Overview
The Dynamic Volatility Trading System is a quantitative futures trading strategy specifically designed for high-volatility markets, particularly suited for cryptocurrency and other volatile instruments. This strategy cleverly combines multiple technical indicators to generate consistent trading signals within a fixed timeframe while featuring a dynamic risk management system. The core of the strategy lies in calculating all key indicators (including EMA, MACD, RSI, ATR, and custom Supertrend) on a fixed timeframe (default 15 minutes), ensuring consistency in signal generation across any chart resolution, while also equipped with an extreme market condition monitoring mechanism that automatically closes positions during violent market fluctuations to reduce risk.

Strategy Principles
The Dynamic Volatility Trading System is based on the synergistic effect of multiple technical indicators, calculating key metrics on a fixed timeframe using TradingView's request.security() function. Its core logic is as follows:

1. Fixed Timeframe Calculations: All indicators are computed on a selected fixed timeframe (default 15 minutes), ensuring trading signals remain consistent regardless of the chart resolution being viewed.

2. Multi-Indicator System:

50-period EMA as a trend filter
MACD crossovers for momentum indication
RSI to monitor overbought/oversold conditions
ATR for dynamic take-profit levels and trailing stops
Custom Supertrend for additional trend confirmation

1. Entry Conditions:

Long: Close above EMA, MACD golden cross, Supertrend up, RSI not overbought
Short: Close below EMA, MACD death cross, Supertrend down, RSI not oversold

1. Exit Mechanisms:

ATR-based take-profit levels
ATR-based trailing stop to protect profits while allowing winning trades to develop fully

1. Extreme market monitoring: forces position closure when price movement exceeds user-defined threshold (default 2%)

2. Risk Management: The strategy limits positions to one direction at a time, ensuring consistency and simplicity in capital management.

Strategy Advantages
The Dynamic Volatility Trading System offers the following significant advantages:

1. Consistent Signal Generation: By calculating all indicators on a fixed timeframe, the strategy ensures stability and consistency in trading signals, avoiding confusion caused by switching between different timeframes.

2. Multiple Confirmation Mechanism: The combination of multiple technical indicators (EMA, MACD, RSI, Supertrend) for entry signals significantly reduces the risk of false signals and improves signal quality.

3. Dynamic Risk Management: ATR-based take-profit and trailing stop levels automatically adjust according to market volatility, allowing profits to grow while protecting capital. This dynamic approach is particularly suitable for highly volatile markets.

4. Extreme Market Protection: By monitoring significant price movements (pumps or dumps), the strategy automatically closes positions under extreme market conditions, effectively mitigating potential losses—an important safety mechanism often overlooked by traditional strategies.

5. High Adaptability: The strategy can be used across multiple timeframes (1-minute, 5-minute, 15-minute, etc.) while maintaining consistency in signal generation, providing traders with greater flexibility.

Strategy Risks
Despite its multiple advantages, the Dynamic Volatility Trading System has the following potential risks:

1. Overtrading Risk: The multi-indicator system may generate excessive signals under certain market conditions, leading to frequent trading and increased transaction costs. Solution: Consider adding additional filtering conditions or extending signal confirmation time.

2. Market Noise Sensitivity: Especially on lower timeframes, the strategy may be sensitive to market noise, triggering unnecessary trades. Solution: Adjust indicator parameters to reduce noise impact, such as increasing EMA length or adjusting RSI boundaries.

3. Parameter Optimization Dependency: Strategy performance is highly dependent on the optimization of multiple parameters (EMA length, MACD parameters, ATR multiplier, etc.), and different market conditions may require different parameter settings. Solution: Regularly backtest and adjust parameters, or consider implementing an adaptive parameter system.

4. Delayed Reaction to Extreme Volatility: Despite having extreme market monitoring, the strategy's reaction may still be delayed in cases of instantaneous extreme volatility, resulting in less-than-ideal exit prices. Solution: Consider adding more sensitive trigger mechanisms based on the rate of price change.

5. Single Timeframe Limitation: While the strategy calculates indicators on a fixed timeframe for consistency, this may also lead to overlooking important market information provided by higher or lower timeframes. Solution: Consider adding multi-timeframe analysis components.

Strategy Optimization Directions
Based on an in-depth analysis of the strategy, here are several possible optimization directions:

1. Multi-Timeframe Coordination System: In addition to the current fixed timeframe, add a higher timeframe trend filter (such as 60-minute or 4-hour) to ensure trading direction aligns with the larger trend. This is beneficial because higher timeframes typically display more stable market trends, reducing the likelihood of counter-trend trading.

2. Dynamic Parameter Adjustment: Implement a mechanism that automatically adjusts strategy parameters based on market volatility or other market indicators. This optimization allows the strategy to better adapt to changing market conditions without manual intervention.

3. Advanced Stop-Loss Management: Building on the current ATR foundation, introduce multi-level trailing stops or intelligent stop-loss systems based on support/resistance. This allows for more refined risk management, protecting profits while allowing trades to develop fully.

4. Sentiment Analysis Integration: Consider adding market sentiment indicators (such as volume analysis, price volatility pattern recognition) to provide an additional dimension for entry and exit decisions. Market sentiment is often a leading indicator of price movements and can improve the timeliness of signal generation.

5. Machine Learning Optimization: Utilize machine learning algorithms to optimize parameter selection and signal filtering, training models through large amounts of historical data to improve strategy performance. Machine learning can identify complex market patterns that traditional technical analysis might struggle to capture.

6. Enhanced Capital Management: Introduce more sophisticated risk management systems, such as dynamic position sizing based on drawdown control or Kelly criterion optimization based on win rate. Scientific capital management is crucial for the long-term profitability of the strategy.

Summary
The Dynamic Volatility Trading System is an advanced futures trading strategy that comprehensively utilizes technical analysis and dynamic risk management, particularly suitable for highly volatile markets. By calculating multiple technical indicators (EMA, MACD, RSI, Supertrend) on a fixed timeframe, the strategy can generate consistent and robust trading signals. Its dynamic take-profit and stop-loss system, along with extreme market monitoring mechanisms, provides multi-layered protection for capital safety.

While the strategy has potential risks such as parameter dependency and market noise sensitivity, these risks can be effectively mitigated through the suggested optimization directions, including multi-timeframe analysis, dynamic parameter adjustment, and advanced stop-loss management. Further integration of machine learning and market sentiment analysis can enhance the strategy's adaptability and profitability.

For traders seeking a systematic trading approach, especially those focusing on volatile markets, the Dynamic Volatility Trading System offers a comprehensive solution that balances technical indicators and risk management, with the potential to maintain stable performance under various market conditions.

Strategy source code

/*backtest
start: 2024-03-05 00:00:00
end: 2024-09-16 00:00:00
period: 1h
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT"}]
*/

//@version=6
strategy("Futures Trading Expert Strategy with Extreme Move Check (Fixed TF)", 
     overlay=true, 
     initial_capital=10000, 
     default_qty_type=strategy.percent_of_equity, 
     default_qty_value=10, 
     calc_on_every_tick=true)

// ========== INPUTS ==========
fixedTF = input.timeframe("15", title="Fixed Timeframe for Signals")

emaLength         = input.int(50, title="EMA Length", minval=1)
atrLength         = input.int(14, title="ATR Length", minval=1)
atrMultiplier     = input.float(3.0, title="ATR Multiplier for TP", step=0.1)
macdFast          = input.int(12, title="MACD Fast Length")
macdSlow          = input.int(26, title="MACD Slow Length")
macdSignal        = input.int(9, title="MACD Signal Smoothing")
stATRPeriod       = input.int(10, title="Supertrend ATR Period", minval=1)
stFactor          = input.float(3.0, title="Supertrend Factor", step=0.1)
rsiLength         = input.int(14, title="RSI Length")
rsiOverbought     = input.int(70, title="RSI Overbought Level")
rsiOversold       = input.int(30, title="RSI Oversold Level")
trailStopMultiplier = input.float(2.0, title="Trailing Stop ATR Multiplier", step=0.1)
extremePct        = input.float(2.0, title="Extreme % Threshold", step=0.1)  // e.g., 2%

// ========== FIXED TIMEFRAME INDICATOR VALUES ==========
// Fetch fixed timeframe OHLC values
ft_close = request.security(syminfo.tickerid, fixedTF, close)
ft_high  = request.security(syminfo.tickerid, fixedTF, high)
ft_low   = request.security(syminfo.tickerid, fixedTF, low)

// EMA calculated on fixed timeframe
emaValue = request.security(syminfo.tickerid, fixedTF, ta.ema(close, emaLength))

// MACD calculated on fixed timeframe
[macdLine, signalLine, _] = request.security(syminfo.tickerid, fixedTF, ta.macd(close, macdFast, macdSlow, macdSignal))

// RSI calculated on fixed timeframe
rsiValue = request.security(syminfo.tickerid, fixedTF, ta.rsi(close, rsiLength))

// ATR calculated on fixed timeframe
atrValue = request.security(syminfo.tickerid, fixedTF, ta.atr(atrLength))

// Supertrend Calculation Function
f_supertrend(_atrPeriod, _factor) =>
    _atr = ta.atr(_atrPeriod)
    _up = (high + low) / 2 - _factor * _atr
    _down = (high + low) / 2 + _factor * _atr
    var float _st = na
    _st := na(_st) ? ((high + low) / 2) : (close[1] > _st ? math.max(_up, _st) : math.min(_down, _st))
    _st

// Compute supertrend on fixed timeframe
supertrend = request.security(syminfo.tickerid, fixedTF, f_supertrend(stATRPeriod, stFactor))
trend = ft_close > supertrend ? 1 : -1

// ========== EXTREME MOVE CHECK (using fixed timeframe values) ==========
prev_ft_close = request.security(syminfo.tickerid, fixedTF, close[1])
btcMovePct = (ft_close - prev_ft_close) / prev_ft_close * 100
pump = btcMovePct > extremePct    // Pump: price increased more than extremePct%
dump = btcMovePct < -extremePct   // Dump: price dropped more than extremePct%

// ========== ENTRY CONDITIONS ==========
// Pre-calculate MACD crossovers on fixed timeframe values
macdLongCrossover    = ta.crossover(macdLine, signalLine)
macdShortCrossunder  = ta.crossunder(macdLine, signalLine)

// Long entry: fixed close > EMA, MACD cross upward, supertrend is up, RSI is not overbought
longCondition  = (ft_close > emaValue) and macdLongCrossover and (trend == 1) and (rsiValue < rsiOverbought)

// Short entry: fixed close < EMA, MACD cross downward, supertrend is down, RSI is not oversold
shortCondition = (ft_close < emaValue) and macdShortCrossunder and (trend == -1) and (rsiValue > rsiOversold)

// ========== TRADE EXECUTION ==========
// Long Trades
if (longCondition and strategy.position_size <= 0)
    if strategy.position_size < 0
        strategy.close("Short", comment="Close Short for Long")
    longTP = ft_close + atrMultiplier * atrValue
    strategy.entry("Long", strategy.long, comment="Long Entry")
    strategy.exit("Long Exit", from_entry="Long", limit=longTP, 
                  trail_price=na, trail_offset=atrValue * trailStopMultiplier, 
                  comment="Long TP & Trailing Stop")

// Short Trades
if (shortCondition and strategy.position_size >= 0)
    if strategy.position_size > 0
        strategy.close("Long", comment="Close Long for Short")
    shortTP = ft_close - atrMultiplier * atrValue
    strategy.entry("Short", strategy.short, comment="Short Entry")
    strategy.exit("Short Exit", from_entry="Short", limit=shortTP, 
                  trail_price=na, trail_offset=atrValue * trailStopMultiplier, 
                  comment="Short TP & Trailing Stop")

// ========== EXTRA EXIT CONDITIONS BASED ON EXTREME MOVES ==========
// If BTC is pumping really hard and you're short, exit the short.
// If BTC is dumping really hard and you're long, exit the long.
if pump and strategy.position_size < 0
    strategy.close("Short", comment="Close Short on BTC Pump")
if dump and strategy.position_size > 0
    strategy.close("Long", comment="Close Long on BTC Dump")

// ========== PLOTTING ==========
// Plot fixed timeframe values for visual reference
plot(emaValue, color=color.blue, title="50 EMA (Fixed TF)")
plot(supertrend, color=(trend == 1 ? color.green : color.red), title="Supertrend (Fixed TF)")
plot(macdLine, title="MACD (Fixed TF)", color=color.aqua)
plot(signalLine, title="Signal (Fixed TF)", color=color.orange)
hline(0, color=color.gray, linestyle=hline.style_dotted)

// Plot entry signals
plotshape(longCondition,  title="Long Signal",  location=location.belowbar, color=color.green, style=shape.labelup,   text="LONG")
plotshape(shortCondition, title="Short Signal", location=location.abovebar, color=color.red,   style=shape.labeldown, text="SHORT")

Strategy Parameters

The original address: Dynamic Volatility Trading System: Multi-Timeframe Nested Technical Indicator Strategy with Extreme Market Condition Monitoring for Futures