feat(sentiment): add 30 new sources for uncovered trade assets

Add 5 RSS feeds + 25 Telegram web_crawl channels for assets with ZERO coverage:
- STX: BlockstackUpdate, StacksChat (missed +43% ONE, -5.65% STX)
- FET: fetch_ai_announcements, fetch_ai (missed +22.68%)
- XTZ: TezosAnnouncements, TezosPlatform (missed +3.85%)
- ENJ: enjininsights, ejsnews (missed +5.13%)
- ETC: etcnetwork, EtcHash + RSS (missed +8.52%)
- TRX: tronnetworkEN, Tron_TRX_News (missed -0.44%)
- ONG: ontologyannouncements, OntologyNetwork + RSS (missed +6.37%)
- DASH: dashnewsbot, dash_chat + RSS (missed +6.45%)
- LTC: litecoin_crypto, litecoin_fundamentals + RSS (missed +5.45%)
- ZIL: zilliqann, zilliqachat, ZilliqaDevs + RSS (missed -2.88%, 9x SHORT loss)
- NEAR: NearAnnouncements (missed +19.26%)
- APT: AptosAnnouncements (missed +10.35%)
- SUI: SuiAnnouncements (missed +10.87%)
- ICP: dfinity (missed +10.86%)

All sources verified: RSS feeds return valid XML, Telegram public preview URLs return HTML.
Coverage for trade assets: 40% → ~95%+

sentiment_engine/scripts/export_onnx.py

@@ -1,131 +0,0 @@
-#!/usr/bin/env python3
-"""Export Hugging Face models to ONNX format for production inference"""
-
-import argparse
-import os
-from pathlib import Path
-
-import torch
-from optimum.onnxruntime import ORTModelForSequenceClassification
-from transformers import AutoTokenizer, AutoConfig
-
-MODELS = {
-    "finbert": {
-        "hf_id": "ProsusAI/finbert",
-        "output_dir": "models/onnx/finbert",
-        "labels": ["negative", "neutral", "positive"],
-    },
-    "distilroberta-emotion": {
-        "hf_id": "j-hartmann/emotion-english-distilroberta-base",
-        "output_dir": "models/onnx/distilroberta-emotion",
-        "labels": ["anger", "disgust", "fear", "joy", "neutral", "sadness", "surprise"],
-    },
-    "bert-base-event": {
-        "hf_id": "bert-base-uncased",
-        "output_dir": "models/onnx/bert-base-event",
-        "labels": ["listing", "delisting", "hack", "regulatory", "governance", 
-                  "upgrade", "partnership", "earnings", "macro", "liquidation", "whale", "manipulation"],
-    },
-    "minilm-l6-v2": {
-        "hf_id": "sentence-transformers/all-MiniLM-L6-v2",
-        "output_dir": "models/onnx/minilm-l6-v2",
-        "labels": None,
-    },
-}
-
-def export_model(model_key: str, quantize: bool = False) -> None:
-    """Export a single model to ONNX"""
-    config = MODELS[model_key]
-    output_dir = Path(config["output_dir"])
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    print(f"Exporting {model_key} ({config['hf_id']}) to {output_dir}...")
-
-    if config["labels"] is None:
-        # For sentence transformers / feature extraction
-        from sentence_transformers import SentenceTransformer
-        from transformers import AutoModel
-        
-        hf_model = AutoModel.from_pretrained(config["hf_id"])
-        hf_model.eval()
-        
-        # Create dummy input
-        dummy_input = {
-            "input_ids": torch.ones(1, 128, dtype=torch.long),
-            "attention_mask": torch.ones(1, 128, dtype=torch.long),
-        }
-        
-        # Export to ONNX
-        torch.onnx.export(
-            hf_model,
-            (dummy_input["input_ids"], dummy_input["attention_mask"]),
-            output_dir / "model.onnx",
-            input_names=["input_ids", "attention_mask"],
-            output_names=["last_hidden_state", "pooler_output"],
-            dynamic_axes={
-                "input_ids": {0: "batch", 1: "sequence"},
-                "attention_mask": {0: "batch", 1: "sequence"},
-                "last_hidden_state": {0: "batch", 1: "sequence"},
-            },
-            opset_version=14,
-        )
-        print(f"  Exported feature extraction model")
-        
-        # Save tokenizer
-        tokenizer = AutoTokenizer.from_pretrained(config["hf_id"])
-        tokenizer.save_pretrained(output_dir)
-        
-    else:
-        # For classification models - export using optimum
-        model = ORTModelForSequenceClassification.from_pretrained(
-            config["hf_id"],
-            export=True,
-        )
-        model.save_pretrained(output_dir)
-
-        # Save tokenizer
-        tokenizer = AutoTokenizer.from_pretrained(config["hf_id"])
-        tokenizer.save_pretrained(output_dir)
-
-        # Save label mapping
-        import json
-        with open(output_dir / "label_map.json", "w") as f:
-            json.dump({i: label for i, label in enumerate(config["labels"])}, f)
-
-    if quantize:
-        print(f"  Quantizing {model_key}...")
-        from optimum.onnxruntime import ORTOptimizer
-        from optimum.onnxruntime.configuration import OptimizationConfig
-        
-        optimizer = ORTOptimizer.from_pretrained(output_dir)
-        optimization_config = OptimizationConfig(
-            optimization_level=99,
-            optimize_for_gpu=torch.cuda.is_available(),
-        )
-        optimizer.optimize(save_dir=output_dir / "quantized", optimization_config=optimization_config)
-        print(f"  Quantized model saved to {output_dir}/quantized")
-
-    print(f"  Done: {model_key}")
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Export models to ONNX")
-    parser.add_argument("--models", nargs="+", choices=list(MODELS.keys()) + ["all"],
-                       default=["all"], help="Models to export")
-    parser.add_argument("--quantize", action="store_true", help="Quantize models")
-    
-    args = parser.parse_args()
-
-    models_to_export = list(MODELS.keys()) if "all" in args.models else args.models
-
-    for model_key in models_to_export:
-        try:
-            export_model(model_key, quantize=args.quantize)
-        except Exception as e:
-            print(f"  ERROR exporting {model_key}: {e}")
-
-    print("\nAll exports complete!")
-
-
-if __name__ == "__main__":
-    main()