Trading Analytics
SignalDeck captures RSI, MACD, and SMA values at the exact minute of your entry. Every trade comes with execution quality metrics, behavioral tags, and post-mortem grades — then exports as clean forensic CSVs for LLMs, Monte Carlo, or your own analysis.
Or check your edge — free expectancy calculator →
Win rate tells you nothing on its own. A 42% win rate with 2.5R winners is more profitable than a 75% win rate with 0.3R winners. SignalDeck tracks every trade outcome as an R-multiple — risk-normalized — so SQN and expectancy are based on real edge measurement, not raw dollars.
SQN above 2.0 is a statistically real edge. Kelly Criterion gives you the mathematically optimal position size from your own R data. These aren't vanity metrics — they're the numbers that determine if you should be scaling up or fixing the system first.
Most journals store the outcome. SignalDeck also stores the context — RSI, MACD, and SMA values at the exact minute of your entry, snapshotted automatically.
When you go back to review a trade, you see not just what happened but what the market looked like when you pulled the trigger. And when you export to CSV, that context travels with every row — ready for ML feature engineering or pattern analysis.
Strategy decay is the most expensive thing that happens silently. A system that worked for 40 trades can stop working in week 7 — and if you're not tracking rolling expectancy, you'll find out via drawdown, not data.
SignalDeck tracks rolling expectancy and flags when a strategy's recent performance diverges from its baseline — so you can pause and diagnose before the drawdown compounds.
Every metric, every indicator, every tag exports to a clean CSV. Use it in Python, feed it to an LLM, run your own Monte Carlo — the data is yours and it's already formatted for the lab.
The full trade record exports as a clean CSV — not just the outcome. Every execution field, every indicator value captured at entry, every behavioral tag, every post-mortem grade, and the screenshot URLs for every chart image you attached to the trade.
Use it in Python to build ML features. Feed it to an LLM for pattern analysis. Run your own Monte Carlo against real trade sequences. The export is structured for direct use — no cleaning, no reshaping.
38 columns per trade row — execution, technical context at entry, save-time snapshot, and behavioral review data.
SQN, expectancy, MAE/MFE, execution indicators at entry, behavioral tags, and forensic CSV export. Built for the lab, not just the ledger. Free during beta.
Start free in SignalDeckSQN (System Quality Number) measures statistical trading quality. Above 2.0 is a real edge. It normalizes expectancy by the standard deviation of R-multiples — so consistent results score higher than wild swings, even with the same average.
Expectancy is the average R-multiple per trade across your full history. It's the only number that reliably predicts forward profitability — because it normalizes for position size and accounts for both win rate and reward-to-risk ratio simultaneously.
MAE (Maximum Adverse Excursion) is the worst intra-trade drawdown before close. MFE (Maximum Favorable Excursion) is the best intra-trade profit before close. Comparing these to your final P&L reveals execution quality — if MFE consistently exceeds final profit, you're exiting too early.
Yes. SignalDeck exports clean forensic CSVs including R-multiples, execution indicators (RSI/MACD/SMA at entry), MAE/MFE, behavioral tags, post-mortem grades, and strategy labels — designed for direct use in Python, Excel, or LLM pipelines.