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| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import numpy as np | |
| import pandas as pd | |
| import json | |
| from pattern_logic import PatternLogic | |
| class PatternAnalyzer: | |
| def __init__(self): | |
| self.model = AutoModelForCausalLM.from_pretrained("tmm-dev/codellama-pattern-analysis") | |
| self.tokenizer = AutoTokenizer.from_pretrained("tmm-dev/codellama-pattern-analysis") | |
| self.basic_patterns = { | |
| 'channel': {'min_points': 4, 'confidence_threshold': 0.7}, | |
| 'triangle': {'min_points': 3, 'confidence_threshold': 0.75}, | |
| 'support': {'min_touches': 2, 'confidence_threshold': 0.8}, | |
| 'resistance': {'min_touches': 2, 'confidence_threshold': 0.8}, | |
| 'double_top': {'max_deviation': 0.02, 'confidence_threshold': 0.85}, | |
| 'double_bottom': {'max_deviation': 0.02, 'confidence_threshold': 0.85} | |
| } | |
| self.pattern_logic = PatternLogic() | |
| def analyze_data(self, ohlcv_data): | |
| data_prompt = f"""TASK: Identify high-confidence technical patterns only. | |
| Minimum confidence threshold: 0.8 | |
| Required pattern criteria: | |
| 1. Channel: Must have at least 3 touching points | |
| 2. Triangle: Must have clear convergence point | |
| 3. Support: Minimum 3 price bounces | |
| 4. Resistance: Minimum 3 price rejections | |
| INPUT DATA: | |
| {ohlcv_data.to_json(orient='records')} | |
| Return ONLY high-confidence patterns (>0.8) in JSON format with exact price coordinates.""" | |
| inputs = self.tokenizer(data_prompt, return_tensors="pt") | |
| outputs = self.model.generate(**inputs, max_length=1000) | |
| analysis = self.tokenizer.decode(outputs[0]) | |
| return self.parse_analysis(analysis) | |
| def parse_analysis(self, analysis_text): | |
| try: | |
| json_start = analysis_text.find('{') | |
| json_end = analysis_text.rfind('}') + 1 | |
| json_str = analysis_text[json_start:json_end] | |
| analysis_data = json.loads(json_str) | |
| patterns = [] | |
| for pattern in analysis_data.get('patterns', []): | |
| pattern_type = pattern.get('type') | |
| if pattern_type in self.basic_patterns: | |
| threshold = self.basic_patterns[pattern_type]['confidence_threshold'] | |
| if pattern.get('confidence', 0) >= threshold: | |
| patterns.append({ | |
| 'type': pattern_type, | |
| 'coordinates': pattern.get('coordinates', []), | |
| 'confidence': pattern.get('confidence'), | |
| 'metadata': { | |
| 'rules': self.basic_patterns[pattern_type], | |
| 'timestamp': pd.Timestamp.now().isoformat() | |
| } | |
| }) | |
| return patterns | |
| except json.JSONDecodeError: | |
| return [] | |