# coding:utf-8
# Author: 阿财（Rgveda@github）（4910163#qq.com）
# Created date: 2020-02-27
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import numpy as np
import pandas as pd
from QUANTAXIS.QAIndicator.base import *
try:
import talib
except:
pass
#print('PLEASE install TALIB to call these methods')
"""
完全用nparray传递参数的talib库，原因是用nparray因为没有 Series MultiIndex 的问题。
处理和补完速度都比pandas更快，转 pd.DataFrame/pd.Series 只需要一个构造函数。
我还引入了一些talib没有，但是写出来不超过10行代码，很容易实现好指标，源自外网(TradingView或者MQ4/5)找到的。
例如
Moving Average ADX
Hull Moving Average
Volume HMA(完成)
"""
# 定义MACD函数
def TA_MACD(prices:np.ndarray,
fastperiod:int=12,
slowperiod:int=26,
signalperiod:int=9) -> np.ndarray:
'''
参数设置:
fastperiod = 12
slowperiod = 26
signalperiod = 9
返回: macd - dif, signal - dea, hist * 2 - bar, delta
'''
macd, signal, hist = talib.MACD(prices,
fastperiod=fastperiod,
slowperiod=slowperiod,
signalperiod=signalperiod)
hist = (macd - signal) * 2
delta = np.r_[np.nan, np.diff(hist)]
return np.c_[macd, signal, hist, delta]
# 定义RSI函数
def TA_RSI(prices:np.ndarray,
timeperiod:int=12) -> np.ndarray:
'''
参数设置:
timeperiod = 12
返回: ma
'''
rsi = talib.RSI(prices, timeperiod=timeperiod)
delta = np.r_[np.nan, np.diff(rsi)]
return np.c_[rsi, delta]
# 定义RSI函数
def TA_BBANDS(prices:np.ndarray,
timeperiod:int=5,
nbdevup:int=2,
nbdevdn:int=2,
matype:int=0) -> np.ndarray:
'''
参数设置:
timeperiod = 5
nbdevup = 2
nbdevdn = 2
返回: up, middle, low
'''
up, middle, low = talib.BBANDS(prices,
timeperiod,
nbdevup,
nbdevdn,
matype)
ch = (up - low) / middle
delta = np.r_[np.nan, np.diff(ch)]
return np.c_[up, middle, low, ch, delta]
def TA_KDJ(high:np.ndarray,
low:np.ndarray,
close:np.ndarray,
fastk_period:int=9,
slowk_matype:int=0,
slowk_period:int=3,
slowd_period:int=3) -> np.ndarray:
'''
参数设置:
fastk_period = 9
lowk_matype = 0,
slowk_period = 3,
slowd_period = 3
返回: K, D, J
'''
K, D = talib.STOCH(high,
low,
close,
fastk_period=fastk_period,
slowk_matype=slowk_matype,
slowk_period=slowk_period,
slowd_period=slowd_period)
J = 3 * K - 2 * D
delta = np.r_[np.nan, np.diff(J)]
return np.c_[K, D, J, delta]
def TA_ADX(high, low, close, timeperiod=14) -> np.ndarray:
"""
ADX - Average Directional Movement Index
"""
real = talib.ADX(high, low, close, timeperiod=timeperiod)
return np.c_[real]
def TA_ADXR(high, low, close, timeperiod=14) -> np.ndarray:
"""
名称：平均趋向指数的趋向指数
简介：使用ADXR指标，指标判断ADX趋势。
ADXR - Average Directional Movement Index Rating
"""
real = talib.ADXR(high, low, close, timeperiod=timeperiod)
return np.c_[real]
def TA_CCI(high:np.ndarray,
low:np.ndarray,
close:np.ndarray,
timeperiod:int=14) -> np.ndarray:
"""
名称：平均趋向指数的趋向指数
简介：使用CCI指标，指标判断CCI趋势。
CCI - Commodity Channel Index
"""
real = talib.CCI(high,
low,
close,
timeperiod=timeperiod)
delta = np.r_[np.nan, np.diff(real)]
return np.c_[real, delta]
def TA_KAMA(close, timeperiod=30):
"""
请直接用 talib.KAMA(close, timeperiod)
KAMA - Kaufman Adaptive Moving Average
"""
real = talib.KAMA(close, timeperiod=timeperiod)
return np.c_[real]
def TA_HMA(close, period):
"""
赫尔移动平均线(HMA)
Hull Moving Average.
Formula:
HMA = WMA(2*WMA(n/2) - WMA(n)), sqrt(n)
"""
hma = talib.WMA(2 * talib.WMA(close, int(period / 2)) - talib.WMA(close, period), int(np.sqrt(period)))
return hma
def ADX_MA(data, period=14, smooth=14, limit=18):
"""
Moving Average ADX
ADX Smoothing Trend Color Change on Moving Average and ADX Cross. Use on Hourly Charts - Green UpTrend - Red DownTrend - Black Choppy No Trend
Source: https://www.tradingview.com/script/owwws7dM-Moving-Average-ADX/
Translator: 阿财（Rgveda@github）（4910163#qq.com）
Parameters
----------
data : (N,) array_like
传入 OHLC Kline 序列。
The OHLC Kline.
period : int or None, optional
DI 统计周期 默认值为 14
DI Length period. Default value is 10.
smooth : int or None, optional
ADX 平滑周期 默认值为 14
ADX smoothing length period. Default value is 10.
limit : int or None, optional
ADX 限制阈值 默认值为 18
ADX MA Active limit threshold. Default value is 18.
Returns
-------
adx, ADXm : ndarray
ADXm 指标和趋势指示方向 (-1, 0, 1) 分别代表 (下跌, 无明显趋势, 上涨)
ADXm indicator and thread directions sequence. (-1, 0, 1) means for (Neagtive, No Trend, Positive)
"""
up = data.high.pct_change()
down = data.low.pct_change() * -1
trur = TA_HMA(talib.TRANGE(data.high.values, data.low.values, data.close.values) , period)
plus = 100 * TA_HMA(np.where(((up > down) & (up > 0)), up, 0), period) / trur
minus = 100 * TA_HMA(np.where(((down > up) & (down > 0)), down, 0), period) / trur
# 这里是dropna的替代解决办法，因为我觉得nparray的传递方式如果随便drop了可能会跟 data.index
# 对不上，所以我选择补零替代dropna
plus = np.r_[np.zeros(period + 2), plus[(period + 2):]]
minus = np.r_[np.zeros(period + 2), minus[(period + 2):]]
sum = plus + minus
adx = 100 * TA_HMA(abs(plus - minus) / (np.where((sum == 0), 1, sum)), smooth)
adx = np.r_[np.zeros(smooth + 2), adx[(smooth + 2):]]
ADXm = np.where(((adx > limit) & (plus > minus)), 1, np.where(((adx > limit) & (plus < minus)), -1, 0))
return adx, ADXm
def ATR_RSI_Stops(data, period=10):
"""
ATR 趋势判断指标 RSI在40~60区间实现牛熊趋势变化指示
This simple indicator gives you a bias on the market that can be used as a filter, an entry indicator for pullbacks,...
It shows the special relationship I discovered between the rsi and the 27 period ema
and the relation between the 40/60 levels of the rsi and the atr offset of the 27 ema line
Source: https://cn.tradingview.com/script/rqzryhZ2-Rsi-Stops-JD/
Translator: 阿财（Rgveda@github）（4910163#qq.com）
Parameters
----------
data : (N,) array_like
传入 OHLC Kline 序列。
The OHLC Kline.
period : int or None, optional
DI 统计周期 默认值为 10
DI Length period. Default value is 10.
Returns
-------
rsi_ma, stop_line, directions : ndarray
rsi_ma, stop_line 指标和 directions 趋势指示方向 (-1, 0, 1) 分别代表 (下跌, 无明显趋势, 上涨)
rsi_ma, stop_line indicator and thread directions sequence. (-1, 0, 1) means for (Neagtive, No Trend, Positive)
"""
rsi_ma = talib.EMA((data.open + data.high + data.low + data.close) / 4, 10)
ATR = talib.ATR(data.high, data.low, data.close, 10)
top_line = rsi_ma + ATR
bottom_line = rsi_ma - ATR
rsi_ma = pd.Series(rsi_ma, index=data.index)
PRICE_PREDICT = pd.DataFrame(columns=['POSITION'], index=data.index)
PREDICT_JX = (CROSS(data.close, top_line) == 1)
PREDICT_SX = (CROSS(bottom_line, data.close) == 1)
PREDICT_JX = PREDICT_JX[PREDICT_JX.apply(lambda x: x == True)] # eqv. Trim(x == False)
PREDICT_SX = PREDICT_SX[PREDICT_SX.apply(lambda x: x == True)] # eqv. Trim(x == False)
PRICE_PREDICT.loc[PREDICT_JX.index, 'POSITION'] = 1
PRICE_PREDICT.loc[PREDICT_SX.index, 'POSITION'] = -1
PRICE_PREDICT['POSITION'] = PRICE_PREDICT['POSITION'].ffill()
stop_line = rsi_ma - PRICE_PREDICT['POSITION'] * ATR
return rsi_ma, stop_line, PRICE_PREDICT['POSITION'].values
def ATR_SuperTrend_cross(klines, length=12, Factor=3):
"""
ATR 趋势判断指标，可以实现快速而精准的牛熊趋势判断
the Super Trend ATR allows you to quickly identify trends and the acceleration phase and accumulation
Source: https://cn.tradingview.com/script/alvd6EHP-Bollinger-Bands-V2-Super-Trend/
Translator: 阿财（Rgveda@github）（4910163#qq.com）
Parameters
----------
data : (N,) array_like
传入 OHLC Kline 序列。
The OHLC Kline.
period : int or None, optional
DI 统计周期 默认值为 10
DI Length period. Default value is 10.
Returns
-------
Tsl, Trend : ndarray
Tsl 指标和 Trend 趋势指示方向 (-1, 0, 1) 分别代表 (下跌, 无明显趋势, 上涨)
the Tsl indicator and thread directions sequence. (-1, 0, 1) means for (Neagtive, No Trend, Positive)
"""
src = klines.close.values
Factor = 3 # Factor of Super Trend
ATR_period = 12 # ATR period
Up = (klines.high + klines.low) / 2 - (Factor * talib.ATR(klines.high,
klines.low,
klines.close,
ATR_period))
Dn = (klines.high + klines.low) / 2 + (Factor * talib.ATR(klines.high,
klines.low,
klines.close,
ATR_period))
TUp = np.full([len(src)], np.nan)
for i in np.arange(1, len(src)):
TUp[i] = max(Up[i], TUp[i - 1]) if (src[i - 1] > TUp[i - 1]) else Up[i]
TDown = np.full([len(src)], np.nan)
for i in np.arange(1, len(src)):
TDown[i] = min(Dn[i], TDown[i - 1]) if (src[i - 1] < TDown[i - 1]) else Dn[i]
Trend = np.full([len(src)], np.nan)
for i in np.arange(1, len(src)):
Trend[i] = 1 if (src[i] > TDown[i - 1]) else (-1 if (src[i] < TUp[i - 1]) else Trend[i - 1])
Tsl = np.where(Trend == 1, TUp, TDown)
return Tsl, Trend
def Volume_HMA(klines, period=5):
"""
交易量加权船型移动平均线 HMA，方向指示性类似于 Moving Average ADX，但它们通过不同的指标实现。
Hull Moving Average with Volume weighted, diretions similar like ADX_MA
Source: https://www.tradingview.com/script/XTViDINu-VHMA/
Translator: 阿财（Rgveda@github）（4910163#qq.com）
Parameters
----------
klines : (N,) array_like
传入 OHLC Kline 序列。
The OHLC Kline.
period : int or None, optional
DI 统计周期 默认值为 10
DI Length period. Default value is 10.
Returns
-------
vhma, Trend : ndarray
vhma 指标和 Trend 趋势指示方向 (-1/-2, 0, 1/2) 分别代表 (下跌, 无明显趋势, 上涨)
the vhma indicator and thread directions sequence. (-1/-2, 0, 1/2) means for (Neagtive, No Trend, Positive)
"""
src1 = talib.EMA(klines.close * klines.volume, period) / talib.EMA(klines.volume, period)
vhma = TA_HMA(src1, period)
vhma_s = pd.Series(vhma)
lineDirection = np.where((vhma > vhma_s.shift(1).values), 1, -1)
hu = np.where((vhma > vhma_s.shift(2).values), 1, -1)
return vhma, lineDirection + hu
