Confluence analysis (methodologies agree = high-probability setup):

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

Score interpretation:

0.7-1.0: High conviction entry 0.4-0.7: Wait for more confluence 0.0-0.4: No trade

Cost-effective routing: DeepSeek-V3 for pattern detection → Claude Sonnet for critical decisions

Analysis is successful when:

Multiple methodologies provide signals (not just one) Regime identified (trending/ranging/volatile) before analysis Confluence score calculated with regime-weighted methodology fusion Cost-optimized: bulk processing on DeepSeek, critical decisions on Claude Clear action (BUY/SELL/WAIT) with supporting rationale NO OPENAI used in model routing

Standardized patterns for technical analysis across trading projects.

Quick Reference Methodology Purpose When to Use Elliott Wave Wave position + targets Trend structure, cycle timing Turtle Trading Breakout system Trend following Fibonacci Support/resistance Entry/exit zones, golden pocket Wyckoff Accumulation/distribution Institutional activity Markov Regime Market state classification Position sizing, strategy selection Pattern Recognition Candlestick + chart patterns Entry confirmation Swarm Consensus Multi-LLM voting High-conviction decisions Confluence Detection

When methodologies agree = high-probability setup.

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)
    }

Score Interpretation:

0.7-1.0: High conviction entry 0.4-0.7: Wait for more confluence 0.0-0.4: No trade File Structure 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

Cost-Effective Model Routing Task Model Cost Pattern detection DeepSeek-V3 $0.27/1M Confluence scoring Qwen-72B $0.40/1M Critical decisions Claude Sonnet $3.00/1M Swarm consensus Mixed tier ~$1.50/1M avg Integration Notes Data Sources: yfinance, CCXT, Alpaca API Pairs with: runpod-deployment-skill (model serving) Projects: ThetaRoom, swaggy-stacks, alpha-lens Reference Files

Core Methodologies:

reference/elliott-wave.md - Wave rules, halving supercycle, targets reference/turtle-trading.md - Donchian channels, ATR sizing, pyramiding reference/fibonacci.md - Levels, golden pocket, on-chain enhanced reference/wyckoff.md - Phase state machines, VSA, composite operator reference/markov-regime.md - 7-state model, transition probabilities

Advanced Patterns:

reference/pattern-recognition.md - Candlestick + chart patterns reference/swarm-consensus.md - Multi-LLM voting system reference/chinese-llm-stack.md - Cost-optimized Chinese LLMs for trading

trading-signals

安装