qtpylib/indicators.py updated
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hroff-1902
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193
freqtrade/vendor/qtpylib/indicators.py
vendored
193
freqtrade/vendor/qtpylib/indicators.py
vendored
@ -4,13 +4,13 @@
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# QTPyLib: Quantitative Trading Python Library
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# https://github.com/ranaroussi/qtpylib
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#
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# Copyright 2016 Ran Aroussi
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# Copyright 2016-2018 Ran Aroussi
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#
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# Licensed under the GNU Lesser General Public License, v3.0 (the "License");
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.gnu.org/licenses/lgpl-3.0.en.html
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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@ -19,8 +19,8 @@
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# limitations under the License.
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#
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import sys
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import warnings
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import sys
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from datetime import datetime, timedelta
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import numpy as np
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@ -62,19 +62,20 @@ def numpy_rolling_series(func):
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@numpy_rolling_series
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def numpy_rolling_mean(data, window, as_source=False):
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return np.mean(numpy_rolling_window(data, window), -1)
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return np.mean(numpy_rolling_window(data, window), axis=-1)
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@numpy_rolling_series
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def numpy_rolling_std(data, window, as_source=False):
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return np.std(numpy_rolling_window(data, window), -1)
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return np.std(numpy_rolling_window(data, window), axis=-1, ddof=1)
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# ---------------------------------------------
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def session(df, start='17:00', end='16:00'):
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""" remove previous globex day from df """
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if len(df) == 0:
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if df.empty:
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return df
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# get start/end/now as decimals
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@ -103,47 +104,50 @@ def session(df, start='17:00', end='16:00'):
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return df.copy()
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# ---------------------------------------------
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def heikinashi(bars):
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bars = bars.copy()
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bars['ha_close'] = (bars['open'] + bars['high'] +
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bars['low'] + bars['close']) / 4
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bars['ha_open'] = (bars['open'].shift(1) + bars['close'].shift(1)) / 2
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bars.loc[:1, 'ha_open'] = bars['open'].values[0]
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for x in range(2):
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bars.loc[1:, 'ha_open'] = (
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(bars['ha_open'].shift(1) + bars['ha_close'].shift(1)) / 2)[1:]
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# ha open
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bars.loc[:1, 'ha_open'] = (bars['open'] + bars['close']) / 2
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prev_open = bars[:1]['ha_open'].values[0]
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for idx, _ in bars[1:][['ha_open', 'ha_close']].iterrows():
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loc = bars.index.get_loc(idx)
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prev_open = (prev_open + bars['ha_close'].values[loc - 1]) / 2
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bars.loc[loc:loc + 1, 'ha_open'] = prev_open
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bars['ha_high'] = bars.loc[:, ['high', 'ha_open', 'ha_close']].max(axis=1)
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bars['ha_low'] = bars.loc[:, ['low', 'ha_open', 'ha_close']].min(axis=1)
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return pd.DataFrame(
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index=bars.index,
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data={
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'open': bars['ha_open'],
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return pd.DataFrame(index=bars.index,
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data={'open': bars['ha_open'],
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'high': bars['ha_high'],
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'low': bars['ha_low'],
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'close': bars['ha_close']})
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# ---------------------------------------------
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def tdi(series, rsi_len=13, bollinger_len=34, rsi_smoothing=2,
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rsi_signal_len=7, bollinger_std=1.6185):
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rsi_series = rsi(series, rsi_len)
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bb_series = bollinger_bands(rsi_series, bollinger_len, bollinger_std)
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signal = sma(rsi_series, rsi_signal_len)
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rsi_series = sma(rsi_series, rsi_smoothing)
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def tdi(series, rsi_lookback=13, rsi_smooth_len=2,
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rsi_signal_len=7, bb_lookback=34, bb_std=1.6185):
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rsi_data = rsi(series, rsi_lookback)
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rsi_smooth = sma(rsi_data, rsi_smooth_len)
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rsi_signal = sma(rsi_data, rsi_signal_len)
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bb_series = bollinger_bands(rsi_data, bb_lookback, bb_std)
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return pd.DataFrame(index=series.index, data={
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"rsi": rsi_series,
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"signal": signal,
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"bbupper": bb_series['upper'],
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"bblower": bb_series['lower'],
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"bbmid": bb_series['mid']
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"rsi": rsi_data,
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"rsi_signal": rsi_signal,
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"rsi_smooth": rsi_smooth,
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"rsi_bb_upper": bb_series['upper'],
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"rsi_bb_lower": bb_series['lower'],
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"rsi_bb_mid": bb_series['mid']
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})
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# ---------------------------------------------
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@ -163,8 +167,8 @@ def awesome_oscillator(df, weighted=False, fast=5, slow=34):
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# ---------------------------------------------
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def nans(len=1):
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mtx = np.empty(len)
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def nans(length=1):
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mtx = np.empty(length)
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mtx[:] = np.nan
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return mtx
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@ -222,7 +226,7 @@ def crossed(series1, series2, direction=None):
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if isinstance(series1, np.ndarray):
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series1 = pd.Series(series1)
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if isinstance(series2, int) or isinstance(series2, float) or isinstance(series2, np.ndarray):
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if isinstance(series2, (float, int, np.ndarray)):
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series2 = pd.Series(index=series1.index, data=series2)
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if direction is None or direction == "above":
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@ -256,7 +260,7 @@ def rolling_std(series, window=200, min_periods=None):
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else:
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try:
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return series.rolling(window=window, min_periods=min_periods).std()
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except BaseException:
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except Exception as e:
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return pd.Series(series).rolling(window=window, min_periods=min_periods).std()
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# ---------------------------------------------
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@ -269,7 +273,7 @@ def rolling_mean(series, window=200, min_periods=None):
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else:
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try:
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return series.rolling(window=window, min_periods=min_periods).mean()
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except BaseException:
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except Exception as e:
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return pd.Series(series).rolling(window=window, min_periods=min_periods).mean()
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# ---------------------------------------------
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@ -279,7 +283,7 @@ def rolling_min(series, window=14, min_periods=None):
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min_periods = window if min_periods is None else min_periods
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try:
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return series.rolling(window=window, min_periods=min_periods).min()
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except BaseException:
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except Exception as e:
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return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
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@ -289,7 +293,7 @@ def rolling_max(series, window=14, min_periods=None):
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min_periods = window if min_periods is None else min_periods
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try:
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return series.rolling(window=window, min_periods=min_periods).min()
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except BaseException:
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except Exception as e:
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return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
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@ -299,16 +303,17 @@ def rolling_weighted_mean(series, window=200, min_periods=None):
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min_periods = window if min_periods is None else min_periods
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try:
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return series.ewm(span=window, min_periods=min_periods).mean()
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except BaseException:
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except Exception as e:
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return pd.ewma(series, span=window, min_periods=min_periods)
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# ---------------------------------------------
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def hull_moving_average(series, window=200):
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wma = (2 * rolling_weighted_mean(series, window=window / 2)) - \
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rolling_weighted_mean(series, window=window)
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return rolling_weighted_mean(wma, window=np.sqrt(window))
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def hull_moving_average(series, window=200, min_periods=None):
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min_periods = window if min_periods is None else min_periods
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ma = (2 * rolling_weighted_mean(series, window / 2, min_periods)) - \
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rolling_weighted_mean(series, window, min_periods)
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return rolling_weighted_mean(ma, np.sqrt(window), min_periods)
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# ---------------------------------------------
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@ -325,8 +330,8 @@ def wma(series, window=200, min_periods=None):
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# ---------------------------------------------
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def hma(series, window=200):
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return hull_moving_average(series, window=window)
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def hma(series, window=200, min_periods=None):
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return hull_moving_average(series, window=window, min_periods=min_periods)
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# ---------------------------------------------
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@ -361,7 +366,7 @@ def rolling_vwap(bars, window=200, min_periods=None):
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min_periods=min_periods).sum()
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right = volume.rolling(window=window, min_periods=min_periods).sum()
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return pd.Series(index=bars.index, data=(left / right))
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return pd.Series(index=bars.index, data=(left / right)).replace([np.inf, -np.inf], float('NaN')).ffill()
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# ---------------------------------------------
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@ -370,6 +375,7 @@ def rsi(series, window=14):
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"""
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compute the n period relative strength indicator
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"""
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# 100-(100/relative_strength)
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deltas = np.diff(series)
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seed = deltas[:window + 1]
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@ -406,13 +412,13 @@ def macd(series, fast=3, slow=10, smooth=16):
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using a fast and slow exponential moving avg'
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return value is emaslow, emafast, macd which are len(x) arrays
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"""
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macd = rolling_weighted_mean(series, window=fast) - \
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macd_line = rolling_weighted_mean(series, window=fast) - \
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rolling_weighted_mean(series, window=slow)
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signal = rolling_weighted_mean(macd, window=smooth)
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histogram = macd - signal
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# return macd, signal, histogram
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signal = rolling_weighted_mean(macd_line, window=smooth)
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histogram = macd_line - signal
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# return macd_line, signal, histogram
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return pd.DataFrame(index=series.index, data={
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'macd': macd.values,
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'macd': macd_line.values,
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'signal': signal.values,
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'histogram': histogram.values
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})
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@ -421,14 +427,14 @@ def macd(series, fast=3, slow=10, smooth=16):
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# ---------------------------------------------
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def bollinger_bands(series, window=20, stds=2):
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sma = rolling_mean(series, window=window)
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std = rolling_std(series, window=window)
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upper = sma + std * stds
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lower = sma - std * stds
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ma = rolling_mean(series, window=window, min_periods=1)
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std = rolling_std(series, window=window, min_periods=1)
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upper = ma + std * stds
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lower = ma - std * stds
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return pd.DataFrame(index=series.index, data={
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'upper': upper,
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'mid': sma,
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'mid': ma,
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'lower': lower
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})
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@ -454,7 +460,7 @@ def returns(series):
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try:
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res = (series / series.shift(1) -
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1).replace([np.inf, -np.inf], float('NaN'))
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except BaseException:
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except Exception as e:
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res = nans(len(series))
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return pd.Series(index=series.index, data=res)
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@ -466,7 +472,7 @@ def log_returns(series):
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try:
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res = np.log(series / series.shift(1)
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).replace([np.inf, -np.inf], float('NaN'))
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except BaseException:
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except Exception as e:
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res = nans(len(series))
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return pd.Series(index=series.index, data=res)
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@ -479,7 +485,7 @@ def implied_volatility(series, window=252):
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logret = np.log(series / series.shift(1)
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).replace([np.inf, -np.inf], float('NaN'))
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res = numpy_rolling_std(logret, window) * np.sqrt(window)
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except BaseException:
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except Exception as e:
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res = nans(len(series))
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return pd.Series(index=series.index, data=res)
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@ -530,32 +536,52 @@ def stoch(df, window=14, d=3, k=3, fast=False):
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compute the n period relative strength indicator
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http://excelta.blogspot.co.il/2013/09/stochastic-oscillator-technical.html
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"""
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highs_ma = pd.concat([df['high'].shift(i)
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for i in np.arange(window)], 1).apply(list, 1)
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highs_ma = highs_ma.T.max().T
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lows_ma = pd.concat([df['low'].shift(i)
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for i in np.arange(window)], 1).apply(list, 1)
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lows_ma = lows_ma.T.min().T
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my_df = pd.DataFrame(index=df.index)
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fast_k = ((df['close'] - lows_ma) / (highs_ma - lows_ma)) * 100
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fast_d = numpy_rolling_mean(fast_k, d)
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my_df['rolling_max'] = df['high'].rolling(window).max()
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my_df['rolling_min'] = df['low'].rolling(window).min()
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my_df['fast_k'] = 100 * (df['close'] - my_df['rolling_min'])/(my_df['rolling_max'] - my_df['rolling_min'])
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my_df['fast_d'] = my_df['fast_k'].rolling(d).mean()
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if fast:
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data = {
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'k': fast_k,
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'd': fast_d
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}
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return my_df.loc[:, ['fast_k', 'fast_d']]
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else:
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slow_k = numpy_rolling_mean(fast_k, k)
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slow_d = numpy_rolling_mean(slow_k, d)
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data = {
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'k': slow_k,
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'd': slow_d
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}
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my_df['slow_k'] = my_df['fast_k'].rolling(k).mean()
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my_df['slow_d'] = my_df['slow_k'].rolling(d).mean()
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return pd.DataFrame(index=df.index, data=data)
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return my_df.loc[:, ['slow_k', 'slow_d']]
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# ---------------------------------------------
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def zlma(series, window=20, min_periods=None, kind="ema"):
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"""
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John Ehlers' Zero lag (exponential) moving average
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https://en.wikipedia.org/wiki/Zero_lag_exponential_moving_average
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"""
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min_periods = window if min_periods is None else min_periods
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lag = (window - 1) // 2
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series = 2 * series - series.shift(lag)
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if kind in ['ewm', 'ema']:
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return wma(series, lag, min_periods)
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elif kind == "hma":
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return hma(series, lag, min_periods)
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return sma(series, lag, min_periods)
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def zlema(series, window, min_periods=None):
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return zlma(series, window, min_periods, kind="ema")
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def zlsma(series, window, min_periods=None):
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return zlma(series, window, min_periods, kind="sma")
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def zlhma(series, window, min_periods=None):
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return zlma(series, window, min_periods, kind="hma")
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# ---------------------------------------------
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@ -571,13 +597,13 @@ def zscore(bars, window=20, stds=1, col='close'):
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def pvt(bars):
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""" Price Volume Trend """
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pvt = ((bars['close'] - bars['close'].shift(1)) /
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trend = ((bars['close'] - bars['close'].shift(1)) /
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bars['close'].shift(1)) * bars['volume']
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return pvt.cumsum()
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return trend.cumsum()
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# =============================================
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PandasObject.session = session
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PandasObject.atr = atr
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PandasObject.bollinger_bands = bollinger_bands
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@ -613,4 +639,11 @@ PandasObject.rolling_weighted_mean = rolling_weighted_mean
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PandasObject.sma = sma
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PandasObject.wma = wma
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PandasObject.ema = wma
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PandasObject.hma = hma
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PandasObject.zlsma = zlsma
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PandasObject.zlwma = zlema
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PandasObject.zlema = zlema
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PandasObject.zlhma = zlhma
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PandasObject.zlma = zlma
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