Overview
The RSI-Adaptive T3 and Squeeze Momentum Hybrid Trading System is a dynamic trend-following strategy that combines an RSI-responsive T3 moving average with Squeeze Momentum detection. This strategy adapts in real-time to market volatility to enhance entry precision and optimize risk management. The core of the system lies in the T3 moving average length that dynamically adjusts according to RSI values, while also integrating Bollinger Bands and Keltner Channels to identify trend buildup phases. This design enables the strategy to capture the early phase of a trend and generate consistent entry signals, making it suitable for traders from beginner to advanced levels.

Strategy Principles
The core principles of this strategy are based on two main components: the RSI-responsive T3 moving average and the Squeeze Momentum indicator.

First, the RSI-responsive T3 is an adaptive moving average whose length parameter dynamically adjusts according to RSI values. When RSI values are low, indicating potential oversold conditions, the T3 length increases to provide a smoother trend line; when RSI values are high, indicating potential overbought conditions, the T3 length decreases to provide a more sensitive trend line. The T3 moving average itself is an advanced smoothing technique that applies EMA multiple times with specific coefficients to reduce lag while maintaining smoothness.

Second, the Squeeze Momentum indicator combines Bollinger Bands and Keltner Channels to identify market compression and release phases. When Bollinger Bands are inside Keltner Channels, it's considered a "squeeze" state, indicating reduced market volatility with potential for a breakout; when Bollinger Bands break outside Keltner Channels, it's considered a "squeeze release" state, indicating increased market volatility with potential for a new trend. The momentum value is calculated using linear regression, reflecting the direction and strength of price changes relative to a midpoint.

The trading logic is as follows:

• Long Entry: When T3 crosses upward above its previous value, momentum is positive, and the squeeze has just been released
• Short Entry: When T3 crosses downward below its previous value, momentum is negative, and the squeeze has just been released
• Exit (Reversal): When the opposite condition to the entry is triggered, the position is reversed

Strategy Advantages
In-depth analysis of the strategy code reveals the following significant advantages:

1. Strong Adaptability: The T3 length adjusts dynamically based on RSI values, allowing the strategy to adapt to different market conditions. It becomes more sensitive in highly volatile markets and more stable in steady markets.

2. High Signal Quality: By combining T3 crossovers, momentum direction, and squeeze release as triple confirmation, the strategy significantly improves the quality of trading signals and reduces false signals.

3. Early Trend Capture: The strategy is specifically designed to capture the early phases of trends, offering greater sensitivity compared to traditional trend-following methods.

4. Visual Support: The strategy provides visual representation of T3 slope direction, squeeze status, and momentum bars, enabling traders to quickly analyze trends and execute trades.

5. Excellent Performance: According to backtest data, the strategy demonstrated a profit factor of 2.01 and a win rate of 47.8% on the BTC/USD 30-minute chart, with a net profit of 173.16 units and a maximum drawdown of only 5.77%.

6. Hybrid System Benefits: The strategy fuses the characteristics of trend-reversal and momentum breakout detection systems, enabling it to identify both trend direction and confirm momentum strength.

Strategy Risks
Despite its many advantages, the strategy also presents some potential risks:

1. Parameter Sensitivity: The strategy uses multiple parameters (RSI length, T3 minimum and maximum lengths, Bollinger Bands and Keltner Channel parameters, etc.), and improper parameter selection could lead to significant performance degradation. The solution is to conduct comprehensive parameter optimization and robustness testing.

2. Market Condition Limitations: In ranging markets or markets without clear trends, the strategy may generate frequent false signals. The solution is to add market environment filters or adjust strategy parameters for specific market conditions.

3. Lag Risk: Although the T3 moving average reduces lag, any system based on moving averages has some degree of lag. The solution is to incorporate leading indicators or optimize T3 parameters.

4. Overtrading Risk: Under certain market conditions, the strategy may generate too many trading signals, increasing trading costs. The solution is to implement trade frequency limitations or add signal confirmation mechanisms.

5. Backtest Overfitting Risk: The strategy may perform well on specific historical data but underperform in future market conditions. The solution is to conduct cross-market, cross-timeframe backtesting validation and forward testing.

Optimization Directions
Based on code analysis, the strategy can be optimized in the following directions:

1. Adaptive Parameter Optimization: Not only can the T3 length be adaptively adjusted, but the multipliers for Bollinger Bands and Keltner Channels can also be dynamically adjusted based on market volatility to adapt to different market environments.

2. Market State Filtering: Add market state recognition mechanisms to employ different trading strategies or parameters in different market states (trending, ranging, consolidating).

3. Stop-Loss and Profit-Taking Mechanisms: The current strategy primarily relies on reverse signals for exits. Adding dynamic stop-loss and profit targets based on ATR or volatility can better control risk and lock in profits.

4. Volume Analysis Integration: Incorporating volume indicators to confirm trend strength can improve signal quality. Especially during squeeze release phases, increased volume can confirm the validity of breakouts.

5. Multi-Timeframe Analysis: Integrating signal confirmation mechanisms across multiple timeframes can enhance the strategy's robustness. For example, executing trades only when the higher timeframe trend direction is consistent.

6. Machine Learning Optimization: Using machine learning algorithms to optimize parameter selection and signal generation logic can help the strategy better adapt to different market environments.

These optimization directions are important because they can significantly improve the strategy's robustness and adaptability, reduce false signals, enhance profitability, and better control risk.

Summary
The RSI-Adaptive T3 and Squeeze Momentum Hybrid Trading System is an innovative quantitative trading strategy that achieves high-precision early trend capture and momentum confirmation by combining adaptive T3 moving averages with squeeze momentum indicators. The strategy not only has a solid theoretical foundation and clear logic but also demonstrates good performance in actual backtesting.

The main advantages of the strategy lie in its adaptability and signal quality, as it can dynamically adjust parameters based on market conditions while reducing false signals through multiple confirmation mechanisms. However, users should also be aware of potential risks such as parameter sensitivity and market condition limitations.

By optimizing market state filtering, stop-loss mechanisms, volume analysis, and multi-timeframe confirmation, the strategy has the potential to further improve its robustness and profitability. For traders seeking highly repeatable and adaptive trading tools, this is a worthy option to consider.

It's important to emphasize that although the strategy has performed well on historical data, past performance does not guarantee future results. Traders applying this strategy should always use appropriate money management and risk control measures.

Strategy source code

/*backtest
start: 2024-07-04 00:00:00
end: 2025-07-02 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT"}]
*/

// This Pine Script® code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © PakunFX

//@version=6
strategy("RSI-Adaptive T3 + Squeeze Momentum Strategy", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=100)

// ====== User Inputs ======
src = close
rsiLen = input.int(14, 'RSI Length', group="T3")
minLen = input.int(5, 'Min T3 Length', group="T3")
maxLen = input.int(50, 'Max T3 Length', group="T3")
v = input.float(0.7, 'T3 Volume Factor', step=0.01, maxval=2, minval=0.1, group="T3")

length = input(27, title="BB Length", group="Squeeze")
mult = input(2.0, title="BB MultFactor", group="Squeeze")
lengthKC = input(20, title="KC Length", group="Squeeze")
multKC = input(1.5, title="KC MultFactor", group="Squeeze")
useTrueRange = input(true, title="Use TrueRange (KC)", group="Squeeze")

// ====== T3 Calculation ======
rsi = ta.rsi(src, rsiLen)
rsi_scale = 1 - rsi / 100
len = math.round(minLen + (maxLen - minLen) * rsi_scale)

pine_ema(s, l) =>
    alpha = 2 / (l + 1)
    sum = 0.0
    sum := na(sum[1]) ? s : alpha * s + (1 - alpha) * nz(sum[1])
    sum

e1 = pine_ema(src, len)
e2 = pine_ema(e1, len)
e3 = pine_ema(e2, len)
e4 = pine_ema(e3, len)
e5 = pine_ema(e4, len)
e6 = pine_ema(e5, len)

c1 = -v * v * v
c2 = 3 * v * v + 3 * v * v * v
c3 = -6 * v * v - 3 * v - 3 * v * v * v
c4 = 1 + 3 * v + v * v * v + 3 * v * v
t3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3

// ====== Squeeze Momentum Calculation ======
basis = ta.sma(src, length)
dev = multKC * ta.stdev(src, length)
upperBB = basis + dev
lowerBB = basis - dev

ma = ta.sma(src, lengthKC)
kcrange = useTrueRange ? ta.tr : (high - low)
kcrangema = ta.sma(kcrange, lengthKC)
upperKC = ma + kcrangema * multKC
lowerKC = ma - kcrangema * multKC

sqzOn = (lowerBB > lowerKC) and (upperBB < upperKC)
sqzOff = (lowerBB < lowerKC) and (upperBB > upperKC)

midLine = (ta.highest(high, lengthKC) + ta.lowest(low, lengthKC)) / 2
val = ta.linreg(src - (midLine + ta.sma(close, lengthKC)) / 2, lengthKC, 0)

// ====== Strategy Logic ======
longCondition = ta.crossover(t3, t3[1]) and val > 0 and sqzOff
shortCondition = ta.crossunder(t3, t3[1]) and val < 0 and sqzOff

if (longCondition)
    strategy.entry("Long", strategy.long)
if (shortCondition)
    strategy.entry("Short", strategy.short)

Strategy parameters

The original address: RSI-Adaptive T3 and Squeeze Momentum Hybrid Trading System