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Strategy vault / Trend following

TrendSync Pro SMC Multi Timeframe Trend Following Strategy

TrendSync Pro (SMC) is a quantitative trading strategy based on a Higher Timeframe (HTF) filter and trend momentum, designed to capture strong market trend movements. The strategy provides traders with a systematic…

Pine ScriptTrend followingMulti-timeframeScalpingATRVWAPPivots / S&R
Author
ianzeng123
Complexity
Starter · 70 lines
Parameters
0
Backtest bar
1h · intraday
Tested on
BTC/USDT · futures
Jump to codeView in vaultOriginal on FMZ

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Overview

TrendSync Pro (SMC) is a quantitative trading strategy based on a Higher Timeframe (HTF) filter and trend momentum, designed to capture strong market trend movements. The strategy provides traders with a systematic trading approach by combining multi-timeframe analysis, trend line detection, and strict risk management.

Strategy Principles

The core principles of the strategy include the following key components:

  1. Higher Timeframe (HTF) Filter: Use higher-level timeframes (such as 1-hour, 4-hour, or daily) to confirm the overall market trend direction, ensuring trades align with the primary trend.

  2. Trend Line Detection: Dynamically identify market trend direction by analyzing key turning points (pivot highs and lows) and visualize trend lines.

  3. Entry and Exit Logic:

    • Long Entry Conditions: Price breaks above trend value with HTF trend upward
    • Short Entry Conditions: Price breaks below trend value with HTF trend downward

  4. Risk Management:

    • Fixed Stop Loss: Set at 1% of entry price
    • Take Profit Target: Set at 10% of entry price
    • Optional dynamic stop loss using ATR (Average True Range)

Strategy Advantages

  1. Multi-Timeframe Confirmation: Combining different timeframes reduces the probability of false signals.

  2. Trend Following: Focus on capturing strong trending market movements rather than frequent, low-quality trades.

  3. Strict Risk Management:

    • Small stop loss (1%) protects capital
    • High risk-reward ratio (1:10)
    • Profitable even with a 50% win rate

  4. Flexibility: Adjustable higher timeframe settings for different trading types (scalping, day trading, swing trading)

  5. Visual Assistance: Provides trend line drawing to help traders intuitively understand market trends.

Strategy Risks

  1. Market Condition Limitations:

    • Poor performance in ranging or non-trending markets
    • Ineffective in low volatility environments

  2. Parameter Sensitivity:

    • Trend period and higher timeframe selection directly impact strategy performance
    • Requires parameter adjustments for different markets and trading instruments

  3. Stop Loss Risks:

    • Fixed 1% stop loss might be too tight in high volatility markets
    • Increased likelihood of being "stopped out"

Strategy Optimization Directions

  1. Dynamic Stop Loss:

    • Introduce ATR-based dynamic stop loss methods
    • Adjust stop loss range based on market volatility

  2. Filter Enhancement:

    • Integrate volume analysis
    • Incorporate liquidity sweeps
    • Add order block confirmation

  3. Multi-Strategy Combination:

    • Combine with ICT Power of 3 method
    • Integrate VWAP and market profile analysis
    • Incorporate liquidation heatmap (especially in crypto markets)

  4. Machine Learning Optimization:

    • Use machine learning algorithms to optimize parameter selection
    • Develop adaptive parameter adjustment mechanisms

Summary

TrendSync Pro (SMC) is a strategy that prioritizes quality over quantity. By providing multi-timeframe confirmation, strict risk management, and trend-following logic, the strategy offers traders a systematic trading framework. The key is selective trading - capturing just 1-2 high-quality trade opportunities per day, rather than frequent but inefficient trading.

Source Pine Script

/*backtest
start: 2024-04-02 00:00:00
end: 2024-07-12 00:00:00
period: 1h
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=6
strategy('TrendSync Pro (SMC)', overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=1)

// Created by Shubham Singh

// Inputs
bool show_trendlines = input.bool(true, "Show Trendlines", group="Visual Settings")
int trend_period = input(20, 'Trend Period', group="Strategy Settings")
string htf = input.timeframe("60", "Higher Timeframe", group="Strategy Settings")

// Risk Management
float sl_percent = input.float(1.0, "Stop Loss (%)", minval=0.1, maxval=10, step=0.1, group="Risk Management")
float tp_percent = input.float(2.0, "Take Profit (%)", minval=0.1, maxval=10, step=0.1, group="Risk Management")

// Created by Shubham Singh

// Trendline Detection
var line trendline = na
var float trend_value = na
var bool trend_direction_up = false  // Initialize with default value

pivot_high = ta.pivothigh(high, trend_period, trend_period)
pivot_low = ta.pivotlow(low, trend_period, trend_period)

if not na(pivot_high)
    trend_value := pivot_high
    trend_direction_up := false


if not na(pivot_low)
    trend_value := pivot_low
    trend_direction_up := true


// Created by Shubham Singh

// Higher Timeframe Filter
htf_close = request.security(syminfo.tickerid, htf, close)
htf_trend_up = htf_close > htf_close[1]
htf_trend_down = htf_close < htf_close[1]

// Trading Logic
long_condition = ta.crossover(close, trend_value) and htf_trend_up and trend_direction_up
short_condition = ta.crossunder(close, trend_value) and htf_trend_down and not trend_direction_up
// Created by Shubham Singh

// Entry/Exit with SL/TP
if strategy.position_size == 0
    if long_condition
        strategy.entry("Long", strategy.long)
        strategy.exit("Long Exit", "Long", stop=close*(1-sl_percent/100), limit=close*(1+tp_percent/100))
    
    if short_condition
        strategy.entry("Short", strategy.short)
        strategy.exit("Short Exit", "Short", stop=close*(1+sl_percent/100), limit=close*(1-tp_percent/100))
// Created by Shubham Singh

// Manual Trendline Exit
if strategy.position_size > 0 and ta.crossunder(close, trend_value)
    strategy.close("Long")
if strategy.position_size < 0 and ta.crossover(close, trend_value)
    strategy.close("Short")

Published by ianzeng123 on the FMZ strategy square · last modified 2025-04-02. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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