Overview
This strategy is a short-term trading system that combines MACD (Moving Average Convergence Divergence) with multiple moving averages, primarily applied to short-period charts and specifically designed to capture short-term momentum shifts in the market. The core logic involves identifying high-probability trend reversal points through multiple technical indicators working in tandem, including crossovers between fast and slow EMAs (Exponential Moving Averages), crossovers between MACD line and signal line, and the relationship between price and moving averages. The strategy also incorporates strict risk management mechanisms, including trade cooldown periods, consecutive loss limits, and daily maximum loss percentage controls to protect account capital.

Strategy Principles
The operation of this strategy is based on the principle of collaborative confirmation from multiple technical analysis indicators, with detailed logic as follows:

Moving Average System: The strategy employs three EMA lines - a 5-period fast EMA, a 13-period slow EMA, and a 50-period trend EMA. These three lines represent short-term, medium-term, and long-term trends respectively.

MACD Indicator Settings: Uses standard MACD parameters (12,26,9) to capture momentum changes and confirm trend direction.

Multiple Confirmation Entry Conditions:

• Bullish Signal: Fast EMA crosses above Slow EMA + MACD line crosses above Signal line + MACD histogram is positive and increasing + Price is above all EMAs
• Bearish Signal: Fast EMA crosses below Slow EMA + MACD line crosses below Signal line + MACD histogram is negative and decreasing + Price is below all EMAs

Risk Management Mechanisms:

• Trade Cooldown: A specific number of periods must elapse after each trade before initiating the next one
• Consecutive Loss Limit: Trading stops after reaching a set number of consecutive losses per day
• Daily Loss Limit: Trading stops when daily losses reach a specific percentage of the account

Fixed Holding Period: The strategy employs a fixed holding time of 4 candles (approximately 2 minutes), a design particularly suited for capturing short-term price movements.

At the code level, the strategy implements complete signal generation, risk control, and graphical visualization functions, enabling traders to intuitively monitor market conditions and strategy performance.

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

1. Multiple Confirmation Mechanism: Combining EMA crossover, MACD crossover, and price position triple confirmation significantly improves signal reliability and reduces the risk of false breakouts.

2. Trend Direction Filtering: Confirms the larger timeframe trend direction through the 50-period EMA, only entering trades in alignment with the main trend, avoiding the high risk of counter-trend trading.

3. Dynamic Risk Management: The built-in trade cooldown mechanism prevents overtrading; consecutive loss limits and daily loss percentage controls effectively protect account capital.

4. Strong Adaptability: Strategy parameters can be adjusted according to different market conditions and personal risk preferences, demonstrating strong adaptability.

5. Visualization of Trading Signals: Clear graphical markers intuitively display trading signals, facilitating real-time monitoring and decision-making.

6. Precise Time Management: Built-in timer functionality helps traders accurately grasp entry timing and holding periods.

7. Complete Strategy Framework: The code implements a complete closed loop from signal generation to trade execution to risk management, serving as a foundational framework for building other short-term trading systems.

Strategy Risks
Despite the strategy's refined design, the following potential risks still exist:

1. Short-term Volatility Sensitivity: Since the strategy targets short-period charts, it is extremely sensitive to market noise and short-term fluctuations, potentially leading to frequent false signals. Solution: Additional filtering conditions can be added, such as volatility indicators or support/resistance confirmation.

2. Rapid Market Reversal Risk: In highly volatile markets, prices may quickly reverse after position entry, and the 2-minute fixed holding period may be insufficient to respond. Solution: Dynamic stop-loss mechanisms can be added or holding times can be extended/shortened under specific market conditions.

3. Transaction Cost Impact: Frequent trading will generate significant fee costs, potentially eroding strategy profits. Solution: Optimize entry conditions, reduce low-quality signals, and improve trade success rates.

4. Indicator Lag: Both EMA and MACD are lagging indicators and may miss optimal entry points in rapidly changing markets. Solution: Incorporate leading indicators such as Relative Strength Index (RSI) or stochastic indicators for confirmation.

5. Parameter Sensitivity: Strategy performance is sensitive to EMA and MACD parameter settings, and parameter changes may lead to performance differences. Solution: Conduct comprehensive backtesting and parameter optimization to find the most stable parameter combination.

Optimization Directions
Based on in-depth analysis of the code, the strategy can be optimized in the following directions:

1. Adaptive Parameter Adjustment: Dynamically adjust EMA and MACD parameters based on market volatility, enabling the strategy to better adapt to different market environments. This optimization can be implemented by calculating the recent Average True Range (ATR), using longer-period parameters in high-volatility markets and shorter-period parameters in low-volatility markets.

2. Time Filter: Add trading time filters to avoid low-liquidity periods and major economic data release times, which will effectively reduce false signals and improve win rates.

3. Dynamic Stop-Loss/Take-Profit: Replace the fixed holding period with a dynamic stop-loss/take-profit mechanism based on market volatility, such as setting stop-loss positions using ATR multiples.

4. Volume Confirmation: Incorporate volume analysis into the signal confirmation system, only trading when supported by volume, improving signal quality.

5. Machine Learning Enhancement: Introduce simple machine learning algorithms to score and filter signals based on historical data, prioritizing high-probability successful trading patterns.

6. Multi-Timeframe Analysis: Extend the current strategy to include higher timeframe trend confirmation, ensuring trade direction aligns with larger cycle trends.

7. Capital Management Optimization: Implement more sophisticated money management algorithms, dynamically adjusting position sizes based on signal strength, recent strategy performance, and market volatility.

These optimization directions can effectively enhance the strategy's stability and profitability while reducing risk levels, making it more suitable for live trading environments.

Summary
The Multi-Momentum Confirmation MACD and Moving Average Crossover Short-Term Trend Optimization Trading Strategy is a well-designed short-term trading system that provides a complete trading solution for short-term markets through the synergy of multiple technical indicators and strict risk management. The core advantages of the strategy lie in its multiple confirmation mechanisms and comprehensive risk control system, which give it high reliability in capturing short-term trend reversal points.

However, as a short-term trading strategy, it also faces challenges such as market noise, false signals, and transaction costs. By implementing the optimization directions proposed in this article, especially adaptive parameter adjustment, dynamic stop-loss/take-profit, and multi-timeframe analysis, the strategy's robustness and long-term performance can be significantly enhanced.

It is worth noting that any trading strategy requires thorough backtesting and simulated trading verification, and appropriate adjustments based on individual risk tolerance and market understanding. This strategy provides a solid foundational framework that traders can customize according to their own needs to create a trading system that suits them.

Strategy source code

/*backtest
start: 2024-07-03 00:00:00
end: 2025-07-02 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"BNB_USDT"}]
*/

//@version=5
strategy("MACD + MA 2-Min Binary Options Strategy (Strategy Mode)", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=100)

// === INPUTS ===
emaFastLen = input.int(5, "Fast EMA Length")
emaSlowLen = input.int(13, "Slow EMA Length")
emaTrendLen = input.int(50, "Trend EMA Length")
macdSrc = input.source(close, "MACD Source")
macdFastLen = input.int(12, "MACD Fast Length")
macdSlowLen = input.int(26, "MACD Slow Length")
macdSignalLen = input.int(9, "MACD Signal Smoothing")
tradeCooldown = input.int(10, "Cooldown Bars Between Trades")
maxLossStreak = input.int(3, "Max Consecutive Losses (Daily)")
dailyEquityLossLimit = input.float(5.0, "Max Daily Loss %", step=0.1)

// === MOVING AVERAGES ===
emaFast = ta.ema(close, emaFastLen)
emaSlow = ta.ema(close, emaSlowLen)
emaTrend = ta.ema(close, emaTrendLen)

// === MACD ===
[macdLine, signalLine, _] = ta.macd(macdSrc, macdFastLen, macdSlowLen, macdSignalLen)
macdHist = macdLine - signalLine

// === CONDITIONS ===
longCond = ta.crossover(emaFast, emaSlow) and ta.crossover(macdLine, signalLine) and macdHist > 0 and close > emaFast and close > emaSlow and close > emaTrend
shortCond = ta.crossunder(emaFast, emaSlow) and ta.crossunder(macdLine, signalLine) and macdHist < 0 and close < emaFast and close < emaSlow and close < emaTrend

// === TRADE FILTERING ===
var int lastTradeBar = na
canTrade = na(lastTradeBar) or (bar_index - lastTradeBar > tradeCooldown)

var int lossStreak = 0
var float dailyProfit = 0.0
var int prevDay = na
newDay = (dayofmonth != prevDay)
if newDay
    lossStreak := 0
    dailyProfit := 0.0
prevDay := dayofmonth

// === TRACK EQUITY ===
var float lastEquity = strategy.equity
profitToday = strategy.equity - lastEquity
lastEquity := strategy.equity

// Update daily PnL
if not newDay
    dailyProfit += profitToday

// Trade rules
allowLossLimit = (strategy.equity - lastEquity) / lastEquity * 100 > -dailyEquityLossLimit
allowTrade = canTrade and lossStreak < maxLossStreak and allowLossLimit

// === PLOT SIGNALS ===
plotshape(longCond and allowTrade, title="CALL Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="CALL")
plotshape(shortCond and allowTrade, title="PUT Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="PUT")

// === PLOT EMAs ===
plot(emaFast, title="EMA 5", color=color.orange)
plot(emaSlow, title="EMA 13", color=color.blue)
plot(emaTrend, title="EMA 50", color=color.purple)

// === ALERTS ===
alertcondition(longCond, title="CALL Alert", message="CALL Signal (Buy) detected!")
alertcondition(shortCond, title="PUT Alert", message="PUT Signal (Sell) detected!")

// === TIMER ===
timeSinceBar = (timenow - time) / 1000  // seconds since bar opened
secondsPerBar = (time - time[1]) / 1000
barCountdown = secondsPerBar - timeSinceBar
plot(barCountdown, title="Bar Countdown (sec)", color=color.gray, linewidth=1, style=plot.style_line)

// === STRATEGY EXECUTION ===
if (longCond and allowTrade)
    strategy.entry("CALL", strategy.long)
    lastTradeBar := bar_index

if (shortCond and allowTrade)
    strategy.entry("PUT", strategy.short)
    lastTradeBar := bar_index

// Exit after 4 bars (2 minutes on 30s timeframe)
if strategy.position_size != 0
    isCall = strategy.opentrades.entry_id(0) == "CALL"
    isPut = strategy.opentrades.entry_id(0) == "PUT"
    barsInTrade = bar_index - strategy.opentrades.entry_bar_index(0)
    if barsInTrade >= 4
        stratClose = false
        if isCall and close > strategy.opentrades.entry_price(0)
            lossStreak := 0
            stratClose := true
        else if isPut and close < strategy.opentrades.entry_price(0)
            lossStreak := 0
            stratClose := true
        else
            lossStreak += 1
            stratClose := true
        if stratClose
            strategy.close("CALL")
            strategy.close("PUT")

// === PLOT EQUITY ===
plot(strategy.equity, title="Equity Curve", color=color.green, linewidth=2, style=plot.style_line)

Strategy parameters

The original address: MACD and Moving Average Momentum Crossover Short-Term Trend Optimization Trading Strategy