Overview
The Multi-Signal Adaptive Trading Strategy is a comprehensive quantitative trading system that combines multiple technical analysis indicators to generate trading signals. The strategy primarily utilizes three core technical indicators: EMA crossover, RSI overbought/oversold conditions, and MACD signals, complemented by volume filters and higher timeframe confirmation mechanisms to form a complete trading system. The strategy also includes a risk management module using fixed percentage stop-loss, take-profit, and ATR trailing stops to effectively control risk on each trade.

Strategy Principle
The core principle of this strategy is to improve trading decision accuracy through the combination of multiple trading signals. The specific implementation is as follows:

EMA Crossover Signal: Uses the crossover between fast EMA (default 9 periods) and slow EMA (default 21 periods) to identify trend changes. A buy signal is generated when the fast EMA crosses above the slow EMA, and a sell signal is generated when the fast EMA crosses below the slow EMA.

RSI Overbought/Oversold Signal: Utilizes the Relative Strength Index (RSI) to identify market overbought and oversold conditions. When RSI falls below 30 (default), it's considered oversold, generating a buy signal; when RSI rises above 70 (default), it's considered overbought, generating a sell signal.

MACD Signal: Uses the crossover between the MACD line and signal line to confirm trend direction. A buy signal is generated when the MACD line crosses above the signal line, and a sell signal is generated when the MACD line crosses below the signal line.

Signal Combination Logic: The strategy provides two combination methods - "Any" (triggered by any signal) and "All" (triggered only when all enabled signals align). In "Any" mode, a trading signal is generated when any enabled signal is triggered; in "All" mode, all enabled signals must be triggered simultaneously to generate a trading signal.

Filter Mechanisms:

• Volume Filter: Ensures trading only when volume is above its moving average.
• Higher Timeframe Confirmation: Uses a higher timeframe EMA to confirm the overall trend direction, trading only when the trend direction aligns.

Position Management: The strategy uses a percentage of equity method to determine position size for each trade, defaulting to 10% of account equity.

Risk Management:

• Fixed percentage stop-loss and take-profit
• ATR trailing stop, using a multiple of ATR to set dynamic stop-loss levels

Strategy Advantages

1. Multi-dimensional Signal Analysis: By combining multiple technical indicators, the strategy can analyze the market from different angles, reducing the impact of false signals and improving the reliability of trading decisions.

2. Flexible Signal Combination: Users can choose between "Any" or "All" signal combination modes, adapting to different trading styles and market conditions. In highly volatile markets, "All" mode can reduce false signals; in clear trends, "Any" mode can more sensitively capture opportunities.

3. Multi-level Filtering Mechanism: The volume filter and higher timeframe confirmation mechanism add additional validation layers, effectively reducing erroneous trading signals, especially during market consolidation.

4. Comprehensive Risk Management: The strategy has a complete risk control system, including percentage stop-loss/take-profit and ATR trailing stops, which can automatically adjust stop positions based on market volatility changes, effectively protecting capital.

5. High Customizability: The strategy allows users to adjust various parameters, including EMA lengths, RSI thresholds, MACD parameters, etc., enabling traders to optimize according to their trading style and target market.

6. Intuitive Visual Feedback: The strategy provides clear chart indications, including EMA lines and buy/sell signal arrows, allowing traders to intuitively understand and evaluate trading signals.

Strategy Risks

1. Parameter Over-optimization: Excessive parameter optimization may lead to the strategy performing well in historical tests but poorly in actual trading (overfitting risk). The solution is to use sufficiently long backtesting periods and conduct robustness tests.

2. Signal Conflicts: Under certain market conditions, different signals may contradict each other, leading to confusion. For example, EMA might indicate an uptrend while RSI is already in the overbought zone. The solution is to clearly define signal priorities or use "All" mode to ensure consistency.

3. Lagging Issue: All technical indicators used have a certain degree of lag, especially EMA and MACD. In rapidly changing markets, this may result in suboptimal entry or exit timing. The solution is to consider shortening indicator periods or incorporating price action analysis.

4. Market Adaptability Limitations: The strategy performs well in markets with clear trends but may generate more false signals in range-bound markets. The solution is to add trend strength filters or pause trading when range-bound markets are identified.

5. Capital Risk: Although the strategy includes stop-loss mechanisms, in extreme market conditions (such as large gaps or liquidity shortages), stops may not execute as expected. The solution is to appropriately reduce the percentage of funds per trade and use more conservative stop settings.

Strategy Optimization Directions

1. Add Trend Strength Filter: Adding ADX or similar indicators to measure trend strength and only trading in clear trends can significantly reduce false signals in oscillating markets. This improvement can solve the problem of the strategy generating false signals in sideways markets.

2. Add Time Filter: Markets have different characteristics at different times; adding a time filter can avoid trading during inefficient periods. For example, avoiding the high volatility periods at market open and close, or only trading during specific sessions.

3. Dynamic Parameter Adjustment: Automatically adjust indicator parameters based on market volatility. For example, lengthening EMA periods in high volatility environments and shortening them in low volatility environments. This adaptive adjustment can improve the strategy's adaptability under different market conditions.

4. Add Machine Learning Component: Introduce machine learning algorithms to optimize signal weight allocation, dynamically adjusting the importance of each signal based on historical performance. This can enable the strategy to automatically adjust its decision logic as market conditions change.

5. Improve Position Management: Implement volatility-based position sizing, increasing positions in low volatility environments and reducing them in high volatility environments. This can improve capital efficiency while keeping relative risk constant.

6. Add Fundamental Filters: For certain markets, incorporating fundamental indicators (such as earnings seasons, economic data releases, etc.) can avoid trading before and after major uncertainty events, reducing potential risks.

7. Improve Stop-Loss Strategy: Implement intelligent stop-loss based on support and resistance levels, rather than relying solely on fixed percentages or ATR multiples. This approach can better adapt to market structure and avoid unnecessary stops due to market noise.

Summary
The Multi-Signal Adaptive Trading Strategy is a comprehensive and flexible trading system that provides relatively reliable trading signals by combining multiple technical indicators and filtering mechanisms. The core advantages of the strategy lie in its comprehensive analysis capabilities and complete risk management system, allowing it to maintain effectiveness under different market conditions.

However, the strategy also has some inherent risks and limitations, such as parameter over-optimization and signal lag issues. By implementing the suggested optimization directions, especially adding trend strength filters and implementing dynamic parameter adjustments, the robustness and adaptability of the strategy can be further improved.

Ultimately, no matter how sophisticated a strategy is, it needs to be adjusted according to specific market environments and personal trading goals. Continuous monitoring of strategy performance, regular evaluation, and optimization are key to maintaining long-term effectiveness. This strategy provides a good starting point for quantitative traders, on which more complex and personalized trading systems can be further developed.

Strategy source code

/*backtest
start: 2024-04-22 00:00:00
end: 2025-04-21 00:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"TRX_USD"}]
*/

//@version=5
strategy("Full‑Featured Multi‑Signal Strategy By Andi Tan", overlay=true)

// === POSITION SIZE ===
posPct = input.float(10, "Position Size (% Equity)", minval=0.1, step=0.1)

// === INPUTS SIGNALS ===
useEMA     = input.bool(true, "Enable EMA Crossover")
emaFastLen = input.int(9,     "EMA Fast Length", minval=1)
emaSlowLen = input.int(21,    "EMA Slow Length", minval=1)

useRSI     = input.bool(true, "Enable RSI Signal")
rsiLen     = input.int(14,    "RSI Length", minval=1)
rsiOB      = input.int(70,    "RSI Overbought", minval=50, maxval=100)
rsiOS      = input.int(30,    "RSI Oversold", minval=0,  maxval=50)

useMACD    = input.bool(true, "Enable MACD Signal")
macdFast   = input.int(12,    "MACD Fast Length",   minval=1)
macdSlow   = input.int(26,    "MACD Slow Length",   minval=1)
macdSig    = input.int(9,     "MACD Signal Length", minval=1)

mode       = input.string("Any", "Signal Combination", options=["Any","All"])
showArrows = input.bool(true, "Show Buy/Sell Arrows")

// === RISK MANAGEMENT ===
slPct     = input.float(1.0, "Stop‑Loss (%)", minval=0) / 100
tpPct     = input.float(2.0, "Take‑Profit (%)", minval=0) / 100

useTrail  = input.bool(true, "Enable ATR Trailing Stop")
atrLen    = input.int(14,    "ATR Length", minval=1)
trailMul  = input.float(1.5, "ATR Multiplier", minval=0.1)

// === FILTERS ===
useVolFilt  = input.bool(true, "Enable Volume Filter")
volLen      = input.int(20,   "Volume MA Length", minval=1)

useHigherTF = input.bool(true, "Enable Higher‑TF Confirmation")
higherTF    = input.string("60", "Higher‑TF Timeframe", options=["5","15","60","240","D","W"])

// === CALCULATIONS ===
// EMA crossover
emaFast = ta.ema(close, emaFastLen)
emaSlow = ta.ema(close, emaSlowLen)
emaUp   = ta.crossover(emaFast, emaSlow)
emaDown = ta.crossunder(emaFast, emaSlow)

// RSI
rsiVal  = ta.rsi(close, rsiLen)
rsiBuy  = rsiVal < rsiOS
rsiSell = rsiVal > rsiOB

// MACD
[macdLine, macdSignal, _] = ta.macd(close, macdFast, macdSlow, macdSig)
macdBuy  = ta.crossover(macdLine, macdSignal)
macdSell = ta.crossunder(macdLine, macdSignal)

// Combine base signals with if…else (bukan ternary terpecah)
var bool buyBase  = false
var bool sellBase = false
if mode == "Any"
    buyBase  := (useEMA and emaUp)   or (useRSI and rsiBuy)   or (useMACD and macdBuy)
    sellBase := (useEMA and emaDown) or (useRSI and rsiSell)  or (useMACD and macdSell)
else
    buyBase  := ((not useEMA) or emaUp)   and ((not useRSI) or rsiBuy)   and ((not useMACD) or macdBuy)
    sellBase := ((not useEMA) or emaDown) and ((not useRSI) or rsiSell)  and ((not useMACD) or macdSell)

// Volume filter
volMA = ta.sma(volume, volLen)
buyF  = buyBase  and (not useVolFilt or volume > volMA)
sellF = sellBase and (not useVolFilt or volume > volMA)

// ——— HIGHER‑TF EMA (dipanggil di top‑scope) ———
htEMA = request.security(syminfo.tickerid, higherTF, ta.ema(close, emaSlowLen))

// Final buy/sell signals
buySignal  = buyF  and (not useHigherTF or close > htEMA)
sellSignal = sellF and (not useHigherTF or close < htEMA)

// ATR untuk trailing
atrVal = ta.atr(atrLen)

// === ORDERS ===
if buySignal
    float qty = (strategy.equity * posPct/100) / close
    strategy.entry("Long", strategy.long, qty=qty)
if sellSignal
    float qty = (strategy.equity * posPct/100) / close
    strategy.entry("Short", strategy.short, qty=qty)

strategy.exit("Exit Long",  from_entry="Long",
     loss=slPct * close, profit=tpPct * close,
     trail_points = useTrail ? atrVal * trailMul : na)

strategy.exit("Exit Short", from_entry="Short",
     loss=slPct * close, profit=tpPct * close,
     trail_points = useTrail ? atrVal * trailMul : na)

// === PLOTS ===
plot(useEMA ? emaFast : na, title="EMA Fast", color=color.orange)
plot(useEMA ? emaSlow : na, title="EMA Slow", color=color.blue)

plotshape(showArrows and buySignal,  title="Buy",  location=location.belowbar,
     style=shape.arrowup,   text="BUY")
plotshape(showArrows and sellSignal, title="Sell", location=location.abovebar,
     style=shape.arrowdown, text="SELL")

Strategy parameters

The original address: Multi-Signal Adaptive Trading Strategy