617 lines
18 KiB
Python
617 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 sys
|
|
import warnings
|
|
from datetime import datetime, timedelta
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
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 not is_same_day:
|
|
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]
|
|
for x in range(2):
|
|
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
|
|
if min_periods == window and len(series) > window:
|
|
return numpy_rolling_std(series, window, True)
|
|
else:
|
|
try:
|
|
return series.rolling(window=window, min_periods=min_periods).std()
|
|
except BaseException:
|
|
return pd.Series(series).rolling(window=window, min_periods=min_periods).std()
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def rolling_mean(series, window=200, min_periods=None):
|
|
min_periods = window if min_periods is None else min_periods
|
|
if min_periods == window and len(series) > window:
|
|
return numpy_rolling_mean(series, window, True)
|
|
else:
|
|
try:
|
|
return series.rolling(window=window, min_periods=min_periods).mean()
|
|
except BaseException:
|
|
return pd.Series(series).rolling(window=window, min_periods=min_periods).mean()
|
|
|
|
# ---------------------------------------------
|
|
|
|
|
|
def rolling_min(series, window=14, min_periods=None):
|
|
min_periods = window if min_periods is None else min_periods
|
|
try:
|
|
return series.rolling(window=window, min_periods=min_periods).min()
|
|
except BaseException:
|
|
return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
def rolling_max(series, window=14, min_periods=None):
|
|
min_periods = window if min_periods is None else min_periods
|
|
try:
|
|
return series.rolling(window=window, min_periods=min_periods).min()
|
|
except BaseException:
|
|
return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
|
|
|
|
|
|
# ---------------------------------------------
|
|
|
|
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 BaseException:
|
|
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 BaseException:
|
|
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 BaseException:
|
|
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 BaseException:
|
|
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
|