Overview
This strategy is a dynamic timing trading system based on volatility, combining the characteristics of trend tracking and risk management. The core of the strategy is to identify market trend changes through volatility channels, while introducing a dynamic position management mechanism based on ATR to achieve precise control of trading risks. This strategy is particularly suitable for operating in a market environment with high volatility, and can adjust positions adaptively to market fluctuations.

Strategy Principle
The core logic of the strategy is based on the following key components:

Volatility channel calculation: Use the ATR (Average True Range) indicator to measure market volatility and build a dynamic volatility channel. The width of the channel is determined by the ATR value and the multiplier factor, and can be flexibly adjusted according to market characteristics.
Trend determination mechanism: The trend direction is determined by the relative position of the price and the volatility channel. When the price crosses the channel, the upward trend is considered to be established, and when the price crosses the channel, the downward trend is considered to be established.
Position management system: Based on the initial capital and the preset risk ratio of each transaction, combined with the real-time stop loss distance, the number of open positions is dynamically calculated to ensure consistent risk exposure for each transaction.
Risk control mechanism: A dynamic stop loss based on the volatility channel is set. When the price reaches the stop loss level, the position is automatically closed, and the position is forced to be liquidated before the closing to avoid overnight risks.

Strategy Advantages

Strong adaptability: The strategy can automatically adjust trading parameters according to changes in market volatility and adapt to different market environments.
Controllable risks: Through dynamic position management and stop-loss mechanism, ensure that the risk exposure of each transaction is within the preset range.
Accurate trend grasp: Using volatility channels can effectively filter out false breakthroughs and improve the accuracy of trend judgment.
Standardized operations: The entry and exit conditions of the strategy are clear, reducing the uncertainty caused by subjective judgment.
Scientific fund management: Introduced a risk-based position management approach to avoid excessive risks that may be caused by fixed positions.

Strategy Risks

Risk of volatile market: Frequent trading may result in continuous small losses in a sideways and volatile market.
Slippage impact: During periods of high volatility, there may be a greater risk of slippage, which may affect strategy performance.
Parameter sensitivity: The strategy effect is sensitive to the choice of ATR period and multiple factors. Improper parameter selection may affect strategy performance.
Capital requirements: Dynamic position management may require larger initial capital to ensure effective risk control.

Strategy Optimization Direction

Market environment filtering: You can add trend strength indicators, suspend trading in a sideways market, and reduce losses in a volatile market.
Multi-time period analysis: Combined with longer-term trend judgment, it improves the accuracy of trading direction.
Optimization of stop-profit mechanism: Dynamic stop-profit conditions can be designed based on volatility to improve profit control.
Optimize entry timing: You can add price patterns or momentum indicators as auxiliary indicators to improve the accuracy of entry timing.
Drawdown Control: Add a dynamic risk control mechanism based on account equity to reduce positions or suspend trading when losses continue.

Summary
This is a complete trading system that combines volatility, trend tracking and risk management. The strategy captures trend changes through volatility channels and uses scientific fund management methods to control risks. Although it may not perform well in volatile markets, it can operate stably in most market environments through reasonable parameter optimization and additional filtering mechanisms. The core advantage of the strategy lies in its adaptability and risk control capabilities, which is suitable for expansion and optimization as the basic framework of medium- and long-term strategies.

Strategy source code

/*backtest
start: 2019-12-23 08:00:00
end: 2024-12-10 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("BNF FUT 5 min Volatility Strategy", overlay=true)

// Inputs
length = input.int(20, "Length", minval=2)
src = input.source(close, "Source")
factor = input.float(2.0, "Multiplier", minval=0.25, step=0.25)
initial_capital = input.float(100000, "Initial Capital ($)")
risk_per_trade = input.float(1.0, "Risk per Trade (%)", minval=0.1, maxval=10.0)

// Volatility Stop Function
volStop(src, atrlen, atrfactor) =>
    if not na(src)
        var max = src
        var min = src
        var uptrend = true
        var float stop = na
        atrM = nz(ta.atr(atrlen) * atrfactor, ta.tr)
        max := math.max(max, src)
        min := math.min(min, src)
        stop := nz(uptrend ? math.max(stop, max - atrM) : math.min(stop, min + atrM), src)
        uptrend := src - stop >= 0.0
        if uptrend != nz(uptrend[1], true)
            max := src
            min := src
            stop := uptrend ? max - atrM : min + atrM
        [stop, uptrend]

// Calculate Volatility Stop
[vStop, uptrend] = volStop(src, length, factor)

// Plot Volatility Stop
plot(vStop, "Volatility Stop", style=plot.style_cross, color=uptrend ? #009688 : #F44336)

// Risk Management and Position Sizing
atr = ta.atr(length)
stop_distance = math.abs(close - vStop) // Distance to stop level
position_size = (initial_capital * (risk_per_trade / 100)) / stop_distance // Position size based on risk per trade
position_size := math.max(position_size, 1) // Ensure minimum size of 1

// Strategy Logic
if not na(vStop)
    if uptrend and not uptrend[1] // Transition to uptrend
        strategy.close("Short")
        strategy.entry("Long", strategy.long, qty=position_size)
    if not uptrend and uptrend[1] // Transition to downtrend
        strategy.close("Long")
        strategy.entry("Short", strategy.short, qty=position_size)

// Exit on Stop Hit
if strategy.position_size > 0 and low < vStop // Exit long if stop hit
    strategy.close("Long", comment="Stop Hit")
if strategy.position_size < 0 and high > vStop // Exit short if stop hit
    strategy.close("Short", comment="Stop Hit")
if (hour == 15 and minute == 15)
    strategy.close_all()

Strategy Parameters

The original address: Dynamic Timing and Position Management Strategy Based on Volatility