Spaces:
Running
Running
File size: 11,438 Bytes
0bf918d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 |
# ml_engine/patterns.py
import pandas as pd
import numpy as np
class ChartPatternAnalyzer:
def __init__(self):
self.pattern_cache = {}
async def detect_chart_patterns(self, ohlcv_data):
"""اكتشاف الأنماط البيانية لجميع الأطر الزمنية"""
patterns = {
'pattern_detected': 'no_clear_pattern',
'pattern_confidence': 0,
'predicted_direction': 'neutral',
'timeframe_analysis': {},
'all_patterns': []
}
try:
for timeframe, candles in ohlcv_data.items():
if candles and len(candles) >= 20:
dataframe = self._create_dataframe(candles)
timeframe_pattern = await self._analyze_timeframe_patterns(dataframe, timeframe)
patterns['timeframe_analysis'][timeframe] = timeframe_pattern
patterns['all_patterns'].append(timeframe_pattern)
if timeframe_pattern['confidence'] > patterns['pattern_confidence']:
patterns.update({
'pattern_detected': timeframe_pattern['pattern'],
'pattern_confidence': timeframe_pattern['confidence'],
'predicted_direction': timeframe_pattern['direction']
})
return patterns
except Exception as e:
print(f"❌ خطأ في اكتشاف الأنماط: {e}")
return patterns
def _create_dataframe(self, candles):
"""إنشاء DataFrame من بيانات الشموع"""
try:
df = pd.DataFrame(candles, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
df[['open', 'high', 'low', 'close', 'volume']] = df[['open', 'high', 'low', 'close', 'volume']].astype(float)
return df
except Exception as e:
print(f"❌ خطأ في إنشاء DataFrame: {e}")
return pd.DataFrame()
async def _analyze_timeframe_patterns(self, dataframe, timeframe):
"""تحليل الأنماط لإطار زمني محدد"""
pattern_info = {
'pattern': 'no_clear_pattern',
'confidence': 0,
'direction': 'neutral',
'timeframe': timeframe,
'details': {}
}
try:
if dataframe is None or dataframe.empty or len(dataframe) < 20:
return pattern_info
closes = dataframe['close'].values
highs = dataframe['high'].values
lows = dataframe['low'].values
current_price = closes[-1]
patterns_detected = []
double_pattern = self._detect_double_pattern(highs, lows, closes)
if double_pattern['detected']:
patterns_detected.append(double_pattern)
breakout_pattern = self._detect_breakout_pattern(highs, lows, closes)
if breakout_pattern['detected']:
patterns_detected.append(breakout_pattern)
trend_pattern = self._detect_trend_pattern(dataframe)
if trend_pattern['detected']:
patterns_detected.append(trend_pattern)
support_resistance_pattern = self._detect_support_resistance(highs, lows, closes)
if support_resistance_pattern['detected']:
patterns_detected.append(support_resistance_pattern)
if patterns_detected:
best_pattern = max(patterns_detected, key=lambda x: x['confidence'])
pattern_info.update({
'pattern': best_pattern['pattern'],
'confidence': best_pattern['confidence'],
'direction': best_pattern.get('direction', 'neutral'),
'details': best_pattern.get('details', {})
})
return pattern_info
except Exception as e:
print(f"❌ خطأ في تحليل الأنماط للإطار {timeframe}: {e}")
return pattern_info
def _detect_double_pattern(self, highs, lows, closes):
"""كشف نمط القمة المزدوجة أو القاع المزدوج"""
try:
if len(highs) < 15:
return {'detected': False}
recent_highs = highs[-15:]
recent_lows = lows[-15:]
high_indices = np.argsort(recent_highs)[-2:]
high_indices.sort()
low_indices = np.argsort(recent_lows)[:2]
low_indices.sort()
double_top = False
double_bottom = False
if len(high_indices) == 2:
high1 = recent_highs[high_indices[0]]
high2 = recent_highs[high_indices[1]]
time_diff = high_indices[1] - high_indices[0]
if (abs(high1 - high2) / high1 < 0.02 and
time_diff >= 3 and time_diff <= 10 and
closes[-1] < min(high1, high2)):
double_top = True
if len(low_indices) == 2:
low1 = recent_lows[low_indices[0]]
low2 = recent_lows[low_indices[1]]
time_diff = low_indices[1] - low_indices[0]
if (abs(low1 - low2) / low1 < 0.02 and
time_diff >= 3 and time_diff <= 10 and
closes[-1] > max(low1, low2)):
double_bottom = True
if double_top:
return {
'detected': True,
'pattern': 'Double Top',
'confidence': 0.75,
'direction': 'down',
'details': {
'resistance_level': np.mean([high1, high2]),
'breakdown_level': min(lows[-5:])
}
}
elif double_bottom:
return {
'detected': True,
'pattern': 'Double Bottom',
'confidence': 0.75,
'direction': 'up',
'details': {
'support_level': np.mean([low1, low2]),
'breakout_level': max(highs[-5:])
}
}
return {'detected': False}
except Exception as e:
return {'detected': False}
def _detect_breakout_pattern(self, highs, lows, closes):
"""كشف نمط الاختراق"""
try:
if len(highs) < 25:
return {'detected': False}
current_price = closes[-1]
resistance = np.max(highs[-25:-5])
support = np.min(lows[-25:-5])
if current_price > resistance * 1.01:
return {
'detected': True,
'pattern': 'Breakout Up',
'confidence': 0.8,
'direction': 'up',
'details': {
'breakout_level': resistance,
'target_level': resistance * 1.05
}
}
elif current_price < support * 0.99:
return {
'detected': True,
'pattern': 'Breakout Down',
'confidence': 0.8,
'direction': 'down',
'details': {
'breakdown_level': support,
'target_level': support * 0.95
}
}
return {'detected': False}
except Exception as e:
return {'detected': False}
def _detect_trend_pattern(self, dataframe):
"""كشف نمط الاتجاه"""
try:
if dataframe is None or dataframe.empty or len(dataframe) < 20:
return {'detected': False}
closes = dataframe['close'].values
ma_short = np.mean(closes[-5:])
ma_medium = np.mean(closes[-13:])
ma_long = np.mean(closes[-21:])
if ma_short > ma_medium > ma_long and closes[-1] > ma_short:
trend_strength = (ma_short - ma_long) / ma_long
confidence = min(0.3 + trend_strength * 10, 0.8)
return {
'detected': True,
'pattern': 'Uptrend',
'confidence': confidence,
'direction': 'up',
'details': {
'trend_strength': trend_strength,
'support_level': ma_medium
}
}
elif ma_short < ma_medium < ma_long and closes[-1] < ma_short:
trend_strength = (ma_long - ma_short) / ma_long
confidence = min(0.3 + trend_strength * 10, 0.8)
return {
'detected': True,
'pattern': 'Downtrend',
'confidence': confidence,
'direction': 'down',
'details': {
'trend_strength': trend_strength,
'resistance_level': ma_medium
}
}
return {'detected': False}
except Exception as e:
return {'detected': False}
def _detect_support_resistance(self, highs, lows, closes):
"""كشف مستويات الدعم والمقاومة"""
try:
if len(highs) < 20:
return {'detected': False}
current_price = closes[-1]
resistance_level = np.max(highs[-20:])
support_level = np.min(lows[-20:])
position = (current_price - support_level) / (resistance_level - support_level)
if position < 0.2:
return {
'detected': True,
'pattern': 'Near Support',
'confidence': 0.6,
'direction': 'up',
'details': {
'support_level': support_level,
'resistance_level': resistance_level,
'position': position
}
}
elif position > 0.8:
return {
'detected': True,
'pattern': 'Near Resistance',
'confidence': 0.6,
'direction': 'down',
'details': {
'support_level': support_level,
'resistance_level': resistance_level,
'position': position
}
}
return {'detected': False}
except Exception as e:
return {'detected': False}
print("✅ ML Module: Chart Pattern Analyzer loaded") |