TradingView Backtest Analysis with Pineify Deep Report
Pineify Backtest Deep Report analyzes an exported trade history. It complements TradingView's Strategy Tester; it does not rerun Pine Script against a new price feed or establish that a strategy will remain profitable.
Start with a reproducible export
Open the strategy report in TradingView and export its trade list. Keep a copy of the script version, symbol and exchange, chart interval, date range, initial capital, position sizing, commission, slippage, and fill settings. These settings explain the trades; the CSV alone cannot reconstruct every decision.
Open Pineify Backtest Report and import the supported CSV. Set the starting capital and check the parsed number of closed trades, net profit, currency, and timestamps against the original report. Investigate a mismatch before interpreting additional charts: partial exits, open positions, date parsing, and exported columns can affect comparisons.
What the extra analysis answers
TradingView already reports risk-adjusted measures including Sharpe ratio. Its report is not limited to net profit and win rate. Comparing two tools also requires matching their return period and calculation conventions; the same metric name can produce different values for legitimate reasons.
Pineify provides rolling statistics, excursion analysis, calendar breakdowns, Monte Carlo resampling, and Excel export. Use each view for a particular question:
| View | Question | Interpretation limit |
|---|---|---|
| Long/short results | Does one direction account for most results? | Small subsets may be unstable. |
| Rolling statistics | Did recent trades differ from earlier ones? | A weak window does not prove permanent loss of an edge. |
| MFE/MAE | How far did trades move favorably and adversely before exit? | Excursions do not establish executable alternative fills. |
| Calendar heatmaps | Were results concentrated in particular periods? | Selecting the best cells after seeing results creates overfitting. |
| Excel export | Can another person inspect the same calculations? | Save the input and settings with the workbook. |
Monte Carlo: resampling trades, not forecasting markets
The current implementation runs 1,000 simulations by sampling historical trade P&L with replacement. A trade may appear more than once or be omitted. This is different from merely shuffling the original trade order. The chart retains a subset of simulated paths for display.
The drawdown percentiles describe this particular resampling model. They are not a 95% or 99% guarantee about future market losses. Independent sampling does not preserve serial dependence, changing volatility, or losses absent from the historical sample.
The displayed risk-of-ruin calculation currently counts simulations whose final equity is zero or negative. It does not count every path that temporarily crosses zero and later recovers. Treat it as that narrower statistic, not an estimate of your real-world liquidation probability. Initial capital and the historical position sizes materially affect it.
Using excursions without inventing a profit improvement
MFE is maximum favorable excursion during a trade; MAE is maximum adverse excursion. A winner with substantial MFE does not mean a trader could have sold at its maximum. Likewise, a large MAE does not by itself prove that a tighter stop would help.
Form a specific proposed exit rule, implement it in the strategy, and rerun it with realistic costs on separate data. Compare trade count, drawdown, and net results. This article does not claim a measured 15–30% improvement or provide an audited performance test.
Data handling and AI analysis
CSV parsing and the numerical analysis run in the browser. That does not mean every optional feature keeps all information on the device: requesting the AI report sends summary metrics, monthly-return summaries, the latest rolling statistics, and Monte Carlo summaries to the analysis service. The current request excludes the raw trade and equity-curve arrays. Review the feature before sending sensitive statistics.
Export the results only after reconciling the input. Neither an AI explanation nor a successful calculation verifies the underlying strategy logic, execution assumptions, or future profitability.
