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advanced_chart_learner.py
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216 lines (177 loc) · 8.67 KB
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import numpy as np
from typing import List, Dict, Tuple, Optional
from collections import deque
from chart_storage import Chart
class AdvancedChartLearner:
def __init__(self):
self.chart_pattern_memory = {}
self.successful_patterns = deque(maxlen=1000)
self.failed_patterns = deque(maxlen=1000)
self.pattern_confidence_history = {}
self.chart_shape_memory = {}
self.regime_aware_patterns = {}
def learn_from_chart_outcome(self, chart_data: List[float],
predicted_profitable: bool,
was_profitable: bool,
confidence: float,
market_regime: str,
indicators: Dict,
patterns: Dict):
chart_signature = self._generate_chart_signature(chart_data)
if chart_signature not in self.chart_pattern_memory:
self.chart_pattern_memory[chart_signature] = {
'total_occurrences': 0,
'successful': 0,
'failed': 0,
'avg_confidence': 0.0,
'regimes': {},
'pattern_indicators': {}
}
memory = self.chart_pattern_memory[chart_signature]
memory['total_occurrences'] += 1
if predicted_profitable == was_profitable:
memory['successful'] += 1
self.successful_patterns.append({
'chart': chart_data,
'confidence': confidence,
'regime': market_regime,
'indicators': indicators,
'patterns': patterns
})
else:
memory['failed'] += 1
self.failed_patterns.append({
'chart': chart_data,
'confidence': confidence,
'regime': market_regime,
'indicators': indicators,
'patterns': patterns
})
memory['avg_confidence'] = (
(memory['avg_confidence'] * (memory['total_occurrences'] - 1) + confidence) /
memory['total_occurrences']
)
if market_regime not in memory['regimes']:
memory['regimes'][market_regime] = {'total': 0, 'successful': 0}
memory['regimes'][market_regime]['total'] += 1
if predicted_profitable == was_profitable:
memory['regimes'][market_regime]['successful'] += 1
key_patterns = self._extract_key_patterns(patterns, indicators)
for pattern_key, pattern_value in key_patterns.items():
if pattern_key not in memory['pattern_indicators']:
memory['pattern_indicators'][pattern_key] = {'total': 0, 'successful': 0}
memory['pattern_indicators'][pattern_key]['total'] += 1
if predicted_profitable == was_profitable:
memory['pattern_indicators'][pattern_key]['successful'] += 1
def get_pattern_confidence_boost(self, chart_data: List[float],
market_regime: str,
patterns: Dict,
indicators: Dict) -> float:
chart_signature = self._generate_chart_signature(chart_data)
if chart_signature not in self.chart_pattern_memory:
return 0.0
memory = self.chart_pattern_memory[chart_signature]
if memory['total_occurrences'] < 3:
return 0.0
success_rate = memory['successful'] / memory['total_occurrences']
if success_rate > 0.7:
boost = (success_rate - 0.5) * 20.0
elif success_rate < 0.3:
boost = (success_rate - 0.5) * 15.0
else:
boost = 0.0
regime_key = f"{chart_signature}_{market_regime}"
if regime_key in self.regime_aware_patterns:
regime_memory = self.regime_aware_patterns[regime_key]
if regime_memory['total'] > 2:
regime_success_rate = regime_memory['successful'] / regime_memory['total']
if regime_success_rate > 0.75:
boost += 5.0
elif regime_success_rate < 0.25:
boost -= 5.0
key_patterns = self._extract_key_patterns(patterns, indicators)
for pattern_key, pattern_value in key_patterns.items():
if pattern_key in memory['pattern_indicators']:
pattern_stats = memory['pattern_indicators'][pattern_key]
if pattern_stats['total'] > 2:
pattern_success_rate = pattern_stats['successful'] / pattern_stats['total']
if pattern_success_rate > 0.7:
boost += 2.0
elif pattern_success_rate < 0.3:
boost -= 2.0
return boost
def find_similar_successful_charts(self, chart_data: List[float],
top_n: int = 5) -> List[Dict]:
chart_signature = self._generate_chart_signature(chart_data)
similar_charts = []
for pattern in self.successful_patterns:
similarity = self._calculate_shape_similarity(chart_data, pattern['chart'])
if similarity > 0.7:
similar_charts.append({
'chart': pattern['chart'],
'similarity': similarity,
'confidence': pattern['confidence'],
'regime': pattern['regime'],
'indicators': pattern['indicators'],
'patterns': pattern['patterns']
})
similar_charts.sort(key=lambda x: x['similarity'], reverse=True)
return similar_charts[:top_n]
def _generate_chart_signature(self, chart_data: List[float]) -> str:
if len(chart_data) < 5:
return "short"
prices = np.array(chart_data)
normalized = (prices - prices.min()) / (prices.max() - prices.min() + 1e-10)
features = [
np.mean(normalized),
np.std(normalized),
(normalized[-1] - normalized[0]),
len([i for i in range(1, len(normalized)) if normalized[i] > normalized[i-1]]),
len([i for i in range(1, len(normalized)) if normalized[i] < normalized[i-1]])
]
signature = "_".join([f"{f:.3f}" for f in features])
return signature
def _calculate_shape_similarity(self, chart1: List[float], chart2: List[float]) -> float:
if len(chart1) < 3 or len(chart2) < 3:
return 0.0
min_len = min(len(chart1), len(chart2))
arr1 = np.array(chart1[:min_len])
arr2 = np.array(chart2[:min_len])
arr1_norm = (arr1 - arr1.min()) / (arr1.max() - arr1.min() + 1e-10)
arr2_norm = (arr2 - arr2.min()) / (arr2.max() - arr2.min() + 1e-10)
std1 = np.std(arr1_norm)
std2 = np.std(arr2_norm)
if std1 < 1e-10 or std2 < 1e-10:
correlation = 1.0 if np.allclose(arr1_norm, arr2_norm) else 0.0
else:
with np.errstate(divide='ignore', invalid='ignore'):
correlation = np.corrcoef(arr1_norm, arr2_norm)[0, 1]
if np.isnan(correlation) or np.isinf(correlation):
correlation = 0.0
euclidean = np.sqrt(np.mean((arr1_norm - arr2_norm) ** 2))
similarity = (max(0.0, correlation) + (1.0 / (1.0 + euclidean))) / 2.0
return float(similarity)
def _extract_key_patterns(self, patterns: Dict, indicators: Dict) -> Dict:
key_patterns = {}
double_pattern = patterns.get('double_pattern', (0.0, 'none'))
if double_pattern[1] != 'none':
key_patterns[f"double_{double_pattern[1]}"] = double_pattern[0]
triangle = patterns.get('triangle', (0.0, 'none'))
if triangle[1] != 'none':
key_patterns[f"triangle_{triangle[1]}"] = triangle[0]
trend = indicators.get('trend', 'neutral')
if trend != 'neutral':
key_patterns[f"trend_{trend}"] = 1.0
rsi = indicators.get('rsi', 50.0)
if rsi < 30:
key_patterns['rsi_oversold'] = 1.0
elif rsi > 70:
key_patterns['rsi_overbought'] = 1.0
macd = indicators.get('macd', {})
if isinstance(macd, dict):
histogram = macd.get('histogram', 0)
if histogram > 0:
key_patterns['macd_bullish'] = abs(histogram)
elif histogram < 0:
key_patterns['macd_bearish'] = abs(histogram)
return key_patterns