Merge pull request #1915 from freqtrade/feat/drop_incomplete_optional

Make dropping the last candle optional (configured per exchange)
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Matthias 2019-06-10 14:58:19 +02:00 committed by GitHub
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10 changed files with 197 additions and 25 deletions

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@ -303,6 +303,25 @@ This configuration enables binance, as well as rate limiting to avoid bans from
Optimal settings for rate limiting depend on the exchange and the size of the whitelist, so an ideal parameter will vary on many other settings.
We try to provide sensible defaults per exchange where possible, if you encounter bans please make sure that `"enableRateLimit"` is enabled and increase the `"rateLimit"` parameter step by step.
#### Advanced FreqTrade Exchange configuration
Advanced options can be configured using the `_ft_has_params` setting, which will override Defaults and exchange-specific behaviours.
Available options are listed in the exchange-class as `_ft_has_default`.
For example, to test the order type `FOK` with Kraken, and modify candle_limit to 200 (so you only get 200 candles per call):
```json
"exchange": {
"name": "kraken",
"_ft_has_params": {
"order_time_in_force": ["gtc", "fok"],
"ohlcv_candle_limit": 200
}
```
!!! Warning
Please make sure to fully understand the impacts of these settings before modifying them.
### What values can be used for fiat_display_currency?

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@ -81,6 +81,51 @@ Please also run `self._validate_whitelist(pairs)` and to check and remove pairs
This is a simple method used by `VolumePairList` - however serves as a good example.
It implements caching (`@cached(TTLCache(maxsize=1, ttl=1800))`) as well as a configuration option to allow different (but similar) strategies to work with the same PairListProvider.
## Implement a new Exchange (WIP)
!!! Note
This section is a Work in Progress and is not a complete guide on how to test a new exchange with FreqTrade.
Most exchanges supported by CCXT should work out of the box.
### Stoploss On Exchange
Check if the new exchange supports Stoploss on Exchange orders through their API.
Since CCXT does not provide unification for Stoploss On Exchange yet, we'll need to implement the exchange-specific parameters ourselfs. Best look at `binance.py` for an example implementation of this. You'll need to dig through the documentation of the Exchange's API on how exactly this can be done. [CCXT Issues](https://github.com/ccxt/ccxt/issues) may also provide great help, since others may have implemented something similar for their projects.
### Incomplete candles
While fetching OHLCV data, we're may end up getting incomplete candles (Depending on the exchange).
To demonstrate this, we'll use daily candles (`"1d"`) to keep things simple.
We query the api (`ct.fetch_ohlcv()`) for the timeframe and look at the date of the last entry. If this entry changes or shows the date of a "incomplete" candle, then we should drop this since having incomplete candles is problematic because indicators assume that only complete candles are passed to them, and will generate a lot of false buy signals. By default, we're therefore removing the last candle assuming it's incomplete.
To check how the new exchange behaves, you can use the following snippet:
``` python
import ccxt
from datetime import datetime
from freqtrade.data.converter import parse_ticker_dataframe
ct = ccxt.binance()
timeframe = "1d"
pair = "XLM/BTC" # Make sure to use a pair that exists on that exchange!
raw = ct.fetch_ohlcv(pair, timeframe=timeframe)
# convert to dataframe
df1 = parse_ticker_dataframe(raw, timeframe, drop_incomplete=False)
print(df1["date"].tail(1))
print(datetime.utcnow())
```
``` output
19 2019-06-08 00:00:00+00:00
2019-06-09 12:30:27.873327
```
The output will show the last entry from the Exchange as well as the current UTC date.
If the day shows the same day, then the last candle can be assumed as incomplete and should be dropped (leave the setting `"ohlcv_partial_candle"` from the exchange-class untouched / True). Otherwise, set `"ohlcv_partial_candle"` to `False` to not drop Candles (shown in the example above).
## Creating a release
This part of the documentation is aimed at maintainers, and shows how to create a release.

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@ -10,14 +10,16 @@ from pandas import DataFrame, to_datetime
logger = logging.getLogger(__name__)
def parse_ticker_dataframe(ticker: list, ticker_interval: str,
fill_missing: bool = True) -> DataFrame:
def parse_ticker_dataframe(ticker: list, ticker_interval: str, *,
fill_missing: bool = True,
drop_incomplete: bool = True) -> DataFrame:
"""
Converts a ticker-list (format ccxt.fetch_ohlcv) to a Dataframe
:param ticker: ticker list, as returned by exchange.async_get_candle_history
:param ticker_interval: ticker_interval (e.g. 5m). Used to fill up eventual missing data
:param fill_missing: fill up missing candles with 0 candles
(see ohlcv_fill_up_missing_data for details)
:param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
:return: DataFrame
"""
logger.debug("Parsing tickerlist to dataframe")
@ -43,8 +45,10 @@ def parse_ticker_dataframe(ticker: list, ticker_interval: str,
'close': 'last',
'volume': 'max',
})
frame.drop(frame.tail(1).index, inplace=True) # eliminate partial candle
logger.debug('Dropping last candle')
# eliminate partial candle
if drop_incomplete:
frame.drop(frame.tail(1).index, inplace=True)
logger.debug('Dropping last candle')
if fill_missing:
return ohlcv_fill_up_missing_data(frame, ticker_interval)

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@ -81,10 +81,20 @@ def load_pair_history(pair: str,
timerange: TimeRange = TimeRange(None, None, 0, 0),
refresh_pairs: bool = False,
exchange: Optional[Exchange] = None,
fill_up_missing: bool = True
fill_up_missing: bool = True,
drop_incomplete: bool = True
) -> DataFrame:
"""
Loads cached ticker history for the given pair.
:param pair: Pair to load data for
:param ticker_interval: Ticker-interval (e.g. "5m")
:param datadir: Path to the data storage location.
:param timerange: Limit data to be loaded to this timerange
:param refresh_pairs: Refresh pairs from exchange.
(Note: Requires exchange to be passed as well.)
:param exchange: Exchange object (needed when using "refresh_pairs")
:param fill_up_missing: Fill missing values with "No action"-candles
:param drop_incomplete: Drop last candle assuming it may be incomplete.
:return: DataFrame with ohlcv data
"""
@ -106,7 +116,9 @@ def load_pair_history(pair: str,
logger.warning('Missing data at end for pair %s, data ends at %s',
pair,
arrow.get(pairdata[-1][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
return parse_ticker_dataframe(pairdata, ticker_interval, fill_up_missing)
return parse_ticker_dataframe(pairdata, ticker_interval,
fill_missing=fill_up_missing,
drop_incomplete=drop_incomplete)
else:
logger.warning(
f'No history data for pair: "{pair}", interval: {ticker_interval}. '

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@ -2,23 +2,24 @@
"""
Cryptocurrency Exchanges support
"""
import logging
import asyncio
import inspect
from random import randint
from typing import List, Dict, Tuple, Any, Optional
import logging
from copy import deepcopy
from datetime import datetime
from math import floor, ceil
from math import ceil, floor
from random import randint
from typing import Any, Dict, List, Optional, Tuple
import arrow
import asyncio
import ccxt
import ccxt.async_support as ccxt_async
from pandas import DataFrame
from freqtrade import (constants, DependencyException, OperationalException,
TemporaryError, InvalidOrderException)
from freqtrade import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError, constants)
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.misc import deep_merge_dicts
logger = logging.getLogger(__name__)
@ -68,12 +69,15 @@ class Exchange(object):
_params: Dict = {}
# Dict to specify which options each exchange implements
# TODO: this should be merged with attributes from subclasses
# To avoid having to copy/paste this to all subclasses.
_ft_has: Dict = {
# This defines defaults, which can be selectively overridden by subclasses using _ft_has
# or by specifying them in the configuration.
_ft_has_default: Dict = {
"stoploss_on_exchange": False,
"order_time_in_force": ["gtc"],
"ohlcv_candle_limit": 500,
"ohlcv_partial_candle": True,
}
_ft_has: Dict = {}
def __init__(self, config: dict) -> None:
"""
@ -100,6 +104,19 @@ class Exchange(object):
logger.info('Instance is running with dry_run enabled')
exchange_config = config['exchange']
# Deep merge ft_has with default ft_has options
self._ft_has = deep_merge_dicts(self._ft_has, deepcopy(self._ft_has_default))
if exchange_config.get("_ft_has_params"):
self._ft_has = deep_merge_dicts(exchange_config.get("_ft_has_params"),
self._ft_has)
logger.info("Overriding exchange._ft_has with config params, result: %s", self._ft_has)
# Assign this directly for easy access
self._ohlcv_candle_limit = self._ft_has['ohlcv_candle_limit']
self._ohlcv_partial_candle = self._ft_has['ohlcv_partial_candle']
# Initialize ccxt objects
self._api: ccxt.Exchange = self._init_ccxt(
exchange_config, ccxt_kwargs=exchange_config.get('ccxt_config'))
self._api_async: ccxt_async.Exchange = self._init_ccxt(
@ -506,10 +523,8 @@ class Exchange(object):
async def _async_get_history(self, pair: str,
ticker_interval: str,
since_ms: int) -> List:
# Assume exchange returns 500 candles
_LIMIT = 500
one_call = timeframe_to_msecs(ticker_interval) * _LIMIT
one_call = timeframe_to_msecs(ticker_interval) * self._ohlcv_candle_limit
logger.debug(
"one_call: %s msecs (%s)",
one_call,
@ -566,7 +581,8 @@ class Exchange(object):
self._pairs_last_refresh_time[(pair, ticker_interval)] = ticks[-1][0] // 1000
# keeping parsed dataframe in cache
self._klines[(pair, ticker_interval)] = parse_ticker_dataframe(
ticks, ticker_interval, fill_missing=True)
ticks, ticker_interval, fill_missing=True,
drop_incomplete=self._ohlcv_partial_candle)
return tickers
def _now_is_time_to_refresh(self, pair: str, ticker_interval: str) -> bool:

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@ -117,6 +117,8 @@ def format_ms_time(date: int) -> str:
def deep_merge_dicts(source, destination):
"""
Values from Source override destination, destination is returned (and modified!!)
Sample:
>>> a = { 'first' : { 'rows' : { 'pass' : 'dog', 'number' : '1' } } }
>>> b = { 'first' : { 'rows' : { 'fail' : 'cat', 'number' : '5' } } }
>>> merge(b, a) == { 'first' : { 'rows' : { 'pass' : 'dog', 'fail' : 'cat', 'number' : '5' } } }

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@ -649,7 +649,7 @@ def ticker_history_list():
@pytest.fixture
def ticker_history(ticker_history_list):
return parse_ticker_dataframe(ticker_history_list, "5m", True)
return parse_ticker_dataframe(ticker_history_list, "5m", fill_missing=True)
@pytest.fixture
@ -854,7 +854,7 @@ def tickers():
@pytest.fixture
def result():
with open('freqtrade/tests/testdata/UNITTEST_BTC-1m.json') as data_file:
return parse_ticker_dataframe(json.load(data_file), '1m', True)
return parse_ticker_dataframe(json.load(data_file), '1m', fill_missing=True)
# FIX:
# Create an fixture/function

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@ -96,3 +96,50 @@ def test_ohlcv_fill_up_missing_data2(caplog):
assert log_has(f"Missing data fillup: before: {len(data)} - after: {len(data2)}",
caplog.record_tuples)
def test_ohlcv_drop_incomplete(caplog):
ticker_interval = '1d'
ticks = [[
1559750400000, # 2019-06-04
8.794e-05, # open
8.948e-05, # high
8.794e-05, # low
8.88e-05, # close
2255, # volume (in quote currency)
],
[
1559836800000, # 2019-06-05
8.88e-05,
8.942e-05,
8.88e-05,
8.893e-05,
9911,
],
[
1559923200000, # 2019-06-06
8.891e-05,
8.893e-05,
8.875e-05,
8.877e-05,
2251
],
[
1560009600000, # 2019-06-07
8.877e-05,
8.883e-05,
8.895e-05,
8.817e-05,
123551
]
]
caplog.set_level(logging.DEBUG)
data = parse_ticker_dataframe(ticks, ticker_interval, fill_missing=False, drop_incomplete=False)
assert len(data) == 4
assert not log_has("Dropping last candle", caplog.record_tuples)
# Drop last candle
data = parse_ticker_dataframe(ticks, ticker_interval, fill_missing=False, drop_incomplete=True)
assert len(data) == 3
assert log_has("Dropping last candle", caplog.record_tuples)

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@ -1435,3 +1435,30 @@ def test_stoploss_limit_order_dry_run(default_conf, mocker):
assert order['type'] == order_type
assert order['price'] == 220
assert order['amount'] == 1
def test_merge_ft_has_dict(default_conf, mocker):
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=MagicMock()))
mocker.patch('freqtrade.exchange.Exchange._load_async_markets', MagicMock())
mocker.patch('freqtrade.exchange.Exchange.validate_pairs', MagicMock())
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
ex = Exchange(default_conf)
assert ex._ft_has == Exchange._ft_has_default
ex = Kraken(default_conf)
assert ex._ft_has == Exchange._ft_has_default
# Binance defines different values
ex = Binance(default_conf)
assert ex._ft_has != Exchange._ft_has_default
assert ex._ft_has['stoploss_on_exchange']
assert ex._ft_has['order_time_in_force'] == ['gtc', 'fok', 'ioc']
conf = copy.deepcopy(default_conf)
conf['exchange']['_ft_has_params'] = {"DeadBeef": 20,
"stoploss_on_exchange": False}
# Use settings from configuration (overriding stoploss_on_exchange)
ex = Binance(conf)
assert ex._ft_has != Exchange._ft_has_default
assert not ex._ft_has['stoploss_on_exchange']
assert ex._ft_has['DeadBeef'] == 20

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@ -111,7 +111,7 @@ def test_tickerdata_to_dataframe(default_conf) -> None:
timerange = TimeRange(None, 'line', 0, -100)
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m', timerange=timerange)
tickerlist = {'UNITTEST/BTC': parse_ticker_dataframe(tick, '1m', True)}
tickerlist = {'UNITTEST/BTC': parse_ticker_dataframe(tick, '1m', fill_missing=True)}
data = strategy.tickerdata_to_dataframe(tickerlist)
assert len(data['UNITTEST/BTC']) == 102 # partial candle was removed