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）
2026-06-12 10:26:45 +08:00
parent 039bfb5075
commit 212f6fedad
7 changed files with 899 additions and 0 deletions
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+"""
+趋势指标 — 移动平均线、MACD
+
+所有函数返回与输入等长的 list[float]，不足周期位置填 0.0。
+"""
+
+from functools import lru_cache
+
+
+def sma(data: list[float], period: int) -> list[float]:
+    """简单移动平均 (SMA)
+
+    Args:
+        data: 价格序列
+        period: 周期
+
+    Returns:
+        与 data 等长的 SMA 序列，前 period-1 位置为 0
+    """
+    n = len(data)
+    result = [0.0] * n
+    if n < period or period <= 0:
+        return result
+
+    window_sum = sum(data[:period])
+    result[period - 1] = window_sum / period
+
+    for i in range(period, n):
+        window_sum += data[i] - data[i - period]
+        result[i] = window_sum / period
+
+    return result
+
+
+def ema(data: list[float], period: int) -> list[float]:
+    """指数移动平均 (EMA)
+
+    使用 Wilder 平滑方式：k = 2 / (period + 1)
+
+    Args:
+        data: 价格序列
+        period: 周期
+
+    Returns:
+        与 data 等长的 EMA 序列，前 period-1 位置为 0
+    """
+    n = len(data)
+    result = [0.0] * n
+    if n < period or period <= 0:
+        return result
+
+    k = 2.0 / (period + 1)
+    # 初始值使用 SMA
+    result[period - 1] = sum(data[:period]) / period
+
+    for i in range(period, n):
+        result[i] = data[i] * k + result[i - 1] * (1 - k)
+
+    return result
+
+
+def macd(
+    data: list[float],
+    fast: int = 12,
+    slow: int = 26,
+    signal: int = 9,
+):
+    """MACD 指标
+
+    MACD 线 = EMA(fast) - EMA(slow)
+    信号线 = EMA(MACD线, signal)
+    柱状图 = MACD 线 - 信号线
+
+    Args:
+        data: 价格序列
+        fast: 快线周期
+        slow: 慢线周期
+        signal: 信号线周期
+
+    Returns:
+        (macd_line, signal_line, histogram) 三个等长序列
+    """
+    fast_ema = ema(data, fast)
+    slow_ema = ema(data, slow)
+
+    macd_line = [0.0] * len(data)
+    for i in range(len(data)):
+        macd_line[i] = fast_ema[i] - slow_ema[i]
+
+    signal_line = ema(macd_line, signal)
+    histogram = [macd_line[i] - signal_line[i] for i in range(len(data))]
+
+    return macd_line, signal_line, histogram
+
+
+def macd_signal(
+    data: list[float],
+    fast: int = 12,
+    slow: int = 26,
+    signal: int = 9,
+) -> list[float]:
+    """MACD 信号线"""
+    _, sig, _ = macd(data, fast, slow, signal)
+    return sig
+
+
+def macd_histogram(
+    data: list[float],
+    fast: int = 12,
+    slow: int = 26,
+    signal: int = 9,
+) -> list[float]:
+    """MACD 柱状图"""
+    _, _, hist = macd(data, fast, slow, signal)
+    return hist
+
+
+def adx(
+    high: list[float],
+    low: list[float],
+    close: list[float],
+    period: int = 14,
+) -> list[float]:
+    """平均趋向指数 (ADX)
+
+    判断趋势强度：ADX > 25 表示强趋势，ADX < 20 表示震荡。
+
+    Args:
+        high: 最高价序列
+        low: 最低价序列
+        close: 收盘价序列
+        period: 周期（默认 14）
+
+    Returns:
+        与输入等长的 ADX 序列 [0, 100]，前 2*period 位置为 0
+    """
+    n = len(close)
+    result = [0.0] * n
+    if n < period * 2:
+        return result
+
+    # True Range, +DM, -DM
+    tr = [0.0] * n
+    plus_dm = [0.0] * n
+    minus_dm = [0.0] * n
+
+    for i in range(1, n):
+        tr[i] = max(
+            high[i] - low[i],
+            abs(high[i] - close[i - 1]),
+            abs(low[i] - close[i - 1]),
+        )
+        up_move = high[i] - high[i - 1]
+        down_move = low[i - 1] - low[i]
+        if up_move > down_move and up_move > 0:
+            plus_dm[i] = up_move
+        if down_move > up_move and down_move > 0:
+            minus_dm[i] = down_move
+
+    # Wilder 平滑
+    tr_smooth = [0.0] * n
+    plus_dm_smooth = [0.0] * n
+    minus_dm_smooth = [0.0] * n
+
+    tr_smooth[period] = sum(tr[1:period + 1])
+    plus_dm_smooth[period] = sum(plus_dm[1:period + 1])
+    minus_dm_smooth[period] = sum(minus_dm[1:period + 1])
+
+    for i in range(period + 1, n):
+        tr_smooth[i] = tr_smooth[i - 1] - tr_smooth[i - 1] / period + tr[i]
+        plus_dm_smooth[i] = plus_dm_smooth[i - 1] - plus_dm_smooth[i - 1] / period + plus_dm[i]
+        minus_dm_smooth[i] = minus_dm_smooth[i - 1] - minus_dm_smooth[i - 1] / period + minus_dm[i]
+
+    # +DI, -DI, DX, ADX
+    dx = [0.0] * n
+    for i in range(period, n):
+        if tr_smooth[i] > 0:
+            pdi = 100 * plus_dm_smooth[i] / tr_smooth[i]
+            mdi = 100 * minus_dm_smooth[i] / tr_smooth[i]
+            di_sum = pdi + mdi
+            if di_sum > 0:
+                dx[i] = 100 * abs(pdi - mdi) / di_sum
+
+    # ADX = EMA of DX
+    for i in range(2 * period, n):
+        if i == 2 * period:
+            result[i] = sum(dx[period + 1:2 * period + 1]) / period
+        else:
+            result[i] = (result[i - 1] * (period - 1) + dx[i]) / period
+
+    return result