Analyze TradingView CSV: From Trade List to 16-KPI Strategy Report

The dashboard groups your metrics into categories: performance, risk-adjusted returns, drawdown and recovery, distribution analysis, and trade efficiency. Here is a practical walkthrough of what each section means for your strategy.

Risk-adjusted returns: Sharpe, Sortino, Calmar, SQN

These four ratios tell you whether your profits came from skill or from taking on more risk. Sharpe ratio divides your average return by its volatility. A Sharpe above 1 is decent. Above 2 is rare and probably overfitted. Sortino only penalizes downside volatility, which matters more for most trading strategies because upside volatility is not really a problem. The Calmar ratio compares annualized return to maximum drawdown, so it directly penalizes strategies that blow up. The SQN, or System Quality Number, normalizes your average trade by its standard deviation and adjusts for trade count.

The Calmar ratio calculator and the SQN calculator give you a standalone deep dive on each metric if you want to understand the math behind them.

Drawdown and recovery: Ulcer Index, Recovery Factor, UPI

Max drawdown from TradingView tells you one number: the worst peak-to-trough drop. But one number does not tell you how deep the drawdowns were on average or how long they lasted. The Ulcer Index measures the depth and duration of drawdowns over the full period. Recovery Factor divides net profit by max drawdown to show how well the strategy bounces back. The UPI, or Martin Ratio, combines the Ulcer Index with returns for a risk-adjusted metric that only penalizes downside.

For a detailed breakdown of each, the Ulcer Index calculator and the Recovery Factor calculator walk through the formulas and interpretation.

Tail risk: Value at Risk and CVaR

VaR at 95 percent confidence tells you the worst loss you can expect 95 percent of the time. The other 5 percent falls in the tail. That tail is where strategies blow up. CVaR, or Expected Shortfall, goes further and tells you the average loss in that worst 5 percent. Two strategies with the same VaR can have very different CVaR numbers. I saw this firsthand comparing two trend-following strategies on ES futures: both had VaR around 2 percent, but one had CVaR of 3.8 percent and the other 7.2 percent. The difference was a few outlier trades that would have been catastrophic. The CVaR calculator and the Value at Risk calculator explain each in more depth.

Distribution: Skewness and Kurtosis

Skewness tells you if your returns lean positive or negative. Positive skew means you have more big wins and small losses. Negative skew means small wins and the occasional blow-up. Kurtosis measures how frequently extreme outcomes happen. Higher kurtosis means fatter tails, which means the strategy carries more tail risk than a normal distribution would suggest. I have a strategy on NQ that showed a perfectly flat equity curve with a 1.5 profit factor and Sharpe of 1.1. The kurtosis was 12.4, which is extremely high. That told me the strategy was coasting on a few lucky trades. Without kurtosis, I would have called it solid.

A single backtest covers years of data, but markets change. A strategy that crushed it in 2021 might fail in 2022. Rolling window analysis slides a 20-trade window across your trade history and recalculates Sharpe, Sortino, and win rate at each step. You see how your strategy performs through different market regimes.

When I ran this on a trend-following strategy on SPY, the rolling Sharpe ratio stayed above 1.0 for the first three years. Then it dropped to 0.3 over a period of 40 trades and stayed there. The equity curve was still going up, but the quality of the returns had degraded. The rolling analysis caught the decay 3 months before the total returns started to flatten. That heads-up alone saved me from doubling down on a strategy that was quietly dying.

Heatmaps break your returns down by time: monthly, weekly, daily, and by time of day. A monthly heatmap shows you which months your strategy actually makes money. I saw one strategy that made 70 percent of its annual profit in January and February alone. The other 10 months were essentially flat. That is a seasonal strategy, not a year-round edge. The heatmap caught it.

The returns distribution histogram plots your trade returns as bars and overlays a normal distribution curve. If your distribution has fatter tails than the normal curve, your strategy carries more extreme-event risk than a naive assessment would suggest. If it is skewed left (more mass on the negative side), the strategy has more losing days than winning ones, even if the win rate looks fine. These visual checks are immediate flags I look for before checking any single metric.

Click one button and the tool generates a structured AI report on your backtest. It scores the strategy from 0 to 100, lists strengths and weaknesses based on your actual metrics, rates the risk level, and suggests specific improvements. Every claim in the AI report ties to a number in your KPI dashboard.

I am honest about the limits here. The AI does not know your market or your trading style. It reads the numbers and flags what stands out statistically. A strategy that scores 70 might be perfect for a specific regime you know how to trade but the AI does not recognize. Use the verdict as a second opinion, not a final ruling. One of my best strategies, a volatility breakout on crude oil, consistently scores around 55 on the AI analysis because its returns are lumpy. But I know the strategy well enough to trade through the drawdowns, and it has been profitable for 3 years.

Here is the workflow I actually follow. Not the theory version, but what I do every time I test a new strategy.

1. Run the backtest in TradingView. I use at least 5 years of data, on daily or 4-hour timeframe. I look for at least 200 closed trades.
2. Export the List of Trades CSV. Only the trade list matters. I ignore the default metrics tab.
3. Upload to Pineify. I check the KPI dashboard first. If the Sharpe ratio is below 0.5, I stop. If it is above 0.8, I dig into the other tabs.
4. Check Monte Carlo. I want at least 75 percent profitable runs. Below that, I move on to the next idea.
5. Check rolling analysis. I look for consistent Sharpe across the full period. If the rolling Sharpe drops below 0 for more than 20 trades, the strategy has regime problems.
6. Check MFE and MAE. I look at where losing trades cluster and whether my stops make sense.
7. Check the heatmap. If the strategy only makes money in 3 months of the year, it is seasonal, not consistent.
8. Export the Excel workbook. Save it to my strategy folder. Then I decide whether to paper trade for another month or allocate real capital.

This process takes about 5 minutes per strategy. Without the tool, checking these metrics manually would take hours of spreadsheet work or require Python. The 100 percent client-side processing means I can do this from any machine without worrying about my strategy data ending up on someone else's server.