Overview
The Multi-Indicator Synergistic Trend-Momentum Trading System integrates the Ultimate Trend Bot (UT Bot), Hull Moving Average (HMA), and JCFBV indicator to identify high-probability trading signals. The strategy employs a triple-filter mechanism to confirm signal reliability and includes trading session filtering for selective execution during London, New York, and Tokyo sessions. The system uses preset stop-loss and take-profit points to protect capital while securing reasonable profits.

Strategy Principle
The core is screening quality signals through multi-indicator confirmation:

1. UT Bot Component: Uses ATR to calculate price volatility and establish a dynamic trailing stop line. When price breaks above this line, it generates a potential buy signal.

2. HMA Trend Filter: Confirms market trend direction using HMA. Only when price breaks above HMA is the buy signal valid, ensuring trades align with the overall trend.

3. JCFBV Momentum Confirmation: A momentum indicator calculated through Weighted Moving Average. When the raw line crosses above the signal line and remains above it, it indicates strengthening momentum suitable for entry.

4. Trading Session Filter: Executes only during specific trading sessions, avoiding low liquidity periods.

5. Risk Management: Employs fixed point stop-loss and take-profit settings for clear risk control and profit targets.

The strategy only triggers a buy signal when all conditions are simultaneously met, significantly improving signal reliability.

Strategy Advantages
Analysis of this strategy reveals these advantages:

1. Multi-layer Filtering: Integrates three different indicator types, effectively reducing false signals and improving trading success rate.

2. Adaptive Performance: Dynamically adjusts the trailing stop based on ATR, adapting to different market volatility conditions.

3. Trend Confirmation: Ensures trading direction aligns with the main trend, avoiding counter-trend trading risks.

4. Momentum Verification: Identifies strong market momentum, improving entry timing precision.

5. Session Optimization: Focuses on high market activity periods, avoiding inefficient trading environments.

6. Clear Risk Control: Provides a defined risk-reward ratio, facilitating capital management.

7. Visual Assistance: Plots indicator lines and entry signals for intuitive monitoring.

Strategy Risks
Despite being well-designed, the strategy has potential risks:

1. Parameter Sensitivity: Multiple key parameters significantly impact performance; improper selection may lead to over-optimization.

2. Multi-condition Restrictions: May reduce trading frequency, potentially missing favorable opportunities.

3. Fixed Stop-Loss/Take-Profit Limitations: Doesn't account for market volatility changes, potentially unsuitable for all market conditions.

4. Trend Reversal Risk: Primarily suitable for trending markets; may perform poorly during consolidation or reversals.

5. Session Dependency: Over-reliance on specific sessions may cause missed opportunities in others.

6. Indicator Synergy Delay: Multiple indicators may produce lag, leading to suboptimal entry points.

7. Mitigation methods include thorough backtesting, adaptive risk management, market environment filtering, and regular parameter adjustment.

Strategy Optimization Directions
Based on code analysis, these optimization directions emerge:

1. Dynamic Risk Management: Implement ATR-based dynamic stop-loss and take-profit to automatically adapt to market volatility.

2. Market Environment Filtering: Add indicators to assess market conditions and pause trading during uncertainty or excessive volatility.

3. Parameter Adaptive Mechanism: Develop algorithms for automatic parameter adjustment based on recent market performance.

4. Partial Position Management: Introduce phased entry and exit for better risk management and optimized average entry price.

5. Reversal Protection: Design mechanisms to detect rapid market reversals and exit early when strong reversal signals appear.

6. Related Asset Confirmation: Add confirmation signals from related assets or indices to increase reliability.

7. Time Decay Factor: Introduce time-based position management to avoid profit giveback in extended trades.

Summary
The Multi-Indicator Synergistic Trend-Momentum Trading System achieves multi-dimensional signal confirmation by integrating UT Bot, HMA, and JCFBV. It requires trend, momentum, and price action confirmation before entry, combined with session filtering and risk management to form a complete trading system.

Its main advantages include multi-layer filtering and adaptive performance, reducing false signals across different market conditions. However, it has limitations such as parameter sensitivity that require careful management.

Optimization focuses primarily on dynamic risk management, market environment filtering, and parameter adaptivity. Any quantitative strategy requires regular evaluation and adjustment to adapt to changing markets.

Overall, this is a well-designed, logically clear strategy suitable for experienced quantitative traders. Thorough backtesting and gradual position sizing are recommended before live implementation.

Strategy source code

/*backtest
start: 2025-05-06 00:00:00
end: 2025-05-13 00:00:00
period: 1m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT"}]
*/

//@version=6
strategy("Clarity Strategy: UT Bot + HMA + JCFBV (v6 fixed)", overlay=true, max_labels_count=500)

// === INPUTS === //
ut_keyvalue = input.float(3, title="UT Bot Key Value", step=0.5)
ut_atrperiod = input.int(10, title="UT Bot ATR Period")

hma_period = input.int(50, title="HMA Period")

jcfb_depth = input.int(15, "JCFBV Depth")
jcfb_smooth = input.int(30, "Signal Smoothing Period")

sl_points = input.int(1000, title="Stop Loss (Points)")
tp_points = input.int(2000, title="Take Profit (Points)")

enable_london = input.bool(true, title="Allow London Session?")
enable_newyork = input.bool(true, title="Allow New York Session?")
enable_tokyo = input.bool(true, title="Allow Tokyo Session?")

// === SESSION FILTERING === //
hr = hour(time)
in_london  = (hr >= 3 and hr < 12)
in_newyork = (hr >= 8 and hr < 17)
in_tokyo   = (hr >= 19 or hr < 4)
session_ok = (enable_london and in_london) or (enable_newyork and in_newyork) or (enable_tokyo and in_tokyo)

// === UT BOT LOGIC === //
src = close
atr = ta.atr(ut_atrperiod)
nLoss = ut_keyvalue * atr

var float trailing_stop = na
trailing_stop := src > nz(trailing_stop[1]) ? math.max(nz(trailing_stop[1]), src - nLoss) :
                 src < nz(trailing_stop[1]) ? math.min(nz(trailing_stop[1]), src + nLoss) :
                 nz(trailing_stop[1])

ut_buy = ta.crossover(src, trailing_stop)
plot(trailing_stop, color=color.gray, title="UT Bot Trailing Stop")

// === HMA LOGIC === //
hma_raw = 2 * ta.wma(close, math.round(hma_period / 2)) - ta.wma(close, hma_period)
hma = ta.wma(hma_raw, math.round(math.sqrt(hma_period)))
plot(hma, color=color.orange, title="HMA 50")
cross_above_hma = ta.crossover(close, hma)

// === JCFBV (SIMPLIFIED) === //
jcfb_raw = ta.wma(close - close[1], jcfb_depth)
jcfb_signal = ta.wma(jcfb_raw, jcfb_smooth)
vol_rising = ta.crossover(jcfb_raw, jcfb_signal)
yellow_bar = jcfb_raw >= jcfb_signal

plot(jcfb_raw, color=color.gray, title="JCFBV Line")
plot(jcfb_signal, color=color.yellow, title="JCFBV Signal")

// === COMBINED ENTRY CONDITION === //
long_entry = ut_buy and cross_above_hma and vol_rising and yellow_bar and session_ok

if (long_entry)
    strategy.entry("Long", strategy.long)
    strategy.exit("Exit", from_entry="Long", loss=sl_points, profit=tp_points)

plotshape(long_entry, title="Long Entry Signal", location=location.belowbar, color=color.green, style=shape.triangleup, size=size.small)

Strategy parameters

The original address: Multi-Indicator Synergistic Trend-Momentum Trading System