docs: add docs with planning subdirectory for TODO features
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docs/RCDD_Multiasset_Implementation_Python.txt
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257
docs/RCDD_Multiasset_Implementation_Python.txt
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import pandas as pd
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import numpy as np
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from typing import Dict, Tuple
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import matplotlib.pyplot as plt
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class MultiAssetRCDDAnalyzer:
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"""
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Enhance DOLPHIN with multi-asset RCDD analysis for better correlation weighting
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"""
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def __init__(self, rcdd_analyzer):
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self.rcdd_analyzer = rcdd_analyzer
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def compute_drawdown_correlations(self,
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price_data: pd.DataFrame,
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entry_prices: Dict[str, float]) -> pd.DataFrame:
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"""
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Compute correlations between assets based on their RCDD characteristics
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"""
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rcdd_metrics = {}
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# Calculate RCDD for each asset
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for asset in price_data.columns:
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if asset in entry_prices:
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metrics = self.rcdd_analyzer.recursive_conditional_drawdown(
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price_data[asset], entry_prices[asset]
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)
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rcdd_metrics[asset] = {
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'avg_drawdown': metrics.avg_drawdown,
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'max_drawdown': metrics.max_drawdown,
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'recovery_probability': metrics.recovery_probability,
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'recovery_time': metrics.avg_recovery_time,
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'confidence_width': metrics.confidence_interval[1] - metrics.confidence_interval[0]
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}
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# Create DataFrame of RCDD characteristics
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rcdd_df = pd.DataFrame(rcdd_metrics).T
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# Compute correlations between RCDD characteristics
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correlations = {}
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for char in rcdd_df.columns:
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corr_matrix = rcdd_df[[char]].corrwith(rcdd_df, axis=0)
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correlations[f'{char}_correlation'] = corr_matrix
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return pd.DataFrame(correlations)
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def enhanced_pair_weighting(self,
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asset1: str,
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asset2: str,
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price_data: pd.DataFrame,
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entry_prices: Dict[str, float]) -> Dict:
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"""
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Enhanced weighting that combines price correlation with RCDD analysis
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"""
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# Traditional price correlation
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price_corr = price_data[asset1].corr(price_data[asset2])
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# RCDD-based analysis
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metrics1 = self.rcdd_analyzer.recursive_conditional_drawdown(
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price_data[asset1], entry_prices[asset1]
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)
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metrics2 = self.rcdd_analyzer.recursive_conditional_drawdown(
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price_data[asset2], entry_prices[asset2]
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)
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# Drawdown behavior similarity
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dd_similarity = 1 - abs(metrics1.avg_drawdown - metrics2.avg_drawdown) / \
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max(metrics1.avg_drawdown, metrics2.avg_drawdown, 0.01)
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recovery_similarity = 1 - abs(metrics1.recovery_probability - metrics2.recovery_probability)
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# Confidence reliability
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conf_width1 = metrics1.confidence_interval[1] - metrics1.confidence_interval[0]
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conf_width2 = metrics2.confidence_interval[1] - metrics2.confidence_interval[0]
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# Lower confidence width = more reliable
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reliability1 = 1 / (1 + conf_width1)
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reliability2 = 1 / (1 + conf_width2)
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combined_reliability = (reliability1 + reliability2) / 2
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# Enhanced weighting formula
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# Independence factor (lower correlation = higher weight)
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independence_factor = 1 - abs(price_corr)
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# Behavior diversity factor (different RCDD behavior = higher weight)
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diversity_factor = 1 - (dd_similarity + recovery_similarity) / 2
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# Reliability factor (more reliable RCDD = higher weight)
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# Combined weight
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enhanced_weight = (independence_factor * 0.4 +
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diversity_factor * 0.3 +
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combined_reliability * 0.3)
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return {
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'price_correlation': price_corr,
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'drawdown_similarity': dd_similarity,
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'recovery_similarity': recovery_similarity,
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'combined_reliability': combined_reliability,
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'independence_factor': independence_factor,
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'diversity_factor': diversity_factor,
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'enhanced_weight': enhanced_weight,
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'traditional_weight': independence_factor, # For comparison
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'weight_improvement': enhanced_weight - independence_factor
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}
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def demonstrate_concept():
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"""
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Demonstrate how multi-asset RCDD analysis enhances DOLPHIN weighting
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"""
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print("🐬 MULTI-ASSET RCDD ENHANCEMENT FOR DOLPHIN")
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print("="*55)
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# Create sample data with different correlation and RCDD patterns
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np.random.seed(42)
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dates = pd.date_range('2023-01-01', periods=200, freq='1H')
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# Simulate different asset types
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assets = {
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'BTC': {'base_price': 50000, 'volatility': 0.03, 'trend': 0.001},
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'ETH': {'base_price': 3000, 'volatility': 0.035, 'trend': 0.0012}, # Similar to BTC
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'GOLD': {'base_price': 2000, 'volatility': 0.015, 'trend': 0.0002}, # Different behavior
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'SPY': {'base_price': 400, 'volatility': 0.02, 'trend': 0.0008}, # Different again
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'BONDS': {'base_price': 100, 'volatility': 0.008, 'trend': -0.0001} # Very different
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}
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price_data = {}
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entry_prices = {}
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for asset, params in assets.items():
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returns = np.random.normal(params['trend'], params['volatility'], len(dates))
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# Add some correlation between BTC and ETH
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if asset == 'ETH':
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btc_returns = np.array(list(price_data.values())[0]) if price_data else returns
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returns = 0.7 * returns + 0.3 * np.random.normal(0, 0.01, len(dates))
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prices = params['base_price'] * np.exp(np.cumsum(returns))
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price_data[asset] = prices
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entry_prices[asset] = prices[50] # Entry point after some history
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price_df = pd.DataFrame(price_data, index=dates)
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print("📊 Sample Data Created:")
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print(f"Assets: {list(price_df.columns)}")
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print(f"Entry points: {entry_prices}")
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# Simulate basic RCDD analyzer for demo
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class MockRCDDAnalyzer:
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def recursive_conditional_drawdown(self, price_series, entry_price):
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from dataclasses import dataclass
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@dataclass
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class MockMetrics:
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avg_drawdown: float
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max_drawdown: float
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recovery_probability: float
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avg_recovery_time: float
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confidence_interval: Tuple[float, float]
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# Simulate RCDD metrics based on price characteristics
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volatility = price_series.pct_change().std()
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recent_drawdown = max(0, entry_price - price_series.min())
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return MockMetrics(
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avg_drawdown=recent_drawdown * 0.7,
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max_drawdown=recent_drawdown,
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recovery_probability=max(0.3, min(0.9, 1 - volatility * 10)),
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avg_recovery_time=volatility * 100,
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confidence_interval=(recent_drawdown * 0.5, recent_drawdown * 1.2)
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)
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# Initialize analyzer
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mock_rcdd = MockRCDDAnalyzer()
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multi_asset_analyzer = MultiAssetRCDDAnalyzer(mock_rcdd)
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print("\n🔍 ANALYZING ASSET PAIRS:")
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print("="*40)
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# Analyze key pairs
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test_pairs = [
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('BTC', 'ETH'), # High price correlation, similar RCDD
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('BTC', 'GOLD'), # Low price correlation, different RCDD
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('BTC', 'BONDS'), # Very different everything
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('ETH', 'SPY'), # Medium correlation, different RCDD
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]
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results = []
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for asset1, asset2 in test_pairs:
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analysis = multi_asset_analyzer.enhanced_pair_weighting(
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asset1, asset2, price_df, entry_prices
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)
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results.append({
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'pair': f'{asset1}-{asset2}',
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**analysis
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})
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print(f"\n{asset1} vs {asset2}:")
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print(f" Price Correlation: {analysis['price_correlation']:.3f}")
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print(f" Drawdown Similarity: {analysis['drawdown_similarity']:.3f}")
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print(f" Recovery Similarity: {analysis['recovery_similarity']:.3f}")
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print(f" Combined Reliability: {analysis['combined_reliability']:.3f}")
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print(f" Traditional Weight: {analysis['traditional_weight']:.3f}")
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print(f" Enhanced Weight: {analysis['enhanced_weight']:.3f}")
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print(f" Improvement: {analysis['weight_improvement']:+.3f}")
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# Summary comparison
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results_df = pd.DataFrame(results)
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print(f"\n📈 WEIGHT COMPARISON SUMMARY:")
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print("="*35)
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print(f"Average Traditional Weight: {results_df['traditional_weight'].mean():.3f}")
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print(f"Average Enhanced Weight: {results_df['enhanced_weight'].mean():.3f}")
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print(f"Pairs with Improvement: {(results_df['weight_improvement'] > 0).sum()}/{len(results_df)}")
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# Identify best and worst pairs
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best_pair = results_df.loc[results_df['enhanced_weight'].idxmax()]
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worst_pair = results_df.loc[results_df['enhanced_weight'].idxmin()]
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print(f"\n🏆 HIGHEST WEIGHTED PAIR: {best_pair['pair']}")
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print(f" Enhanced Weight: {best_pair['enhanced_weight']:.3f}")
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print(f" Why: Independent price movement + diverse RCDD behavior")
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print(f"\n⚠️ LOWEST WEIGHTED PAIR: {worst_pair['pair']}")
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print(f" Enhanced Weight: {worst_pair['enhanced_weight']:.3f}")
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print(f" Why: Correlated prices + similar RCDD behavior")
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print(f"\n💡 KEY INSIGHTS:")
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print("="*20)
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print("✅ Enhanced weighting considers:")
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print(" • Price correlation (independence)")
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print(" • RCDD behavior diversity")
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print(" • Prediction reliability")
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print(" • Multi-dimensional asset relationships")
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print("\n✅ Benefits for DOLPHIN:")
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print(" • More nuanced pair weighting")
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print(" • Better regime detection accuracy")
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print(" • Reduced false signals from correlated behavior")
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print(" • Adaptive to changing market structures")
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print(f"\n🔧 PRACTICAL IMPLEMENTATION:")
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print("="*30)
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print("1. Calculate RCDD for all assets in universe")
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print("2. Compute enhanced weights for all pairs")
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print("3. Use enhanced weights in DOLPHIN algorithm")
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print("4. Monitor weight stability over time")
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print("5. Recalibrate periodically")
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return results_df, price_df
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# Run the demonstration
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if __name__ == "__main__":
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results, data = demonstrate_concept()
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