Tags: SMCHTF

The SMC Market High-Low Breakout Strategy is a quantitative trading strategy based on the principles of Superior Market Concepts (SMC). It identifies significant buying/selling pressure areas (order blocks) on higher timeframes and seeks optimal breakout entry points on the current timeframe. This aligns with the SMC principle that these blocks often act as support or resistance levels. The strategy considers trend direction, inducement patterns, and risk-reward ratio to optimize entry levels and profit targets.

- Identify uptrends and downtrends on the higher timeframe (e.g., 1-hour chart). An uptrend is defined as a higher close and higher low compared to the previous period. A downtrend is the opposite.
- Look for inducement patterns on the higher timeframe. A bullish inducement occurs in an uptrend when the previous high is higher than the highs of the past two and three periods. A bearish inducement occurs in a downtrend when the previous low is lower than the lows of the past two and three periods.
- Identify order blocks on the higher timeframe. After a bullish inducement, the high and low of that period define the upper and lower boundaries of the order block. The opposite applies to a bearish inducement.
- Find optimal entry points on the current timeframe (e.g., 15-minute chart). A long entry occurs when the current close breaks above the lower boundary of the order block, and the previous close is within the block. A short entry occurs when the close breaks below the upper boundary.
- Set stop-loss and take-profit levels. The stop-loss is placed at the boundary of the order block, while the take-profit is calculated based on the set risk-reward ratio (e.g., 1:1.5).

- Based on SMC principles, it captures major trends and key support/resistance levels on higher timeframes, avoiding noise interference on lower timeframes.
- Identifying inducement patterns helps gauge trend strength and sustainability, providing more basis for entry.
- Precise breakout entries on the current timeframe reduce false signals and drawdown risks.
- Flexible risk-reward ratio settings can be adjusted according to individual risk preferences.

- During market consolidation or early trend reversals, the strategy may face drawdown risks.
- In extreme market conditions (e.g., sharp rises or falls), order blocks may become invalid, leading to overly loose stop-losses.
- Considering only price action and ignoring other important indicators like volume may lead to biased judgments.

- Introduce more higher timeframes (e.g., daily, weekly) for filtering to ensure capturing long-term trends.
- Combine moving average systems, momentum indicators, etc., to improve the accuracy of trend and inducement pattern identification.
- Dynamically optimize order block boundaries, such as considering Average True Range (ATR) or channel width, to adapt to different market conditions.
- Implement trailing stop-losses after entry, such as tracking ATR or Parabolic SAR, to reduce holding risks.
- Consider market sentiment indicators (e.g., VIX) or macroeconomic data to identify potential trend reversals or black swan events.

The SMC Market High-Low Breakout Strategy is a quantitative trading strategy based on SMC principles. It identifies key pressure areas on higher timeframes and seeks optimal breakout entry points on the current timeframe. The strategy comprehensively considers trend direction, inducement patterns, and risk-reward ratio to optimize entry levels and profit targets. Its advantages lie in filtering out noise based on higher timeframes, precisely capturing trends, and providing flexible risk management features. However, the strategy may face drawdown risks during market consolidation or early trend reversals. Future optimizations can introduce more timeframes, optimize order block boundaries, implement dynamic stop-losses, and consider market sentiment to improve the strategy’s robustness and adaptability.

//@version=5 strategy("SMC Indian Market Strategy", overlay=true) // Input Parameters htf = input.timeframe("60", title="Higher Timeframe") // For Inducement & Order Block riskRewardRatio = input.float(1.5, title="Risk:Reward Ratio", minval=0.1) // Higher Timeframe Data [htfOpen, htfHigh, htfLow, htfClose] = request.security(syminfo.tickerid, htf, [open, high, low, close]) // Trend Identification (HTF) bool htfUptrend = htfClose > htfClose[1] and htfLow > htfLow[1] // Price action bool htfDowntrend = htfClose < htfClose[1] and htfHigh < htfHigh[1] // Inducement Identification (HTF) bool htfInducementHigh = htfUptrend and high[1] > high[2] and high[1] > high[3] bool htfInducementLow = htfDowntrend and low[1] < low[2] and low[1] < low[3] float inducementLevel = htfInducementHigh ? high[1] : htfInducementLow ? low[1] : na // Order Block Identification (HTF) var float htfOBHigh = na // Highest high within the order block var float htfOBLow = na // Lowest low within the order block if htfInducementHigh htfOBHigh := htfHigh htfOBLow := htfLow else if htfInducementLow htfOBHigh := htfHigh htfOBLow := htfLow // Optimal Entry (Current Timeframe) bool longEntry = htfUptrend and close > htfOBLow and close[1] < htfOBLow // Break of OB low bool shortEntry = htfDowntrend and close < htfOBHigh and close[1] > htfOBHigh // Break of OB high // Stop Loss and Take Profit float longSL = htfOBLow float longTP = close + (close - longSL) * riskRewardRatio float shortSL = htfOBHigh float shortTP = close - (shortSL - close) * riskRewardRatio // Strategy Execution if longEntry strategy.entry("Long", strategy.long, stop=longSL, limit=longTP) else if shortEntry strategy.entry("Short", strategy.short, stop=shortSL, limit=shortTP)

- SMC & EMA Strategy with P&L Projections
- HTF Zigzag Path Strategy
- Markov Chain Probability Transition State Quantitative Trading Strategy
- Support-Resistance breakout
- Bollinger + RSI, Double Strategy v1.1
- Dynamic Stop Loss and Take Profit Bollinger Bands Strategy
- Dynamic Trend Following Strategy
- Bollinger Bands Stochastic Oscillator Strategy
- RSI统计套利策略
- Bollinger Bands Standard Deviation Breakout Strategy

- Short-term Short Selling Strategy for High-liquidity Currency Pairs
- MOST Indicator Dual Position Adaptive Strategy
- Bollinger Bands RSI Trading Strategy
- Buy and Sell Volume Heatmap with Real-Time Price Strategy
- Dual Moving Average Regression Trading Strategy
- Multi-Indicator Quantitative Trading Strategy - Super Indicator 7-in-1 Strategy
- SMK ULTRA TREND Dual Moving Average Crossover Strategy
- Quintuple Strong Moving Average Strategy
- EMA-SMA Crossover Bull Market Support Band Strategy
- Multi-Timeframe Trend Following Strategy with 200 EMA Filter - Long Only
- Dynamic Trend Momentum Trading Strategy
- EMA Crossover with Short-term Signals Strategy
- Ichimoku Cloud and ATR Strategy
- BONK Multi-Factor Trading Strategy
- Dual Moving Average Crossover Strategy
- HalfTrend Bullish and Bearish Trend Following Stop-Limit Buy Strategy
- Alligator Long-Term Trend Following Trading Strategy
- Multi-timeframe Trend Tracking Strategy Based on Impulse MACD and Dual Moving Average Crossover
- EMA5 and EMA13 Crossover Strategy
- EMA SAR Medium-to-Long-Term Trend Following Strategy