Merge pull request #79 from gcarq/qtpylib

Include new indicators from qtpylib

freqtrade/analyze.py

@@ -5,6 +5,7 @@ from datetime import timedelta
 import arrow
 import talib.abstract as ta
 from pandas import DataFrame
+from qtpylib.indicators import awesome_oscillator, crossed_above
 
 from freqtrade import exchange
 from freqtrade.exchange import Bittrex, get_ticker_history
@@ -41,6 +42,8 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
     dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
     dataframe['mfi'] = ta.MFI(dataframe)
     dataframe['cci'] = ta.CCI(dataframe)
+    dataframe['ao'] = awesome_oscillator(dataframe)
+
     return dataframe
 
 

freqtrade/tests/test_analyze.py

@@ -1,4 +1,5 @@
 # pragma pylint: disable=missing-docstring
+import json
 import pytest
 import arrow
 from pandas import DataFrame
@@ -6,31 +7,19 @@ from pandas import DataFrame
 from freqtrade.analyze import parse_ticker_dataframe, populate_buy_trend, populate_indicators, \
     get_buy_signal
 
-RESULT_BITTREX = {
-    'success': True,
-    'message': '',
-    'result': [
-        {'O': 0.00065311, 'H': 0.00065311, 'L': 0.00065311, 'C': 0.00065311, 'V': 22.17210568, 'T': '2017-08-30T10:40:00', 'BV': 0.01448082},
-        {'O': 0.00066194, 'H': 0.00066195, 'L': 0.00066194, 'C': 0.00066195, 'V': 33.4727437, 'T': '2017-08-30T10:34:00', 'BV': 0.02215696},
-        {'O': 0.00065311, 'H': 0.00065311, 'L': 0.00065311, 'C': 0.00065311, 'V': 53.85127609, 'T': '2017-08-30T10:37:00', 'BV': 0.0351708},
-        {'O': 0.00066194, 'H': 0.00066194, 'L': 0.00065311, 'C': 0.00065311, 'V': 46.29210665, 'T': '2017-08-30T10:42:00', 'BV': 0.03063118},
-    ]
-}
-
 @pytest.fixture
 def result():
-    return parse_ticker_dataframe(RESULT_BITTREX['result'], arrow.get('2017-08-30T10:00:00'))
+    with open('freqtrade/tests/testdata/btc-eth.json') as data_file:
+        data = json.load(data_file)
+
+    return parse_ticker_dataframe(data['result'], arrow.get('2017-08-30T10:00:00'))
 
 def test_dataframe_has_correct_columns(result):
     assert result.columns.tolist() == \
                         ['close', 'high', 'low', 'open', 'date', 'volume']
 
-def test_orders_by_date(result):
+def test_dataframe_has_correct_length(result):
-    assert result['date'].tolist() == \
+    assert len(result.index) == 5751
-                        ['2017-08-30T10:34:00',
-                        '2017-08-30T10:37:00',
-                        '2017-08-30T10:40:00',
-                        '2017-08-30T10:42:00']
 
 def test_populates_buy_trend(result):
     dataframe = populate_buy_trend(populate_indicators(result))

freqtrade/tests/test_hyperopt.py

@@ -7,6 +7,7 @@ from functools import reduce
 import pytest
 import arrow
 from pandas import DataFrame
+from qtpylib.indicators import crossed_above
 
 from hyperopt import fmin, tpe, hp
 
@@ -63,6 +64,7 @@ def buy_strategy_generator(params):
         triggers = {
             'lower_bb': dataframe['tema'] <= dataframe['blower'],
             'faststoch10': (dataframe['fastd'] >= 10) & (prev_fastd < 10),
+            'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
         }
         conditions.append(triggers.get(params['trigger']['type']))
 
@@ -124,7 +126,8 @@ def test_hyperopt(conf, pairs, mocker):
         ]),
         'trigger': hp.choice('trigger', [
             {'type': 'lower_bb'},
-            {'type': 'faststoch10'}
+            {'type': 'faststoch10'},
+            {'type': 'ao_cross_zero'}
         ]),
     }
     print('Best parameters {}'.format(fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=40)))

qtpylib/indicators.py

@@ -0,0 +1,619 @@
+#!/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