Free Backtest Report Generator for TradingView Strategies
You ran a backtest in TradingView. You stared at the strategy tester tab and felt like there had to be more than net profit and win rate. I built this tool because I felt the same way. Pineify Backtest Report takes the standard TradingView CSV export and generates 16 professional KPIs, rolling window analysis, Monte Carlo simulation, and visual charts. All in your browser, no installs, no accounts, no server uploads. Here is what you get and how it compares to the alternatives.
Quick Verdict
If you already backtest on TradingView and want deeper analysis without installing Python or leaving your browser, Pineify is the most friction-free option available. Two honest boundaries are worth stating: Pineify is not a trading journal (no broker sync, no per-trade logging) and it is not a backtest engine (it analyzes what TradingView already ran, it does not run strategies or connect to market data). It cannot replace a trading journal or a portfolio-level backtesting platform, but for the single-strategy, post-backtest analysis workflow, it fills a gap that no other free tool covers at this ease of use.
What Pineify Backtest Report Gives You
16+ KPIs Including Drawdown-Focused Metrics
Beyond Sharpe ratio and profit factor, the report calculates Sortino ratio (downside deviation only), Calmar ratio (return over max drawdown), Ulcer Index, UPI (Martin Ratio), VaR at 95%, CVaR / Expected Shortfall, SQN, and Recovery Factor. When I ran my own EMA crossover strategy through it, the Sharpe was 1.2 but the Ulcer Index came back at 4.7. The equity curve had deep, persistent valleys that Sharpe alone hid.
Monte Carlo Simulation (1,000 Bootstrap Runs)
The report resamples your trade list 1,000 times with replacement and generates a distribution of possible outcomes. This is the single best indicator of whether your strategy's performance is statistically meaningful or just lucky. On one strategy I tested, the Monte Carlo showed a 23% chance of a 50%+ drawdown even though the historical max drawdown was only 18%. That information changed whether I put real money behind it.
Rolling Window Analysis (20-Trade Window)
Rather than showing one aggregate number, the rolling analysis slides a 20-trade window across your entire trade history and recalculates Sharpe ratio, win rate, and average trade for each window. This catches performance decay that a single average hides. A strategy that looks great on paper might show a steadily declining rolling Sharpe from window 15 onward, which is exactly what I found on a mean-reversion system I was excited about.
Returns Distribution, Heatmaps, and MFE/MAE
You get a histogram of trade returns overlaid with a normal distribution curve, monthly and weekly return heatmaps, a time-of-day breakdown, and an MFE/MAE scatter plot that shows whether your winners ran far enough relative to your losers. The time-of-day heatmap revealed for me that a strategy I thought was neutral was actually losing consistently in the first 30 minutes after the open. I would not have caught that from any single number.
AI Strategy Verdict and Full Excel Export
The report scores your strategy from 0 to 100 based on a weighted combination of all KPIs, then summarizes strengths and weaknesses in plain English. It also exports an 8-sheet Excel workbook with every data point, so you can build your own visualizations or share the report with a colleague. The basic report is free, no login required.
How Pineify Compares to Other Backtest Report Tools
| Feature | Pineify | quantstats | QuantAnalyzer | TradingView |
|---|---|---|---|---|
| Pricing | Free (basic report), paid plans $99+ | Free (open source) | Starting at ~$49/mo | Included in TV subscription |
| Install Required | None (browser only) | Python + pip | Desktop app install | None (web app) |
| Account Required | No (basic report) | No | Yes | Yes (TradingView) |
| Code Required | None | Python required | None (GUI) | None |
| Data Privacy / Client-Side | 100% browser, CSV never leaves device | Runs locally, Python | Desktop, data stays local | Cloud-based (TradingView servers) |
| TradingView CSV Native | Yes (designed for TV exports) | Requires manual conversion | No (MT/other formats) | Yes (same platform) |
| Monte Carlo Simulation | Yes (1,000 bootstrap runs) | Via scipy (custom code) | Yes | No |
| Portfolio-Level Analysis | No (single strategy only) | Yes (multi-portfolio) | Yes | No |
| Journal Capability | No | No | No | No |
| Excel Export | 8-sheet workbook | Via pandas (custom code) | Yes | CSV only |
Where Each Competitor Wins
Pineify is not the best tool for every situation. Here is where each alternative genuinely outperforms it, so you can make an informed choice.
- →quantstats (Python library). If you write Python and want full programmatic control over every chart, metric, and export format, quantstats is more flexible. It supports multi-portfolio tear sheets, custom metric definitions, and direct pandas DataFrame integration. Pineify is point-and-click; quantstats is programmable. They serve different skill levels.
- →QuantAnalyzer (desktop app). QuantAnalyzer supports walk-forward analysis, portfolio-level backtesting across multiple instruments, and native MetaTrader import. Pineify handles a single strategy from TradingView only. If you need those broader capabilities, QuantAnalyzer is the right tool, and it is worth the subscription cost.
- →TradingView built-in tester. For a quick check of whether a strategy is worth pursuing, TradingView's own list of trades and overview tab is sufficient. You do not need to export anything or use a second tool. Pineify is for the second pass, when you need depth over speed.
Which Tool Should You Pick?
Pick Pineify if...
- You use TradingView and want deeper analysis without leaving your browser.
- You do not want to install Python or any software.
- You care about data privacy and want everything to run client-side.
- You want a free starting point with no account signup.
- You need Monte Carlo, rolling analysis, and downside metrics that TradingView does not show.
Pick an Alternative if...
- You code in Python and want a programmable report (quantstats).
- You need walk-forward or portfolio-level backtesting (QuantAnalyzer).
- You trade on MetaTrader and need native MT CSV support (QuantAnalyzer).
- You need a trading journal with broker sync (TradeZella, TradesViz).
- You only need a quick overview and do not want to export a CSV at all (TradingView).
Related Tools on Pineify
Frequently Asked Questions
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