Stop measuring how close you were. Start measuring if you would have won.
For research and educational purposes only. Not financial, investment, or trading advice.
Drop any CSV with predictions & actual outcomes (6 columns maximum)
Works for stocks, sales, weather, sports-anything
Actuals stay visually dominant, the strongest forecast stays emphasized, and weaker candidates remain visible without taking over the chart.
Think of it like this: If you bet money on your predictions, would you make profit?
Being close isn't enough. Wrong direction can lose money.
Anyone who needs predictions that work, not just look good on paper.
Move from evaluation into advisory or execution.
Diagnose the dataset, select the target, assess dataset health, and get a structured model path before training.
Open Decision IntelligenceCarry forward the recommended shortlist, benchmark concrete candidates, and keep execution aligned with the upstream decision flow.
Open AutoMLThis works for any prediction, not just finance
Scenario: You predict a stock at $101, it goes to $99.
Traditional: "Close! Only 2% error."
Quantsynth: "Wrong direction = you lost money."
Scenario: You predict 75°F, actual is 73°F.
Traditional: "Good job!"
Quantsynth: "Did people bring jackets? That's what matters."
Scenario: You predict a 10-point win, they win by 3.
Traditional: "Pretty close!"
Quantsynth: "You predicted a win and they won = profit."
Scenario: Predict $100K sales, actual is $80K.
Traditional: "20% error"
Quantsynth: "Did you overstaff? That costs real money."
Scenario: Model A: 95% accurate. Model B: 88% accurate.
Traditional: "Pick A"
Quantsynth: "Which one makes fewer expensive mistakes?"
Scenario: Predict price increase, it decreases 5%.
Traditional: "Small error"
Quantsynth: "Wrong direction = you bought at the peak."
Comparing what matters vs what gets measured
| WHAT MATTERS | TRADITIONAL APPROACH | QUANTSYNTH APPROACH |
|---|---|---|
| Getting Direction Right | ✕ Completely ignored (only cares about distance) | ✓ Primary focus (80% of success) |
| Actual Success Rate | ✕ No connection to outcomes | ✓ Directly measures if it works |
| When You're Wrong | ✕ Big mistakes kill your score | ✓ Understands some errors matter more |
| Confidence Levels | ✕ Treats all predictions equally | ✓ Rewards being confident when right |
| Real-World Use | ✕ Great metrics, bad decisions | ✓ Optimizes for actual utility |
Not how close your predictions were — but how useful they actually are
FIS measures whether a prediction would have led to a good decision. Instead of rewarding forecasts for being close to the target, it rewards them for being right in the moments that matter.
CER answers a different question: How much confidence do you earn per unit of error? It balances how strong a prediction is with how efficiently it achieves that strength.
FIS tells you whether a prediction is worth acting on. CER tells you whether that confidence is efficiently earned. Together, they reveal why traditional accuracy metrics can be misleading.
How traditional metrics get fooled by common prediction patterns
This model just predicts yesterday's price. It's incredibly common (moving averages, momentum strategies) and looks great on traditional metrics because it's "always close." But it's completely untradeable.
By the time you get the signal, the move already happened. You're always entering after the profitable moment. It's like reading yesterday's news.
✓ "Excellent fit!"
✓ "Very accurate!"
✕ "Worthless for trading"
Real Trading Result: You'd lose money on transaction costs alone. Every trade is a day late.
The transparency and privacy you need for sensitive data
Predictions processed in-memory. We evaluate your utility, we don't steal your alpha. No data storage, no tracking, no leaks.
Math is documented. Implementation stays private. You know what we do, just not how we optimize it.
Download reports include all the necessary data to complete your analysis. No black boxes.
50,000+ Monte Carlo simulations. Bootstrap confidence intervals. Peer-reviewed methodology. Not guesswork.