1. Data Collection & Preprocessing
python
# The system gathers multiple data dimensions:
class DataCollector:
def collect_market_signals(self):
return {
'price_data': self.get_price_movement(),
'volume_data': self.get_volume_analysis(),
'sentiment_data': self.get_market_sentiment(),
'technical_indicators': self.get_technical_analysis(),
'market_metrics': self.get_market_metrics()
}
2. Multi-Dimensional Analysis Framework
Price Movement Analysis:
python
def analyze_price_movement(self, prices):
# Trend analysis
short_trend = self.calculate_trend(prices[-10:]) # 10-day trend
medium_trend = self.calculate_trend(prices[-30:]) # 30-day trend
long_trend = self.calculate_trend(prices[-90:]) # 90-day trend
# Volatility analysis
volatility = self.calculate_volatility(prices[-30:])
# Support/Resistance levels
support_levels = self.identify_support_levels(prices)
resistance_levels = self.identify_resistance_levels(prices)
Volume Analysis:
python
def analyze_volume_patterns(self, volumes, prices):
# Volume trend analysis
volume_trend = self.calculate_volume_trend(volumes)
# Volume-price correlation
volume_price_correlation = self.calculate_correlation(volumes, prices)
# Abnormal volume detection
volume_spikes = self.detect_volume_spikes(volumes)
3. LLM Classification Engine
Rule-Based Intelligence Layer:
python
class LLMClassificationEngine:
def classify_market_condition(self, analysis_data):
# Multi-factor weighted scoring
score = self.calculate_composite_score(analysis_data)
# Pattern recognition
patterns = self.identify_market_patterns(analysis_data)
# Sentiment classification
sentiment = self.determine_market_sentiment(score, patterns)
return {
'classification': sentiment,
'confidence_score': self.calculate_confidence(analysis_data),
'key_factors': self.extract_key_factors(analysis_data),
'risk_assessment': self.assess_risk_level(analysis_data)
}
Crucial Data Points for Accurate Classification
1. Price-Based Metrics:
- 24-hour price change – Immediate momentum
- 7-day/30-day price performance – Medium-term trend
- Price volatility – Market stability assessment
- Support/Resistance levels – Key price zones
- Moving averages – Trend confirmation
2. Volume-Based Metrics:
- Trading volume trends – Market participation
- Volume spikes – Institutional activity
- Volume-price correlation – Trend validation
- Relative volume – Compared to historical averages
3. Market Structure Metrics:
- Buyer/Seller ratio – Market sentiment
- Order book analysis – Liquidity depth
- Market depth – Support/resistance strength
4. Technical Indicators:
- RSI (Relative Strength Index) – Overbought/oversold
- MACD – Trend momentum
- Bollinger Bands – Volatility and price levels
The Classification Process
Step 1: Data Normalization
python
def normalize_market_data(self, raw_data):
# Convert all metrics to standardized scores (0-100)
normalized_data = {}
for metric, value in raw_data.items():
normalized_data[metric] = self.min_max_normalize(value)
return normalized_data
Step 2: Weighted Scoring System
python
def calculate_composite_score(self, normalized_data):
weights = {
'price_momentum': 0.25, # Most important
'volume_trend': 0.20, # Very important
'volatility': 0.15, # Important for risk
'support_levels': 0.15, # Important for entry points
'market_sentiment': 0.15, # Contextual
'technical_indicators': 0.10 # Confirmatory
}
composite_score = 0
for metric, weight in weights.items():
composite_score += normalized_data[metric] * weight
return composite_score
Step 3: Pattern Recognition
python
def identify_market_patterns(self, data):
patterns = []
# Bullish patterns
if self.detect_bullish_engulfing(data):
patterns.append('bullish_engulfing')
if self.detect_support_bounce(data):
patterns.append('support_bounce')
# Bearish patterns
if self.detect_resistance_rejection(data):
patterns.append('resistance_rejection')
if self.detect_breakdown(data):
patterns.append('breakdown')
return patterns
Step 4: Sentiment Classification
python
def determine_market_sentiment(self, score, patterns):
if score >= 70 and 'bullish_engulfing' in patterns:
return "strong_bullish"
elif score >= 60:
return "bullish"
elif score >= 40:
return "neutral"
elif score >= 30:
return "bearish"
else:
return "strong_bearish"
Biggest Challenges in Classification
1. Data Quality & Completeness
python
# Challenge: Incomplete or delayed data from free API
def handle_data_limitations(self):
challenges = {
'rate_limiting': "5 calls/minute restricts real-time analysis",
'historical_depth': "Limited to 365 days max",
'data_granularity': "No minute-level data for free tier",
'missing_metrics': "No order book or advanced indicators"
}
return challenges
2. Market Noise Filtering
python
# Challenge: Separating signal from noise in volatile crypto markets
def filter_market_noise(self, price_data):
techniques = {
'moving_averages': "Smooth out short-term fluctuations",
'volatility_adjustment': "Weight recent data appropriately",
'outlier_detection': "Identify and handle anomalous data points",
'trend_confirmation': "Require multiple confirming signals"
}
3. Contextual Understanding
python
# Challenge: Crypto markets behave differently from traditional markets
def adapt_to_crypto_dynamics(self):
crypto_specific_challenges = {
'higher_volatility': "2-3x more volatile than stocks",
'24_7_market': "No closing hours, continuous data flow",
'sentiment_driven': "More influenced by news and social media",
'regulatory_impact': "Sudden regulatory news can cause 20%+ moves"
}
4. Real-time Adaptation
python
# Challenge: Markets change rapidly, models must adapt
def ensure_model_adaptability(self):
adaptation_mechanisms = {
'dynamic_weighting': "Adjust feature weights based on market regime",
'regime_detection': "Identify bull/bear/neutral markets",
'volatility_scaling': "Adjust sensitivity during high volatility",
'feedback_loops': "Learn from classification accuracy over time"
}
Advanced Classification Techniques Used
1. Multi-Timeframe Analysis
python
def multi_timeframe_analysis(self, data):
timeframes = {
'intraday': "1h-4h charts for immediate signals",
'daily': "1d charts for primary trend",
'weekly': "1w charts for broader context",
'monthly': "1m charts for long-term perspective"
}
# Combine signals from all timeframes
consensus_signal = self.aggregate_timeframe_signals(timeframes)
return consensus_signal
2. Confidence Scoring
python
def calculate_confidence_score(self, analysis_data):
confidence_factors = {
'data_quality': self.assess_data_completeness(),
'signal_strength': self.measure_signal_clarity(),
'pattern_confirmation': self.check_multiple_confirmations(),
'market_conditions': self.assess_market_stability()
}
return min(confidence_factors.values()) # Conservative approach
3. Risk Assessment Integration
python
def integrate_risk_assessment(self, classification):
risk_levels = {
'strong_bullish': "Medium risk - confirmed uptrend",
'bullish': "Medium-High risk - emerging trend",
'neutral': "High risk - uncertain direction",
'bearish': "High risk - confirmed downtrend",
'strong_bearish': "Very High risk - strong downtrend"
}
return risk_levels.get(classification, "Unknown risk")
Key Success Factors
1. Data Quality Over Quantity
- Focus on high-signal data points
- Clean, normalized data inputs
- Multiple data source verification
2. Conservative Classification
- Require multiple confirming signals
- Higher confidence thresholds
- Clear risk disclosure
3. Continuous Learning
- Monitor classification accuracy
- Adjust weightings based on performance
- Incorporate new market patterns
This LLM-based classification system provides sophisticated market analysis by combining multiple data dimensions, applying weighted scoring, and adapting to crypto market specifics while handling the challenges of limited API data and market volatility.