stable/qtpylib/indicators.py
2017-10-25 18:37:20 +03:00

620 lines
18 KiB
Python

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# QTPyLib: Quantitative Trading Python Library
# https://github.com/ranaroussi/qtpylib
#
# Copyright 2016 Ran Aroussi
#
# Licensed under the GNU Lesser General Public License, v3.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.gnu.org/licenses/lgpl-3.0.en.html
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import numpy as np
import pandas as pd
import warnings
import sys
from datetime import datetime, timedelta
from pandas.core.base import PandasObject
# =============================================
# check min, python version
if sys.version_info < (3, 4):
raise SystemError("QTPyLib requires Python version >= 3.4")
# =============================================
warnings.simplefilter(action="ignore", category=RuntimeWarning)
# =============================================
def numpy_rolling_window(data, window):
shape = data.shape[:-1] + (data.shape[-1] - window + 1, window)
strides = data.strides + (data.strides[-1],)
return np.lib.stride_tricks.as_strided(data, shape=shape, strides=strides)
def numpy_rolling_series(func):
def func_wrapper(data, window, as_source=False):
series = data.values if isinstance(data, pd.Series) else data
new_series = np.empty(len(series)) * np.nan
calculated = func(series, window)
new_series[-len(calculated):] = calculated
if as_source and isinstance(data, pd.Series):
return pd.Series(index=data.index, data=new_series)
return new_series
return func_wrapper
@numpy_rolling_series
def numpy_rolling_mean(data, window, as_source=False):
return np.mean(numpy_rolling_window(data, window), -1)
@numpy_rolling_series
def numpy_rolling_std(data, window, as_source=False):
return np.std(numpy_rolling_window(data, window), -1)
# ---------------------------------------------
def session(df, start='17:00', end='16:00'):
""" remove previous globex day from df """
if len(df) == 0:
return df
# get start/end/now as decimals
int_start = list(map(int, start.split(':')))
int_start = (int_start[0] + int_start[1] - 1 / 100) - 0.0001
int_end = list(map(int, end.split(':')))
int_end = int_end[0] + int_end[1] / 100
int_now = (df[-1:].index.hour[0] + (df[:1].index.minute[0]) / 100)
# same-dat session?
is_same_day = int_end > int_start
# set pointers
curr = prev = df[-1:].index[0].strftime('%Y-%m-%d')
# globex/forex session
if is_same_day == False:
prev = (datetime.strptime(curr, '%Y-%m-%d') -
timedelta(1)).strftime('%Y-%m-%d')
# slice
if int_now >= int_start:
df = df[df.index >= curr + ' ' + start]
else:
df = df[df.index >= prev + ' ' + start]
return df.copy()
# ---------------------------------------------
def heikinashi(bars):
bars = bars.copy()
bars['ha_close'] = (bars['open'] + bars['high'] +
bars['low'] + bars['close']) / 4
bars['ha_open'] = (bars['open'].shift(1) + bars['close'].shift(1)) / 2
bars.loc[:1, 'ha_open'] = bars['open'].values[0]
bars.loc[1:, 'ha_open'] = (
(bars['ha_open'].shift(1) + bars['ha_close'].shift(1)) / 2)[1:]
bars['ha_high'] = bars.loc[:, ['high', 'ha_open', 'ha_close']].max(axis=1)
bars['ha_low'] = bars.loc[:, ['low', 'ha_open', 'ha_close']].min(axis=1)
return pd.DataFrame(index=bars.index, data={'open': bars['ha_open'],
'high': bars['ha_high'], 'low': bars['ha_low'], 'close': bars['ha_close']})
# ---------------------------------------------
def tdi(series, rsi_len=13, bollinger_len=34, rsi_smoothing=2, rsi_signal_len=7, bollinger_std=1.6185):
rsi_series = rsi(series, rsi_len)
bb_series = bollinger_bands(rsi_series, bollinger_len, bollinger_std)
signal = sma(rsi_series, rsi_signal_len)
rsi_series = sma(rsi_series, rsi_smoothing)
return pd.DataFrame(index=series.index, data={
"rsi": rsi_series,
"signal": signal,
"bbupper": bb_series['upper'],
"bblower": bb_series['lower'],
"bbmid": bb_series['mid']
})
# ---------------------------------------------
def awesome_oscillator(df, weighted=False, fast=5, slow=34):
midprice = (df['high'] + df['low']) / 2
if weighted:
ao = (midprice.ewm(fast).mean() - midprice.ewm(slow).mean()).values
else:
ao = numpy_rolling_mean(midprice, fast) - \
numpy_rolling_mean(midprice, slow)
return pd.Series(index=df.index, data=ao)
# ---------------------------------------------
def nans(len=1):
mtx = np.empty(len)
mtx[:] = np.nan
return mtx
# ---------------------------------------------
def typical_price(bars):
res = (bars['high'] + bars['low'] + bars['close']) / 3.
return pd.Series(index=bars.index, data=res)
# ---------------------------------------------
def mid_price(bars):
res = (bars['high'] + bars['low']) / 2.
return pd.Series(index=bars.index, data=res)
# ---------------------------------------------
def ibs(bars):
""" Internal bar strength """
res = np.round((bars['close'] - bars['low']) /
(bars['high'] - bars['low']), 2)
return pd.Series(index=bars.index, data=res)
# ---------------------------------------------
def true_range(bars):
return pd.DataFrame({
"hl": bars['high'] - bars['low'],
"hc": abs(bars['high'] - bars['close'].shift(1)),
"lc": abs(bars['low'] - bars['close'].shift(1))
}).max(axis=1)
# ---------------------------------------------
def atr(bars, window=14, exp=False):
tr = true_range(bars)
if exp:
res = rolling_weighted_mean(tr, window)
else:
res = rolling_mean(tr, window)
res = pd.Series(res)
return (res.shift(1) * (window - 1) + res) / window
# ---------------------------------------------
def crossed(series1, series2, direction=None):
if isinstance(series1, np.ndarray):
series1 = pd.Series(series1)
if isinstance(series2, int) or isinstance(series2, float) or isinstance(series2, np.ndarray):
series2 = pd.Series(index=series1.index, data=series2)
if direction is None or direction == "above":
above = pd.Series((series1 > series2) & (
series1.shift(1) <= series2.shift(1)))
if direction is None or direction == "below":
below = pd.Series((series1 < series2) & (
series1.shift(1) >= series2.shift(1)))
if direction is None:
return above or below
return above if direction is "above" else below
def crossed_above(series1, series2):
return crossed(series1, series2, "above")
def crossed_below(series1, series2):
return crossed(series1, series2, "below")
# ---------------------------------------------
def rolling_std(series, window=200, min_periods=None):
min_periods = window if min_periods is None else min_periods
try:
if min_periods == window:
return numpy_rolling_std(series, window, True)
else:
try:
return series.rolling(window=window, min_periods=min_periods).std()
except:
return pd.Series(series).rolling(window=window, min_periods=min_periods).std()
except:
return pd.rolling_std(series, window=window, min_periods=min_periods)
# ---------------------------------------------
def rolling_mean(series, window=200, min_periods=None):
min_periods = window if min_periods is None else min_periods
try:
if min_periods == window:
return numpy_rolling_mean(series, window, True)
else:
try:
return series.rolling(window=window, min_periods=min_periods).mean()
except:
return pd.Series(series).rolling(window=window, min_periods=min_periods).mean()
except:
return pd.rolling_mean(series, window=window, min_periods=min_periods)
# ---------------------------------------------
def rolling_min(series, window=14, min_periods=None):
min_periods = window if min_periods is None else min_periods
try:
try:
return series.rolling(window=window, min_periods=min_periods).min()
except:
return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
except:
return pd.rolling_min(series, window=window, min_periods=min_periods)
# ---------------------------------------------
def rolling_max(series, window=14, min_periods=None):
min_periods = window if min_periods is None else min_periods
try:
try:
return series.rolling(window=window, min_periods=min_periods).min()
except:
return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
except:
return pd.rolling_min(series, window=window, min_periods=min_periods)
# ---------------------------------------------
def rolling_weighted_mean(series, window=200, min_periods=None):
min_periods = window if min_periods is None else min_periods
try:
return series.ewm(span=window, min_periods=min_periods).mean()
except:
return pd.ewma(series, span=window, min_periods=min_periods)
# ---------------------------------------------
def hull_moving_average(series, window=200):
wma = (2 * rolling_weighted_mean(series, window=window / 2)) - \
rolling_weighted_mean(series, window=window)
return rolling_weighted_mean(wma, window=np.sqrt(window))
# ---------------------------------------------
def sma(series, window=200, min_periods=None):
return rolling_mean(series, window=window, min_periods=min_periods)
# ---------------------------------------------
def wma(series, window=200, min_periods=None):
return rolling_weighted_mean(series, window=window, min_periods=min_periods)
# ---------------------------------------------
def hma(series, window=200):
return hull_moving_average(series, window=window)
# ---------------------------------------------
def vwap(bars):
"""
calculate vwap of entire time series
(input can be pandas series or numpy array)
bars are usually mid [ (h+l)/2 ] or typical [ (h+l+c)/3 ]
"""
typical = ((bars['high'] + bars['low'] + bars['close']) / 3).values
volume = bars['volume'].values
return pd.Series(index=bars.index,
data=np.cumsum(volume * typical) / np.cumsum(volume))
# ---------------------------------------------
def rolling_vwap(bars, window=200, min_periods=None):
"""
calculate vwap using moving window
(input can be pandas series or numpy array)
bars are usually mid [ (h+l)/2 ] or typical [ (h+l+c)/3 ]
"""
min_periods = window if min_periods is None else min_periods
typical = ((bars['high'] + bars['low'] + bars['close']) / 3)
volume = bars['volume']
left = (volume * typical).rolling(window=window,
min_periods=min_periods).sum()
right = volume.rolling(window=window, min_periods=min_periods).sum()
return pd.Series(index=bars.index, data=(left / right))
# ---------------------------------------------
def rsi(series, window=14):
"""
compute the n period relative strength indicator
"""
# 100-(100/relative_strength)
deltas = np.diff(series)
seed = deltas[:window + 1]
# default values
ups = seed[seed > 0].sum() / window
downs = -seed[seed < 0].sum() / window
rsival = np.zeros_like(series)
rsival[:window] = 100. - 100. / (1. + ups / downs)
# period values
for i in range(window, len(series)):
delta = deltas[i - 1]
if delta > 0:
upval = delta
downval = 0
else:
upval = 0
downval = -delta
ups = (ups * (window - 1) + upval) / window
downs = (downs * (window - 1.) + downval) / window
rsival[i] = 100. - 100. / (1. + ups / downs)
# return rsival
return pd.Series(index=series.index, data=rsival)
# ---------------------------------------------
def macd(series, fast=3, slow=10, smooth=16):
"""
compute the MACD (Moving Average Convergence/Divergence)
using a fast and slow exponential moving avg'
return value is emaslow, emafast, macd which are len(x) arrays
"""
macd = rolling_weighted_mean(series, window=fast) - \
rolling_weighted_mean(series, window=slow)
signal = rolling_weighted_mean(macd, window=smooth)
histogram = macd - signal
# return macd, signal, histogram
return pd.DataFrame(index=series.index, data={
'macd': macd.values,
'signal': signal.values,
'histogram': histogram.values
})
# ---------------------------------------------
def bollinger_bands(series, window=20, stds=2):
sma = rolling_mean(series, window=window)
std = rolling_std(series, window=window)
upper = sma + std * stds
lower = sma - std * stds
return pd.DataFrame(index=series.index, data={
'upper': upper,
'mid': sma,
'lower': lower
})
# ---------------------------------------------
def weighted_bollinger_bands(series, window=20, stds=2):
ema = rolling_weighted_mean(series, window=window)
std = rolling_std(series, window=window)
upper = ema + std * stds
lower = ema - std * stds
return pd.DataFrame(index=series.index, data={
'upper': upper.values,
'mid': ema.values,
'lower': lower.values
})
# ---------------------------------------------
def returns(series):
try:
res = (series / series.shift(1) -
1).replace([np.inf, -np.inf], float('NaN'))
except:
res = nans(len(series))
return pd.Series(index=series.index, data=res)
# ---------------------------------------------
def log_returns(series):
try:
res = np.log(series / series.shift(1)
).replace([np.inf, -np.inf], float('NaN'))
except:
res = nans(len(series))
return pd.Series(index=series.index, data=res)
# ---------------------------------------------
def implied_volatility(series, window=252):
try:
logret = np.log(series / series.shift(1)
).replace([np.inf, -np.inf], float('NaN'))
res = numpy_rolling_std(logret, window) * np.sqrt(window)
except:
res = nans(len(series))
return pd.Series(index=series.index, data=res)
# ---------------------------------------------
def keltner_channel(bars, window=14, atrs=2):
typical_mean = rolling_mean(typical_price(bars), window)
atrval = atr(bars, window) * atrs
upper = typical_mean + atrval
lower = typical_mean - atrval
return pd.DataFrame(index=bars.index, data={
'upper': upper.values,
'mid': typical_mean.values,
'lower': lower.values
})
# ---------------------------------------------
def roc(series, window=14):
"""
compute rate of change
"""
res = (series - series.shift(window)) / series.shift(window)
return pd.Series(index=series.index, data=res)
# ---------------------------------------------
def cci(series, window=14):
"""
compute commodity channel index
"""
price = typical_price(series)
typical_mean = rolling_mean(price, window)
res = (price - typical_mean) / (.015 * np.std(typical_mean))
return pd.Series(index=series.index, data=res)
# ---------------------------------------------
def stoch(df, window=14, d=3, k=3, fast=False):
"""
compute the n period relative strength indicator
http://excelta.blogspot.co.il/2013/09/stochastic-oscillator-technical.html
"""
highs_ma = pd.concat([df['high'].shift(i)
for i in np.arange(window)], 1).apply(list, 1)
highs_ma = highs_ma.T.max().T
lows_ma = pd.concat([df['low'].shift(i)
for i in np.arange(window)], 1).apply(list, 1)
lows_ma = lows_ma.T.min().T
fast_k = ((df['close'] - lows_ma) / (highs_ma - lows_ma)) * 100
fast_d = numpy_rolling_mean(fast_k, d)
if fast:
data = {
'k': fast_k,
'd': fast_d
}
else:
slow_k = numpy_rolling_mean(fast_k, k)
slow_d = numpy_rolling_mean(slow_k, d)
data = {
'k': slow_k,
'd': slow_d
}
return pd.DataFrame(index=df.index, data=data)
# ---------------------------------------------
def zscore(bars, window=20, stds=1, col='close'):
""" get zscore of price """
std = numpy_rolling_std(bars[col], window)
mean = numpy_rolling_mean(bars[col], window)
return (bars[col] - mean) / (std * stds)
# ---------------------------------------------
def pvt(bars):
""" Price Volume Trend """
pvt = ((bars['close'] - bars['close'].shift(1)) /
bars['close'].shift(1)) * bars['volume']
return pvt.cumsum()
# =============================================
PandasObject.session = session
PandasObject.atr = atr
PandasObject.bollinger_bands = bollinger_bands
PandasObject.cci = cci
PandasObject.crossed = crossed
PandasObject.crossed_above = crossed_above
PandasObject.crossed_below = crossed_below
PandasObject.heikinashi = heikinashi
PandasObject.hull_moving_average = hull_moving_average
PandasObject.ibs = ibs
PandasObject.implied_volatility = implied_volatility
PandasObject.keltner_channel = keltner_channel
PandasObject.log_returns = log_returns
PandasObject.macd = macd
PandasObject.returns = returns
PandasObject.roc = roc
PandasObject.rolling_max = rolling_max
PandasObject.rolling_min = rolling_min
PandasObject.rolling_mean = rolling_mean
PandasObject.rolling_std = rolling_std
PandasObject.rsi = rsi
PandasObject.stoch = stoch
PandasObject.zscore = zscore
PandasObject.pvt = pvt
PandasObject.tdi = tdi
PandasObject.true_range = true_range
PandasObject.mid_price = mid_price
PandasObject.typical_price = typical_price
PandasObject.vwap = vwap
PandasObject.rolling_vwap = rolling_vwap
PandasObject.weighted_bollinger_bands = weighted_bollinger_bands
PandasObject.rolling_weighted_mean = rolling_weighted_mean
PandasObject.sma = sma
PandasObject.wma = wma
PandasObject.hma = hma