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Merge branch 'develop' into no-ticker-2

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This commit is contained in:
hroff-1902
2020-03-13 16:43:52 +03:00
committed by GitHub
parent a7ed51c642 c6bb32d419
commit 51f52c8609
32 changed files with 252 additions and 74 deletions
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3
freqtrade/commands/arguments.py
View File

@@ -69,7 +69,8 @@ ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
"hyperopt_list_min_avg_time", "hyperopt_list_max_avg_time",
"hyperopt_list_min_avg_profit", "hyperopt_list_max_avg_profit",
"hyperopt_list_min_total_profit", "hyperopt_list_max_total_profit",
"print_colorized", "print_json", "hyperopt_list_no_details"]
"print_colorized", "print_json", "hyperopt_list_no_details",
"export_csv"]
ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperopt_show_index",
"print_json", "hyperopt_show_no_header"]

7
freqtrade/commands/cli_options.py
View File

@@ -221,6 +221,13 @@ AVAILABLE_CLI_OPTIONS = {
action='store_true',
default=False,
),
"export_csv": Arg(
'--export-csv',
help='Export to CSV-File.'
' This will disable table print.'
' Example: --export-csv hyperopt.csv',
metavar='FILE',
),
"hyperopt_jobs": Arg(
'-j', '--job-workers',
help='The number of concurrently running jobs for hyperoptimization '

17
freqtrade/commands/hyperopt_commands.py
View File

@@ -21,6 +21,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
print_colorized = config.get('print_colorized', False)
print_json = config.get('print_json', False)
export_csv = config.get('export_csv', None)
no_details = config.get('hyperopt_list_no_details', False)
no_header = False
@@ -49,17 +50,23 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
if print_colorized:
colorama_init(autoreset=True)
try:
Hyperopt.print_result_table(config, trials, total_epochs,
not filteroptions['only_best'], print_colorized, 0)
except KeyboardInterrupt:
print('User interrupted..')
if not export_csv:
try:
Hyperopt.print_result_table(config, trials, total_epochs,
not filteroptions['only_best'], print_colorized, 0)
except KeyboardInterrupt:
print('User interrupted..')
if trials and not no_details:
sorted_trials = sorted(trials, key=itemgetter('loss'))
results = sorted_trials[0]
Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header)
if trials and export_csv:
Hyperopt.export_csv_file(
config, trials, total_epochs, not filteroptions['only_best'], export_csv
)
def start_hyperopt_show(args: Dict[str, Any]) -> None:
"""

13
freqtrade/commands/optimize_commands.py
View File

@@ -17,10 +17,15 @@ def setup_optimize_configuration(args: Dict[str, Any], method: RunMode) -> Dict[
"""
config = setup_utils_configuration(args, method)
if method == RunMode.BACKTEST:
if config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT:
raise DependencyException('stake amount could not be "%s" for backtesting' %
constants.UNLIMITED_STAKE_AMOUNT)
no_unlimited_runmodes = {
RunMode.BACKTEST: 'backtesting',
RunMode.HYPEROPT: 'hyperoptimization',
}
if (method in no_unlimited_runmodes.keys() and
config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT):
raise DependencyException(
f'The value of `stake_amount` cannot be set as "{constants.UNLIMITED_STAKE_AMOUNT}" '
f'for {no_unlimited_runmodes[method]}')
return config

3
freqtrade/configuration/configuration.py
View File

@@ -282,6 +282,9 @@ class Configuration:
self._args_to_config(config, argname='print_json',
logstring='Parameter --print-json detected ...')
self._args_to_config(config, argname='export_csv',
logstring='Parameter --export-csv detected: {}')
self._args_to_config(config, argname='hyperopt_jobs',
logstring='Parameter -j/--job-workers detected: {}')

45
freqtrade/data/history/idatahandler.py
View File

@@ -145,27 +145,40 @@ class IDataHandler(ABC):
if startup_candles > 0 and timerange_startup:
timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
df = self._ohlcv_load(pair, timeframe, timerange=timerange_startup)
if df.empty:
pairdf = self._ohlcv_load(pair, timeframe,
timerange=timerange_startup)
if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
return pairdf
else:
enddate = df.iloc[-1]['date']
if timerange_startup:
self._validate_pairdata(pair, pairdf, timerange_startup)
pairdf = trim_dataframe(pairdf, timerange_startup)
if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
return pairdf
# incomplete candles should only be dropped if we didn't trim the end beforehand.
pairdf = clean_ohlcv_dataframe(pairdf, timeframe,
pair=pair,
fill_missing=fill_missing,
drop_incomplete=(drop_incomplete and
enddate == pairdf.iloc[-1]['date']))
self._check_empty_df(pairdf, pair, timeframe, warn_no_data)
return pairdf
def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str, warn_no_data: bool):
"""
Warn on empty dataframe
"""
if pairdf.empty:
if warn_no_data:
logger.warning(
f'No history data for pair: "{pair}", timeframe: {timeframe}. '
'Use `freqtrade download-data` to download the data'
)
return df
else:
enddate = df.iloc[-1]['date']
if timerange_startup:
self._validate_pairdata(pair, df, timerange_startup)
df = trim_dataframe(df, timerange_startup)
# incomplete candles should only be dropped if we didn't trim the end beforehand.
return clean_ohlcv_dataframe(df, timeframe,
pair=pair,
fill_missing=fill_missing,
drop_incomplete=(drop_incomplete and
enddate == df.iloc[-1]['date']))
return True
return False
def _validate_pairdata(self, pair, pairdata: DataFrame, timerange: TimeRange):
"""

62
freqtrade/optimize/hyperopt.py
View File

@@ -23,6 +23,8 @@ from joblib import (Parallel, cpu_count, delayed, dump, load,
wrap_non_picklable_objects)
from pandas import DataFrame, json_normalize, isna
import tabulate
from os import path
import io
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.history import get_timerange
@@ -330,10 +332,10 @@ class Hyperopt:
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: ('{:,.2f}%'.format(x)).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: ('{:,.1f} m'.format(x)).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
@@ -381,6 +383,62 @@ class Hyperopt:
)
print(table)
@staticmethod
def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
csv_file: str) -> None:
"""
Log result to csv-file
"""
if not results:
return
# Verification for overwrite
if path.isfile(csv_file):
logger.error("CSV-File already exists!")
return
try:
io.open(csv_file, 'w+').close()
except IOError:
logger.error("Filed to create CSV-File!")
return
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
trials['Stake currency'] = config['stake_currency']
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '*'
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Epoch'] = trials['Epoch'].astype(str)
trials['Trades'] = trials['Trades'].astype(str)
trials['Total profit'] = trials['Total profit'].apply(
lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
)
trials['Profit'] = trials['Profit'].apply(
lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
print("CSV-File created!")
def has_space(self, space: str) -> bool:
"""
Tell if the space value is contained in the configuration

2
freqtrade/optimize/hyperopt_loss_sharpe.py
View File

@@ -36,7 +36,7 @@ class SharpeHyperOptLoss(IHyperOptLoss):
expected_returns_mean = total_profit.sum() / days_period
up_stdev = np.std(total_profit)
if (np.std(total_profit) != 0.):
if up_stdev != 0:
sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.

2
freqtrade/optimize/hyperopt_loss_sharpe_daily.py
View File

@@ -51,7 +51,7 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
expected_returns_mean = total_profit.mean()
up_stdev = total_profit.std()
if (up_stdev != 0.):
if up_stdev != 0:
sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.

2
freqtrade/optimize/hyperopt_loss_sortino.py
View File

@@ -39,7 +39,7 @@ class SortinoHyperOptLoss(IHyperOptLoss):
results.loc[total_profit < 0, 'downside_returns'] = results['profit_percent']
down_stdev = np.std(results['downside_returns'])
if np.std(total_profit) != 0.0:
if down_stdev != 0:
sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
else:
# Define high (negative) sortino ratio to be clear that this is NOT optimal.

2
freqtrade/optimize/hyperopt_loss_sortino_daily.py
View File

@@ -59,7 +59,7 @@ class SortinoHyperOptLossDaily(IHyperOptLoss):
# where P = sum_daily["profit_percent_after_slippage"]
down_stdev = math.sqrt((total_downside**2).sum() / len(total_downside))
if (down_stdev != 0.):
if down_stdev != 0:
sortino_ratio = expected_returns_mean / down_stdev * math.sqrt(days_in_year)
else:
# Define high (negative) sortino ratio to be clear that this is NOT optimal.

25
freqtrade/pairlist/IPairList.py
View File

@@ -67,21 +67,37 @@ class IPairList(ABC):
"""
@staticmethod
def verify_blacklist(pairlist: List[str], blacklist: List[str]) -> List[str]:
def verify_blacklist(pairlist: List[str], blacklist: List[str],
aswarning: bool) -> List[str]:
"""
Verify and remove items from pairlist - returning a filtered pairlist.
Logs a warning or info depending on `aswarning`.
Pairlists explicitly using this method shall use `aswarning=False`!
:param pairlist: Pairlist to validate
:param blacklist: Blacklist to validate pairlist against
:param aswarning: Log message as Warning or info
:return: pairlist - blacklisted pairs
"""
for pair in deepcopy(pairlist):
if pair in blacklist:
logger.warning(f"Pair {pair} in your blacklist. Removing it from whitelist...")
if aswarning:
logger.warning(f"Pair {pair} in your blacklist. Removing it from whitelist...")
else:
logger.info(f"Pair {pair} in your blacklist. Removing it from whitelist...")
pairlist.remove(pair)
return pairlist
def _verify_blacklist(self, pairlist: List[str]) -> List[str]:
def _verify_blacklist(self, pairlist: List[str], aswarning: bool = True) -> List[str]:
"""
Proxy method to verify_blacklist for easy access for child classes.
Logs a warning or info depending on `aswarning`.
Pairlists explicitly using this method shall use aswarning=False!
:param pairlist: Pairlist to validate
:param aswarning: Log message as Warning or info.
:return: pairlist - blacklisted pairs
"""
return IPairList.verify_blacklist(pairlist, self._pairlistmanager.blacklist)
return IPairList.verify_blacklist(pairlist, self._pairlistmanager.blacklist,
aswarning=aswarning)
def _whitelist_for_active_markets(self, pairlist: List[str]) -> List[str]:
"""
@@ -113,6 +129,5 @@ class IPairList(ABC):
if pair not in sanitized_whitelist:
sanitized_whitelist.append(pair)
sanitized_whitelist = self._verify_blacklist(sanitized_whitelist)
# We need to remove pairs that are unknown
return sanitized_whitelist

2
freqtrade/pairlist/VolumePairList.py
View File

@@ -106,7 +106,7 @@ class VolumePairList(IPairList):
# Validate whitelist to only have active market pairs
pairs = self._whitelist_for_active_markets([s['symbol'] for s in sorted_tickers])
pairs = self._verify_blacklist(pairs)
pairs = self._verify_blacklist(pairs, aswarning=False)
# Limit to X number of pairs
pairs = pairs[:self._number_pairs]
logger.info(f"Searching {self._number_pairs} pairs: {pairs}")

2
freqtrade/pairlist/pairlistmanager.py
View File

@@ -91,6 +91,6 @@ class PairListManager():
pairlist = pl.filter_pairlist(pairlist, tickers)
# Validation against blacklist happens after the pairlists to ensure blacklist is respected.
pairlist = IPairList.verify_blacklist(pairlist, self.blacklist)
pairlist = IPairList.verify_blacklist(pairlist, self.blacklist, True)
self._whitelist = pairlist

2
freqtrade/templates/base_config.json.j2
View File

@@ -24,7 +24,7 @@
"price_side": "ask",
"use_order_book": false,
"order_book_min": 1,
"order_book_max": 9,
"order_book_max": 1,
"use_sell_signal": true,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false

21
freqtrade/templates/base_hyperopt.py.j2
View File

@@ -21,7 +21,7 @@ class {{ hyperopt }}(IHyperOpt):
"""
This is a Hyperopt template to get you started.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
You should:
- Add any lib you need to build your hyperopt.
@@ -29,11 +29,14 @@ class {{ hyperopt }}(IHyperOpt):
You must keep:
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
copied in every custom hyperopt. However, you may override them if you need the
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
Sample implementation of these methods can be found in
https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
The methods roi_space, generate_roi_table and stoploss_space are not required
and are provided by default.
However, you may override them if you need 'roi' and 'stoploss' spaces that
differ from the defaults offered by Freqtrade.
Sample implementation of these methods will be copied to `user_data/hyperopts` when
creating the user-data directory using `freqtrade create-userdir --userdir user_data`,
or is available online under the following URL:
https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
"""
@staticmethod
@@ -63,6 +66,9 @@ class {{ hyperopt }}(IHyperOpt):
dataframe['close'], dataframe['sar']
))
# Check that the candle had volume
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
@@ -108,6 +114,9 @@ class {{ hyperopt }}(IHyperOpt):
dataframe['sar'], dataframe['close']
))
# Check that the candle had volume
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),

25
freqtrade/templates/sample_hyperopt.py
View File

@@ -20,23 +20,28 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
class SampleHyperOpt(IHyperOpt):
"""
This is a sample Hyperopt to inspire you.
Feel free to customize it.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
You should:
- Rename the class name to some unique name.
- Add any methods you want to build your hyperopt.
- Add any lib you need to build your hyperopt.
An easier way to get a new hyperopt file is by using
`freqtrade new-hyperopt --hyperopt MyCoolHyperopt`.
You must keep:
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
copied in every custom hyperopt. However, you may override them if you need the
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
Sample implementation of these methods can be found in
https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
The methods roi_space, generate_roi_table and stoploss_space are not required
and are provided by default.
However, you may override them if you need 'roi' and 'stoploss' spaces that
differ from the defaults offered by Freqtrade.
Sample implementation of these methods will be copied to `user_data/hyperopts` when
creating the user-data directory using `freqtrade create-userdir --userdir user_data`,
or is available online under the following URL:
https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
"""
@staticmethod
@@ -73,6 +78,9 @@ class SampleHyperOpt(IHyperOpt):
dataframe['close'], dataframe['sar']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
@@ -133,6 +141,9 @@ class SampleHyperOpt(IHyperOpt):
dataframe['sar'], dataframe['close']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),

13
freqtrade/templates/sample_hyperopt_advanced.py
View File

@@ -22,7 +22,7 @@ class AdvancedSampleHyperOpt(IHyperOpt):
This is a sample hyperopt to inspire you.
Feel free to customize it.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
You should:
- Rename the class name to some unique name.
@@ -32,8 +32,9 @@ class AdvancedSampleHyperOpt(IHyperOpt):
You must keep:
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
copied in every custom hyperopt. However, you may override them if you need the
The methods roi_space, generate_roi_table and stoploss_space are not required
and are provided by default.
However, you may override them if you need the
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
This sample illustrates how to override these methods.
@@ -92,6 +93,9 @@ class AdvancedSampleHyperOpt(IHyperOpt):
dataframe['close'], dataframe['sar']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
@@ -152,6 +156,9 @@ class AdvancedSampleHyperOpt(IHyperOpt):
dataframe['sar'], dataframe['close']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),