# SILOQY Market Sensing System - Complete Conversation (Verbatim) ## Initial User Request The user provided documents and stated: > I shall now described a putative crypto sensing, regime detection, and signaling system, alias SILOQY - See PRELIMINARY NOTES in Project Knowlege for rationale.- > The JERICHO market "sensing" system, is described in the so-called document in project knowlege.- > > The "regime detection subsystem" is described in Python code inside the project knowledge, ALIAS "DOLPHIN" (they lead the way of ships) NG.- > > JERICHO, basically describes a *state machine*.- > > The "parameters" with which DOLPHIN and JERICHO work together have been hand-tuned for Bitcoin as a particular asset. This of course has implications for "hyperparms" such as volatility (over 600 15m candles) and market cap for the asset in question. Sufice it for now. For the record, the hand-tunning was performed during a two hour period between 11 and 1330 CET, on Monday.- > > After the JERICHO implementation, first order of business - in a chat to that effect - will be to *every so slightly and safely* modify DOLPHIN code (NOT THE MAIN ALGORITHM, KEEP THAT INTACT) to provide: > > - For a given ASSET (BTC, pre configured) the current "distances" from current price to the Bollinger Bands, in terms of percentage, as stated in the JERICHO implementation document (see project knowledge). This distance should be expressed in a JERICHO-compatible, pre-agreed, way, in percentage from closest band.- > > - A process loop. On first instance, this process loop SHALL just make sure the "DOLPHIN" (main.py) code gets ran as is: ie. fetch data, run algorithm, output results. As a parameter, and for the record, DOLPHIN has been running in a simple outside-driven execution "loop" via, command: > > python -c "import os,time;exec('while True: os.system(\"python main.py\"); time.sleep(5)')" > > - A websocket interface through which to feed "SCAN" (see "THE SENSING SYSTEM" document in project knowledge) information to JERICHO and others. This server should be longstanding.- > > - A simple TUI (text UI) through which all this shows output. Any TUI library will do. Something modern.- > > During all coding tasks, keep in mind: > - Code should be HFT-standard, best-of-breed.- > - System(s) should run on both Windows and Linux(es).- > - All design should yield data strutures amenable to (later later later) incorporation of ML approaches.- > > JERICHO, thus, SHALL also be comprised of a lightweight, DOLPHIN comnpatible server. It SHALL query and or listen to DOLPHIN and process accordingly generating signals and output to its own TUI. To wit, particularly, it shall show: > > - All status > - Current DOLPHIN connection status.- > - Last implemented "decision" (ie. "LONGING") > - AND AT MOST TWO succint lines of output "explaining" it's logic (ie. "Market regime BULLISH, SENTIMENT BULLISH sustained sufficient epochs @92.35%").- > > JERICHO SHALL also provide *outgoing* Websocket interfaces for it's signals, in order to feed a prospective, future, trading simulation system ("HARLEQUIN") that shall at first help test the strategy with mock trading, then, deo volente, trade.- > > Thanks very much for your help in this. Breathe deeply prior to any and all work, thought.- > PS. The project motto for JERICHO is: "Through music, not war".- > > Throughout, be brief (not incomplete), but *brief* - I'd like to avoid running out of context space in each "tasks" conversation. Also do not single-handedly output artifacts or code without prior authorization.- > > JERICHO and DOLPHIN and other components, together, form SILOQY.- > > I would like to incorporate a "time memory" feature, with a view to comparison/predictive power, including (future) ML > > - Any advantage, vs just encoding timestamps, in (ie.) naming every minute and quantizing over those? ## Assistant's Initial Response I'll analyze the JERICHO state machine specification and provide a comprehensive review. Let me breathe deeply and focus on the core elements. ### **Current State Machine Structure (As Specified)** **Core States:** - `STANDING_BY` (default action) - `WATCHING` (attention elevated) - `WATCHING_FOR_BREAKOUT/BREAKDOWN` (BB special states) - `LONGING/SHORTING` (action states, 1 SCAN period) - `LONG/SHORT` (position states) - `EXITING` (1 SCAN period) **Key Variables:** - SENTIMENT: {BULLISH, BEARISH, SIDEWAYS, NEUTRAL} - POSITION: {NONE, LONG, SHORT} - ACTION: {STANDING_BY, LONGING, SHORTING} - ATTENTION: {NORMAL, WATCHING, WATCHING_FOR_BREAKOUT, WATCHING_FOR_BREAKDOWN} ### **Critical Gaps & Clarifications Needed** 1. **Missing State Transitions:** - No explicit transition from WATCHING back to STANDING_BY - No handling of SIDEWAYS sentiment during position holding - No cooldown mechanism after exits (prevents thrashing) 2. **Ambiguous Rules:** - "Two consecutive 10% drops" - from what reference point? Current position entry or rolling window? - "Every 2x consecutive opposite regimes between SIDEWAYs count as 1x" - implementation unclear - BB proximity "1%" - absolute percentage or relative to volatility? 3. **Human Psychology Enhancement Opportunities:** - Add position sizing based on confidence - Implement momentum confirmation (don't catch falling knives) - Add exhaustion detection (volume divergence) - Fear/greed oscillator (sentiment extremes) ### **Time Memory Feature Recommendation** For your time memory feature, I recommend **semantic time encoding** over simple timestamps: ```python TimeContext = { "market_session": "asian_open" | "london_open" | "ny_open" | "overnight", "day_type": "monday" | "tuesday" | ... | "friday", "month_phase": "early" | "mid" | "late", "volatility_regime": "low" | "normal" | "high" | "extreme" } ``` This provides ML algorithms with meaningful temporal patterns rather than raw timestamps. ### **Proposed State Machine Enhancements** 1. **Add COOLDOWN state** (prevents immediate re-entry) 2. **Add confidence scoring** (0-100% for each decision) 3. **Add momentum filters** (require price confirmation) 4. **Add position sizing logic** (based on confidence + volatility) ### **DOLPHIN Integration Requirements** Your DOLPHIN sample outputs show: - `up_ratio`: 0.04-0.89 (maps to bull_pct) - `down_ratio`: 0.11-0.96 (maps to bear_pct) - `regime`: "BULL"/"BEAR" (maps to sentiment) **Missing for JERICHO:** - Bollinger Band distances (need to add) - Price momentum indicators - Volume confirmation - Confidence metrics ### **Key Questions for Complete Specification** 1. **BB Distance Calculation:** Percentage from current price to nearest band, or signed distance showing direction? 2. **10% Drop Reference:** Is this from position entry price, or rolling 10-period high? 3. **Sentiment Confidence:** Should 51% vs 99% bullish candles trigger different behaviors? 4. **Position Sizing:** Fixed size or confidence-weighted? 5. **Cooldown Duration:** How many SCAN periods after exit before re-entry allowed? ### **Recommended Next Steps** 1. **Clarify ambiguous rules** above 2. **Define confidence metrics** for decision quality 3. **Specify BB calculation method** for price proximity 4. **Add momentum confirmation** requirements 5. **Design position sizing logic** Would you like me to: - **A)** Draft a complete, unambiguous state transition table - **B)** Design the DOLPHIN enhancement specification for BB distances - **C)** Create the websocket message schemas for DOLPHIN↔JERICHO communication The current spec is 80% complete but needs these clarifications for robust implementation. The "Through music, not war" philosophy suggests we should add harmony detection - confirming multiple signals align before major position changes. ## User Response 1 > Don't think you answered my question ## Assistant Response 1 You're absolutely right. Let me directly answer your specific question about time memory: ### Time Memory Feature: Quantized Named Minutes vs Timestamps **Advantage of naming/quantizing every minute:** Yes, there are significant advantages over raw timestamps: 1. **Pattern Recognition**: ML algorithms can learn "Monday 9:30 AM behavior" vs "Friday 3:00 PM behavior" as discrete features rather than trying to extract cyclical patterns from continuous timestamps. 2. **Market Microstructure**: Each minute has distinct characteristics: - "London_Open_+5min" (high volatility) - "NY_Lunch_Hour" (low volume) - "Asian_Close_-10min" (position squaring) 3. **Regime Persistence**: You can encode "this pattern typically lasts 15 minutes from this starting context" rather than learning time-series dependencies. 4. **Cross-Asset Synchronization**: "SPY_9:30_behavior" can inform "BTC_9:30_behavior" even across different markets. **Recommended Implementation:** ```python time_context = { "minute_of_day": 570, # 9:30 AM = minute 570 "session_phase": "opening_rush", "day_type": "monday_after_weekend", "relative_position": "session_start_+5min" } ``` This gives ML models structured temporal features that encode market participant behavior patterns rather than just linear time. **Answer: Yes, quantized named minutes have substantial advantages over raw timestamps for predictive power.** ## User Response 2 > Not minute of day. Thinking "minute of *year*" as a "bucket" ## Assistant Response 2 Ah! You mean **minute-of-year as discrete buckets** - like minute 1 through minute 525,600 (365 × 24 × 60). ### Minute-of-Year Bucketing: Advantages **Yes, significant advantages over timestamps:** 1. **Seasonal Pattern Learning**: ML can learn that "minute 157,680" (around March 15th, 10:30 AM) has specific regime characteristics that repeat annually. 2. **Calendar Effect Encoding**: Minute buckets naturally encode: - Tax deadline proximity (minute ~105,000) - Earnings season timing - Holiday effects - Quarter-end patterns 3. **Multi-Year Pattern Recognition**: Minute 262,800 (May 1st) can aggregate learnings across multiple years of that exact same market timing. 4. **Sparse Feature Benefits**: 525,600 discrete buckets allow ML to learn very specific temporal patterns without needing to decompose cyclical functions. 5. **Regime Memory**: You could build "this minute bucket typically sees regime X lasting Y minutes" based on historical data at that exact calendar position. **Implementation:** ```python minute_of_year = (day_of_year - 1) * 1440 + hour * 60 + minute # March 15, 10:30 AM = minute 157,680 ``` **Advantage over timestamps**: Instead of learning "time 1723456789 had pattern X," ML learns "calendar minute 157,680 typically has pattern X" - much more generalizable across years. **Answer: Yes, minute-of-year bucketing provides superior predictive power by creating repeatable temporal contexts for pattern recognition.** ## User Response 3 > RCDD over a *entire market* - or index of it? ## Assistant Response 3 Ah, you're asking about **Regime Change Detection and Direction (RCDD)** applied to an **entire market/index** rather than individual assets. ### Market-Wide RCDD: Advantages **Yes, massive advantages over single-asset analysis:** 1. **Signal Strength**: Market-wide regime changes are typically: - More reliable (500+ assets vs 1 asset voting) - Earlier warning (portfolio effects precede individual breakdowns) - Higher confidence (systemic vs idiosyncratic) 2. **DOLPHIN's Design Intent**: Your 500-symbol analysis IS market-wide RCDD: - `up_ratio: 0.89` = 89% of market bullish - `down_ratio: 0.04` = 4% bearish - This captures broad regime shifts better than BTC alone 3. **Regime Cascade Effect**: - Market regime changes → Individual asset regime changes - Your JERICHO gets "early warning" from market-wide signal - Then confirms with BTC-specific BB/momentum 4. **Crisis Detection**: Market coherence jumps (all correlations spike) signal systemic events that individual assets miss. **Implementation Advantage:** ``` DOLPHIN (500 assets) → Market regime signal JERICHO FSM → Applies market signal to BTC-specific entry/exit BB distance + momentum → Asset-specific confirmation ``` **Answer: Yes, market-wide RCDD (what DOLPHIN already does) provides superior signal quality and early warning compared to single-asset regime detection. Your architecture is correctly designed around this principle.** The 500-asset DOLPHIN feeding BTC-specific JERICHO is the optimal approach. ## User Final Request > Please, write the *full conversation* (verbatim) and all full artifacts and snippets to an MD file using ctx MCP --- # Referenced Artifacts and Documents ## Document 1: JERICHO Specification (from project knowledge) THE SENSING SYSTEM - JERICHO The REGIME DETECTION subsystem produces a continued SCAN of BULLISH vs. BEARISH total assets, expressed as percentage bullish and percentage bearish, and other information. This report is - by default - produced every 5 seconds. Most references to percentages below are to these percentages of bullish and/or bearish number of total market 15m candles per SCAN period. References to 6x or 9x are to a number of consecutive SCAN periods.- SENTIMENT and REGIME are "BULLISH" and "BEARISH" or "SIDEWAYS". SENTIMENT and REGIME are interchangeable. At any point, the REGIME DETECTION subsystem provides a "CURRENT MARKET REGIME".- A STREAK is a secuence of 9x reports of a given SENTIMENT. Any SCAN with the relevant (BULISH/BEARISH) value <20% breaks a STREAK.- A SIDEWAYS sentiment "resets" the streak, except prior SENTIMENT values in the interrupted STREAK are "accounted for" for the ongoing (previous STREAK) as stated below.- Default sentiment is NEUTRAL. Default attention state is NORMAL.- Default state POSITION is NONE. It can also be LONG or SHORT, meaning in a position.- Default ACTION is "STANDING BY". it can be "LONGING" or "SHORTING" when such actions are triggered, as below.- 90% is the magic number.- Any decrease or increase (depending on the current sentiment vs. the opposite) >2%. Attention state becomes "WATCHING".- - ie. if currently BULISH, and BULISH % drops > 2% or BEARISH and bearish % drops >2%.- Any "jump" to 95% or greater in any value becomes "WATCHING".- IMPORTANT: Price (or index value, if market) at 1% proximity to an UPPER, MIDDLE (from both directions) or LOWER, BOLLINGER BAND enters "WATCHING" attention. any slight % over (the upper) or under (the lower) BOLLINGER BAND becomes "WATCHING FOR BREAKOUT" and "WATCHING FOR BREAKDOWN" respectively. These are special states.- Two consecutive 10% drops (even while a given % in a given REGIME is still major) exits any position, if held, from a given starting point (ie. 80%/ vs. 70%/75% - the last two are ten percent drops relative to the first reading).- (These drop, however, do not change the SENTIMENT per. Regular rules apply).- 2x SCANS with >99% or 98.5% for any given candle type (BULISH/BEARISH) buy "into" the sentiment, whatever it might be (ie SHORT for BEARISH, LONG for BULLISH). If NEUTRAL, do not enter or exit any POSITION. POSITION becomes LONGING or SHORTING for the next SCAN period, LONG or SHORT for the remaining, until position is closed (ie. we assume entering or exiting a POSITION takes one SCAN PERIOD). Exiting a position yields a STANDING BY state.- WATCHING FOR PRICE REVERSALS - Sudden "turn" (BULL/BEAR BEAR/BULL) *without* SIDEWAYS, any SENTIMENT >90% confirms the SENTIMENT turnaround.- - Turns "via" a SIDEWAYS need 9x streaks confirming - A SIDEWAYS raises attention to "WATCHING". 9x to CONFIRM state SWITCH. A new SIDEWAYS, resets count. Every 2x consecutive opposite regimes "between" SIDEWAYs count as 1x to CONFIRM. A CONTRARY sentiment "resets" state to last known sentiment.- ## Document 2: DOLPHIN Sample Outputs Sample 1: ```json { "regime": "BULL", "up_ratio": 0.7627118644067796, "down_ratio": 0.23728813559322035, "total_symbols": 405, "correlation_symbols": 405, "direction_symbols": 405, "timestamp": "2025-08-12T17:10:16.389625" } ``` Sample 2: ```json { "regime": "BEAR", "up_ratio": 0.04, "down_ratio": 0.96, "total_symbols": 405, "correlation_symbols": 405, "direction_symbols": 405, "timestamp": "2025-08-12T13:13:43.247235" } ``` Sample 3: ```json { "regime": "BULL", "up_ratio": 0.8920308483290489, "down_ratio": 0.10796915167095116, "total_symbols": 405, "correlation_symbols": 405, "direction_symbols": 405, "timestamp": "2025-08-11T18:54:06.718689" } ``` ## Document 3: DOLPHIN Main Code (main.py) ```python """ Main execution script for Crypto Market Regime Detection """ import asyncio import json from datetime import datetime from market_regime_detector import MarketRegimeDetector, RegimeThresholds from data_sources import DataSourceManager from regime_analyzer import RegimeAnalyzer from symbol_discovery import SymbolDiscovery # Configuration SYMBOL_LIMIT = 500 # Number of symbols to analyze CACHE_SYMBOLS = True # Whether to cache discovered symbols REGIME_THRESHOLDS = RegimeThresholds( bull_threshold=0.60, # 60% symbols up for bull market bear_threshold=0.55, # 55% symbols DOWN for bear market transition_change_threshold=0.15 # 15% change triggers transition ) class MarketRegimeApp: """Main application class""" def __init__(self): self.symbol_discovery = SymbolDiscovery() self.symbols = [] self.detector = None # Will be initialized after symbol discovery self.data_manager = DataSourceManager() self.analyzer = None # Will be initialized after detector async def initialize_symbols(self): """Discover and initialize symbols for analysis""" print("Initializing symbol discovery...") # Try to load cached symbols first if CACHE_SYMBOLS: cached_symbols = self.symbol_discovery.load_symbols_from_file() if cached_symbols: self.symbols = cached_symbols[:SYMBOL_LIMIT] print(f"Using {len(self.symbols)} cached symbols") else: print("No cached symbols found, discovering from Binance...") self.symbols = await self.symbol_discovery.discover_symbols(SYMBOL_LIMIT) if CACHE_SYMBOLS and self.symbols: self.symbol_discovery.save_symbols_to_file(self.symbols) else: # Always fetch fresh symbols self.symbols = await self.symbol_discovery.discover_symbols(SYMBOL_LIMIT) if not self.symbols: raise ValueError("Failed to discover any symbols") # Initialize detector and analyzer with discovered symbols self.detector = MarketRegimeDetector(self.symbols, REGIME_THRESHOLDS) self.analyzer = RegimeAnalyzer(self.detector) print(f"Initialized with {len(self.symbols)} symbols for analysis") async def fetch_all_data(self): """Fetch price data for all discovered symbols""" print(f"Fetching data for {len(self.symbols)} symbols...") tasks = [] for symbol in self.symbols: task = self.data_manager.fetch_price_data(symbol, "15m", 300) tasks.append(task) # Fetch all data concurrently results = await asyncio.gather(*tasks, return_exceptions=True) # Store results successful_fetches = 0 failed_fetches = 0 for i, result in enumerate(results): symbol = self.symbols[i] if isinstance(result, Exception): print(f"ERROR fetching data for {symbol}: {result}") self.detector.price_data[symbol] = [] failed_fetches += 1 else: self.detector.price_data[symbol] = result if len(result) > 0: successful_fetches += 1 print(f"Fetched {len(result)} candles for {symbol}") else: print(f"No data returned for {symbol}") failed_fetches += 1 print(f"\nFetch Summary:") print(f" Successful: {successful_fetches}/{len(self.symbols)} symbols") print(f" Failed: {failed_fetches}/{len(self.symbols)} symbols") async def run_single_detection(self): """Run a single regime detection cycle""" print("\n" + "="*50) print("CRYPTO MARKET REGIME DETECTION") print("="*50) # Initialize symbols if not already done if not self.symbols or not self.detector: await self.initialize_symbols() # Fetch fresh data await self.fetch_all_data() # Check data quality total_symbols = len(self.detector.price_data) valid_symbols = [s for s, data in self.detector.price_data.items() if len(data) >= 250] direction_symbols = [s for s, data in self.detector.price_data.items() if len(data) >= 1] print(f"Data Summary:") print(f" Total symbols: {total_symbols}") print(f" Symbols with ≥250 candles (for correlation): {len(valid_symbols)}") print(f" Symbols with ≥1 candle (for live sentiment): {len(direction_symbols)}") if len(valid_symbols) < 5: print(f"Insufficient data for correlation analysis. Need at least 5 symbols with 250+ candles.") return None print(f"\nAnalyzing {len(valid_symbols)} symbols for correlations...") # Calculate correlations correlation_matrix = self.detector.calculate_correlations() # Order pairs by correlation ordered_pairs = self.detector.order_pairs_by_correlation(correlation_matrix) # Calculate up/down ratios up_ratio, down_ratio = self.detector.calculate_price_direction_ratios() # Detect regime current_regime = self.detector.detect_regime(up_ratio, down_ratio) # Create result result = { "regime": current_regime, "up_ratio": up_ratio, "down_ratio": down_ratio, "total_symbols": total_symbols, "correlation_symbols": len(valid_symbols), "direction_symbols": len(direction_symbols), "correlation_pairs": len(ordered_pairs), "least_correlated_pair": ordered_pairs[0] if ordered_pairs else None, "most_correlated_pair": ordered_pairs[-1] if ordered_pairs else None, "timestamp": datetime.now() } # Add to analyzer history self.analyzer.add_regime_entry(result) # Print results self.print_results(result, correlation_matrix) return result async def main(): """Main entry point""" app = MarketRegimeApp() try: # Run single detection (will auto-initialize symbols) result = await app.run_single_detection() except Exception as e: print(f"Error during execution: {e}") return if result: # Optionally save results with open(f"regime_result_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json", "w") as f: json.dump({ "regime": result["regime"].value, "up_ratio": result["up_ratio"], "down_ratio": result["down_ratio"], "total_symbols": result["total_symbols"], "correlation_symbols": result["correlation_symbols"], "direction_symbols": result["direction_symbols"], "timestamp": result["timestamp"].isoformat() }, f, indent=2) print(f"\nResults saved to file.") if __name__ == "__main__": asyncio.run(main()) ``` --- *End of Complete Verbatim Conversation Record*