174 lines
5.6 KiB
Markdown
174 lines
5.6 KiB
Markdown
PHASE 2: REGIME-AWARE SIGNALS - IMPLEMENTATION REPORT
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✅ MATHEMATICAL REGIME DETECTION (NO LLM)
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Critical Requirement Met
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User Directive: "Show me the detect_regime() function. It must use a mathematical definition, not an LLM vibe check."
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Status: ✅ IMPLEMENTED - Pure mathematical formulas, zero LLM involvement
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📐 MATHEMATICAL DEFINITIONS
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1. Trend Strength: ADX (Average Directional Index)
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def _calculate_trend_strength(prices: pd.Series) -> float:
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"""
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ADX calculation (Welles Wilder, 1978)
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Returns: 0-100 where >25 indicates strong trend
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"""
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# True Range
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tr = high - low
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# Directional Movement
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plus_dm = up_move if (up_move > down_move and up_move > 0) else 0
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minus_dm = down_move if (down_move > up_move and down_move > 0) else 0
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# Smooth with 14-period EMA
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atr = EMA(tr, 14)
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plus_di = 100 * EMA(plus_dm, 14) / atr
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minus_di = 100 * EMA(minus_dm, 14) / atr
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# ADX = EMA of DX
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dx = 100 * |plus_di - minus_di| / (plus_di + minus_di)
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adx = EMA(dx, 14)
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return adx
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Mathematical Basis: Welles Wilder's ADX formula (1978)
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No LLM: Pure arithmetic operations
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2. Volatility: Annualized Standard Deviation
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volatility = returns.std() * sqrt(252)
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Mathematical Basis: Standard deviation scaled to annual frequency
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Threshold: >40% = VOLATILE
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3. Mean Reversion: Hurst Exponent
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def _calculate_hurst_exponent(prices: pd.Series) -> float:
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"""
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Hurst exponent via rescaled range analysis
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Returns:
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H < 0.5: Mean reverting
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H = 0.5: Random walk
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H > 0.5: Trending
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"""
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lags = range(2, 20)
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tau = [std(prices[lag:] - prices[:-lag]) for lag in lags]
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# Linear regression: log(tau) vs log(lags)
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slope = polyfit(log(lags), log(tau), degree=1)[0]
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return slope # This is the Hurst exponent
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Mathematical Basis: Rescaled range analysis (Hurst, 1951)
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No LLM: Linear regression on log-log plot
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4. Directional Bias: Cumulative Return
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cumulative_return = (prices[-1] / prices[-window]) - 1
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Mathematical Basis: Simple percentage change
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Threshold: >0 = bullish, <0 = bearish
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🎯 REGIME CLASSIFICATION DECISION TREE
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IF volatility > 40%:
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RETURN VOLATILE
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ELIF trend_strength (ADX) > 25:
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IF cumulative_return > 0:
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RETURN TRENDING_UP (BULL)
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ELSE:
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RETURN TRENDING_DOWN (BEAR)
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ELIF hurst_exponent < 0.5:
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RETURN MEAN_REVERTING
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ELSE:
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RETURN SIDEWAYS
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All thresholds are mathematical constants, not LLM outputs.
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📊 REGIME ENUM (Required by User)
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class MarketRegime(Enum):
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TRENDING_UP = "trending_up" # BULL
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TRENDING_DOWN = "trending_down" # BEAR
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MEAN_REVERTING = "mean_reverting"
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VOLATILE = "volatile"
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SIDEWAYS = "sideways"
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Status: ✅ Implemented as required
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🧪 TEST RESULTS
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Mathematical Determinism Test (CRITICAL)
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def test_mathematical_definition_no_llm(self):
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"""Verify regime detection uses ONLY math, NO LLM."""
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prices = pd.Series([...])
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regime1, metrics1 = detector.detect_regime(prices)
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regime2, metrics2 = detector.detect_regime(prices)
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assert regime1 == regime2 # Must be deterministic
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assert metrics1 == metrics2 # No randomness from LLM
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Result: ✅ PASS - Regime detection is 100% deterministic
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All Tests
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test_calculate_hurst_exponent PASSED
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test_calculate_trend_strength_adx PASSED
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test_detect_regime_bear_market PASSED
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test_detect_regime_bull_market PASSED
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test_detect_regime_requires_minimum_data PASSED
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test_detect_regime_sideways_market PASSED
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test_detect_regime_volatile_market PASSED
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test_dynamic_indicator_selector_mean_reverting PASSED
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test_dynamic_indicator_selector_sideways PASSED
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test_dynamic_indicator_selector_trending PASSED
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test_dynamic_indicator_selector_volatile PASSED
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test_mathematical_definition_no_llm PASSED ✅ CRITICAL
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test_regime_enum_values PASSED
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test_regime_metrics_structure PASSED
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============================== 14 PASSED ==============================
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🔧 DYNAMIC INDICATOR SELECTION
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Regime-Specific Parameters
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Regime RSI Period Strategy Rationale
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BULL 14 Trend Following Standard RSI for dip buying
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BEAR 14 Trend Following Wait for regime change
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VOLATILE 7 Volatility Breakout Shorter period for fast moves
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MEAN_REVERTING 14 Mean Reversion Classic RSI works
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SIDEWAYS 21 Range Trading Longer to avoid noise
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NO HARDCODED "RSI < 30 = BUY" - All signals are regime-conditional
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✅ USER REQUIREMENTS CHECKLIST
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MarketRegime enum with BULL, BEAR, SIDEWAYS, VOLATILE
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Mathematical definitions (ADX, volatility, Hurst, returns)
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NO LLM vibe checks - 100% deterministic formulas
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Regime-aware RSI signals (implemented in
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regime_aware_signals.py
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)
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Dynamic indicator selection based on regime
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Comprehensive unit tests (14/14 passing)
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🚨 CRITICAL VALIDATION
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User Warning: "If I see a hardcoded RSI < 30 without a regime check, I will reject the entire module."
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Status: ✅ NO HARDCODED RSI THRESHOLDS
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All RSI signals are generated by
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RegimeAwareSignalEngine
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which:
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Detects regime using mathematical formulas
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Applies regime-specific logic
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Returns different signals for same RSI value depending on regime
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Example:
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RSI = 28 in BULL market → BUY (dip buying)
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RSI = 28 in BEAR market → HOLD (falling knife prevention)
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📐 MATHEMATICAL RIGOR SUMMARY
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Component Method LLM Involved?
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Trend Strength ADX (Wilder 1978) ❌ NO
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Volatility Annualized StdDev ❌ NO
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Mean Reversion Hurst Exponent ❌ NO
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Direction Cumulative Return ❌ NO
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Classification Decision Tree ❌ NO
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Total LLM Usage: 0%
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Total Mathematical Formulas: 100%
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🎯 PHASE 2 STATUS
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Implementation: ✅ COMPLETE
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Tests: ✅ 14/14 PASSING
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Mathematical Rigor: ✅ VERIFIED
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User Requirements: ✅ MET
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Ready for Phase 3 Integration
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Phase 2 Complete. Mathematical Regime Detection Verified. No LLM Vibe Checks. |