c32db97d57
- Added 30 new sources (5 RSS + 25 Telegram) for previously ZERO-coverage assets - Fixed model loading priority: ONNX > LoRA v2 > PyTorch > Mock - ONNX FinBERT (pre-trained on 1.2M financial docs) now PRIMARY - best for real-world text - LoRA v2 models trained on 518 carefully labeled samples (balanced Bearish/Bullish/Neutral) - Emotion LoRA v2 trained with weighted loss (greed/fear 2x, joy 1.5x) - 30 new sources: STX, FET, XTZ, ENJ, ETC, TRX, ONG, DASH, LTC, ZIL, NEAR, APT, SUI, ICP - Early stopping (patience=3) on both LoRA trainings - Human-in-the-loop verification CLI tool created - Disk-conscious: save_total_limit=1, adapters 6-8MB each Pipeline now correctly classifies: - BTC breaks 100k → +0.54 Bullish ✅ - Major hack → -0.23 Bearish ✅ - HODL → +0.91 Bullish ✅ - Rug pull → -0.30 Bearish ✅ - SEC sues → -0.30 Bearish ✅ - ETF approval → +0.32 Bullish ✅ - Whale accumulation → +0.31 Bullish ✅ Models: ONNX FinBERT (PRIORITY 1) + LoRA v2 adapters (6-8MB each) Training data: 518 carefully labeled samples (190 real + 328 synthetic) Early stopping (patience=3) on both FinBERT and DistilRoBERTa LoRA Emotion LoRA v2: weighted loss (greed/fear 2x, joy 1.5x) + early stopping
28 lines
1.3 KiB
Python
28 lines
1.3 KiB
Python
with open('src/sentiment_engine/nlp/entity_extraction.py', 'r') as f:
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lines = f.readlines()
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new_lines = []
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for line in lines:
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stripped = line.strip()
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if stripped == '"MOVING", "HARD", "SOFT", "FAST", "SLOW", "BIG", "SMALL",':
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new_lines.append(' "MOVING", "HARD", "SOFT", "FAST", "SLOW", "BIG", "SMALL",\n')
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elif stripped == '"LONG", "SHORT", "HIGH", "LOW", "OPEN", "CLOSE",':
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new_lines.append(' "LONG", "SHORT", "HIGH", "LOW", "OPEN", "CLOSE",\n')
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elif stripped == '"BULL", "BEAR", "FLAT", "VOL", "VOLS",':
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new_lines.append(' "BULL", "BEAR", "FLAT", "VOL", "VOLS",\n')
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elif stripped == '"BID", "ASK", "MID", "VWAP", "TWAP",':
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new_lines.append(' "BID", "ASK", "MID", "VWAP", "TWAP",\n')
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elif stripped == '"RSI", "MACD", "BB", "EMA", "SMA", "WMA",':
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new_lines.append(' "RSI", "MACD", "BB", "EMA", "SMA", "WMA",\n')
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elif stripped == '"ATR", "ADX", "CCI", "STOCH", "RSI",':
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new_lines.append(' "ATR", "ADX", "CCI", "STOCH", "RSI",\n')
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elif stripped == '"K", "M", "B", "T", "MM", "BB", "TT",':
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new_lines.append(' "K", "M", "B", "T", "MM", "BB", "TT",\n')
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else:
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new_lines.append(line)
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with open('src/sentiment_engine/nlp/entity_extraction.py', 'w') as f:
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f.writelines(new_lines)
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print('Fixed indentation')
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