金融交易操作系统

graph TD
    User["User Terminal"] --> UI["Gradio Unified Dashboard"]

    subgraph CORE1["CORE 1: EXECUTION ENGINE (Async Loop)"]
        UI --> TradingFloor["Trading Floor Orchestrator"]
        TradingFloor --> Agents["Agent Committee"]

        Agents --> Warren["Claude-3-Opus (Warren)"]
        Agents --> George["Gemini-Pro (George)"]
        Agents --> Cathie["GPT-4 (Cathie)"]

        Agents --> MCP["Model Context Protocol"]
        MCP --> Polygon["Polygon.io API"]
        MCP --> Portfolio["SQLite Ledger"]
        MCP --> Risk["VaR and Drawdown Guard"]
    end

    subgraph CORE2["CORE 2: INTELLIGENCE ENGINE (LangGraph)"]
        UI --> Chatbot["Analyst Chatbot"]
        Chatbot --> Router["Intent Router"]

        Router --> Sentiment["Sentiment Engine"]
        Sentiment --> News["Market News"]

        Router --> RAG["RAG Pipeline"]
        RAG --> VectorDB["ChromaDB"]
    end

    Risk --> Alerts["Pushover Alerts"]

prices = np.array(prices)
deltas = np.diff(prices)
seed = deltas[:period+1]
up = seed[seed >= 0].sum() / period
down = -seed[seed < 0].sum() / period
rs = up / down if down != 0 else 100
rsi = np.zeros_like(prices)
rsi[:period] = 100. - 100. / (1. + rs)

for i in range(period, len(prices)):
    delta = deltas[i-1]
    if delta > 0:
        upval = delta
        downval = 0.
    else:
        upval = 0.
        downval = -delta

    up = (up * (period - 1) + upval) / period
    down = (down * (period - 1) + downval) / period
    rs = up / down if down != 0 else 100
    rsi[i] = 100. - 100. / (1. + rs)

return rsi.tolist()

prices = pd.Series(prices)
ema_fast = prices.ewm(span=fast).mean()
ema_slow = prices.ewm(span=slow).mean()
macd_line = ema_fast - ema_slow
signal_line = macd_line.ewm(span=signal).mean()
histogram = macd_line - signal_line

return macd_line.tolist(), signal_line.tolist(), histogram.tolist()

prices = pd.Series(prices)
sma = prices.rolling(window=period).mean()
std = prices.rolling(window=period).std()

upper_band = sma + (std * std_dev)
lower_band = sma - (std * std_dev)

return upper_band.bfill().tolist(), sma.bfill().tolist(), lower_band.bfill().tolist()

    for i in range(days):
        change = random.uniform(-0.05, 0.05)
        trend = 0.001 if i > 25 else -0.001
        base_price *= (1 + change + trend)
        prices.append(max(10, base_price))
    
    rsi = calculate_rsi(prices)
    macd, macd_signal, macd_hist = calculate_macd(prices)
    bb_upper, bb_middle, bb_lower = calculate_bollinger_bands(prices)
    
    current_rsi = rsi[-1]
    current_macd = macd[-1]
    current_price = prices[-1]
    
    signals = []
    
    if current_rsi > 70:
        signals.append({"type": "RSI", "signal": "OVERBOUGHT", "value": current_rsi, "color": "#FF6B6B"})
    elif current_rsi < 30:
        signals.append({"type": "RSI", "signal": "OVERSOLD", "value": current_rsi, "color": "#00FF88"})
    else:
        signals.append({"type": "RSI", "signal": "NEUTRAL", "value": current_rsi, "color": "#FFD93D"})
    
    if current_macd > 0:
        signals.append({"type": "MACD", "signal": "BULLISH", "value": current_macd, "color": "#00FF88"})
    else:
        signals.append({"type": "MACD", "signal": "BEARISH", "value": current_macd, "color": "#FF6B6B"})
    
    if current_price > bb_upper[-1]:
        signals.append({"type": "BB", "signal": "OVERBOUGHT", "value": current_price, "color": "#FF6B6B"})
    elif current_price < bb_lower[-1]:
        signals.append({"type": "BB", "signal": "OVERSOLD", "value": current_price, "color": "#00FF88"})
    else:
        signals.append({"type": "BB", "signal": "NORMAL", "value": current_price, "color": "#00D2FF"})
    
    return {
        "symbol": symbol,
        "signals": signals,
        "rsi": current_rsi,
        "macd": current_macd,
        "price": current_price,
        "timestamp": datetime.now().isoformat()
    }
    
except Exception as e:
    return {"symbol": symbol, "signals": [], "error": str(e)}