Optimizing TradingView Backtests: Performance, Settings, and Metrics

• Commission and slippage: set per-market norms (e.g., futures vs equities).
• Initial capital and order sizing: fixed size vs percent of equity.
• Pyramiding and concurrent entries: align with your actual execution plan.

• Reuse calculations; avoid heavy loops and redundant request.security() calls.
• Compute higher-timeframe signals once, then reference.
• Plot only what you need on large datasets to keep charts responsive.

• Net profit, max drawdown, and profit factor are basics; add return/drawdown ratio.
• Expectancy and win/loss distribution—check for outlier dependence.
• Sensitivity analysis: vary key inputs and verify stability across symbols/timeframes.

• Look for smoothness and absence of sudden cliffs; inspect periods around drawdown spikes.
• Compare long vs short contributions; ensure edge isn’t one-sided unless intentional.
• Check trade frequency and exposure; too few trades inflate uncertainty.

• Grid-search key inputs and visualize heatmaps where metrics stay stable.
• Use walk-forward windows (e.g., 6m optimize, 3m test) to imitate live adaptation.
• Avoid chasing the best single configuration; prefer robust neighborhoods.

• Capture assumptions (fees, slippage, sizing) next to metrics for transparency.
• Save parameter sets and results to compare revisions; annotate what changed and why.
• Watch for regime shifts where performance patterns break; adjust or disable accordingly.

PineScripter.app produces production-leaning code with clearer inputs and exits, making it easier to test realistic settings. For quick idea drafts, tryPineify orPine Script Wizard—but always verify assumptions before trusting results.