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Merge branch 'develop' into pr/yazeed/3055

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This commit is contained in:
Matthias
2020-08-24 07:21:48 +02:00
parent bfa55f31c0 a55dd8444d
commit 8b767eedfd
152 changed files with 6643 additions and 2380 deletions
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3
freqtrade/commands/__init__.py
View File

@@ -9,7 +9,8 @@ Note: Be careful with file-scoped imports in these subfiles.
from freqtrade.commands.arguments import Arguments
from freqtrade.commands.build_config_commands import start_new_config
from freqtrade.commands.data_commands import (start_convert_data,
start_download_data)
start_download_data,
start_list_data)
from freqtrade.commands.deploy_commands import (start_create_userdir,
start_new_hyperopt,
start_new_strategy)

240
freqtrade/commands/arguments.py
View File

@@ -15,7 +15,7 @@ ARGS_STRATEGY = ["strategy", "strategy_path"]
ARGS_TRADE = ["db_url", "sd_notify", "dry_run"]
ARGS_COMMON_OPTIMIZE = ["ticker_interval", "timerange",
ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange",
"max_open_trades", "stake_amount", "fee"]
ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
@@ -54,15 +54,17 @@ ARGS_BUILD_HYPEROPT = ["user_data_dir", "hyperopt", "template"]
ARGS_CONVERT_DATA = ["pairs", "format_from", "format_to", "erase"]
ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes"]
ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs"]
ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchange",
"timeframes", "erase", "dataformat_ohlcv", "dataformat_trades"]
ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
"db_url", "trade_source", "export", "exportfilename",
"timerange", "ticker_interval", "no_trades"]
"timerange", "timeframe", "no_trades"]
ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
"trade_source", "ticker_interval"]
"trade_source", "timeframe"]
ARGS_SHOW_TRADES = ["db_url", "trade_ids", "print_json"]
@@ -71,6 +73,7 @@ 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",
"hyperopt_list_min_objective", "hyperopt_list_max_objective",
"print_colorized", "print_json", "hyperopt_list_no_details",
"export_csv"]
@@ -78,7 +81,7 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
"print_json", "hyperopt_show_no_header"]
NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
"list-markets", "list-pairs", "list-strategies",
"list-markets", "list-pairs", "list-strategies", "list-data",
"list-hyperopts", "hyperopt-list", "hyperopt-show",
"plot-dataframe", "plot-profit", "show-trades"]
@@ -159,7 +162,7 @@ class Arguments:
self._build_args(optionlist=['version'], parser=self.parser)
from freqtrade.commands import (start_create_userdir, start_convert_data,
start_download_data,
start_download_data, start_list_data,
start_hyperopt_list, start_hyperopt_show,
start_list_exchanges, start_list_hyperopts,
start_list_markets, start_list_strategies,
@@ -181,25 +184,6 @@ class Arguments:
trade_cmd.set_defaults(func=start_trading)
self._build_args(optionlist=ARGS_TRADE, parser=trade_cmd)
# Add backtesting subcommand
backtesting_cmd = subparsers.add_parser('backtesting', help='Backtesting module.',
parents=[_common_parser, _strategy_parser])
backtesting_cmd.set_defaults(func=start_backtesting)
self._build_args(optionlist=ARGS_BACKTEST, parser=backtesting_cmd)
# Add edge subcommand
edge_cmd = subparsers.add_parser('edge', help='Edge module.',
parents=[_common_parser, _strategy_parser])
edge_cmd.set_defaults(func=start_edge)
self._build_args(optionlist=ARGS_EDGE, parser=edge_cmd)
# Add hyperopt subcommand
hyperopt_cmd = subparsers.add_parser('hyperopt', help='Hyperopt module.',
parents=[_common_parser, _strategy_parser],
)
hyperopt_cmd.set_defaults(func=start_hyperopt)
self._build_args(optionlist=ARGS_HYPEROPT, parser=hyperopt_cmd)
# add create-userdir subcommand
create_userdir_cmd = subparsers.add_parser('create-userdir',
help="Create user-data directory.",
@@ -213,79 +197,17 @@ class Arguments:
build_config_cmd.set_defaults(func=start_new_config)
self._build_args(optionlist=ARGS_BUILD_CONFIG, parser=build_config_cmd)
# add new-strategy subcommand
build_strategy_cmd = subparsers.add_parser('new-strategy',
help="Create new strategy")
build_strategy_cmd.set_defaults(func=start_new_strategy)
self._build_args(optionlist=ARGS_BUILD_STRATEGY, parser=build_strategy_cmd)
# add new-hyperopt subcommand
build_hyperopt_cmd = subparsers.add_parser('new-hyperopt',
help="Create new hyperopt")
build_hyperopt_cmd.set_defaults(func=start_new_hyperopt)
self._build_args(optionlist=ARGS_BUILD_HYPEROPT, parser=build_hyperopt_cmd)
# Add list-strategies subcommand
list_strategies_cmd = subparsers.add_parser(
'list-strategies',
help='Print available strategies.',
parents=[_common_parser],
)
list_strategies_cmd.set_defaults(func=start_list_strategies)
self._build_args(optionlist=ARGS_LIST_STRATEGIES, parser=list_strategies_cmd)
# Add list-hyperopts subcommand
list_hyperopts_cmd = subparsers.add_parser(
'list-hyperopts',
help='Print available hyperopt classes.',
parents=[_common_parser],
)
list_hyperopts_cmd.set_defaults(func=start_list_hyperopts)
self._build_args(optionlist=ARGS_LIST_HYPEROPTS, parser=list_hyperopts_cmd)
# Add list-exchanges subcommand
list_exchanges_cmd = subparsers.add_parser(
'list-exchanges',
help='Print available exchanges.',
parents=[_common_parser],
)
list_exchanges_cmd.set_defaults(func=start_list_exchanges)
self._build_args(optionlist=ARGS_LIST_EXCHANGES, parser=list_exchanges_cmd)
# Add list-timeframes subcommand
list_timeframes_cmd = subparsers.add_parser(
'list-timeframes',
help='Print available ticker intervals (timeframes) for the exchange.',
parents=[_common_parser],
)
list_timeframes_cmd.set_defaults(func=start_list_timeframes)
self._build_args(optionlist=ARGS_LIST_TIMEFRAMES, parser=list_timeframes_cmd)
# Add list-markets subcommand
list_markets_cmd = subparsers.add_parser(
'list-markets',
help='Print markets on exchange.',
parents=[_common_parser],
)
list_markets_cmd.set_defaults(func=partial(start_list_markets, pairs_only=False))
self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_markets_cmd)
# Add list-pairs subcommand
list_pairs_cmd = subparsers.add_parser(
'list-pairs',
help='Print pairs on exchange.',
parents=[_common_parser],
)
list_pairs_cmd.set_defaults(func=partial(start_list_markets, pairs_only=True))
self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_pairs_cmd)
# Add test-pairlist subcommand
test_pairlist_cmd = subparsers.add_parser(
'test-pairlist',
help='Test your pairlist configuration.',
)
test_pairlist_cmd.set_defaults(func=start_test_pairlist)
self._build_args(optionlist=ARGS_TEST_PAIRLIST, parser=test_pairlist_cmd)
# add new-strategy subcommand
build_strategy_cmd = subparsers.add_parser('new-strategy',
help="Create new strategy")
build_strategy_cmd.set_defaults(func=start_new_strategy)
self._build_args(optionlist=ARGS_BUILD_STRATEGY, parser=build_strategy_cmd)
# Add download-data subcommand
download_data_cmd = subparsers.add_parser(
@@ -314,32 +236,33 @@ class Arguments:
convert_trade_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=False))
self._build_args(optionlist=ARGS_CONVERT_DATA, parser=convert_trade_data_cmd)
# Add Plotting subcommand
plot_dataframe_cmd = subparsers.add_parser(
'plot-dataframe',
help='Plot candles with indicators.',
parents=[_common_parser, _strategy_parser],
)
plot_dataframe_cmd.set_defaults(func=start_plot_dataframe)
self._build_args(optionlist=ARGS_PLOT_DATAFRAME, parser=plot_dataframe_cmd)
# Plot profit
plot_profit_cmd = subparsers.add_parser(
'plot-profit',
help='Generate plot showing profits.',
# Add list-data subcommand
list_data_cmd = subparsers.add_parser(
'list-data',
help='List downloaded data.',
parents=[_common_parser],
)
plot_profit_cmd.set_defaults(func=start_plot_profit)
self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd)
list_data_cmd.set_defaults(func=start_list_data)
self._build_args(optionlist=ARGS_LIST_DATA, parser=list_data_cmd)
# Add show-trades subcommand
show_trades = subparsers.add_parser(
'show-trades',
help='Show trades.',
parents=[_common_parser],
)
show_trades.set_defaults(func=start_show_trades)
self._build_args(optionlist=ARGS_SHOW_TRADES, parser=show_trades)
# Add backtesting subcommand
backtesting_cmd = subparsers.add_parser('backtesting', help='Backtesting module.',
parents=[_common_parser, _strategy_parser])
backtesting_cmd.set_defaults(func=start_backtesting)
self._build_args(optionlist=ARGS_BACKTEST, parser=backtesting_cmd)
# Add edge subcommand
edge_cmd = subparsers.add_parser('edge', help='Edge module.',
parents=[_common_parser, _strategy_parser])
edge_cmd.set_defaults(func=start_edge)
self._build_args(optionlist=ARGS_EDGE, parser=edge_cmd)
# Add hyperopt subcommand
hyperopt_cmd = subparsers.add_parser('hyperopt', help='Hyperopt module.',
parents=[_common_parser, _strategy_parser],
)
hyperopt_cmd.set_defaults(func=start_hyperopt)
self._build_args(optionlist=ARGS_HYPEROPT, parser=hyperopt_cmd)
# Add hyperopt-list subcommand
hyperopt_list_cmd = subparsers.add_parser(
@@ -358,3 +281,92 @@ class Arguments:
)
hyperopt_show_cmd.set_defaults(func=start_hyperopt_show)
self._build_args(optionlist=ARGS_HYPEROPT_SHOW, parser=hyperopt_show_cmd)
# Add list-exchanges subcommand
list_exchanges_cmd = subparsers.add_parser(
'list-exchanges',
help='Print available exchanges.',
parents=[_common_parser],
)
list_exchanges_cmd.set_defaults(func=start_list_exchanges)
self._build_args(optionlist=ARGS_LIST_EXCHANGES, parser=list_exchanges_cmd)
# Add list-hyperopts subcommand
list_hyperopts_cmd = subparsers.add_parser(
'list-hyperopts',
help='Print available hyperopt classes.',
parents=[_common_parser],
)
list_hyperopts_cmd.set_defaults(func=start_list_hyperopts)
self._build_args(optionlist=ARGS_LIST_HYPEROPTS, parser=list_hyperopts_cmd)
# Add list-markets subcommand
list_markets_cmd = subparsers.add_parser(
'list-markets',
help='Print markets on exchange.',
parents=[_common_parser],
)
list_markets_cmd.set_defaults(func=partial(start_list_markets, pairs_only=False))
self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_markets_cmd)
# Add list-pairs subcommand
list_pairs_cmd = subparsers.add_parser(
'list-pairs',
help='Print pairs on exchange.',
parents=[_common_parser],
)
list_pairs_cmd.set_defaults(func=partial(start_list_markets, pairs_only=True))
self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_pairs_cmd)
# Add list-strategies subcommand
list_strategies_cmd = subparsers.add_parser(
'list-strategies',
help='Print available strategies.',
parents=[_common_parser],
)
list_strategies_cmd.set_defaults(func=start_list_strategies)
self._build_args(optionlist=ARGS_LIST_STRATEGIES, parser=list_strategies_cmd)
# Add list-timeframes subcommand
list_timeframes_cmd = subparsers.add_parser(
'list-timeframes',
help='Print available timeframes for the exchange.',
parents=[_common_parser],
)
list_timeframes_cmd.set_defaults(func=start_list_timeframes)
self._build_args(optionlist=ARGS_LIST_TIMEFRAMES, parser=list_timeframes_cmd)
# Add show-trades subcommand
show_trades = subparsers.add_parser(
'show-trades',
help='Show trades.',
parents=[_common_parser],
)
show_trades.set_defaults(func=start_show_trades)
self._build_args(optionlist=ARGS_SHOW_TRADES, parser=show_trades)
# Add test-pairlist subcommand
test_pairlist_cmd = subparsers.add_parser(
'test-pairlist',
help='Test your pairlist configuration.',
)
test_pairlist_cmd.set_defaults(func=start_test_pairlist)
self._build_args(optionlist=ARGS_TEST_PAIRLIST, parser=test_pairlist_cmd)
# Add Plotting subcommand
plot_dataframe_cmd = subparsers.add_parser(
'plot-dataframe',
help='Plot candles with indicators.',
parents=[_common_parser, _strategy_parser],
)
plot_dataframe_cmd.set_defaults(func=start_plot_dataframe)
self._build_args(optionlist=ARGS_PLOT_DATAFRAME, parser=plot_dataframe_cmd)
# Plot profit
plot_profit_cmd = subparsers.add_parser(
'plot-profit',
help='Generate plot showing profits.',
parents=[_common_parser, _strategy_parser],
)
plot_profit_cmd.set_defaults(func=start_plot_profit)
self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd)

4
freqtrade/commands/build_config_commands.py
View File

@@ -75,8 +75,8 @@ def ask_user_config() -> Dict[str, Any]:
},
{
"type": "text",
"name": "ticker_interval",
"message": "Please insert your timeframe (ticker interval):",
"name": "timeframe",
"message": "Please insert your desired timeframe (e.g. 5m):",
"default": "5m",
},
{

28
freqtrade/commands/cli_options.py
View File

@@ -110,8 +110,8 @@ AVAILABLE_CLI_OPTIONS = {
action='store_true',
),
# Optimize common
"ticker_interval": Arg(
'-i', '--ticker-interval',
"timeframe": Arg(
'-i', '--timeframe', '--ticker-interval',
help='Specify ticker interval (`1m`, `5m`, `30m`, `1h`, `1d`).',
),
"timerange": Arg(
@@ -455,37 +455,49 @@ AVAILABLE_CLI_OPTIONS = {
),
"hyperopt_list_min_avg_time": Arg(
'--min-avg-time',
help='Select epochs on above average time.',
help='Select epochs above average time.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_max_avg_time": Arg(
'--max-avg-time',
help='Select epochs on under average time.',
help='Select epochs below average time.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_min_avg_profit": Arg(
'--min-avg-profit',
help='Select epochs on above average profit.',
help='Select epochs above average profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_max_avg_profit": Arg(
'--max-avg-profit',
help='Select epochs on below average profit.',
help='Select epochs below average profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_min_total_profit": Arg(
'--min-total-profit',
help='Select epochs on above total profit.',
help='Select epochs above total profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_max_total_profit": Arg(
'--max-total-profit',
help='Select epochs on below total profit.',
help='Select epochs below total profit.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_min_objective": Arg(
'--min-objective',
help='Select epochs above objective.',
type=float,
metavar='FLOAT',
),
"hyperopt_list_max_objective": Arg(
'--max-objective',
help='Select epochs below objective.',
type=float,
metavar='FLOAT',
),

29
freqtrade/commands/data_commands.py
View File

@@ -1,5 +1,6 @@
import logging
import sys
from collections import defaultdict
from typing import Any, Dict, List
import arrow
@@ -11,6 +12,7 @@ from freqtrade.data.history import (convert_trades_to_ohlcv,
refresh_backtest_ohlcv_data,
refresh_backtest_trades_data)
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.resolvers import ExchangeResolver
from freqtrade.state import RunMode
@@ -88,3 +90,30 @@ def start_convert_data(args: Dict[str, Any], ohlcv: bool = True) -> None:
convert_trades_format(config,
convert_from=args['format_from'], convert_to=args['format_to'],
erase=args['erase'])
def start_list_data(args: Dict[str, Any]) -> None:
"""
List available backtest data
"""
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
from freqtrade.data.history.idatahandler import get_datahandler
from tabulate import tabulate
dhc = get_datahandler(config['datadir'], config['dataformat_ohlcv'])
paircombs = dhc.ohlcv_get_available_data(config['datadir'])
if args['pairs']:
paircombs = [comb for comb in paircombs if comb[0] in args['pairs']]
print(f"Found {len(paircombs)} pair / timeframe combinations.")
groupedpair = defaultdict(list)
for pair, timeframe in sorted(paircombs, key=lambda x: (x[0], timeframe_to_minutes(x[1]))):
groupedpair[pair].append(timeframe)
if groupedpair:
print(tabulate([(pair, ', '.join(timeframes)) for pair, timeframes in groupedpair.items()],
headers=("Pair", "Timeframe"),
tablefmt='psql', stralign='right'))

59
freqtrade/commands/hyperopt_commands.py
View File

@@ -35,7 +35,9 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None)
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
'filter_min_objective': config.get('hyperopt_list_min_objective', None),
'filter_max_objective': config.get('hyperopt_list_max_objective', None),
}
results_file = (config['user_data_dir'] /
@@ -45,7 +47,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
epochs = Hyperopt.load_previous_results(results_file)
total_epochs = len(epochs)
epochs = _hyperopt_filter_epochs(epochs, filteroptions)
epochs = hyperopt_filter_epochs(epochs, filteroptions)
if print_colorized:
colorama_init(autoreset=True)
@@ -92,14 +94,16 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None)
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
'filter_min_objective': config.get('hyperopt_list_min_objective', None),
'filter_max_objective': config.get('hyperopt_list_max_objective', None)
}
# Previous evaluations
epochs = Hyperopt.load_previous_results(results_file)
total_epochs = len(epochs)
epochs = _hyperopt_filter_epochs(epochs, filteroptions)
epochs = hyperopt_filter_epochs(epochs, filteroptions)
filtered_epochs = len(epochs)
if n > filtered_epochs:
@@ -119,7 +123,7 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
header_str="Epoch details")
def _hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
"""
Filter our items from the list of hyperopt results
"""
@@ -127,6 +131,24 @@ def _hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
epochs = [x for x in epochs if x['is_best']]
if filteroptions['only_profitable']:
epochs = [x for x in epochs if x['results_metrics']['profit'] > 0]
epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions)
epochs = _hyperopt_filter_epochs_duration(epochs, filteroptions)
epochs = _hyperopt_filter_epochs_profit(epochs, filteroptions)
epochs = _hyperopt_filter_epochs_objective(epochs, filteroptions)
logger.info(f"{len(epochs)} " +
("best " if filteroptions['only_best'] else "") +
("profitable " if filteroptions['only_profitable'] else "") +
"epochs found.")
return epochs
def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_trades'] > 0:
epochs = [
x for x in epochs
@@ -137,6 +159,11 @@ def _hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
x for x in epochs
if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
]
return epochs
def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_avg_time'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = [
@@ -149,6 +176,12 @@ def _hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
x for x in epochs
if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
]
return epochs
def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_avg_profit'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = [
@@ -173,10 +206,18 @@ def _hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
x for x in epochs
if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
]
return epochs
logger.info(f"{len(epochs)} " +
("best " if filteroptions['only_best'] else "") +
("profitable " if filteroptions['only_profitable'] else "") +
"epochs found.")
def _hyperopt_filter_epochs_objective(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_objective'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = [x for x in epochs if x['loss'] < filteroptions['filter_min_objective']]
if filteroptions['filter_max_objective'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']]
return epochs

8
freqtrade/commands/list_commands.py
View File

@@ -14,7 +14,7 @@ from freqtrade.configuration import setup_utils_configuration
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import (available_exchanges, ccxt_exchanges,
market_is_active, symbol_is_pair)
market_is_active)
from freqtrade.misc import plural
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
@@ -102,8 +102,8 @@ def start_list_timeframes(args: Dict[str, Any]) -> None:
Print ticker intervals (timeframes) available on Exchange
"""
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
# Do not use ticker_interval set in the config
config['ticker_interval'] = None
# Do not use timeframe set in the config
config['timeframe'] = None
# Init exchange
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
@@ -163,7 +163,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
tabular_data.append({'Id': v['id'], 'Symbol': v['symbol'],
'Base': v['base'], 'Quote': v['quote'],
'Active': market_is_active(v),
**({'Is pair': symbol_is_pair(v['symbol'])}
**({'Is pair': exchange.market_is_tradable(v)}
if not pairs_only else {})})
if (args.get('print_one_column', False) or

1
freqtrade/commands/pairlist_commands.py
View File

@@ -25,7 +25,6 @@ def start_test_pairlist(args: Dict[str, Any]) -> None:
results = {}
for curr in quote_currencies:
config['stake_currency'] = curr
# Do not use ticker_interval set in the config
pairlists = PairListManager(exchange, config)
pairlists.refresh_pairlist()
results[curr] = pairlists.whitelist

18
freqtrade/configuration/configuration.py
View File

@@ -199,14 +199,14 @@ class Configuration:
config['exportfilename'] = Path(config['exportfilename'])
else:
config['exportfilename'] = (config['user_data_dir']
/ 'backtest_results/backtest-result.json')
/ 'backtest_results')
def _process_optimize_options(self, config: Dict[str, Any]) -> None:
# This will override the strategy configuration
self._args_to_config(config, argname='ticker_interval',
logstring='Parameter -i/--ticker-interval detected ... '
'Using ticker_interval: {} ...')
self._args_to_config(config, argname='timeframe',
logstring='Parameter -i/--timeframe detected ... '
'Using timeframe: {} ...')
self._args_to_config(config, argname='position_stacking',
logstring='Parameter --enable-position-stacking detected ...')
@@ -242,8 +242,8 @@ class Configuration:
self._args_to_config(config, argname='strategy_list',
logstring='Using strategy list of {} strategies', logfun=len)
self._args_to_config(config, argname='ticker_interval',
logstring='Overriding ticker interval with Command line argument')
self._args_to_config(config, argname='timeframe',
logstring='Overriding timeframe with Command line argument')
self._args_to_config(config, argname='export',
logstring='Parameter --export detected: {} ...')
@@ -334,6 +334,12 @@ class Configuration:
self._args_to_config(config, argname='hyperopt_list_max_total_profit',
logstring='Parameter --max-total-profit detected: {}')
self._args_to_config(config, argname='hyperopt_list_min_objective',
logstring='Parameter --min-objective detected: {}')
self._args_to_config(config, argname='hyperopt_list_max_objective',
logstring='Parameter --max-objective detected: {}')
self._args_to_config(config, argname='hyperopt_list_no_details',
logstring='Parameter --no-details detected: {}')

13
freqtrade/configuration/deprecated_settings.py
View File

@@ -60,10 +60,21 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
if (config.get('edge', {}).get('enabled', False)
and 'capital_available_percentage' in config.get('edge', {})):
logger.warning(
raise OperationalException(
"DEPRECATED: "
"Using 'edge.capital_available_percentage' has been deprecated in favor of "
"'tradable_balance_ratio'. Please migrate your configuration to "
"'tradable_balance_ratio' and remove 'capital_available_percentage' "
"from the edge configuration."
)
if 'ticker_interval' in config:
logger.warning(
"DEPRECATED: "
"Please use 'timeframe' instead of 'ticker_interval."
)
if 'timeframe' in config:
raise OperationalException(
"Both 'timeframe' and 'ticker_interval' detected."
"Please remove 'ticker_interval' from your configuration to continue operating."
)
config['timeframe'] = config['ticker_interval']

25
freqtrade/constants.py
View File

@@ -3,6 +3,9 @@
"""
bot constants
"""
from typing import List, Tuple
DEFAULT_CONFIG = 'config.json'
DEFAULT_EXCHANGE = 'bittrex'
PROCESS_THROTTLE_SECS = 5 # sec
@@ -19,15 +22,19 @@ ORDERBOOK_SIDES = ['ask', 'bid']
ORDERTYPE_POSSIBILITIES = ['limit', 'market']
ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'PrecisionFilter', 'PriceFilter', 'ShuffleFilter', 'SpreadFilter']
'AgeFilter', 'PrecisionFilter', 'PriceFilter',
'ShuffleFilter', 'SpreadFilter']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz']
DRY_RUN_WALLET = 1000
DATETIME_PRINT_FORMAT = '%Y-%m-%d %H:%M:%S'
MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons
DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume']
# Don't modify sequence of DEFAULT_TRADES_COLUMNS
# it has wide consequences for stored trades files
DEFAULT_TRADES_COLUMNS = ['timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost']
LAST_BT_RESULT_FN = '.last_result.json'
USERPATH_HYPEROPTS = 'hyperopts'
USERPATH_STRATEGIES = 'strategies'
USERPATH_NOTEBOOKS = 'notebooks'
@@ -68,7 +75,7 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'max_open_trades': {'type': ['integer', 'number'], 'minimum': -1},
'ticker_interval': {'type': 'string'},
'timeframe': {'type': 'string'},
'stake_currency': {'type': 'string'},
'stake_amount': {
'type': ['number', 'string'],
@@ -152,7 +159,9 @@ CONF_SCHEMA = {
'emergencysell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss_on_exchange': {'type': 'boolean'},
'stoploss_on_exchange_interval': {'type': 'number'}
'stoploss_on_exchange_interval': {'type': 'number'},
'stoploss_on_exchange_limit_ratio': {'type': 'number', 'minimum': 0.0,
'maximum': 1.0}
},
'required': ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
},
@@ -218,12 +227,16 @@ CONF_SCHEMA = {
},
'username': {'type': 'string'},
'password': {'type': 'string'},
'jwt_secret_key': {'type': 'string'},
'CORS_origins': {'type': 'array', 'items': {'type': 'string'}},
'verbosity': {'type': 'string', 'enum': ['error', 'info']},
},
'required': ['enabled', 'listen_ip_address', 'listen_port', 'username', 'password']
},
'db_url': {'type': 'string'},
'initial_state': {'type': 'string', 'enum': ['running', 'stopped']},
'forcebuy_enable': {'type': 'boolean'},
'disable_dataframe_checks': {'type': 'boolean'},
'internals': {
'type': 'object',
'default': {},
@@ -282,7 +295,6 @@ CONF_SCHEMA = {
'process_throttle_secs': {'type': 'integer', 'minimum': 600},
'calculate_since_number_of_days': {'type': 'integer'},
'allowed_risk': {'type': 'number'},
'capital_available_percentage': {'type': 'number'},
'stoploss_range_min': {'type': 'number'},
'stoploss_range_max': {'type': 'number'},
'stoploss_range_step': {'type': 'number'},
@@ -299,6 +311,7 @@ CONF_SCHEMA = {
SCHEMA_TRADE_REQUIRED = [
'exchange',
'timeframe',
'max_open_trades',
'stake_currency',
'stake_amount',
@@ -329,3 +342,7 @@ CANCEL_REASON = {
"ALL_CANCELLED": "cancelled (all unfilled and partially filled open orders cancelled)",
"CANCELLED_ON_EXCHANGE": "cancelled on exchange",
}
# List of pairs with their timeframes
PairWithTimeframe = Tuple[str, str]
ListPairsWithTimeframes = List[PairWithTimeframe]

184
freqtrade/data/btanalysis.py
View File

@@ -3,52 +3,123 @@ Helpers when analyzing backtest data
"""
import logging
from pathlib import Path
from typing import Dict, Union, Tuple
from typing import Dict, Union, Tuple, Any, Optional
import numpy as np
import pandas as pd
from datetime import timezone
from freqtrade import persistence
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.misc import json_load
from freqtrade.persistence import Trade
logger = logging.getLogger(__name__)
# must align with columns in backtest.py
BT_DATA_COLUMNS = ["pair", "profitperc", "open_time", "close_time", "index", "duration",
BT_DATA_COLUMNS = ["pair", "profit_percent", "open_date", "close_date", "index", "trade_duration",
"open_rate", "close_rate", "open_at_end", "sell_reason"]
def load_backtest_data(filename: Union[Path, str]) -> pd.DataFrame:
def get_latest_backtest_filename(directory: Union[Path, str]) -> str:
"""
Load backtest data file.
:param filename: pathlib.Path object, or string pointing to the file.
:return: a dataframe with the analysis results
Get latest backtest export based on '.last_result.json'.
:param directory: Directory to search for last result
:return: string containing the filename of the latest backtest result
:raises: ValueError in the following cases:
* Directory does not exist
* `directory/.last_result.json` does not exist
* `directory/.last_result.json` has the wrong content
"""
if isinstance(filename, str):
filename = Path(filename)
if isinstance(directory, str):
directory = Path(directory)
if not directory.is_dir():
raise ValueError(f"Directory '{directory}' does not exist.")
filename = directory / LAST_BT_RESULT_FN
if not filename.is_file():
raise ValueError(f"File {filename} does not exist.")
raise ValueError(
f"Directory '{directory}' does not seem to contain backtest statistics yet.")
with filename.open() as file:
data = json_load(file)
df = pd.DataFrame(data, columns=BT_DATA_COLUMNS)
if 'latest_backtest' not in data:
raise ValueError(f"Invalid '{LAST_BT_RESULT_FN}' format.")
df['open_time'] = pd.to_datetime(df['open_time'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['close_time'] = pd.to_datetime(df['close_time'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['profit'] = df['close_rate'] - df['open_rate']
df = df.sort_values("open_time").reset_index(drop=True)
return data['latest_backtest']
def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
"""
Load backtest statistics file.
:param filename: pathlib.Path object, or string pointing to the file.
:return: a dictionary containing the resulting file.
"""
if isinstance(filename, str):
filename = Path(filename)
if filename.is_dir():
filename = filename / get_latest_backtest_filename(filename)
if not filename.is_file():
raise ValueError(f"File {filename} does not exist.")
logger.info(f"Loading backtest result from {filename}")
with filename.open() as file:
data = json_load(file)
return data
def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = None) -> pd.DataFrame:
"""
Load backtest data file.
:param filename: pathlib.Path object, or string pointing to a file or directory
:param strategy: Strategy to load - mainly relevant for multi-strategy backtests
Can also serve as protection to load the correct result.
:return: a dataframe with the analysis results
:raise: ValueError if loading goes wrong.
"""
data = load_backtest_stats(filename)
if not isinstance(data, list):
# new, nested format
if 'strategy' not in data:
raise ValueError("Unknown dataformat.")
if not strategy:
if len(data['strategy']) == 1:
strategy = list(data['strategy'].keys())[0]
else:
raise ValueError("Detected backtest result with more than one strategy. "
"Please specify a strategy.")
if strategy not in data['strategy']:
raise ValueError(f"Strategy {strategy} not available in the backtest result.")
data = data['strategy'][strategy]['trades']
df = pd.DataFrame(data)
df['open_date'] = pd.to_datetime(df['open_date'],
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
utc=True,
infer_datetime_format=True
)
else:
# old format - only with lists.
df = pd.DataFrame(data, columns=BT_DATA_COLUMNS)
df['open_date'] = pd.to_datetime(df['open_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['profit_abs'] = df['close_rate'] - df['open_rate']
df = df.sort_values("open_date").reset_index(drop=True)
return df
@@ -62,9 +133,9 @@ def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataF
"""
from freqtrade.exchange import timeframe_to_minutes
timeframe_min = timeframe_to_minutes(timeframe)
dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time,
dates = [pd.Series(pd.date_range(row[1]['open_date'], row[1]['close_date'],
freq=f"{timeframe_min}min"))
for row in results[['open_time', 'close_time']].iterrows()]
for row in results[['open_date', 'close_date']].iterrows()]
deltas = [len(x) for x in dates]
dates = pd.Series(pd.concat(dates).values, name='date')
df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)
@@ -90,20 +161,25 @@ def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
return df_final[df_final['open_trades'] > max_open_trades]
def load_trades_from_db(db_url: str) -> pd.DataFrame:
def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataFrame:
"""
Load trades from a DB (using dburl)
:param db_url: Sqlite url (default format sqlite:///tradesv3.dry-run.sqlite)
:param strategy: Strategy to load - mainly relevant for multi-strategy backtests
Can also serve as protection to load the correct result.
:return: Dataframe containing Trades
"""
trades: pd.DataFrame = pd.DataFrame([], columns=BT_DATA_COLUMNS)
persistence.init(db_url, clean_open_orders=False)
columns = ["pair", "open_time", "close_time", "profit", "profitperc",
"open_rate", "close_rate", "amount", "duration", "sell_reason",
columns = ["pair", "open_date", "close_date", "profit", "profit_percent",
"open_rate", "close_rate", "amount", "trade_duration", "sell_reason",
"fee_open", "fee_close", "open_rate_requested", "close_rate_requested",
"stake_amount", "max_rate", "min_rate", "id", "exchange",
"stop_loss", "initial_stop_loss", "strategy", "ticker_interval"]
"stop_loss", "initial_stop_loss", "strategy", "timeframe"]
filters = []
if strategy:
filters.append(Trade.strategy == strategy)
trades = pd.DataFrame([(t.pair,
t.open_date.replace(tzinfo=timezone.utc),
@@ -121,16 +197,16 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
t.min_rate,
t.id, t.exchange,
t.stop_loss, t.initial_stop_loss,
t.strategy, t.ticker_interval
t.strategy, t.timeframe
)
for t in Trade.get_trades().all()],
for t in Trade.get_trades(filters).all()],
columns=columns)
return trades
def load_trades(source: str, db_url: str, exportfilename: Path,
no_trades: bool = False) -> pd.DataFrame:
no_trades: bool = False, strategy: Optional[str] = None) -> pd.DataFrame:
"""
Based on configuration option "trade_source":
* loads data from DB (using `db_url`)
@@ -148,7 +224,7 @@ def load_trades(source: str, db_url: str, exportfilename: Path,
if source == "DB":
return load_trades_from_db(db_url)
elif source == "file":
return load_backtest_data(exportfilename)
return load_backtest_data(exportfilename, strategy)
def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
@@ -163,11 +239,31 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
else:
trades_start = dataframe.iloc[0]['date']
trades_stop = dataframe.iloc[-1]['date']
trades = trades.loc[(trades['open_time'] >= trades_start) &
(trades['close_time'] <= trades_stop)]
trades = trades.loc[(trades['open_date'] >= trades_start) &
(trades['close_date'] <= trades_stop)]
return trades
def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close") -> float:
"""
Calculate market change based on "column".
Calculation is done by taking the first non-null and the last non-null element of each column
and calculating the pctchange as "(last - first) / first".
Then the results per pair are combined as mean.
:param data: Dict of Dataframes, dict key should be pair.
:param column: Column in the original dataframes to use
:return:
"""
tmp_means = []
for pair, df in data.items():
start = df[column].dropna().iloc[0]
end = df[column].dropna().iloc[-1]
tmp_means.append((end - start) / start)
return np.mean(tmp_means)
def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
column: str = "close") -> pd.DataFrame:
"""
@@ -190,15 +286,19 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
"""
Adds a column `col_name` with the cumulative profit for the given trades array.
:param df: DataFrame with date index
:param trades: DataFrame containing trades (requires columns close_time and profitperc)
:param trades: DataFrame containing trades (requires columns close_date and profit_percent)
:param col_name: Column name that will be assigned the results
:param timeframe: Timeframe used during the operations
:return: Returns df with one additional column, col_name, containing the cumulative profit.
:raise: ValueError if trade-dataframe was found empty.
"""
if len(trades) == 0:
raise ValueError("Trade dataframe empty.")
from freqtrade.exchange import timeframe_to_minutes
timeframe_minutes = timeframe_to_minutes(timeframe)
# Resample to timeframe to make sure trades match candles
_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_time')[['profitperc']].sum()
_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_date'
)[['profit_percent']].sum()
df.loc[:, col_name] = _trades_sum.cumsum()
# Set first value to 0
df.loc[df.iloc[0].name, col_name] = 0
@@ -207,14 +307,14 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
return df
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_time',
value_col: str = 'profitperc'
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_percent'
) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
"""
Calculate max drawdown and the corresponding close dates
:param trades: DataFrame containing trades (requires columns close_time and profitperc)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_time')
:param value_col: Column in DataFrame to use for values (defaults to 'profitperc')
:param trades: DataFrame containing trades (requires columns close_date and profit_percent)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
:param value_col: Column in DataFrame to use for values (defaults to 'profit_percent')
:return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
:raise: ValueError if trade-dataframe was found empty.
"""

6
freqtrade/data/converter.py
View File

@@ -197,7 +197,7 @@ def trades_to_ohlcv(trades: List, timeframe: str) -> DataFrame:
df_new['date'] = df_new.index
# Drop 0 volume rows
df_new = df_new.dropna()
return df_new[DEFAULT_DATAFRAME_COLUMNS]
return df_new.loc[:, DEFAULT_DATAFRAME_COLUMNS]
def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
@@ -236,12 +236,12 @@ def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to:
from freqtrade.data.history.idatahandler import get_datahandler
src = get_datahandler(config['datadir'], convert_from)
trg = get_datahandler(config['datadir'], convert_to)
timeframes = config.get('timeframes', [config.get('ticker_interval')])
timeframes = config.get('timeframes', [config.get('timeframe')])
logger.info(f"Converting candle (OHLCV) for timeframe {timeframes}")
if 'pairs' not in config:
config['pairs'] = []
# Check timeframes or fall back to ticker_interval.
# Check timeframes or fall back to timeframe.
for timeframe in timeframes:
config['pairs'].extend(src.ohlcv_get_pairs(config['datadir'],
timeframe))

46
freqtrade/data/dataprovider.py
View File

@@ -5,16 +5,17 @@ including ticker and orderbook data, live and historical candle (OHLCV) data
Common Interface for bot and strategy to access data.
"""
import logging
from typing import Any, Dict, List, Optional
from datetime import datetime, timezone
from typing import Any, Dict, List, Optional, Tuple
from arrow import Arrow
from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes, PairWithTimeframe
from freqtrade.data.history import load_pair_history
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exceptions import ExchangeError, OperationalException
from freqtrade.exchange import Exchange
from freqtrade.state import RunMode
from freqtrade.typing import ListPairsWithTimeframes
logger = logging.getLogger(__name__)
@@ -25,6 +26,18 @@ class DataProvider:
self._config = config
self._exchange = exchange
self._pairlists = pairlists
self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
def _set_cached_df(self, pair: str, timeframe: str, dataframe: DataFrame) -> None:
"""
Store cached Dataframe.
Using private method as this should never be used by a user
(but the class is exposed via `self.dp` to the strategy)
:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
:param dataframe: analyzed dataframe
"""
self.__cached_pairs[(pair, timeframe)] = (dataframe, Arrow.utcnow().datetime)
def refresh(self,
pairlist: ListPairsWithTimeframes,
@@ -55,7 +68,7 @@ class DataProvider:
Use False only for read-only operations (where the dataframe is not modified)
"""
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
return self._exchange.klines((pair, timeframe or self._config['ticker_interval']),
return self._exchange.klines((pair, timeframe or self._config['timeframe']),
copy=copy)
else:
return DataFrame()
@@ -67,7 +80,7 @@ class DataProvider:
:param timeframe: timeframe to get data for
"""
return load_pair_history(pair=pair,
timeframe=timeframe or self._config['ticker_interval'],
timeframe=timeframe or self._config['timeframe'],
datadir=self._config['datadir']
)
@@ -89,6 +102,20 @@ class DataProvider:
logger.warning(f"No data found for ({pair}, {timeframe}).")
return data
def get_analyzed_dataframe(self, pair: str, timeframe: str) -> Tuple[DataFrame, datetime]:
"""
:param pair: pair to get the data for
:param timeframe: timeframe to get data for
:return: Tuple of (Analyzed Dataframe, lastrefreshed) for the requested pair / timeframe
combination.
Returns empty dataframe and Epoch 0 (1970-01-01) if no dataframe was cached.
"""
if (pair, timeframe) in self.__cached_pairs:
return self.__cached_pairs[(pair, timeframe)]
else:
return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
def market(self, pair: str) -> Optional[Dict[str, Any]]:
"""
Return market data for the pair
@@ -105,17 +132,18 @@ class DataProvider:
"""
try:
return self._exchange.fetch_ticker(pair)
except DependencyException:
except ExchangeError:
return {}
def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
"""
fetch latest orderbook data
Fetch latest l2 orderbook data
Warning: Does a network request - so use with common sense.
:param pair: pair to get the data for
:param maximum: Maximum number of orderbook entries to query
:return: dict including bids/asks with a total of `maximum` entries.
"""
return self._exchange.get_order_book(pair, maximum)
return self._exchange.fetch_l2_order_book(pair, maximum)
@property
def runmode(self) -> RunMode:

5
freqtrade/data/history/history_utils.py
View File

@@ -270,6 +270,11 @@ def _download_trades_history(exchange: Exchange,
# DEFAULT_TRADES_COLUMNS: 0 -> timestamp
# DEFAULT_TRADES_COLUMNS: 1 -> id
if trades and since < trades[0][0]:
# since is before the first trade
logger.info(f"Start earlier than available data. Redownloading trades for {pair}...")
trades = []
from_id = trades[-1][1] if trades else None
if trades and since < trades[-1][0]:
# Reset since to the last available point

9
freqtrade/data/history/idatahandler.py
View File

@@ -13,6 +13,7 @@ from typing import List, Optional, Type
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.converter import (clean_ohlcv_dataframe,
trades_remove_duplicates, trim_dataframe)
from freqtrade.exchange import timeframe_to_seconds
@@ -28,6 +29,14 @@ class IDataHandler(ABC):
def __init__(self, datadir: Path) -> None:
self._datadir = datadir
@abstractclassmethod
def ohlcv_get_available_data(cls, datadir: Path) -> ListPairsWithTimeframes:
"""
Returns a list of all pairs with ohlcv data available in this datadir
:param datadir: Directory to search for ohlcv files
:return: List of Tuples of (pair, timeframe)
"""
@abstractclassmethod
def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
"""

15
freqtrade/data/history/jsondatahandler.py
View File

@@ -8,7 +8,8 @@ from pandas import DataFrame, read_json, to_datetime
from freqtrade import misc
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS,
ListPairsWithTimeframes)
from freqtrade.data.converter import trades_dict_to_list
from .idatahandler import IDataHandler, TradeList
@@ -21,6 +22,18 @@ class JsonDataHandler(IDataHandler):
_use_zip = False
_columns = DEFAULT_DATAFRAME_COLUMNS
@classmethod
def ohlcv_get_available_data(cls, datadir: Path) -> ListPairsWithTimeframes:
"""
Returns a list of all pairs with ohlcv data available in this datadir
:param datadir: Directory to search for ohlcv files
:return: List of Tuples of (pair, timeframe)
"""
_tmp = [re.search(r'^([a-zA-Z_]+)\-(\d+\S+)(?=.json)', p.name)
for p in datadir.glob(f"*.{cls._get_file_extension()}")]
return [(match[1].replace('_', '/'), match[2]) for match in _tmp
if match and len(match.groups()) > 1]
@classmethod
def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
"""

33
freqtrade/edge/edge_positioning.py
View File

@@ -9,7 +9,7 @@ import utils_find_1st as utf1st
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, DATETIME_PRINT_FORMAT
from freqtrade.exceptions import OperationalException
from freqtrade.data.history import get_timerange, load_data, refresh_data
from freqtrade.strategy.interface import SellType
@@ -57,9 +57,7 @@ class Edge:
if self.config['stake_amount'] != UNLIMITED_STAKE_AMOUNT:
raise OperationalException('Edge works only with unlimited stake amount')
# Deprecated capital_available_percentage. Will use tradable_balance_ratio in the future.
self._capital_percentage: float = self.edge_config.get(
'capital_available_percentage', self.config['tradable_balance_ratio'])
self._capital_ratio: float = self.config['tradable_balance_ratio']
self._allowed_risk: float = self.edge_config.get('allowed_risk')
self._since_number_of_days: int = self.edge_config.get('calculate_since_number_of_days', 14)
self._last_updated: int = 0 # Timestamp of pairs last updated time
@@ -100,14 +98,14 @@ class Edge:
datadir=self.config['datadir'],
pairs=pairs,
exchange=self.exchange,
timeframe=self.strategy.ticker_interval,
timeframe=self.strategy.timeframe,
timerange=self._timerange,
)
data = load_data(
datadir=self.config['datadir'],
pairs=pairs,
timeframe=self.strategy.ticker_interval,
timeframe=self.strategy.timeframe,
timerange=self._timerange,
startup_candles=self.strategy.startup_candle_count,
data_format=self.config.get('dataformat_ohlcv', 'json'),
@@ -123,12 +121,9 @@ class Edge:
# Print timeframe
min_date, max_date = get_timerange(preprocessed)
logger.info(
'Measuring data from %s up to %s (%s days) ...',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
logger.info(f'Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
trades: list = []
@@ -157,7 +152,7 @@ class Edge:
def stake_amount(self, pair: str, free_capital: float,
total_capital: float, capital_in_trade: float) -> float:
stoploss = self.stoploss(pair)
available_capital = (total_capital + capital_in_trade) * self._capital_percentage
available_capital = (total_capital + capital_in_trade) * self._capital_ratio
allowed_capital_at_risk = available_capital * self._allowed_risk
max_position_size = abs(allowed_capital_at_risk / stoploss)
position_size = min(max_position_size, free_capital)
@@ -242,7 +237,7 @@ class Edge:
# All returned values are relative, they are defined as ratios.
stake = 0.015
result['trade_duration'] = result['close_time'] - result['open_time']
result['trade_duration'] = result['close_date'] - result['open_date']
result['trade_duration'] = result['trade_duration'].map(
lambda x: int(x.total_seconds() / 60))
@@ -283,8 +278,8 @@ class Edge:
#
# Removing Pumps
if self.edge_config.get('remove_pumps', False):
results = results.groupby(['pair', 'stoploss']).apply(
lambda x: x[x['profit_abs'] < 2 * x['profit_abs'].std() + x['profit_abs'].mean()])
results = results[results['profit_abs'] < 2 * results['profit_abs'].std()
+ results['profit_abs'].mean()]
##########################################################################
# Removing trades having a duration more than X minutes (set in config)
@@ -432,10 +427,8 @@ class Edge:
'stoploss': stoploss,
'profit_ratio': '',
'profit_abs': '',
'open_time': date_column[open_trade_index],
'close_time': date_column[exit_index],
'open_index': start_point + open_trade_index,
'close_index': start_point + exit_index,
'open_date': date_column[open_trade_index],
'close_date': date_column[exit_index],
'trade_duration': '',
'open_rate': round(open_price, 15),
'close_rate': round(exit_price, 15),

33
freqtrade/exceptions.py
View File

@@ -21,7 +21,22 @@ class DependencyException(FreqtradeException):
"""
class InvalidOrderException(FreqtradeException):
class PricingError(DependencyException):
"""
Subclass of DependencyException.
Indicates that the price could not be determined.
Implicitly a buy / sell operation.
"""
class ExchangeError(DependencyException):
"""
Error raised out of the exchange.
Has multiple Errors to determine the appropriate error.
"""
class InvalidOrderException(ExchangeError):
"""
This is returned when the order is not valid. Example:
If stoploss on exchange order is hit, then trying to cancel the order
@@ -29,7 +44,14 @@ class InvalidOrderException(FreqtradeException):
"""
class TemporaryError(FreqtradeException):
class RetryableOrderError(InvalidOrderException):
"""
This is returned when the order is not found.
This Error will be repeated with increasing backof (in line with DDosError).
"""
class TemporaryError(ExchangeError):
"""
Temporary network or exchange related error.
This could happen when an exchange is congested, unavailable, or the user
@@ -37,6 +59,13 @@ class TemporaryError(FreqtradeException):
"""
class DDosProtection(TemporaryError):
"""
Temporary error caused by DDOS protection.
Bot will wait for a second and then retry.
"""
class StrategyError(FreqtradeException):
"""
Errors with custom user-code deteced.

3
freqtrade/exchange/__init__.py
View File

@@ -12,8 +12,7 @@ from freqtrade.exchange.exchange import (timeframe_to_seconds,
timeframe_to_msecs,
timeframe_to_next_date,
timeframe_to_prev_date)
from freqtrade.exchange.exchange import (market_is_active,
symbol_is_pair)
from freqtrade.exchange.exchange import (market_is_active)
from freqtrade.exchange.kraken import Kraken
from freqtrade.exchange.binance import Binance
from freqtrade.exchange.bibox import Bibox

17
freqtrade/exchange/binance.py
View File

@@ -4,9 +4,11 @@ from typing import Dict
import ccxt
from freqtrade.exceptions import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exceptions import (DDosProtection, ExchangeError,
InvalidOrderException, OperationalException,
TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
logger = logging.getLogger(__name__)
@@ -20,7 +22,7 @@ class Binance(Exchange):
"trades_pagination_arg": "fromId",
}
def get_order_book(self, pair: str, limit: int = 100) -> dict:
def fetch_l2_order_book(self, pair: str, limit: int = 100) -> dict:
"""
get order book level 2 from exchange
@@ -30,7 +32,7 @@ class Binance(Exchange):
# get next-higher step in the limit_range list
limit = min(list(filter(lambda x: limit <= x, limit_range)))
return super().get_order_book(pair, limit)
return super().fetch_l2_order_book(pair, limit)
def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
"""
@@ -39,6 +41,7 @@ class Binance(Exchange):
"""
return order['type'] == 'stop_loss_limit' and stop_loss > float(order['info']['stopPrice'])
@retrier(retries=0)
def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
"""
creates a stoploss limit order.
@@ -77,8 +80,8 @@ class Binance(Exchange):
'stop price: %s. limit: %s', pair, stop_price, rate)
return order
except ccxt.InsufficientFunds as e:
raise DependencyException(
f'Insufficient funds to create {ordertype} sell order on market {pair}.'
raise ExchangeError(
f'Insufficient funds to create {ordertype} sell order on market {pair}. '
f'Tried to sell amount {amount} at rate {rate}. '
f'Message: {e}') from e
except ccxt.InvalidOrder as e:
@@ -88,6 +91,8 @@ class Binance(Exchange):
f'Could not create {ordertype} sell order on market {pair}. '
f'Tried to sell amount {amount} at rate {rate}. '
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not place sell order due to {e.__class__.__name__}. Message: {e}') from e

63
freqtrade/exchange/common.py
View File

@@ -1,6 +1,10 @@
import asyncio
import logging
import time
from functools import wraps
from freqtrade.exceptions import TemporaryError
from freqtrade.exceptions import (DDosProtection, RetryableOrderError,
TemporaryError)
logger = logging.getLogger(__name__)
@@ -88,6 +92,13 @@ MAP_EXCHANGE_CHILDCLASS = {
}
def calculate_backoff(retrycount, max_retries):
"""
Calculate backoff
"""
return (max_retries - retrycount) ** 2 + 1
def retrier_async(f):
async def wrapper(*args, **kwargs):
count = kwargs.pop('count', API_RETRY_COUNT)
@@ -96,9 +107,13 @@ def retrier_async(f):
except TemporaryError as ex:
logger.warning('%s() returned exception: "%s"', f.__name__, ex)
if count > 0:
logger.warning('retrying %s() still for %s times', f.__name__, count)
count -= 1
kwargs.update({'count': count})
logger.warning('retrying %s() still for %s times', f.__name__, count)
if isinstance(ex, DDosProtection):
backoff_delay = calculate_backoff(count + 1, API_RETRY_COUNT)
logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")
await asyncio.sleep(backoff_delay)
return await wrapper(*args, **kwargs)
else:
logger.warning('Giving up retrying: %s()', f.__name__)
@@ -106,19 +121,31 @@ def retrier_async(f):
return wrapper
def retrier(f):
def wrapper(*args, **kwargs):
count = kwargs.pop('count', API_RETRY_COUNT)
try:
return f(*args, **kwargs)
except TemporaryError as ex:
logger.warning('%s() returned exception: "%s"', f.__name__, ex)
if count > 0:
count -= 1
kwargs.update({'count': count})
logger.warning('retrying %s() still for %s times', f.__name__, count)
return wrapper(*args, **kwargs)
else:
logger.warning('Giving up retrying: %s()', f.__name__)
raise ex
return wrapper
def retrier(_func=None, retries=API_RETRY_COUNT):
def decorator(f):
@wraps(f)
def wrapper(*args, **kwargs):
count = kwargs.pop('count', retries)
try:
return f(*args, **kwargs)
except (TemporaryError, RetryableOrderError) as ex:
logger.warning('%s() returned exception: "%s"', f.__name__, ex)
if count > 0:
logger.warning('retrying %s() still for %s times', f.__name__, count)
count -= 1
kwargs.update({'count': count})
if isinstance(ex, DDosProtection) or isinstance(ex, RetryableOrderError):
# increasing backoff
backoff_delay = calculate_backoff(count + 1, retries)
logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")
time.sleep(backoff_delay)
return wrapper(*args, **kwargs)
else:
logger.warning('Giving up retrying: %s()', f.__name__)
raise ex
return wrapper
# Support both @retrier and @retrier(retries=2) syntax
if _func is None:
return decorator
else:
return decorator(_func)

167
freqtrade/exchange/exchange.py
View File

@@ -18,12 +18,13 @@ from ccxt.base.decimal_to_precision import (ROUND_DOWN, ROUND_UP, TICK_SIZE,
TRUNCATE, decimal_to_precision)
from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.exceptions import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exceptions import (DDosProtection, ExchangeError,
InvalidOrderException, OperationalException,
RetryableOrderError, TemporaryError)
from freqtrade.exchange.common import BAD_EXCHANGES, retrier, retrier_async
from freqtrade.misc import deep_merge_dicts, safe_value_fallback
from freqtrade.typing import ListPairsWithTimeframes
from freqtrade.misc import deep_merge_dicts, safe_value_fallback2
CcxtModuleType = Any
@@ -79,7 +80,7 @@ class Exchange:
if config['dry_run']:
logger.info('Instance is running with dry_run enabled')
logger.info(f"Using CCXT {ccxt.__version__}")
exchange_config = config['exchange']
# Deep merge ft_has with default ft_has options
@@ -98,12 +99,14 @@ class Exchange:
# Initialize ccxt objects
ccxt_config = self._ccxt_config.copy()
ccxt_config = deep_merge_dicts(exchange_config.get('ccxt_config', {}),
ccxt_config)
self._api = self._init_ccxt(
exchange_config, ccxt_kwargs=ccxt_config)
ccxt_config = deep_merge_dicts(exchange_config.get('ccxt_config', {}), ccxt_config)
ccxt_config = deep_merge_dicts(exchange_config.get('ccxt_sync_config', {}), ccxt_config)
self._api = self._init_ccxt(exchange_config, ccxt_kwargs=ccxt_config)
ccxt_async_config = self._ccxt_config.copy()
ccxt_async_config = deep_merge_dicts(exchange_config.get('ccxt_config', {}),
ccxt_async_config)
ccxt_async_config = deep_merge_dicts(exchange_config.get('ccxt_async_config', {}),
ccxt_async_config)
self._api_async = self._init_ccxt(
@@ -113,7 +116,7 @@ class Exchange:
if validate:
# Check if timeframe is available
self.validate_timeframes(config.get('ticker_interval'))
self.validate_timeframes(config.get('timeframe'))
# Initial markets load
self._load_markets()
@@ -184,11 +187,16 @@ class Exchange:
def timeframes(self) -> List[str]:
return list((self._api.timeframes or {}).keys())
@property
def ohlcv_candle_limit(self) -> int:
"""exchange ohlcv candle limit"""
return int(self._ohlcv_candle_limit)
@property
def markets(self) -> Dict:
"""exchange ccxt markets"""
if not self._api.markets:
logger.warning("Markets were not loaded. Loading them now..")
logger.info("Markets were not loaded. Loading them now..")
self._load_markets()
return self._api.markets
@@ -214,7 +222,7 @@ class Exchange:
if quote_currencies:
markets = {k: v for k, v in markets.items() if v['quote'] in quote_currencies}
if pairs_only:
markets = {k: v for k, v in markets.items() if symbol_is_pair(v['symbol'])}
markets = {k: v for k, v in markets.items() if self.market_is_tradable(v)}
if active_only:
markets = {k: v for k, v in markets.items() if market_is_active(v)}
return markets
@@ -238,6 +246,19 @@ class Exchange:
"""
return self.markets.get(pair, {}).get('base', '')
def market_is_tradable(self, market: Dict[str, Any]) -> bool:
"""
Check if the market symbol is tradable by Freqtrade.
By default, checks if it's splittable by `/` and both sides correspond to base / quote
"""
symbol_parts = market['symbol'].split('/')
return (len(symbol_parts) == 2 and
len(symbol_parts[0]) > 0 and
len(symbol_parts[1]) > 0 and
symbol_parts[0] == market.get('base') and
symbol_parts[1] == market.get('quote')
)
def klines(self, pair_interval: Tuple[str, str], copy: bool = True) -> DataFrame:
if pair_interval in self._klines:
return self._klines[pair_interval].copy() if copy else self._klines[pair_interval]
@@ -250,8 +271,8 @@ class Exchange:
api.urls['api'] = api.urls['test']
logger.info("Enabled Sandbox API on %s", name)
else:
logger.warning(name, "No Sandbox URL in CCXT, exiting. "
"Please check your config.json")
logger.warning(
f"No Sandbox URL in CCXT for {name}, exiting. Please check your config.json")
raise OperationalException(f'Exchange {name} does not provide a sandbox api')
def _load_async_markets(self, reload: bool = False) -> None:
@@ -273,8 +294,8 @@ class Exchange:
except ccxt.BaseError as e:
logger.warning('Unable to initialize markets. Reason: %s', e)
def _reload_markets(self) -> None:
"""Reload markets both sync and async, if refresh interval has passed"""
def reload_markets(self) -> None:
"""Reload markets both sync and async if refresh interval has passed """
# Check whether markets have to be reloaded
if (self._last_markets_refresh > 0) and (
self._last_markets_refresh + self.markets_refresh_interval
@@ -283,6 +304,8 @@ class Exchange:
logger.debug("Performing scheduled market reload..")
try:
self._api.load_markets(reload=True)
# Also reload async markets to avoid issues with newly listed pairs
self._load_async_markets(reload=True)
self._last_markets_refresh = arrow.utcnow().timestamp
except ccxt.BaseError:
logger.exception("Could not reload markets.")
@@ -347,7 +370,7 @@ class Exchange:
for pair in [f"{curr_1}/{curr_2}", f"{curr_2}/{curr_1}"]:
if pair in self.markets and self.markets[pair].get('active'):
return pair
raise DependencyException(f"Could not combine {curr_1} and {curr_2} to get a valid pair.")
raise ExchangeError(f"Could not combine {curr_1} and {curr_2} to get a valid pair.")
def validate_timeframes(self, timeframe: Optional[str]) -> None:
"""
@@ -470,6 +493,7 @@ class Exchange:
"id": order_id,
'pair': pair,
'price': rate,
'average': rate,
'amount': _amount,
'cost': _amount * rate,
'type': ordertype,
@@ -514,15 +538,17 @@ class Exchange:
amount, rate_for_order, params)
except ccxt.InsufficientFunds as e:
raise DependencyException(
f'Insufficient funds to create {ordertype} {side} order on market {pair}.'
raise ExchangeError(
f'Insufficient funds to create {ordertype} {side} order on market {pair}. '
f'Tried to {side} amount {amount} at rate {rate}.'
f'Message: {e}') from e
except ccxt.InvalidOrder as e:
raise DependencyException(
f'Could not create {ordertype} {side} order on market {pair}.'
f'Tried to {side} amount {amount} at rate {rate}.'
raise ExchangeError(
f'Could not create {ordertype} {side} order on market {pair}. '
f'Tried to {side} amount {amount} at rate {rate}. '
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not place {side} order due to {e.__class__.__name__}. Message: {e}') from e
@@ -602,6 +628,8 @@ class Exchange:
balances.pop("used", None)
return balances
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get balance due to {e.__class__.__name__}. Message: {e}') from e
@@ -616,6 +644,8 @@ class Exchange:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching tickers in batch. '
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not load tickers due to {e.__class__.__name__}. Message: {e}') from e
@@ -626,9 +656,11 @@ class Exchange:
def fetch_ticker(self, pair: str) -> dict:
try:
if pair not in self._api.markets or not self._api.markets[pair].get('active'):
raise DependencyException(f"Pair {pair} not available")
raise ExchangeError(f"Pair {pair} not available")
data = self._api.fetch_ticker(pair)
return data
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not load ticker due to {e.__class__.__name__}. Message: {e}') from e
@@ -762,6 +794,8 @@ class Exchange:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching historical '
f'candle (OHLCV) data. Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(f'Could not fetch historical candle (OHLCV) data '
f'for pair {pair} due to {e.__class__.__name__}. '
@@ -798,6 +832,8 @@ class Exchange:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching historical trade data.'
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(f'Could not load trade history due to {e.__class__.__name__}. '
f'Message: {e}') from e
@@ -887,14 +923,19 @@ class Exchange:
Async wrapper handling downloading trades using either time or id based methods.
"""
logger.debug(f"_async_get_trade_history(), pair: {pair}, "
f"since: {since}, until: {until}, from_id: {from_id}")
if until is None:
until = ccxt.Exchange.milliseconds()
logger.debug(f"Exchange milliseconds: {until}")
if self._trades_pagination == 'time':
return await self._async_get_trade_history_time(
pair=pair, since=since,
until=until or ccxt.Exchange.milliseconds())
pair=pair, since=since, until=until)
elif self._trades_pagination == 'id':
return await self._async_get_trade_history_id(
pair=pair, since=since,
until=until or ccxt.Exchange.milliseconds(), from_id=from_id
pair=pair, since=since, until=until, from_id=from_id
)
else:
raise OperationalException(f"Exchange {self.name} does use neither time, "
@@ -924,7 +965,7 @@ class Exchange:
def check_order_canceled_empty(self, order: Dict) -> bool:
"""
Verify if an order has been cancelled without being partially filled
:param order: Order dict as returned from get_order()
:param order: Order dict as returned from fetch_order()
:return: True if order has been cancelled without being filled, False otherwise.
"""
return order.get('status') in ('closed', 'canceled') and order.get('filled') == 0.0
@@ -939,12 +980,17 @@ class Exchange:
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Could not cancel order. Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not cancel order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
# Assign method to cancel_stoploss_order to allow easy overriding in other classes
cancel_stoploss_order = cancel_order
def is_cancel_order_result_suitable(self, corder) -> bool:
if not isinstance(corder, dict):
return False
@@ -956,7 +1002,7 @@ class Exchange:
"""
Cancel order returning a result.
Creates a fake result if cancel order returns a non-usable result
and get_order does not work (certain exchanges don't return cancelled orders)
and fetch_order does not work (certain exchanges don't return cancelled orders)
:param order_id: Orderid to cancel
:param pair: Pair corresponding to order_id
:param amount: Amount to use for fake response
@@ -967,17 +1013,17 @@ class Exchange:
if self.is_cancel_order_result_suitable(corder):
return corder
except InvalidOrderException:
logger.warning(f"Could not cancel order {order_id}.")
logger.warning(f"Could not cancel order {order_id} for {pair}.")
try:
order = self.get_order(order_id, pair)
order = self.fetch_order(order_id, pair)
except InvalidOrderException:
logger.warning(f"Could not fetch cancelled order {order_id}.")
order = {'fee': {}, 'status': 'canceled', 'amount': amount, 'info': {}}
return order
@retrier
def get_order(self, order_id: str, pair: str) -> Dict:
@retrier(retries=5)
def fetch_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
try:
order = self._dry_run_open_orders[order_id]
@@ -988,22 +1034,30 @@ class Exchange:
f'Tried to get an invalid dry-run-order (id: {order_id}). Message: {e}') from e
try:
return self._api.fetch_order(order_id, pair)
except ccxt.OrderNotFound as e:
raise RetryableOrderError(
f'Order not found (pair: {pair} id: {order_id}). Message: {e}') from e
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Tried to get an invalid order (id: {order_id}). Message: {e}') from e
f'Tried to get an invalid order (pair: {pair} id: {order_id}). Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier
def get_order_book(self, pair: str, limit: int = 100) -> dict:
"""
get order book level 2 from exchange
# Assign method to fetch_stoploss_order to allow easy overriding in other classes
fetch_stoploss_order = fetch_order
Notes:
20180619: bittrex doesnt support limits -.-
@retrier
def fetch_l2_order_book(self, pair: str, limit: int = 100) -> dict:
"""
Get L2 order book from exchange.
Can be limited to a certain amount (if supported).
Returns a dict in the format
{'asks': [price, volume], 'bids': [price, volume]}
"""
try:
@@ -1012,6 +1066,8 @@ class Exchange:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching order book.'
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get order book due to {e.__class__.__name__}. Message: {e}') from e
@@ -1048,7 +1104,8 @@ class Exchange:
matched_trades = [trade for trade in my_trades if trade['order'] == order_id]
return matched_trades
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get trades due to {e.__class__.__name__}. Message: {e}') from e
@@ -1065,6 +1122,8 @@ class Exchange:
return self._api.calculate_fee(symbol=symbol, type=type, side=side, amount=amount,
price=price, takerOrMaker=taker_or_maker)['rate']
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get fee info due to {e.__class__.__name__}. Message: {e}') from e
@@ -1099,19 +1158,22 @@ class Exchange:
if fee_curr in self.get_pair_base_currency(order['symbol']):
# Base currency - divide by amount
return round(
order['fee']['cost'] / safe_value_fallback(order, order, 'filled', 'amount'), 8)
order['fee']['cost'] / safe_value_fallback2(order, order, 'filled', 'amount'), 8)
elif fee_curr in self.get_pair_quote_currency(order['symbol']):
# Quote currency - divide by cost
return round(order['fee']['cost'] / order['cost'], 8)
return round(order['fee']['cost'] / order['cost'], 8) if order['cost'] else None
else:
# If Fee currency is a different currency
if not order['cost']:
# If cost is None or 0.0 -> falsy, return None
return None
try:
comb = self.get_valid_pair_combination(fee_curr, self._config['stake_currency'])
tick = self.fetch_ticker(comb)
fee_to_quote_rate = safe_value_fallback(tick, tick, 'last', 'ask')
fee_to_quote_rate = safe_value_fallback2(tick, tick, 'last', 'ask')
return round((order['fee']['cost'] * fee_to_quote_rate) / order['cost'], 8)
except DependencyException:
except ExchangeError:
return None
def extract_cost_curr_rate(self, order: Dict) -> Tuple[float, str, Optional[float]]:
@@ -1124,7 +1186,6 @@ class Exchange:
return (order['fee']['cost'],
order['fee']['currency'],
self.calculate_fee_rate(order))
# calculate rate ? (order['fee']['cost'] / (order['amount'] * order['price']))
def is_exchange_bad(exchange_name: str) -> bool:
@@ -1210,20 +1271,6 @@ def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
def symbol_is_pair(market_symbol: str, base_currency: str = None,
quote_currency: str = None) -> bool:
"""
Check if the market symbol is a pair, i.e. that its symbol consists of the base currency and the
quote currency separated by '/' character. If base_currency and/or quote_currency is passed,
it also checks that the symbol contains appropriate base and/or quote currency part before
and after the separating character correspondingly.
"""
symbol_parts = market_symbol.split('/')
return (len(symbol_parts) == 2 and
(symbol_parts[0] == base_currency if base_currency else len(symbol_parts[0]) > 0) and
(symbol_parts[1] == quote_currency if quote_currency else len(symbol_parts[1]) > 0))
def market_is_active(market: Dict) -> bool:
"""
Return True if the market is active.

124
freqtrade/exchange/ftx.py
View File

@@ -1,8 +1,14 @@
""" FTX exchange subclass """
import logging
from typing import Dict
from typing import Any, Dict
import ccxt
from freqtrade.exceptions import (DDosProtection, ExchangeError,
InvalidOrderException, OperationalException,
TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
logger = logging.getLogger(__name__)
@@ -10,5 +16,121 @@ logger = logging.getLogger(__name__)
class Ftx(Exchange):
_ft_has: Dict = {
"stoploss_on_exchange": True,
"ohlcv_candle_limit": 1500,
}
def market_is_tradable(self, market: Dict[str, Any]) -> bool:
"""
Check if the market symbol is tradable by Freqtrade.
Default checks + check if pair is spot pair (no futures trading yet).
"""
parent_check = super().market_is_tradable(market)
return (parent_check and
market.get('spot', False) is True)
def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return order['type'] == 'stop' and stop_loss > float(order['price'])
@retrier(retries=0)
def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
"""
Creates a stoploss order.
depending on order_types.stoploss configuration, uses 'market' or limit order.
Limit orders are defined by having orderPrice set, otherwise a market order is used.
"""
limit_price_pct = order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
limit_rate = stop_price * limit_price_pct
ordertype = "stop"
stop_price = self.price_to_precision(pair, stop_price)
if self._config['dry_run']:
dry_order = self.dry_run_order(
pair, ordertype, "sell", amount, stop_price)
return dry_order
try:
params = self._params.copy()
if order_types.get('stoploss', 'market') == 'limit':
# set orderPrice to place limit order, otherwise it's a market order
params['orderPrice'] = limit_rate
amount = self.amount_to_precision(pair, amount)
order = self._api.create_order(symbol=pair, type=ordertype, side='sell',
amount=amount, price=stop_price, params=params)
logger.info('stoploss order added for %s. '
'stop price: %s.', pair, stop_price)
return order
except ccxt.InsufficientFunds as e:
raise ExchangeError(
f'Insufficient funds to create {ordertype} sell order on market {pair}. '
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Could not create {ordertype} sell order on market {pair}. '
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not place sell order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier(retries=5)
def fetch_stoploss_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
try:
order = self._dry_run_open_orders[order_id]
return order
except KeyError as e:
# Gracefully handle errors with dry-run orders.
raise InvalidOrderException(
f'Tried to get an invalid dry-run-order (id: {order_id}). Message: {e}') from e
try:
orders = self._api.fetch_orders(pair, None, params={'type': 'stop'})
order = [order for order in orders if order['id'] == order_id]
if len(order) == 1:
return order[0]
else:
raise InvalidOrderException(f"Could not get stoploss order for id {order_id}")
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Tried to get an invalid order (id: {order_id}). Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier
def cancel_stoploss_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
return {}
try:
return self._api.cancel_order(order_id, pair, params={'type': 'stop'})
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Could not cancel order. Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not cancel order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e

26
freqtrade/exchange/kraken.py
View File

@@ -1,11 +1,12 @@
""" Kraken exchange subclass """
import logging
from typing import Dict
from typing import Any, Dict
import ccxt
from freqtrade.exceptions import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exceptions import (DDosProtection, ExchangeError,
InvalidOrderException, OperationalException,
TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
@@ -21,6 +22,16 @@ class Kraken(Exchange):
"trades_pagination_arg": "since",
}
def market_is_tradable(self, market: Dict[str, Any]) -> bool:
"""
Check if the market symbol is tradable by Freqtrade.
Default checks + check if pair is darkpool pair.
"""
parent_check = super().market_is_tradable(market)
return (parent_check and
market.get('darkpool', False) is False)
@retrier
def get_balances(self) -> dict:
if self._config['dry_run']:
@@ -45,6 +56,8 @@ class Kraken(Exchange):
balances[bal]['free'] = balances[bal]['total'] - balances[bal]['used']
return balances
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get balance due to {e.__class__.__name__}. Message: {e}') from e
@@ -58,6 +71,7 @@ class Kraken(Exchange):
"""
return order['type'] == 'stop-loss' and stop_loss > float(order['price'])
@retrier(retries=0)
def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict) -> Dict:
"""
Creates a stoploss market order.
@@ -84,8 +98,8 @@ class Kraken(Exchange):
'stop price: %s.', pair, stop_price)
return order
except ccxt.InsufficientFunds as e:
raise DependencyException(
f'Insufficient funds to create {ordertype} sell order on market {pair}.'
raise ExchangeError(
f'Insufficient funds to create {ordertype} sell order on market {pair}. '
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e
except ccxt.InvalidOrder as e:
@@ -93,6 +107,8 @@ class Kraken(Exchange):
f'Could not create {ordertype} sell order on market {pair}. '
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not place sell order due to {e.__class__.__name__}. Message: {e}') from e

181
freqtrade/freqtradebot.py
View File

@@ -11,16 +11,16 @@ from typing import Any, Dict, List, Optional
import arrow
from cachetools import TTLCache
from requests.exceptions import RequestException
from freqtrade import __version__, constants, persistence
from freqtrade.configuration import validate_config_consistency
from freqtrade.data.converter import order_book_to_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.edge import Edge
from freqtrade.exceptions import DependencyException, InvalidOrderException
from freqtrade.exceptions import (DependencyException, ExchangeError,
InvalidOrderException, PricingError)
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date
from freqtrade.misc import safe_value_fallback
from freqtrade.misc import safe_value_fallback, safe_value_fallback2
from freqtrade.pairlist.pairlistmanager import PairListManager
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
@@ -119,6 +119,8 @@ class FreqtradeBot:
if self.config['cancel_open_orders_on_exit']:
self.cancel_all_open_orders()
self.check_for_open_trades()
self.rpc.cleanup()
persistence.cleanup()
@@ -139,8 +141,8 @@ class FreqtradeBot:
:return: True if one or more trades has been created or closed, False otherwise
"""
# Check whether markets have to be reloaded
self.exchange._reload_markets()
# Check whether markets have to be reloaded and reload them when it's needed
self.exchange.reload_markets()
# Query trades from persistence layer
trades = Trade.get_open_trades()
@@ -151,6 +153,10 @@ class FreqtradeBot:
self.dataprovider.refresh(self.pairlists.create_pair_list(self.active_pair_whitelist),
self.strategy.informative_pairs())
strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)()
self.strategy.analyze(self.active_pair_whitelist)
with self._sell_lock:
# Check and handle any timed out open orders
self.check_handle_timedout()
@@ -175,6 +181,24 @@ class FreqtradeBot:
if self.config['cancel_open_orders_on_exit']:
self.cancel_all_open_orders()
def check_for_open_trades(self):
"""
Notify the user when the bot is stopped
and there are still open trades active.
"""
open_trades = Trade.get_trades([Trade.is_open == 1]).all()
if len(open_trades) != 0:
msg = {
'type': RPCMessageType.WARNING_NOTIFICATION,
'status': f"{len(open_trades)} open trades active.\n\n"
f"Handle these trades manually on {self.exchange.name}, "
f"or '/start' the bot again and use '/stopbuy' "
f"to handle open trades gracefully. \n"
f"{'Trades are simulated.' if self.config['dry_run'] else ''}",
}
self.rpc.send_msg(msg)
def _refresh_active_whitelist(self, trades: List[Trade] = []) -> List[str]:
"""
Refresh active whitelist from pairlist or edge and extend it with
@@ -251,7 +275,7 @@ class FreqtradeBot:
rate = self._buy_rate_cache.get(pair)
# Check if cache has been invalidated
if rate:
logger.info(f"Using cached buy rate for {pair}.")
logger.debug(f"Using cached buy rate for {pair}.")
return rate
bid_strategy = self.config.get('bid_strategy', {})
@@ -260,12 +284,19 @@ class FreqtradeBot:
f"Getting price from order book {bid_strategy['price_side'].capitalize()} side."
)
order_book_top = bid_strategy.get('order_book_top', 1)
order_book = self.exchange.get_order_book(pair, order_book_top)
order_book = self.exchange.fetch_l2_order_book(pair, order_book_top)
logger.debug('order_book %s', order_book)
# top 1 = index 0
order_book_rate = order_book[f"{bid_strategy['price_side']}s"][order_book_top - 1][0]
logger.info(f'...top {order_book_top} order book buy rate {order_book_rate:.8f}')
used_rate = order_book_rate
try:
rate_from_l2 = order_book[f"{bid_strategy['price_side']}s"][order_book_top - 1][0]
except (IndexError, KeyError) as e:
logger.warning(
"Buy Price from orderbook could not be determined."
f"Orderbook: {order_book}"
)
raise PricingError from e
logger.info(f'...top {order_book_top} order book buy rate {rate_from_l2:.8f}')
used_rate = rate_from_l2
else:
logger.info(f"Using Last {bid_strategy['price_side'].capitalize()} / Last Price")
ticker = self.exchange.fetch_ticker(pair)
@@ -413,8 +444,8 @@ class FreqtradeBot:
return False
# running get_signal on historical data fetched
dataframe = self.dataprovider.ohlcv(pair, self.strategy.ticker_interval)
(buy, sell) = self.strategy.get_signal(pair, self.strategy.ticker_interval, dataframe)
analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(pair, self.strategy.timeframe)
(buy, sell) = self.strategy.get_signal(pair, self.strategy.timeframe, analyzed_df)
if buy and not sell:
stake_amount = self.get_trade_stake_amount(pair)
@@ -445,7 +476,7 @@ class FreqtradeBot:
"""
conf_bids_to_ask_delta = conf.get('bids_to_ask_delta', 0)
logger.info(f"Checking depth of market for {pair} ...")
order_book = self.exchange.get_order_book(pair, 1000)
order_book = self.exchange.fetch_l2_order_book(pair, 1000)
order_book_data_frame = order_book_to_dataframe(order_book['bids'], order_book['asks'])
order_book_bids = order_book_data_frame['b_size'].sum()
order_book_asks = order_book_data_frame['a_size'].sum()
@@ -487,6 +518,12 @@ class FreqtradeBot:
amount = stake_amount / buy_limit_requested
order_type = self.strategy.order_types['buy']
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=amount, rate=buy_limit_requested,
time_in_force=time_in_force):
logger.info(f"User requested abortion of buying {pair}")
return False
amount = self.exchange.amount_to_precision(pair, amount)
order = self.exchange.buy(pair=pair, ordertype=order_type,
amount=amount, rate=buy_limit_requested,
time_in_force=time_in_force)
@@ -495,6 +532,7 @@ class FreqtradeBot:
# we assume the order is executed at the price requested
buy_limit_filled_price = buy_limit_requested
amount_requested = amount
if order_status == 'expired' or order_status == 'rejected':
order_tif = self.strategy.order_time_in_force['buy']
@@ -515,15 +553,15 @@ class FreqtradeBot:
order['filled'], order['amount'], order['remaining']
)
stake_amount = order['cost']
amount = order['amount']
buy_limit_filled_price = order['price']
amount = safe_value_fallback(order, 'filled', 'amount')
buy_limit_filled_price = safe_value_fallback(order, 'average', 'price')
order_id = None
# in case of FOK the order may be filled immediately and fully
elif order_status == 'closed':
stake_amount = order['cost']
amount = order['amount']
buy_limit_filled_price = order['price']
amount = safe_value_fallback(order, 'filled', 'amount')
buy_limit_filled_price = safe_value_fallback(order, 'average', 'price')
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
@@ -531,6 +569,7 @@ class FreqtradeBot:
pair=pair,
stake_amount=stake_amount,
amount=amount,
amount_requested=amount_requested,
fee_open=fee,
fee_close=fee,
open_rate=buy_limit_filled_price,
@@ -539,7 +578,7 @@ class FreqtradeBot:
exchange=self.exchange.id,
open_order_id=order_id,
strategy=self.strategy.get_strategy_name(),
ticker_interval=timeframe_to_minutes(self.config['ticker_interval'])
timeframe=timeframe_to_minutes(self.config['timeframe'])
)
# Update fees if order is closed
@@ -561,6 +600,7 @@ class FreqtradeBot:
Sends rpc notification when a buy occured.
"""
msg = {
'trade_id': trade.id,
'type': RPCMessageType.BUY_NOTIFICATION,
'exchange': self.exchange.name.capitalize(),
'pair': trade.pair,
@@ -584,6 +624,7 @@ class FreqtradeBot:
current_rate = self.get_buy_rate(trade.pair, False)
msg = {
'trade_id': trade.id,
'type': RPCMessageType.BUY_CANCEL_NOTIFICATION,
'exchange': self.exchange.name.capitalize(),
'pair': trade.pair,
@@ -621,7 +662,7 @@ class FreqtradeBot:
trades_closed += 1
except DependencyException as exception:
logger.warning('Unable to sell trade: %s', exception)
logger.warning('Unable to sell trade %s: %s', trade.pair, exception)
# Updating wallets if any trade occured
if trades_closed:
@@ -634,7 +675,7 @@ class FreqtradeBot:
"""
Helper generator to query orderbook in loop (used for early sell-order placing)
"""
order_book = self.exchange.get_order_book(pair, order_book_max)
order_book = self.exchange.fetch_l2_order_book(pair, order_book_max)
for i in range(order_book_min, order_book_max + 1):
yield order_book[side][i - 1][0]
@@ -652,7 +693,7 @@ class FreqtradeBot:
rate = self._sell_rate_cache.get(pair)
# Check if cache has been invalidated
if rate:
logger.info(f"Using cached sell rate for {pair}.")
logger.debug(f"Using cached sell rate for {pair}.")
return rate
ask_strategy = self.config.get('ask_strategy', {})
@@ -661,10 +702,15 @@ class FreqtradeBot:
logger.info(
f"Getting price from order book {ask_strategy['price_side'].capitalize()} side."
)
rate = next(self._order_book_gen(pair, f"{ask_strategy['price_side']}s"))
try:
rate = next(self._order_book_gen(pair, f"{ask_strategy['price_side']}s"))
except (IndexError, KeyError) as e:
logger.warning("Sell Price at location from orderbook could not be determined.")
raise PricingError from e
else:
rate = self.exchange.fetch_ticker(pair)[ask_strategy['price_side']]
if rate is None:
raise PricingError(f"Sell-Rate for {pair} was empty.")
self._sell_rate_cache[pair] = rate
return rate
@@ -684,23 +730,33 @@ class FreqtradeBot:
if (config_ask_strategy.get('use_sell_signal', True) or
config_ask_strategy.get('ignore_roi_if_buy_signal', False)):
(buy, sell) = self.strategy.get_signal(
trade.pair, self.strategy.ticker_interval,
self.dataprovider.ohlcv(trade.pair, self.strategy.ticker_interval))
analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(trade.pair,
self.strategy.timeframe)
(buy, sell) = self.strategy.get_signal(trade.pair, self.strategy.timeframe, analyzed_df)
if config_ask_strategy.get('use_order_book', False):
logger.debug(f'Using order book for selling {trade.pair}...')
# logger.debug('Order book %s',orderBook)
order_book_min = config_ask_strategy.get('order_book_min', 1)
order_book_max = config_ask_strategy.get('order_book_max', 1)
logger.debug(f'Using order book between {order_book_min} and {order_book_max} '
f'for selling {trade.pair}...')
order_book = self._order_book_gen(trade.pair, f"{config_ask_strategy['price_side']}s",
order_book_min=order_book_min,
order_book_max=order_book_max)
for i in range(order_book_min, order_book_max + 1):
sell_rate = next(order_book)
try:
sell_rate = next(order_book)
except (IndexError, KeyError) as e:
logger.warning(
f"Sell Price at location {i} from orderbook could not be determined."
)
raise PricingError from e
logger.debug(f" order book {config_ask_strategy['price_side']} top {i}: "
f"{sell_rate:0.8f}")
# Assign sell-rate to cache - otherwise sell-rate is never updated in the cache,
# resulting in outdated RPC messages
self._sell_rate_cache[trade.pair] = sell_rate
if self._check_and_execute_sell(trade, sell_rate, buy, sell):
return True
@@ -714,7 +770,7 @@ class FreqtradeBot:
logger.debug('Found no sell signal for %s.', trade)
return False
def create_stoploss_order(self, trade: Trade, stop_price: float, rate: float) -> bool:
def create_stoploss_order(self, trade: Trade, stop_price: float) -> bool:
"""
Abstracts creating stoploss orders from the logic.
Handles errors and updates the trade database object.
@@ -733,7 +789,7 @@ class FreqtradeBot:
logger.warning('Selling the trade forcefully')
self.execute_sell(trade, trade.stop_loss, sell_reason=SellType.EMERGENCY_SELL)
except DependencyException:
except ExchangeError:
trade.stoploss_order_id = None
logger.exception('Unable to place a stoploss order on exchange.')
return False
@@ -751,18 +807,18 @@ class FreqtradeBot:
try:
# First we check if there is already a stoploss on exchange
stoploss_order = self.exchange.get_order(trade.stoploss_order_id, trade.pair) \
if trade.stoploss_order_id else None
stoploss_order = self.exchange.fetch_stoploss_order(
trade.stoploss_order_id, trade.pair) if trade.stoploss_order_id else None
except InvalidOrderException as exception:
logger.warning('Unable to fetch stoploss order: %s', exception)
# We check if stoploss order is fulfilled
if stoploss_order and stoploss_order['status'] == 'closed':
if stoploss_order and stoploss_order['status'] in ('closed', 'triggered'):
trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value
self.update_trade_state(trade, stoploss_order, sl_order=True)
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(trade.pair,
timeframe_to_next_date(self.config['ticker_interval']))
timeframe_to_next_date(self.config['timeframe']))
self._notify_sell(trade, "stoploss")
return True
@@ -773,20 +829,17 @@ class FreqtradeBot:
return False
# If buy order is fulfilled but there is no stoploss, we add a stoploss on exchange
if (not stoploss_order):
if not stoploss_order:
stoploss = self.edge.stoploss(pair=trade.pair) if self.edge else self.strategy.stoploss
stop_price = trade.open_rate * (1 + stoploss)
if self.create_stoploss_order(trade=trade, stop_price=stop_price, rate=stop_price):
if self.create_stoploss_order(trade=trade, stop_price=stop_price):
trade.stoploss_last_update = datetime.now()
return False
# If stoploss order is canceled for some reason we add it
if stoploss_order and stoploss_order['status'] == 'canceled':
if self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss,
rate=trade.stop_loss):
if stoploss_order and stoploss_order['status'] in ('canceled', 'cancelled'):
if self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss):
return False
else:
trade.stoploss_order_id = None
@@ -817,14 +870,13 @@ class FreqtradeBot:
logger.info('Trailing stoploss: cancelling current stoploss on exchange (id:{%s}) '
'in order to add another one ...', order['id'])
try:
self.exchange.cancel_order(order['id'], trade.pair)
self.exchange.cancel_stoploss_order(order['id'], trade.pair)
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {order['id']} "
f"for pair {trade.pair}")
# Create new stoploss order
if not self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss,
rate=trade.stop_loss):
if not self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss):
logger.warning(f"Could not create trailing stoploss order "
f"for pair {trade.pair}.")
@@ -868,8 +920,8 @@ class FreqtradeBot:
try:
if not trade.open_order_id:
continue
order = self.exchange.get_order(trade.open_order_id, trade.pair)
except (RequestException, DependencyException, InvalidOrderException):
order = self.exchange.fetch_order(trade.open_order_id, trade.pair)
except (ExchangeError):
logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
continue
@@ -901,8 +953,8 @@ class FreqtradeBot:
for trade in Trade.get_open_order_trades():
try:
order = self.exchange.get_order(trade.open_order_id, trade.pair)
except (DependencyException, InvalidOrderException):
order = self.exchange.fetch_order(trade.open_order_id, trade.pair)
except (ExchangeError):
logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
continue
@@ -924,6 +976,12 @@ class FreqtradeBot:
reason = constants.CANCEL_REASON['TIMEOUT']
corder = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
trade.amount)
# Avoid race condition where the order could not be cancelled coz its already filled.
# Simply bailing here is the only safe way - as this order will then be
# handled in the next iteration.
if corder.get('status') not in ('canceled', 'closed'):
logger.warning(f"Order {trade.open_order_id} for {trade.pair} not cancelled.")
return False
else:
# Order was cancelled already, so we can reuse the existing dict
corder = order
@@ -932,7 +990,7 @@ class FreqtradeBot:
logger.info('Buy order %s for %s.', reason, trade)
# Using filled to determine the filled amount
filled_amount = safe_value_fallback(corder, order, 'filled', 'filled')
filled_amount = safe_value_fallback2(corder, order, 'filled', 'filled')
if isclose(filled_amount, 0.0, abs_tol=constants.MATH_CLOSE_PREC):
logger.info('Buy order fully cancelled. Removing %s from database.', trade)
@@ -1045,7 +1103,7 @@ class FreqtradeBot:
# First cancelling stoploss on exchange ...
if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
try:
self.exchange.cancel_order(trade.stoploss_order_id, trade.pair)
self.exchange.cancel_stoploss_order(trade.stoploss_order_id, trade.pair)
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
@@ -1055,12 +1113,20 @@ class FreqtradeBot:
order_type = self.strategy.order_types.get("emergencysell", "market")
amount = self._safe_sell_amount(trade.pair, trade.amount)
time_in_force = self.strategy.order_time_in_force['sell']
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair, trade=trade, order_type=order_type, amount=amount, rate=limit,
time_in_force=time_in_force,
sell_reason=sell_reason.value):
logger.info(f"User requested abortion of selling {trade.pair}")
return False
# Execute sell and update trade record
order = self.exchange.sell(pair=str(trade.pair),
ordertype=order_type,
amount=amount, rate=limit,
time_in_force=self.strategy.order_time_in_force['sell']
time_in_force=time_in_force
)
trade.open_order_id = order['id']
@@ -1072,7 +1138,7 @@ class FreqtradeBot:
Trade.session.flush()
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(trade.pair, timeframe_to_next_date(self.config['ticker_interval']))
self.strategy.lock_pair(trade.pair, timeframe_to_next_date(self.config['timeframe']))
self._notify_sell(trade, order_type)
@@ -1091,6 +1157,7 @@ class FreqtradeBot:
msg = {
'type': RPCMessageType.SELL_NOTIFICATION,
'trade_id': trade.id,
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
'gain': gain,
@@ -1133,6 +1200,7 @@ class FreqtradeBot:
msg = {
'type': RPCMessageType.SELL_CANCEL_NOTIFICATION,
'trade_id': trade.id,
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
'gain': gain,
@@ -1180,14 +1248,15 @@ class FreqtradeBot:
# Update trade with order values
logger.info('Found open order for %s', trade)
try:
order = action_order or self.exchange.get_order(order_id, trade.pair)
order = action_order or self.exchange.fetch_order(order_id, trade.pair)
except InvalidOrderException as exception:
logger.warning('Unable to fetch order %s: %s', order_id, exception)
return False
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order, order_amount)
if not isclose(order['amount'], new_amount, abs_tol=constants.MATH_CLOSE_PREC):
if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount,
abs_tol=constants.MATH_CLOSE_PREC):
order['amount'] = new_amount
order.pop('filled', None)
trade.recalc_open_trade_price()
@@ -1233,7 +1302,7 @@ class FreqtradeBot:
"""
# Init variables
if order_amount is None:
order_amount = order['amount']
order_amount = safe_value_fallback(order, 'filled', 'amount')
# Only run for closed orders
if trade.fee_updated(order.get('side', '')) or order['status'] == 'open':
return order_amount

8
freqtrade/loggers.py
View File

@@ -11,7 +11,7 @@ from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
def _set_loggers(verbosity: int = 0) -> None:
def _set_loggers(verbosity: int = 0, api_verbosity: str = 'info') -> None:
"""
Set the logging level for third party libraries
:return: None
@@ -28,6 +28,10 @@ def _set_loggers(verbosity: int = 0) -> None:
)
logging.getLogger('telegram').setLevel(logging.INFO)
logging.getLogger('werkzeug').setLevel(
logging.ERROR if api_verbosity == 'error' else logging.INFO
)
def setup_logging(config: Dict[str, Any]) -> None:
"""
@@ -77,5 +81,5 @@ def setup_logging(config: Dict[str, Any]) -> None:
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=log_handlers
)
_set_loggers(verbosity)
_set_loggers(verbosity, config.get('api_server', {}).get('verbosity', 'info'))
logger.info('Verbosity set to %s', verbosity)

16
freqtrade/misc.py
View File

@@ -134,7 +134,21 @@ def round_dict(d, n):
return {k: (round(v, n) if isinstance(v, float) else v) for k, v in d.items()}
def safe_value_fallback(dict1: dict, dict2: dict, key1: str, key2: str, default_value=None):
def safe_value_fallback(obj: dict, key1: str, key2: str, default_value=None):
"""
Search a value in obj, return this if it's not None.
Then search key2 in obj - return that if it's not none - then use default_value.
Else falls back to None.
"""
if key1 in obj and obj[key1] is not None:
return obj[key1]
else:
if key2 in obj and obj[key2] is not None:
return obj[key2]
return default_value
def safe_value_fallback2(dict1: dict, dict2: dict, key1: str, key2: str, default_value=None):
"""
Search a value in dict1, return this if it's not None.
Fall back to dict2 - return key2 from dict2 if it's not None.

118
freqtrade/optimize/backtesting.py
View File

@@ -13,17 +13,18 @@ from pandas import DataFrame
from freqtrade.configuration import (TimeRange, remove_credentials,
validate_config_consistency)
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data import history
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.optimize.optimize_reports import (show_backtest_results,
store_backtest_result)
from freqtrade.optimize.optimize_reports import (generate_backtest_stats,
show_backtest_results,
store_backtest_stats)
from freqtrade.pairlist.pairlistmanager import PairListManager
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
logger = logging.getLogger(__name__)
@@ -36,14 +37,15 @@ class BacktestResult(NamedTuple):
pair: str
profit_percent: float
profit_abs: float
open_time: datetime
close_time: datetime
open_index: int
close_index: int
open_date: datetime
open_rate: float
open_fee: float
close_date: datetime
close_rate: float
close_fee: float
amount: float
trade_duration: float
open_at_end: bool
open_rate: float
close_rate: float
sell_reason: SellType
@@ -64,23 +66,8 @@ class Backtesting:
self.strategylist: List[IStrategy] = []
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
self.pairlists = PairListManager(self.exchange, self.config)
if 'VolumePairList' in self.pairlists.name_list:
raise OperationalException("VolumePairList not allowed for backtesting.")
self.pairlists.refresh_pairlist()
if len(self.pairlists.whitelist) == 0:
raise OperationalException("No pair in whitelist.")
if config.get('fee'):
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0])
if self.config.get('runmode') != RunMode.HYPEROPT:
self.dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = self.dataprovider
dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = dataprovider
if self.config.get('strategy_list', None):
for strat in list(self.config['strategy_list']):
@@ -94,12 +81,31 @@ class Backtesting:
self.strategylist.append(StrategyResolver.load_strategy(self.config))
validate_config_consistency(self.config)
if "ticker_interval" not in self.config:
if "timeframe" not in self.config:
raise OperationalException("Timeframe (ticker interval) needs to be set in either "
"configuration or as cli argument `--ticker-interval 5m`")
self.timeframe = str(self.config.get('ticker_interval'))
"configuration or as cli argument `--timeframe 5m`")
self.timeframe = str(self.config.get('timeframe'))
self.timeframe_min = timeframe_to_minutes(self.timeframe)
self.pairlists = PairListManager(self.exchange, self.config)
if 'VolumePairList' in self.pairlists.name_list:
raise OperationalException("VolumePairList not allowed for backtesting.")
if len(self.strategylist) > 1 and 'PrecisionFilter' in self.pairlists.name_list:
raise OperationalException(
"PrecisionFilter not allowed for backtesting multiple strategies."
)
self.pairlists.refresh_pairlist()
if len(self.pairlists.whitelist) == 0:
raise OperationalException("No pair in whitelist.")
if config.get('fee', None) is not None:
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0])
# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
# Load one (first) strategy
@@ -131,10 +137,10 @@ class Backtesting:
min_date, max_date = history.get_timerange(data)
logger.info(
'Loading data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
logger.info(f'Loading data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
# Adjust startts forward if not enough data is available
timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
self.required_startup, min_date)
@@ -219,7 +225,7 @@ class Backtesting:
open_rate=buy_row.open,
open_date=buy_row.date,
stake_amount=stake_amount,
amount=stake_amount / buy_row.open,
amount=round(stake_amount / buy_row.open, 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
@@ -240,14 +246,15 @@ class Backtesting:
return BacktestResult(pair=pair,
profit_percent=trade.calc_profit_ratio(rate=closerate),
profit_abs=trade.calc_profit(rate=closerate),
open_time=buy_row.date,
close_time=sell_row.date,
trade_duration=trade_dur,
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=False,
open_date=buy_row.date,
open_rate=buy_row.open,
open_fee=self.fee,
close_date=sell_row.date,
close_rate=closerate,
close_fee=self.fee,
amount=trade.amount,
trade_duration=trade_dur,
open_at_end=False,
sell_reason=sell.sell_type
)
if partial_ohlcv:
@@ -256,15 +263,16 @@ class Backtesting:
bt_res = BacktestResult(pair=pair,
profit_percent=trade.calc_profit_ratio(rate=sell_row.open),
profit_abs=trade.calc_profit(rate=sell_row.open),
open_time=buy_row.date,
close_time=sell_row.date,
open_date=buy_row.date,
open_rate=buy_row.open,
open_fee=self.fee,
close_date=sell_row.date,
close_rate=sell_row.open,
close_fee=self.fee,
amount=trade.amount,
trade_duration=int((
sell_row.date - buy_row.date).total_seconds() // 60),
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=True,
open_rate=buy_row.open,
close_rate=sell_row.open,
sell_reason=SellType.FORCE_SELL
)
logger.debug(f"{pair} - Force selling still open trade, "
@@ -350,8 +358,8 @@ class Backtesting:
if trade_entry:
logger.debug(f"{pair} - Locking pair till "
f"close_time={trade_entry.close_time}")
lock_pair_until[pair] = trade_entry.close_time
f"close_date={trade_entry.close_date}")
lock_pair_until[pair] = trade_entry.close_date
trades.append(trade_entry)
else:
# Set lock_pair_until to end of testing period if trade could not be closed
@@ -394,10 +402,9 @@ class Backtesting:
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = history.get_timerange(preprocessed)
logger.info(
'Backtesting with data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
# Execute backtest and print results
all_results[self.strategy.get_strategy_name()] = self.backtest(
processed=preprocessed,
@@ -408,7 +415,10 @@ class Backtesting:
position_stacking=position_stacking,
)
stats = generate_backtest_stats(self.config, data, all_results,
min_date=min_date, max_date=max_date)
if self.config.get('export', False):
store_backtest_result(self.config['exportfilename'], all_results)
store_backtest_stats(self.config['exportfilename'], stats)
# Show backtest results
show_backtest_results(self.config, data, all_results)
show_backtest_results(self.config, stats)

202
freqtrade/optimize/default_hyperopt.py
View File

@@ -1,202 +0,0 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from functools import reduce
from typing import Any, Callable, Dict, List
import talib.abstract as ta
from pandas import DataFrame
from skopt.space import Categorical, Dimension, Integer
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class DefaultHyperOpt(IHyperOpt):
"""
Default hyperopt provided by the Freqtrade bot.
You can override it with your own Hyperopt
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Add several indicators needed for buy and sell strategies defined below.
"""
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
# Minus-DI
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['bb_upperband'] = bollinger['upper']
# SAR
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 'trigger' in params:
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']
))
if conditions:
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 buy 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 sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by Hyperopt.
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
conditions.append(dataframe['adx'] < params['sell-adx-value'])
if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
# TRIGGERS
if 'sell-trigger' in params:
if params['sell-trigger'] == 'sell-bb_upper':
conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['sell-trigger'] == 'sell-macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
))
if params['sell-trigger'] == 'sell-sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['sar'], dataframe['close']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe
return populate_sell_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters.
"""
return [
Integer(75, 100, name='sell-mfi-value'),
Integer(50, 100, name='sell-fastd-value'),
Integer(50, 100, name='sell-adx-value'),
Integer(60, 100, name='sell-rsi-value'),
Categorical([True, False], name='sell-mfi-enabled'),
Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-bb_upper',
'sell-macd_cross_signal',
'sell-sar_reversal'], name='sell-trigger')
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include buy space.
"""
dataframe.loc[
(
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['mfi'] < 16) &
(dataframe['adx'] > 25) &
(dataframe['rsi'] < 21)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include sell space.
"""
dataframe.loc[
(
(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
)) &
(dataframe['fastd'] > 54)
),
'sell'] = 1
return dataframe

4
freqtrade/optimize/default_hyperopt_loss.py
View File

@@ -42,8 +42,8 @@ class DefaultHyperOptLoss(IHyperOptLoss):
* 0.25: Avoiding trade loss
* 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
"""
total_profit = results.profit_percent.sum()
trade_duration = results.trade_duration.mean()
total_profit = results['profit_percent'].sum()
trade_duration = results['trade_duration'].mean()
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)

75
freqtrade/optimize/hyperopt.py
View File

@@ -4,27 +4,28 @@
This module contains the hyperopt logic
"""
import io
import locale
import logging
import random
import warnings
from math import ceil
from collections import OrderedDict
from math import ceil
from operator import itemgetter
from pathlib import Path
from pprint import pprint
from pprint import pformat
from typing import Any, Dict, List, Optional
import progressbar
import rapidjson
import tabulate
from colorama import Fore, Style
from colorama import init as colorama_init
from joblib import (Parallel, cpu_count, delayed, dump, load,
wrap_non_picklable_objects)
from pandas import DataFrame, json_normalize, isna
import progressbar
import tabulate
from os import path
import io
from pandas import DataFrame, isna, json_normalize
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.history import get_timerange
from freqtrade.exceptions import OperationalException
@@ -32,9 +33,11 @@ from freqtrade.misc import plural, round_dict
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import \
IHyperOptLoss # noqa: F401
from freqtrade.resolvers.hyperopt_resolver import (HyperOptLossResolver,
HyperOptResolver)
from freqtrade.strategy import IStrategy
# Suppress scikit-learn FutureWarnings from skopt
with warnings.catch_warnings():
@@ -230,6 +233,9 @@ class Hyperopt:
if space in ['buy', 'sell']:
result_dict.setdefault('params', {}).update(space_params)
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
# Convert keys in min_roi dict to strings because
# rapidjson cannot dump dicts with integer keys...
# OrderedDict is used to keep the numeric order of the items
@@ -244,11 +250,24 @@ class Hyperopt:
def _params_pretty_print(params, space: str, header: str) -> None:
if space in params:
space_params = Hyperopt._space_params(params, space, 5)
params_result = f"\n# {header}\n"
if space == 'stoploss':
print(header, space_params.get('stoploss'))
params_result += f"stoploss = {space_params.get('stoploss')}"
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
minimal_roi_result = rapidjson.dumps(
OrderedDict(
(str(k), v) for k, v in space_params.items()
),
default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
params_result += f"minimal_roi = {minimal_roi_result}"
else:
print(header)
pprint(space_params, indent=4)
params_result += f"{space}_params = {pformat(space_params, indent=4)}"
params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
params_result = params_result.replace("\n", "\n ")
print(params_result)
@staticmethod
def _space_params(params, space: str, r: int = None) -> Dict:
@@ -296,11 +315,16 @@ class Hyperopt:
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
if 'results_metrics.winsdrawslosses' not in trials.columns:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.winsdrawslosses'] = 'N/A'
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.winsdrawslosses',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit',
trials.columns = ['Best', 'Epoch', 'Trades', 'W/D/L', 'Avg profit', 'Total profit',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* '
@@ -374,7 +398,7 @@ class Hyperopt:
return
# Verification for overwrite
if path.isfile(csv_file):
if Path(csv_file).is_file():
logger.error(f"CSV file already exists: {csv_file}")
return
@@ -542,9 +566,17 @@ class Hyperopt:
}
def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
wins = len(backtesting_results[backtesting_results.profit_percent > 0])
draws = len(backtesting_results[backtesting_results.profit_percent == 0])
losses = len(backtesting_results[backtesting_results.profit_percent < 0])
return {
'trade_count': len(backtesting_results.index),
'wins': wins,
'draws': draws,
'losses': losses,
'winsdrawslosses': f"{wins}/{draws}/{losses}",
'avg_profit': backtesting_results.profit_percent.mean() * 100.0,
'median_profit': backtesting_results.profit_percent.median() * 100.0,
'total_profit': backtesting_results.profit_abs.sum(),
'profit': backtesting_results.profit_percent.sum() * 100.0,
'duration': backtesting_results.trade_duration.mean(),
@@ -556,7 +588,10 @@ class Hyperopt:
"""
stake_cur = self.config['stake_currency']
return (f"{results_metrics['trade_count']:6d} trades. "
f"{results_metrics['wins']}/{results_metrics['draws']}"
f"/{results_metrics['losses']} Wins/Draws/Losses. "
f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
f"Median profit {results_metrics['median_profit']: 6.2f}%. "
f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
f"Avg duration {results_metrics['duration']:5.1f} min."
@@ -609,15 +644,17 @@ class Hyperopt:
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = get_timerange(data)
logger.info(
'Hyperopting with data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
dump(preprocessed, self.data_pickle_file)
# We don't need exchange instance anymore while running hyperopt
self.backtesting.exchange = None # type: ignore
self.backtesting.pairlists = None # type: ignore
self.backtesting.strategy.dp = None # type: ignore
IStrategy.dp = None # type: ignore
self.epochs = self.load_previous_results(self.results_file)
@@ -628,6 +665,10 @@ class Hyperopt:
self.dimensions: List[Dimension] = self.hyperopt_space()
self.opt = self.get_optimizer(self.dimensions, config_jobs)
if self.print_colorized:
colorama_init(autoreset=True)
try:
with Parallel(n_jobs=config_jobs) as parallel:
jobs = parallel._effective_n_jobs()

11
freqtrade/optimize/hyperopt_interface.py
View File

@@ -31,13 +31,15 @@ class IHyperOpt(ABC):
Class attributes you can use:
ticker_interval -> int: value of the ticker interval to use for the strategy
"""
ticker_interval: str
ticker_interval: str # DEPRECATED
timeframe: str
def __init__(self, config: dict) -> None:
self.config = config
# Assign ticker_interval to be used in hyperopt
IHyperOpt.ticker_interval = str(config['ticker_interval'])
IHyperOpt.ticker_interval = str(config['timeframe']) # DEPRECATED
IHyperOpt.timeframe = str(config['timeframe'])
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
@@ -218,9 +220,10 @@ class IHyperOpt(ABC):
# Why do I still need such shamanic mantras in modern python?
def __getstate__(self):
state = self.__dict__.copy()
state['ticker_interval'] = self.ticker_interval
state['timeframe'] = self.timeframe
return state
def __setstate__(self, state):
self.__dict__.update(state)
IHyperOpt.ticker_interval = state['ticker_interval']
IHyperOpt.ticker_interval = state['timeframe']
IHyperOpt.timeframe = state['timeframe']

2
freqtrade/optimize/hyperopt_loss_interface.py
View File

@@ -14,7 +14,7 @@ class IHyperOptLoss(ABC):
Interface for freqtrade hyperopt Loss functions.
Defines the custom loss function (`hyperopt_loss_function()` which is evaluated every epoch.)
"""
ticker_interval: str
timeframe: str
@staticmethod
@abstractmethod

2
freqtrade/optimize/hyperopt_loss_onlyprofit.py
View File

@@ -34,5 +34,5 @@ class OnlyProfitHyperOptLoss(IHyperOptLoss):
"""
Objective function, returns smaller number for better results.
"""
total_profit = results.profit_percent.sum()
total_profit = results['profit_percent'].sum()
return 1 - total_profit / EXPECTED_MAX_PROFIT

2
freqtrade/optimize/hyperopt_loss_sharpe_daily.py
View File

@@ -43,7 +43,7 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
normalize=True)
sum_daily = (
results.resample(resample_freq, on='close_time').agg(
results.resample(resample_freq, on='close_date').agg(
{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
)

2
freqtrade/optimize/hyperopt_loss_sortino_daily.py
View File

@@ -45,7 +45,7 @@ class SortinoHyperOptLossDaily(IHyperOptLoss):
normalize=True)
sum_daily = (
results.resample(resample_freq, on='close_time').agg(
results.resample(resample_freq, on='close_date').agg(
{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
)

496
freqtrade/optimize/optimize_reports.py
View File

@@ -1,186 +1,166 @@
import logging
from datetime import timedelta
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Dict
from typing import Any, Dict, List
from arrow import Arrow
from pandas import DataFrame
from numpy import int64
from tabulate import tabulate
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data.btanalysis import calculate_max_drawdown, calculate_market_change
from freqtrade.misc import file_dump_json
logger = logging.getLogger(__name__)
def store_backtest_result(recordfilename: Path, all_results: Dict[str, DataFrame]) -> None:
def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> None:
"""
Stores backtest results to file (one file per strategy)
:param recordfilename: Destination filename
:param all_results: Dict of Dataframes, one results dataframe per strategy
Stores backtest results
:param recordfilename: Path object, which can either be a filename or a directory.
Filenames will be appended with a timestamp right before the suffix
while for diectories, <directory>/backtest-result-<datetime>.json will be used as filename
:param stats: Dataframe containing the backtesting statistics
"""
for strategy, results in all_results.items():
records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value)
for index, t in results.iterrows()]
if recordfilename.is_dir():
filename = (recordfilename /
f'backtest-result-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.json')
else:
filename = Path.joinpath(
recordfilename.parent,
f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}'
).with_suffix(recordfilename.suffix)
file_dump_json(filename, stats)
if records:
filename = recordfilename
if len(all_results) > 1:
# Inject strategy to filename
filename = Path.joinpath(
recordfilename.parent,
f'{recordfilename.stem}-{strategy}').with_suffix(recordfilename.suffix)
logger.info(f'Dumping backtest results to {filename}')
file_dump_json(filename, records)
latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
results: DataFrame, skip_nan: bool = False) -> str:
def _get_line_floatfmt() -> List[str]:
"""
Generates and returns a text table for the given backtest data and the results dataframe
Generate floatformat (goes in line with _generate_result_line())
"""
return ['s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', 'd', 'd', 'd']
def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
"""
Generate header lines (goes in line with _generate_result_line())
"""
return [first_column, 'Buys', 'Avg Profit %', 'Cum Profit %',
f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
'Wins', 'Draws', 'Losses']
def _generate_result_line(result: DataFrame, max_open_trades: int, first_column: str) -> Dict:
"""
Generate one result dict, with "first_column" as key.
"""
return {
'key': first_column,
'trades': len(result),
'profit_mean': result['profit_percent'].mean() if len(result) > 0 else 0.0,
'profit_mean_pct': result['profit_percent'].mean() * 100.0 if len(result) > 0 else 0.0,
'profit_sum': result['profit_percent'].sum(),
'profit_sum_pct': result['profit_percent'].sum() * 100.0,
'profit_total_abs': result['profit_abs'].sum(),
'profit_total': result['profit_percent'].sum() / max_open_trades,
'profit_total_pct': result['profit_percent'].sum() * 100.0 / max_open_trades,
'duration_avg': str(timedelta(
minutes=round(result['trade_duration'].mean()))
) if not result.empty else '0:00',
# 'duration_max': str(timedelta(
# minutes=round(result['trade_duration'].max()))
# ) if not result.empty else '0:00',
# 'duration_min': str(timedelta(
# minutes=round(result['trade_duration'].min()))
# ) if not result.empty else '0:00',
'wins': len(result[result['profit_abs'] > 0]),
'draws': len(result[result['profit_abs'] == 0]),
'losses': len(result[result['profit_abs'] < 0]),
}
def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
results: DataFrame, skip_nan: bool = False) -> List[Dict]:
"""
Generates and returns a list for the given backtest data and the results dataframe
:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades
:param results: Dataframe containing the backtest results
:param skip_nan: Print "left open" open trades
:return: pretty printed table with tabulate as string
:return: List of Dicts containing the metrics per pair
"""
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
tabular_data = []
headers = [
'Pair',
'Buys',
'Avg Profit %',
'Cum Profit %',
f'Tot Profit {stake_currency}',
'Tot Profit %',
'Avg Duration',
'Wins',
'Draws',
'Losses'
]
for pair in data:
result = results[results.pair == pair]
if skip_nan and result.profit_abs.isnull().all():
result = results[results['pair'] == pair]
if skip_nan and result['profit_abs'].isnull().all():
continue
tabular_data.append([
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_percent.sum() * 100.0,
result.profit_abs.sum(),
result.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(
minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
len(result[result.profit_abs > 0]),
len(result[result.profit_abs == 0]),
len(result[result.profit_abs < 0])
])
tabular_data.append(_generate_result_line(result, max_open_trades, pair))
# Append Total
tabular_data.append([
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
results.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs == 0]),
len(results[results.profit_abs < 0])
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
tabular_data.append(_generate_result_line(results, max_open_trades, 'TOTAL'))
return tabular_data
def generate_text_table_sell_reason(stake_currency: str, max_open_trades: int,
results: DataFrame) -> str:
def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]:
"""
Generate small table outlining Backtest results
:param stake_currency: Stakecurrency used
:param max_open_trades: Max_open_trades parameter
:param results: Dataframe containing the backtest results
:return: pretty printed table with tabulate as string
:param results: Dataframe containing the backtest result for one strategy
:return: List of Dicts containing the metrics per Sell reason
"""
tabular_data = []
headers = [
"Sell Reason",
"Sells",
"Wins",
"Draws",
"Losses",
"Avg Profit %",
"Cum Profit %",
f"Tot Profit {stake_currency}",
"Tot Profit %",
]
for reason, count in results['sell_reason'].value_counts().iteritems():
result = results.loc[results['sell_reason'] == reason]
wins = len(result[result['profit_abs'] > 0])
draws = len(result[result['profit_abs'] == 0])
loss = len(result[result['profit_abs'] < 0])
profit_mean = round(result['profit_percent'].mean() * 100.0, 2)
profit_sum = round(result["profit_percent"].sum() * 100.0, 2)
profit_tot = result['profit_abs'].sum()
profit_mean = result['profit_percent'].mean()
profit_sum = result["profit_percent"].sum()
profit_percent_tot = round(result['profit_percent'].sum() * 100.0 / max_open_trades, 2)
tabular_data.append(
[
reason.value,
count,
wins,
draws,
loss,
profit_mean,
profit_sum,
profit_tot,
profit_percent_tot,
]
{
'sell_reason': reason.value,
'trades': count,
'wins': len(result[result['profit_abs'] > 0]),
'draws': len(result[result['profit_abs'] == 0]),
'losses': len(result[result['profit_abs'] < 0]),
'profit_mean': profit_mean,
'profit_mean_pct': round(profit_mean * 100, 2),
'profit_sum': profit_sum,
'profit_sum_pct': round(profit_sum * 100, 2),
'profit_total_abs': result['profit_abs'].sum(),
'profit_total_pct': profit_percent_tot,
}
)
return tabulate(tabular_data, headers=headers, tablefmt="orgtbl", stralign="right")
return tabular_data
def generate_text_table_strategy(stake_currency: str, max_open_trades: str,
all_results: Dict) -> str:
def generate_strategy_metrics(stake_currency: str, max_open_trades: int,
all_results: Dict) -> List[Dict]:
"""
Generate summary table per strategy
Generate summary per strategy
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades used for backtest
:param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
:return: pretty printed table with tabulate as string
:return: List of Dicts containing the metrics per Strategy
"""
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
tabular_data = []
headers = ['Strategy', 'Buys', 'Avg Profit %', 'Cum Profit %',
f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
'Wins', 'Draws', 'Losses']
for strategy, results in all_results.items():
tabular_data.append([
strategy,
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
results.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs == 0]),
len(results[results.profit_abs < 0])
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
tabular_data.append(_generate_result_line(results, max_open_trades, strategy))
return tabular_data
def generate_edge_table(results: dict) -> str:
floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', '.d')
floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', 'd', 'd')
tabular_data = []
headers = ['Pair', 'Stoploss', 'Win Rate', 'Risk Reward Ratio',
'Required Risk Reward', 'Expectancy', 'Total Number of Trades',
@@ -204,40 +184,264 @@ def generate_edge_table(results: dict) -> str:
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def show_backtest_results(config: Dict, btdata: Dict[str, DataFrame],
all_results: Dict[str, DataFrame]):
def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
if len(results) == 0:
return {
'backtest_best_day': 0,
'backtest_worst_day': 0,
'winning_days': 0,
'draw_days': 0,
'losing_days': 0,
'winner_holding_avg': timedelta(),
'loser_holding_avg': timedelta(),
}
daily_profit = results.resample('1d', on='close_date')['profit_percent'].sum()
worst = min(daily_profit)
best = max(daily_profit)
winning_days = sum(daily_profit > 0)
draw_days = sum(daily_profit == 0)
losing_days = sum(daily_profit < 0)
winning_trades = results.loc[results['profit_percent'] > 0]
losing_trades = results.loc[results['profit_percent'] < 0]
return {
'backtest_best_day': best,
'backtest_worst_day': worst,
'winning_days': winning_days,
'draw_days': draw_days,
'losing_days': losing_days,
'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
if not winning_trades.empty else timedelta()),
'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
if not losing_trades.empty else timedelta()),
}
def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
all_results: Dict[str, DataFrame],
min_date: Arrow, max_date: Arrow
) -> Dict[str, Any]:
"""
:param config: Configuration object used for backtest
:param btdata: Backtest data
:param all_results: backtest result - dictionary with { Strategy: results}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:return:
Dictionary containing results per strategy and a stratgy summary.
"""
stake_currency = config['stake_currency']
max_open_trades = config['max_open_trades']
result: Dict[str, Any] = {'strategy': {}}
market_change = calculate_market_change(btdata, 'close')
for strategy, results in all_results.items():
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
max_open_trades=max_open_trades,
results=results, skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
results=results)
left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
max_open_trades=max_open_trades,
results=results.loc[results['open_at_end']],
skip_nan=True)
daily_stats = generate_daily_stats(results)
results['open_timestamp'] = results['open_date'].astype(int64) // 1e6
results['close_timestamp'] = results['close_date'].astype(int64) // 1e6
backtest_days = (max_date - min_date).days
strat_stats = {
'trades': results.to_dict(orient='records'),
'results_per_pair': pair_results,
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
'total_trades': len(results),
'profit_mean': results['profit_percent'].mean() if len(results) > 0 else 0,
'profit_total': results['profit_percent'].sum(),
'profit_total_abs': results['profit_abs'].sum(),
'backtest_start': min_date.datetime,
'backtest_start_ts': min_date.timestamp * 1000,
'backtest_end': max_date.datetime,
'backtest_end_ts': max_date.timestamp * 1000,
'backtest_days': backtest_days,
'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
'market_change': market_change,
'pairlist': list(btdata.keys()),
'stake_amount': config['stake_amount'],
'stake_currency': config['stake_currency'],
'max_open_trades': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
'timeframe': config['timeframe'],
**daily_stats,
}
result['strategy'][strategy] = strat_stats
try:
max_drawdown, drawdown_start, drawdown_end = calculate_max_drawdown(
results, value_col='profit_percent')
strat_stats.update({
'max_drawdown': max_drawdown,
'drawdown_start': drawdown_start,
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
'drawdown_end': drawdown_end,
'drawdown_end_ts': drawdown_end.timestamp() * 1000,
})
except ValueError:
strat_stats.update({
'max_drawdown': 0.0,
'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_start_ts': 0,
'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_end_ts': 0,
})
strategy_results = generate_strategy_metrics(stake_currency=stake_currency,
max_open_trades=max_open_trades,
all_results=all_results)
result['strategy_comparison'] = strategy_results
return result
###
# Start output section
###
def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
:param stake_currency: stake-currency - used to correctly name headers
:return: pretty printed table with tabulate as string
"""
headers = _get_line_header('Pair', stake_currency)
floatfmt = _get_line_floatfmt()
output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
] for t in pair_results]
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_currency: str) -> str:
"""
Generate small table outlining Backtest results
:param sell_reason_stats: Sell reason metrics
:param stake_currency: Stakecurrency used
:return: pretty printed table with tabulate as string
"""
headers = [
'Sell Reason',
'Sells',
'Wins',
'Draws',
'Losses',
'Avg Profit %',
'Cum Profit %',
f'Tot Profit {stake_currency}',
'Tot Profit %',
]
output = [[
t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'],
t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], t['profit_total_pct'],
] for t in sell_reason_stats]
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
def text_table_strategy(strategy_results, stake_currency: str) -> str:
"""
Generate summary table per strategy
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades used for backtest
:param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
:return: pretty printed table with tabulate as string
"""
floatfmt = _get_line_floatfmt()
headers = _get_line_header('Strategy', stake_currency)
output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
] for t in strategy_results]
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
def text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0:
min_trade = min(strat_results['trades'], key=lambda x: x['open_date'])
metrics = [
('Backtesting from', strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
('Backtesting to', strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
('Total trades', strat_results['total_trades']),
('First trade', min_trade['open_date'].strftime(DATETIME_PRINT_FORMAT)),
('First trade Pair', min_trade['pair']),
('Total Profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
('Trades per day', strat_results['trades_per_day']),
('Best day', f"{round(strat_results['backtest_best_day'] * 100, 2)}%"),
('Worst day', f"{round(strat_results['backtest_worst_day'] * 100, 2)}%"),
('Days win/draw/lose', f"{strat_results['winning_days']} / "
f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
('', ''), # Empty line to improve readability
('Max Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
]
return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
else:
return ''
def show_backtest_results(config: Dict, backtest_stats: Dict):
stake_currency = config['stake_currency']
for strategy, results in backtest_stats['strategy'].items():
# Print results
print(f"Result for strategy {strategy}")
table = generate_text_table(btdata, stake_currency=config['stake_currency'],
max_open_trades=config['max_open_trades'],
results=results)
table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
if isinstance(table, str):
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
table = generate_text_table_sell_reason(stake_currency=config['stake_currency'],
max_open_trades=config['max_open_trades'],
results=results)
if isinstance(table, str):
table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = generate_text_table(btdata,
stake_currency=config['stake_currency'],
max_open_trades=config['max_open_trades'],
results=results.loc[results.open_at_end], skip_nan=True)
if isinstance(table, str):
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str):
table = text_table_add_metrics(results)
if isinstance(table, str) and len(table) > 0:
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str) and len(table) > 0:
print('=' * len(table.splitlines()[0]))
print()
if len(all_results) > 1:
if len(backtest_stats['strategy']) > 1:
# Print Strategy summary table
table = generate_text_table_strategy(config['stake_currency'],
config['max_open_trades'],
all_results=all_results)
table = text_table_strategy(backtest_stats['strategy_comparison'], stake_currency)
print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '='))
print(table)
print('=' * len(table.splitlines()[0]))

83
freqtrade/pairlist/AgeFilter.py Normal file
View File

@@ -0,0 +1,83 @@
"""
Minimum age (days listed) pair list filter
"""
import logging
import arrow
from typing import Any, Dict
from freqtrade.exceptions import OperationalException
from freqtrade.misc import plural
from freqtrade.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)
class AgeFilter(IPairList):
# Checked symbols cache (dictionary of ticker symbol => timestamp)
_symbolsChecked: Dict[str, int] = {}
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
self._min_days_listed = pairlistconfig.get('min_days_listed', 10)
if self._min_days_listed < 1:
raise OperationalException("AgeFilter requires min_days_listed to be >= 1")
if self._min_days_listed > exchange.ohlcv_candle_limit:
raise OperationalException("AgeFilter requires min_days_listed to not exceed "
"exchange max request size "
f"({exchange.ohlcv_candle_limit})")
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requires tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
return True
def short_desc(self) -> str:
"""
Short whitelist method description - used for startup-messages
"""
return (f"{self.name} - Filtering pairs with age less than "
f"{self._min_days_listed} {plural(self._min_days_listed, 'day')}.")
def _validate_pair(self, ticker: dict) -> bool:
"""
Validate age for the ticker
:param ticker: ticker dict as returned from ccxt.load_markets()
:return: True if the pair can stay, False if it should be removed
"""
# Check symbol in cache
if ticker['symbol'] in self._symbolsChecked:
return True
since_ms = int(arrow.utcnow()
.floor('day')
.shift(days=-self._min_days_listed)
.float_timestamp) * 1000
daily_candles = self._exchange.get_historic_ohlcv(pair=ticker['symbol'],
timeframe='1d',
since_ms=since_ms)
if daily_candles is not None:
if len(daily_candles) > self._min_days_listed:
# We have fetched at least the minimum required number of daily candles
# Add to cache, store the time we last checked this symbol
self._symbolsChecked[ticker['symbol']] = int(arrow.utcnow().float_timestamp) * 1000
return True
else:
self.log_on_refresh(logger.info, f"Removed {ticker['symbol']} from whitelist, "
f"because age {len(daily_candles)} is less than "
f"{self._min_days_listed} "
f"{plural(self._min_days_listed, 'day')}")
return False
return False

61
freqtrade/pairlist/IPairList.py
View File

@@ -3,10 +3,12 @@ PairList Handler base class
"""
import logging
from abc import ABC, abstractmethod, abstractproperty
from copy import deepcopy
from typing import Any, Dict, List
from cachetools import TTLCache, cached
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import market_is_active
@@ -25,6 +27,8 @@ class IPairList(ABC):
:param pairlistconfig: Configuration for this Pairlist Handler - can be empty.
:param pairlist_pos: Position of the Pairlist Handler in the chain
"""
self._enabled = True
self._exchange = exchange
self._pairlistmanager = pairlistmanager
self._config = config
@@ -64,7 +68,7 @@ class IPairList(ABC):
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requries tickers, an empty List is passed
If no Pairlist requires tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
@@ -75,16 +79,59 @@ class IPairList(ABC):
-> Please overwrite in subclasses
"""
@abstractmethod
def _validate_pair(self, ticker) -> bool:
"""
Check one pair against Pairlist Handler's specific conditions.
Either implement it in the Pairlist Handler or override the generic
filter_pairlist() method.
:param ticker: ticker dict as returned from ccxt.load_markets()
:return: True if the pair can stay, false if it should be removed
"""
raise NotImplementedError()
def gen_pairlist(self, cached_pairlist: List[str], tickers: Dict) -> List[str]:
"""
Generate the pairlist.
This method is called once by the pairlistmanager in the refresh_pairlist()
method to supply the starting pairlist for the chain of the Pairlist Handlers.
Pairlist Filters (those Pairlist Handlers that cannot be used at the first
position in the chain) shall not override this base implementation --
it will raise the exception if a Pairlist Handler is used at the first
position in the chain.
:param cached_pairlist: Previously generated pairlist (cached)
:param tickers: Tickers (from exchange.get_tickers()).
:return: List of pairs
"""
raise OperationalException("This Pairlist Handler should not be used "
"at the first position in the list of Pairlist Handlers.")
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Filters and sorts pairlist and returns the whitelist again.
Called on each bot iteration - please use internal caching if necessary
-> Please overwrite in subclasses
This generic implementation calls self._validate_pair() for each pair
in the pairlist.
Some Pairlist Handlers override this generic implementation and employ
own filtration.
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new whitelist
"""
if self._enabled:
# Copy list since we're modifying this list
for p in deepcopy(pairlist):
# Filter out assets
if not self._validate_pair(tickers[p]):
pairlist.remove(p)
return pairlist
def verify_blacklist(self, pairlist: List[str], logmethod) -> List[str]:
"""
@@ -103,6 +150,9 @@ class IPairList(ABC):
black_listed
"""
markets = self._exchange.markets
if not markets:
raise OperationalException(
'Markets not loaded. Make sure that exchange is initialized correctly.')
sanitized_whitelist: List[str] = []
for pair in pairlist:
@@ -112,6 +162,11 @@ class IPairList(ABC):
f"{self._exchange.name}. Removing it from whitelist..")
continue
if not self._exchange.market_is_tradable(markets[pair]):
logger.warning(f"Pair {pair} is not tradable with Freqtrade."
"Removing it from whitelist..")
continue
if self._exchange.get_pair_quote_currency(pair) != self._config['stake_currency']:
logger.warning(f"Pair {pair} is not compatible with your stake currency "
f"{self._config['stake_currency']}. Removing it from whitelist..")

34
freqtrade/pairlist/PrecisionFilter.py
View File

@@ -2,11 +2,10 @@
Precision pair list filter
"""
import logging
from copy import deepcopy
from typing import Any, Dict, List
from typing import Any, Dict
from freqtrade.pairlist.IPairList import IPairList
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
@@ -18,14 +17,21 @@ class PrecisionFilter(IPairList):
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
if 'stoploss' not in self._config:
raise OperationalException(
'PrecisionFilter can only work with stoploss defined. Please add the '
'stoploss key to your configuration (overwrites eventual strategy settings).')
self._stoploss = self._config['stoploss']
self._enabled = self._stoploss != 0
# Precalculate sanitized stoploss value to avoid recalculation for every pair
self._stoploss = 1 - abs(self._config['stoploss'])
self._stoploss = 1 - abs(self._stoploss)
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requries tickers, an empty List is passed
If no Pairlist requires tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
return True
@@ -36,16 +42,14 @@ class PrecisionFilter(IPairList):
"""
return f"{self.name} - Filtering untradable pairs."
def _validate_precision_filter(self, ticker: dict, stoploss: float) -> bool:
def _validate_pair(self, ticker: dict) -> bool:
"""
Check if pair has enough room to add a stoploss to avoid "unsellable" buys of very
low value pairs.
:param ticker: ticker dict as returned from ccxt.load_markets()
:param stoploss: stoploss value as set in the configuration
(already cleaned to be 1 - stoploss)
:return: True if the pair can stay, False if it should be removed
"""
stop_price = ticker['ask'] * stoploss
stop_price = ticker['ask'] * self._stoploss
# Adjust stop-prices to precision
sp = self._exchange.price_to_precision(ticker["symbol"], stop_price)
@@ -60,15 +64,3 @@ class PrecisionFilter(IPairList):
return False
return True
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Filters and sorts pairlists and assigns and returns them again.
"""
# Copy list since we're modifying this list
for p in deepcopy(pairlist):
# Filter out assets which would not allow setting a stoploss
if not self._validate_precision_filter(tickers[p], self._stoploss):
pairlist.remove(p)
return pairlist

81
freqtrade/pairlist/PriceFilter.py
View File

@@ -2,9 +2,9 @@
Price pair list filter
"""
import logging
from copy import deepcopy
from typing import Any, Dict, List
from typing import Any, Dict
from freqtrade.exceptions import OperationalException
from freqtrade.pairlist.IPairList import IPairList
@@ -19,12 +19,23 @@ class PriceFilter(IPairList):
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
self._low_price_ratio = pairlistconfig.get('low_price_ratio', 0)
if self._low_price_ratio < 0:
raise OperationalException("PriceFilter requires low_price_ratio to be >= 0")
self._min_price = pairlistconfig.get('min_price', 0)
if self._min_price < 0:
raise OperationalException("PriceFilter requires min_price to be >= 0")
self._max_price = pairlistconfig.get('max_price', 0)
if self._max_price < 0:
raise OperationalException("PriceFilter requires max_price to be >= 0")
self._enabled = ((self._low_price_ratio > 0) or
(self._min_price > 0) or
(self._max_price > 0))
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requries tickers, an empty List is passed
If no Pairlist requires tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
return True
@@ -33,40 +44,52 @@ class PriceFilter(IPairList):
"""
Short whitelist method description - used for startup-messages
"""
return f"{self.name} - Filtering pairs priced below {self._low_price_ratio * 100}%."
active_price_filters = []
if self._low_price_ratio != 0:
active_price_filters.append(f"below {self._low_price_ratio * 100}%")
if self._min_price != 0:
active_price_filters.append(f"below {self._min_price:.8f}")
if self._max_price != 0:
active_price_filters.append(f"above {self._max_price:.8f}")
def _validate_ticker_lowprice(self, ticker) -> bool:
if len(active_price_filters):
return f"{self.name} - Filtering pairs priced {' or '.join(active_price_filters)}."
return f"{self.name} - No price filters configured."
def _validate_pair(self, ticker) -> bool:
"""
Check if if one price-step (pip) is > than a certain barrier.
:param ticker: ticker dict as returned from ccxt.load_markets()
:return: True if the pair can stay, false if it should be removed
"""
if ticker['last'] is None:
if ticker['last'] is None or ticker['last'] == 0:
self.log_on_refresh(logger.info,
f"Removed {ticker['symbol']} from whitelist, because "
"ticker['last'] is empty (Usually no trade in the last 24h).")
return False
compare = self._exchange.price_get_one_pip(ticker['symbol'], ticker['last'])
changeperc = compare / ticker['last']
if changeperc > self._low_price_ratio:
self.log_on_refresh(logger.info, f"Removed {ticker['symbol']} from whitelist, "
f"because 1 unit is {changeperc * 100:.3f}%")
return False
# Perform low_price_ratio check.
if self._low_price_ratio != 0:
compare = self._exchange.price_get_one_pip(ticker['symbol'], ticker['last'])
changeperc = compare / ticker['last']
if changeperc > self._low_price_ratio:
self.log_on_refresh(logger.info, f"Removed {ticker['symbol']} from whitelist, "
f"because 1 unit is {changeperc * 100:.3f}%")
return False
# Perform min_price check.
if self._min_price != 0:
if ticker['last'] < self._min_price:
self.log_on_refresh(logger.info, f"Removed {ticker['symbol']} from whitelist, "
f"because last price < {self._min_price:.8f}")
return False
# Perform max_price check.
if self._max_price != 0:
if ticker['last'] > self._max_price:
self.log_on_refresh(logger.info, f"Removed {ticker['symbol']} from whitelist, "
f"because last price > {self._max_price:.8f}")
return False
return True
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Filters and sorts pairlist and returns the whitelist again.
Called on each bot iteration - please use internal caching if necessary
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new whitelist
"""
if self._low_price_ratio:
# Copy list since we're modifying this list
for p in deepcopy(pairlist):
# Filter out assets which would not allow setting a stoploss
if not self._validate_ticker_lowprice(tickers[p]):
pairlist.remove(p)
return pairlist

7
freqtrade/pairlist/ShuffleFilter.py
View File

@@ -3,7 +3,7 @@ Shuffle pair list filter
"""
import logging
import random
from typing import Dict, List
from typing import Any, Dict, List
from freqtrade.pairlist.IPairList import IPairList
@@ -13,7 +13,8 @@ logger = logging.getLogger(__name__)
class ShuffleFilter(IPairList):
def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
@@ -24,7 +25,7 @@ class ShuffleFilter(IPairList):
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requries tickers, an empty List is passed
If no Pairlist requires tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
return False

28
freqtrade/pairlist/SpreadFilter.py
View File

@@ -2,8 +2,7 @@
Spread pair list filter
"""
import logging
from copy import deepcopy
from typing import Dict, List
from typing import Any, Dict
from freqtrade.pairlist.IPairList import IPairList
@@ -13,17 +12,19 @@ logger = logging.getLogger(__name__)
class SpreadFilter(IPairList):
def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
self._max_spread_ratio = pairlistconfig.get('max_spread_ratio', 0.005)
self._enabled = self._max_spread_ratio != 0
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requries tickers, an empty List is passed
If no Pairlist requires tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
return True
@@ -35,7 +36,7 @@ class SpreadFilter(IPairList):
return (f"{self.name} - Filtering pairs with ask/bid diff above "
f"{self._max_spread_ratio * 100}%.")
def _validate_spread(self, ticker: dict) -> bool:
def _validate_pair(self, ticker: dict) -> bool:
"""
Validate spread for the ticker
:param ticker: ticker dict as returned from ccxt.load_markets()
@@ -51,20 +52,3 @@ class SpreadFilter(IPairList):
else:
return True
return False
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Filters and sorts pairlist and returns the whitelist again.
Called on each bot iteration - please use internal caching if necessary
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new whitelist
"""
# Copy list since we're modifying this list
for p in deepcopy(pairlist):
ticker = tickers[p]
# Filter out assets
if not self._validate_spread(ticker):
pairlist.remove(p)
return pairlist

25
freqtrade/pairlist/StaticPairList.py
View File

@@ -4,8 +4,9 @@ Static Pair List provider
Provides pair white list as it configured in config
"""
import logging
from typing import Dict, List
from typing import Any, Dict, List
from freqtrade.exceptions import OperationalException
from freqtrade.pairlist.IPairList import IPairList
@@ -14,11 +15,20 @@ logger = logging.getLogger(__name__)
class StaticPairList(IPairList):
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
if self._pairlist_pos != 0:
raise OperationalException(f"{self.name} can only be used in the first position "
"in the list of Pairlist Handlers.")
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requries tickers, an empty List is passed
If no Pairlist requires tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
return False
@@ -30,6 +40,15 @@ class StaticPairList(IPairList):
"""
return f"{self.name}"
def gen_pairlist(self, cached_pairlist: List[str], tickers: Dict) -> List[str]:
"""
Generate the pairlist
:param cached_pairlist: Previously generated pairlist (cached)
:param tickers: Tickers (from exchange.get_tickers()).
:return: List of pairs
"""
return self._whitelist_for_active_markets(self._config['exchange']['pair_whitelist'])
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Filters and sorts pairlist and returns the whitelist again.
@@ -38,4 +57,4 @@ class StaticPairList(IPairList):
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new whitelist
"""
return self._whitelist_for_active_markets(self._config['exchange']['pair_whitelist'])
return pairlist

69
freqtrade/pairlist/VolumePairList.py
View File

@@ -19,7 +19,8 @@ SORT_VALUES = ['askVolume', 'bidVolume', 'quoteVolume']
class VolumePairList(IPairList):
def __init__(self, exchange, pairlistmanager, config: Dict[str, Any], pairlistconfig: dict,
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
@@ -36,8 +37,8 @@ class VolumePairList(IPairList):
if not self._exchange.exchange_has('fetchTickers'):
raise OperationalException(
'Exchange does not support dynamic whitelist.'
'Please edit your config and restart the bot'
'Exchange does not support dynamic whitelist. '
'Please edit your config and restart the bot.'
)
if not self._validate_keys(self._sort_key):
@@ -53,7 +54,7 @@ class VolumePairList(IPairList):
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requries tickers, an empty List is passed
If no Pairlist requires tickers, an empty List is passed
as tickers argument to filter_pairlist
"""
return True
@@ -67,6 +68,31 @@ class VolumePairList(IPairList):
"""
return f"{self.name} - top {self._pairlistconfig['number_assets']} volume pairs."
def gen_pairlist(self, cached_pairlist: List[str], tickers: Dict) -> List[str]:
"""
Generate the pairlist
:param cached_pairlist: Previously generated pairlist (cached)
:param tickers: Tickers (from exchange.get_tickers()).
:return: List of pairs
"""
# Generate dynamic whitelist
# Must always run if this pairlist is not the first in the list.
if self._last_refresh + self.refresh_period < datetime.now().timestamp():
self._last_refresh = int(datetime.now().timestamp())
# Use fresh pairlist
# Check if pair quote currency equals to the stake currency.
filtered_tickers = [
v for k, v in tickers.items()
if (self._exchange.get_pair_quote_currency(k) == self._stake_currency
and v[self._sort_key] is not None)]
pairlist = [s['symbol'] for s in filtered_tickers]
else:
# Use the cached pairlist if it's not time yet to refresh
pairlist = cached_pairlist
return pairlist
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Filters and sorts pairlist and returns the whitelist again.
@@ -75,37 +101,8 @@ class VolumePairList(IPairList):
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new whitelist
"""
# Generate dynamic whitelist
# Must always run if this pairlist is not the first in the list.
if (self._pairlist_pos != 0 or
(self._last_refresh + self.refresh_period < datetime.now().timestamp())):
self._last_refresh = int(datetime.now().timestamp())
pairs = self._gen_pair_whitelist(pairlist, tickers)
else:
pairs = pairlist
self.log_on_refresh(logger.info, f"Searching {self._number_pairs} pairs: {pairs}")
return pairs
def _gen_pair_whitelist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Updates the whitelist with with a dynamically generated list
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()).
:return: List of pairs
"""
if self._pairlist_pos == 0:
# If VolumePairList is the first in the list, use fresh pairlist
# Check if pair quote currency equals to the stake currency.
filtered_tickers = [
v for k, v in tickers.items()
if (self._exchange.get_pair_quote_currency(k) == self._stake_currency
and v[self._sort_key] is not None)]
else:
# If other pairlist is in front, use the incoming pairlist.
filtered_tickers = [v for k, v in tickers.items() if k in pairlist]
# Use the incoming pairlist.
filtered_tickers = [v for k, v in tickers.items() if k in pairlist]
if self._min_value > 0:
filtered_tickers = [
@@ -119,4 +116,6 @@ class VolumePairList(IPairList):
# Limit pairlist to the requested number of pairs
pairs = pairs[:self._number_pairs]
self.log_on_refresh(logger.info, f"Searching {self._number_pairs} pairs: {pairs}")
return pairs

9
freqtrade/pairlist/pairlistmanager.py
View File

@@ -10,7 +10,7 @@ from cachetools import TTLCache, cached
from freqtrade.exceptions import OperationalException
from freqtrade.pairlist.IPairList import IPairList
from freqtrade.resolvers import PairListResolver
from freqtrade.typing import ListPairsWithTimeframes
from freqtrade.constants import ListPairsWithTimeframes
logger = logging.getLogger(__name__)
@@ -87,6 +87,9 @@ class PairListManager():
# Adjust whitelist if filters are using tickers
pairlist = self._prepare_whitelist(self._whitelist.copy(), tickers)
# Generate the pairlist with first Pairlist Handler in the chain
pairlist = self._pairlist_handlers[0].gen_pairlist(self._whitelist, tickers)
# Process all Pairlist Handlers in the chain
for pairlist_handler in self._pairlist_handlers:
pairlist = pairlist_handler.filter_pairlist(pairlist, tickers)
@@ -128,6 +131,6 @@ class PairListManager():
def create_pair_list(self, pairs: List[str], timeframe: str = None) -> ListPairsWithTimeframes:
"""
Create list of pair tuples with (pair, ticker_interval)
Create list of pair tuples with (pair, timeframe)
"""
return [(pair, timeframe or self._config['ticker_interval']) for pair in pairs]
return [(pair, timeframe or self._config['timeframe']) for pair in pairs]

77
freqtrade/persistence.py
View File

@@ -2,7 +2,7 @@
This module contains the class to persist trades into SQLite
"""
import logging
from datetime import datetime
from datetime import datetime, timezone
from decimal import Decimal
from typing import Any, Dict, List, Optional
@@ -17,6 +17,7 @@ from sqlalchemy.orm.session import sessionmaker
from sqlalchemy.pool import StaticPool
from freqtrade.exceptions import OperationalException
from freqtrade.misc import safe_value_fallback
logger = logging.getLogger(__name__)
@@ -86,7 +87,7 @@ def check_migrate(engine) -> None:
logger.debug(f'trying {table_back_name}')
# Check for latest column
if not has_column(cols, 'sell_order_status'):
if not has_column(cols, 'amount_requested'):
logger.info(f'Running database migration - backup available as {table_back_name}')
fee_open = get_column_def(cols, 'fee_open', 'fee')
@@ -107,13 +108,19 @@ def check_migrate(engine) -> None:
min_rate = get_column_def(cols, 'min_rate', 'null')
sell_reason = get_column_def(cols, 'sell_reason', 'null')
strategy = get_column_def(cols, 'strategy', 'null')
ticker_interval = get_column_def(cols, 'ticker_interval', 'null')
# If ticker-interval existed use that, else null.
if has_column(cols, 'ticker_interval'):
timeframe = get_column_def(cols, 'timeframe', 'ticker_interval')
else:
timeframe = get_column_def(cols, 'timeframe', 'null')
open_trade_price = get_column_def(cols, 'open_trade_price',
f'amount * open_rate * (1 + {fee_open})')
close_profit_abs = get_column_def(
cols, 'close_profit_abs',
f"(amount * close_rate * (1 - {fee_close})) - {open_trade_price}")
sell_order_status = get_column_def(cols, 'sell_order_status', 'null')
amount_requested = get_column_def(cols, 'amount_requested', 'amount')
# Schema migration necessary
engine.execute(f"alter table trades rename to {table_back_name}")
@@ -129,11 +136,11 @@ def check_migrate(engine) -> None:
fee_open, fee_open_cost, fee_open_currency,
fee_close, fee_close_cost, fee_open_currency, open_rate,
open_rate_requested, close_rate, close_rate_requested, close_profit,
stake_amount, amount, open_date, close_date, open_order_id,
stake_amount, amount, amount_requested, open_date, close_date, open_order_id,
stop_loss, stop_loss_pct, initial_stop_loss, initial_stop_loss_pct,
stoploss_order_id, stoploss_last_update,
max_rate, min_rate, sell_reason, sell_order_status, strategy,
ticker_interval, open_trade_price, close_profit_abs
timeframe, open_trade_price, close_profit_abs
)
select id, lower(exchange),
case
@@ -148,14 +155,14 @@ def check_migrate(engine) -> None:
{fee_close_cost} fee_close_cost, {fee_close_currency} fee_close_currency,
open_rate, {open_rate_requested} open_rate_requested, close_rate,
{close_rate_requested} close_rate_requested, close_profit,
stake_amount, amount, open_date, close_date, open_order_id,
stake_amount, amount, {amount_requested}, open_date, close_date, open_order_id,
{stop_loss} stop_loss, {stop_loss_pct} stop_loss_pct,
{initial_stop_loss} initial_stop_loss,
{initial_stop_loss_pct} initial_stop_loss_pct,
{stoploss_order_id} stoploss_order_id, {stoploss_last_update} stoploss_last_update,
{max_rate} max_rate, {min_rate} min_rate, {sell_reason} sell_reason,
{sell_order_status} sell_order_status,
{strategy} strategy, {ticker_interval} ticker_interval,
{strategy} strategy, {timeframe} timeframe,
{open_trade_price} open_trade_price, {close_profit_abs} close_profit_abs
from {table_back_name}
""")
@@ -210,6 +217,7 @@ class Trade(_DECL_BASE):
close_profit_abs = Column(Float)
stake_amount = Column(Float, nullable=False)
amount = Column(Float)
amount_requested = Column(Float)
open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
close_date = Column(DateTime)
open_order_id = Column(String)
@@ -232,7 +240,7 @@ class Trade(_DECL_BASE):
sell_reason = Column(String, nullable=True)
sell_order_status = Column(String, nullable=True)
strategy = Column(String, nullable=True)
ticker_interval = Column(Integer, nullable=True)
timeframe = Column(Integer, nullable=True)
def __init__(self, **kwargs):
super().__init__(**kwargs)
@@ -249,37 +257,59 @@ class Trade(_DECL_BASE):
'trade_id': self.id,
'pair': self.pair,
'is_open': self.is_open,
'exchange': self.exchange,
'amount': round(self.amount, 8),
'amount_requested': round(self.amount_requested, 8) if self.amount_requested else None,
'stake_amount': round(self.stake_amount, 8),
'strategy': self.strategy,
'ticker_interval': self.timeframe, # DEPRECATED
'timeframe': self.timeframe,
'fee_open': self.fee_open,
'fee_open_cost': self.fee_open_cost,
'fee_open_currency': self.fee_open_currency,
'fee_close': self.fee_close,
'fee_close_cost': self.fee_close_cost,
'fee_close_currency': self.fee_close_currency,
'open_date_hum': arrow.get(self.open_date).humanize(),
'open_date': self.open_date.strftime("%Y-%m-%d %H:%M:%S"),
'open_timestamp': int(self.open_date.replace(tzinfo=timezone.utc).timestamp() * 1000),
'open_rate': self.open_rate,
'open_rate_requested': self.open_rate_requested,
'open_trade_price': round(self.open_trade_price, 8),
'close_date_hum': (arrow.get(self.close_date).humanize()
if self.close_date else None),
'close_date': (self.close_date.strftime("%Y-%m-%d %H:%M:%S")
if self.close_date else None),
'open_rate': self.open_rate,
'open_rate_requested': self.open_rate_requested,
'open_trade_price': self.open_trade_price,
'close_timestamp': int(self.close_date.replace(
tzinfo=timezone.utc).timestamp() * 1000) if self.close_date else None,
'close_rate': self.close_rate,
'close_rate_requested': self.close_rate_requested,
'amount': round(self.amount, 8),
'stake_amount': round(self.stake_amount, 8),
'close_profit': self.close_profit,
'close_profit_abs': self.close_profit_abs,
'sell_reason': self.sell_reason,
'sell_order_status': self.sell_order_status,
'stop_loss': self.stop_loss,
'stop_loss': self.stop_loss, # Deprecated - should not be used
'stop_loss_abs': self.stop_loss,
'stop_loss_ratio': self.stop_loss_pct if self.stop_loss_pct else None,
'stop_loss_pct': (self.stop_loss_pct * 100) if self.stop_loss_pct else None,
'initial_stop_loss': self.initial_stop_loss,
'stoploss_order_id': self.stoploss_order_id,
'stoploss_last_update': (self.stoploss_last_update.strftime("%Y-%m-%d %H:%M:%S")
if self.stoploss_last_update else None),
'stoploss_last_update_timestamp': int(self.stoploss_last_update.replace(
tzinfo=timezone.utc).timestamp() * 1000) if self.stoploss_last_update else None,
'initial_stop_loss': self.initial_stop_loss, # Deprecated - should not be used
'initial_stop_loss_abs': self.initial_stop_loss,
'initial_stop_loss_ratio': (self.initial_stop_loss_pct
if self.initial_stop_loss_pct else None),
'initial_stop_loss_pct': (self.initial_stop_loss_pct * 100
if self.initial_stop_loss_pct else None),
'min_rate': self.min_rate,
'max_rate': self.max_rate,
'strategy': self.strategy,
'ticker_interval': self.ticker_interval,
'open_order_id': self.open_order_id,
}
@@ -335,27 +365,27 @@ class Trade(_DECL_BASE):
def update(self, order: Dict) -> None:
"""
Updates this entity with amount and actual open/close rates.
:param order: order retrieved by exchange.get_order()
:param order: order retrieved by exchange.fetch_order()
:return: None
"""
order_type = order['type']
# Ignore open and cancelled orders
if order['status'] == 'open' or order['price'] is None:
if order['status'] == 'open' or safe_value_fallback(order, 'average', 'price') is None:
return
logger.info('Updating trade (id=%s) ...', self.id)
if order_type in ('market', 'limit') and order['side'] == 'buy':
# Update open rate and actual amount
self.open_rate = Decimal(order['price'])
self.amount = Decimal(order.get('filled', order['amount']))
self.open_rate = Decimal(safe_value_fallback(order, 'average', 'price'))
self.amount = Decimal(safe_value_fallback(order, 'filled', 'amount'))
self.recalc_open_trade_price()
logger.info('%s_BUY has been fulfilled for %s.', order_type.upper(), self)
self.open_order_id = None
elif order_type in ('market', 'limit') and order['side'] == 'sell':
self.close(order['price'])
self.close(safe_value_fallback(order, 'average', 'price'))
logger.info('%s_SELL has been fulfilled for %s.', order_type.upper(), self)
elif order_type in ('stop_loss_limit', 'stop-loss'):
elif order_type in ('stop_loss_limit', 'stop-loss', 'stop'):
self.stoploss_order_id = None
self.close_rate_requested = self.stop_loss
logger.info('%s is hit for %s.', order_type.upper(), self)
@@ -544,6 +574,7 @@ class Trade(_DECL_BASE):
def get_best_pair():
"""
Get best pair with closed trade.
:returns: Tuple containing (pair, profit_sum)
"""
best_pair = Trade.session.query(
Trade.pair, func.sum(Trade.close_profit).label('profit_sum')

66
freqtrade/plot/plotting.py
View File

@@ -8,12 +8,16 @@ from freqtrade.configuration import TimeRange
from freqtrade.data.btanalysis import (calculate_max_drawdown,
combine_dataframes_with_mean,
create_cum_profit,
extract_trades_of_period, load_trades)
extract_trades_of_period,
load_trades)
from freqtrade.data.converter import trim_dataframe
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history import load_data
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.misc import pair_to_filename
from freqtrade.resolvers import StrategyResolver
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.strategy import IStrategy
logger = logging.getLogger(__name__)
@@ -44,25 +48,28 @@ def init_plotscript(config):
data = load_data(
datadir=config.get("datadir"),
pairs=pairs,
timeframe=config.get('ticker_interval', '5m'),
timeframe=config.get('timeframe', '5m'),
timerange=timerange,
data_format=config.get('dataformat_ohlcv', 'json'),
)
no_trades = False
filename = config.get('exportfilename')
if config.get('no_trades', False):
no_trades = True
elif not config['exportfilename'].is_file() and config['trade_source'] == 'file':
logger.warning("Backtest file is missing skipping trades.")
no_trades = True
elif config['trade_source'] == 'file':
if not filename.is_dir() and not filename.is_file():
logger.warning("Backtest file is missing skipping trades.")
no_trades = True
trades = load_trades(
config['trade_source'],
db_url=config.get('db_url'),
exportfilename=config.get('exportfilename'),
no_trades=no_trades
exportfilename=filename,
no_trades=no_trades,
strategy=config.get("strategy"),
)
trades = trim_dataframe(trades, timerange, 'open_time')
trades = trim_dataframe(trades, timerange, 'open_date')
return {"ohlcv": data,
"trades": trades,
@@ -161,11 +168,12 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
# Trades can be empty
if trades is not None and len(trades) > 0:
# Create description for sell summarizing the trade
trades['desc'] = trades.apply(lambda row: f"{round(row['profitperc'] * 100, 1)}%, "
f"{row['sell_reason']}, {row['duration']} min",
trades['desc'] = trades.apply(lambda row: f"{round(row['profit_percent'] * 100, 1)}%, "
f"{row['sell_reason']}, "
f"{row['trade_duration']} min",
axis=1)
trade_buys = go.Scatter(
x=trades["open_time"],
x=trades["open_date"],
y=trades["open_rate"],
mode='markers',
name='Trade buy',
@@ -180,9 +188,9 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
)
trade_sells = go.Scatter(
x=trades.loc[trades['profitperc'] > 0, "close_time"],
y=trades.loc[trades['profitperc'] > 0, "close_rate"],
text=trades.loc[trades['profitperc'] > 0, "desc"],
x=trades.loc[trades['profit_percent'] > 0, "close_date"],
y=trades.loc[trades['profit_percent'] > 0, "close_rate"],
text=trades.loc[trades['profit_percent'] > 0, "desc"],
mode='markers',
name='Sell - Profit',
marker=dict(
@@ -193,9 +201,9 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
)
)
trade_sells_loss = go.Scatter(
x=trades.loc[trades['profitperc'] <= 0, "close_time"],
y=trades.loc[trades['profitperc'] <= 0, "close_rate"],
text=trades.loc[trades['profitperc'] <= 0, "desc"],
x=trades.loc[trades['profit_percent'] <= 0, "close_date"],
y=trades.loc[trades['profit_percent'] <= 0, "close_rate"],
text=trades.loc[trades['profit_percent'] <= 0, "desc"],
mode='markers',
name='Sell - Loss',
marker=dict(
@@ -414,9 +422,12 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
for pair in pairs:
profit_col = f'cum_profit_{pair}'
df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col, timeframe)
fig = add_profit(fig, 3, df_comb, profit_col, f"Profit {pair}")
try:
df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col,
timeframe)
fig = add_profit(fig, 3, df_comb, profit_col, f"Profit {pair}")
except ValueError:
pass
return fig
@@ -463,6 +474,8 @@ def load_and_plot_trades(config: Dict[str, Any]):
"""
strategy = StrategyResolver.load_strategy(config)
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
IStrategy.dp = DataProvider(config, exchange)
plot_elements = init_plotscript(config)
trades = plot_elements['trades']
pair_counter = 0
@@ -483,7 +496,7 @@ def load_and_plot_trades(config: Dict[str, Any]):
plot_config=strategy.plot_config if hasattr(strategy, 'plot_config') else {}
)
store_plot_file(fig, filename=generate_plot_filename(pair, config['ticker_interval']),
store_plot_file(fig, filename=generate_plot_filename(pair, config['timeframe']),
directory=config['user_data_dir'] / "plot")
logger.info('End of plotting process. %s plots generated', pair_counter)
@@ -502,12 +515,15 @@ def plot_profit(config: Dict[str, Any]) -> None:
# Remove open pairs - we don't know the profit yet so can't calculate profit for these.
# Also, If only one open pair is left, then the profit-generation would fail.
trades = trades[(trades['pair'].isin(plot_elements["pairs"]))
& (~trades['close_time'].isnull())
& (~trades['close_date'].isnull())
]
if len(trades) == 0:
raise OperationalException("No trades found, cannot generate Profit-plot without "
"trades from either Backtest result or database.")
# Create an average close price of all the pairs that were involved.
# this could be useful to gauge the overall market trend
fig = generate_profit_graph(plot_elements["pairs"], plot_elements["ohlcv"],
trades, config.get('ticker_interval', '5m'))
trades, config.get('timeframe', '5m'))
store_plot_file(fig, filename='freqtrade-profit-plot.html',
directory=config['user_data_dir'] / "plot", auto_open=True)

19
freqtrade/resolvers/hyperopt_resolver.py
View File

@@ -23,7 +23,7 @@ class HyperOptResolver(IResolver):
object_type = IHyperOpt
object_type_str = "Hyperopt"
user_subdir = USERPATH_HYPEROPTS
initial_search_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
initial_search_path = None
@staticmethod
def load_hyperopt(config: Dict) -> IHyperOpt:
@@ -42,14 +42,14 @@ class HyperOptResolver(IResolver):
extra_dir=config.get('hyperopt_path'))
if not hasattr(hyperopt, 'populate_indicators'):
logger.warning("Hyperopt class does not provide populate_indicators() method. "
"Using populate_indicators from the strategy.")
logger.info("Hyperopt class does not provide populate_indicators() method. "
"Using populate_indicators from the strategy.")
if not hasattr(hyperopt, 'populate_buy_trend'):
logger.warning("Hyperopt class does not provide populate_buy_trend() method. "
"Using populate_buy_trend from the strategy.")
logger.info("Hyperopt class does not provide populate_buy_trend() method. "
"Using populate_buy_trend from the strategy.")
if not hasattr(hyperopt, 'populate_sell_trend'):
logger.warning("Hyperopt class does not provide populate_sell_trend() method. "
"Using populate_sell_trend from the strategy.")
logger.info("Hyperopt class does not provide populate_sell_trend() method. "
"Using populate_sell_trend from the strategy.")
return hyperopt
@@ -77,8 +77,9 @@ class HyperOptLossResolver(IResolver):
config, kwargs={},
extra_dir=config.get('hyperopt_path'))
# Assign ticker_interval to be used in hyperopt
hyperoptloss.__class__.ticker_interval = str(config['ticker_interval'])
# Assign timeframe to be used in hyperopt
hyperoptloss.__class__.ticker_interval = str(config['timeframe'])
hyperoptloss.__class__.timeframe = str(config['timeframe'])
if not hasattr(hyperoptloss, 'hyperopt_loss_function'):
raise OperationalException(

60
freqtrade/resolvers/strategy_resolver.py
View File

@@ -50,39 +50,51 @@ class StrategyResolver(IResolver):
if 'ask_strategy' not in config:
config['ask_strategy'] = {}
if hasattr(strategy, 'ticker_interval') and not hasattr(strategy, 'timeframe'):
# Assign ticker_interval to timeframe to keep compatibility
if 'timeframe' not in config:
logger.warning(
"DEPRECATED: Please migrate to using 'timeframe' instead of 'ticker_interval'."
)
strategy.timeframe = strategy.ticker_interval
# Set attributes
# Check if we need to override configuration
# (Attribute name, default, ask_strategy)
attributes = [("minimal_roi", {"0": 10.0}, False),
("ticker_interval", None, False),
("stoploss", None, False),
("trailing_stop", None, False),
("trailing_stop_positive", None, False),
("trailing_stop_positive_offset", 0.0, False),
("trailing_only_offset_is_reached", None, False),
("process_only_new_candles", None, False),
("order_types", None, False),
("order_time_in_force", None, False),
("stake_currency", None, False),
("stake_amount", None, False),
("startup_candle_count", None, False),
("unfilledtimeout", None, False),
("use_sell_signal", True, True),
("sell_profit_only", False, True),
("ignore_roi_if_buy_signal", False, True),
# (Attribute name, default, subkey)
attributes = [("minimal_roi", {"0": 10.0}, None),
("timeframe", None, None),
("stoploss", None, None),
("trailing_stop", None, None),
("trailing_stop_positive", None, None),
("trailing_stop_positive_offset", 0.0, None),
("trailing_only_offset_is_reached", None, None),
("process_only_new_candles", None, None),
("order_types", None, None),
("order_time_in_force", None, None),
("stake_currency", None, None),
("stake_amount", None, None),
("startup_candle_count", None, None),
("unfilledtimeout", None, None),
("use_sell_signal", True, 'ask_strategy'),
("sell_profit_only", False, 'ask_strategy'),
("ignore_roi_if_buy_signal", False, 'ask_strategy'),
("disable_dataframe_checks", False, None),
]
for attribute, default, ask_strategy in attributes:
if ask_strategy:
StrategyResolver._override_attribute_helper(strategy, config['ask_strategy'],
for attribute, default, subkey in attributes:
if subkey:
StrategyResolver._override_attribute_helper(strategy, config.get(subkey, {}),
attribute, default)
else:
StrategyResolver._override_attribute_helper(strategy, config,
attribute, default)
# Assign deprecated variable - to not break users code relying on this.
strategy.ticker_interval = strategy.timeframe
# Loop this list again to have output combined
for attribute, _, exp in attributes:
if exp and attribute in config['ask_strategy']:
logger.info("Strategy using %s: %s", attribute, config['ask_strategy'][attribute])
for attribute, _, subkey in attributes:
if subkey and attribute in config[subkey]:
logger.info("Strategy using %s: %s", attribute, config[subkey][attribute])
elif attribute in config:
logger.info("Strategy using %s: %s", attribute, config[attribute])

43
freqtrade/rpc/api_server.py
View File

@@ -16,7 +16,9 @@ from werkzeug.security import safe_str_cmp
from werkzeug.serving import make_server
from freqtrade.__init__ import __version__
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.rpc.rpc import RPC, RPCException
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
logger = logging.getLogger(__name__)
@@ -31,7 +33,7 @@ class ArrowJSONEncoder(JSONEncoder):
elif isinstance(obj, date):
return obj.strftime("%Y-%m-%d")
elif isinstance(obj, datetime):
return obj.strftime("%Y-%m-%d %H:%M:%S")
return obj.strftime(DATETIME_PRINT_FORMAT)
iterable = iter(obj)
except TypeError:
pass
@@ -55,7 +57,7 @@ def require_login(func: Callable[[Any, Any], Any]):
# Type should really be Callable[[ApiServer], Any], but that will create a circular dependency
def rpc_catch_errors(func: Callable[[Any], Any]):
def rpc_catch_errors(func: Callable[..., Any]):
def func_wrapper(obj, *args, **kwargs):
@@ -89,7 +91,11 @@ class ApiServer(RPC):
self._config = freqtrade.config
self.app = Flask(__name__)
self._cors = CORS(self.app, resources={r"/api/*": {"origins": "*"}})
self._cors = CORS(self.app,
resources={r"/api/*": {
"supports_credentials": True,
"origins": self._config['api_server'].get('CORS_origins', [])}}
)
# Setup the Flask-JWT-Extended extension
self.app.config['JWT_SECRET_KEY'] = self._config['api_server'].get(
@@ -101,6 +107,9 @@ class ApiServer(RPC):
# Register application handling
self.register_rest_rpc_urls()
if self._config.get('fiat_display_currency', None):
self._fiat_converter = CryptoToFiatConverter()
thread = threading.Thread(target=self.run, daemon=True)
thread.start()
@@ -170,8 +179,8 @@ class ApiServer(RPC):
self.app.add_url_rule(f'{BASE_URI}/stop', 'stop', view_func=self._stop, methods=['POST'])
self.app.add_url_rule(f'{BASE_URI}/stopbuy', 'stopbuy',
view_func=self._stopbuy, methods=['POST'])
self.app.add_url_rule(f'{BASE_URI}/reload_conf', 'reload_conf',
view_func=self._reload_conf, methods=['POST'])
self.app.add_url_rule(f'{BASE_URI}/reload_config', 'reload_config',
view_func=self._reload_config, methods=['POST'])
# Info commands
self.app.add_url_rule(f'{BASE_URI}/balance', 'balance',
view_func=self._balance, methods=['GET'])
@@ -192,6 +201,8 @@ class ApiServer(RPC):
view_func=self._ping, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/trades', 'trades',
view_func=self._trades, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/trades/<int:tradeid>', 'trades_delete',
view_func=self._trades_delete, methods=['DELETE'])
# Combined actions and infos
self.app.add_url_rule(f'{BASE_URI}/blacklist', 'blacklist', view_func=self._blacklist,
methods=['GET', 'POST'])
@@ -302,12 +313,12 @@ class ApiServer(RPC):
@require_login
@rpc_catch_errors
def _reload_conf(self):
def _reload_config(self):
"""
Handler for /reload_conf.
Handler for /reload_config.
Triggers a config file reload
"""
msg = self._rpc_reload_conf()
msg = self._rpc_reload_config()
return self.rest_dump(msg)
@require_login
@@ -358,7 +369,6 @@ class ApiServer(RPC):
Returns a cumulative profit statistics
:return: stats
"""
logger.info("LocalRPC - Profit Command Called")
stats = self._rpc_trade_statistics(self._config['stake_currency'],
self._config.get('fiat_display_currency')
@@ -375,8 +385,6 @@ class ApiServer(RPC):
Returns a cumulative performance statistics
:return: stats
"""
logger.info("LocalRPC - performance Command Called")
stats = self._rpc_performance()
return self.rest_dump(stats)
@@ -419,6 +427,19 @@ class ApiServer(RPC):
results = self._rpc_trade_history(limit)
return self.rest_dump(results)
@require_login
@rpc_catch_errors
def _trades_delete(self, tradeid):
"""
Handler for DELETE /trades/<tradeid> endpoint.
Removes the trade from the database (tries to cancel open orders first!)
get:
param:
tradeid: Numeric trade-id assigned to the trade.
"""
result = self._rpc_delete(tradeid)
return self.rest_dump(result)
@require_login
@rpc_catch_errors
def _whitelist(self):

177
freqtrade/rpc/rpc.py
View File

@@ -6,12 +6,14 @@ from abc import abstractmethod
from datetime import date, datetime, timedelta
from enum import Enum
from math import isnan
from typing import Any, Dict, List, Optional, Tuple
from typing import Any, Dict, List, Optional, Tuple, Union
import arrow
from numpy import NAN, mean
from freqtrade.exceptions import DependencyException, TemporaryError
from freqtrade.exceptions import (ExchangeError,
PricingError)
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_msecs
from freqtrade.misc import shorten_date
from freqtrade.persistence import Trade
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
@@ -101,10 +103,15 @@ class RPC:
'trailing_stop_positive': config.get('trailing_stop_positive'),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset'),
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached'),
'ticker_interval': config['ticker_interval'],
'ticker_interval': config['timeframe'], # DEPRECATED
'timeframe': config['timeframe'],
'timeframe_ms': timeframe_to_msecs(config['timeframe']),
'timeframe_min': timeframe_to_minutes(config['timeframe']),
'exchange': config['exchange']['name'],
'strategy': config['strategy'],
'forcebuy_enabled': config.get('forcebuy_enable', False),
'ask_strategy': config.get('ask_strategy', {}),
'bid_strategy': config.get('bid_strategy', {}),
'state': str(self._freqtrade.state)
}
return val
@@ -123,21 +130,36 @@ class RPC:
for trade in trades:
order = None
if trade.open_order_id:
order = self._freqtrade.exchange.get_order(trade.open_order_id, trade.pair)
order = self._freqtrade.exchange.fetch_order(trade.open_order_id, trade.pair)
# calculate profit and send message to user
try:
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
except DependencyException:
except (ExchangeError, PricingError):
current_rate = NAN
current_profit = trade.calc_profit_ratio(current_rate)
current_profit_abs = trade.calc_profit(current_rate)
# Calculate guaranteed profit (in case of trailing stop)
stoploss_entry_dist = trade.calc_profit(trade.stop_loss)
stoploss_entry_dist_ratio = trade.calc_profit_ratio(trade.stop_loss)
# calculate distance to stoploss
stoploss_current_dist = trade.stop_loss - current_rate
stoploss_current_dist_ratio = stoploss_current_dist / current_rate
fmt_close_profit = (f'{round(trade.close_profit * 100, 2):.2f}%'
if trade.close_profit else None)
if trade.close_profit is not None else None)
trade_dict = trade.to_json()
trade_dict.update(dict(
base_currency=self._freqtrade.config['stake_currency'],
close_profit=fmt_close_profit,
close_profit=trade.close_profit if trade.close_profit is not None else None,
close_profit_pct=fmt_close_profit,
current_rate=current_rate,
current_profit=round(current_profit * 100, 2),
current_profit=current_profit,
current_profit_pct=round(current_profit * 100, 2),
current_profit_abs=current_profit_abs,
stoploss_current_dist=stoploss_current_dist,
stoploss_current_dist_ratio=round(stoploss_current_dist_ratio, 8),
stoploss_entry_dist=stoploss_entry_dist,
stoploss_entry_dist_ratio=round(stoploss_entry_dist_ratio, 8),
open_order='({} {} rem={:.8f})'.format(
order['type'], order['side'], order['remaining']
) if order else None,
@@ -156,7 +178,7 @@ class RPC:
# calculate profit and send message to user
try:
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
except DependencyException:
except (PricingError, ExchangeError):
current_rate = NAN
trade_percent = (100 * trade.calc_profit_ratio(current_rate))
trade_profit = trade.calc_profit(current_rate)
@@ -202,22 +224,20 @@ class RPC:
]).order_by(Trade.close_date).all()
curdayprofit = sum(trade.close_profit_abs for trade in trades)
profit_days[profitday] = {
'amount': f'{curdayprofit:.8f}',
'amount': curdayprofit,
'trades': len(trades)
}
data = [
{
'date': key,
'abs_profit': f'{float(value["amount"]):.8f}',
'fiat_value': '{value:.3f}'.format(
value=self._fiat_converter.convert_amount(
'abs_profit': value["amount"],
'fiat_value': self._fiat_converter.convert_amount(
value['amount'],
stake_currency,
fiat_display_currency
) if self._fiat_converter else 0,
),
'trade_count': f'{value["trades"]}',
'trade_count': value["trades"],
}
for key, value in profit_days.items()
]
@@ -230,9 +250,10 @@ class RPC:
def _rpc_trade_history(self, limit: int) -> Dict:
""" Returns the X last trades """
if limit > 0:
trades = Trade.get_trades().order_by(Trade.id.desc()).limit(limit)
trades = Trade.get_trades([Trade.is_open.is_(False)]).order_by(
Trade.id.desc()).limit(limit)
else:
trades = Trade.get_trades().order_by(Trade.id.desc()).all()
trades = Trade.get_trades([Trade.is_open.is_(False)]).order_by(Trade.id.desc()).all()
output = [trade.to_json() for trade in trades]
@@ -251,6 +272,8 @@ class RPC:
profit_closed_coin = []
profit_closed_ratio = []
durations = []
winning_trades = 0
losing_trades = 0
for trade in trades:
current_rate: float = 0.0
@@ -264,11 +287,15 @@ class RPC:
profit_ratio = trade.close_profit
profit_closed_coin.append(trade.close_profit_abs)
profit_closed_ratio.append(profit_ratio)
if trade.close_profit >= 0:
winning_trades += 1
else:
losing_trades += 1
else:
# Get current rate
try:
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
except DependencyException:
except (PricingError, ExchangeError):
current_rate = NAN
profit_ratio = trade.calc_profit_ratio(rate=current_rate)
@@ -279,15 +306,11 @@ class RPC:
best_pair = Trade.get_best_pair()
if not best_pair:
raise RPCException('no closed trade')
bp_pair, bp_rate = best_pair
# Prepare data to display
profit_closed_coin_sum = round(sum(profit_closed_coin), 8)
profit_closed_percent = (round(mean(profit_closed_ratio) * 100, 2) if profit_closed_ratio
else 0.0)
profit_closed_ratio_mean = mean(profit_closed_ratio) if profit_closed_ratio else 0.0
profit_closed_ratio_sum = sum(profit_closed_ratio) if profit_closed_ratio else 0.0
profit_closed_fiat = self._fiat_converter.convert_amount(
profit_closed_coin_sum,
stake_currency,
@@ -295,27 +318,43 @@ class RPC:
) if self._fiat_converter else 0
profit_all_coin_sum = round(sum(profit_all_coin), 8)
profit_all_percent = round(mean(profit_all_ratio) * 100, 2) if profit_all_ratio else 0.0
profit_all_ratio_mean = mean(profit_all_ratio) if profit_all_ratio else 0.0
profit_all_ratio_sum = sum(profit_all_ratio) if profit_all_ratio else 0.0
profit_all_fiat = self._fiat_converter.convert_amount(
profit_all_coin_sum,
stake_currency,
fiat_display_currency
) if self._fiat_converter else 0
first_date = trades[0].open_date if trades else None
last_date = trades[-1].open_date if trades else None
num = float(len(durations) or 1)
return {
'profit_closed_coin': profit_closed_coin_sum,
'profit_closed_percent': profit_closed_percent,
'profit_closed_percent': round(profit_closed_ratio_mean * 100, 2), # DEPRECATED
'profit_closed_percent_mean': round(profit_closed_ratio_mean * 100, 2),
'profit_closed_ratio_mean': profit_closed_ratio_mean,
'profit_closed_percent_sum': round(profit_closed_ratio_sum * 100, 2),
'profit_closed_ratio_sum': profit_closed_ratio_sum,
'profit_closed_fiat': profit_closed_fiat,
'profit_all_coin': profit_all_coin_sum,
'profit_all_percent': profit_all_percent,
'profit_all_percent': round(profit_all_ratio_mean * 100, 2), # DEPRECATED
'profit_all_percent_mean': round(profit_all_ratio_mean * 100, 2),
'profit_all_ratio_mean': profit_all_ratio_mean,
'profit_all_percent_sum': round(profit_all_ratio_sum * 100, 2),
'profit_all_ratio_sum': profit_all_ratio_sum,
'profit_all_fiat': profit_all_fiat,
'trade_count': len(trades),
'first_trade_date': arrow.get(trades[0].open_date).humanize(),
'latest_trade_date': arrow.get(trades[-1].open_date).humanize(),
'closed_trade_count': len([t for t in trades if not t.is_open]),
'first_trade_date': arrow.get(first_date).humanize() if first_date else '',
'first_trade_timestamp': int(first_date.timestamp() * 1000) if first_date else 0,
'latest_trade_date': arrow.get(last_date).humanize() if last_date else '',
'latest_trade_timestamp': int(last_date.timestamp() * 1000) if last_date else 0,
'avg_duration': str(timedelta(seconds=sum(durations) / num)).split('.')[0],
'best_pair': bp_pair,
'best_rate': round(bp_rate * 100, 2),
'best_pair': best_pair[0] if best_pair else '',
'best_rate': round(best_pair[1] * 100, 2) if best_pair else 0,
'winning_trades': winning_trades,
'losing_trades': losing_trades,
}
def _rpc_balance(self, stake_currency: str, fiat_display_currency: str) -> Dict:
@@ -324,7 +363,7 @@ class RPC:
total = 0.0
try:
tickers = self._freqtrade.exchange.get_tickers()
except (TemporaryError, DependencyException):
except (ExchangeError):
raise RPCException('Error getting current tickers.')
self._freqtrade.wallets.update(require_update=False)
@@ -345,7 +384,7 @@ class RPC:
if pair.startswith(stake_currency):
rate = 1.0 / rate
est_stake = rate * balance.total
except (TemporaryError, DependencyException):
except (ExchangeError):
logger.warning(f" Could not get rate for pair {coin}.")
continue
total = total + (est_stake or 0)
@@ -391,9 +430,9 @@ class RPC:
return {'status': 'already stopped'}
def _rpc_reload_conf(self) -> Dict[str, str]:
""" Handler for reload_conf. """
self._freqtrade.state = State.RELOAD_CONF
def _rpc_reload_config(self) -> Dict[str, str]:
""" Handler for reload_config. """
self._freqtrade.state = State.RELOAD_CONFIG
return {'status': 'reloading config ...'}
def _rpc_stopbuy(self) -> Dict[str, str]:
@@ -404,7 +443,7 @@ class RPC:
# Set 'max_open_trades' to 0
self._freqtrade.config['max_open_trades'] = 0
return {'status': 'No more buy will occur from now. Run /reload_conf to reset.'}
return {'status': 'No more buy will occur from now. Run /reload_config to reset.'}
def _rpc_forcesell(self, trade_id: str) -> Dict[str, str]:
"""
@@ -414,7 +453,7 @@ class RPC:
def _exec_forcesell(trade: Trade) -> None:
# Check if there is there is an open order
if trade.open_order_id:
order = self._freqtrade.exchange.get_order(trade.open_order_id, trade.pair)
order = self._freqtrade.exchange.fetch_order(trade.open_order_id, trade.pair)
# Cancel open LIMIT_BUY orders and close trade
if order and order['status'] == 'open' \
@@ -483,7 +522,7 @@ class RPC:
# check if valid pair
# check if pair already has an open pair
trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair.is_(pair)]).first()
trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first()
if trade:
raise RPCException(f'position for {pair} already open - id: {trade.id}')
@@ -492,11 +531,51 @@ class RPC:
# execute buy
if self._freqtrade.execute_buy(pair, stakeamount, price):
trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair.is_(pair)]).first()
trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first()
return trade
else:
return None
def _rpc_delete(self, trade_id: str) -> Dict[str, Union[str, int]]:
"""
Handler for delete <id>.
Delete the given trade and close eventually existing open orders.
"""
with self._freqtrade._sell_lock:
c_count = 0
trade = Trade.get_trades(trade_filter=[Trade.id == trade_id]).first()
if not trade:
logger.warning('delete trade: Invalid argument received')
raise RPCException('invalid argument')
# Try cancelling regular order if that exists
if trade.open_order_id:
try:
self._freqtrade.exchange.cancel_order(trade.open_order_id, trade.pair)
c_count += 1
except (ExchangeError):
pass
# cancel stoploss on exchange ...
if (self._freqtrade.strategy.order_types.get('stoploss_on_exchange')
and trade.stoploss_order_id):
try:
self._freqtrade.exchange.cancel_stoploss_order(trade.stoploss_order_id,
trade.pair)
c_count += 1
except (ExchangeError):
pass
Trade.session.delete(trade)
Trade.session.flush()
self._freqtrade.wallets.update()
return {
'result': 'success',
'trade_id': trade_id,
'result_msg': f'Deleted trade {trade_id}. Closed {c_count} open orders.',
'cancel_order_count': c_count,
}
def _rpc_performance(self) -> List[Dict[str, Any]]:
"""
Handler for performance.
@@ -529,16 +608,26 @@ class RPC:
def _rpc_blacklist(self, add: List[str] = None) -> Dict:
""" Returns the currently active blacklist"""
errors = {}
if add:
stake_currency = self._freqtrade.config.get('stake_currency')
for pair in add:
if (self._freqtrade.exchange.get_pair_quote_currency(pair) == stake_currency
and pair not in self._freqtrade.pairlists.blacklist):
self._freqtrade.pairlists.blacklist.append(pair)
if self._freqtrade.exchange.get_pair_quote_currency(pair) == stake_currency:
if pair not in self._freqtrade.pairlists.blacklist:
self._freqtrade.pairlists.blacklist.append(pair)
else:
errors[pair] = {
'error_msg': f'Pair {pair} already in pairlist.'}
else:
errors[pair] = {
'error_msg': f"Pair {pair} does not match stake currency."
}
res = {'method': self._freqtrade.pairlists.name_list,
'length': len(self._freqtrade.pairlists.blacklist),
'blacklist': self._freqtrade.pairlists.blacklist,
'errors': errors,
}
return res

4
freqtrade/rpc/rpc_manager.py
View File

@@ -72,7 +72,7 @@ class RPCManager:
minimal_roi = config['minimal_roi']
stoploss = config['stoploss']
trailing_stop = config['trailing_stop']
ticker_interval = config['ticker_interval']
timeframe = config['timeframe']
exchange_name = config['exchange']['name']
strategy_name = config.get('strategy', '')
self.send_msg({
@@ -81,7 +81,7 @@ class RPCManager:
f'*Stake per trade:* `{stake_amount} {stake_currency}`\n'
f'*Minimum ROI:* `{minimal_roi}`\n'
f'*{"Trailing " if trailing_stop else ""}Stoploss:* `{stoploss}`\n'
f'*Ticker Interval:* `{ticker_interval}`\n'
f'*Timeframe:* `{timeframe}`\n'
f'*Strategy:* `{strategy_name}`'
})
self.send_msg({

270
freqtrade/rpc/telegram.py
View File

@@ -3,7 +3,9 @@
"""
This module manage Telegram communication
"""
import json
import logging
import arrow
from typing import Any, Callable, Dict
from tabulate import tabulate
@@ -19,7 +21,6 @@ logger = logging.getLogger(__name__)
logger.debug('Included module rpc.telegram ...')
MAX_TELEGRAM_MESSAGE_LENGTH = 4096
@@ -29,6 +30,7 @@ def authorized_only(command_handler: Callable[..., None]) -> Callable[..., Any]:
:param command_handler: Telegram CommandHandler
:return: decorated function
"""
def wrapper(self, *args, **kwargs):
""" Decorator logic """
update = kwargs.get('update') or args[0]
@@ -91,11 +93,13 @@ class Telegram(RPC):
CommandHandler('stop', self._stop),
CommandHandler('forcesell', self._forcesell),
CommandHandler('forcebuy', self._forcebuy),
CommandHandler('trades', self._trades),
CommandHandler('delete', self._delete_trade),
CommandHandler('performance', self._performance),
CommandHandler('daily', self._daily),
CommandHandler('count', self._count),
CommandHandler('reload_conf', self._reload_conf),
CommandHandler('show_config', self._show_config),
CommandHandler(['reload_config', 'reload_conf'], self._reload_config),
CommandHandler(['show_config', 'show_conf'], self._show_config),
CommandHandler('stopbuy', self._stopbuy),
CommandHandler('whitelist', self._whitelist),
CommandHandler('blacklist', self._blacklist),
@@ -133,7 +137,7 @@ class Telegram(RPC):
else:
msg['stake_amount_fiat'] = 0
message = ("*{exchange}:* Buying {pair}\n"
message = ("\N{LARGE BLUE CIRCLE} *{exchange}:* Buying {pair}\n"
"*Amount:* `{amount:.8f}`\n"
"*Open Rate:* `{limit:.8f}`\n"
"*Current Rate:* `{current_rate:.8f}`\n"
@@ -144,7 +148,8 @@ class Telegram(RPC):
message += ")`"
elif msg['type'] == RPCMessageType.BUY_CANCEL_NOTIFICATION:
message = "*{exchange}:* Cancelling Open Buy Order for {pair}".format(**msg)
message = ("\N{WARNING SIGN} *{exchange}:* "
"Cancelling Open Buy Order for {pair}".format(**msg))
elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
msg['amount'] = round(msg['amount'], 8)
@@ -153,7 +158,9 @@ class Telegram(RPC):
microsecond=0) - msg['open_date'].replace(microsecond=0)
msg['duration_min'] = msg['duration'].total_seconds() / 60
message = ("*{exchange}:* Selling {pair}\n"
msg['emoji'] = self._get_sell_emoji(msg)
message = ("{emoji} *{exchange}:* Selling {pair}\n"
"*Amount:* `{amount:.8f}`\n"
"*Open Rate:* `{open_rate:.8f}`\n"
"*Current Rate:* `{current_rate:.8f}`\n"
@@ -165,21 +172,21 @@ class Telegram(RPC):
# Check if all sell properties are available.
# This might not be the case if the message origin is triggered by /forcesell
if (all(prop in msg for prop in ['gain', 'fiat_currency', 'stake_currency'])
and self._fiat_converter):
and self._fiat_converter):
msg['profit_fiat'] = self._fiat_converter.convert_amount(
msg['profit_amount'], msg['stake_currency'], msg['fiat_currency'])
message += (' `({gain}: {profit_amount:.8f} {stake_currency}'
' / {profit_fiat:.3f} {fiat_currency})`').format(**msg)
elif msg['type'] == RPCMessageType.SELL_CANCEL_NOTIFICATION:
message = ("*{exchange}:* Cancelling Open Sell Order "
message = ("\N{WARNING SIGN} *{exchange}:* Cancelling Open Sell Order "
"for {pair}. Reason: {reason}").format(**msg)
elif msg['type'] == RPCMessageType.STATUS_NOTIFICATION:
message = '*Status:* `{status}`'.format(**msg)
elif msg['type'] == RPCMessageType.WARNING_NOTIFICATION:
message = '*Warning:* `{status}`'.format(**msg)
message = '\N{WARNING SIGN} *Warning:* `{status}`'.format(**msg)
elif msg['type'] == RPCMessageType.CUSTOM_NOTIFICATION:
message = '{status}'.format(**msg)
@@ -189,6 +196,20 @@ class Telegram(RPC):
self._send_msg(message)
def _get_sell_emoji(self, msg):
"""
Get emoji for sell-side
"""
if float(msg['profit_percent']) >= 5.0:
return "\N{ROCKET}"
elif float(msg['profit_percent']) >= 0.0:
return "\N{EIGHT SPOKED ASTERISK}"
elif msg['sell_reason'] == "stop_loss":
return"\N{WARNING SIGN}"
else:
return "\N{CROSS MARK}"
@authorized_only
def _status(self, update: Update, context: CallbackContext) -> None:
"""
@@ -215,13 +236,15 @@ class Telegram(RPC):
"*Open Rate:* `{open_rate:.8f}`",
"*Close Rate:* `{close_rate}`" if r['close_rate'] else "",
"*Current Rate:* `{current_rate:.8f}`",
"*Close Profit:* `{close_profit}`" if r['close_profit'] else "",
"*Current Profit:* `{current_profit:.2f}%`",
("*Close Profit:* `{close_profit_pct}`"
if r['close_profit_pct'] is not None else ""),
"*Current Profit:* `{current_profit_pct:.2f}%`",
# Adding initial stoploss only if it is different from stoploss
"*Initial Stoploss:* `{initial_stop_loss:.8f}` " +
("`({initial_stop_loss_pct:.2f}%)`" if r['initial_stop_loss_pct'] else "")
if r['stop_loss'] != r['initial_stop_loss'] else "",
("`({initial_stop_loss_pct:.2f}%)`") if (
r['stop_loss'] != r['initial_stop_loss']
and r['initial_stop_loss_pct'] is not None) else "",
# Adding stoploss and stoploss percentage only if it is not None
"*Stoploss:* `{stop_loss:.8f}` " +
@@ -282,8 +305,8 @@ class Telegram(RPC):
)
stats_tab = tabulate(
[[day['date'],
f"{day['abs_profit']} {stats['stake_currency']}",
f"{day['fiat_value']} {stats['fiat_display_currency']}",
f"{day['abs_profit']:.8f} {stats['stake_currency']}",
f"{day['fiat_value']:.3f} {stats['fiat_display_currency']}",
f"{day['trade_count']} trades"] for day in stats['data']],
headers=[
'Day',
@@ -309,38 +332,50 @@ class Telegram(RPC):
stake_cur = self._config['stake_currency']
fiat_disp_cur = self._config.get('fiat_display_currency', '')
try:
stats = self._rpc_trade_statistics(
stake_cur,
fiat_disp_cur)
profit_closed_coin = stats['profit_closed_coin']
profit_closed_percent = stats['profit_closed_percent']
profit_closed_fiat = stats['profit_closed_fiat']
profit_all_coin = stats['profit_all_coin']
profit_all_percent = stats['profit_all_percent']
profit_all_fiat = stats['profit_all_fiat']
trade_count = stats['trade_count']
first_trade_date = stats['first_trade_date']
latest_trade_date = stats['latest_trade_date']
avg_duration = stats['avg_duration']
best_pair = stats['best_pair']
best_rate = stats['best_rate']
stats = self._rpc_trade_statistics(
stake_cur,
fiat_disp_cur)
profit_closed_coin = stats['profit_closed_coin']
profit_closed_percent_mean = stats['profit_closed_percent_mean']
profit_closed_percent_sum = stats['profit_closed_percent_sum']
profit_closed_fiat = stats['profit_closed_fiat']
profit_all_coin = stats['profit_all_coin']
profit_all_percent_mean = stats['profit_all_percent_mean']
profit_all_percent_sum = stats['profit_all_percent_sum']
profit_all_fiat = stats['profit_all_fiat']
trade_count = stats['trade_count']
first_trade_date = stats['first_trade_date']
latest_trade_date = stats['latest_trade_date']
avg_duration = stats['avg_duration']
best_pair = stats['best_pair']
best_rate = stats['best_rate']
if stats['trade_count'] == 0:
markdown_msg = 'No trades yet.'
else:
# Message to display
markdown_msg = "*ROI:* Close trades\n" \
f"∙ `{profit_closed_coin:.8f} {stake_cur} "\
f"({profit_closed_percent:.2f}%)`\n" \
f"∙ `{profit_closed_fiat:.3f} {fiat_disp_cur}`\n" \
f"*ROI:* All trades\n" \
f"∙ `{profit_all_coin:.8f} {stake_cur} ({profit_all_percent:.2f}%)`\n" \
f"∙ `{profit_all_fiat:.3f} {fiat_disp_cur}`\n" \
f"*Total Trade Count:* `{trade_count}`\n" \
f"*First Trade opened:* `{first_trade_date}`\n" \
f"*Latest Trade opened:* `{latest_trade_date}`\n" \
f"*Avg. Duration:* `{avg_duration}`\n" \
f"*Best Performing:* `{best_pair}: {best_rate:.2f}%`"
self._send_msg(markdown_msg)
except RPCException as e:
self._send_msg(str(e))
if stats['closed_trade_count'] > 0:
markdown_msg = ("*ROI:* Closed trades\n"
f"∙ `{profit_closed_coin:.8f} {stake_cur} "
f"({profit_closed_percent_mean:.2f}%) "
f"({profit_closed_percent_sum} \N{GREEK CAPITAL LETTER SIGMA}%)`\n"
f"∙ `{profit_closed_fiat:.3f} {fiat_disp_cur}`\n")
else:
markdown_msg = "`No closed trade` \n"
markdown_msg += (f"*ROI:* All trades\n"
f"∙ `{profit_all_coin:.8f} {stake_cur} "
f"({profit_all_percent_mean:.2f}%) "
f"({profit_all_percent_sum} \N{GREEK CAPITAL LETTER SIGMA}%)`\n"
f"∙ `{profit_all_fiat:.3f} {fiat_disp_cur}`\n"
f"*Total Trade Count:* `{trade_count}`\n"
f"*First Trade opened:* `{first_trade_date}`\n"
f"*Latest Trade opened:* `{latest_trade_date}\n`"
f"*Win / Loss:* `{stats['winning_trades']} / {stats['losing_trades']}`"
)
if stats['closed_trade_count'] > 0:
markdown_msg += (f"\n*Avg. Duration:* `{avg_duration}`\n"
f"*Best Performing:* `{best_pair}: {best_rate:.2f}%`")
self._send_msg(markdown_msg)
@authorized_only
def _balance(self, update: Update, context: CallbackContext) -> None:
@@ -356,14 +391,14 @@ class Telegram(RPC):
"This mode is still experimental!\n"
"Starting capital: "
f"`{self._config['dry_run_wallet']}` {self._config['stake_currency']}.\n"
)
)
for currency in result['currencies']:
if currency['est_stake'] > 0.0001:
curr_output = "*{currency}:*\n" \
"\t`Available: {free: .8f}`\n" \
"\t`Balance: {balance: .8f}`\n" \
"\t`Pending: {used: .8f}`\n" \
"\t`Est. {stake}: {est_stake: .8f}`\n".format(**currency)
curr_output = ("*{currency}:*\n"
"\t`Available: {free: .8f}`\n"
"\t`Balance: {balance: .8f}`\n"
"\t`Pending: {used: .8f}`\n"
"\t`Est. {stake}: {est_stake: .8f}`\n").format(**currency)
else:
curr_output = "*{currency}:* not showing <1$ amount \n".format(**currency)
@@ -374,9 +409,9 @@ class Telegram(RPC):
else:
output += curr_output
output += "\n*Estimated Value*:\n" \
"\t`{stake}: {total: .8f}`\n" \
"\t`{symbol}: {value: .2f}`\n".format(**result)
output += ("\n*Estimated Value*:\n"
"\t`{stake}: {total: .8f}`\n"
"\t`{symbol}: {value: .2f}`\n").format(**result)
self._send_msg(output)
except RPCException as e:
self._send_msg(str(e))
@@ -406,15 +441,15 @@ class Telegram(RPC):
self._send_msg('Status: `{status}`'.format(**msg))
@authorized_only
def _reload_conf(self, update: Update, context: CallbackContext) -> None:
def _reload_config(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /reload_conf.
Handler for /reload_config.
Triggers a config file reload
:param bot: telegram bot
:param update: message update
:return: None
"""
msg = self._rpc_reload_conf()
msg = self._rpc_reload_config()
self._send_msg('Status: `{status}`'.format(**msg))
@authorized_only
@@ -464,6 +499,62 @@ class Telegram(RPC):
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _trades(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /trades <n>
Returns last n recent trades.
:param bot: telegram bot
:param update: message update
:return: None
"""
stake_cur = self._config['stake_currency']
try:
nrecent = int(context.args[0])
except (TypeError, ValueError, IndexError):
nrecent = 10
try:
trades = self._rpc_trade_history(
nrecent
)
trades_tab = tabulate(
[[arrow.get(trade['open_date']).humanize(),
trade['pair'],
f"{(100 * trade['close_profit']):.2f}% ({trade['close_profit_abs']})"]
for trade in trades['trades']],
headers=[
'Open Date',
'Pair',
f'Profit ({stake_cur})',
],
tablefmt='simple')
message = (f"<b>{min(trades['trades_count'], nrecent)} recent trades</b>:\n"
+ (f"<pre>{trades_tab}</pre>" if trades['trades_count'] > 0 else ''))
self._send_msg(message, parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _delete_trade(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /delete <id>.
Delete the given trade
:param bot: telegram bot
:param update: message update
:return: None
"""
trade_id = context.args[0] if len(context.args) > 0 else None
try:
msg = self._rpc_delete(trade_id)
self._send_msg((
'`{result_msg}`\n'
'Please make sure to take care of this asset on the exchange manually.'
).format(**msg))
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _performance(self, update: Update, context: CallbackContext) -> None:
"""
@@ -532,6 +623,11 @@ class Telegram(RPC):
try:
blacklist = self._rpc_blacklist(context.args)
errmsgs = []
for pair, error in blacklist['errors'].items():
errmsgs.append(f"Error adding `{pair}` to blacklist: `{error['error_msg']}`")
if errmsgs:
self._send_msg('\n'.join(errmsgs))
message = f"Blacklist contains {blacklist['length']} pairs\n"
message += f"`{', '.join(blacklist['blacklist'])}`"
@@ -564,32 +660,34 @@ class Telegram(RPC):
:param update: message update
:return: None
"""
forcebuy_text = "*/forcebuy <pair> [<rate>]:* `Instantly buys the given pair. " \
"Optionally takes a rate at which to buy.` \n"
message = "*/start:* `Starts the trader`\n" \
"*/stop:* `Stops the trader`\n" \
"*/status [table]:* `Lists all open trades`\n" \
" *table :* `will display trades in a table`\n" \
" `pending buy orders are marked with an asterisk (*)`\n" \
" `pending sell orders are marked with a double asterisk (**)`\n" \
"*/profit:* `Lists cumulative profit from all finished trades`\n" \
"*/forcesell <trade_id>|all:* `Instantly sells the given trade or all trades, " \
"regardless of profit`\n" \
f"{forcebuy_text if self._config.get('forcebuy_enable', False) else '' }" \
"*/performance:* `Show performance of each finished trade grouped by pair`\n" \
"*/daily <n>:* `Shows profit or loss per day, over the last n days`\n" \
"*/count:* `Show number of trades running compared to allowed number of trades`" \
"\n" \
"*/balance:* `Show account balance per currency`\n" \
"*/stopbuy:* `Stops buying, but handles open trades gracefully` \n" \
"*/reload_conf:* `Reload configuration file` \n" \
"*/show_config:* `Show running configuration` \n" \
"*/whitelist:* `Show current whitelist` \n" \
"*/blacklist [pair]:* `Show current blacklist, or adds one or more pairs " \
"to the blacklist.` \n" \
"*/edge:* `Shows validated pairs by Edge if it is enabled` \n" \
"*/help:* `This help message`\n" \
"*/version:* `Show version`"
forcebuy_text = ("*/forcebuy <pair> [<rate>]:* `Instantly buys the given pair. "
"Optionally takes a rate at which to buy.` \n")
message = ("*/start:* `Starts the trader`\n"
"*/stop:* `Stops the trader`\n"
"*/status [table]:* `Lists all open trades`\n"
" *table :* `will display trades in a table`\n"
" `pending buy orders are marked with an asterisk (*)`\n"
" `pending sell orders are marked with a double asterisk (**)`\n"
"*/trades [limit]:* `Lists last closed trades (limited to 10 by default)`\n"
"*/profit:* `Lists cumulative profit from all finished trades`\n"
"*/forcesell <trade_id>|all:* `Instantly sells the given trade or all trades, "
"regardless of profit`\n"
f"{forcebuy_text if self._config.get('forcebuy_enable', False) else ''}"
"*/delete <trade_id>:* `Instantly delete the given trade in the database`\n"
"*/performance:* `Show performance of each finished trade grouped by pair`\n"
"*/daily <n>:* `Shows profit or loss per day, over the last n days`\n"
"*/count:* `Show number of trades running compared to allowed number of trades`"
"\n"
"*/balance:* `Show account balance per currency`\n"
"*/stopbuy:* `Stops buying, but handles open trades gracefully` \n"
"*/reload_config:* `Reload configuration file` \n"
"*/show_config:* `Show running configuration` \n"
"*/whitelist:* `Show current whitelist` \n"
"*/blacklist [pair]:* `Show current blacklist, or adds one or more pairs "
"to the blacklist.` \n"
"*/edge:* `Shows validated pairs by Edge if it is enabled` \n"
"*/help:* `This help message`\n"
"*/version:* `Show version`")
self._send_msg(message)
@@ -631,8 +729,10 @@ class Telegram(RPC):
f"*Stake per trade:* `{val['stake_amount']} {val['stake_currency']}`\n"
f"*Max open Trades:* `{val['max_open_trades']}`\n"
f"*Minimum ROI:* `{val['minimal_roi']}`\n"
f"*Ask strategy:* ```\n{json.dumps(val['ask_strategy'])}```\n"
f"*Bid strategy:* ```\n{json.dumps(val['bid_strategy'])}```\n"
f"{sl_info}"
f"*Ticker Interval:* `{val['ticker_interval']}`\n"
f"*Timeframe:* `{val['timeframe']}`\n"
f"*Strategy:* `{val['strategy']}`\n"
f"*Current state:* `{val['state']}`"
)

2
freqtrade/state.py
View File

@@ -12,7 +12,7 @@ class State(Enum):
"""
RUNNING = 1
STOPPED = 2
RELOAD_CONF = 3
RELOAD_CONFIG = 3
def __str__(self):
return f"{self.name.lower()}"

184
freqtrade/strategy/interface.py
View File

@@ -7,20 +7,19 @@ import warnings
from abc import ABC, abstractmethod
from datetime import datetime, timezone
from enum import Enum
from typing import Dict, NamedTuple, Optional, Tuple
from typing import Dict, List, NamedTuple, Optional, Tuple
import arrow
from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import StrategyError
from freqtrade.exceptions import StrategyError, OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.persistence import Trade
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.typing import ListPairsWithTimeframes
from freqtrade.wallets import Wallets
logger = logging.getLogger(__name__)
@@ -45,6 +44,10 @@ class SellType(Enum):
EMERGENCY_SELL = "emergency_sell"
NONE = ""
def __str__(self):
# explicitly convert to String to help with exporting data.
return self.value
class SellCheckTuple(NamedTuple):
"""
@@ -62,7 +65,7 @@ class IStrategy(ABC):
Attributes you can use:
minimal_roi -> Dict: Minimal ROI designed for the strategy
stoploss -> float: optimal stoploss designed for the strategy
ticker_interval -> str: value of the timeframe (ticker interval) to use with the strategy
timeframe -> str: value of the timeframe (ticker interval) to use with the strategy
"""
# Strategy interface version
# Default to version 2
@@ -85,8 +88,9 @@ class IStrategy(ABC):
trailing_stop_positive_offset: float = 0.0
trailing_only_offset_is_reached = False
# associated ticker interval
ticker_interval: str
# associated timeframe
ticker_interval: str # DEPRECATED
timeframe: str
# Optional order types
order_types: Dict = {
@@ -106,6 +110,9 @@ class IStrategy(ABC):
# run "populate_indicators" only for new candle
process_only_new_candles: bool = False
# Disable checking the dataframe (converts the error into a warning message)
disable_dataframe_checks: bool = False
# Count of candles the strategy requires before producing valid signals
startup_candle_count: int = 0
@@ -187,6 +194,63 @@ class IStrategy(ABC):
"""
return False
def bot_loop_start(self, **kwargs) -> None:
"""
Called at the start of the bot iteration (one loop).
Might be used to perform pair-independent tasks
(e.g. gather some remote resource for comparison)
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
"""
pass
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, **kwargs) -> bool:
"""
Called right before placing a buy order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be bought.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in target (quote) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the buy-order is placed on the exchange.
False aborts the process
"""
return True
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
"""
Called right before placing a regular sell order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be sold.
:param trade: trade object.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in quote currency.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param sell_reason: Sell reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell']
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is placed on the exchange.
False aborts the process
"""
return True
def informative_pairs(self) -> ListPairsWithTimeframes:
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
@@ -200,6 +264,10 @@ class IStrategy(ABC):
"""
return []
###
# END - Intended to be overridden by strategy
###
def get_strategy_name(self) -> str:
"""
Returns strategy class name
@@ -269,6 +337,8 @@ class IStrategy(ABC):
# Defs that only make change on new candle data.
dataframe = self.analyze_ticker(dataframe, metadata)
self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date']
if self.dp:
self.dp._set_cached_df(pair, self.timeframe, dataframe)
else:
logger.debug("Skipping TA Analysis for already analyzed candle")
dataframe['buy'] = 0
@@ -280,14 +350,53 @@ class IStrategy(ABC):
return dataframe
def analyze_pair(self, pair: str) -> None:
"""
Fetch data for this pair from dataprovider and analyze.
Stores the dataframe into the dataprovider.
The analyzed dataframe is then accessible via `dp.get_analyzed_dataframe()`.
:param pair: Pair to analyze.
"""
if not self.dp:
raise OperationalException("DataProvider not found.")
dataframe = self.dp.ohlcv(pair, self.timeframe)
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning('Empty candle (OHLCV) data for pair %s', pair)
return
try:
df_len, df_close, df_date = self.preserve_df(dataframe)
dataframe = strategy_safe_wrapper(
self._analyze_ticker_internal, message=""
)(dataframe, {'pair': pair})
self.assert_df(dataframe, df_len, df_close, df_date)
except StrategyError as error:
logger.warning(f"Unable to analyze candle (OHLCV) data for pair {pair}: {error}")
return
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return
def analyze(self, pairs: List[str]) -> None:
"""
Analyze all pairs using analyze_pair().
:param pairs: List of pairs to analyze
"""
for pair in pairs:
self.analyze_pair(pair)
@staticmethod
def preserve_df(dataframe: DataFrame) -> Tuple[int, float, datetime]:
""" keep some data for dataframes """
return len(dataframe), dataframe["close"].iloc[-1], dataframe["date"].iloc[-1]
@staticmethod
def assert_df(dataframe: DataFrame, df_len: int, df_close: float, df_date: datetime):
""" make sure data is unmodified """
def assert_df(self, dataframe: DataFrame, df_len: int, df_close: float, df_date: datetime):
"""
Ensure dataframe (length, last candle) was not modified, and has all elements we need.
"""
message = ""
if df_len != len(dataframe):
message = "length"
@@ -296,64 +405,48 @@ class IStrategy(ABC):
elif df_date != dataframe["date"].iloc[-1]:
message = "last date"
if message:
raise StrategyError(f"Dataframe returned from strategy has mismatching {message}.")
if self.disable_dataframe_checks:
logger.warning(f"Dataframe returned from strategy has mismatching {message}.")
else:
raise StrategyError(f"Dataframe returned from strategy has mismatching {message}.")
def get_signal(self, pair: str, interval: str, dataframe: DataFrame) -> Tuple[bool, bool]:
def get_signal(self, pair: str, timeframe: str, dataframe: DataFrame) -> Tuple[bool, bool]:
"""
Calculates current signal based several technical analysis indicators
Calculates current signal based based on the buy / sell columns of the dataframe.
Used by Bot to get the signal to buy or sell
:param pair: pair in format ANT/BTC
:param interval: Interval to use (in min)
:param dataframe: Dataframe to analyze
:param timeframe: timeframe to use
:param dataframe: Analyzed dataframe to get signal from.
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning('Empty candle (OHLCV) data for pair %s', pair)
return False, False
try:
df_len, df_close, df_date = self.preserve_df(dataframe)
dataframe = strategy_safe_wrapper(
self._analyze_ticker_internal, message=""
)(dataframe, {'pair': pair})
self.assert_df(dataframe, df_len, df_close, df_date)
except StrategyError as error:
logger.warning(f"Unable to analyze candle (OHLCV) data for pair {pair}: {error}")
return False, False
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
logger.warning(f'Empty candle (OHLCV) data for pair {pair}')
return False, False
latest_date = dataframe['date'].max()
latest = dataframe.loc[dataframe['date'] == latest_date].iloc[-1]
interval_minutes = timeframe_to_minutes(interval)
# Explicitly convert to arrow object to ensure the below comparison does not fail
latest_date = arrow.get(latest_date)
# Check if dataframe is out of date
timeframe_minutes = timeframe_to_minutes(timeframe)
offset = self.config.get('exchange', {}).get('outdated_offset', 5)
if latest_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + offset))):
if latest_date < (arrow.utcnow().shift(minutes=-(timeframe_minutes * 2 + offset))):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair,
int((arrow.utcnow() - latest_date).total_seconds() // 60)
pair, int((arrow.utcnow() - latest_date).total_seconds() // 60)
)
return False, False
# Check if dataframe has new candle
if (arrow.utcnow() - latest_date).total_seconds() // 60 >= interval_minutes:
if (arrow.utcnow() - latest_date).total_seconds() // 60 >= timeframe_minutes:
logger.warning('Old candle for pair %s. Last candle is %s minutes old',
pair, int((arrow.utcnow() - latest_date).total_seconds() // 60))
return False, False
(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
logger.debug(
'trigger: %s (pair=%s) buy=%s sell=%s',
latest['date'],
pair,
str(buy),
str(sell)
)
logger.debug('trigger: %s (pair=%s) buy=%s sell=%s',
latest['date'], pair, str(buy), str(sell))
return buy, sell
def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
@@ -499,7 +592,8 @@ class IStrategy(ABC):
def ohlcvdata_to_dataframe(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
"""
Creates a dataframe and populates indicators for given candle (OHLCV) data
Populates indicators for given candle (OHLCV) data (for multiple pairs)
Does not run advice_buy or advise_sell!
Used by optimize operations only, not during dry / live runs.
Using .copy() to get a fresh copy of the dataframe for every strategy run.
Has positive effects on memory usage for whatever reason - also when

6
freqtrade/strategy/strategy_wrapper.py
View File

@@ -5,7 +5,7 @@ from freqtrade.exceptions import StrategyError
logger = logging.getLogger(__name__)
def strategy_safe_wrapper(f, message: str = "", default_retval=None):
def strategy_safe_wrapper(f, message: str = "", default_retval=None, supress_error=False):
"""
Wrapper around user-provided methods and functions.
Caches all exceptions and returns either the default_retval (if it's not None) or raises
@@ -20,7 +20,7 @@ def strategy_safe_wrapper(f, message: str = "", default_retval=None):
f"Strategy caused the following exception: {error}"
f"{f}"
)
if default_retval is None:
if default_retval is None and not supress_error:
raise StrategyError(str(error)) from error
return default_retval
except Exception as error:
@@ -28,7 +28,7 @@ def strategy_safe_wrapper(f, message: str = "", default_retval=None):
f"{message}"
f"Unexpected error {error} calling {f}"
)
if default_retval is None:
if default_retval is None and not supress_error:
raise StrategyError(str(error)) from error
return default_retval

12
freqtrade/templates/base_config.json.j2
View File

@@ -4,7 +4,7 @@
"stake_amount": {{ stake_amount }},
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "{{ fiat_display_currency }}",
"ticker_interval": "{{ ticker_interval }}",
"timeframe": "{{ timeframe }}",
"dry_run": {{ dry_run | lower }},
"cancel_open_orders_on_exit": false,
"unfilledtimeout": {
@@ -53,6 +53,16 @@
"token": "{{ telegram_token }}",
"chat_id": "{{ telegram_chat_id }}"
},
"api_server": {
"enabled": false,
"listen_ip_address": "127.0.0.1",
"listen_port": 8080,
"verbosity": "info",
"jwt_secret_key": "somethingrandom",
"CORS_origins": [],
"username": "",
"password": ""
},
"initial_state": "running",
"forcebuy_enable": false,
"internals": {

4
freqtrade/templates/base_strategy.py.j2
View File

@@ -51,8 +51,8 @@ class {{ strategy }}(IStrategy):
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Optimal ticker interval for the strategy.
ticker_interval = '5m'
# Optimal timeframe for the strategy.
timeframe = '5m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False

2
freqtrade/templates/sample_strategy.py
View File

@@ -53,7 +53,7 @@ class SampleStrategy(IStrategy):
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Optimal ticker interval for the strategy.
ticker_interval = '5m'
timeframe = '5m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False

51
freqtrade/templates/strategy_analysis_example.ipynb
View File

@@ -34,7 +34,7 @@
"# config = Configuration.from_files([\"config.json\"])\n",
"\n",
"# Define some constants\n",
"config[\"ticker_interval\"] = \"5m\"\n",
"config[\"timeframe\"] = \"5m\"\n",
"# Name of the strategy class\n",
"config[\"strategy\"] = \"SampleStrategy\"\n",
"# Location of the data\n",
@@ -53,7 +53,7 @@
"from freqtrade.data.history import load_pair_history\n",
"\n",
"candles = load_pair_history(datadir=data_location,\n",
" timeframe=config[\"ticker_interval\"],\n",
" timeframe=config[\"timeframe\"],\n",
" pair=pair)\n",
"\n",
"# Confirm success\n",
@@ -136,10 +136,51 @@
"metadata": {},
"outputs": [],
"source": [
"from freqtrade.data.btanalysis import load_backtest_data\n",
"from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats\n",
"\n",
"# Load backtest results\n",
"trades = load_backtest_data(config[\"user_data_dir\"] / \"backtest_results/backtest-result.json\")\n",
"# if backtest_dir points to a directory, it'll automatically load the last backtest file.\n",
"backtest_dir = config[\"user_data_dir\"] / \"backtest_results\"\n",
"# backtest_dir can also point to a specific file \n",
"# backtest_dir = config[\"user_data_dir\"] / \"backtest_results/backtest-result-2020-07-01_20-04-22.json\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# You can get the full backtest statistics by using the following command.\n",
"# This contains all information used to generate the backtest result.\n",
"stats = load_backtest_stats(backtest_dir)\n",
"\n",
"strategy = 'SampleStrategy'\n",
"# All statistics are available per strategy, so if `--strategy-list` was used during backtest, this will be reflected here as well.\n",
"# Example usages:\n",
"print(stats['strategy'][strategy]['results_per_pair'])\n",
"# Get pairlist used for this backtest\n",
"print(stats['strategy'][strategy]['pairlist'])\n",
"# Get market change (average change of all pairs from start to end of the backtest period)\n",
"print(stats['strategy'][strategy]['market_change'])\n",
"# Maximum drawdown ()\n",
"print(stats['strategy'][strategy]['max_drawdown'])\n",
"# Maximum drawdown start and end\n",
"print(stats['strategy'][strategy]['drawdown_start'])\n",
"print(stats['strategy'][strategy]['drawdown_end'])\n",
"\n",
"\n",
"# Get strategy comparison (only relevant if multiple strategies were compared)\n",
"print(stats['strategy_comparison'])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load backtested trades as dataframe\n",
"trades = load_backtest_data(backtest_dir)\n",
"\n",
"# Show value-counts per pair\n",
"trades.groupby(\"pair\")[\"sell_reason\"].value_counts()"

61
freqtrade/templates/subtemplates/strategy_methods_advanced.j2
View File

@@ -1,4 +1,65 @@
def bot_loop_start(self, **kwargs) -> None:
"""
Called at the start of the bot iteration (one loop).
Might be used to perform pair-independent tasks
(e.g. gather some remote ressource for comparison)
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, this simply does nothing.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
"""
pass
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, **kwargs) -> bool:
"""
Called right before placing a buy order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be bought.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in target (quote) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the buy-order is placed on the exchange.
False aborts the process
"""
return True
def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
"""
Called right before placing a regular sell order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be sold.
:param trade: trade object.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in quote currency.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param sell_reason: Sell reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell']
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is placed on the exchange.
False aborts the process
"""
return True
def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
"""
Check buy timeout function callback.

8
freqtrade/typing.py
View File

@@ -1,8 +0,0 @@
"""
Common Freqtrade types
"""
from typing import List, Tuple
# List of pairs with their timeframes
ListPairsWithTimeframes = List[Tuple[str, str]]

5
freqtrade/worker.py
View File

@@ -71,7 +71,7 @@ class Worker:
state = None
while True:
state = self._worker(old_state=state)
if state == State.RELOAD_CONF:
if state == State.RELOAD_CONFIG:
self._reconfigure()
def _worker(self, old_state: Optional[State]) -> State:
@@ -90,6 +90,9 @@ class Worker:
if state == State.RUNNING:
self.freqtrade.startup()
if state == State.STOPPED:
self.freqtrade.check_for_open_trades()
# Reset heartbeat timestamp to log the heartbeat message at
# first throttling iteration when the state changes
self._heartbeat_msg = 0