Strategy Overview
The Bollinger Precision Risk Optimization Strategy is a trading system that combines Bollinger Bands and the Relative Strength Index (RSI) to capture high-probability trading opportunities. This strategy is based on the mean-reversion principle, capitalizing on price movements returning to average levels after reaching extremes. Through a systematic risk-reward management framework, the strategy ensures trading discipline, helping traders optimize performance and minimize losses.

The strategy identifies potential trading signals by monitoring price relationships with Bollinger Bands and RSI readings. Buy signals are generated when prices cross above the lower band and RSI is in oversold territory; sell signals occur when prices cross below the upper band and RSI is in overbought territory. Additionally, the strategy employs a fixed 1:2 risk-reward ratio, with predefined stop-loss and take-profit levels for each trade to ensure controlled risk.

Strategy Principles
The core of this strategy lies in combining two powerful technical indicators to enhance trading signal accuracy:

Bollinger Bands: Calculate price volatility range based on standard deviations, consisting of three lines:

• Middle Band: 20-period Simple Moving Average (SMA)
• Upper Band: Middle band plus 2 standard deviations
• Lower Band: Middle band minus 2 standard deviations

RSI Indicator: Measures the speed and magnitude of price movements to confirm overbought or oversold conditions:

• RSI below 30 is considered oversold
• RSI above 70 is considered overbought

The trading logic works as follows:

• Buy Condition: Price crosses above the lower Bollinger Band and RSI is below 30 (oversold)
• Sell Condition: Price crosses below the upper Bollinger Band and RSI is above 70 (overbought)

For risk management, the strategy utilizes fixed percentage stop-loss and take-profit levels:

• Stop-loss is set at 4% of the entry price
• Take-profit target is 8% of the entry price, maintaining a 1:2 risk-reward ratio

The code also allows users to adjust various parameters according to personal preferences, including Bollinger Bands length and multiplier, RSI period and thresholds, and risk management parameters.

Strategy Advantages

1. Signal Enhancement Filtering: By combining Bollinger Bands and RSI, the strategy reduces false signals, only trading when both indicators confirm, thereby improving trading accuracy.

2. Adaptability: Bollinger Bands, calculated based on price standard deviation, automatically adapt to changes in market volatility, maintaining effectiveness across different market environments.

3. Clear Trading Rules: The strategy provides explicit entry and exit conditions, eliminating subjective judgment and helping traders maintain emotional stability.

4. Fixed Risk-Reward Ratio: The preset 1:2 risk-reward ratio ensures long-term profitability potential, even with a win rate that isn't particularly high, as long as it remains above 50%.

5. Flexible Parameter Settings: Users can adjust parameters for different assets and timeframes to optimize strategy performance.

6. Comprehensive Risk Management: Built-in stop-loss and take-profit mechanisms protect capital and prevent excessive losses from single trades.

Strategy Risks

1. False Breakout Risk: In low-volatility or ranging markets, prices may frequently touch Bollinger Band boundaries without forming true reversals, leading to increased false signals. Solution: Avoid trading during low-liquidity periods or add additional confirmation indicators.

2. Delayed Signals: Since the strategy generates signals only after price crosses Bollinger Bands and RSI thresholds, entries may be slightly late, missing some potential profits. Solution: Consider using more sensitive parameter settings or shorter-period moving averages.

3. Fixed Stop-Loss Risk: The 4% fixed stop-loss may not be suitable for all market conditions, especially during high-volatility periods when it can be easily triggered. Solution: Dynamically adjust stop-loss levels based on the Average True Range (ATR) of the asset.

4. Parameter Sensitivity: Bollinger Bands and RSI parameter settings significantly impact strategy performance, and inappropriate parameters may lead to overtrading or missed opportunities. Solution: Find optimal parameter combinations for specific assets and timeframes through backtesting.

5. Trend Market Performance: As a mean-reversion strategy, it may underperform in strong trending markets, frequently generating counter-trend signals. Solution: Add trend filters, only trade in the direction of the trend, or pause the strategy during strong trends.

Strategy Optimization Directions

1. Add Trend Filters: Introduce additional trend indicators (such as moving average direction or ADX) to only trade in the trend direction, avoiding counter-trend operations. This optimization can significantly improve strategy performance in trending markets.

2. Dynamic Stop-Loss Settings: Replace fixed percentage stop-losses with volatility-based dynamic stops, such as using ATR multiples, making risk management more adaptable to current market conditions. This optimization can reduce unnecessary stops caused by changes in market volatility.

3. Implement Time Filters: Avoid trading during high-volatility periods around market opens and closes, as well as during important economic data releases, to reduce false signals caused by low liquidity or sudden events.

4. Add Volume Conditions: Incorporate volume indicators into the confirmation system to ensure trades are only executed with sufficient market participation, improving signal quality.

5. Optimize Parameter Adaptability: Implement automatic parameter optimization, dynamically adjusting Bollinger Bands and RSI parameters based on recent market data, allowing the strategy to better adapt to changing market conditions.

6. Implement Partial Profit-Taking: Develop partial profit-locking functionality, such as closing half the position upon reaching a certain profit level while letting the remainder run, both securing profits and not missing potential larger moves.

Conclusion
The Bollinger Precision Risk Optimization Strategy is a complete trading system combining technical analysis and risk management. Through the synergy of Bollinger Bands and RSI, the strategy can identify potential reversal points in price fluctuations, while strict risk control measures ensure trading sustainability.

This strategy is particularly suitable for moderately volatile market environments and is an ideal choice for investors seeking steady trading. Through the suggested optimization directions, traders can further enhance the strategy's adaptability and profitability, maintaining competitiveness across different market cycles.

Most importantly, regardless of which strategy is used, traders should conduct thorough backtesting and forward testing to ensure the strategy aligns with personal risk preferences and trading objectives. Continuous monitoring and adjustment are also key to maintaining the long-term effectiveness of the strategy.

Strategy source code

/*backtest
start: 2024-03-03 00:00:00
end: 2024-05-17 00:00:00
period: 1h
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT"}]
*/

//@version=6
strategy("Bollinger Precision Strategy", overlay=true, initial_capital=10000, currency=currency.USD, default_qty_type=strategy.percent_of_equity, default_qty_value=2)

// === Input Settings ===
// Bollinger Bands settings
bb_length = input.int(20, title="BB Length", minval=1)
bb_mult   = input.float(2.0, title="BB Multiplier", step=0.1)

// RSI settings (used as an additional filter)
rsiPeriod  = input.int(14, title="RSI Period")
oversold   = input.int(30, title="RSI Oversold Level")
overbought = input.int(70, title="RSI Overbought Level")

// === Risk Management Inputs ===
enable_stop_loss   = input.bool(true, title="Enable Stop-Loss")
enable_take_profit = input.bool(true, title="Enable Take-Profit")
stop_loss_percent  = input.float(4.0, title="Stop-Loss (%)", step=0.1)
take_profit_percent = input.float(8.0, title="Take-Profit (%)", step=0.1)

// === Bollinger Bands Calculations ===
basis = ta.sma(close, bb_length)
dev   = bb_mult * ta.stdev(close, bb_length)
upper = basis + dev
lower = basis - dev

// Plot Bollinger Bands
plot(basis, color=color.blue, title="Basis")
plot(upper, color=color.red, title="Upper Band")
plot(lower, color=color.green, title="Lower Band")

// === RSI Calculation ===
rsiValue = ta.rsi(close, rsiPeriod)

// === Entry Conditions ===
buySignal  = ta.crossover(close, lower) and (rsiValue < oversold)
sellSignal = ta.crossunder(close, upper) and (rsiValue > overbought)

// Variable to store the entry price
var float entry_price = na

// === Trading Logic with Copied Risk–Reward Function ===
if buySignal
    entry_price := close
    strategy.entry("Long", strategy.long)
    strategy.close("Short")

    // Risk–Reward Management for Long Trades
    sl_long = enable_stop_loss ? entry_price * (1 - stop_loss_percent / 100) : na
    tp_long = enable_take_profit ? entry_price * (1 + take_profit_percent / 100) : na
    strategy.exit("Exit Long", from_entry="Long", stop=sl_long, limit=tp_long)

    // If both SL and TP are disabled, close the Long position on signal
    if not enable_stop_loss and not enable_take_profit
        strategy.close("Long")

if sellSignal
    entry_price := close
    strategy.entry("Short", strategy.short)
    strategy.close("Long")

    // Risk–Reward Management for Short Trades
    sl_short = enable_stop_loss ? entry_price * (1 + stop_loss_percent / 100) : na
    tp_short = enable_take_profit ? entry_price * (1 - take_profit_percent / 100) : na
    strategy.exit("Exit Short", from_entry="Short", stop=sl_short, limit=tp_short)

    // If both SL and TP are disabled, close the Short position on signal
    if not enable_stop_loss and not enable_take_profit
        strategy.close("Short")

Strategy Parameters

The original address: Bollinger Precision Risk Optimization Strategy