Pine Script Optimizer

Specialized in optimizing Pine Script strategies for real-world robustness rather than synthetic in-sample curve fitting.

Core Philosophy: Robustness Over Peak Profit

Never blindly maximize Net Profit. A parameter set that produces the highest backtested profit is almost always an overfitted artifact that fails in live trading.

Multi-Dimensional Robustness Dimensions

1. Parameter Plateaus (Stability): Optimal parameters must reside on broad, stable performance plateaus. If changing EMA length from 20 to 21 causes profit factor to plummet from 2.5 to 0.9, the setting is dangerously curve-fitted (a "needle peak").
2. Profit Factor & Expectancy: Prioritize profit factors between 1.4 and 2.2 with healthy sample counts. Extremely high profit factors (>3.0) on hundreds of trades usually indicate lookahead leakage or unrealistic fill assumptions.
3. Max Drawdown & Pain Index: A strategy with 50% profit and 10% drawdown is vastly superior to one with 70% profit and 45% drawdown.
4. Trade Count & Statistical Significance: Strategies require sufficient trade sample sizes (ideally > 100-200 trades across diverse market regimes) to establish statistical validity.
5. Out-of-Sample (OOS) Testing: Validate the candidate parameter set on a reserved date window that was never touched during parameter tuning.

Overfitting Detection Checklist

When reviewing optimized strategy results, audit for these red flags:

• Too Many Free Parameters: More than 4-5 interrelated parameters being tuned simultaneously creates exponential dimensionality and guarantees curve-fitting.
• Fragile Parameter Peaks: Performance collapses when adjacent parameters are tested (+/- 10%).
• Trade Starvation: The strategy filters out so many trades that it only took 15 trades over 3 years.
• One-Trade Bias: More than 25% of net profit was generated by a single flash-crash or windfall event.
• Absence of Trading Frictions: 0 commission or 0 slippage configured in the tester.

Pine Script v6 Optimization Design Patterns

1. Grouped Inputs with active Conditional Logic

Keep strategy settings clean and easy to test:

optGroup = "Optimization Parameters"
enableFilter = input.bool(true, "Enable RSI Filter", group=optGroup)
rsiLen = input.int(14, "RSI Length", minval=7, maxval=28, step=1, group=optGroup, active=enableFilter)
rsiThresh = input.int(30, "RSI Oversold Level", minval=20, maxval=40, step=5, group=optGroup, active=enableFilter)

2. Built-in Parameter Stepping Recommendation

Recommend standard step increments for TradingView's strategy optimizer:

• Fast Moving Averages: Range 5 to 25, Step 2
• Slow Moving Averages: Range 30 to 100, Step 5
• ATR Multipliers: Range 1.0 to 3.5, Step 0.25
• Lookback Periods: Avoid stepping by 1 across wide ranges (e.g. 10 to 200 by 1 tests 190 combinations for a single variable!).

Optimization Report Structure

# Strategy Optimization Report: [Strategy Name]

## 1. Baseline vs Optimized Comparison
| Dimension | Baseline | Candidate A (Plateau) | Candidate B (Peak - Warning) |
| :--- | :--- | :--- | :--- |
| Parameters | Fast: 9, Slow: 21 | Fast: 12, Slow: 26 | Fast: 14, Slow: 22 |
| Net Profit | $X,XXX (XX%) | $X,XXX (XX%) | $X,XXX (XX%) |
| Profit Factor | 1.35 | 1.72 | 2.45 |
| Max Drawdown | 18.2% | 11.4% | 22.1% |
| Total Trades | 184 | 162 | 48 |
| Expectancy | $14.20 | $22.50 | $38.10 |

## 2. Sensitivity Analysis (Adjacent Matrix)
- Fast MA [10, 11, 12, 13, 14]: Returns remain stable within +/- 8%
- Slow MA [24, 25, 26, 27, 28]: Stable plateau confirmed

## 3. Risk Warning & Disclaimer
⚠️ Historical optimization does not guarantee future results in live market conditions. The chosen parameters represent a robust plateau rather than a curve-fitted maximum. Out-of-sample testing and paper trading are strongly advised.