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Merge branch 'develop' into stoploss_on_exchange

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misagh 2018-11-22 09:39:01 +01:00
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5
config_full.json.example
View File

@ -33,6 +33,11 @@
"order_book_min": 1,
"order_book_max": 9
},
"order_types": {
"buy": "limit",
"sell": "limit",
"stoploss": "market"
},
"exchange": {
"name": "bittrex",
"key": "your_exchange_key",

2
docs/bot-usage.md
View File

@ -204,6 +204,8 @@ optional arguments:
number)
--timerange TIMERANGE
specify what timerange of data to use.
--hyperopt PATH specify hyperopt file (default:
freqtrade/optimize/default_hyperopt.py)
-e INT, --epochs INT specify number of epochs (default: 100)
-s {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...], --spaces {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...]
Specify which parameters to hyperopt. Space separate

17
docs/configuration.md
View File

@ -39,6 +39,7 @@ The table below will list all configuration parameters.
| `ask_strategy.use_order_book` | false | No | Allows selling of open traded pair using the rates in Order Book Asks.
| `ask_strategy.order_book_min` | 0 | No | Bot will scan from the top min to max Order Book Asks searching for a profitable rate.
| `ask_strategy.order_book_max` | 0 | No | Bot will scan from the top min to max Order Book Asks searching for a profitable rate.
| `order_types` | None | No | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`).
| `exchange.name` | bittrex | Yes | Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename).
| `exchange.key` | key | No | API key to use for the exchange. Only required when you are in production mode.
| `exchange.secret` | secret | No | API secret to use for the exchange. Only required when you are in production mode.
@ -138,6 +139,22 @@ use the `last` price and values between those interpolate between ask and last
price. Using `ask` price will guarantee quick success in bid, but bot will also
end up paying more then would probably have been necessary.
### Understand order_types
`order_types` contains a dict mapping order-types to market-types. This allows to buy using limit orders, sell using limit-orders, and create stoploss orders using market.
This can be set in the configuration or in the strategy. Configuration overwrites strategy configurations.
If this is configured, all 3 values (`"buy"`, `"sell"` and `"stoploss"`) need to be present, otherwise the bot warn about it and will fail to start.
The below is the default which is used if this is not configured in either Strategy or configuration.
``` json
"order_types": {
"buy": "limit",
"sell": "limit",
"stoploss": "market"
},
```
### What values for exchange.name?
Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports 115 cryptocurrency

32
docs/hyperopt.md
View File

@ -19,18 +19,27 @@ and still take a long time.
## Prepare Hyperopting
We recommend you start by taking a look at `hyperopt.py` file located in [freqtrade/optimize](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py)
Before we start digging in Hyperopt, we recommend you to take a look at
an example hyperopt file located into [user_data/hyperopts/](https://github.com/gcarq/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py)
### 1. Install a Custom Hyperopt File
This is very simple. Put your hyperopt file into the folder
`user_data/hyperopts`.
Let assume you want a hyperopt file `awesome_hyperopt.py`:
1. Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts/awesome_hyperopt.py`
### Configure your Guards and Triggers
### 2. Configure your Guards and Triggers
There are two places you need to change in your hyperopt file to add a
new buy hyperopt for testing:
- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L230-L251).
- Inside [indicator_space()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L207-L223).
There are two places you need to change to add a new buy strategy for testing:
- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L231-L264).
- Inside [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L213-L224)
and the associated methods `indicator_space`, `roi_space`, `stoploss_space`.
There you have two different types of indicators: 1. `guards` and 2. `triggers`.
There you have two different type of indicators: 1. `guards` and 2. `triggers`.
1. Guards are conditions like "never buy if ADX < 10", or "never buy if
current price is over EMA10".
1. Guards are conditions like "never buy if ADX < 10", or never buy if
current price is over EMA10.
2. Triggers are ones that actually trigger buy in specific moment, like
"buy when EMA5 crosses over EMA10" or "buy when close price touches lower
bollinger band".
@ -124,9 +133,12 @@ Because hyperopt tries a lot of combinations to find the best parameters it will
We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
```bash
python3 ./freqtrade/main.py -c config.json hyperopt -e 5000
python3 ./freqtrade/main.py -s <strategyname> --hyperopt <hyperoptname> -c config.json hyperopt -e 5000
```
Use `<strategyname>` and `<hyperoptname>` as the names of the custom strategy
(only required for generating sells) and the custom hyperopt used.
The `-e` flag will set how many evaluations hyperopt will do. We recommend
running at least several thousand evaluations.

8
freqtrade/arguments.py
View File

@ -104,6 +104,14 @@ class Arguments(object):
type=str,
metavar='PATH',
)
self.parser.add_argument(
'--customhyperopt',
help='specify hyperopt class name (default: %(default)s)',
dest='hyperopt',
default=constants.DEFAULT_HYPEROPT,
type=str,
metavar='NAME',
)
self.parser.add_argument(
'--dynamic-whitelist',
help='dynamically generate and update whitelist'

3
freqtrade/configuration.py
View File

@ -53,6 +53,9 @@ class Configuration(object):
if self.args.strategy_path:
config.update({'strategy_path': self.args.strategy_path})
# Add the hyperopt file to use
config.update({'hyperopt': self.args.hyperopt})
# Load Common configuration
config = self._load_common_config(config)

12
freqtrade/constants.py
View File

@ -9,9 +9,12 @@ TICKER_INTERVAL = 5 # min
HYPEROPT_EPOCH = 100 # epochs
RETRY_TIMEOUT = 30 # sec
DEFAULT_STRATEGY = 'DefaultStrategy'
DEFAULT_HYPEROPT = 'DefaultHyperOpts'
DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
DEFAULT_DB_DRYRUN_URL = 'sqlite://'
UNLIMITED_STAKE_AMOUNT = 'unlimited'
REQUIRED_ORDERTYPES = ['buy', 'sell', 'stoploss']
ORDERTYPE_POSSIBILITIES = ['limit', 'market']
TICKER_INTERVAL_MINUTES = {
@ -101,6 +104,15 @@ CONF_SCHEMA = {
'order_book_max': {'type': 'number', 'minimum': 1, 'maximum': 50}
}
},
'order_types': {
'type': 'object',
'properties': {
'buy': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'sell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES}
},
'required': ['buy', 'sell', 'stoploss']
},
'exchange': {'$ref': '#/definitions/exchange'},
'edge': {'$ref': '#/definitions/edge'},
'experimental': {

39
freqtrade/edge/__init__.py
View File

@ -1,8 +1,7 @@
# pragma pylint: disable=W0603
""" Edge positioning package """
import logging
from typing import Any, Dict
from collections import namedtuple
from typing import Any, Dict, NamedTuple
import arrow
import numpy as np
@ -18,6 +17,16 @@ from freqtrade.strategy.interface import SellType
logger = logging.getLogger(__name__)
class PairInfo(NamedTuple):
stoploss: float
winrate: float
risk_reward_ratio: float
required_risk_reward: float
expectancy: float
nb_trades: int
avg_trade_duration: float
class Edge():
"""
Calculates Win Rate, Risk Reward Ratio, Expectancy
@ -30,13 +39,6 @@ class Edge():
config: Dict = {}
_cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
# pair info data type
_pair_info = namedtuple(
'pair_info',
['stoploss', 'winrate', 'risk_reward_ratio', 'required_risk_reward', 'expectancy',
'nb_trades', 'avg_trade_duration']
)
def __init__(self, config: Dict[str, Any], exchange, strategy) -> None:
self.config = config
@ -294,16 +296,15 @@ class Edge():
final = {}
for x in df.itertuples():
info = {
'stoploss': x.stoploss,
'winrate': x.winrate,
'risk_reward_ratio': x.risk_reward_ratio,
'required_risk_reward': x.required_risk_reward,
'expectancy': x.expectancy,
'nb_trades': x.nb_trades,
'avg_trade_duration': x.avg_trade_duration
}
final[x.pair] = self._pair_info(**info)
final[x.pair] = PairInfo(
x.stoploss,
x.winrate,
x.risk_reward_ratio,
x.required_risk_reward,
x.expectancy,
x.nb_trades,
x.avg_trade_duration
)
# Returning a list of pairs in order of "expectancy"
return final

27
freqtrade/exchange/__init__.py
View File

@ -102,7 +102,7 @@ class Exchange(object):
self.markets = self._load_markets()
# Check if all pairs are available
self.validate_pairs(config['exchange']['pair_whitelist'])
self.validate_ordertypes(config.get('order_types', {}))
if config.get('ticker_interval'):
# Check if timeframe is available
self.validate_timeframes(config['ticker_interval'])
@ -218,6 +218,15 @@ class Exchange(object):
raise OperationalException(
f'Invalid ticker {timeframe}, this Exchange supports {timeframes}')
def validate_ordertypes(self, order_types: Dict) -> None:
"""
Checks if order-types configured in strategy/config are supported
"""
if any(v == 'market' for k, v in order_types.items()):
if not self.exchange_has('createMarketOrder'):
raise OperationalException(
f'Exchange {self.name} does not support market orders.')
def exchange_has(self, endpoint: str) -> bool:
"""
Checks if exchange implements a specific API endpoint.
@ -249,14 +258,14 @@ class Exchange(object):
price = ceil(big_price) / pow(10, symbol_prec)
return price
def buy(self, pair: str, rate: float, amount: float) -> Dict:
def buy(self, pair: str, ordertype: str, amount: float, rate: float) -> Dict:
if self._conf['dry_run']:
order_id = f'dry_run_buy_{randint(0, 10**6)}'
self._dry_run_open_orders[order_id] = {
'pair': pair,
'price': rate,
'amount': amount,
'type': 'limit',
'type': ordertype,
'side': 'buy',
'remaining': 0.0,
'datetime': arrow.utcnow().isoformat(),
@ -268,9 +277,9 @@ class Exchange(object):
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)
rate = self.symbol_price_prec(pair, rate) if ordertype != 'market' else None
return self._api.create_limit_buy_order(pair, amount, rate)
return self._api.create_order(pair, ordertype, 'buy', amount, rate)
except ccxt.InsufficientFunds as e:
raise DependencyException(
f'Insufficient funds to create limit buy order on market {pair}.'
@ -287,14 +296,14 @@ class Exchange(object):
except ccxt.BaseError as e:
raise OperationalException(e)
def sell(self, pair: str, rate: float, amount: float) -> Dict:
def sell(self, pair: str, ordertype: str, amount: float, rate: float) -> Dict:
if self._conf['dry_run']:
order_id = f'dry_run_sell_{randint(0, 10**6)}'
self._dry_run_open_orders[order_id] = {
'pair': pair,
'price': rate,
'amount': amount,
'type': 'limit',
'type': ordertype,
'side': 'sell',
'remaining': 0.0,
'datetime': arrow.utcnow().isoformat(),
@ -305,9 +314,9 @@ class Exchange(object):
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)
rate = self.symbol_price_prec(pair, rate) if ordertype != 'market' else None
return self._api.create_limit_sell_order(pair, amount, rate)
return self._api.create_order(pair, ordertype, 'sell', amount, rate)
except ccxt.InsufficientFunds as e:
raise DependencyException(
f'Insufficient funds to create limit sell order on market {pair}.'

33
freqtrade/freqtradebot.py
View File

@ -17,6 +17,7 @@ from cachetools import TTLCache, cached
from freqtrade import (DependencyException, OperationalException,
TemporaryError, __version__, constants, persistence)
from freqtrade.exchange import Exchange
from freqtrade.wallets import Wallets
from freqtrade.edge import Edge
from freqtrade.persistence import Trade
from freqtrade.rpc import RPCManager, RPCMessageType
@ -56,6 +57,7 @@ class FreqtradeBot(object):
self.rpc: RPCManager = RPCManager(self)
self.persistence = None
self.exchange = Exchange(self.config)
self.wallets = Wallets(self.exchange)
# Initializing Edge only if enabled
self.edge = Edge(self.config, self.exchange, self.strategy) if \
@ -194,9 +196,6 @@ class FreqtradeBot(object):
self.edge.calculate()
self.active_pair_whitelist = self.edge.adjust(self.active_pair_whitelist)
# Refreshing candles
self.exchange.refresh_tickers(self.active_pair_whitelist, self.strategy.ticker_interval)
# Query trades from persistence layer
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
@ -338,7 +337,9 @@ class FreqtradeBot(object):
else:
stake_amount = self.config['stake_amount']
# TODO: should come from the wallet
avaliable_amount = self.exchange.get_balance(self.config['stake_currency'])
# avaliable_amount = self.wallets.wallets[self.config['stake_currency']].free
if stake_amount == constants.UNLIMITED_STAKE_AMOUNT:
open_trades = len(Trade.query.filter(Trade.is_open.is_(True)).all())
@ -475,7 +476,8 @@ class FreqtradeBot(object):
amount = stake_amount / buy_limit
order_id = self.exchange.buy(pair, buy_limit, amount)['id']
order_id = self.exchange.buy(pair=pair, ordertype=self.strategy.order_types['buy'],
amount=amount, rate=buy_limit)['id']
self.rpc.send_msg({
'type': RPCMessageType.BUY_NOTIFICATION,
@ -505,6 +507,10 @@ class FreqtradeBot(object):
)
Trade.session.add(trade)
Trade.session.flush()
# Updating wallets
self.wallets.update()
return True
def process_maybe_execute_buy(self) -> bool:
@ -549,7 +555,14 @@ class FreqtradeBot(object):
if trade.is_open and trade.open_order_id is None:
# Check if we can sell our current pair
return self.handle_trade(trade)
result = self.handle_trade(trade)
# Updating wallets if any trade occured
if result:
self.wallets.update()
return result
except DependencyException as exception:
logger.warning('Unable to sell trade: %s', exception)
return False
@ -688,14 +701,17 @@ class FreqtradeBot(object):
# Check if trade is still actually open
if int(order['remaining']) == 0:
self.wallets.update()
continue
# Check if trade is still actually open
if order['status'] == 'open':
if order['side'] == 'buy' and ordertime < buy_timeoutthreashold:
self.handle_timedout_limit_buy(trade, order)
self.wallets.update()
elif order['side'] == 'sell' and ordertime < sell_timeoutthreashold:
self.handle_timedout_limit_sell(trade, order)
self.wallets.update()
# FIX: 20180110, why is cancel.order unconditionally here, whereas
# it is conditionally called in the
@ -762,8 +778,13 @@ class FreqtradeBot(object):
:param sellreason: Reason the sell was triggered
:return: None
"""
sell_type = 'sell'
if sell_reason in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
sell_type = 'stoploss'
# Execute sell and update trade record
order_id = self.exchange.sell(str(trade.pair), limit, trade.amount)['id']
order_id = self.exchange.sell(pair=str(trade.pair),
ordertype=self.strategy.order_types[sell_type],
amount=trade.amount, rate=limit)['id']
trade.open_order_id = order_id
trade.close_rate_requested = limit
trade.sell_reason = sell_reason.value

1
freqtrade/optimize/__init__.py
View File

@ -20,6 +20,7 @@ from pandas import DataFrame
from freqtrade import misc, constants, OperationalException
from freqtrade.exchange import Exchange
from freqtrade.arguments import TimeRange
from freqtrade.optimize.default_hyperopt import DefaultHyperOpts # noqa: F401
logger = logging.getLogger(__name__)

130
freqtrade/optimize/default_hyperopt.py Normal file
View File

@ -0,0 +1,130 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
import talib.abstract as ta
from pandas import DataFrame
from typing import Dict, Any, Callable, List
from functools import reduce
from skopt.space import Categorical, Dimension, Integer, Real
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class_name = 'DefaultHyperOpts'
class DefaultHyperOpts(IHyperOpt):
"""
Default hyperopt provided by freqtrade bot.
You can override it with your own hyperopt
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by hyperopt
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Generate the ROI table that will be used by Hyperopt
"""
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
return roi_table
@staticmethod
def stoploss_space() -> List[Dimension]:
"""
Stoploss Value to search
"""
return [
Real(-0.5, -0.02, name='stoploss'),
]
@staticmethod
def roi_space() -> List[Dimension]:
"""
Values to search for each ROI steps
"""
return [
Integer(10, 120, name='roi_t1'),
Integer(10, 60, name='roi_t2'),
Integer(10, 40, name='roi_t3'),
Real(0.01, 0.04, name='roi_p1'),
Real(0.01, 0.07, name='roi_p2'),
Real(0.01, 0.20, name='roi_p3'),
]

147
freqtrade/optimize/hyperopt.py
View File

@ -9,22 +9,21 @@ import multiprocessing
import os
import sys
from argparse import Namespace
from functools import reduce
from math import exp
from operator import itemgetter
from typing import Any, Callable, Dict, List
from typing import Any, Dict, List
import talib.abstract as ta
from pandas import DataFrame
from sklearn.externals.joblib import Parallel, delayed, dump, load
from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects
from skopt import Optimizer
from skopt.space import Categorical, Dimension, Integer, Real
from skopt.space import Dimension
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.optimize import load_data
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.optimize.hyperopt_resolver import HyperOptResolver
logger = logging.getLogger(__name__)
@ -42,6 +41,9 @@ class Hyperopt(Backtesting):
"""
def __init__(self, config: Dict[str, Any]) -> None:
super().__init__(config)
self.config = config
self.custom_hyperopt = HyperOptResolver(self.config).hyperopt
# set TARGET_TRADES to suit your number concurrent trades so its realistic
# to the number of days
self.target_trades = 600
@ -74,24 +76,6 @@ class Hyperopt(Backtesting):
arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
return arg_dict
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
def save_trials(self) -> None:
"""
Save hyperopt trials to file
@ -121,7 +105,8 @@ class Hyperopt(Backtesting):
best_result['params']
)
if 'roi_t1' in best_result['params']:
logger.info('ROI table:\n%s', self.generate_roi_table(best_result['params']))
logger.info('ROI table:\n%s',
self.custom_hyperopt.generate_roi_table(best_result['params']))
def log_results(self, results) -> None:
"""
@ -149,59 +134,6 @@ class Hyperopt(Backtesting):
result = trade_loss + profit_loss + duration_loss
return result
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Generate the ROI table that will be used by Hyperopt
"""
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
return roi_table
@staticmethod
def roi_space() -> List[Dimension]:
"""
Values to search for each ROI steps
"""
return [
Integer(10, 120, name='roi_t1'),
Integer(10, 60, name='roi_t2'),
Integer(10, 40, name='roi_t3'),
Real(0.01, 0.04, name='roi_p1'),
Real(0.01, 0.07, name='roi_p2'),
Real(0.01, 0.20, name='roi_p3'),
]
@staticmethod
def stoploss_space() -> List[Dimension]:
"""
Stoploss search space
"""
return [
Real(-0.5, -0.02, name='stoploss'),
]
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
def has_space(self, space: str) -> bool:
"""
Tell if a space value is contained in the configuration
@ -216,61 +148,20 @@ class Hyperopt(Backtesting):
"""
spaces: List[Dimension] = []
if self.has_space('buy'):
spaces += Hyperopt.indicator_space()
spaces += self.custom_hyperopt.indicator_space()
if self.has_space('roi'):
spaces += Hyperopt.roi_space()
spaces += self.custom_hyperopt.roi_space()
if self.has_space('stoploss'):
spaces += Hyperopt.stoploss_space()
spaces += self.custom_hyperopt.stoploss_space()
return spaces
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by hyperopt
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
def generate_optimizer(self, _params) -> Dict:
def generate_optimizer(self, _params: Dict) -> Dict:
params = self.get_args(_params)
if self.has_space('roi'):
self.strategy.minimal_roi = self.generate_roi_table(params)
self.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(params)
if self.has_space('buy'):
self.advise_buy = self.buy_strategy_generator(params)
self.advise_buy = self.custom_hyperopt.buy_strategy_generator(params)
if self.has_space('stoploss'):
self.strategy.stoploss = params['stoploss']
@ -329,7 +220,8 @@ class Hyperopt(Backtesting):
)
def run_optimizer_parallel(self, parallel, asked) -> List:
return parallel(delayed(self.generate_optimizer)(v) for v in asked)
return parallel(delayed(
wrap_non_picklable_objects(self.generate_optimizer))(v) for v in asked)
def load_previous_results(self):
""" read trials file if we have one """
@ -351,7 +243,8 @@ class Hyperopt(Backtesting):
)
if self.has_space('buy'):
self.strategy.advise_indicators = Hyperopt.populate_indicators # type: ignore
self.strategy.advise_indicators = \
self.custom_hyperopt.populate_indicators # type: ignore
dump(self.strategy.tickerdata_to_dataframe(data), TICKERDATA_PICKLE)
self.exchange = None # type: ignore
self.load_previous_results()

66
freqtrade/optimize/hyperopt_interface.py Normal file
View File

@ -0,0 +1,66 @@
"""
IHyperOpt interface
This module defines the interface to apply for hyperopts
"""
from abc import ABC, abstractmethod
from typing import Dict, Any, Callable, List
from pandas import DataFrame
from skopt.space import Dimension
class IHyperOpt(ABC):
"""
Interface for freqtrade hyperopts
Defines the mandatory structure must follow any custom strategies
Attributes you can use:
minimal_roi -> Dict: Minimal ROI designed for the strategy
stoploss -> float: optimal stoploss designed for the strategy
ticker_interval -> int: value of the ticker interval to use for the strategy
"""
@staticmethod
@abstractmethod
def populate_indicators(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()
:return: a Dataframe with all mandatory indicators for the strategies
"""
@staticmethod
@abstractmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Create a buy strategy generator
"""
@staticmethod
@abstractmethod
def indicator_space() -> List[Dimension]:
"""
Create an indicator space
"""
@staticmethod
@abstractmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Create an roi table
"""
@staticmethod
@abstractmethod
def stoploss_space() -> List[Dimension]:
"""
Create a stoploss space
"""
@staticmethod
@abstractmethod
def roi_space() -> List[Dimension]:
"""
Create a roi space
"""

104
freqtrade/optimize/hyperopt_resolver.py Normal file
View File

@ -0,0 +1,104 @@
# pragma pylint: disable=attribute-defined-outside-init
"""
This module load custom hyperopts
"""
import importlib.util
import inspect
import logging
import os
from typing import Optional, Dict, Type
from freqtrade.constants import DEFAULT_HYPEROPT
from freqtrade.optimize.hyperopt_interface import IHyperOpt
logger = logging.getLogger(__name__)
class HyperOptResolver(object):
"""
This class contains all the logic to load custom hyperopt class
"""
__slots__ = ['hyperopt']
def __init__(self, config: Optional[Dict] = None) -> None:
"""
Load the custom class from config parameter
:param config: configuration dictionary or None
"""
config = config or {}
# Verify the hyperopt is in the configuration, otherwise fallback to the default hyperopt
hyperopt_name = config.get('hyperopt') or DEFAULT_HYPEROPT
self.hyperopt = self._load_hyperopt(hyperopt_name, extra_dir=config.get('hyperopt_path'))
def _load_hyperopt(
self, hyperopt_name: str, extra_dir: Optional[str] = None) -> IHyperOpt:
"""
Search and loads the specified hyperopt.
:param hyperopt_name: name of the module to import
:param extra_dir: additional directory to search for the given hyperopt
:return: HyperOpt instance or None
"""
current_path = os.path.dirname(os.path.realpath(__file__))
abs_paths = [
os.path.join(current_path, '..', '..', 'user_data', 'hyperopts'),
current_path,
]
if extra_dir:
# Add extra hyperopt directory on top of search paths
abs_paths.insert(0, extra_dir)
for path in abs_paths:
hyperopt = self._search_hyperopt(path, hyperopt_name)
if hyperopt:
logger.info('Using resolved hyperopt %s from \'%s\'', hyperopt_name, path)
return hyperopt
raise ImportError(
"Impossible to load Hyperopt '{}'. This class does not exist"
" or contains Python code errors".format(hyperopt_name)
)
@staticmethod
def _get_valid_hyperopts(module_path: str, hyperopt_name: str) -> Optional[Type[IHyperOpt]]:
"""
Returns a list of all possible hyperopts for the given module_path
:param module_path: absolute path to the module
:param hyperopt_name: Class name of the hyperopt
:return: Tuple with (name, class) or None
"""
# Generate spec based on absolute path
spec = importlib.util.spec_from_file_location('user_data.hyperopts', module_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module) # type: ignore # importlib does not use typehints
valid_hyperopts_gen = (
obj for name, obj in inspect.getmembers(module, inspect.isclass)
if hyperopt_name == name and IHyperOpt in obj.__bases__
)
return next(valid_hyperopts_gen, None)
@staticmethod
def _search_hyperopt(directory: str, hyperopt_name: str) -> Optional[IHyperOpt]:
"""
Search for the hyperopt_name in the given directory
:param directory: relative or absolute directory path
:return: name of the hyperopt class
"""
logger.debug('Searching for hyperopt %s in \'%s\'', hyperopt_name, directory)
for entry in os.listdir(directory):
# Only consider python files
if not entry.endswith('.py'):
logger.debug('Ignoring %s', entry)
continue
hyperopt = HyperOptResolver._get_valid_hyperopts(
os.path.abspath(os.path.join(directory, entry)), hyperopt_name
)
if hyperopt:
return hyperopt()
return None

7
freqtrade/strategy/default_strategy.py
View File

@ -28,6 +28,13 @@ class DefaultStrategy(IStrategy):
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optional order type mapping
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'limit'
}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame

7
freqtrade/strategy/interface.py
View File

@ -75,6 +75,13 @@ class IStrategy(ABC):
# associated ticker interval
ticker_interval: str
# Optional order types
order_types: Dict = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'limit'
}
# run "populate_indicators" only for new candle
process_only_new_candles: bool = False

13
freqtrade/strategy/resolver.py
View File

@ -75,6 +75,19 @@ class StrategyResolver(object):
else:
config['process_only_new_candles'] = self.strategy.process_only_new_candles
if 'order_types' in config:
self.strategy.order_types = config['order_types']
logger.info(
"Override strategy 'order_types' with value in config file: %s.",
config['order_types']
)
else:
config['order_types'] = self.strategy.order_types
if not all(k in self.strategy.order_types for k in constants.REQUIRED_ORDERTYPES):
raise ImportError(f"Impossible to load Strategy '{self.strategy.__class__.__name__}'. "
f"Order-types mapping is incomplete.")
# Sort and apply type conversions
self.strategy.minimal_roi = OrderedDict(sorted(
{int(key): value for (key, value) in self.strategy.minimal_roi.items()}.items(),

1
freqtrade/tests/conftest.py
View File

@ -30,6 +30,7 @@ def log_has(line, logs):
def patch_exchange(mocker, api_mock=None) -> None:
mocker.patch('freqtrade.exchange.Exchange._load_markets', MagicMock(return_value={}))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
mocker.patch('freqtrade.exchange.Exchange.validate_ordertypes', MagicMock())
mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value="Bittrex"))
mocker.patch('freqtrade.exchange.Exchange.id', PropertyMock(return_value="bittrex"))
if api_mock:

14
freqtrade/tests/edge/test_edge.py
View File

@ -4,7 +4,7 @@
import pytest
import logging
from freqtrade.tests.conftest import get_patched_freqtradebot
from freqtrade.edge import Edge
from freqtrade.edge import Edge, PairInfo
from pandas import DataFrame, to_datetime
from freqtrade.strategy.interface import SellType
from freqtrade.tests.optimize import (BTrade, BTContainer, _build_backtest_dataframe,
@ -128,9 +128,9 @@ def test_adjust(mocker, default_conf):
edge = Edge(default_conf, freqtrade.exchange, freqtrade.strategy)
mocker.patch('freqtrade.edge.Edge._cached_pairs', mocker.PropertyMock(
return_value={
'E/F': Edge._pair_info(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
'C/D': Edge._pair_info(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
'N/O': Edge._pair_info(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60)
'E/F': PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
'C/D': PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
'N/O': PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60)
}
))
@ -143,9 +143,9 @@ def test_stoploss(mocker, default_conf):
edge = Edge(default_conf, freqtrade.exchange, freqtrade.strategy)
mocker.patch('freqtrade.edge.Edge._cached_pairs', mocker.PropertyMock(
return_value={
'E/F': Edge._pair_info(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
'C/D': Edge._pair_info(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
'N/O': Edge._pair_info(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60)
'E/F': PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
'C/D': PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
'N/O': PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60)
}
))

108
freqtrade/tests/exchange/test_exchange.py
View File

@ -355,6 +355,36 @@ def test_validate_timeframes_not_in_config(default_conf, mocker):
Exchange(default_conf)
def test_validate_order_types(default_conf, mocker):
api_mock = MagicMock()
type(api_mock).has = PropertyMock(return_value={'createMarketOrder': True})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange._load_markets', MagicMock(return_value={}))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
default_conf['order_types'] = {'buy': 'limit', 'sell': 'limit', 'stoploss': 'market'}
Exchange(default_conf)
type(api_mock).has = PropertyMock(return_value={'createMarketOrder': False})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
default_conf['order_types'] = {'buy': 'limit', 'sell': 'limit', 'stoploss': 'market'}
with pytest.raises(OperationalException,
match=r'Exchange .* does not support market orders.'):
Exchange(default_conf)
def test_validate_order_types_not_in_config(default_conf, mocker):
api_mock = MagicMock()
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange._load_markets', MagicMock(return_value={}))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
conf = copy.deepcopy(default_conf)
Exchange(conf)
def test_exchange_has(default_conf, mocker):
exchange = get_patched_exchange(mocker, default_conf)
assert not exchange.exchange_has('ASDFASDF')
@ -373,7 +403,7 @@ def test_buy_dry_run(default_conf, mocker):
default_conf['dry_run'] = True
exchange = get_patched_exchange(mocker, default_conf)
order = exchange.buy(pair='ETH/BTC', rate=200, amount=1)
order = exchange.buy(pair='ETH/BTC', ordertype='limit', amount=1, rate=200)
assert 'id' in order
assert 'dry_run_buy_' in order['id']
@ -381,47 +411,64 @@ def test_buy_dry_run(default_conf, mocker):
def test_buy_prod(default_conf, mocker):
api_mock = MagicMock()
order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
api_mock.create_limit_buy_order = MagicMock(return_value={
order_type = 'market'
api_mock.create_order = MagicMock(return_value={
'id': order_id,
'info': {
'foo': 'bar'
}
})
default_conf['dry_run'] = False
mocker.patch('freqtrade.exchange.Exchange.symbol_amount_prec', lambda s, x, y: y)
mocker.patch('freqtrade.exchange.Exchange.symbol_price_prec', lambda s, x, y: y)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
order = exchange.buy(pair='ETH/BTC', rate=200, amount=1)
order = exchange.buy(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
assert 'id' in order
assert 'info' in order
assert order['id'] == order_id
assert api_mock.create_order.call_args[0][0] == 'ETH/BTC'
assert api_mock.create_order.call_args[0][1] == order_type
assert api_mock.create_order.call_args[0][2] == 'buy'
assert api_mock.create_order.call_args[0][3] == 1
assert api_mock.create_order.call_args[0][4] is None
api_mock.create_order.reset_mock()
order_type = 'limit'
order = exchange.buy(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
assert api_mock.create_order.call_args[0][0] == 'ETH/BTC'
assert api_mock.create_order.call_args[0][1] == order_type
assert api_mock.create_order.call_args[0][2] == 'buy'
assert api_mock.create_order.call_args[0][3] == 1
assert api_mock.create_order.call_args[0][4] == 200
# test exception handling
with pytest.raises(DependencyException):
api_mock.create_limit_buy_order = MagicMock(side_effect=ccxt.InsufficientFunds)
api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.buy(pair='ETH/BTC', rate=200, amount=1)
exchange.buy(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
with pytest.raises(DependencyException):
api_mock.create_limit_buy_order = MagicMock(side_effect=ccxt.InvalidOrder)
api_mock.create_order = MagicMock(side_effect=ccxt.InvalidOrder)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.buy(pair='ETH/BTC', rate=200, amount=1)
exchange.buy(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
with pytest.raises(TemporaryError):
api_mock.create_limit_buy_order = MagicMock(side_effect=ccxt.NetworkError)
api_mock.create_order = MagicMock(side_effect=ccxt.NetworkError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.buy(pair='ETH/BTC', rate=200, amount=1)
exchange.buy(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
with pytest.raises(OperationalException):
api_mock.create_limit_buy_order = MagicMock(side_effect=ccxt.BaseError)
api_mock.create_order = MagicMock(side_effect=ccxt.BaseError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.buy(pair='ETH/BTC', rate=200, amount=1)
exchange.buy(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
def test_sell_dry_run(default_conf, mocker):
default_conf['dry_run'] = True
exchange = get_patched_exchange(mocker, default_conf)
order = exchange.sell(pair='ETH/BTC', rate=200, amount=1)
order = exchange.sell(pair='ETH/BTC', ordertype='limit', amount=1, rate=200)
assert 'id' in order
assert 'dry_run_sell_' in order['id']
@ -429,7 +476,8 @@ def test_sell_dry_run(default_conf, mocker):
def test_sell_prod(default_conf, mocker):
api_mock = MagicMock()
order_id = 'test_prod_sell_{}'.format(randint(0, 10 ** 6))
api_mock.create_limit_sell_order = MagicMock(return_value={
order_type = 'market'
api_mock.create_order = MagicMock(return_value={
'id': order_id,
'info': {
'foo': 'bar'
@ -438,32 +486,48 @@ def test_sell_prod(default_conf, mocker):
default_conf['dry_run'] = False
exchange = get_patched_exchange(mocker, default_conf, api_mock)
mocker.patch('freqtrade.exchange.Exchange.symbol_amount_prec', lambda s, x, y: y)
mocker.patch('freqtrade.exchange.Exchange.symbol_price_prec', lambda s, x, y: y)
order = exchange.sell(pair='ETH/BTC', rate=200, amount=1)
order = exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
assert 'id' in order
assert 'info' in order
assert order['id'] == order_id
assert api_mock.create_order.call_args[0][0] == 'ETH/BTC'
assert api_mock.create_order.call_args[0][1] == order_type
assert api_mock.create_order.call_args[0][2] == 'sell'
assert api_mock.create_order.call_args[0][3] == 1
assert api_mock.create_order.call_args[0][4] is None
api_mock.create_order.reset_mock()
order_type = 'limit'
order = exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
assert api_mock.create_order.call_args[0][0] == 'ETH/BTC'
assert api_mock.create_order.call_args[0][1] == order_type
assert api_mock.create_order.call_args[0][2] == 'sell'
assert api_mock.create_order.call_args[0][3] == 1
assert api_mock.create_order.call_args[0][4] == 200
# test exception handling
with pytest.raises(DependencyException):
api_mock.create_limit_sell_order = MagicMock(side_effect=ccxt.InsufficientFunds)
api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.sell(pair='ETH/BTC', rate=200, amount=1)
exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
with pytest.raises(DependencyException):
api_mock.create_limit_sell_order = MagicMock(side_effect=ccxt.InvalidOrder)
api_mock.create_order = MagicMock(side_effect=ccxt.InvalidOrder)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.sell(pair='ETH/BTC', rate=200, amount=1)
exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
with pytest.raises(TemporaryError):
api_mock.create_limit_sell_order = MagicMock(side_effect=ccxt.NetworkError)
api_mock.create_order = MagicMock(side_effect=ccxt.NetworkError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.sell(pair='ETH/BTC', rate=200, amount=1)
exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
with pytest.raises(OperationalException):
api_mock.create_limit_sell_order = MagicMock(side_effect=ccxt.BaseError)
api_mock.create_order = MagicMock(side_effect=ccxt.BaseError)
exchange = get_patched_exchange(mocker, default_conf, api_mock)
exchange.sell(pair='ETH/BTC', rate=200, amount=1)
exchange.sell(pair='ETH/BTC', ordertype=order_type, amount=1, rate=200)
def test_get_balance_dry_run(default_conf, mocker):

13
freqtrade/tests/optimize/test_edge_cli.py
View File

@ -4,7 +4,7 @@
from unittest.mock import MagicMock
import json
from typing import List
from freqtrade.edge import Edge
from freqtrade.edge import PairInfo
from freqtrade.arguments import Arguments
from freqtrade.optimize.edge_cli import (EdgeCli, setup_configuration, start)
from freqtrade.tests.conftest import log_has, patch_exchange
@ -123,17 +123,8 @@ def test_generate_edge_table(edge_conf, mocker):
edge_cli = EdgeCli(edge_conf)
results = {}
info = {
'stoploss': -0.01,
'winrate': 0.60,
'risk_reward_ratio': 2,
'required_risk_reward': 1,
'expectancy': 3,
'nb_trades': 10,
'avg_trade_duration': 60
}
results['ETH/BTC'] = PairInfo(-0.01, 0.60, 2, 1, 3, 10, 60)
results['ETH/BTC'] = Edge._pair_info(**info)
assert edge_cli._generate_edge_table(results).count(':|') == 7
assert edge_cli._generate_edge_table(results).count('| ETH/BTC |') == 1
assert edge_cli._generate_edge_table(results).count(

10
freqtrade/tests/optimize/test_hyperopt.py
View File

@ -175,7 +175,7 @@ def test_roi_table_generation(hyperopt) -> None:
'roi_p3': 3,
}
assert hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
assert hyperopt.custom_hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
@ -243,7 +243,8 @@ def test_populate_indicators(hyperopt) -> None:
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
tickerlist = {'UNITTEST/BTC': tick}
dataframes = hyperopt.strategy.tickerdata_to_dataframe(tickerlist)
dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
dataframe = hyperopt.custom_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
@ -255,9 +256,10 @@ def test_buy_strategy_generator(hyperopt) -> None:
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
tickerlist = {'UNITTEST/BTC': tick}
dataframes = hyperopt.strategy.tickerdata_to_dataframe(tickerlist)
dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
{'pair': 'UNITTEST/BTC'})
populate_buy_trend = hyperopt.buy_strategy_generator(
populate_buy_trend = hyperopt.custom_hyperopt.buy_strategy_generator(
{
'adx-value': 20,
'fastd-value': 20,

40
freqtrade/tests/strategy/test_strategy.py
View File

@ -88,8 +88,8 @@ def test_load_strategy_invalid_directory(result, caplog):
def test_load_not_found_strategy():
strategy = StrategyResolver()
with pytest.raises(ImportError,
match=r'Impossible to load Strategy \'NotFoundStrategy\'.'
r' This class does not exist or contains Python code errors'):
match=r"Impossible to load Strategy 'NotFoundStrategy'."
r" This class does not exist or contains Python code errors"):
strategy._load_strategy(strategy_name='NotFoundStrategy', config={})
@ -182,6 +182,42 @@ def test_strategy_override_process_only_new_candles(caplog):
) in caplog.record_tuples
def test_strategy_override_order_types(caplog):
caplog.set_level(logging.INFO)
order_types = {
'buy': 'market',
'sell': 'limit',
'stoploss': 'limit'
}
config = {
'strategy': 'DefaultStrategy',
'order_types': order_types
}
resolver = StrategyResolver(config)
assert resolver.strategy.order_types
for method in ['buy', 'sell', 'stoploss']:
assert resolver.strategy.order_types[method] == order_types[method]
assert ('freqtrade.strategy.resolver',
logging.INFO,
"Override strategy 'order_types' with value in config file:"
" {'buy': 'market', 'sell': 'limit', 'stoploss': 'limit'}."
) in caplog.record_tuples
config = {
'strategy': 'DefaultStrategy',
'order_types': {'buy': 'market'}
}
# Raise error for invalid configuration
with pytest.raises(ImportError,
match=r"Impossible to load Strategy 'DefaultStrategy'. "
r"Order-types mapping is incomplete."):
StrategyResolver(config)
def test_deprecate_populate_indicators(result):
default_location = path.join(path.dirname(path.realpath(__file__)))
resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',

18
freqtrade/tests/test_freqtradebot.py
View File

@ -553,7 +553,7 @@ def test_create_trade_minimal_amount(default_conf, ticker, limit_buy_order,
patch_get_signal(freqtrade)
freqtrade.create_trade()
rate, amount = buy_mock.call_args[0][1], buy_mock.call_args[0][2]
rate, amount = buy_mock.call_args[1]['rate'], buy_mock.call_args[1]['amount']
assert rate * amount >= default_conf['stake_amount']
@ -863,10 +863,10 @@ def test_execute_buy(mocker, default_conf, fee, markets, limit_buy_order) -> Non
assert freqtrade.execute_buy(pair, stake_amount)
assert get_bid.call_count == 1
assert buy_mm.call_count == 1
call_args = buy_mm.call_args_list[0][0]
assert call_args[0] == pair
assert call_args[1] == bid
assert call_args[2] == stake_amount / bid
call_args = buy_mm.call_args_list[0][1]
assert call_args['pair'] == pair
assert call_args['rate'] == bid
assert call_args['amount'] == stake_amount / bid
# Test calling with price
fix_price = 0.06
@ -875,10 +875,10 @@ def test_execute_buy(mocker, default_conf, fee, markets, limit_buy_order) -> Non
assert get_bid.call_count == 1
assert buy_mm.call_count == 2
call_args = buy_mm.call_args_list[1][0]
assert call_args[0] == pair
assert call_args[1] == fix_price
assert call_args[2] == stake_amount / fix_price
call_args = buy_mm.call_args_list[1][1]
assert call_args['pair'] == pair
assert call_args['rate'] == fix_price
assert call_args['amount'] == stake_amount / fix_price
def test_process_maybe_execute_buy(mocker, default_conf) -> None:

84
freqtrade/tests/test_wallets.py Normal file
View File

@ -0,0 +1,84 @@
# pragma pylint: disable=missing-docstring
from freqtrade.tests.conftest import get_patched_freqtradebot
from unittest.mock import MagicMock
def test_sync_wallet_at_boot(mocker, default_conf):
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_balances=MagicMock(return_value={
"BNT": {
"free": 1.0,
"used": 2.0,
"total": 3.0
},
"GAS": {
"free": 0.260739,
"used": 0.0,
"total": 0.260739
},
})
)
freqtrade = get_patched_freqtradebot(mocker, default_conf)
assert len(freqtrade.wallets.wallets) == 2
assert freqtrade.wallets.wallets['BNT'].free == 1.0
assert freqtrade.wallets.wallets['BNT'].used == 2.0
assert freqtrade.wallets.wallets['BNT'].total == 3.0
assert freqtrade.wallets.wallets['GAS'].free == 0.260739
assert freqtrade.wallets.wallets['GAS'].used == 0.0
assert freqtrade.wallets.wallets['GAS'].total == 0.260739
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_balances=MagicMock(return_value={
"BNT": {
"free": 1.2,
"used": 1.9,
"total": 3.5
},
"GAS": {
"free": 0.270739,
"used": 0.1,
"total": 0.260439
},
})
)
freqtrade.wallets.update()
assert len(freqtrade.wallets.wallets) == 2
assert freqtrade.wallets.wallets['BNT'].free == 1.2
assert freqtrade.wallets.wallets['BNT'].used == 1.9
assert freqtrade.wallets.wallets['BNT'].total == 3.5
assert freqtrade.wallets.wallets['GAS'].free == 0.270739
assert freqtrade.wallets.wallets['GAS'].used == 0.1
assert freqtrade.wallets.wallets['GAS'].total == 0.260439
def test_sync_wallet_missing_data(mocker, default_conf):
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_balances=MagicMock(return_value={
"BNT": {
"free": 1.0,
"used": 2.0,
"total": 3.0
},
"GAS": {
"free": 0.260739,
"total": 0.260739
},
})
)
freqtrade = get_patched_freqtradebot(mocker, default_conf)
assert len(freqtrade.wallets.wallets) == 2
assert freqtrade.wallets.wallets['BNT'].free == 1.0
assert freqtrade.wallets.wallets['BNT'].used == 2.0
assert freqtrade.wallets.wallets['BNT'].total == 3.0
assert freqtrade.wallets.wallets['GAS'].free == 0.260739
assert freqtrade.wallets.wallets['GAS'].used is None
assert freqtrade.wallets.wallets['GAS'].total == 0.260739

44
freqtrade/wallets.py Normal file
View File

@ -0,0 +1,44 @@
# pragma pylint: disable=W0603
""" Wallet """
import logging
from typing import Dict, Any, NamedTuple
from collections import namedtuple
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Wallet(NamedTuple):
exchange: str
currency: str
free: float = 0
used: float = 0
total: float = 0
class Wallets(object):
# wallet data structure
wallet = namedtuple(
'wallet',
['exchange', 'currency', 'free', 'used', 'total']
)
def __init__(self, exchange: Exchange) -> None:
self.exchange = exchange
self.wallets: Dict[str, Any] = {}
self.update()
def update(self) -> None:
balances = self.exchange.get_balances()
for currency in balances:
self.wallets[currency] = Wallet(
self.exchange.id,
currency,
balances[currency].get('free', None),
balances[currency].get('used', None),
balances[currency].get('total', None)
)
logger.info('Wallets synced ...')

3
requirements.txt
View File

@ -1,4 +1,4 @@
ccxt==1.17.498
ccxt==1.17.522
SQLAlchemy==1.2.14
python-telegram-bot==11.1.0
arrow==0.12.1
@ -8,6 +8,7 @@ urllib3==1.24.1
wrapt==1.10.11
pandas==0.23.4
scikit-learn==0.20.0
joblib==0.13.0
scipy==1.1.0
jsonschema==2.6.0
numpy==1.15.4

1
setup.py
View File

@ -31,6 +31,7 @@ setup(name='freqtrade',
'pandas',
'scikit-learn',
'scipy',
'joblib',
'jsonschema',
'TA-Lib',
'tabulate',

0
user_data/hyperopts/__init__.py Normal file
View File

139
user_data/hyperopts/sample_hyperopt.py Normal file
View File

@ -0,0 +1,139 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
import talib.abstract as ta
from pandas import DataFrame
from typing import Dict, Any, Callable, List
from functools import reduce
import numpy
from skopt.space import Categorical, Dimension, Integer, Real
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class_name = 'SampleHyperOpts'
# This class is a sample. Feel free to customize it.
class SampleHyperOpts(IHyperOpt):
"""
This is a test hyperopt to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
You can:
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your hyperopt
- Add any lib you need to build your hyperopt
You must keep:
- the prototype for the methods: populate_indicators, indicator_space, buy_strategy_generator,
roi_space, generate_roi_table, stoploss_space
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by hyperopt
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Generate the ROI table that will be used by Hyperopt
"""
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
return roi_table
@staticmethod
def stoploss_space() -> List[Dimension]:
"""
Stoploss Value to search
"""
return [
Real(-0.5, -0.02, name='stoploss'),
]
@staticmethod
def roi_space() -> List[Dimension]:
"""
Values to search for each ROI steps
"""
return [
Integer(10, 120, name='roi_t1'),
Integer(10, 60, name='roi_t2'),
Integer(10, 40, name='roi_t3'),
Real(0.01, 0.04, name='roi_p1'),
Real(0.01, 0.07, name='roi_p2'),
Real(0.01, 0.20, name='roi_p3'),
]

7
user_data/strategies/test_strategy.py
View File

@ -48,6 +48,13 @@ class TestStrategy(IStrategy):
# run "populate_indicators" only for new candle
ta_on_candle = False
# Optional order type mapping
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market'
}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame