🔭 Model Observatory
37 ML models · ensemble voting · Darwin tracks accuracy · GPU retraining weekly
Total Models
37
ensemble running
Avg Accuracy
—%
across all 37
Best Model
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Drift Alerts
0
all stable
Transformers7 models
TFT
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PatchTST
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iTransformer
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Autoformer
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Informer
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Crossformer
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TimesNet
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Recurrent (RNN)4 models
BiLSTM
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GRU
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Neural ODE
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Liquid NN
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CNN4 models
CNN-1D
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CNN-2D
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WaveNet
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TCN
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Gradient Boosting5 models
XGBoost
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LightGBM
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CatBoost
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Random Forest
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Extra Trees
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Reinforcement Learning4 models
PPO
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SAC
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TD3
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A2C
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Graph Neural Networks3 models
GCN
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GAT
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Temporal GNN
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Statistical4 models
Ridge
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Elastic Net
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NBEATS
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DLinear
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Anomaly Detection3 models
VAE
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LSTM-AE
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Isolation Forest
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NLP / Sentiment3 models
FinBERT
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RoBERTa
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VADER
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🏆 Accuracy Leaderboard — Top 10Updated by Darwin hourly
| # | Model | Accuracy | Bar | Predictions Today | Drift | Last Eval |
|---|---|---|---|---|---|---|
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🔬 Ensemble Architecture
Layer 1: 37 independent models predict direction (−1 to +1) concurrently
Layer 2: Weighted meta-learner stacks outputs (weights updated weekly by Darwin)
Layer 3: Temperature scaling calibrates final probability
Output: Signal type + calibrated confidence [0–1] + SHAP feature attribution
Training: Modal Labs A100 GPU · 5-fold time-series cross-validation · Optuna HPO
Data: 5 years OHLCV + 400+ features per symbol · Updated weekly
Layer 2: Weighted meta-learner stacks outputs (weights updated weekly by Darwin)
Layer 3: Temperature scaling calibrates final probability
Output: Signal type + calibrated confidence [0–1] + SHAP feature attribution
Training: Modal Labs A100 GPU · 5-fold time-series cross-validation · Optuna HPO
Data: 5 years OHLCV + 400+ features per symbol · Updated weekly
🧠 SHAP Feature Attribution — Oracle (Latest RELIANCE Signal)shapley values · additive explanation
Base value: 50%Output: 81.0% BUY confidence