weakness in sideways markets. Ways to further enhance this strategy include optimizing moving average parameters, stop loss methods, adding trend filtering indicators etc.
[/trans]
Strategy Arguments
| Argument | Default | Description |
|---|---|---|
| v_input_1_close | 0 | Source Price vs MA: close |
| v_input_2 | 0 | Type of MA: SMA |
| v_input_3 | 32 | MA Length |
| v_input_4 | 2017 | Backtest Start Year |
| v_input_5 | true | Backtest Start Month |
| v_input_6 | true | Backtest Start Day |
| v_input_7 | 2020 | Backtest Stop Year |
| v_input_8 | 12 | Backtest Stop Month |
| v_input_9 | 31 | Backtest Stop Day |
| v_input_10 | 600 | Backtest Profit Goal (in USD) |
| v_input_11 | 300 | Backtest STOP Goal (in USD) |
Source (PineScript)
/*backtest
start: 2023-12-30 00:00:00
end: 2024-01-29 00:00:00
period: 3h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//@version=4
//@author=Daveatt
StrategyName = "BEST Engulfing + MA"
ShortStrategyName = "BEST Engulfing + MA"
strategy(title=StrategyName, shorttitle=ShortStrategyName, overlay=true)
includeEngulfing = true
includeMA = true
source_ma = input(title="Source Price vs MA", type=input.source, defval=close)
typeofMA = input(title="Type of MA", defval="SMA", options=["RMA", "SMA", "EMA", "WMA", "VWMA", "SMMA", "KMA", "TMA", "HullMA", "DEMA", "TEMA"])
length_ma = input(32, title = "MA Length", type=input.integer)
// ---------- Candle components and states
GreenCandle = close > open
RedCandle = close < open
NoBody = close==open
Body = abs(close-open)
// bullish conditions
isBullishEngulfing1 = max(close[1],open[1]) < max(close,open) and min(close[1],open[1]) > min(close,open) and Body > Body[1] and GreenCandle and RedCandle[1]
isBullishEngulfing2 = max(close[1],open[1]) < max(close,open) and min(close[1],open[1]) <= min(close,open) and Body > Body[1] and GreenCandle and RedCandle[1]
// bearish conditions
isBearishEngulfing1 = max(close[1],open[1]) < max(close,open) and min(close[1],open[1]) > min(close,open) and Body > Body[1] and RedCandle and GreenCandle[1]
isBearishEngulfing2 = max(close[1],open[1]) >= max(close,open) and min(close[1],open[1]) > min(close,open) and Body > Body[1] and RedCandle and GreenCandle[1]
// consolidation of conditions
isBullishEngulfing = isBullishEngulfing1 or isBullishEngulfing2
isBearishEngulfing = isBearishEngulfing1 or isBearishEngulfing2
//isBullishEngulfing = max(close[1],open[1]) < max(close,open) and min(close[1],open[1]) > min(close,open) and Body > Body[1] and GreenCandle and RedCandle[1]
//isBearishEngulfing = max(close[1],open[1]) < max(close,open) and min(close[1],open[1]) > min(close,open) and Body > Body[1] and RedCandle and GreenCandle[1]
Engulf_curr = 0 - barssince(isBearishEngulfing) + barssince(isBullishEngulfing)
Engulf_Buy = Engulf_curr < 0 ? 1 : 0
Engulf_Sell = Engulf_curr > 0 ? 1 : 0
// Price vs MM
smma(src, len) =>
smma = 0.0
smma := na(smma[1]) ? sma(src, len) : (smma[1] * (len - 1) + src) / len
smma
ma(smoothing, src, length) =>
if smoothing == "RMA"
rma(src, length)
else
if smoothing == "SMA"
sma(src, length)
else
if smoothing == "EMA"
ema(src, length)
else
if smoothing == "WMA"
wma(src, length)
else
if smoothing == "VWMA"
vwma(src, length)
else
if smoothing == "SMMA"
smma(src, length)
else
if smoothing == "HullMA"
wma(2 * wma(src, length / 2) - wma(src, length), round(sqrt(length)))
else
if smoothing == "LSMA"
src
else
if smoothing == "KMA"
xPrice = src
xvnoise = abs(xPrice - xPrice[1])
nfastend = 0.666
nslowend = 0.0645
nsignal = abs(xPrice - xPrice[length])
nnoise = sum(xvnoise, length)
nefratio = iff(nnoise != 0, nsignal / nnoise, 0)
nsmooth = pow(nefratio * (nfastend - nslowend) + nslowend, 2)
nAMA = 0.0
nAMA := nz(nAMA[1]) + nsmooth * (xPrice - nz(nAMA[1]))
nAMA
else
if smoothing == "TMA"
sma(sma(close, length), length)
else
if smoothing == "DEMA"
2 * src - ema(src, length)
else
if smoothing == "TEMA"
3 * (src - ema(src, length)) + ema(ema(src, length), length)
else
src
MA = ma(typeofMA, source_ma, length_ma)
plot(MA, color=#006400FF, title="MA breakout", linewidth=3)
macrossover = crossover (source_ma, MA)
macrossunder = crossunder(source_ma, MA)
since_ma_buy = barssince(macrossover)
since_ma_sell = barssince(macrossunder)
macross_curr = 0 - since_ma_sell + since_ma_buy
bullish_MA_cond = macross_curr < 0 ? 1 : 0
bearish_MA_cond = macross_curr > 0 ? 1 : 0
posUp = (Engulf_Buy ? 1 : 0) + (bullish_MA_cond ? 1 : 0)
posDn = (Engulf_Sell ? 1 : 0) + (bearish_MA_cond ? 1 : 0)
conditionUP = posUp == 2 and posUp[1] < 2
conditionDN = posDn == 2 and posDn[1] < 2
sinceUP = barssince(conditionUP)
sinceDN = barssince(conditionDN)
// primary-first signal of the trend
nUP = crossunder(sinceUP,sinceDN)
nDN = crossover(sinceUP,sinceDN)
// and the following secondary signals
// save of the primary signal
sinceNUP = barssince(nUP)
sinceNDN = barssince(nDN)
buy_trend = sinceNDN > sinceNUP
sell_trend = sinceNDN < sinceNUP
// engulfing by
barcolor(nUP ? color.orange : na, title="Bullish condition")
barcolor(nDN ? color.yellow : na, title="Bearish condition")
isLong = nUP
isShort = nDN
long_entry_price = valuewhen(nUP, close, 0)
short_entry_price = valuewhen(nDN, close, 0)
longClose = close[1] < MA
shortClose = close[1] > MA
///////////////////////////////////////////////
//* Backtesting Period Selector | Component *//
///////////////////////////////////////////////
StartYear = input(2017, "Backtest Start Year",minval=1980)
StartMonth = input(1, "Backtest Start Month",minval=1,maxval=12)
StartDay = input(1, "Backtest Start Day",minval=1,maxval=31)
testPeriodStart = timestamp(StartYear,StartMonth,StartDay,0,0)
StopYear = input(2020, "Backtest Stop Year",minval=1980)
StopMonth = input(12, "Backtest Stop Month",minval=1,maxval=12)
StopDay = input(31, "Backtest Stop Day",minval=1,maxval=31)
testPeriodStop = timestamp(StopYear,StopMonth,StopDay,0,0)
testPeriod() => true
//////////////////////////
//* Profit Component *//
//////////////////////////
input_tp_pips = input(600, "Backtest Profit Goal (in USD)",minval=0)
input_sl_pips = input(300, "Backtest STOP Goal (in USD)",minval=0)
tp = buy_trend? long_entry_price + input_tp_pips : short_entry_price - input_tp_pips
sl = buy_trend? long_entry_price - input_sl_pips : short_entry_price + input_sl_pips
long_TP_exit = buy_trend and high >= tp
short_TP_exit = sell_trend and low <= tp
plot(tp, title="TP", style=plot.style_circles, linewidth=3, color=color.blue)
plot(sl, title="SL", style=plot.style_circles, linewidth=3, color=color.red)
if testPeriod()
strategy.entry("Long", 1, when=isLong)
strategy.close("Long", when=longClose )
strategy.exit("XL","Long", limit=tp, when=buy_trend, stop=sl)
if testPeriod()
strategy.entry("Short", 0, when=isShort)
strategy.close("Short", when=shortClose )
strategy.exit("XS","Short", when=sell_trend, limit=tp, stop=sl)
Detail
https://www.fmz.com/strategy/440459
Last Modified
2024-01-30 17:20:59