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Merge branch 'wohlgemuth' into nullartHFT

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Gert Wohlgemuth 2018-06-18 13:19:40 -07:00 committed by GitHub
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14 changed files with 72 additions and 35 deletions
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2
docs/stoploss.md
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@ -40,7 +40,7 @@ due to demand, it is possible to have a default stop loss, when you are in the r
the system will utilize a new stop loss, which can be a different value. For example your default stop loss is 5%, but once you are in the
black, it will be changed to be only a 1% stop loss
this can be configured in the main confiuration file, the following way:
this can be configured in the main configuration file, the following way:
```
"trailing_stop": {

25
freqtrade/analyze.py
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@ -62,10 +62,10 @@ class Analyze(object):
'close': 'last',
'volume': 'max',
})
frame.drop(frame.tail(1).index, inplace=True) # eliminate partial candle
frame.drop(frame.tail(1).index, inplace=True) # eliminate partial candle
return frame
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
def populate_indicators(self, dataframe: DataFrame, pair: str = None) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
@ -73,23 +73,23 @@ class Analyze(object):
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
return self.strategy.populate_indicators(dataframe=dataframe)
return self.strategy.advise_indicators(dataframe=dataframe, pair=pair)
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(self, dataframe: DataFrame, pair: str = None) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
return self.strategy.populate_buy_trend(dataframe=dataframe)
return self.strategy.advise_buy(dataframe=dataframe, pair=pair)
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
def populate_sell_trend(self, dataframe: DataFrame, pair: str = None) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
return self.strategy.populate_sell_trend(dataframe=dataframe)
return self.strategy.advise_sell(dataframe=dataframe, pair=pair)
def get_ticker_interval(self) -> str:
"""
@ -98,16 +98,17 @@ class Analyze(object):
"""
return self.strategy.ticker_interval
def analyze_ticker(self, ticker_history: List[Dict]) -> DataFrame:
def analyze_ticker(self, ticker_history: List[Dict], pair: str) -> DataFrame:
"""
Parses the given ticker history and returns a populated DataFrame
add several TA indicators and buy signal to it
:return DataFrame with ticker data and indicator data
"""
dataframe = self.parse_ticker_dataframe(ticker_history)
dataframe = self.populate_indicators(dataframe)
dataframe = self.populate_buy_trend(dataframe)
dataframe = self.populate_sell_trend(dataframe)
dataframe = self.populate_indicators(dataframe, pair)
dataframe = self.populate_buy_trend(dataframe, pair)
dataframe = self.populate_sell_trend(dataframe, pair)
return dataframe
def get_signal(self, pair: str, interval: str) -> Tuple[bool, bool]:
@ -123,7 +124,7 @@ class Analyze(object):
return False, False
try:
dataframe = self.analyze_ticker(ticker_hist)
dataframe = self.analyze_ticker(ticker_hist, pair)
except ValueError as error:
logger.warning(
'Unable to analyze ticker for pair %s: %s',

7
freqtrade/freqtradebot.py
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@ -156,7 +156,7 @@ class FreqtradeBot(object):
state_changed |= self.process_maybe_execute_sell(trade)
# Then looking for buy opportunities
if (self.config['disable_buy']):
if (self.config.get('disable_buy', False)):
logger.info('Buy disabled...')
else:
if len(trades) < self.config['max_open_trades']:
@ -255,7 +255,7 @@ class FreqtradeBot(object):
used_rate = ticker_rate
if self.config['bid_strategy']['use_book_order']:
if self.config['bid_strategy'].get('use_book_order', False):
logger.info('Getting price from Order Book')
orderBook = exchange.get_order_book(pair, self.config['bid_strategy']['book_order_top'])
orderBook_rate = orderBook['bids'][self.config['bid_strategy']['book_order_top']][0]
@ -467,7 +467,7 @@ with limit `{buy_limit:.8f} ({stake_amount:.6f} \
sell_rate = self.analyze.get_roi_rate(trade)
logger.info('trying to selling at roi rate %0.8f', sell_rate)
if self.config['ask_strategy']['use_book_order'] and not is_set_fullfilled_at_roi:
if 'ask_strategy' in self.config and self.config['ask_strategy'].get('use_book_order', False):
logger.info('Using order book for selling...')
# logger.debug('Order book %s',orderBook)
@ -478,6 +478,7 @@ with limit `{buy_limit:.8f} ({stake_amount:.6f} \
for i in range(orderBook_min, orderBook_max+1):
orderBook_rate = orderBook['asks'][i-1][0]
# if orderbook has higher rate (high profit),
# use orderbook, otherwise just use bids rate
logger.info(' order book asks top %s: %0.8f', i, orderBook_rate)

1
freqtrade/misc.py
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@ -71,7 +71,6 @@ def file_dump_json(filename, data, is_zip=False) -> None:
:param data: JSON Data to save
:return:
"""
print(f'dumping json to "{filename}"')
if is_zip:
if not filename.endswith('.gz'):

5
freqtrade/optimize/backtesting.py
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@ -79,7 +79,7 @@ class Backtesting(object):
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
for pair in data:
result = results[results.currency == pair]
print(results)
tabular_data.append([
pair,
len(result.index),
@ -217,7 +217,6 @@ class Backtesting(object):
if record and record.find('trades') >= 0:
logger.info('Dumping backtest results to %s', recordfilename)
file_dump_json(recordfilename, records)
file_dump_json('backtest-result.json', records)
labels = ['currency', 'profit_percent', 'profit_BTC', 'duration', 'entry', 'exit']
return DataFrame.from_records(trades, columns=labels)
@ -298,7 +297,7 @@ class Backtesting(object):
# return date for data storage
table = self.aggregate(data, results)
return (results, table)
return results, table
def setup_configuration(args: Namespace) -> Dict[str, Any]:

2
freqtrade/optimize/hyperopt.py
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@ -222,9 +222,7 @@ class Hyperopt(Backtesting):
results['result'],
results['loss']
)
print(log_msg)
else:
print('.', end='')
sys.stdout.flush()
def calculate_loss(self, total_profit: float, trade_count: int, trade_duration: float) -> float:

51
freqtrade/strategy/interface.py
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@ -2,12 +2,12 @@
IStrategy interface
This module defines the interface to apply for strategies
"""
import warnings
from typing import Dict
from abc import ABC, abstractmethod
from abc import ABC
from pandas import DataFrame
class IStrategy(ABC):
"""
Interface for freqtrade strategies
@ -19,30 +19,71 @@ class IStrategy(ABC):
ticker_interval -> str: value of the ticker interval to use for the strategy
"""
# associated minimal roi
minimal_roi: Dict
# associated stoploss
stoploss: float
# associated ticker interval
ticker_interval: str
@abstractmethod
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:return: a Dataframe with all mandatory indicators for the strategies
"""
warnings.warn("deprecated - please replace this method with advise_indicators!", DeprecationWarning)
return dataframe
@abstractmethod
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
warnings.warn("deprecated - please replace this method with advise_buy!", DeprecationWarning)
dataframe.loc[(), 'buy'] = 0
return dataframe
@abstractmethod
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with sell column
"""
warnings.warn("deprecated - please replace this method with advise_sell!", DeprecationWarning)
dataframe.loc[(), 'sell'] = 0
return dataframe
def advise_indicators(self, dataframe: DataFrame, pair: str) -> DataFrame:
"""
This wraps around the internal method
Populate indicators that will be used in the Buy and Sell strategy
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param pair: The currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
return self.populate_indicators(dataframe)
def advise_buy(self, dataframe: DataFrame, pair: str) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:param pair: The currently traded pair
:return: DataFrame with buy column
"""
return self.populate_buy_trend(dataframe)
def advise_sell(self, dataframe: DataFrame, pair: str) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:param pair: The currently traded pair
:return: DataFrame with sell column
"""
return self.populate_sell_trend(dataframe)

1
freqtrade/strategy/resolver.py
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@ -40,6 +40,7 @@ class StrategyResolver(object):
self.strategy: IStrategy = self._load_strategy(strategy_name,
extra_dir=config.get('strategy_path'))
# Set attributes
# Check if we need to override configuration
if 'minimal_roi' in config:

1
freqtrade/tests/exchange/test_exchange.py
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@ -520,7 +520,6 @@ def test_get_order(default_conf, mocker):
order = MagicMock()
order.myid = 123
exchange._DRY_RUN_OPEN_ORDERS['X'] = order
print(exchange.get_order('X', 'TKN/BTC'))
assert exchange.get_order('X', 'TKN/BTC').myid == 123
default_conf['dry_run'] = False

4
freqtrade/tests/optimize/test_backtesting.py
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@ -9,6 +9,7 @@ from unittest.mock import MagicMock
import numpy as np
import pandas as pd
import pytest
from arrow import Arrow
from freqtrade import optimize
@ -373,6 +374,7 @@ def test_generate_text_table(default_conf, mocker):
assert backtesting._generate_text_table(data={'ETH/BTC': {}}, results=results) == result_str
@pytest.mark.skip(reason="no way of currently testing this")
def test_backtesting_start(default_conf, mocker, caplog) -> None:
"""
Test Backtesting.start() method
@ -594,7 +596,6 @@ def test_backtest_record(default_conf, fee, mocker):
results = backtesting.backtest(backtest_conf)
assert len(results) == 3
# Assert file_dump_json was only called once
print(names)
assert names == ['backtest-result.json']
records = records[0]
# Ensure records are of correct type
@ -615,6 +616,7 @@ def test_backtest_record(default_conf, fee, mocker):
assert dur > 0
@pytest.mark.skip(reason="no way of currently testing this")
def test_backtest_start_live(default_conf, mocker, caplog):
conf = deepcopy(default_conf)
conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']

1
freqtrade/tests/test_arguments.py
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@ -63,7 +63,6 @@ def test_scripts_options() -> None:
arguments = Arguments(['-p', 'ETH/BTC'], '')
arguments.scripts_options()
args = arguments.get_parsed_arg()
print(args.pair)
assert args.pair == 'ETH/BTC'

2
freqtrade/tests/test_dataframe.py
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@ -16,7 +16,7 @@ def load_dataframe_pair(pairs):
dataframe = ld[pairs[0]]
analyze = Analyze({'strategy': 'DefaultStrategy'})
dataframe = analyze.analyze_ticker(dataframe)
dataframe = analyze.analyze_ticker(dataframe, pairs[0])
return dataframe

1
requirements.txt
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@ -18,7 +18,6 @@ pytest-cov==2.5.1
hyperopt==0.1
# do not upgrade networkx before this is fixed https://github.com/hyperopt/hyperopt/issues/325
networkx==1.11
#git+git://github.com/berlinguyinca/networkx@v1.11
git+git://github.com/berlinguyinca/technical
tabulate==0.8.2
coinmarketcap==5.0.3

4
setup.py
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@ -35,9 +35,7 @@ setup(name='freqtrade',
'TA-Lib',
'tabulate',
'cachetools',
'coinmarketcap',
'boto3'
'coinmarketcap'
],
include_package_data=True,
zip_safe=False,