147 lines
6.0 KiB
Markdown
147 lines
6.0 KiB
Markdown
# Market Regime Detection Algorithm
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**Created:** 8/11/2025 12:54
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**Updated:** 8/11/2025 12:55
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**Exported:** 8/28/2025 22:09
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**Link:** [https://claude.ai/chat/9450955e-9d56-427a-88de-e4c0da98ba27](https://claude.ai/chat/9450955e-9d56-427a-88de-e4c0da98ba27)
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## Prompt:
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11/8/2025, 12:54:08
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> File: Dolphin_System_General_Architechture_Rev1.00.md
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> File: Pairwise Correlation Regime Detection Academic Analysis Enhancement Framework.txt
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> File: Price_component_Market_Regime_Detection_Algorithm.md
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Also consider *these* documents, describing the Algorithm.-
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## Response:
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11/8/2025, 12:54:58
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````plaintext
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Thought process: Decoded market regime detection algorithm's intricate computational strategy.
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Looking at the provided documents, I need to analyze the core algorithm described for market regime detection. Let me identify the key components:
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1. The system takes a universe of cryptocurrency tokens/assets
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2. Computes pairwise correlations between all assets
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3. Ranks pairs from least to most correlated
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4. Removes perfectly correlated pairs (1.0 correlation)
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5. Applies exponential weighting (lambda=0.94) to prioritize least correlated pairs
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6. Calculates regime based on weighted bull vs bear pair ratios
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The documents show this is implemented in:
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- `processing_loop.py` - orchestrates the entire system
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- `correlation_calculator.py` - calculates pairwise correlations
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- `exponential_weighting.py` - applies exponential weighting to pairs
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- `statistical_validation.py` - optional validation layer
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- `market_regime_detection.py` - final regime classification
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- `multi_exchange_data_fetcher.py` - fetches real market data
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Key workflow from `processing_loop.py`:
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1. Fetch market data (real or mock)
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2. Calculate returns data
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3. Run correlation calculation
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4. Apply statistical validation (optional)
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5. Apply exponential weighting
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6. Run regime detection
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7. Optional ensemble classifier
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The algorithm classifies regimes as:
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- Bull: >60% of weighted pairs trending up
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- Bear: >60% of weighted pairs trending down
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- Sideways: Neither threshold met
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- Transition: High volatility/mixed signals
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The academic documents validate this approach and suggest enhancements like VIX integration, multi-timeframe analysis, and ensemble methods.
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````
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Based on my analysis of the provided documents, here's a comprehensive overview of the Market Regime Detection algorithm:
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## Core Algorithm Overview
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The system implements a **pairwise correlation-based regime detection algorithm** with the following key steps:
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### 1. Data Collection & Processing
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- **Universe Selection**: Takes a configurable list of cryptocurrency assets (default: top 20-200 cryptos)
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- **Data Fetching**: Uses `MultiExchangeDataFetcherBlock` to fetch OHLCV data from multiple exchanges (Binance, Kraken, Coinbase)
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- **Returns Calculation**: Converts price data to returns for correlation analysis
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### 2. Correlation Analysis (`correlation_calculator.py`)
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- **Pairwise Calculation**: Computes Pearson correlations for all unique asset pairs
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- **Matrix Generation**: Creates full correlation matrix using `statistics.correlation()`
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- **Pair Ranking**: Sorts pairs by absolute correlation (least to most correlated)
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- **Perfect Correlation Removal**: Excludes pairs with |correlation| ≥ 0.999
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### 3. Exponential Weighting (`exponential_weighting.py`)
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- **Weight Application**: Uses λ^rank weighting (default λ=0.94)
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- **Prioritization**: Gives higher weights to least correlated pairs
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- **Volatility Adjustment**: Optional adjustment based on correlation volatility
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- **Normalization**: Ensures weights sum to 1.0
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### 4. Regime Classification (`market_regime_detection.py`)
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- **Trend Analysis**: Classifies each symbol as bull (>1% avg return) or bear (<-1% avg return)
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- **Weighted Voting**: Calculates weighted percentage of bull vs bear pairs
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- **Regime Decision**:
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- **Bull**: >60% weighted pairs bullish
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- **Bear**: >60% weighted pairs bearish
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- **Sideways**: Neither threshold met
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- **Transition**: High volatility + mixed signals
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### 5. Optional Enhancements
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- **Statistical Validation**: Significance testing, confidence intervals
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- **Ensemble Classifier**: ML models (Random Forest, XGBoost) for enhanced accuracy
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- **Multi-timeframe Analysis**: Different correlation windows for various perspectives
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## Key Implementation Details
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### Processing Flow (`processing_loop.py`)
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```python
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# Simplified workflow
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1. Fetch market data →
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2. Extract returns →
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3. Calculate correlations →
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4. Apply exponential weighting →
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5. Detect regime →
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6. Broadcast results
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```
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### Critical Parameters
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- **Correlation Window**: 20-50 periods (default: 20)
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- **Lambda (λ)**: 0.94 for exponential decay
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- **Bull Threshold**: 60% of weighted pairs
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- **Bear Threshold**: 60% of weighted pairs
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- **Transition Volatility**: 30% threshold
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## Algorithm Strengths
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1. **Mathematically Sound**: Prioritizing least correlated pairs captures diversification breakdown
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2. **Computationally Efficient**: O(n²) complexity for correlations, suitable for real-time
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3. **Interpretable**: Clear relationship between correlations and regime
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4. **Adaptive**: Exponential weighting responds to recent market changes
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## Academic Validation
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The documents confirm this approach aligns with:
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- **Pelletier (2006)**: Pairwise correlations outperform univariate methods
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- **Preis et al. (2012)**: Correlation breakdown signals market stress
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- **Random Matrix Theory**: Filtering perfect correlations removes noise
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## Recommended Enhancements
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1. **VIX Integration**: Add volatility index for 25-35% better crisis detection
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2. **Multi-timeframe**: Wavelet decomposition for frequency analysis
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3. **Dynamic Thresholds**: Adjust bull/bear thresholds based on market volatility
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4. **Market Cap Weighting**: Prioritize large-cap cryptocurrencies for stability
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The system is well-architected with modular blocks that can be independently tested and enhanced. The exponential weighting approach (λ=0.94) is academically validated as superior to logarithmic weighting for financial applications.
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---
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