Converting HalfTrend indicator to TradingView Strategy (Part-2)

In Part 1 of this series, we explored the basics of the HalfTrend indicator — a popular trend-following tool available on TradingView — and why it’s suitable for building a rule-based strategy. Now, in Part 2, we move from theory to application. We'll decode its logic, turn it into a backtestable strategy, and evaluate its performance across markets. Finally, we’ll attempt to enhance results using traditional filters — and introduce smarter alternatives when those fall short.

Read the Part 1 Article 

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🔍 Understanding the HalfTrend Logic

The HalfTrend indicator signals potential trend reversals based on moving averages and price extremes:

📈 Buy Signal:

• The SMA of lows rises above the highest high

• The closing price is higher than the previous high

📉 Sell Signal:

• The SMA of highs drops below the lowest low

• The closing price is lower than the previous low

The plotted HalfTrend line:

• Equals maxLowPrice during an uptrend

• Equals minHighPrice during a downtrend

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🛠️ Building the Strategy in Pine Script

We convert this logic into a Pine Script strategy:

1. Open Pine Editor and start a new strategy template

2. Paste the HalfTrend indicator logic

3. Use the generated buySignal and sellSignal as longCondition and shortCondition

4. Add entry logic using strategy.entry()

The basic strategy is now ready for testing

📌 Click here to view the strategy on TradingView

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📊 Backtesting Across Markets

We tested the basic HalfTrend strategy across five markets and four timeframes from Jan 1, 2023 to Mar 1, 2025:

Markets:

• NIFTY Futures (NSE India)
• BANKNIFTY Futures (NSE India)
• GOLD MINI (MCX India)
• CRUDE MINI (MCX India)
• BTCUSD (Binance)

Timeframes: 5M, 15M, 1H, 4H

Evaluation Metrics: Total Trades, Win Rate, Profit Factor, Sharpe Ratio

🔽 Summary of Results — Basic Strategy

NIFTY

 📈 See the full performance report here

⚠️ Note: This is a research strategy, meant to showcase the power of algorithmic optimization — not yet recommended for live trading.

⚠️ Curve-Fitting Warning

Machine learning and algorithmic filters can sometimes produce overfitted results — systems that perform well in backtests but fail in live trading.

To avoid this, you must perform:

• Out-of-sample testing

• Walk-forward analysis

• Monte Carlo simulations

We'll explore these topics next.

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🔮 What’s Coming in Part 3

Read The Trader’s Mindset: Building a Robust Foundation Before Going Live(Bonus Article – Before Deploying Our Strategy)

In the final part of this series, we’ll:

• Explore robustness testing

• Analyze live deployment readiness

• Create a multi-strategy portfolio

• Deploy a strategy for live tracking
  

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👉 Stay tuned and subscribe to follow the full journey from TradingView script to robust, trade-ready system.