ianzeng123
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:
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.
Trend Line Detection: Dynamically identify market trend direction by analyzing key turning points (pivot highs and lows) and visualize trend lines.
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
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
Multi-Timeframe Confirmation: Combining different timeframes reduces the probability of false signals.
Trend Following: Focus on capturing strong trending market movements rather than frequent, low-quality trades.
Strict Risk Management:
- Small stop loss (1%) protects capital
- High risk-reward ratio (1:10)
- Profitable even with a 50% win rate
Flexibility: Adjustable higher timeframe settings for different trading types (scalping, day trading, swing trading)
Visual Assistance: Provides trend line drawing to help traders intuitively understand market trends.
Strategy Risks
Market Condition Limitations:
- Poor performance in ranging or non-trending markets
- Ineffective in low volatility environments
Parameter Sensitivity:
- Trend period and higher timeframe selection directly impact strategy performance
- Requires parameter adjustments for different markets and trading instruments
Stop Loss Risks:
- Fixed 1% stop loss might be too tight in high volatility markets
- Increased likelihood of being “stopped out”
Strategy Optimization Directions
Dynamic Stop Loss:
- Introduce ATR-based dynamic stop loss methods
- Adjust stop loss range based on market volatility
Filter Enhancement:
- Integrate volume analysis
- Incorporate liquidity sweeps
- Add order block confirmation
Multi-Strategy Combination:
- Combine with ICT Power of 3 method
- Integrate VWAP and market profile analysis
- Incorporate liquidation heatmap (especially in crypto markets)
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.
/*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")