159 lines
3.2 KiB
Python
159 lines
3.2 KiB
Python
"""
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技术指标计算模块
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提供常用技术指标的计算功能
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"""
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import pandas as pd
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import numpy as np
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from typing import Tuple
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def calculate_ma(data: pd.Series, period: int = 5) -> pd.Series:
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"""
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计算移动平均线（MA）
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Args:
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data: 价格数据
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period: 周期
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Returns:
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MA值
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"""
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return data.rolling(window=period).mean()
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def calculate_ema(data: pd.Series, period: int = 12) -> pd.Series:
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"""
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计算指数移动平均线（EMA）
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Args:
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data: 价格数据
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period: 周期
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Returns:
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EMA值
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"""
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return data.ewm(span=period, adjust=False).mean()
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def calculate_macd(
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data: pd.Series,
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fast_period: int = 12,
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slow_period: int = 26,
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signal_period: int = 9
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) -> Tuple[pd.Series, pd.Series, pd.Series]:
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"""
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计算MACD指标
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Args:
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data: 价格数据
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fast_period: 快线周期
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slow_period: 慢线周期
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signal_period: 信号线周期
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Returns:
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(DIF, DEA, MACD柱)
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"""
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ema_fast = calculate_ema(data, fast_period)
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ema_slow = calculate_ema(data, slow_period)
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dif = ema_fast - ema_slow
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dea = dif.ewm(span=signal_period, adjust=False).mean()
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macd = (dif - dea) * 2
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return dif, dea, macd
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def calculate_rsi(data: pd.Series, period: int = 14) -> pd.Series:
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"""
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计算相对强弱指标（RSI）
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Args:
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data: 价格数据
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period: 周期
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Returns:
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RSI值
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"""
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delta = data.diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
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rs = gain / loss
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rsi = 100 - (100 / (1 + rs))
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return rsi
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def calculate_kdj(
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high: pd.Series,
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low: pd.Series,
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close: pd.Series,
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period: int = 9,
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m1: int = 3,
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m2: int = 3
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) -> Tuple[pd.Series, pd.Series, pd.Series]:
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"""
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计算KDJ指标
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Args:
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high: 最高价
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low: 最低价
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close: 收盘价
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period: 周期
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m1: K值平滑参数
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m2: D值平滑参数
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Returns:
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(K, D, J)
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"""
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low_min = low.rolling(window=period).min()
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high_max = high.rolling(window=period).max()
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rsv = (close - low_min) / (high_max - low_min) * 100
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k = rsv.ewm(com=m1 - 1, adjust=False).mean()
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d = k.ewm(com=m2 - 1, adjust=False).mean()
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j = 3 * k - 2 * d
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return k, d, j
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def calculate_boll(
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data: pd.Series,
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period: int = 20,
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std_dev: float = 2.0
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) -> Tuple[pd.Series, pd.Series, pd.Series]:
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"""
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计算布林带（BOLL）
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Args:
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data: 价格数据
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period: 周期
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std_dev: 标准差倍数
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Returns:
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(上轨, 中轨, 下轨)
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"""
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middle = data.rolling(window=period).mean()
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std = data.rolling(window=period).std()
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upper = middle + (std * std_dev)
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lower = middle - (std * std_dev)
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return upper, middle, lower
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def calculate_volume_ma(volume: pd.Series, period: int = 5) -> pd.Series:
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"""
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计算成交量移动平均
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Args:
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volume: 成交量数据
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period: 周期
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Returns:
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成交量MA
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"""
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return volume.rolling(window=period).mean()
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