Overview
This strategy is a high-frequency trading method that combines multi-timeframe analysis with candlestick pattern recognition. It primarily uses the 15-minute timeframe to determine the overall trend direction while identifying key candlestick breakout patterns (engulfing patterns) on the 5-minute chart for precise entry. The most significant feature of the strategy is its implementation of a 1:3 risk-reward ratio, meaning the potential profit is three times the potential risk, which helps maintain overall profitability even when the success rate doesn't need to be very high. Additionally, the strategy dynamically sets stop-loss positions by identifying price swing highs and lows, enhancing the flexibility of risk management.

Strategy Principles
The core principles of this strategy are based on multi-timeframe analysis and price action theory. Specifically, the strategy operates through the following key steps:

1. Trend Direction Determination: The overall trend is determined through price action analysis on the 15-minute timeframe. The strategy calculates the highest highs and lowest lows over the past 5 periods. If both the highs and lows are rising, it's considered an uptrend; if both are falling, it's considered a downtrend.

2. Support/Resistance Identification: On the 5-minute chart, the strategy identifies key support and resistance levels by calculating the lowest and highest prices over the past 5 periods. These price levels serve as reference points for stop-loss positions.

3. Candlestick Pattern Recognition: The strategy focuses on identifying powerful engulfing patterns. A bullish engulfing pattern occurs when the current candle's closing price is higher than its opening price and completely engulfs the previous candle's price range. A bearish engulfing pattern is the opposite.

4. Entry Conditions: Trade signals are only generated when the 15-minute trend direction aligns with the 5-minute candlestick pattern. For example, a buy signal is generated when a bullish engulfing pattern appears during an uptrend; a sell signal is generated when a bearish engulfing pattern appears during a downtrend.

5. Risk Management: The strategy employs a 1:3 risk-reward ratio, with the stop-loss set at recent swing lows (for long positions) or swing highs (for short positions), and the take-profit target set at three times the stop-loss distance.

Strategy Advantages
Through in-depth analysis of the strategy's code implementation, the following significant advantages can be summarized:

1. Multi-Timeframe Synergy: By combining 15-minute and 5-minute timeframes, the strategy reduces false signals and only enters trades when supported by the larger trend, increasing the success rate.

2. Clear Entry Logic: Using the classic engulfing pattern as an entry trigger condition, a pattern widely recognized in technical analysis as a powerful reversal or continuation signal.

3. Optimized Risk-Reward Ratio: The fixed 1:3 risk-reward ratio setting theoretically allows the strategy to break even with a win rate of just 25%, with any actual win rate above this threshold generating net profits.

4. Dynamic Stop-Loss Setting: Stop-loss positions are based on recent price swing highs and lows rather than fixed points, allowing the strategy to better adapt to different market volatility environments.

5. Visual Feedback Mechanism: The strategy marks buy and sell signals and entry positions on the chart, allowing traders to visually evaluate and verify strategy performance.

6. Integrated Capital Management: The strategy defaults to using 2% of account equity for each trade, with this proportional position management helping to control single-trade risk.

Strategy Risks
Despite being well-designed, the strategy still has the following potential risks:

1. Market Event Risk: During major news or unexpected events, prices may rapidly break through stop-loss levels, resulting in actual losses exceeding expectations. The solution is to pause the strategy before important economic data or news releases.

2. Low Liquidity Environment Risk: In markets with insufficient liquidity, slippage may occur, causing actual entry or exit prices to deviate from expectations. It is recommended to use this strategy during major trading sessions and avoid periods of lower liquidity.

3. False Breakout Risk: Engulfing patterns are not 100% reliable and false breakouts may occur. The solution is to consider adding confirmation indicators, such as volume confirmation or other technical indicator filters.

4. Trend Determination Delay: Using 5 periods to calculate trends may result in a certain lag in trend determination. In strongly oscillating markets, this lag may lead to incorrect signals. Consider adjusting the number of periods or adding additional trend confirmation indicators.

5. Limitations of Fixed Risk-Reward Ratio: Although a 1:3 risk-reward ratio is theoretically attractive, not all market environments are suitable for this setting. In highly volatile or undefined trend markets, it may be difficult to achieve profit targets that are three times the stop-loss distance.

Strategy Optimization Directions
Through in-depth analysis, here are directions for further optimization of the strategy:

1. Dynamic Risk-Reward Ratio Adjustment: The risk-reward ratio can be dynamically adjusted based on market volatility (ATR indicator), using more conservative settings (such as 1:2) in low-volatility environments and more aggressive settings (such as 1:4) in strong trend environments. This better adapts to different market states.

2. Add Volume Confirmation: Add a volume filter to entry conditions, only entering when the engulfing pattern is accompanied by a significant increase in volume, which can reduce the risk of false breakouts.

3. Introduce Momentum Indicators: Combine momentum indicators such as RSI or MACD as additional filtering conditions to ensure entry points have not only pattern support but also momentum support.

4. Optimize Timeframe Selection: The current strategy uses fixed 15-minute and 5-minute timeframes. Consider using adjustable parameters that allow users to select the optimal timeframe combination based on the trading instrument and personal preferences.

5. Partial Profit Locking Mechanism: Implement a staged exit strategy, such as locking in part of the profit when the price reaches a 1:1 risk-reward ratio and adjusting the stop-loss to breakeven for the remaining position, allowing the remaining position to pursue higher return targets.

6. Add Trading Time Filter: Add a trading time filter to avoid trading during high-volatility, low-liquidity periods around market opening and closing, or to avoid major economic data release times.

7. Adaptive Parameter Optimization: Implement a mechanism that automatically adjusts strategy parameters based on recent market performance, such as adjusting the number of periods for trend determination based on market characteristics over the most recent 20-50 trading periods.

Summary
The Multi-Timeframe Candlestick Breakout with Risk-Reward Optimization Strategy is a comprehensive trading system that combines trend analysis, price action, and risk management. It enhances signal quality through multi-timeframe synergistic analysis, provides precise entry points through classic candlestick patterns, and ensures long-term profitability through an optimized risk-reward ratio.

This strategy is particularly suitable for traders seeking short-term high-frequency trading opportunities, especially performing better in market environments with clear trends. However, like all trading strategies, it is not perfect and requires traders to make appropriate adjustments based on their risk tolerance and trading objectives.

By implementing the optimization suggestions proposed in this article, especially dynamic risk-reward ratio adjustments and adding additional confirmation indicators, the robustness and adaptability of the strategy can be further enhanced. Ultimately, the success of this strategy depends not only on the algorithm itself but also on the trader's understanding of the market and continuous monitoring and improvement of the strategy.

Strategy source code

/*backtest
start: 2025-01-01 00:00:00
end: 2025-06-11 00:00:00
period: 5m
basePeriod: 5m
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT"}]
*/

//@version=5
strategy("5-Min Gold Scalping Strategy with 1:3 RR", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=2)

// Trend Direction (Using 15-Minute Price Action)
higherHigh = ta.highest(high, 5)
higherLow = ta.highest(low, 5)
lowerHigh = ta.lowest(high, 5)
lowerLow = ta.lowest(low, 5)
trendUp_15min = request.security(syminfo.tickerid, "15", higherHigh > higherHigh[1] and higherLow > higherLow[1])
trendDown_15min = request.security(syminfo.tickerid, "15", lowerHigh < lowerHigh[1] and lowerLow < lowerLow[1])

// Price Action on 5-Minute Chart
// Support/Resistance (Swing Lows/Highs)
swing_low = ta.lowest(low, 5)
swing_high = ta.highest(high, 5)

// Candlestick Patterns (Bullish/Bearish Engulfing)
bullishEngulfing = close > open and close[1] < open[1] and close > high[1] and open < low[1]
bearishEngulfing = close < open and close[1] > open[1] and close < low[1] and open > high[1]

// Buy and Sell Conditions
buySignal = trendUp_15min and bullishEngulfing
sellSignal = trendDown_15min and bearishEngulfing

// Auto Buy Entry
if (buySignal)
    strategy.entry("Buy", strategy.long)
    label.new(bar_index, low, "BUY", color=color.green, style=label.style_label_up, textcolor=color.white)

// Auto Buy Exit (1:3 RR)
if (strategy.position_size > 0)
    stopLossBuy = swing_low
    takeProfitBuy = close + (close - stopLossBuy) * 3
    strategy.exit("Buy Exit", "Buy", stop=stopLossBuy, limit=takeProfitBuy)

// Auto Sell Entry
if (sellSignal)
    strategy.entry("Sell", strategy.short)
    label.new(bar_index, high, "SELL", color=color.red, style=label.style_label_down, textcolor=color.white)

// Auto Sell Exit (1:3 RR)
if (strategy.position_size < 0)
    stopLossSell = swing_high
    takeProfitSell = close - (stopLossSell - close) * 3
    strategy.exit("Sell Exit", "Sell", stop=stopLossSell, limit=takeProfitSell)

// Plot Buy/Sell Signals with Shapes
plotshape(buySignal, title="Buy Signal", location=location.belowbar, color=color.green, style=shape.triangleup, size=size.small)
plotshape(sellSignal, title="Sell Signal", location=location.abovebar, color=color.red, style=shape.triangledown, size=size.small)

The original address: Multi-Timeframe Candlestick Breakout with Risk-Reward Optimization Strategy