239 lines
9.4 KiB
Python
239 lines
9.4 KiB
Python
# -*- encoding:utf-8 -*-
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"""
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ATR
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ATR又称 Average true range平均真实波动范围，简称ATR指标，是由J.Welles Wilder 发明的，ATR指标主要是用来衡量市场波动的强烈度，
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即为了显示市场变化率的指标。
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计算方法：
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1. TR=∣最高价-最低价∣，∣最高价-昨收∣，∣昨收-最低价∣中的最大值
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2. 真实波幅（ATR）= MA(TR,N)（TR的N日简单移动平均）
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3. 常用参数N设置为14日或者21日
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"""
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from __future__ import absolute_import
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from __future__ import print_function
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from __future__ import division
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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from ..CoreBu.ABuPdHelper import pd_ewm_mean
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from ..UtilBu import ABuScalerUtil
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from .ABuNDBase import plot_from_order, g_calc_type, ECalcType
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__author__ = '阿布'
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__weixin__ = 'abu_quant'
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# noinspection PyUnresolvedReferences
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def _calc_atr_from_ta(high, low, close, time_period=14):
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"""
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使用talib计算atr，即透传talib.ATR计算结果
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:param high: 最高价格序列，pd.Series或者np.array
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:param low: 最低价格序列，pd.Series或者np.array
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:param close: 收盘价格序列，pd.Series或者np.array
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:param time_period: atr的N值默认值14，int
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:return: atr值序列，np.array对象
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"""
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import talib
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if isinstance(high, pd.Series):
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high = high.values
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if isinstance(low, pd.Series):
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low = low.values
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if isinstance(close, pd.Series):
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close = close.values
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atr = talib.ATR(high, low, close, timeperiod=time_period)
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return atr
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def _calc_atr_from_pd(high, low, close, time_period=14):
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"""
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通过atr公式手动计算atr
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:param high: 最高价格序列，pd.Series或者np.array
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:param low: 最低价格序列，pd.Series或者np.array
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:param close: 收盘价格序列，pd.Series或者np.array
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:param time_period: atr的N值默认值14，int
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:return: atr值序列，np.array对象
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"""
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if isinstance(close, pd.Series):
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# shift(1)构成昨天收盘价格序列
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pre_close = close.shift(1).values
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else:
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from scipy.ndimage.interpolation import shift
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# 也可以暂时转换为pd.Series进行shift
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pre_close = shift(close, 1)
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pre_close[0] = pre_close[1]
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if isinstance(high, pd.Series):
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high = high.values
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if isinstance(low, pd.Series):
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low = low.values
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# ∣最高价 - 最低价∣
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tr_hl = np.abs(high - low)
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# ∣最高价 - 昨收∣
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tr_hc = np.abs(high - pre_close)
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# ∣昨收 - 最低价∣
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tr_cl = np.abs(pre_close - low)
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# TR =∣最高价 - 最低价∣，∣最高价 - 昨收∣，∣昨收 - 最低价∣中的最大值
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tr = np.maximum(np.maximum(tr_hl, tr_hc), tr_cl)
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# （ATR）= MA(TR, N)（TR的N日简单移动平均）, 这里没有完全按照标准公式使用简单移动平均，使用了pd_ewm_mean，即加权移动平均
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atr = pd_ewm_mean(pd.Series(tr), span=time_period, min_periods=1)
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# 返回atr值序列，np.array对象
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return atr.values
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"""通过在ABuNDBase中尝试import talib来统一确定指标计算方式"""
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calc_atr = _calc_atr_from_pd if g_calc_type == ECalcType.E_FROM_PD else _calc_atr_from_ta
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def atr14(high, low, close):
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"""
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通过high, low, close计算atr14序列值
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:param high: 最高价格序列，pd.Series或者np.array
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:param low: 最低价格序列，pd.Series或者np.array
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:param close: 收盘价格序列，pd.Series或者np.array
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:return: atr值序列，np.array对象
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"""
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atr = calc_atr(high, low, close, 14)
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return atr
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def atr21(high, low, close):
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"""
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通过high, low, close计算atr21序列值
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:param high: 最高价格序列，pd.Series或者np.array
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:param low: 最低价格序列，pd.Series或者np.array
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:param close: 收盘价格序列，pd.Series或者np.array
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:return: atr值序列，np.array对象
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"""
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atr = calc_atr(high, low, close, 21)
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return atr
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def atr14_min(high, low, close):
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"""
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确定常数阀值时使用，通过high, low, close计算atr14序列值，返回计算结果atr14序列中的最小值
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:param high: 最高价格序列，pd.Series或者np.array
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:param low: 最低价格序列，pd.Series或者np.array
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:param close: 收盘价格序列，pd.Series或者np.array
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:return: atr值序列，atr14序列中的最小值，float
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"""
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_atr14 = atr14(high, low, close)
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_atr14 = pd.Series(_atr14)
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_atr14.fillna(method='bfill', inplace=True)
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_atr14 = _atr14.min()
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return _atr14
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def atr14_max(high, low, close):
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"""
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确定常数阀值时使用，通过high, low, close计算atr14序列值，返回计算结果atr14序列中的最大值
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:param high: 最高价格序列，pd.Series或者np.array
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:param low: 最低价格序列，pd.Series或者np.array
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:param close: 收盘价格序列，pd.Series或者np.array
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:return: atr值序列，atr14序列中的最大值，float
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"""
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_atr14 = atr14(high, low, close)
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_atr14 = pd.Series(_atr14)
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_atr14.fillna(method='bfill', inplace=True)
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_atr14 = _atr14.max()
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return _atr14
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def atr21_min(high, low, close):
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"""
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确定常数阀值时使用，通过high, low, close计算atr21序列值，返回计算结果atr21序列中的最小值
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:param high: 最高价格序列，pd.Series或者np.array
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:param low: 最低价格序列，pd.Series或者np.array
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:param close: 收盘价格序列，pd.Series或者np.array
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:return: atr值序列，atr21序列中的最小值，float
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"""
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_atr21 = atr21(high, low, close)
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_atr21 = pd.Series(_atr21)
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_atr21.fillna(method='bfill', inplace=True)
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_atr21 = _atr21.min()
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return _atr21
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def atr21_max(high, low, close):
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"""
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确定常数阀值时使用，通过high, low, close计算atr21序列值，返回计算结果atr21序列中的最大值
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:param high: 最高价格序列，pd.Series或者np.array
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:param low: 最低价格序列，pd.Series或者np.array
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:param close: 收盘价格序列，pd.Series或者np.array
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:return: atr值序列，atr21序列中的最大值，float
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"""
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_atr21 = atr21(high, low, close)
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_atr21 = pd.Series(_atr21)
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_atr21.fillna(method='bfill', inplace=True)
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_atr21 = _atr21.max()
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return _atr21
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def plot_atr_from_klpd(kl_pd, with_points=None, with_points_ext=None, **kwargs):
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"""
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封装plot_atr，绘制收盘价格，atr曲线
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:param kl_pd: 金融时间序列，pd.DataFrame对象
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:param with_points: 这里的常规用途是传入买入order, with_points=buy_index=pd.to_datetime(orders['buy_date']))
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:param with_points_ext: 这里的常规用途是传入卖出order, with_points_ext=sell_index=pd.to_datetime(orders['sell_date']))
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:param kwargs: 绘制技术指标需要的其它关键字参数，time_period, 最终透传给plot_atr
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"""
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plot_atr(kl_pd.high, kl_pd.low, kl_pd.close, kl_pd.index,
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with_points=with_points, with_points_ext=with_points_ext, **kwargs)
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def plot_atr_from_order(order, date_ext=120, **kwargs):
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"""
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封装ABuNDBase中的plot_from_order与模块中绘制技术指标的函数，完成技术指标可视化及标注买入卖出点位
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:param order: AbuOrder对象转换的pd.DataFrame对象or pd.Series对象
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:param date_ext: int对象 eg. 如交易在2015-06-01执行，如date_ext＝120，择start向前推120天，end向后推120天
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:param kwargs: 绘制技术指标需要的其它关键字参数，time_period, 最终透传给plot_atr
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"""
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return plot_from_order(plot_atr_from_klpd, order, date_ext, **kwargs)
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def plot_atr(high, low, close, kl_index, with_points=None, with_points_ext=None, time_period=14):
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"""
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分别在上下两个子画布上绘制收盘价格，以及对应的atr曲线，如果有with_points点位标注，
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则只画在一个画布上，且将两个曲线进行缩放到一个数值级别
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:param high: 最高价格序列，pd.Series或者np.array
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:param low: 最低价格序列，pd.Series或者np.array
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:param close: 收盘价格序列，pd.Series或者np.array
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:param kl_index: pd.Index时间序列
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:param with_points: 这里的常规用途是传入买入order, with_points=buy_index=pd.to_datetime(orders['buy_date']))
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:param with_points_ext: 这里的常规用途是传入卖出order, with_points_ext=sell_index=pd.to_datetime(orders['sell_date']))
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:param time_period: atr的N值默认值14，int
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"""
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atr = calc_atr(high, low, close, time_period)
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plt.figure(figsize=(14, 7))
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if with_points is not None or with_points_ext is not None:
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# 如果需要标准买入卖出点，就绘制在一个画布上
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p1 = plt.subplot(111)
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p2 = p1
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# 绘制在一个画布上, 将两个曲线进行缩放到一个数值级别
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matrix = ABuScalerUtil.scaler_matrix([atr, close])
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atr, close = matrix[matrix.columns[0]], matrix[matrix.columns[1]]
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# with_points和with_points_ext的点位使用竖线标注
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if with_points is not None:
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p1.axvline(with_points, color='green', linestyle='--')
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if with_points_ext is not None:
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p1.axvline(with_points_ext, color='red')
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else:
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# 绘制在两个子画布上面
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p1 = plt.subplot(211)
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p2 = plt.subplot(212)
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p1.plot(kl_index, close, "b-", label="close")
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p2.plot(kl_index, atr, "r-.", label="period={} atr".format(time_period), lw=2)
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p1.grid(True)
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p1.legend()
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p2.grid(True)
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p2.legend()
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plt.show()
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