Merge branch 'release/0.13.0'

MANIFEST.in

@@ -1,7 +1,5 @@
 include LICENSE
 include README.md
 include config.json.example
-include freqtrade/exchange/*.py
+recursive-include freqtrade *.py
-include freqtrade/rpc/*.py
-include freqtrade/tests/*.py
 include freqtrade/tests/testdata/*.json

freqtrade/__init__.py

@@ -1,3 +1,3 @@
-__version__ = '0.12.0'
+__version__ = '0.13.0'
 
 from . import main

freqtrade/analyze.py

@@ -4,17 +4,18 @@ from datetime import timedelta
 
 import arrow
 import talib.abstract as ta
-from pandas import DataFrame
+from pandas import DataFrame, to_datetime
 
 from freqtrade import exchange
 from freqtrade.exchange import Bittrex, get_ticker_history
+from freqtrade.vendor.qtpylib.indicators import awesome_oscillator
 
 logging.basicConfig(level=logging.DEBUG,
                     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
 
 
-def parse_ticker_dataframe(ticker: list, minimum_date: arrow.Arrow) -> DataFrame:
+def parse_ticker_dataframe(ticker: list) -> DataFrame:
     """
     Analyses the trend for the given pair
     :param pair: pair as str in format BTC_ETH or BTC-ETH
@@ -22,8 +23,9 @@ def parse_ticker_dataframe(ticker: list, minimum_date: arrow.Arrow) -> DataFrame
     """
     df = DataFrame(ticker) \
         .drop('BV', 1) \
-        .rename(columns={'C':'close', 'V':'volume', 'O':'open', 'H':'high', 'L':'low', 'T':'date'}) \
+        .rename(columns={'C':'close', 'V':'volume', 'O':'open', 'H':'high', 'L':'low', 'T':'date'})
-        .sort_values('date')
+    df['date'] = to_datetime(df['date'], utc=True, infer_datetime_format=True)
+    df.sort_values('date', inplace=True)
     return df
 
 
@@ -41,6 +43,17 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
     dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
     dataframe['mfi'] = ta.MFI(dataframe)
     dataframe['cci'] = ta.CCI(dataframe)
+    dataframe['rsi'] = ta.RSI(dataframe)
+    dataframe['mom'] = ta.MOM(dataframe)
+    dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
+    dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+    dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+    dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+    dataframe['ao'] = awesome_oscillator(dataframe)
+    macd = ta.MACD(dataframe)
+    dataframe['macd'] = macd['macd']
+    dataframe['macdsignal'] = macd['macdsignal']
+    dataframe['macdhist'] = macd['macdhist']
     return dataframe
 
 
@@ -50,14 +63,14 @@ def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
     :param dataframe: DataFrame
     :return: DataFrame with buy column
     """
-    dataframe.loc[
+    dataframe.ix[
         (dataframe['close'] < dataframe['sma']) &
         (dataframe['tema'] <= dataframe['blower']) &
         (dataframe['mfi'] < 25) &
         (dataframe['fastd'] < 25) &
         (dataframe['adx'] > 30),
         'buy'] = 1
-    dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
+    dataframe.ix[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
 
     return dataframe
 
@@ -70,7 +83,7 @@ def analyze_ticker(pair: str) -> DataFrame:
     """
     minimum_date = arrow.utcnow().shift(hours=-24)
     data = get_ticker_history(pair, minimum_date)
-    dataframe = parse_ticker_dataframe(data['result'], minimum_date)
+    dataframe = parse_ticker_dataframe(data['result'])
 
     if dataframe.empty:
         logger.warning('Empty dataframe for pair %s', pair)

freqtrade/exchange/__init__.py

@@ -85,6 +85,10 @@ def get_balance(currency: str) -> float:
     return EXCHANGE.get_balance(currency)
 
 
+def get_balances():
+    return EXCHANGE.get_balances()
+
+
 def get_ticker(pair: str) -> dict:
     return EXCHANGE.get_ticker(pair)
 

freqtrade/exchange/bittrex.py

@@ -54,6 +54,12 @@ class Bittrex(Exchange):
             raise RuntimeError('{}: {}'.format(self.name.upper(), data['message']))
         return float(data['result']['Balance'] or 0.0)
 
+    def get_balances(self):
+        data = _API.get_balances()
+        if not data['success']:
+            raise RuntimeError('{}: {}'.format(self.name.upper(), data['message']))
+        return data['result']
+    
     def get_ticker(self, pair: str) -> dict:
         data = _API.get_ticker(pair.replace('_', '-'))
         if not data['success']:

freqtrade/exchange/interface.py

@@ -49,6 +49,21 @@ class Exchange(ABC):
         :return: float
         """
 
+    @abstractmethod
+    def get_balances(self) -> List[dict]:
+        """
+        Gets account balances across currencies
+        :return: List of dicts, format: [
+          {
+            'Currency': str,
+            'Balance': float,
+            'Available': float,
+            'Pending': float,
+          }
+          ...
+        ]
+        """
+
     @abstractmethod
     def get_ticker(self, pair: str) -> dict:
         """

freqtrade/main.py

@@ -6,6 +6,7 @@ import time
 import traceback
 from datetime import datetime
 from typing import Dict, Optional
+from signal import signal, SIGINT, SIGABRT, SIGTERM
 
 from jsonschema import validate
 
@@ -223,6 +224,23 @@ def init(config: dict, db_url: Optional[str] = None) -> None:
     else:
         update_state(State.STOPPED)
 
+    # Register signal handlers
+    for sig in (SIGINT, SIGTERM, SIGABRT):
+        signal(sig, cleanup)
+
+
+def cleanup(*args, **kwargs) -> None:
+    """
+    Cleanup the application state und finish all pending tasks
+    :return: None
+    """
+    telegram.send_msg('*Status:* `Stopping trader...`')
+    logger.info('Stopping trader and cleaning up modules...')
+    update_state(State.STOPPED)
+    persistence.cleanup()
+    telegram.cleanup()
+    exit(0)
+
 
 def app(config: dict) -> None:
     """
@@ -251,10 +269,10 @@ def app(config: dict) -> None:
                 time.sleep(exchange.EXCHANGE.sleep_time)
             old_state = new_state
     except RuntimeError:
-        telegram.send_msg('*Status:* Got RuntimeError: ```\n{}\n```'.format(traceback.format_exc()))
+        telegram.send_msg(
+            '*Status:* Got RuntimeError:\n```\n{}\n```'.format(traceback.format_exc())
+        )
         logger.exception('RuntimeError. Trader stopped!')
-    finally:
-        telegram.send_msg('*Status:* `Trader has stopped`')
 
 
 def main():

freqtrade/persistence.py

@@ -5,7 +5,6 @@ from sqlalchemy import Boolean, Column, DateTime, Float, Integer, String, create
 from sqlalchemy.ext.declarative import declarative_base
 from sqlalchemy.orm.scoping import scoped_session
 from sqlalchemy.orm.session import sessionmaker
-from sqlalchemy.types import Enum
 
 from freqtrade import exchange
 
@@ -37,6 +36,14 @@ def init(config: dict, db_url: Optional[str] = None) -> None:
     Base.metadata.create_all(engine)
 
 
+def cleanup() -> None:
+    """
+    Flushes all pending operations to disk.
+    :return: None
+    """
+    Trade.session.flush()
+
+
 class Trade(Base):
     __tablename__ = 'trades'
 

freqtrade/rpc/telegram.py

@@ -17,7 +17,7 @@ logging.getLogger('requests.packages.urllib3').setLevel(logging.INFO)
 logging.getLogger('telegram').setLevel(logging.INFO)
 logger = logging.getLogger(__name__)
 
-_updater = None
+_updater: Updater = None
 _CONF = {}
 
 
@@ -41,6 +41,7 @@ def init(config: dict) -> None:
     handles = [
         CommandHandler('status', _status),
         CommandHandler('profit', _profit),
+        CommandHandler('balance', _balance),
         CommandHandler('start', _start),
         CommandHandler('stop', _stop),
         CommandHandler('forcesell', _forcesell),
@@ -61,6 +62,14 @@ def init(config: dict) -> None:
     )
 
 
+def cleanup() -> None:
+    """
+    Stops all running telegram threads.
+    :return: None
+    """
+    _updater.stop()
+
+
 def authorized_only(command_handler: Callable[[Bot, Update], None]) -> Callable[..., Any]:
     """
     Decorator to check if the message comes from the correct chat_id
@@ -194,6 +203,27 @@ def _profit(bot: Bot, update: Update) -> None:
     send_msg(markdown_msg, bot=bot)
 
 
+@authorized_only
+def _balance(bot: Bot, update: Update) -> None:
+    """
+    Handler for /balance
+    Returns current account balance per crypto
+    """
+    output = ""
+    balances = exchange.get_balances()
+    for currency in balances:
+        if not currency['Balance'] and not currency['Available'] and not currency['Pending']:
+            continue
+        output += """*Currency*: {Currency}
+*Available*: {Available}
+*Balance*: {Balance}
+*Pending*: {Pending}
+
+""".format(**currency)
+
+    send_msg(output)
+
+
 @authorized_only
 def _start(bot: Bot, update: Update) -> None:
     """
@@ -318,6 +348,7 @@ def _help(bot: Bot, update: Update) -> None:
 */profit:* `Lists cumulative profit from all finished trades`
 */forcesell <trade_id>:* `Instantly sells the given trade, regardless of profit`
 */performance:* `Show performance of each finished trade grouped by pair`
+*/balance:* `Show account balance per currency`
 */help:* `This help message`
     """
     send_msg(message, bot=bot)

freqtrade/tests/test_analyze.py

@@ -1,47 +1,41 @@
 # pragma pylint: disable=missing-docstring
+from datetime import datetime
+import json
 import pytest
-import arrow
 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'])
+
 
 def test_dataframe_has_correct_columns(result):
     assert result.columns.tolist() == \
                         ['close', 'high', 'low', 'open', 'date', 'volume']
 
-def test_orders_by_date(result):
+
-    assert result['date'].tolist() == \
+def test_dataframe_has_correct_length(result):
-                        ['2017-08-30T10:34:00',
+    assert len(result.index) == 5751
-                        '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))
     assert 'buy' in dataframe.columns
     assert 'buy_price' in dataframe.columns
 
+
 def test_returns_latest_buy_signal(mocker):
-    buydf = DataFrame([{'buy': 1, 'date': arrow.utcnow()}])
+    buydf = DataFrame([{'buy': 1, 'date': datetime.today()}])
     mocker.patch('freqtrade.analyze.analyze_ticker', return_value=buydf)
     assert get_buy_signal('BTC-ETH')
 
-    buydf = DataFrame([{'buy': 0, 'date': arrow.utcnow()}])
+    buydf = DataFrame([{'buy': 0, 'date': datetime.today()}])
     mocker.patch('freqtrade.analyze.analyze_ticker', return_value=buydf)
     assert not get_buy_signal('BTC-ETH')

freqtrade/tests/test_backtesting.py

@@ -13,19 +13,27 @@ from freqtrade.persistence import Trade
 
 logging.disable(logging.DEBUG) # disable debug logs that slow backtesting a lot
 
-def print_results(results):
+
-    print('Made {} buys. Average profit {:.2f}%. Total profit was {:.3f}. Average duration {:.1f} mins.'.format(
+def format_results(results):
+    return 'Made {} buys. Average profit {:.2f}%. Total profit was {:.3f}. Average duration {:.1f} mins.'.format(
         len(results.index),
         results.profit.mean() * 100.0,
         results.profit.sum(),
         results.duration.mean() * 5
-    ))
+    )
+
+
+def print_pair_results(pair, results):
+    print('For currency {}:'.format(pair))
+    print(format_results(results[results.currency == pair]))
+
 
 @pytest.fixture
 def pairs():
     return ['btc-neo', 'btc-eth', 'btc-omg', 'btc-edg', 'btc-pay',
             'btc-pivx', 'btc-qtum', 'btc-mtl', 'btc-etc', 'btc-ltc']
 
+
 @pytest.fixture
 def conf():
     return {
@@ -39,39 +47,36 @@ def conf():
     }
 
 
-@pytest.mark.skipif(not os.environ.get('BACKTEST', False), reason="BACKTEST not set")
+def backtest(conf, pairs, mocker):
-def test_backtest(conf, pairs, mocker):
     trades = []
+    mocked_history = mocker.patch('freqtrade.analyze.get_ticker_history')
     mocker.patch.dict('freqtrade.main._CONF', conf)
+    mocker.patch('arrow.utcnow', return_value=arrow.get('2017-08-20T14:50:00'))
     for pair in pairs:
         with open('freqtrade/tests/testdata/'+pair+'.json') as data_file:
             data = json.load(data_file)
-
+            mocked_history.return_value = data
-            mocker.patch('freqtrade.analyze.get_ticker_history', return_value=data)
+            ticker = analyze_ticker(pair)[['close', 'date', 'buy']].copy()
-            mocker.patch('arrow.utcnow', return_value=arrow.get('2017-08-20T14:50:00'))
-            ticker = analyze_ticker(pair)
             # for each buy point
-            for index, row in ticker[ticker.buy == 1].iterrows():
+            for row in ticker[ticker.buy == 1].itertuples(index=True):
-                trade = Trade(
+                trade = Trade(open_rate=row.close, open_date=row.date, amount=1)
-                    open_rate=row['close'],
-                    open_date=arrow.get(row['date']).datetime,
-                    amount=1,
-                )
                 # calculate win/lose forwards from buy point
-                for index2, row2 in ticker[index:].iterrows():
+                for row2 in ticker[row.Index:].itertuples(index=True):
-                    if should_sell(trade, row2['close'], arrow.get(row2['date']).datetime):
+                    if should_sell(trade, row2.close, row2.date):
-                        current_profit = (row2['close'] - trade.open_rate) / trade.open_rate
+                        current_profit = (row2.close - trade.open_rate) / trade.open_rate
 
-                        trades.append((pair, current_profit, index2 - index))
+                        trades.append((pair, current_profit, row2.Index - row.Index))
                         break
-        
     labels = ['currency', 'profit', 'duration']
     results = DataFrame.from_records(trades, columns=labels)
+    return results
+
+
+@pytest.mark.skipif(not os.environ.get('BACKTEST', False), reason="BACKTEST not set")
+def test_backtest(conf, pairs, mocker, report=True):
+    results = backtest(conf, pairs, mocker)
 
     print('====================== BACKTESTING REPORT ================================')
-
+    [print_pair_results(pair, results) for pair in pairs]
-    for pair in pairs:
-        print('For currency {}:'.format(pair))
-        print_results(results[results.currency == pair])
     print('TOTAL OVER ALL TRADES:')
-    print_results(results)
+    print(format_results(results))

freqtrade/tests/test_hyperopt.py

@@ -1,34 +1,29 @@
 # pragma pylint: disable=missing-docstring
-import json
 import logging
 import os
 from functools import reduce
+from math import exp
+from operator import itemgetter
 
 import pytest
-import arrow
+from hyperopt import fmin, tpe, hp, Trials, STATUS_OK
 from pandas import DataFrame
 
-from hyperopt import fmin, tpe, hp
+from freqtrade.tests.test_backtesting import backtest, format_results
-
+from freqtrade.vendor.qtpylib.indicators import crossed_above
-from freqtrade.analyze import analyze_ticker
-from freqtrade.main import should_sell
-from freqtrade.persistence import Trade
 
 logging.disable(logging.DEBUG) # disable debug logs that slow backtesting a lot
 
-def print_results(results):
+# set TARGET_TRADES to suit your number concurrent trades so its realistic to 20days of data
-    print('Made {} buys. Average profit {:.2f}%. Total profit was {:.3f}. Average duration {:.1f} mins.'.format(
+TARGET_TRADES = 1200
-        len(results.index),
+
-        results.profit.mean() * 100.0,
-        results.profit.sum(),
-        results.duration.mean() * 5
-    ))
 
 @pytest.fixture
 def pairs():
     return ['btc-neo', 'btc-eth', 'btc-omg', 'btc-edg', 'btc-pay',
             'btc-pivx', 'btc-qtum', 'btc-mtl', 'btc-etc', 'btc-ltc']
 
+
 @pytest.fixture
 def conf():
     return {
@@ -42,52 +37,14 @@ def conf():
     }
 
 
-def backtest(conf, pairs, mocker, buy_strategy):
-    trades = []
-    mocker.patch.dict('freqtrade.main._CONF', conf)
-    for pair in pairs:
-        with open('freqtrade/tests/testdata/'+pair+'.json') as data_file:
-            data = json.load(data_file)
-
-            mocker.patch('freqtrade.analyze.get_ticker_history', return_value=data)
-            mocker.patch('arrow.utcnow', return_value=arrow.get('2017-08-20T14:50:00'))
-            mocker.patch('freqtrade.analyze.populate_buy_trend', side_effect=buy_strategy)
-            ticker = analyze_ticker(pair)
-            # for each buy point
-            for index, row in ticker[ticker.buy == 1].iterrows():
-                trade = Trade(
-                    open_rate=row['close'],
-                    open_date=arrow.get(row['date']).datetime,
-                    amount=1,
-                )
-                # calculate win/lose forwards from buy point
-                for index2, row2 in ticker[index:].iterrows():
-                    if should_sell(trade, row2['close'], arrow.get(row2['date']).datetime):
-                        current_profit = (row2['close'] - trade.open_rate) / trade.open_rate
-
-                        trades.append((pair, current_profit, index2 - index))
-                        break
-
-    labels = ['currency', 'profit', 'duration']
-    results = DataFrame.from_records(trades, columns=labels)
-
-    print_results(results)
-
-    # set the value below to suit your number concurrent trades so its realistic to 20days of data
-    TARGET_TRADES = 1200
-    if results.profit.sum() == 0 or results.profit.mean() == 0:
-        return 49999999999 # avoid division by zero, return huge value to discard result
-    return abs(len(results.index) - 1200.1) / (results.profit.sum() ** 2) * results.duration.mean() # the smaller the better
-
 def buy_strategy_generator(params):
     print(params)
+
     def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
         conditions = []
         # GUARDS AND TRENDS
-        if params['below_sma']['enabled']:
+        if params['uptrend_long_ema']['enabled']:
-            conditions.append(dataframe['close'] < dataframe['sma'])
+            conditions.append(dataframe['ema50'] > dataframe['ema100'])
-        if params['over_sma']['enabled']:
-            conditions.append(dataframe['close'] > dataframe['sma'])
         if params['mfi']['enabled']:
             conditions.append(dataframe['mfi'] < params['mfi']['value'])
         if params['fastd']['enabled']:
@@ -96,6 +53,8 @@ def buy_strategy_generator(params):
             conditions.append(dataframe['adx'] > params['adx']['value'])
         if params['cci']['enabled']:
             conditions.append(dataframe['cci'] < params['cci']['value'])
+        if params['rsi']['enabled']:
+            conditions.append(dataframe['rsi'] < params['rsi']['value'])
         if params['over_sar']['enabled']:
             conditions.append(dataframe['close'] > dataframe['sar'])
         if params['uptrend_sma']['enabled']:
@@ -107,6 +66,9 @@ 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)),
+            'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),
+            'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
         }
         conditions.append(triggers.get(params['trigger']['type']))
 
@@ -118,34 +80,53 @@ def buy_strategy_generator(params):
         return dataframe
     return populate_buy_trend
 
+
 @pytest.mark.skipif(not os.environ.get('BACKTEST', False), reason="BACKTEST not set")
 def test_hyperopt(conf, pairs, mocker):
+    mocked_buy_trend = mocker.patch('freqtrade.analyze.populate_buy_trend')
 
     def optimizer(params):
-        return backtest(conf, pairs, mocker, buy_strategy_generator(params))
+        mocked_buy_trend.side_effect = buy_strategy_generator(params)
+
+        results = backtest(conf, pairs, mocker)
+
+        result = format_results(results)
+        print(result)
+
+        total_profit = results.profit.sum() * 1000
+        trade_count = len(results.index)
+
+        trade_loss = 1 - 0.8 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5)
+        profit_loss = exp(-total_profit**3 / 10**11)
+
+        return {
+            'loss': trade_loss + profit_loss,
+            'status': STATUS_OK,
+            'result': result
+        }
 
     space = {
         'mfi': hp.choice('mfi', [
             {'enabled': False},
-            {'enabled': True, 'value': hp.uniform('mfi-value', 2, 40)}
+            {'enabled': True, 'value': hp.uniform('mfi-value', 5, 15)}
         ]),
         'fastd': hp.choice('fastd', [
             {'enabled': False},
-            {'enabled': True, 'value': hp.uniform('fastd-value', 2, 40)}
+            {'enabled': True, 'value': hp.uniform('fastd-value', 5, 40)}
         ]),
         'adx': hp.choice('adx', [
             {'enabled': False},
-            {'enabled': True, 'value': hp.uniform('adx-value', 2, 40)}
+            {'enabled': True, 'value': hp.uniform('adx-value', 10, 30)}
         ]),
         'cci': hp.choice('cci', [
             {'enabled': False},
-            {'enabled': True, 'value': hp.uniform('cci-value', -200, -100)}
+            {'enabled': True, 'value': hp.uniform('cci-value', -150, -100)}
         ]),
-        'below_sma': hp.choice('below_sma', [
+        'rsi': hp.choice('rsi', [
             {'enabled': False},
-            {'enabled': True}
+            {'enabled': True, 'value': hp.uniform('rsi-value', 20, 30)}
         ]),
-        'over_sma': hp.choice('over_sma', [
+        'uptrend_long_ema': hp.choice('uptrend_long_ema', [
             {'enabled': False},
             {'enabled': True}
         ]),
@@ -159,8 +140,15 @@ def test_hyperopt(conf, pairs, mocker):
         ]),
         'trigger': hp.choice('trigger', [
             {'type': 'lower_bb'},
-            {'type': 'faststoch10'}
+            {'type': 'faststoch10'},
+            {'type': 'ao_cross_zero'},
+            {'type': 'ema5_cross_ema10'},
+            {'type': 'macd_cross_signal'},
         ]),
     }
-
+    trials = Trials()
-    print('Best parameters {}'.format(fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=40)))
+    best = fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=40, trials=trials)
+    print('\n\n\n\n====================== HYPEROPT BACKTESTING REPORT ================================')
+    print('Best parameters {}'.format(best))
+    newlist = sorted(trials.results, key=itemgetter('loss'))
+    print('Result: {}'.format(newlist[0]['result']))

freqtrade/tests/test_main.py

@@ -48,6 +48,7 @@ def conf():
     validate(configuration, CONF_SCHEMA)
     return configuration
 
+
 def test_create_trade(conf, mocker):
     mocker.patch.dict('freqtrade.main._CONF', conf)
     buy_signal = mocker.patch('freqtrade.main.get_buy_signal', side_effect=lambda _: True)
@@ -82,6 +83,7 @@ def test_create_trade(conf, mocker):
         [call('BTC_ETH'), call('BTC_TKN'), call('BTC_TRST'), call('BTC_SWT')]
     )
 
+
 def test_handle_trade(conf, mocker):
     mocker.patch.dict('freqtrade.main._CONF', conf)
     mocker.patch.multiple('freqtrade.main.telegram', init=MagicMock(), send_msg=MagicMock())
@@ -101,6 +103,7 @@ def test_handle_trade(conf, mocker):
     assert trade.close_date is not None
     assert trade.open_order_id == 'dry_run'
 
+
 def test_close_trade(conf, mocker):
     mocker.patch.dict('freqtrade.main._CONF', conf)
     trade = Trade.query.filter(Trade.is_open.is_(True)).first()
@@ -113,14 +116,17 @@ def test_close_trade(conf, mocker):
     assert closed
     assert not trade.is_open
 
+
 def test_balance_fully_ask_side(mocker):
     mocker.patch.dict('freqtrade.main._CONF', {'bid_strategy': {'ask_last_balance': 0.0}})
     assert get_target_bid({'ask': 20, 'last': 10}) == 20
 
+
 def test_balance_fully_last_side(mocker):
     mocker.patch.dict('freqtrade.main._CONF', {'bid_strategy': {'ask_last_balance': 1.0}})
     assert get_target_bid({'ask': 20, 'last': 10}) == 10
 
+
 def test_balance_when_last_bigger_than_ask(mocker):
     mocker.patch.dict('freqtrade.main._CONF', {'bid_strategy': {'ask_last_balance': 1.0}})
     assert get_target_bid({'ask': 5, 'last': 10}) == 5

freqtrade/tests/test_persistence.py

@@ -2,6 +2,7 @@
 from freqtrade.exchange import Exchanges
 from freqtrade.persistence import Trade
 
+
 def test_exec_sell_order(mocker):
     api_mock = mocker.patch('freqtrade.main.exchange.sell', side_effect='mocked_order_id')
     trade = Trade(

freqtrade/tests/test_telegram.py

@@ -9,7 +9,7 @@ from telegram import Bot, Update, Message, Chat
 from freqtrade.main import init, create_trade
 from freqtrade.misc import update_state, State, get_state, CONF_SCHEMA
 from freqtrade.persistence import Trade
-from freqtrade.rpc.telegram import _status, _profit, _forcesell, _performance, _start, _stop
+from freqtrade.rpc.telegram import _status, _profit, _forcesell, _performance, _start, _stop, _balance
 
 
 @pytest.fixture
@@ -82,6 +82,7 @@ def test_status_handle(conf, update, mocker):
     assert msg_mock.call_count == 2
     assert '[BTC_ETH]' in msg_mock.call_args_list[-1][0][0]
 
+
 def test_profit_handle(conf, update, mocker):
     mocker.patch.dict('freqtrade.main._CONF', conf)
     mocker.patch('freqtrade.main.get_buy_signal', side_effect=lambda _: True)
@@ -112,6 +113,7 @@ def test_profit_handle(conf, update, mocker):
     assert msg_mock.call_count == 2
     assert '(100.00%)' in msg_mock.call_args_list[-1][0][0]
 
+
 def test_forcesell_handle(conf, update, mocker):
     mocker.patch.dict('freqtrade.main._CONF', conf)
     mocker.patch('freqtrade.main.get_buy_signal', side_effect=lambda _: True)
@@ -140,6 +142,7 @@ def test_forcesell_handle(conf, update, mocker):
     assert 'Selling [BTC/ETH]' in msg_mock.call_args_list[-1][0][0]
     assert '0.072561' in msg_mock.call_args_list[-1][0][0]
 
+
 def test_performance_handle(conf, update, mocker):
     mocker.patch.dict('freqtrade.main._CONF', conf)
     mocker.patch('freqtrade.main.get_buy_signal', side_effect=lambda _: True)
@@ -171,6 +174,7 @@ def test_performance_handle(conf, update, mocker):
     assert 'Performance' in msg_mock.call_args_list[-1][0][0]
     assert 'BTC_ETH	100.00%' in msg_mock.call_args_list[-1][0][0]
 
+
 def test_start_handle(conf, update, mocker):
     mocker.patch.dict('freqtrade.main._CONF', conf)
     msg_mock = MagicMock()
@@ -184,6 +188,7 @@ def test_start_handle(conf, update, mocker):
     assert get_state() == State.RUNNING
     assert msg_mock.call_count == 0
 
+
 def test_stop_handle(conf, update, mocker):
     mocker.patch.dict('freqtrade.main._CONF', conf)
     msg_mock = MagicMock()
@@ -197,3 +202,22 @@ def test_stop_handle(conf, update, mocker):
     assert get_state() == State.STOPPED
     assert msg_mock.call_count == 1
     assert 'Stopping trader' in msg_mock.call_args_list[0][0][0]
+
+
+def test_balance_handle(conf, update, mocker):
+    mock_balance = [{
+        'Currency': 'BTC',
+        'Balance': 10.0,
+        'Available': 12.0,
+        'Pending': 0.0,
+        'CryptoAddress': 'XXXX'}]
+    mocker.patch.dict('freqtrade.main._CONF', conf)
+    msg_mock = MagicMock()
+    mocker.patch.multiple('freqtrade.main.telegram', _CONF=conf, init=MagicMock(), send_msg=msg_mock)
+    mocker.patch.multiple('freqtrade.main.exchange',
+                          get_balances=MagicMock(return_value=mock_balance))
+
+    _balance(bot=MagicBot(), update=update)
+    assert msg_mock.call_count == 1
+    assert '*Currency*: BTC' in msg_mock.call_args_list[0][0][0]
+    assert 'Balance' in msg_mock.call_args_list[0][0][0]

freqtrade/tests/testdata/download_backtest_data.py

@@ -5,6 +5,7 @@
 from urllib.request import urlopen
 
 CURRENCIES = ["ok", "neo", "dash", "etc", "eth", "snt"]
+OUTPUT_DIR = 'freqtrade/tests/testdata/'
 
 for cur in CURRENCIES:
     url1 = 'https://bittrex.com/Api/v2.0/pub/market/GetTicks?marketName=BTC-'
@@ -12,5 +13,6 @@ for cur in CURRENCIES:
     x = urlopen(url)
     json_data = x.read()
     json_str = str(json_data, 'utf-8')
-    with open('btc-'+cur+'.json', 'w') as file:
+    output = OUTPUT_DIR + 'btc-'+cur+'.json'
+    with open(output, 'w') as file:
         file.write(json_str)

freqtrade/vendor/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