trading-signals

score = sum(signal.strength * weights[signal.method] for signal in signals)
action = 'BUY' if score >= 0.7 else 'WAIT'

class ConfluenceAnalyzer:
    """Regime-weighted methodology fusion"""

    REGIME_WEIGHTS = {
        'trending_up':   {'elliott': 0.30, 'turtle': 0.30, 'fib': 0.20, 'wyckoff': 0.15},
        'trending_down': {'elliott': 0.30, 'turtle': 0.30, 'fib': 0.20, 'wyckoff': 0.15},
        'ranging':       {'fib': 0.35, 'wyckoff': 0.30, 'elliott': 0.20, 'turtle': 0.05},
        'volatile':      {'fib': 0.30, 'wyckoff': 0.30, 'elliott': 0.20, 'turtle': 0.10},
    }

    def analyze(self, df, regime: str) -> dict:
        weights = self.REGIME_WEIGHTS[regime]
        signals = self._collect_signals(df)

        score = sum(s.strength * weights[s.method] for s in signals)
        return {
            'score': score,  # 0-1.0
            'action': 'BUY' if score >= 0.7 else 'WAIT',
            'confluence': self._calc_agreement(signals)
        }

trading-project/
├── methodologies/
│   ├── elliott_wave.py     # Wave detection + halving cycle
│   ├── turtle_system.py    # Donchian breakouts
│   ├── fibonacci.py        # Levels + golden pocket
│   ├── wyckoff.py          # Phase detection + VSA
│   └── markov_regime.py    # State classification
├── patterns/
│   ├── candlestick.py      # Engulfing, hammer, doji
│   └── chart_patterns.py   # H&S, double bottom, triangles
├── aggregator.py           # Regime-weighted fusion
└── swarm/
    ├── consensus.py        # Multi-LLM voting
    └── adapters/           # Claude, DeepSeek, Gemini