Per-Asset Performance
Crypto
Bitcoin
Directional Accuracy
58.6%
BUY / SELL correct direction
Overall 3-Class Accuracy
44.8%
BUY / SELL / WAIT · random = 33.3%
Crypto
Ethereum
Directional Accuracy
58.9%
BUY / SELL correct direction
Overall 3-Class Accuracy
44.8%
BUY / SELL / WAIT · random = 33.3%
Commodity
Gold
Directional Accuracy
62.4%
BUY / SELL correct direction
Overall 3-Class Accuracy
50.2%
BUY / SELL / WAIT · random = 33.3%
Commodity
Silver
Directional Accuracy
62.0%
BUY / SELL correct direction
Overall 3-Class Accuracy
43.8%
BUY / SELL / WAIT · random = 33.3%
Energy
Oil (WTI)
Directional Accuracy
60.2%
BUY / SELL correct direction
Overall 3-Class Accuracy
51.0%
BUY / SELL / WAIT · random = 33.3%
Energy
Brent Oil
Directional Accuracy
60.5%
BUY / SELL correct direction
Overall 3-Class Accuracy
50.9%
BUY / SELL / WAIT · random = 33.3%
Index
S&P 500
Directional Accuracy
62.1%
BUY / SELL correct direction
Overall 3-Class Accuracy
50.4%
BUY / SELL / WAIT · random = 33.3%
Equity
NVIDIA
Directional Accuracy
62.5%
BUY / SELL correct direction
Overall 3-Class Accuracy
51.1%
BUY / SELL / WAIT · random = 33.3%
Index
Dow Jones
Directional Accuracy
61.3%
BUY / SELL correct direction
Overall 3-Class Accuracy
50.2%
BUY / SELL / WAIT · random = 33.3%
Why two accuracy numbers?
The model outputs three classes: BUY, SELL, or WAIT. Overall accuracy measures all three — including WAIT signals, which are deliberately issued when conviction is low. Directional accuracy only measures the BUY/SELL calls against a 50% coin-flip baseline. That is the number that matters for trading: when the model commits to a direction, how often is it right?
Conviction Calibration — The Higher the Confidence, the More Accurate
| Confidence |
Bitcoin |
Ethereum |
Gold |
Silver |
Oil (WTI) |
Brent |
Dow Jones |
S&P 500 |
NVIDIA |
Rating |
| 45 – 55% |
46.4% (59.9% dir) |
45.8% (59.8% dir) |
46.9% (60.6% dir) |
48.3% (60.7% dir) |
48.2% (58.1% dir) |
48.8% (59.3% dir) |
47.6% (60.0% dir) |
47.9% (59.7% dir) |
47.4% (59.4% dir) |
Moderate |
| 55 – 65% |
54.5% (66.8% dir) |
57.5% (69.7% dir) |
62.5% (70.1% dir) |
61.5% (68.8% dir) |
60.8% (67.9% dir) |
63.3% (69.7% dir) |
64.2% (70.5% dir) |
63.6% (70.7% dir) |
61.6% (69.7% dir) |
High |
| 65 – 75% |
63.6% (72.2% dir) |
71.1% (79.0% dir) |
81.2% (81.7% dir) |
78.8% (78.0% dir) |
75.1% (78.2% dir) |
81.7% (83.2% dir) |
80.9% (82.3% dir) |
78.8% (79.6% dir) |
76.5% (77.9% dir) |
High |
| 75%+ |
84.0% (81.2% dir) |
85.1% (87.8% dir) |
91.3% (88.0% dir) |
87.1% (78.0% dir) |
90.1% (86.2% dir) |
93.5% (90.7% dir) |
91.3% (89.5% dir) |
86.8% (86.0% dir) |
89.5% (85.3% dir) |
Peak |
What this means: Conviction is monotonically calibrated — accuracy rises consistently as the model's confidence increases. At 75%+ conviction, BTC signals have been correct 84.0% of the time across 430 historical instances, and ETH signals 85.1% correct across 322 instances. These high-conviction signals are rare by design: the model holds back unless the evidence is strong. Users can see the current conviction level on every signal in the app.
Signal Quality — Correct Calls Move More
Why the quality gap matters: Even when the model is wrong about direction, the magnitude of the move tends to be smaller. When it is right, the move is larger. This asymmetry — correct calls catching bigger moves, incorrect calls occurring on smaller noise — is the structural trading edge beyond raw accuracy alone.
How the Model Works
Signal Type
3-class classification — BUY, SELL, or WAIT. The WAIT class is a deliberate output, not a fallback. The model withholds a directional call when evidence is insufficient, reducing noise and false positives.
Label Generation
Maximum Favorable Excursion (MFE) scaled by Average True Range. Labels reflect whether a real, volatility-adjusted move occurred in the next 6 bars — not just whether the next candle closed up or down.
Feature Engineering
100+ technical features per bar — momentum indicators, volatility measures, volume signals, trend structure, candlestick patterns, Ichimoku components, and multi-timeframe lags. All features are strictly backward-looking.
Dimensionality Reduction
Return-weighted PCA — a variation of standard PCA where principal components are selected based on their correlation with forward price returns, not just variance explained. This biases the feature space toward market-relevant structure.
Model Architecture
Soft-voting ensemble of gradient-boosted trees and a random forest. Each model produces class probabilities; the ensemble averages them. The final confidence score is the probability of the winning class — a real calibrated number, not a heuristic.
Validation Protocol
Strict chronological split — training on earliest data, testing on most recent. No shuffling. 5-fold time-series cross-validation with each fold advancing forward in time. No data leakage between folds.
Comparative Summary
| Asset |
Directional Accuracy |
Overall Accuracy |
Uplift vs Coin Flip |
Uplift vs Random (3-class) |
Rating |
|
|
62.4% |
50.2% |
+12.4pp |
+16.8pp |
High |
|
|
58.6% |
44.8% |
+8.6pp |
+11.5pp |
High |
|
|
58.9% |
44.8% |
+8.9pp |
+11.5pp |
High |
|
|
62.0% |
43.8% |
+12.0pp |
+10.5pp |
High |
|
|
60.2% |
51.0% |
+10.2pp |
+17.6pp |
High |
|
|
60.5% |
50.9% |
+10.5pp |
+17.6pp |
High |
|
|
61.3% |
50.2% |
+11.3pp |
+16.8pp |
High |
|
|
62.1% |
50.4% |
+12.1pp |
+17.1pp |
High |
|
|
62.5% |
51.1% |
+12.5pp |
+17.8pp |
High |
Past model performance does not guarantee future results. Markets are non-stationary — model accuracy may vary across different market regimes. Enodara provides market intelligence for informational purposes only and does not constitute financial advice. All figures will be updated following each model retrain.