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siloqy/docs/Claude-Financial Crisis Prediction Toolkit Design (Background Reasearch).md

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docs: add docs with planning subdirectory for TODO features
2025-08-31 15:08:08 +02:00
# Financial Crisis Prediction Toolkit Design (Background Reasearch)
**Created:** 8/20/2025 13:30
**Updated:** 8/21/2025 16:11
**Exported:** 8/21/2025 16:11
**Link:** [https://claude.ai/chat/217e1682-1f08-4769-be95-169e7a0fb5ec](https://claude.ai/chat/217e1682-1f08-4769-be95-169e7a0fb5ec)
## Prompt:
20/8/2025, 13:32:08
Implement what follows, based on SILOQY/DOLPHIN BINANCE tick data:
# COMPLETE CONVERSATION SUMMARY: FINANCIAL CRISIS PREDICTION WITH SILOQY/DOLPHIN INTEGRATION
## CONVERSATION OVERVIEW
This conversation focused on building a comprehensive financial crisis prediction system, starting with a detailed request to explain multiple crisis prediction techniques "in the simplest way possible" with full implementation code, then diving into "excruciating detail" about inputs, and finally working toward integrating SILOQY/DOLPHIN data (context from a different chat).
## INITIAL REQUEST & RESPONSE
**User's Original Request:**
- Explain financial crisis prediction techniques in simplest terms
- Provide complete implementation code
- Cover several techniques that can be chained together
- Identify inputs/outputs in detail
- Reference was made to extensive literature review including Dynamic Network Biomarkers (DNB), Hyperdimensional forecasting (TSF-HD), Empirical Intrinsic Geometry (EIG), probabilistic multivariate early warning systems, and Topological Data Analysis (TDA)
**My Response - Complete Crisis Prediction Toolkit:**
I created a comprehensive `FinancialCrisisPredictionToolkit` class implementing 6 major techniques:
### 1. DYNAMIC NETWORK BIOMARKERS (DNB)
**Simple Explanation:** Find groups of variables that become highly connected to each other but disconnect from everything else (like cliques forming at a party before drama happens)
**Technical Implementation:**
- Input: DataFrame with multiple time series (rows=time, cols=variables)
- Algorithm: Calculate intra-group variance, intra-group correlation, inter-group correlation
- Formula: `DNB_score = (intra_variance * intra_correlation) / (inter_correlation + 0.001)`
- Output: DNB score (higher = more crisis-like) + biomarker groups
- Crisis Signal: High DNB score means variables are clustering unusually
### 2. SHANNON ENTROPY GRADIENT
**Simple Explanation:** Measure how predictable vs random the data becomes over time (like weather becoming either perfectly predictable OR completely random before a storm)
**Technical Implementation:**
- Input: Dictionary {horizon: prediction_errors_array}
- Algorithm: Discretize residuals into bins, calculate Shannon entropy across horizons
- Formula: `entropy = -sum(hist * log2(hist))`, then `gradient = d(entropy)/d(horizon)`
- Output: Entropy gradient + entropy values by horizon
- Crisis Signal: Sudden changes in predictability patterns across time horizons
### 3. PREDICTION HORIZON DISCONTINUITY
**Simple Explanation:** Detect when prediction ability suddenly breaks down at certain time horizons (like suddenly only being able to see 100 feet instead of 1 mile while driving)
**Technical Implementation:**
- Input: Dictionary {horizon: [accuracy_scores_over_time]}
- Algorithm: Compare recent vs baseline performance, detect changepoints
- Output: Discontinuity score + detected changepoints
- Crisis Signal: Sudden drops in prediction accuracy at specific horizons
### 4. CROSS-SCALE COHERENCE
**Simple Explanation:** Check if short-term (minutes) and long-term (daily) patterns start moving in lockstep (like heartbeat and breathing syncing unusually when stressed)
**Technical Implementation:**
- Input: High-frequency data + low-frequency data
- Algorithm: Calculate cross-spectral coherence using scipy.signal.coherence
- Output: Coherence score (0-1) + frequency analysis
- Crisis Signal: High coherence means loss of normal scale separation
### 5. TOPOLOGICAL FEATURES
**Simple Explanation:** Analyze the geometric "shape" of data patterns in high dimensions (data creates shapes like loops, clusters, holes - changes warn of transitions)
**Technical Implementation:**
- Input: Single univariate time series
- Algorithm: Time-delay embedding, pairwise distances, cluster persistence across scales
- Output: Persistence score + embedded data points
- Crisis Signal: High persistence score means complex/unstable geometric patterns
### 6. COMPUTATIONAL COMPLEXITY SCALING
**Simple Explanation:** Track if you need increasingly complex models for same prediction accuracy (like needing bigger hammer to drive same nail - wood is getting harder)
**Technical Implementation:**
- Input: Model performance history + complexity metrics (time, parameters, cost)
- Algorithm: Calculate efficiency trends and complexity scaling rates
- Output: Complexity scaling rate + efficiency trend
- Crisis Signal: Rapidly increasing computational requirements
### FUSION ALGORITHM
**Combines all indicators into single crisis probability:**
- Normalize all scores to [0,1] using sigmoid functions
- Weighted probabilistic averaging
- Output: Single crisis probability (0-1)
- Interpretation: >0.7 = High risk, 0.4-0.7 = Moderate risk, <0.4 = Low risk
## DETAILED INPUT SPECIFICATIONS REQUEST
**User's Second Request:** "Could you in excruciating detail explain *the inputs*?"
**My Response - Comprehensive Input Guide:**
I created a massive input specification document covering every parameter in exhaustive detail:
### DYNAMIC NETWORK BIOMARKERS INPUTS
**`data` Parameter:**
- Type: pandas DataFrame
- Shape: (n_timesteps, n_variables)
- Rows: Timestamps (DatetimeIndex preferred, min window_size observations)
- Columns: Financial variables (stocks, ETFs, indicators)
- Values: Consistent units (don't mix prices $150 with returns 0.02)
- Quality: <10% missing data, no extreme outliers
- Examples: ['AAPL', 'MSFT', 'GOOGL'] or ['XLF', 'XLK', 'XLE'] or cross-asset universe
**`group_size` Parameter:**
- Type: int, Default: 5, Range: 2 to min(10, n_variables//2)
- Controls: Number of variables in biomarker clusters
- Selection: 2-3 for small portfolios, 4-6 for sectors, 5-8 for cross-asset
### SHANNON ENTROPY INPUTS
**`residuals_dict` Parameter:**
- Type: dict, Structure: {horizon: numpy.array}
- Keys: Prediction horizons ([1,5,10,20] for daily data)
- Values: Prediction errors (actual - predicted)
- Generation methods: Autoregressive, ML models, GARCH, VAR
**`n_bins` Parameter:**
- Type: int, Default: 10, Range: 5-50
- Rule of thumb: sqrt(n_observations) or sample_size/10
### PREDICTION HORIZON DISCONTINUITY INPUTS
**`performance_dict` Parameter:**
- Type: dict, Structure: {horizon: [accuracy_scores_over_time]}
- Values: Model performance (accuracy, R², Sharpe, IC)
- Length: Min 50 observations, recommended 100+
- Generation: Rolling window evaluation, expanding window evaluation
**`baseline_window` Parameter:**
- Type: int, Default: 50, Range: 20-200
- Selection: 10-20% of total performance history length
### CROSS-SCALE COHERENCE INPUTS
**`micro_data` Parameter:**
- Type: numpy.array/pandas.Series
- Frequency: Higher than macro_data (2x-10x+)
- Alignment: Same time period as macro_data
- Examples: Minute vs daily, tick vs minute, hourly vs daily
**`macro_data` Parameter:**
- Type: numpy.array/pandas.Series
- Relationship: Same underlying asset at different scales
- Aggregation: Could be aggregated version of micro_data
**`sample_rate` Parameter:**
- Type: float, Default: 1.0
- Selection: Based on data frequency and analysis needs
### TOPOLOGICAL FEATURES INPUTS
**`data` Parameter:**
- Type: numpy.array/pandas.Series (univariate)
- Length: Min 50, recommended 200+
- Type: Continuous numerical (preferably stationary)
- Preprocessing: Use returns not prices, remove outliers
**`embedding_dim` Parameter:**
- Type: int, Default: 3, Range: 2-10
- Selection: 3-4 optimal for financial data, data-driven methods available
**`delay` Parameter:**
- Type: int, Default: 1, Range: 1-20
- Selection: Autocorrelation minimum, mutual information minimum
### COMPUTATIONAL COMPLEXITY INPUTS
**`model_performance_history` Parameter:**
- Type: list of float
- Content: Time series of accuracy/performance scores
- Metrics: Accuracy (0-1), R² (0-1), Sharpe (0-3), IC (-1 to 1)
- Collection: Walk-forward validation, production monitoring
**`complexity_history` Parameter:**
- Type: list of float/int
- Content: Computational resource requirements
- Metrics: Training time, parameters, memory, cost
- Alignment: MUST match performance_history length and timing
### DATA QUALITY REQUIREMENTS
- Consistent frequencies across all inputs
- Temporal alignment (same time periods)
- Minimum data lengths (50+ observations)
- Missing data handling (<10% missing)
- Outlier removal (3σ threshold)
- Unit consistency (prices OR returns, not mixed)
## SILOQY/DOLPHIN INTEGRATION REQUEST
**User's Third Request:** "I'd like to plug SILOQY/DOLPHIN data into this"
**Research Findings:**
- I searched extensively but could not find SILOQY/DOLPHIN as a recognized financial data provider
- Found various companies with "DOLPHIN" in name (offshore enterprises, entertainment, capital investors)
- Found SILO Finance (crypto DeFi protocol) but not SILOQY specifically
- User indicated "SILOQY context is in a different chat"
**My Response - Universal Integration System:**
I created a comprehensive `SILOQYDolphinDataIntegrator` class that handles any possible data format:
### UNIVERSAL FORMAT SUPPORT
- **CSV/TSV**: Any delimiter (comma, semicolon, tab, pipe)
- **JSON**: Various structures (arrays, nested objects, records)
- **Excel**: Multiple sheets with automatic sheet detection
- **Parquet**: High-performance columnar format
- **SQL**: SQLite databases with automatic table detection
- **HDF5**: Scientific data format
### INTELLIGENT DATA PROCESSING
- **Automatic format detection**: Based on file extension and content analysis
- **Column categorization**: Auto-identifies dates, prices, volumes, returns
- **Data quality assessment**: Missing data, duplicates, outliers analysis
- **Smart preprocessing**: Missing data filling, outlier removal, frequency resampling
### CRISIS TOOLKIT PREPARATION
- **Data cleaning pipeline**: Handles missing values, removes outliers (3σ)
- **Frequency standardization**: Resample to any target frequency (D, H, min)
- **Price-to-return conversion**: Automatic detection and conversion
- **Micro/macro pair generation**: Creates multi-scale data for coherence analysis
- **Performance history simulation**: Generates synthetic model performance data
### KEY INTEGRATION METHODS
**`load_data()`**: Universal data loading with automatic format detection
**`analyze_data_structure()`**: Comprehensive data structure analysis
**`prepare_for_crisis_prediction()`**: Complete preprocessing pipeline
**`export_for_toolkit()`**: Outputs crisis prediction toolkit-ready inputs
### USAGE PATTERNS
```python
# Basic integration
integrator = SILOQYDolphinDataIntegrator('siloqy_data.csv')
raw_data = integrator.load_data()
prepared_data, metadata = integrator.prepare_for_crisis_prediction()
# Advanced configuration
config = {
'date_columns': ['timestamp'],
'price_columns': ['close_price'],
'missing_data_threshold': 0.05
}
integrator = SILOQYDolphinDataIntegrator(config=config)
# Export for toolkit
toolkit_inputs = integrator.export_for_toolkit()
results, crisis_probability, details = toolkit.analyze_time_series(**toolkit_inputs)
```
## CURRENT STATUS & NEXT STEPS
### WHAT'S BEEN ACCOMPLISHED
1. ✅ Complete crisis prediction toolkit with 6 techniques implemented
2. ✅ Exhaustive input specification documentation
3. ✅ Universal SILOQY/DOLPHIN data integration system
4. ✅ Automatic data processing and preparation pipeline
5. ✅ End-to-end workflow from raw data to crisis predictions
### WHAT'S NEEDED FROM USER
To complete the SILOQY/DOLPHIN integration, I need:
1. **Data Format**: What format is SILOQY/DOLPHIN data in? (CSV, JSON, Excel, database)
2. **Data Structure**: Column names and types, such as:
```
Date, SILOQY_SPY, SILOQY_TLT, SILOQY_VIX, Volume_SPY, etc.
```
3. **Frequency**: Data frequency (daily, hourly, minute, tick-level)
4. **Sample Data**: A few rows showing the actual structure
5. **Special Requirements**: Any SILOQY-specific preprocessing needs
### IMMEDIATE NEXT ACTIONS
1. Get SILOQY/DOLPHIN data specifications from other chat
2. Customize integration code for specific SILOQY format
3. Test integration with real SILOQY data
4. Run complete crisis prediction analysis
5. Validate results and tune parameters
### TECHNICAL ARCHITECTURE SUMMARY
```
SILOQY/DOLPHIN Raw Data
↓
SILOQYDolphinDataIntegrator
• Format detection
• Data loading
• Structure analysis
• Quality assessment
• Preprocessing pipeline
↓
Crisis Prediction Toolkit
• Dynamic Network Biomarkers
• Shannon Entropy Gradient
• Prediction Horizon Discontinuity
• Cross-scale Coherence
• Topological Features
• Computational Complexity
↓
Fusion Algorithm
↓
Crisis Probability (0-1)
```
### CODE ARTIFACTS CREATED
1. **`crisis_prediction_toolkit`**: Complete implementation of all 6 techniques
2. **`input_specification_guide`**: Exhaustive input documentation
3. **`siloqy_dolphin_integration`**: Universal data integration system
The system is ready for immediate use once SILOQY/DOLPHIN data specifications are provided from the other chat context.
## KEY RESEARCH REFERENCES MENTIONED
- Dynamic Network Biomarkers (DNB) theory & reviews
- TSF-HD (Hyperdimensional time-series forecasting) - arXiv:2402.01999
- Empirical Intrinsic Geometry (EIG) - PNAS paper
- Probabilistic multivariate EWS - arXiv:2205.07576
- Topological Data Analysis in finance - Gidea & Katz (250 trading-day lead)
- Critical slowing down in finance - Wang et al. 2016/2018
All techniques are grounded in peer-reviewed research and have been implemented with proper mathematical foundations and practical considerations for financial markets.
## Response:
20/8/2025, 13:32:25
````plaintext
Thought process: Analyzed SILOQY/DOLPHIN integration for financial crisis prediction system.
Let me break down what the user is asking for based on this extensive context:
1. They want to implement the financial crisis prediction system that was developed in a previous conversation
2. They want to integrate it specifically with SILOQY/DOLPHIN Binance tick data
3. From the project knowledge, I can see:
- SILOQY is their overall system name
- DOLPHIN is the regime detection subsystem that produces market sentiment data
- JERICHO is the state machine/signaling system
- DOLPHIN outputs JSON files with regime data like: {"regime": "BULL", "up_ratio": 0.7627, "down_ratio": 0.2373, "total_symbols": 405, "timestamp": "2025-08-12T17:10:16.389625"}
4. The user wants me to build something that integrates the crisis prediction toolkit with this DOLPHIN regime data from Binance
5. They want me to be brief and not output artifacts without authorization
6. They want HFT-standard, best-of-breed code that works on Windows/Linux and is amenable to ML approaches
Let me first understand what they want me to implement specifically and ask for clarification before jumping into code.
````
Thank you for the comprehensive context. I understand you want to integrate the financial crisis prediction toolkit with your SILOQY/DOLPHIN Binance tick data system.
From the project knowledge, I can see:
**DOLPHIN Output Format (from sample files):**
```json
{
"regime": "BULL"/"BEAR",
"up_ratio": 0.7627,
"down_ratio": 0.2373,
"total_symbols": 405,
"timestamp": "2025-08-12T17:10:16.389625"
}
```
**Key Integration Questions:**
1. **Data Integration Point**: Should the crisis prediction system consume:
- DOLPHIN's regime output files directly?
- Real-time websocket feeds from DOLPHIN?
- Raw Binance tick data before DOLPHIN processing?
2. **Crisis Prediction Scope**: Apply crisis detection to:
- DOLPHIN's regime classification patterns over time?
- The underlying 500-symbol correlation matrices from DOLPHIN?
- Individual asset data (like BTCUSDT) with Bollinger Band distances?
3. **Output Integration**: Should crisis signals feed into:
- JERICHO's FSM state machine?
- Separate crisis alert system?
- Combined regime+crisis probability score?
4. **Temporal Alignment**:
- DOLPHIN runs every 5 seconds with 15m candles
- Crisis prediction needs sufficient history - how many DOLPHIN cycles to accumulate?
**Most Logical Architecture:**
```
Binance Tick Data → DOLPHIN (regime detection) → Crisis Prediction Layer → JERICHO (signals) → HARLEQUIN (trading)
```
**Before I implement**, please confirm:
- Which DOLPHIN output data should feed the crisis prediction?
- Where in the SILOQY pipeline should crisis detection occur?
- Should I start with a standalone crisis monitor or integrate into existing components?
This will ensure I build exactly what fits your architecture.
## Prompt:
20/8/2025, 13:35:31
Unless you have a better idea: Combined regime+crisis probability score. Raw Binance tick data before DOLPHIN processing *and* Dolphin outputs. The underlying 500-symbol correlation matrices from DOLPHIN
## Response:
20/8/2025, 13:35:55
````plaintext
Thought process: Engineered multi-scale crisis detection framework for financial analysis.
The user wants a comprehensive approach that combines:
1. **Combined regime+crisis probability score** - This makes sense as it would give a unified signal
2. **Raw Binance tick data before DOLPHIN processing AND DOLPHIN outputs** - This is smart because:
- Raw tick data gives granular micro-structure information
- DOLPHIN outputs give regime-level macro information
- Combined gives multi-scale crisis detection
3. **The underlying 500-symbol correlation matrices from DOLPHIN** - This is excellent because correlation breakdown is a key crisis indicator
This is actually a very sophisticated approach. Let me think through the architecture:
**Multi-Scale Crisis Detection Architecture:**
`​`​`
Raw Binance Tick Data (Micro-scale)
↓
Crisis Features:
- Trade velocity/momentum (from tick aggregation feature request)
- Cross-scale coherence (tick vs 15m bars)
- Topological features (tick patterns)
↓
DOLPHIN Processing (500 symbols → regime detection)
↓
DOLPHIN Outputs (Macro-scale):
- regime classification over time
- up_ratio/down_ratio time series
- 500x500 correlation matrices
↓
Crisis Features:
- Dynamic Network Biomarkers (correlation matrix analysis)
- Shannon Entropy (regime classification predictability)
- Prediction Horizon Discontinuity (regime prediction breakdown)
↓
Combined Crisis+Regime Score
↓
JERICHO FSM (enhanced with crisis awareness)
`​`​`
This gives us:
1. **Micro-scale crisis detection** from raw tick data
2. **Macro-scale crisis detection** from DOLPHIN regime/correlation analysis
3. **Combined scoring** that fuses both scales
4. **Enhanced JERICHO** that considers both normal regime changes and crisis conditions
The user is asking for a very sophisticated multi-scale crisis detection system. This is actually a better idea than what I initially proposed because it captures crisis signals at multiple time scales and data granularities.
I'll draft a concise proposal outlining the key architectural components and validation approach. This will help ensure alignment on the technical strategy before diving into implementation details.
````
Excellent approach! Multi-scale crisis detection using both micro (tick) and macro (regime/correlation) data is superior to single-scale analysis. This captures crisis signals across time scales.
**Proposed Architecture:**
```
┌─────────────────────────────────────────────────────────────┐
│ SILOQY CRISIS DETECTION │
├─────────────────────────────────────────────────────────────┤
│ Raw Binance Tick Data │
│ ↓ │
│ Micro-Scale Crisis Features: │
│ • Trade velocity/momentum (from your tick aggregation req) │
│ • Cross-scale coherence (tick ↔ 15m patterns) │
│ • Topological features (tick clustering patterns) │
│ │
│ DOLPHIN Processing (500 symbols) │
│ ↓ │
│ Macro-Scale Crisis Features: │
│ • Dynamic Network Biomarkers (500×500 correlation matrix) │
│ • Shannon Entropy (regime predictability breakdown) │
│ • Prediction Horizon Discontinuity (regime forecast decay) │
│ • Correlation complexity scaling │
│ │
│ ↓ │
│ Combined Regime+Crisis Score: │
│ • regime_probability: [BULL|BEAR|NEUTRAL] (0-1) │
│ • crisis_probability: [STABLE|WARNING|CRISIS] (0-1) │
│ • combined_confidence: Overall signal strength (0-1) │
│ │
│ ↓ │
│ Enhanced JERICHO FSM │
│ • Normal regime transitions (existing logic) │
│ • Crisis-aware state modifications │
│ • Emergency exit conditions │
└─────────────────────────────────────────────────────────────┘
```
**Key Components:**
1. **CrisisDetectionLayer**: Processes both tick and DOLPHIN data
2. **MultiScaleCrisisAnalyzer**: Fuses micro/macro crisis signals
3. **RegimeCrisisFusion**: Combines regime+crisis into unified score
4. **Enhanced JERICHO**: Crisis-aware state machine
**Output Format:**
```python
{
"timestamp": "2025-08-20T...",
"regime_score": {
"classification": "BULL",
"probability": 0.85,
"up_ratio": 0.76
},
"crisis_score": {
"level": "WARNING",
"probability": 0.35,
"micro_features": {...},
"macro_features": {...}
},
"combined_signal": {
"action_recommendation": "HOLD_WITH_CAUTION",
"confidence": 0.72,
"explanation": ["Regime BULLISH @85%", "Crisis warning @35% - correlation breakdown detected"]
}
}
```
**Questions before implementing:**
1. **DOLPHIN Correlation Matrix Access**: Do you want me to modify DOLPHIN to expose the 500×500 correlation matrices, or capture them via websocket?
2. **Crisis Integration Point**: Should this be a separate `CRISIS-DETECTOR` component alongside DOLPHIN, or embedded within DOLPHIN itself?
3. **Tick Data Source**: Use your existing Nautilus tick aggregation system, or build separate tick processor?
4. **Crisis Thresholds**: Conservative (fewer false alarms) or sensitive (early warnings) for crypto markets?
Shall I proceed with this multi-scale architecture?
## Prompt:
20/8/2025, 15:12:40
1. If Crisis component is part of Dolphin, it should share memory struct w/ Dolphin. Even maybe have dolphin output stats to Nautilus bus.
2. Within Dolphin.
3 Nautilus ticks. As Dolphin fetches
Take care for correct implementation
## Response:
20/8/2025, 15:13:24
````plaintext
Thought process: Architected Dolphin-Crisis integration with Nautilus tick processing
The user wants:
1. Crisis component embedded within DOLPHIN, sharing memory structures with DOLPHIN
2. DOLPHIN should output stats to Nautilus message bus
3. Use Nautilus ticks as DOLPHIN fetches them
4. Correct implementation
From the project knowledge, I can see:
- DOLPHIN currently fetches 15m candles for 500 symbols from Binance
- It calculates correlations and regime detection
- Outputs regime results as JSON files
- The user wants to integrate with Nautilus for tick data
- They want multi-scale crisis detection using both tick data and DOLPHIN's correlation matrices
The architecture should be:
1. Enhanced DOLPHIN that:
- Keeps existing 500-symbol regime detection
- Adds crisis detection component that shares memory with regime detection
- Processes Nautilus tick data for micro-scale features
- Fuses micro + macro crisis signals
- Outputs enhanced regime+crisis data to Nautilus bus
Key implementation considerations:
- Memory sharing between regime detection and crisis detection within DOLPHIN
- Nautilus integration for tick data processing
- Preserving existing DOLPHIN algorithm while adding crisis features
- Correct data structures and threading
- Performance optimization for HFT requirements
I should provide a clear implementation plan that:
1. Shows how to embed crisis detection within DOLPHIN
2. Defines shared memory structures
3. Shows Nautilus tick integration
4. Provides the enhanced output format
5. Maintains DOLPHIN's existing functionality
````
Understood. Embedding crisis detection **within DOLPHIN** with shared memory structures and Nautilus integration. Here's the correct implementation architecture:
## Enhanced DOLPHIN with Embedded Crisis Detection
**Modified DOLPHIN Structure:**
```python
class EnhancedMarketRegimeDetector:
"""DOLPHIN with embedded multi-scale crisis detection"""
def __init__(self, symbols, thresholds):
# Existing DOLPHIN components
self.symbols = symbols
self.thresholds = thresholds
self.correlation_matrix = None # 500x500 shared memory
self.price_data = {}
# NEW: Shared memory structures for crisis detection
self.shared_memory = {
'correlation_matrix': None, # Shared with crisis detector
'regime_history': deque(maxlen=100),
'tick_features': {},
'crisis_metrics': {},
'combined_state': {}
}
# NEW: Embedded crisis detection component
self.crisis_detector = EmbeddedCrisisDetector(
shared_memory=self.shared_memory,
nautilus_bus=None # Set during Nautilus integration
)
# NEW: Nautilus integration for tick data
self.nautilus_tick_processor = None
def integrate_nautilus(self, nautilus_bus):
"""Initialize Nautilus tick processing"""
self.crisis_detector.nautilus_bus = nautilus_bus
self.nautilus_tick_processor = NautilusTickProcessor(
symbols=['BTCUSDT'], # Target asset for tick analysis
shared_memory=self.shared_memory,
crisis_detector=self.crisis_detector
)
```
**Shared Memory Structure:**
```python
class SharedMemoryStructure:
"""Memory-efficient shared data between regime and crisis detection"""
def __init__(self):
# Regime detection data (existing)
self.correlation_matrix = np.zeros((500, 500), dtype=np.float32)
self.up_ratios = deque(maxlen=100)
self.down_ratios = deque(maxlen=100)
self.regime_timestamps = deque(maxlen=100)
# Crisis detection data (new)
self.crisis_scores = deque(maxlen=100)
self.network_biomarkers = {}
self.entropy_metrics = deque(maxlen=50)
self.coherence_scores = deque(maxlen=50)
# Tick-level features (new)
self.tick_velocity = deque(maxlen=1000) # Trade velocity
self.tick_momentum = deque(maxlen=1000) # Price momentum
self.tick_clusters = {} # Topological features
# Combined output (new)
self.combined_signal = {
'regime_prob': 0.0,
'crisis_prob': 0.0,
'confidence': 0.0,
'explanation': []
}
```
**Embedded Crisis Detector:**
```python
class EmbeddedCrisisDetector:
"""Crisis detection embedded within DOLPHIN, sharing memory"""
def __init__(self, shared_memory, nautilus_bus=None):
self.shared_memory = shared_memory
self.nautilus_bus = nautilus_bus
# Crisis detection components
self.network_biomarker = DynamicNetworkBiomarker()
self.entropy_analyzer = ShannonEntropyGradient()
self.coherence_analyzer = CrossScaleCoherence()
self.topology_analyzer = TopologicalFeatures()
def update_from_regime_detection(self, regime_result):
"""Called after each DOLPHIN regime detection cycle"""
# Update shared memory with regime data
self.shared_memory.up_ratios.append(regime_result['up_ratio'])
self.shared_memory.down_ratios.append(regime_result['down_ratio'])
self.shared_memory.regime_timestamps.append(regime_result['timestamp'])
# Update correlation matrix reference (shared memory)
if hasattr(regime_result, 'correlation_matrix'):
self.shared_memory.correlation_matrix = regime_result['correlation_matrix']
# Perform macro-scale crisis analysis
macro_crisis_score = self._analyze_macro_crisis()
# Combine with micro-scale features (from tick processor)
micro_crisis_score = self._get_micro_crisis_score()
# Fuse regime + crisis signals
combined_signal = self._fuse_regime_crisis(regime_result, macro_crisis_score, micro_crisis_score)
# Update shared memory
self.shared_memory.combined_signal = combined_signal
# Send to Nautilus bus
if self.nautilus_bus:
self._send_to_nautilus_bus(combined_signal)
return combined_signal
def _analyze_macro_crisis(self):
"""Macro-scale crisis analysis using 500-symbol correlation matrix"""
# Dynamic Network Biomarkers on correlation matrix
if len(self.shared_memory.up_ratios) >= 50:
biomarker_score = self.network_biomarker.analyze(
correlation_matrix=self.shared_memory.correlation_matrix,
regime_history=list(self.shared_memory.up_ratios)
)
# Shannon Entropy on regime predictability
if len(self.shared_memory.up_ratios) >= 30:
entropy_score = self.entropy_analyzer.analyze(
regime_sequence=list(self.shared_memory.up_ratios)
)
# Combine macro features
macro_score = (biomarker_score + entropy_score) / 2
return macro_score
def _get_micro_crisis_score(self):
"""Get micro-scale crisis score from tick processor"""
if hasattr(self.shared_memory, 'tick_features'):
return self.shared_memory.tick_features.get('crisis_score', 0.0)
return 0.0
def _fuse_regime_crisis(self, regime_result, macro_crisis, micro_crisis):
"""Fuse regime detection with crisis probability"""
# Regime probability (existing DOLPHIN logic)
regime_prob = self._calculate_regime_probability(regime_result)
# Crisis probability (weighted combination)
crisis_prob = 0.7 * macro_crisis + 0.3 * micro_crisis
# Combined confidence
confidence = min(regime_prob, 1.0 - crisis_prob) # High confidence when regime clear AND no crisis
# Action recommendation
action = self._determine_action(regime_prob, crisis_prob, confidence)
# Explanation
explanation = self._generate_explanation(regime_result, crisis_prob, action)
return {
'regime_classification': regime_result['regime'],
'regime_probability': regime_prob,
'crisis_probability': crisis_prob,
'combined_confidence': confidence,
'action_recommendation': action,
'explanation': explanation,
'timestamp': regime_result['timestamp']
}
```
**Nautilus Tick Processor:**
```python
class NautilusTickProcessor:
"""Process Nautilus tick data for micro-scale crisis features"""
def __init__(self, symbols, shared_memory, crisis_detector):
self.symbols = symbols
self.shared_memory = shared_memory
self.crisis_detector = crisis_detector
# Tick aggregation for micro-features
self.tick_aggregator = TickAggregator()
self.velocity_calculator = TradeVelocityCalculator()
self.momentum_calculator = TradeMomentumCalculator()
def on_trade_tick(self, tick):
"""Called for each Nautilus trade tick"""
# Calculate trade velocity (from your requirement)
velocity = self.velocity_calculator.update(tick)
self.shared_memory.tick_velocity.append(velocity)
# Calculate price momentum
momentum = self.momentum_calculator.update(tick.price)
self.shared_memory.tick_momentum.append(momentum)
# Topological analysis (every 100 ticks)
if len(self.shared_memory.tick_momentum) % 100 == 0:
topology_score = self._analyze_tick_topology()
# Cross-scale coherence (tick vs 15m patterns)
coherence_score = self._calculate_cross_scale_coherence()
# Update micro crisis features
micro_crisis_score = (topology_score + coherence_score) / 2
self.shared_memory.tick_features = {
'crisis_score': micro_crisis_score,
'velocity': velocity,
'momentum': momentum,
'topology': topology_score,
'coherence': coherence_score
}
```
**Enhanced DOLPHIN Main Loop:**
```python
async def enhanced_run_single_detection(self):
"""Modified DOLPHIN main loop with embedded crisis detection"""
# Existing DOLPHIN regime detection
regime_result = await self.run_original_regime_detection()
# NEW: Crisis detection using shared memory
combined_result = self.crisis_detector.update_from_regime_detection(regime_result)
# Output enhanced result
self.print_enhanced_results(combined_result)
# Save enhanced results
self.save_enhanced_results(combined_result)
return combined_result
def print_enhanced_results(self, result):
"""Enhanced output with regime + crisis information"""
print(f"\nENHANCED MARKET ANALYSIS:")
print(f"Regime: {result['regime_classification']} ({result['regime_probability']:.1%})")
print(f"Crisis: {result['crisis_probability']:.1%} risk")
print(f"Action: {result['action_recommendation']}")
print(f"Confidence: {result['combined_confidence']:.1%}")
print(f"Logic: {result['explanation'][0]}")
print(f" {result['explanation'][1]}")
```
**Key Implementation Features:**
1. **Shared Memory**: Direct memory sharing between regime and crisis detection
2. **Zero-Copy**: Correlation matrices shared by reference
3. **Nautilus Integration**: Direct tick processing within DOLPHIN
4. **Performance**: Crisis analysis only when sufficient data available
5. **Output Enhancement**: Preserves existing DOLPHIN output + adds crisis layer
**Next Steps:**
1. Modify existing DOLPHIN `main.py` to use `EnhancedMarketRegimeDetector`
2. Integrate with your Nautilus tick aggregation system
3. Add websocket output for JERICHO consumption
4. Test with historical data
Should I proceed with implementing the specific crisis detection algorithms within this embedded architecture?
---
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