Refactor: restructure market module with services, stores, and utils

market/services/tech_indicators.py

@@ -0,0 +1,238 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+技术指标计算模块
+纯函数实现，不依赖外部状态
+"""
+
+from typing import List, Dict
+from ..models import KlineData
+
+
+def calculate_ma(data: List[float], period: int) -> float:
+    """
+    计算移动平均线 (MA)
+
+    Args:
+        data: 数据列表（如收盘价）
+        period: 周期
+
+    Returns:
+        MA 值
+    """
+    if not data:
+        return 0
+    if len(data) < period:
+        return data[-1]
+    return sum(data[-period:]) / period
+
+
+def calculate_adx(klines: List[KlineData], period: int = 14) -> float:
+    """
+    计算 ADX (Average Directional Index)
+
+    ADX > 25: 有趋势
+    ADX > 40: 强趋势
+    ADX < 20: 无明显趋势
+
+    Args:
+        klines: K线数据列表
+        period: 计算周期
+
+    Returns:
+        ADX 值
+    """
+    if len(klines) < period + 1:
+        return 0
+
+    # 计算 +DM 和 -DM
+    plus_dm = []
+    minus_dm = []
+    tr_list = []
+
+    for i in range(1, len(klines)):
+        high = klines[i].high
+        low = klines[i].low
+        prev_high = klines[i - 1].high
+        prev_low = klines[i - 1].low
+        prev_close = klines[i - 1].close
+
+        # +DM
+        up_move = high - prev_high
+        down_move = prev_low - low
+
+        if up_move > down_move and up_move > 0:
+            plus_dm.append(up_move)
+        else:
+            plus_dm.append(0)
+
+        # -DM
+        if down_move > up_move and down_move > 0:
+            minus_dm.append(down_move)
+        else:
+            minus_dm.append(0)
+
+        # True Range
+        tr = max(
+            high - low,
+            abs(high - prev_close),
+            abs(low - prev_close)
+        )
+        tr_list.append(tr)
+
+    if len(tr_list) < period:
+        return 0
+
+    # 计算平滑值
+    atr = sum(tr_list[-period:]) / period
+    smoothed_plus_dm = sum(plus_dm[-period:]) / period
+    smoothed_minus_dm = sum(minus_dm[-period:]) / period
+
+    # 计算 +DI 和 -DI
+    if atr == 0:
+        return 0
+
+    plus_di = (smoothed_plus_dm / atr) * 100
+    minus_di = (smoothed_minus_dm / atr) * 100
+
+    # 计算 DX
+    di_sum = plus_di + minus_di
+    if di_sum == 0:
+        return 0
+
+    dx = abs(plus_di - minus_di) / di_sum * 100
+
+    return dx
+
+
+def calculate_rsi(data: List[float], period: int = 14) -> float:
+    """
+    计算 RSI (Relative Strength Index)
+
+    Args:
+        data: 数据列表（如收盘价）
+        period: 计算周期
+
+    Returns:
+        RSI 值 (0-100)
+    """
+    if len(data) < period + 1:
+        return 50  # 默认中性值
+
+    gains = []
+    losses = []
+
+    for i in range(1, len(data)):
+        change = data[i] - data[i - 1]
+        if change > 0:
+            gains.append(change)
+            losses.append(0)
+        else:
+            gains.append(0)
+            losses.append(abs(change))
+
+    if len(gains) < period:
+        return 50
+
+    avg_gain = sum(gains[-period:]) / period
+    avg_loss = sum(losses[-period:]) / period
+
+    if avg_loss == 0:
+        return 100
+
+    rs = avg_gain / avg_loss
+    rsi = 100 - (100 / (1 + rs))
+
+    return rsi
+
+
+def calculate_macd(data: List[float], fast: int = 12, slow: int = 26, signal: int = 9) -> Dict:
+    """
+    计算 MACD (Moving Average Convergence Divergence)
+
+    Args:
+        data: 数据列表
+        fast: 快线周期
+        slow: 慢线周期
+        signal: 信号线周期
+
+    Returns:
+        {"macd": float, "signal": float, "histogram": float}
+    """
+    if len(data) < slow + signal:
+        return {"macd": 0, "signal": 0, "histogram": 0}
+
+    # 计算快慢 EMA（简化用 SMA 近似）
+    ema_fast = calculate_ema_approx(data, fast)
+    ema_slow = calculate_ema_approx(data, slow)
+
+    # MACD 线
+    macd_line = ema_fast - ema_slow
+
+    # 信号线（MACD 的移动平均）
+    # 简化处理
+    signal_line = macd_line  # 简化
+
+    # 柱状图
+    histogram = macd_line - signal_line
+
+    return {
+        "macd": macd_line,
+        "signal": signal_line,
+        "histogram": histogram
+    }
+
+
+def calculate_ema_approx(data: List[float], period: int) -> float:
+    """
+    计算指数移动平均线 (EMA) 的近似值
+
+    Args:
+        data: 数据列表
+        period: 周期
+
+    Returns:
+        EMA 值
+    """
+    if not data:
+        return 0
+    if len(data) < period:
+        return data[-1]
+
+    # 简化：使用 SMA 近似
+    return sum(data[-period:]) / period
+
+
+def calculate_bollinger_bands(data: List[float], period: int = 20, std_dev: float = 2.0) -> Dict:
+    """
+    计算布林带
+
+    Args:
+        data: 数据列表
+        period: 周期
+        std_dev: 标准差倍数
+
+    Returns:
+        {"upper": float, "middle": float, "lower": float}
+    """
+    if len(data) < period:
+        current = data[-1] if data else 0
+        return {"upper": current, "middle": current, "lower": current}
+
+    # 中轨（SMA）
+    middle = sum(data[-period:]) / period
+
+    # 计算标准差
+    subset = data[-period:]
+    variance = sum((x - middle) ** 2 for x in subset) / period
+    std = variance ** 0.5
+
+    # 上下轨
+    upper = middle + std_dev * std
+    lower = middle - std_dev * std
+
+    return {
+        "upper": upper,
+        "middle": middle,
+        "lower": lower
+    }