RSI and Bollinger Bands Oversold Bounce Quantitative Trading Strategy

Overview
This strategy is a quantitative trading approach that combines the Relative Strength Index (RSI) and Bollinger Bands to identify potential bounce opportunities in oversold market conditions. The strategy looks for situations where the price touches or falls below the lower Bollinger Band while the RSI simultaneously indicates oversold conditions. With a 5% take profit and 2% stop loss setup, the strategy aims to capture reasonable returns while controlling risk. This multi-indicator approach provides more reliable trading signals and reduces the risks associated with false breakouts.

Strategy Principles
The strategy is based on the synergistic effect of two main technical indicators:

1. Bollinger Bands: Consisting of a 20-day Simple Moving Average (SMA) as the middle band, with upper and lower bands set at 2 standard deviations above and below. Bollinger Bands reflect price volatility, and when price touches or breaks below the lower band, it often indicates that the market may be in an oversold condition.

2. Relative Strength Index (RSI): Using a 14-period setting, when the RSI falls below 30, the market is considered oversold, suggesting a potential bounce opportunity.

The trading logic is as follows:

• Entry condition: Price closes at or below the lower Bollinger Band, RSI is below 30, and there is currently no open position.
• Exit condition: Take profit when price reaches 1.05 times the entry price (5% gain), or stop loss when price falls to 0.98 times the entry price (2% loss).

The strategy uses barstate.isconfirmed to ensure trades are executed only after the candle closes, avoiding false signals that might occur during candle formation.

Strategy Advantages

1. Multi-indicator Confirmation: By combining RSI and Bollinger Bands, the strategy provides more reliable trading signals. Single indicators can be misleading, while multiple indicators working together filter out many false signals.

2. Clear Risk Management: The strategy incorporates a 5% take profit and 2% stop loss, creating a risk-reward ratio of 2.5:1, which aligns with healthy trading risk management principles.

3. Simple and Clear Logic: Trading rules are intuitive and easy to understand, without complex conditional judgments, making monitoring and adjustment straightforward.

4. Based on Statistical Principles: Bollinger Bands are based on normal distribution theory, with prices theoretically spending only about 5% of time outside the bands. Combined with RSI oversold signals, this further improves the success rate.

5. Flexible Parameter Settings: The strategy allows adjustment of Bollinger Band length, multiplier, RSI period, and oversold threshold, making it adaptable to different market environments.

6. Fully Automated: The strategy can be executed fully automatically, reducing emotional interference and improving trading discipline.

Strategy Risks

1. Ranging Market Risk: In prolonged sideways markets, prices may repeatedly touch the lower Bollinger Band and trigger multiple trades, but lack sufficient upward momentum to reach the take profit point, resulting in consecutive small losses.

2. Trending Downward Risk: In strong downtrends, prices may continue to make new lows. Even though RSI and Bollinger Bands indicate oversold conditions, the market may continue to decline, triggering stop losses.

3. Parameter Sensitivity: Different market environments may require different parameter settings. Fixed parameters may perform poorly when market conditions change.

4. Slippage and Commission Impact: The strategy sets a 0.1% commission, but in actual trading, slippage can further increase trading costs, especially in volatile markets.

5. Lack of Volume Confirmation: The current strategy only considers price and technical indicators without incorporating volume or market structure factors, potentially missing important market information.

Risk mitigation methods include: setting stricter entry conditions, such as requiring RSI rebound confirmation; adding trend filters to avoid counter-trend trading in strong trends; adjusting parameters for different markets; and considering the inclusion of volume or other indicators as auxiliary confirmation.

Strategy Optimization Directions

1. Add Trend Filters: Consider adding long-period moving averages (such as 50 or 200-day MA) as trend filters, only considering long positions when the average is pointing upward or price is above the average, avoiding counter-trend operations in downtrends.

2. Optimize Entry Confirmation Mechanism: Consider waiting for RSI to show an upturn or for price to close above the lower Bollinger Band after an oversold signal appears before entering, reducing false signals and improving success rates.

3. Dynamic Stop Loss and Take Profit: Replace fixed percentage stop loss and take profit with ATR (Average True Range) based dynamic levels to better adapt to changes in market volatility.

4. Incorporate Volume Analysis: Add volume confirmation to entry conditions, such as requiring increased volume when signals trigger, indicating higher market acceptance of the reversal.

5. Time Filters: Avoid high volatility periods such as important economic data releases, or use different parameter settings for different trading sessions.

6. Add Market Environment Adaptive Mechanism: Automatically adjust Bollinger Band multipliers and RSI thresholds based on market volatility (such as VIX index or ATR values) to maintain stable performance across different market environments.

7. Add Position Management Logic: Consider partial profit-taking and position scaling strategies, such as partially closing positions when reaching certain profit levels to protect gains while allowing remaining positions to continue profiting.

8. Explore Machine Learning Optimization: Use machine learning algorithms to automatically optimize parameter combinations or predict which oversold signals are more likely to lead to successful bounces.

Summary
The RSI and Bollinger Bands Oversold Bounce Quantitative Trading Strategy is a structurally simple but logically rigorous quantitative trading system. By using Bollinger Bands to identify extreme price areas and confirming oversold conditions with RSI, it effectively captures potential market reversal points. The strategy incorporates reasonable risk control measures, including clear stop loss and take profit levels.

While the strategy has advantages such as multi-indicator confirmation and clear risk management, it may face challenges in strongly trending or prolonged ranging markets. To improve robustness, consider adding trend filters, optimizing entry confirmation mechanisms, implementing dynamic stop loss and take profit levels, and incorporating volume analysis among other optimization directions.

This strategy is particularly suitable for medium to short-term traders, especially in relatively stable but volatile market environments. Through continuous monitoring and optimization, this strategy has the potential to become an effective tool in a trading portfolio, providing stable oversold bounce trading opportunities for investors.

Strategy source code

/*backtest
start: 2024-07-22 00:00:00
end: 2025-07-20 08:00:00
period: 2d
basePeriod: 2d
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT","balance":2000000}]
*/

//@version=6
strategy(
 "RSI + Bollinger Bands Long Strategy",
 overlay=true,
 default_qty_type=strategy.percent_of_equity,
 default_qty_value=100,
 commission_type=strategy.commission.percent,
 commission_value=0.1
 )

// Parameters
bbLength = input.int(20, "BB Length")
bbMult = input.float(2.0, "BB Multiplier")
rsiLength = input.int(14, "RSI Length")
rsiOversold = input.int(30, "RSI Oversold Level")

// Bollinger Bands hesaplama
basis = ta.sma(close, bbLength)
dev = bbMult * ta.stdev(close, bbLength)
upperBB = basis + dev
lowerBB = basis - dev

// RSI calculation
rsi = ta.rsi(close, rsiLength)

// Bollinger Bands and RSI plots
plot(basis, "BB Basis", color=color.orange)
plot(upperBB, "BB Upper", color=color.blue)
plot(lowerBB, "BB Lower", color=color.blue)
plot(rsi, "RSI", color=color.purple)
hline(rsiOversold, "RSI Oversold", color=color.green)

// Long condition: price below or equal to the lower band, RSI oversold, no position, bar close
longCondition = (close <= lowerBB) and (rsi < rsiOversold) and (strategy.position_size <= 0) and barstate.isconfirmed

if (longCondition)
    strategy.entry("Long", strategy.long)

// TP and SL
if (strategy.position_size > 0)
    entryPrice = strategy.position_avg_price
    takeProfit = entryPrice * 1.05
    stopLoss = entryPrice * 0.98
    strategy.exit("Long Exit", from_entry="Long", limit=takeProfit, stop=stopLoss)

Strategy parameters

The original address:RSI and Bollinger Bands Oversold Bounce Quantitative Trading Strategy

Comments

[Raxrb Kuech]

Love how this strategy gives RSI and Bollinger Bands a solid reason to finally work together—like two coworkers who usually ignore each other but suddenly nail a project. Oversold bounce plays can be tricky, but this setup looks like it has some real backbone. Time to let the bots do the dip-buying for once 😄