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Merge branch 'develop' into cleaner-tests

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Janne Sinivirta 2018-07-30 21:08:55 +03:00 committed by GitHub
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41
docs/bot-optimization.md
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@ -39,7 +39,6 @@ A strategy file contains all the information needed to build a good strategy:
- Sell strategy rules
- Minimal ROI recommended
- Stoploss recommended
- Hyperopt parameter
The bot also include a sample strategy called `TestStrategy` you can update: `user_data/strategies/test_strategy.py`.
You can test it with the parameter: `--strategy TestStrategy`
@ -61,22 +60,22 @@ file as reference.**
### Buy strategy
Edit the method `populate_buy_trend()` into your strategy file to
update your buy strategy.
Edit the method `populate_buy_trend()` into your strategy file to update your buy strategy.
Sample from `user_data/strategies/test_strategy.py`:
```python
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['adx'] > 30) &
(dataframe['tema'] <= dataframe['blower']) &
(dataframe['tema'] <= dataframe['bb_middleband']) &
(dataframe['tema'] > dataframe['tema'].shift(1))
),
'buy'] = 1
@ -87,38 +86,47 @@ def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
### Sell strategy
Edit the method `populate_sell_trend()` into your strategy file to update your sell strategy.
Please note that the sell-signal is only used if `use_sell_signal` is set to true in the configuration.
Sample from `user_data/strategies/test_strategy.py`:
```python
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['adx'] > 70) &
(dataframe['tema'] > dataframe['blower']) &
(dataframe['tema'] > dataframe['bb_middleband']) &
(dataframe['tema'] < dataframe['tema'].shift(1))
),
'sell'] = 1
return dataframe
```
## Add more Indicator
## Add more Indicators
As you have seen, buy and sell strategies need indicators. You can add
more indicators by extending the list contained in
the method `populate_indicators()` from your strategy file.
As you have seen, buy and sell strategies need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
You should only add the indicators used in either `populate_buy_trend()`, `populate_sell_trend()`, or to populate another indicator, otherwise performance may suffer.
Sample:
```python
def populate_indicators(dataframe: DataFrame) -> DataFrame:
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
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.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
dataframe['sar'] = ta.SAR(dataframe)
dataframe['adx'] = ta.ADX(dataframe)
@ -149,6 +157,11 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
return dataframe
```
### Metadata dict
The metadata-dict (available for `populate_buy_trend`, `populate_sell_trend`, `populate_indicators`) contains additional information.
Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
### Want more indicator examples
Look into the [user_data/strategies/test_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py).

31
freqtrade/exchange/__init__.py
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@ -4,6 +4,7 @@ import logging
from random import randint
from typing import List, Dict, Any, Optional
from datetime import datetime
from math import floor, ceil
import ccxt
import arrow
@ -150,6 +151,28 @@ class Exchange(object):
"""
return endpoint in self._api.has and self._api.has[endpoint]
def symbol_amount_prec(self, pair, amount: float):
'''
Returns the amount to buy or sell to a precision the Exchange accepts
Rounded down
'''
if self._api.markets[pair]['precision']['amount']:
symbol_prec = self._api.markets[pair]['precision']['amount']
big_amount = amount * pow(10, symbol_prec)
amount = floor(big_amount) / pow(10, symbol_prec)
return amount
def symbol_price_prec(self, pair, price: float):
'''
Returns the price buying or selling with to the precision the Exchange accepts
Rounds up
'''
if self._api.markets[pair]['precision']['price']:
symbol_prec = self._api.markets[pair]['precision']['price']
big_price = price * pow(10, symbol_prec)
price = ceil(big_price) / pow(10, symbol_prec)
return price
def buy(self, pair: str, rate: float, amount: float) -> Dict:
if self._conf['dry_run']:
order_id = f'dry_run_buy_{randint(0, 10**6)}'
@ -167,6 +190,10 @@ class Exchange(object):
return {'id': order_id}
try:
# Set the precision for amount and price(rate) as accepted by the exchange
amount = self.symbol_amount_prec(pair, amount)
rate = self.symbol_price_prec(pair, rate)
return self._api.create_limit_buy_order(pair, amount, rate)
except ccxt.InsufficientFunds as e:
raise DependencyException(
@ -200,6 +227,10 @@ class Exchange(object):
return {'id': order_id}
try:
# Set the precision for amount and price(rate) as accepted by the exchange
amount = self.symbol_amount_prec(pair, amount)
rate = self.symbol_price_prec(pair, rate)
return self._api.create_limit_sell_order(pair, amount, rate)
except ccxt.InsufficientFunds as e:
raise DependencyException(

8
freqtrade/optimize/backtesting.py
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@ -57,8 +57,8 @@ class Backtesting(object):
self.strategy: IStrategy = StrategyResolver(self.config).strategy
self.ticker_interval = self.strategy.ticker_interval
self.tickerdata_to_dataframe = self.strategy.tickerdata_to_dataframe
self.populate_buy_trend = self.strategy.populate_buy_trend
self.populate_sell_trend = self.strategy.populate_sell_trend
self.advise_buy = self.strategy.advise_buy
self.advise_sell = self.strategy.advise_sell
# Reset keys for backtesting
self.config['exchange']['key'] = ''
@ -229,8 +229,8 @@ class Backtesting(object):
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
ticker_data = self.populate_sell_trend(
self.populate_buy_trend(pair_data))[headers].copy()
ticker_data = self.advise_sell(
self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
# to avoid using data from future, we buy/sell with signal from previous candle
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)

10
freqtrade/optimize/hyperopt.py
View File

@ -75,7 +75,7 @@ class Hyperopt(Backtesting):
return arg_dict
@staticmethod
def populate_indicators(dataframe: DataFrame) -> DataFrame:
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
@ -228,7 +228,7 @@ class Hyperopt(Backtesting):
"""
Define the buy strategy parameters to be used by hyperopt
"""
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use
"""
@ -270,7 +270,7 @@ class Hyperopt(Backtesting):
self.strategy.minimal_roi = self.generate_roi_table(params)
if self.has_space('buy'):
self.populate_buy_trend = self.buy_strategy_generator(params)
self.advise_buy = self.buy_strategy_generator(params)
if self.has_space('stoploss'):
self.strategy.stoploss = params['stoploss']
@ -351,7 +351,7 @@ class Hyperopt(Backtesting):
)
if self.has_space('buy'):
self.strategy.populate_indicators = Hyperopt.populate_indicators # type: ignore
self.strategy.advise_indicators = Hyperopt.populate_indicators # type: ignore
dump(self.tickerdata_to_dataframe(data), TICKERDATA_PICKLE)
self.exchange = None # type: ignore
self.load_previous_results()
@ -360,7 +360,7 @@ class Hyperopt(Backtesting):
logger.info(f'Found {cpus} CPU cores. Let\'s make them scream!')
opt = self.get_optimizer(cpus)
EVALS = max(self.total_tries//cpus, 1)
EVALS = max(self.total_tries // cpus, 1)
try:
with Parallel(n_jobs=cpus) as parallel:
for i in range(EVALS):

11
freqtrade/strategy/default_strategy.py
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@ -28,13 +28,16 @@ class DefaultStrategy(IStrategy):
# Optimal ticker interval for the strategy
ticker_interval = '5m'
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
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.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
# Momentum Indicator
@ -196,10 +199,11 @@ class DefaultStrategy(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
@ -217,10 +221,11 @@ class DefaultStrategy(IStrategy):
return dataframe
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[

75
freqtrade/strategy/interface.py
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@ -7,6 +7,7 @@ from abc import ABC, abstractmethod
from datetime import datetime
from enum import Enum
from typing import Dict, List, NamedTuple, Tuple
import warnings
import arrow
from pandas import DataFrame
@ -57,34 +58,45 @@ class IStrategy(ABC):
ticker_interval -> str: value of the ticker interval to use for the strategy
"""
_populate_fun_len: int = 0
_buy_fun_len: int = 0
_sell_fun_len: int = 0
# associated minimal roi
minimal_roi: Dict
# associated stoploss
stoploss: float
# associated ticker interval
ticker_interval: str
def __init__(self, config: dict) -> None:
self.config = config
@abstractmethod
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> 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()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
@abstractmethod
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
@abstractmethod
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with sell column
"""
@ -94,16 +106,16 @@ class IStrategy(ABC):
"""
return self.__class__.__name__
def analyze_ticker(self, ticker_history: List[Dict]) -> DataFrame:
def analyze_ticker(self, ticker_history: List[Dict], metadata: dict) -> 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 = parse_ticker_dataframe(ticker_history)
dataframe = self.populate_indicators(dataframe)
dataframe = self.populate_buy_trend(dataframe)
dataframe = self.populate_sell_trend(dataframe)
dataframe = self.advise_indicators(dataframe, metadata)
dataframe = self.advise_buy(dataframe, metadata)
dataframe = self.advise_sell(dataframe, metadata)
return dataframe
def get_signal(self, pair: str, interval: str, ticker_hist: List[Dict]) -> Tuple[bool, bool]:
@ -118,7 +130,7 @@ class IStrategy(ABC):
return False, False
try:
dataframe = self.analyze_ticker(ticker_hist)
dataframe = self.analyze_ticker(ticker_hist, {'pair': pair})
except ValueError as error:
logger.warning(
'Unable to analyze ticker for pair %s: %s',
@ -263,5 +275,50 @@ class IStrategy(ABC):
"""
Creates a dataframe and populates indicators for given ticker data
"""
return {pair: self.populate_indicators(parse_ticker_dataframe(pair_data))
return {pair: self.advise_indicators(parse_ticker_dataframe(pair_data), {'pair': pair})
for pair, pair_data in tickerdata.items()}
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy
This method should not be overridden.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
if self._populate_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)
return self.populate_indicators(dataframe) # type: ignore
else:
return self.populate_indicators(dataframe, metadata)
def advise_buy(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
This method should not be overridden.
:param dataframe: DataFrame
:param pair: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
if self._buy_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)
return self.populate_buy_trend(dataframe) # type: ignore
else:
return self.populate_buy_trend(dataframe, metadata)
def advise_sell(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
This method should not be overridden.
:param dataframe: DataFrame
:param pair: Additional information, like the currently traded pair
:return: DataFrame with sell column
"""
if self._sell_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)
return self.populate_sell_trend(dataframe) # type: ignore
else:
return self.populate_sell_trend(dataframe, metadata)

7
freqtrade/strategy/resolver.py
View File

@ -92,6 +92,13 @@ class StrategyResolver(object):
strategy = self._search_strategy(path, strategy_name=strategy_name, config=config)
if strategy:
logger.info('Using resolved strategy %s from \'%s\'', strategy_name, path)
strategy._populate_fun_len = len(
inspect.getfullargspec(strategy.populate_indicators).args)
strategy._buy_fun_len = len(
inspect.getfullargspec(strategy.populate_buy_trend).args)
strategy._sell_fun_len = len(
inspect.getfullargspec(strategy.populate_sell_trend).args)
return import_strategy(strategy, config=config)
except FileNotFoundError:
logger.warning('Path "%s" does not exist', path)

46
freqtrade/tests/exchange/test_exchange.py
View File

@ -49,6 +49,52 @@ def test_init_exception(default_conf, mocker):
Exchange(default_conf)
def test_symbol_amount_prec(default_conf, mocker):
'''
Test rounds down to 4 Decimal places
'''
api_mock = MagicMock()
api_mock.load_markets = MagicMock(return_value={
'ETH/BTC': '', 'LTC/BTC': '', 'XRP/BTC': '', 'NEO/BTC': ''
})
mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value='binance'))
markets = PropertyMock(return_value={'ETH/BTC': {'precision': {'amount': 4}}})
type(api_mock).markets = markets
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
exchange = Exchange(default_conf)
amount = 2.34559
pair = 'ETH/BTC'
amount = exchange.symbol_amount_prec(pair, amount)
assert amount == 2.3455
def test_symbol_price_prec(default_conf, mocker):
'''
Test rounds up to 4 decimal places
'''
api_mock = MagicMock()
api_mock.load_markets = MagicMock(return_value={
'ETH/BTC': '', 'LTC/BTC': '', 'XRP/BTC': '', 'NEO/BTC': ''
})
mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value='binance'))
markets = PropertyMock(return_value={'ETH/BTC': {'precision': {'price': 4}}})
type(api_mock).markets = markets
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
exchange = Exchange(default_conf)
price = 2.34559
pair = 'ETH/BTC'
price = exchange.symbol_price_prec(pair, price)
assert price == 2.3456
def test_validate_pairs(default_conf, mocker):
api_mock = MagicMock()
api_mock.load_markets = MagicMock(return_value={

28
freqtrade/tests/optimize/test_backtesting.py
View File

@ -145,7 +145,7 @@ def _trend(signals, buy_value, sell_value):
return signals
def _trend_alternate(dataframe=None):
def _trend_alternate(dataframe=None, metadata=None):
signals = dataframe
low = signals['low']
n = len(low)
@ -314,8 +314,8 @@ def test_backtesting_init(mocker, default_conf) -> None:
assert backtesting.config == default_conf
assert backtesting.ticker_interval == '5m'
assert callable(backtesting.tickerdata_to_dataframe)
assert callable(backtesting.populate_buy_trend)
assert callable(backtesting.populate_sell_trend)
assert callable(backtesting.advise_buy)
assert callable(backtesting.advise_sell)
get_fee.assert_called()
assert backtesting.fee == 0.5
@ -562,42 +562,42 @@ def test_backtest_ticks(default_conf, fee, mocker):
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
ticks = [1, 5]
fun = Backtesting(default_conf).populate_buy_trend
fun = Backtesting(default_conf).advise_buy
for _ in ticks:
backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
backtesting = Backtesting(default_conf)
backtesting.populate_buy_trend = fun # Override
backtesting.populate_sell_trend = fun # Override
backtesting.advise_buy = fun # Override
backtesting.advise_sell = fun # Override
results = backtesting.backtest(backtest_conf)
assert not results.empty
def test_backtest_clash_buy_sell(mocker, default_conf):
# Override the default buy trend function in our default_strategy
def fun(dataframe=None):
def fun(dataframe=None, pair=None):
buy_value = 1
sell_value = 1
return _trend(dataframe, buy_value, sell_value)
backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
backtesting = Backtesting(default_conf)
backtesting.populate_buy_trend = fun # Override
backtesting.populate_sell_trend = fun # Override
backtesting.advise_buy = fun # Override
backtesting.advise_sell = fun # Override
results = backtesting.backtest(backtest_conf)
assert results.empty
def test_backtest_only_sell(mocker, default_conf):
# Override the default buy trend function in our default_strategy
def fun(dataframe=None):
def fun(dataframe=None, pair=None):
buy_value = 0
sell_value = 1
return _trend(dataframe, buy_value, sell_value)
backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
backtesting = Backtesting(default_conf)
backtesting.populate_buy_trend = fun # Override
backtesting.populate_sell_trend = fun # Override
backtesting.advise_buy = fun # Override
backtesting.advise_sell = fun # Override
results = backtesting.backtest(backtest_conf)
assert results.empty
@ -606,8 +606,8 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker):
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
backtest_conf = _make_backtest_conf(mocker, conf=default_conf, pair='UNITTEST/BTC')
backtesting = Backtesting(default_conf)
backtesting.populate_buy_trend = _trend_alternate # Override
backtesting.populate_sell_trend = _trend_alternate # Override
backtesting.advise_buy = _trend_alternate # Override
backtesting.advise_sell = _trend_alternate # Override
results = backtesting.backtest(backtest_conf)
backtesting._store_backtest_result("test_.json", results)
assert len(results) == 4

8
freqtrade/tests/optimize/test_hyperopt.py
View File

@ -100,7 +100,7 @@ def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
}
)
out, err = capsys.readouterr()
assert ' 1/2: foo. Loss 1.00000'in out
assert ' 1/2: foo. Loss 1.00000' in out
def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
@ -218,7 +218,7 @@ def test_populate_indicators(hyperopt) -> None:
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
tickerlist = {'UNITTEST/BTC': tick}
dataframes = hyperopt.tickerdata_to_dataframe(tickerlist)
dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'])
dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
# Check if some indicators are generated. We will not test all of them
assert 'adx' in dataframe
@ -230,7 +230,7 @@ def test_buy_strategy_generator(hyperopt) -> None:
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
tickerlist = {'UNITTEST/BTC': tick}
dataframes = hyperopt.tickerdata_to_dataframe(tickerlist)
dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'])
dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
populate_buy_trend = hyperopt.buy_strategy_generator(
{
@ -245,7 +245,7 @@ def test_buy_strategy_generator(hyperopt) -> None:
'trigger': 'bb_lower'
}
)
result = populate_buy_trend(dataframe)
result = populate_buy_trend(dataframe, {'pair': 'UNITTEST/BTC'})
# Check if some indicators are generated. We will not test all of them
assert 'buy' in result
assert 1 in result['buy']

235
freqtrade/tests/strategy/legacy_strategy.py Normal file
View File

@ -0,0 +1,235 @@
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
# This class is a sample. Feel free to customize it.
class TestStrategyLegacy(IStrategy):
"""
This is a test strategy using the legacy function headers, which will be
removed in a future update.
Please do not use this as a template, but refer to user_data/strategy/TestStrategy.py
for a uptodate version of this template.
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = '5m'
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
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.
"""
# Momentum Indicator
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
"""
# Awesome oscillator
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# Minus Directional Indicator / Movement
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# ROC
dataframe['roc'] = ta.ROC(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Stoch RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
"""
# Overlap Studies
# ------------------------------------
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
"""
# EMA - Exponential Moving Average
dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
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)
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
"""
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Cycle Indicator
# ------------------------------------
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
"""
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# Inverted Hammer: values [0, 100]
dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# Dragonfly Doji: values [0, 100]
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# Piercing Line: values [0, 100]
dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# Morningstar: values [0, 100]
dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# Three White Soldiers: values [0, 100]
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
"""
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
"""
# Hanging Man: values [0, 100]
dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# Shooting Star: values [0, 100]
dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# Gravestone Doji: values [0, 100]
dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# Dark Cloud Cover: values [0, 100]
dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# Evening Doji Star: values [0, 100]
dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# Evening Star: values [0, 100]
dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
"""
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
"""
# Three Line Strike: values [0, -100, 100]
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# Spinning Top: values [0, -100, 100]
dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# Engulfing: values [0, -100, 100]
dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# Harami: values [0, -100, 100]
dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# Three Outside Up/Down: values [0, -100, 100]
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# Three Inside Up/Down: values [0, -100, 100]
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
"""
# Chart type
# ------------------------------------
"""
# Heikinashi stategy
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
"""
return dataframe
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
"""
dataframe.loc[
(
(dataframe['adx'] > 30) &
(dataframe['tema'] <= dataframe['bb_middleband']) &
(dataframe['tema'] > dataframe['tema'].shift(1))
),
'buy'] = 1
return dataframe
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 buy column
"""
dataframe.loc[
(
(dataframe['adx'] > 70) &
(dataframe['tema'] > dataframe['bb_middleband']) &
(dataframe['tema'] < dataframe['tema'].shift(1))
),
'sell'] = 1
return dataframe

7
freqtrade/tests/strategy/test_default_strategy.py
View File

@ -25,10 +25,11 @@ def test_default_strategy_structure():
def test_default_strategy(result):
strategy = DefaultStrategy({})
metadata = {'pair': 'ETH/BTC'}
assert type(strategy.minimal_roi) is dict
assert type(strategy.stoploss) is float
assert type(strategy.ticker_interval) is str
indicators = strategy.populate_indicators(result)
indicators = strategy.populate_indicators(result, metadata)
assert type(indicators) is DataFrame
assert type(strategy.populate_buy_trend(indicators)) is DataFrame
assert type(strategy.populate_sell_trend(indicators)) is DataFrame
assert type(strategy.populate_buy_trend(indicators, metadata)) is DataFrame
assert type(strategy.populate_sell_trend(indicators, metadata)) is DataFrame

81
freqtrade/tests/strategy/test_strategy.py
View File

@ -1,8 +1,10 @@
# pragma pylint: disable=missing-docstring, protected-access, C0103
import logging
import os
from os import path
import warnings
import pytest
from pandas import DataFrame
from freqtrade.strategy import import_strategy
from freqtrade.strategy.default_strategy import DefaultStrategy
@ -37,8 +39,8 @@ def test_import_strategy(caplog):
def test_search_strategy():
default_config = {}
default_location = os.path.join(os.path.dirname(
os.path.realpath(__file__)), '..', '..', 'strategy'
default_location = path.join(path.dirname(
path.realpath(__file__)), '..', '..', 'strategy'
)
assert isinstance(
StrategyResolver._search_strategy(
@ -57,12 +59,13 @@ def test_search_strategy():
def test_load_strategy(result):
resolver = StrategyResolver({'strategy': 'TestStrategy'})
assert 'adx' in resolver.strategy.populate_indicators(result)
metadata = {'pair': 'ETH/BTC'}
assert 'adx' in resolver.strategy.advise_indicators(result, metadata=metadata)
def test_load_strategy_invalid_directory(result, caplog):
resolver = StrategyResolver()
extra_dir = os.path.join('some', 'path')
extra_dir = path.join('some', 'path')
resolver._load_strategy('TestStrategy', config={}, extra_dir=extra_dir)
assert (
@ -70,7 +73,8 @@ def test_load_strategy_invalid_directory(result, caplog):
logging.WARNING,
'Path "{}" does not exist'.format(extra_dir),
) in caplog.record_tuples
assert 'adx' in resolver.strategy.populate_indicators(result)
assert 'adx' in resolver.strategy.advise_indicators(result, {'pair': 'ETH/BTC'})
def test_load_not_found_strategy():
@ -85,7 +89,7 @@ def test_strategy(result):
config = {'strategy': 'DefaultStrategy'}
resolver = StrategyResolver(config)
metadata = {'pair': 'ETH/BTC'}
assert resolver.strategy.minimal_roi[0] == 0.04
assert config["minimal_roi"]['0'] == 0.04
@ -95,12 +99,13 @@ def test_strategy(result):
assert resolver.strategy.ticker_interval == '5m'
assert config['ticker_interval'] == '5m'
assert 'adx' in resolver.strategy.populate_indicators(result)
df_indicators = resolver.strategy.advise_indicators(result, metadata=metadata)
assert 'adx' in df_indicators
dataframe = resolver.strategy.populate_buy_trend(resolver.strategy.populate_indicators(result))
dataframe = resolver.strategy.advise_buy(df_indicators, metadata=metadata)
assert 'buy' in dataframe.columns
dataframe = resolver.strategy.populate_sell_trend(resolver.strategy.populate_indicators(result))
dataframe = resolver.strategy.advise_sell(df_indicators, metadata=metadata)
assert 'sell' in dataframe.columns
@ -150,3 +155,59 @@ def test_strategy_override_ticker_interval(caplog):
logging.INFO,
'Override strategy \'ticker_interval\' with value in config file: 60.'
) in caplog.record_tuples
def test_deprecate_populate_indicators(result):
default_location = path.join(path.dirname(path.realpath(__file__)))
resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',
'strategy_path': default_location})
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
indicators = resolver.strategy.advise_indicators(result, 'ETH/BTC')
assert len(w) == 1
assert issubclass(w[-1].category, DeprecationWarning)
assert "deprecated - check out the Sample strategy to see the current function headers!" \
in str(w[-1].message)
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
resolver.strategy.advise_buy(indicators, 'ETH/BTC')
assert len(w) == 1
assert issubclass(w[-1].category, DeprecationWarning)
assert "deprecated - check out the Sample strategy to see the current function headers!" \
in str(w[-1].message)
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
resolver.strategy.advise_sell(indicators, 'ETH_BTC')
assert len(w) == 1
assert issubclass(w[-1].category, DeprecationWarning)
assert "deprecated - check out the Sample strategy to see the current function headers!" \
in str(w[-1].message)
def test_call_deprecated_function(result, monkeypatch):
default_location = path.join(path.dirname(path.realpath(__file__)))
resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',
'strategy_path': default_location})
metadata = {'pair': 'ETH/BTC'}
# Make sure we are using a legacy function
assert resolver.strategy._populate_fun_len == 2
assert resolver.strategy._buy_fun_len == 2
assert resolver.strategy._sell_fun_len == 2
indicator_df = resolver.strategy.advise_indicators(result, metadata=metadata)
assert type(indicator_df) is DataFrame
assert 'adx' in indicator_df.columns
buydf = resolver.strategy.advise_buy(result, metadata=metadata)
assert type(buydf) is DataFrame
assert 'buy' in buydf.columns
selldf = resolver.strategy.advise_sell(result, metadata=metadata)
assert type(selldf) is DataFrame
assert 'sell' in selldf

2
freqtrade/tests/test_dataframe.py
View File

@ -14,7 +14,7 @@ def load_dataframe_pair(pairs, strategy):
assert isinstance(pairs[0], str)
dataframe = ld[pairs[0]]
dataframe = strategy.analyze_ticker(dataframe)
dataframe = strategy.analyze_ticker(dataframe, pairs[0])
return dataframe

2
requirements.txt
View File

@ -1,4 +1,4 @@
ccxt==1.17.45
ccxt==1.17.49
SQLAlchemy==1.2.10
python-telegram-bot==10.1.0
arrow==0.12.1

4
scripts/plot_dataframe.py
View File

@ -159,8 +159,8 @@ def plot_analyzed_dataframe(args: Namespace) -> None:
dataframes = strategy.tickerdata_to_dataframe(tickers)
dataframe = dataframes[pair]
dataframe = strategy.populate_buy_trend(dataframe)
dataframe = strategy.populate_sell_trend(dataframe)
dataframe = strategy.advise_buy(dataframe, {'pair': pair})
dataframe = strategy.advise_sell(dataframe, {'pair': pair})
if len(dataframe.index) > args.plot_limit:
logger.warning('Ticker contained more than %s candles as defined '

16
user_data/strategies/test_strategy.py
View File

@ -18,6 +18,7 @@ class TestStrategy(IStrategy):
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
@ -44,13 +45,16 @@ class TestStrategy(IStrategy):
# Optimal ticker interval for the strategy
ticker_interval = '5m'
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
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.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
# Momentum Indicator
@ -211,10 +215,11 @@ class TestStrategy(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
@ -227,10 +232,11 @@ class TestStrategy(IStrategy):
return dataframe
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[