feat(engine): 添加数据服务层与技术指标库

- data/service.py: 数据拉取服务，从 TimescaleDB 读取 K 线/Ticker 等行情数据
- indicators/momentum.py: 动量类指标（RSI/MACD/Stochastic 等）
- indicators/trend.py: 趋势类指标（EMA/SMA/ADX/SuperTrend 等）
- indicators/volatility.py: 波动率指标（Bollinger/ATR/Keltner 等）
- indicators/volume.py: 成交量指标（OBV/VWAP/MFI 等）

engine/indicators/momentum.py

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+"""
+动量指标 — RSI、Stochastic
+
+所有函数返回与输入等长的 list[float]，不足周期位置填 0.0。
+"""
+
+
+def rsi(data: list[float], period: int = 14) -> list[float]:
+    """相对强弱指数 (RSI)
+
+    使用 Wilder 平滑算法，Wilder's RSI = 100 - [100 / (1 + avg_gain / avg_loss)]
+
+    Args:
+        data: 价格序列
+        period: 周期（默认 14）
+
+    Returns:
+        与 data 等长的 RSI 序列 [0, 100]，前 period 位置为 0
+    """
+    n = len(data)
+    result = [0.0] * n
+    if n < period + 1:
+        return result
+
+    # 计算价格变化
+    changes = [data[i] - data[i - 1] for i in range(1, n)]
+
+    # 初始平均涨幅和跌幅（Simple average of first `period` changes）
+    gains = [max(c, 0) for c in changes[:period]]
+    losses = [abs(min(c, 0)) for c in changes[:period]]
+    avg_gain = sum(gains) / period
+    avg_loss = sum(losses) / period
+
+    # 计算第一个 RSI
+    if avg_loss == 0:
+        result[period] = 100.0
+    else:
+        rs = avg_gain / avg_loss
+        result[period] = 100.0 - (100.0 / (1.0 + rs))
+
+    # Wilder 平滑后续值
+    for i in range(period, n - 1):
+        change = changes[i]
+        gain = max(change, 0.0)
+        loss = abs(min(change, 0.0))
+
+        avg_gain = (avg_gain * (period - 1) + gain) / period
+        avg_loss = (avg_loss * (period - 1) + loss) / period
+
+        if avg_loss == 0:
+            result[i + 1] = 100.0
+        else:
+            rs = avg_gain / avg_loss
+            result[i + 1] = 100.0 - (100.0 / (1.0 + rs))
+
+    return result
+
+
+def stoch(
+    high: list[float],
+    low: list[float],
+    close: list[float],
+    k_period: int = 14,
+    k_smooth: int = 3,
+    d_smooth: int = 3,
+):
+    """Stochastic 指标 (KDJ 中的 K/D)
+
+    %K = 100 * (close - lowest_low) / (highest_high - lowest_low)
+    %K_smoothed = SMA(%K, k_smooth)
+    %D = SMA(%K_smoothed, d_smooth)
+
+    Args:
+        high: 最高价序列
+        low: 最低价序列
+        close: 收盘价序列
+        k_period: %K 窗口
+        k_smooth: %K 平滑周期
+        d_smooth: %D 平滑周期
+
+    Returns:
+        (k_values, d_values) 两个等长序列，范围 [0, 100]
+    """
+    n = len(close)
+    k_raw = [0.0] * n
+    k_values = [0.0] * n
+    d_values = [0.0] * n
+
+    if n < k_period:
+        return k_values, d_values
+
+    # 计算原始 %K
+    for i in range(k_period - 1, n):
+        highest = max(high[i - k_period + 1 : i + 1])
+        lowest = min(low[i - k_period + 1 : i + 1])
+        if highest != lowest:
+            k_raw[i] = 100.0 * (close[i] - lowest) / (highest - lowest)
+        else:
+            k_raw[i] = 50.0
+
+    # 平滑 %K
+    from .trend import sma as _sma
+    k_smoothed = _sma(k_raw, k_smooth)
+    d_smoothed = _sma(k_smoothed, d_smooth)
+
+    return k_smoothed, d_smoothed
+
+
+def stoch_k(
+    high: list[float],
+    low: list[float],
+    close: list[float],
+    k_period: int = 14,
+    k_smooth: int = 3,
+) -> list[float]:
+    """Stochastic %K"""
+    k, _ = stoch(high, low, close, k_period, k_smooth)
+    return k
+
+
+def stoch_d(
+    high: list[float],
+    low: list[float],
+    close: list[float],
+    k_period: int = 14,
+    k_smooth: int = 3,
+    d_smooth: int = 3,
+) -> list[float]:
+    """Stochastic %D"""
+    _, d = stoch(high, low, close, k_period, k_smooth, d_smooth)
+    return d