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Merge branch 'develop' into pr/eatrisno/4308

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This commit is contained in:
Matthias
2021-06-13 20:04:24 +02:00
parent af98e025d1 7b372fbcaa
commit d32508aa75
229 changed files with 10104 additions and 5002 deletions
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15
freqtrade/commands/arguments.py
View File

@@ -14,18 +14,18 @@ ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_dat
ARGS_STRATEGY = ["strategy", "strategy_path"]
ARGS_TRADE = ["db_url", "sd_notify", "dry_run"]
ARGS_TRADE = ["db_url", "sd_notify", "dry_run", "dry_run_wallet", "fee"]
ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange", "dataformat_ohlcv",
"max_open_trades", "stake_amount", "fee"]
"max_open_trades", "stake_amount", "fee", "pairs"]
ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
"enable_protections",
"enable_protections", "dry_run_wallet",
"strategy_list", "export", "exportfilename"]
ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
"position_stacking", "use_max_market_positions",
"enable_protections",
"enable_protections", "dry_run_wallet",
"epochs", "spaces", "print_all",
"print_colorized", "print_json", "hyperopt_jobs",
"hyperopt_random_state", "hyperopt_min_trades",
@@ -60,15 +60,16 @@ ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes"]
ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs"]
ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "timerange", "download_trades", "exchange",
"timeframes", "erase", "dataformat_ohlcv", "dataformat_trades"]
ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "new_pairs_days", "timerange",
"download_trades", "exchange", "timeframes", "erase", "dataformat_ohlcv",
"dataformat_trades"]
ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
"db_url", "trade_source", "export", "exportfilename",
"timerange", "timeframe", "no_trades"]
ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
"trade_source", "timeframe"]
"trade_source", "timeframe", "plot_auto_open"]
ARGS_INSTALL_UI = ["erase_ui_only"]

37
freqtrade/commands/build_config_commands.py
View File

@@ -1,9 +1,11 @@
import logging
import secrets
from pathlib import Path
from typing import Any, Dict, List
from questionary import Separator, prompt
from freqtrade.configuration.directory_operations import chown_user_directory
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import MAP_EXCHANGE_CHILDCLASS, available_exchanges
@@ -93,10 +95,10 @@ def ask_user_config() -> Dict[str, Any]:
"message": "Select exchange",
"choices": [
"binance",
"binanceje",
"binanceus",
"bittrex",
"kraken",
"ftx",
Separator(),
"other",
],
@@ -138,6 +140,32 @@ def ask_user_config() -> Dict[str, Any]:
"message": "Insert Telegram chat id",
"when": lambda x: x['telegram']
},
{
"type": "confirm",
"name": "api_server",
"message": "Do you want to enable the Rest API (includes FreqUI)?",
"default": False,
},
{
"type": "text",
"name": "api_server_listen_addr",
"message": "Insert Api server Listen Address (best left untouched default!)",
"default": "127.0.0.1",
"when": lambda x: x['api_server']
},
{
"type": "text",
"name": "api_server_username",
"message": "Insert api-server username",
"default": "freqtrader",
"when": lambda x: x['api_server']
},
{
"type": "text",
"name": "api_server_password",
"message": "Insert api-server password",
"when": lambda x: x['api_server']
},
]
answers = prompt(questions)
@@ -145,6 +173,9 @@ def ask_user_config() -> Dict[str, Any]:
# Interrupted questionary sessions return an empty dict.
raise OperationalException("User interrupted interactive questions.")
# Force JWT token to be a random string
answers['api_server_jwt_key'] = secrets.token_hex()
return answers
@@ -173,6 +204,9 @@ def deploy_new_config(config_path: Path, selections: Dict[str, Any]) -> None:
arguments=selections)
logger.info(f"Writing config to `{config_path}`.")
logger.info(
"Please make sure to check the configuration contents and adjust settings to your needs.")
config_path.write_text(config_text)
@@ -183,6 +217,7 @@ def start_new_config(args: Dict[str, Any]) -> None:
"""
config_path = Path(args['config'][0])
chown_user_directory(config_path.parent)
if config_path.exists():
overwrite = ask_user_overwrite(config_path)
if overwrite:

24
freqtrade/commands/cli_options.py
View File

@@ -110,10 +110,15 @@ AVAILABLE_CLI_OPTIONS = {
help='Enforce dry-run for trading (removes Exchange secrets and simulates trades).',
action='store_true',
),
"dry_run_wallet": Arg(
'--dry-run-wallet', '--starting-balance',
help='Starting balance, used for backtesting / hyperopt and dry-runs.',
type=float,
),
# Optimize common
"timeframe": Arg(
'-i', '--timeframe', '--ticker-interval',
help='Specify ticker interval (`1m`, `5m`, `30m`, `1h`, `1d`).',
help='Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).',
),
"timerange": Arg(
'--timerange',
@@ -128,7 +133,6 @@ AVAILABLE_CLI_OPTIONS = {
"stake_amount": Arg(
'--stake-amount',
help='Override the value of the `stake_amount` configuration setting.',
type=float,
),
# Backtesting
"position_stacking": Arg(
@@ -191,6 +195,7 @@ AVAILABLE_CLI_OPTIONS = {
'--hyperopt',
help='Specify hyperopt class name which will be used by the bot.',
metavar='NAME',
required=False,
),
"hyperopt_path": Arg(
'--hyperopt-path',
@@ -262,7 +267,7 @@ AVAILABLE_CLI_OPTIONS = {
default=1,
),
"hyperopt_loss": Arg(
'--hyperopt-loss',
'--hyperopt-loss', '--hyperoptloss',
help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). '
'Different functions can generate completely different results, '
'since the target for optimization is different. Built-in Hyperopt-loss-functions are: '
@@ -325,7 +330,7 @@ AVAILABLE_CLI_OPTIONS = {
# Script options
"pairs": Arg(
'-p', '--pairs',
help='Show profits for only these pairs. Pairs are space-separated.',
help='Limit command to these pairs. Pairs are space-separated.',
nargs='+',
),
# Download data
@@ -340,6 +345,12 @@ AVAILABLE_CLI_OPTIONS = {
type=check_int_positive,
metavar='INT',
),
"new_pairs_days": Arg(
'--new-pairs-days',
help='Download data of new pairs for given number of days. Default: `%(default)s`.',
type=check_int_positive,
metavar='INT',
),
"download_trades": Arg(
'--dl-trades',
help='Download trades instead of OHLCV data. The bot will resample trades to the '
@@ -422,6 +433,11 @@ AVAILABLE_CLI_OPTIONS = {
metavar='INT',
default=750,
),
"plot_auto_open": Arg(
'--auto-open',
help='Automatically open generated plot.',
action='store_true',
),
"no_trades": Arg(
'--no-trades',
help='Skip using trades from backtesting file and DB.',

11
freqtrade/commands/data_commands.py
View File

@@ -8,11 +8,11 @@ from freqtrade.configuration import TimeRange, setup_utils_configuration
from freqtrade.data.converter import convert_ohlcv_format, convert_trades_format
from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_ohlcv_data,
refresh_backtest_trades_data)
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.resolvers import ExchangeResolver
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
@@ -62,8 +62,8 @@ def start_download_data(args: Dict[str, Any]) -> None:
if config.get('download_trades'):
pairs_not_available = refresh_backtest_trades_data(
exchange, pairs=expanded_pairs, datadir=config['datadir'],
timerange=timerange, erase=bool(config.get('erase')),
data_format=config['dataformat_trades'])
timerange=timerange, new_pairs_days=config['new_pairs_days'],
erase=bool(config.get('erase')), data_format=config['dataformat_trades'])
# Convert downloaded trade data to different timeframes
convert_trades_to_ohlcv(
@@ -75,8 +75,9 @@ def start_download_data(args: Dict[str, Any]) -> None:
else:
pairs_not_available = refresh_backtest_ohlcv_data(
exchange, pairs=expanded_pairs, timeframes=config['timeframes'],
datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
data_format=config['dataformat_ohlcv'])
datadir=config['datadir'], timerange=timerange,
new_pairs_days=config['new_pairs_days'],
erase=bool(config.get('erase')), data_format=config['dataformat_ohlcv'])
except KeyboardInterrupt:
sys.exit("SIGINT received, aborting ...")

2
freqtrade/commands/deploy_commands.py
View File

@@ -8,9 +8,9 @@ import requests
from freqtrade.configuration import setup_utils_configuration
from freqtrade.configuration.directory_operations import copy_sample_files, create_userdata_dir
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.misc import render_template, render_template_with_fallback
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)

105
freqtrade/commands/hyperopt_commands.py
View File

@@ -6,8 +6,9 @@ from colorama import init as colorama_init
from freqtrade.configuration import setup_utils_configuration
from freqtrade.data.btanalysis import get_latest_hyperopt_file
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.state import RunMode
from freqtrade.optimize.optimize_reports import show_backtest_result
logger = logging.getLogger(__name__)
@@ -17,7 +18,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
"""
List hyperopt epochs previously evaluated
"""
from freqtrade.optimize.hyperopt import Hyperopt
from freqtrade.optimize.hyperopt_tools import HyperoptTools
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
@@ -47,7 +48,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
config.get('hyperoptexportfilename'))
# Previous evaluations
epochs = Hyperopt.load_previous_results(results_file)
epochs = HyperoptTools.load_previous_results(results_file)
total_epochs = len(epochs)
epochs = hyperopt_filter_epochs(epochs, filteroptions)
@@ -57,18 +58,19 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
if not export_csv:
try:
print(Hyperopt.get_result_table(config, epochs, total_epochs,
not filteroptions['only_best'], print_colorized, 0))
print(HyperoptTools.get_result_table(config, epochs, total_epochs,
not filteroptions['only_best'],
print_colorized, 0))
except KeyboardInterrupt:
print('User interrupted..')
if epochs and not no_details:
sorted_epochs = sorted(epochs, key=itemgetter('loss'))
results = sorted_epochs[0]
Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header)
HyperoptTools.show_epoch_details(results, total_epochs, print_json, no_header)
if epochs and export_csv:
Hyperopt.export_csv_file(
HyperoptTools.export_csv_file(
config, epochs, total_epochs, not filteroptions['only_best'], export_csv
)
@@ -77,7 +79,7 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
"""
Show details of a hyperopt epoch previously evaluated
"""
from freqtrade.optimize.hyperopt import Hyperopt
from freqtrade.optimize.hyperopt_tools import HyperoptTools
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
@@ -105,7 +107,7 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
}
# Previous evaluations
epochs = Hyperopt.load_previous_results(results_file)
epochs = HyperoptTools.load_previous_results(results_file)
total_epochs = len(epochs)
epochs = hyperopt_filter_epochs(epochs, filteroptions)
@@ -124,18 +126,26 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
if epochs:
val = epochs[n]
Hyperopt.print_epoch_details(val, total_epochs, print_json, no_header,
header_str="Epoch details")
metrics = val['results_metrics']
if 'strategy_name' in metrics:
show_backtest_result(metrics['strategy_name'], metrics,
metrics['stake_currency'])
HyperoptTools.show_epoch_details(val, total_epochs, print_json, no_header,
header_str="Epoch details")
def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
"""
Filter our items from the list of hyperopt results
TODO: after 2021.5 remove all "legacy" mode queries.
"""
if filteroptions['only_best']:
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 = [x for x in epochs if x['results_metrics'].get(
'profit', x['results_metrics'].get('profit_total', 0)) > 0]
epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions)
@@ -152,34 +162,59 @@ def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
return epochs
def _hyperopt_filter_epochs_trade(epochs: List, trade_count: int):
"""
Filter epochs with trade-counts > trades
"""
return [
x for x in epochs
if x['results_metrics'].get(
'trade_count', x['results_metrics'].get('total_trades', 0)
) > trade_count
]
def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_trades'] > 0:
epochs = [
x for x in epochs
if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades']
]
epochs = _hyperopt_filter_epochs_trade(epochs, filteroptions['filter_min_trades'])
if filteroptions['filter_max_trades'] > 0:
epochs = [
x for x in epochs
if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
if x['results_metrics'].get(
'trade_count', x['results_metrics'].get('total_trades')
) < filteroptions['filter_max_trades']
]
return epochs
def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
def get_duration_value(x):
# Duration in minutes ...
if 'duration' in x['results_metrics']:
return x['results_metrics']['duration']
else:
# New mode
if 'holding_avg_s' in x['results_metrics']:
avg = x['results_metrics']['holding_avg_s']
return avg // 60
raise OperationalException(
"Holding-average not available. Please omit the filter on average time, "
"or rerun hyperopt with this version")
if filteroptions['filter_min_avg_time'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time']
if get_duration_value(x) > filteroptions['filter_min_avg_time']
]
if filteroptions['filter_max_avg_time'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
if get_duration_value(x) < filteroptions['filter_max_avg_time']
]
return epochs
@@ -188,28 +223,36 @@ def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
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 = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics']['avg_profit'] > filteroptions['filter_min_avg_profit']
if x['results_metrics'].get(
'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
) > filteroptions['filter_min_avg_profit']
]
if filteroptions['filter_max_avg_profit'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics']['avg_profit'] < filteroptions['filter_max_avg_profit']
if x['results_metrics'].get(
'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
) < filteroptions['filter_max_avg_profit']
]
if filteroptions['filter_min_total_profit'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit']
if x['results_metrics'].get(
'profit', x['results_metrics'].get('profit_total_abs', 0)
) > filteroptions['filter_min_total_profit']
]
if filteroptions['filter_max_total_profit'] is not None:
epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
if x['results_metrics'].get(
'profit', x['results_metrics'].get('profit_total_abs', 0)
) < filteroptions['filter_max_total_profit']
]
return epochs
@@ -217,11 +260,11 @@ def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
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 = _hyperopt_filter_epochs_trade(epochs, 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 = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']]

40
freqtrade/commands/list_commands.py
View File

@@ -1,7 +1,6 @@
import csv
import logging
import sys
from collections import OrderedDict
from pathlib import Path
from typing import Any, Dict, List
@@ -12,11 +11,11 @@ from tabulate import tabulate
from freqtrade.configuration import setup_utils_configuration
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import available_exchanges, ccxt_exchanges, market_is_active
from freqtrade.exchange import market_is_active, validate_exchanges
from freqtrade.misc import plural
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
@@ -28,14 +27,18 @@ def start_list_exchanges(args: Dict[str, Any]) -> None:
:param args: Cli args from Arguments()
:return: None
"""
exchanges = ccxt_exchanges() if args['list_exchanges_all'] else available_exchanges()
exchanges = validate_exchanges(args['list_exchanges_all'])
if args['print_one_column']:
print('\n'.join(exchanges))
print('\n'.join([e[0] for e in exchanges]))
else:
if args['list_exchanges_all']:
print(f"All exchanges supported by the ccxt library: {', '.join(exchanges)}")
print("All exchanges supported by the ccxt library:")
else:
print(f"Exchanges available for Freqtrade: {', '.join(exchanges)}")
print("Exchanges available for Freqtrade:")
exchanges = [e for e in exchanges if e[1] is not False]
print(tabulate(exchanges, headers=['Exchange name', 'Valid', 'reason']))
def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
@@ -50,15 +53,21 @@ def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
reset = ''
names = [s['name'] for s in objs]
objss_to_print = [{
objs_to_print = [{
'name': s['name'] if s['name'] else "--",
'location': s['location'].name,
'status': (red + "LOAD FAILED" + reset if s['class'] is None
else "OK" if names.count(s['name']) == 1
else yellow + "DUPLICATE NAME" + reset)
} for s in objs]
print(tabulate(objss_to_print, headers='keys', tablefmt='psql', stralign='right'))
for idx, s in enumerate(objs):
if 'hyperoptable' in s:
objs_to_print[idx].update({
'hyperoptable': "Yes" if s['hyperoptable']['count'] > 0 else "No",
'buy-Params': len(s['hyperoptable'].get('buy', [])),
'sell-Params': len(s['hyperoptable'].get('sell', [])),
})
print(tabulate(objs_to_print, headers='keys', tablefmt='psql', stralign='right'))
def start_list_strategies(args: Dict[str, Any]) -> None:
@@ -71,6 +80,11 @@ def start_list_strategies(args: Dict[str, Any]) -> None:
strategy_objs = StrategyResolver.search_all_objects(directory, not args['print_one_column'])
# Sort alphabetically
strategy_objs = sorted(strategy_objs, key=lambda x: x['name'])
for obj in strategy_objs:
if obj['class']:
obj['hyperoptable'] = obj['class'].detect_all_parameters()
else:
obj['hyperoptable'] = {'count': 0}
if args['print_one_column']:
print('\n'.join([s['name'] for s in strategy_objs]))
@@ -99,7 +113,7 @@ def start_list_hyperopts(args: Dict[str, Any]) -> None:
def start_list_timeframes(args: Dict[str, Any]) -> None:
"""
Print ticker intervals (timeframes) available on Exchange
Print timeframes available on Exchange
"""
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
# Do not use timeframe set in the config
@@ -139,7 +153,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
pairs_only=pairs_only,
active_only=active_only)
# Sort the pairs/markets by symbol
pairs = OrderedDict(sorted(pairs.items()))
pairs = dict(sorted(pairs.items()))
except Exception as e:
raise OperationalException(f"Cannot get markets. Reason: {e}") from e
@@ -177,7 +191,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
# human-readable formats.
print()
if len(pairs):
if pairs:
if args.get('print_list', False):
# print data as a list, with human-readable summary
print(f"{summary_str}: {', '.join(pairs.keys())}.")

17
freqtrade/commands/optimize_commands.py
View File

@@ -3,8 +3,9 @@ from typing import Any, Dict
from freqtrade import constants
from freqtrade.configuration import setup_utils_configuration
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.state import RunMode
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.misc import round_coin_value
logger = logging.getLogger(__name__)
@@ -22,11 +23,13 @@ def setup_optimize_configuration(args: Dict[str, Any], method: RunMode) -> Dict[
RunMode.BACKTEST: 'backtesting',
RunMode.HYPEROPT: 'hyperoptimization',
}
if (method in no_unlimited_runmodes.keys() and
config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT):
raise DependencyException(
f'The value of `stake_amount` cannot be set as "{constants.UNLIMITED_STAKE_AMOUNT}" '
f'for {no_unlimited_runmodes[method]}')
if method in no_unlimited_runmodes.keys():
if (config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT
and config['stake_amount'] > config['dry_run_wallet']):
wallet = round_coin_value(config['dry_run_wallet'], config['stake_currency'])
stake = round_coin_value(config['stake_amount'], config['stake_currency'])
raise OperationalException(f"Starting balance ({wallet}) "
f"is smaller than stake_amount {stake}.")
return config

4
freqtrade/commands/pairlist_commands.py
View File

@@ -4,8 +4,8 @@ from typing import Any, Dict
import rapidjson
from freqtrade.configuration import setup_utils_configuration
from freqtrade.enums import RunMode
from freqtrade.resolvers import ExchangeResolver
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
@@ -31,7 +31,7 @@ def start_test_pairlist(args: Dict[str, Any]) -> None:
results[curr] = pairlists.whitelist
for curr, pairlist in results.items():
if not args.get('print_one_column', False):
if not args.get('print_one_column', False) and not args.get('list_pairs_print_json', False):
print(f"Pairs for {curr}: ")
if args.get('print_one_column', False):

2
freqtrade/commands/plot_commands.py
View File

@@ -1,8 +1,8 @@
from typing import Any, Dict
from freqtrade.configuration import setup_utils_configuration
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.state import RunMode
def validate_plot_args(args: Dict[str, Any]) -> None:

16
freqtrade/configuration/check_exchange.py
View File

@@ -1,10 +1,10 @@
import logging
from typing import Any, Dict
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import (available_exchanges, get_exchange_bad_reason, is_exchange_bad,
is_exchange_known_ccxt, is_exchange_officially_supported)
from freqtrade.state import RunMode
from freqtrade.exchange import (available_exchanges, is_exchange_known_ccxt,
is_exchange_officially_supported, validate_exchange)
logger = logging.getLogger(__name__)
@@ -57,9 +57,13 @@ def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
f'{", ".join(available_exchanges())}'
)
if check_for_bad and is_exchange_bad(exchange):
raise OperationalException(f'Exchange "{exchange}" is known to not work with the bot yet. '
f'Reason: {get_exchange_bad_reason(exchange)}')
valid, reason = validate_exchange(exchange)
if not valid:
if check_for_bad:
raise OperationalException(f'Exchange "{exchange}" will not work with Freqtrade. '
f'Reason: {reason}')
else:
logger.warning(f'Exchange "{exchange}" will not work with Freqtrade. Reason: {reason}')
if is_exchange_officially_supported(exchange):
logger.info(f'Exchange "{exchange}" is officially supported '

2
freqtrade/configuration/config_setup.py
View File

@@ -1,7 +1,7 @@
import logging
from typing import Any, Dict
from freqtrade.state import RunMode
from freqtrade.enums import RunMode
from .check_exchange import remove_credentials
from .config_validation import validate_config_consistency

23
freqtrade/configuration/config_validation.py
View File

@@ -6,8 +6,8 @@ from jsonschema import Draft4Validator, validators
from jsonschema.exceptions import ValidationError, best_match
from freqtrade import constants
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
@@ -47,6 +47,8 @@ def validate_config_schema(conf: Dict[str, Any]) -> Dict[str, Any]:
conf_schema = deepcopy(constants.CONF_SCHEMA)
if conf.get('runmode', RunMode.OTHER) in (RunMode.DRY_RUN, RunMode.LIVE):
conf_schema['required'] = constants.SCHEMA_TRADE_REQUIRED
elif conf.get('runmode', RunMode.OTHER) in (RunMode.BACKTEST, RunMode.HYPEROPT):
conf_schema['required'] = constants.SCHEMA_BACKTEST_REQUIRED
else:
conf_schema['required'] = constants.SCHEMA_MINIMAL_REQUIRED
try:
@@ -72,6 +74,7 @@ def validate_config_consistency(conf: Dict[str, Any]) -> None:
# validating trailing stoploss
_validate_trailing_stoploss(conf)
_validate_price_config(conf)
_validate_edge(conf)
_validate_whitelist(conf)
_validate_protections(conf)
@@ -93,6 +96,19 @@ def _validate_unlimited_amount(conf: Dict[str, Any]) -> None:
raise OperationalException("`max_open_trades` and `stake_amount` cannot both be unlimited.")
def _validate_price_config(conf: Dict[str, Any]) -> None:
"""
When using market orders, price sides must be using the "other" side of the price
"""
if (conf.get('order_types', {}).get('buy') == 'market'
and conf.get('bid_strategy', {}).get('price_side') != 'ask'):
raise OperationalException('Market buy orders require bid_strategy.price_side = "ask".')
if (conf.get('order_types', {}).get('sell') == 'market'
and conf.get('ask_strategy', {}).get('price_side') != 'bid'):
raise OperationalException('Market sell orders require ask_strategy.price_side = "bid".')
def _validate_trailing_stoploss(conf: Dict[str, Any]) -> None:
if conf.get('stoploss') == 0.0:
@@ -133,11 +149,6 @@ def _validate_edge(conf: Dict[str, Any]) -> None:
if not conf.get('edge', {}).get('enabled'):
return
if conf.get('pairlist', {}).get('method') == 'VolumePairList':
raise OperationalException(
"Edge and VolumePairList are incompatible, "
"Edge will override whatever pairs VolumePairlist selects."
)
if not conf.get('ask_strategy', {}).get('use_sell_signal', True):
raise OperationalException(
"Edge requires `use_sell_signal` to be True, otherwise no sells will happen."

44
freqtrade/configuration/configuration.py
View File

@@ -11,11 +11,11 @@ from freqtrade import constants
from freqtrade.configuration.check_exchange import check_exchange
from freqtrade.configuration.deprecated_settings import process_temporary_deprecated_settings
from freqtrade.configuration.directory_operations import create_datadir, create_userdata_dir
from freqtrade.configuration.load_config import load_config_file
from freqtrade.configuration.load_config import load_config_file, load_file
from freqtrade.enums import NON_UTIL_MODES, TRADING_MODES, RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.loggers import setup_logging
from freqtrade.misc import deep_merge_dicts, json_load
from freqtrade.state import NON_UTIL_MODES, TRADING_MODES, RunMode
from freqtrade.misc import deep_merge_dicts
logger = logging.getLogger(__name__)
@@ -75,8 +75,6 @@ class Configuration:
# Normalize config
if 'internals' not in config:
config['internals'] = {}
# TODO: This can be deleted along with removal of deprecated
# experimental settings
if 'ask_strategy' not in config:
config['ask_strategy'] = {}
@@ -108,6 +106,8 @@ class Configuration:
self._process_plot_options(config)
self._process_data_options(config)
# Check if the exchange set by the user is supported
check_exchange(config, config.get('experimental', {}).get('block_bad_exchanges', True))
@@ -214,9 +214,6 @@ class Configuration:
self._args_to_config(
config, argname='enable_protections',
logstring='Parameter --enable-protections detected, enabling Protections. ...')
# Setting max_open_trades to infinite if -1
if config.get('max_open_trades') == -1:
config['max_open_trades'] = float('inf')
if 'use_max_market_positions' in self.args and not self.args["use_max_market_positions"]:
config.update({'use_max_market_positions': False})
@@ -228,11 +225,23 @@ class Configuration:
'overriding max_open_trades to: %s ...', config.get('max_open_trades'))
elif config['runmode'] in NON_UTIL_MODES:
logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
# Setting max_open_trades to infinite if -1
if config.get('max_open_trades') == -1:
config['max_open_trades'] = float('inf')
if self.args.get('stake_amount', None):
# Convert explicitly to float to support CLI argument for both unlimited and value
try:
self.args['stake_amount'] = float(self.args['stake_amount'])
except ValueError:
pass
self._args_to_config(config, argname='stake_amount',
logstring='Parameter --stake-amount detected, '
'overriding stake_amount to: {} ...')
self._args_to_config(config, argname='dry_run_wallet',
logstring='Parameter --dry-run-wallet detected, '
'overriding dry_run_wallet to: {} ...')
self._args_to_config(config, argname='fee',
logstring='Parameter --fee detected, '
'setting fee to: {} ...')
@@ -366,6 +375,9 @@ class Configuration:
self._args_to_config(config, argname='plot_limit',
logstring='Limiting plot to: {}')
self._args_to_config(config, argname='plot_auto_open',
logstring='Parameter --auto-open detected.')
self._args_to_config(config, argname='trade_source',
logstring='Using trades from: {}')
@@ -390,6 +402,11 @@ class Configuration:
self._args_to_config(config, argname='dataformat_trades',
logstring='Using "{}" to store trades data.')
def _process_data_options(self, config: Dict[str, Any]) -> None:
self._args_to_config(config, argname='new_pairs_days',
logstring='Detected --new-pairs-days: {}')
def _process_runmode(self, config: Dict[str, Any]) -> None:
self._args_to_config(config, argname='dry_run',
@@ -436,6 +453,7 @@ class Configuration:
"""
if "pairs" in config:
config['exchange']['pair_whitelist'] = config['pairs']
return
if "pairs_file" in self.args and self.args["pairs_file"]:
@@ -445,9 +463,8 @@ class Configuration:
# or if pairs file is specified explicitely
if not pairs_file.exists():
raise OperationalException(f'No pairs file found with path "{pairs_file}".')
with pairs_file.open('r') as f:
config['pairs'] = json_load(f)
config['pairs'].sort()
config['pairs'] = load_file(pairs_file)
config['pairs'].sort()
return
if 'config' in self.args and self.args['config']:
@@ -457,7 +474,6 @@ class Configuration:
# Fall back to /dl_path/pairs.json
pairs_file = config['datadir'] / 'pairs.json'
if pairs_file.exists():
with pairs_file.open('r') as f:
config['pairs'] = json_load(f)
config['pairs'] = load_file(pairs_file)
if 'pairs' in config:
config['pairs'].sort()

19
freqtrade/configuration/directory_operations.py
View File

@@ -24,6 +24,21 @@ def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> Pat
return folder
def chown_user_directory(directory: Path) -> None:
"""
Use Sudo to change permissions of the home-directory if necessary
Only applies when running in docker!
"""
import os
if os.environ.get('FT_APP_ENV') == 'docker':
try:
import subprocess
subprocess.check_output(
['sudo', 'chown', '-R', 'ftuser:', str(directory.resolve())])
except Exception:
logger.warning(f"Could not chown {directory}")
def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
"""
Create userdata directory structure.
@@ -37,6 +52,7 @@ def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "logs",
"notebooks", "plot", "strategies", ]
folder = Path(directory)
chown_user_directory(folder)
if not folder.is_dir():
if create_dir:
folder.mkdir(parents=True)
@@ -72,6 +88,5 @@ def copy_sample_files(directory: Path, overwrite: bool = False) -> None:
if not overwrite:
logger.warning(f"File `{targetfile}` exists already, not deploying sample file.")
continue
else:
logger.warning(f"File `{targetfile}` exists already, overwriting.")
logger.warning(f"File `{targetfile}` exists already, overwriting.")
shutil.copy(str(sourcedir / source), str(targetfile))

9
freqtrade/configuration/load_config.py
View File

@@ -38,6 +38,15 @@ def log_config_error_range(path: str, errmsg: str) -> str:
return ''
def load_file(path: Path) -> Dict[str, Any]:
try:
with path.open('r') as file:
config = rapidjson.load(file, parse_mode=CONFIG_PARSE_MODE)
except FileNotFoundError:
raise OperationalException(f'File "{path}" not found!')
return config
def load_config_file(path: str) -> Dict[str, Any]:
"""
Loads a config file from the given path

14
freqtrade/configuration/timerange.py
View File

@@ -3,10 +3,13 @@ This module contains the argument manager class
"""
import logging
import re
from datetime import datetime
from typing import Optional
import arrow
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
@@ -41,7 +44,7 @@ class TimeRange:
self.startts = self.startts - seconds
def adjust_start_if_necessary(self, timeframe_secs: int, startup_candles: int,
min_date: arrow.Arrow) -> None:
min_date: datetime) -> None:
"""
Adjust startts by <startup_candles> candles.
Applies only if no startup-candles have been available.
@@ -52,11 +55,11 @@ class TimeRange:
:return: None (Modifies the object in place)
"""
if (not self.starttype or (startup_candles
and min_date.int_timestamp >= self.startts)):
and min_date.timestamp() >= self.startts)):
# If no startts was defined, or backtest-data starts at the defined backtest-date
logger.warning("Moving start-date by %s candles to account for startup time.",
startup_candles)
self.startts = (min_date.int_timestamp + timeframe_secs * startup_candles)
self.startts = int(min_date.timestamp() + timeframe_secs * startup_candles)
self.starttype = 'date'
@staticmethod
@@ -103,5 +106,8 @@ class TimeRange:
stop = int(stops) // 1000
else:
stop = int(stops)
if start > stop > 0:
raise OperationalException(
f'Start date is after stop date for timerange "{text}"')
return TimeRange(stype[0], stype[1], start, stop)
raise Exception('Incorrect syntax for timerange "%s"' % text)
raise OperationalException(f'Incorrect syntax for timerange "{text}"')

55
freqtrade/constants.py
View File

@@ -11,6 +11,7 @@ DEFAULT_EXCHANGE = 'bittrex'
PROCESS_THROTTLE_SECS = 5 # sec
HYPEROPT_EPOCH = 100 # epochs
RETRY_TIMEOUT = 30 # sec
TIMEOUT_UNITS = ['minutes', 'seconds']
DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite'
UNLIMITED_STAKE_AMOUNT = 'unlimited'
@@ -26,7 +27,7 @@ HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'AgeFilter', 'PerformanceFilter', 'PrecisionFilter',
'PriceFilter', 'RangeStabilityFilter', 'ShuffleFilter',
'SpreadFilter']
'SpreadFilter', 'VolatilityFilter']
AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
DRY_RUN_WALLET = 1000
@@ -54,6 +55,11 @@ DECIMALS_PER_COIN = {
'ETH': 5,
}
DUST_PER_COIN = {
'BTC': 0.0001,
'ETH': 0.01
}
# Soure files with destination directories within user-directory
USER_DATA_FILES = {
@@ -91,6 +97,7 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'max_open_trades': {'type': ['integer', 'number'], 'minimum': -1},
'new_pairs_days': {'type': 'integer', 'default': 30},
'timeframe': {'type': 'string'},
'stake_currency': {'type': 'string'},
'stake_amount': {
@@ -131,7 +138,8 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'buy': {'type': 'number', 'minimum': 1},
'sell': {'type': 'number', 'minimum': 1}
'sell': {'type': 'number', 'minimum': 1},
'unit': {'type': 'string', 'enum': TIMEOUT_UNITS, 'default': 'minutes'}
}
},
'bid_strategy': {
@@ -160,12 +168,18 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'price_side': {'type': 'string', 'enum': ORDERBOOK_SIDES, 'default': 'ask'},
'bid_last_balance': {
'type': 'number',
'minimum': 0,
'maximum': 1,
'exclusiveMaximum': False,
},
'use_order_book': {'type': 'boolean'},
'order_book_min': {'type': 'integer', 'minimum': 1},
'order_book_max': {'type': 'integer', 'minimum': 1, 'maximum': 50},
'use_sell_signal': {'type': 'boolean'},
'sell_profit_only': {'type': 'boolean'},
'sell_profit_offset': {'type': 'number', 'minimum': 0.0},
'sell_profit_offset': {'type': 'number'},
'ignore_roi_if_buy_signal': {'type': 'boolean'}
}
},
@@ -174,6 +188,8 @@ CONF_SCHEMA = {
'properties': {
'buy': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'sell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'forcesell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'forcebuy': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'emergencysell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss_on_exchange': {'type': 'boolean'},
@@ -230,20 +246,37 @@ CONF_SCHEMA = {
'enabled': {'type': 'boolean'},
'token': {'type': 'string'},
'chat_id': {'type': 'string'},
'balance_dust_level': {'type': 'number', 'minimum': 0.0},
'notification_settings': {
'type': 'object',
'default': {},
'properties': {
'status': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'warning': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'startup': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'buy': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'buy_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}
'buy_fill': {'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
},
'sell': {
'type': ['string', 'object'],
'additionalProperties': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS
}
},
'sell_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell_fill': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
},
}
}
},
'required': ['enabled', 'token', 'chat_id']
'required': ['enabled', 'token', 'chat_id'],
},
'webhook': {
'type': 'object',
@@ -370,6 +403,16 @@ SCHEMA_TRADE_REQUIRED = [
'dataformat_trades',
]
SCHEMA_BACKTEST_REQUIRED = [
'exchange',
'max_open_trades',
'stake_currency',
'stake_amount',
'dry_run_wallet',
'dataformat_ohlcv',
'dataformat_trades',
]
SCHEMA_MINIMAL_REQUIRED = [
'exchange',
'dry_run',

89
freqtrade/data/btanalysis.py
View File

@@ -10,7 +10,7 @@ import pandas as pd
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.misc import json_load
from freqtrade.persistence import Trade, init_db
from freqtrade.persistence import LocalTrade, Trade, init_db
logger = logging.getLogger(__name__)
@@ -156,33 +156,35 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
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
)
if not df.empty:
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_OLD)
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
)
# Create compatibility with new format
df['profit_abs'] = df['close_rate'] - df['open_rate']
if 'profit_ratio' not in df.columns:
df['profit_ratio'] = df['profit_percent']
df = df.sort_values("open_date").reset_index(drop=True)
if not df.empty:
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
)
# Create compatibility with new format
df['profit_abs'] = df['close_rate'] - df['open_rate']
if not df.empty:
if 'profit_ratio' not in df.columns:
df['profit_ratio'] = df['profit_percent']
df = df.sort_values("open_date").reset_index(drop=True)
return df
@@ -224,7 +226,7 @@ def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
return df_final[df_final['open_trades'] > max_open_trades]
def trade_list_to_dataframe(trades: List[Trade]) -> pd.DataFrame:
def trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
"""
Convert list of Trade objects to pandas Dataframe
:param trades: List of trade objects
@@ -337,7 +339,7 @@ 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_date and profit_ratio)
:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
: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.
@@ -349,8 +351,8 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
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_date'
)[['profit_ratio']].sum()
df.loc[:, col_name] = _trades_sum['profit_ratio'].cumsum()
)[['profit_abs']].sum()
df.loc[:, col_name] = _trades_sum['profit_abs'].cumsum()
# Set first value to 0
df.loc[df.iloc[0].name, col_name] = 0
# FFill to get continuous
@@ -360,13 +362,14 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_ratio'
) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]:
"""
Calculate max drawdown and the corresponding close dates
:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
: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_ratio')
:return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
:return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
high and low time and high and low value.
:raise: ValueError if trade-dataframe was found empty.
"""
if len(trades) == 0:
@@ -382,4 +385,26 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
raise ValueError("No losing trade, therefore no drawdown.")
high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
low_date = profit_results.loc[idxmin, date_col]
return abs(min(max_drawdown_df['drawdown'])), high_date, low_date
high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
['high_value'].idxmax(), 'cumulative']
low_val = max_drawdown_df.loc[idxmin, 'cumulative']
return abs(min(max_drawdown_df['drawdown'])), high_date, low_date, high_val, low_val
def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:
"""
Calculate min/max cumsum of trades, to show if the wallet/stake amount ratio is sane
:param trades: DataFrame containing trades (requires columns close_date and profit_percent)
:param starting_balance: Add starting balance to results, to show the wallets high / low points
:return: Tuple (float, float) with cumsum of profit_abs
:raise: ValueError if trade-dataframe was found empty.
"""
if len(trades) == 0:
raise ValueError("Trade dataframe empty.")
csum_df = pd.DataFrame()
csum_df['sum'] = trades['profit_abs'].cumsum()
csum_min = csum_df['sum'].min() + starting_balance
csum_max = csum_df['sum'].max() + starting_balance
return csum_min, csum_max

46
freqtrade/data/converter.py
View File

@@ -110,28 +110,62 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
df.reset_index(inplace=True)
len_before = len(dataframe)
len_after = len(df)
pct_missing = (len_after - len_before) / len_before if len_before > 0 else 0
if len_before != len_after:
logger.info(f"Missing data fillup for {pair}: before: {len_before} - after: {len_after}")
message = (f"Missing data fillup for {pair}: before: {len_before} - after: {len_after}"
f" - {round(pct_missing * 100, 2)}%")
if pct_missing > 0.01:
logger.info(message)
else:
# Don't be verbose if only a small amount is missing
logger.debug(message)
return df
def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date') -> DataFrame:
def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date',
startup_candles: int = 0) -> DataFrame:
"""
Trim dataframe based on given timerange
:param df: Dataframe to trim
:param timerange: timerange (use start and end date if available)
:param: df_date_col: Column in the dataframe to use as Date column
:param df_date_col: Column in the dataframe to use as Date column
:param startup_candles: When not 0, is used instead the timerange start date
:return: trimmed dataframe
"""
if timerange.starttype == 'date':
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
df = df.loc[df[df_date_col] >= start, :]
if startup_candles:
# Trim candles instead of timeframe in case of given startup_candle count
df = df.iloc[startup_candles:, :]
else:
if timerange.starttype == 'date':
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
df = df.loc[df[df_date_col] >= start, :]
if timerange.stoptype == 'date':
stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
df = df.loc[df[df_date_col] <= stop, :]
return df
def trim_dataframes(preprocessed: Dict[str, DataFrame], timerange,
startup_candles: int) -> Dict[str, DataFrame]:
"""
Trim startup period from analyzed dataframes
:param preprocessed: Dict of pair: dataframe
:param timerange: timerange (use start and end date if available)
:param startup_candles: Startup-candles that should be removed
:return: Dict of trimmed dataframes
"""
processed: Dict[str, DataFrame] = {}
for pair, df in preprocessed.items():
trimed_df = trim_dataframe(df, timerange, startup_candles=startup_candles)
if not trimed_df.empty:
processed[pair] = trimed_df
else:
logger.warning(f'{pair} has no data left after adjusting for startup candles, '
f'skipping.')
return processed
def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
"""
TODO: This should get a dedicated test

178
freqtrade/data/dataprovider.py
View File

@@ -12,21 +12,32 @@ from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes, PairWithTimeframe
from freqtrade.data.history import load_pair_history
from freqtrade.enums import RunMode
from freqtrade.exceptions import ExchangeError, OperationalException
from freqtrade.exchange import Exchange
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
NO_EXCHANGE_EXCEPTION = 'Exchange is not available to DataProvider.'
MAX_DATAFRAME_CANDLES = 1000
class DataProvider:
def __init__(self, config: dict, exchange: Exchange, pairlists=None) -> None:
def __init__(self, config: dict, exchange: Optional[Exchange], pairlists=None) -> None:
self._config = config
self._exchange = exchange
self._pairlists = pairlists
self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
self.__slice_index: Optional[int] = None
def _set_dataframe_max_index(self, limit_index: int):
"""
Limit analyzed dataframe to max specified index.
:param limit_index: dataframe index.
"""
self.__slice_index = limit_index
def _set_cached_df(self, pair: str, timeframe: str, dataframe: DataFrame) -> None:
"""
@@ -45,40 +56,6 @@ class DataProvider:
"""
self._pairlists = pairlists
def refresh(self,
pairlist: ListPairsWithTimeframes,
helping_pairs: ListPairsWithTimeframes = None) -> None:
"""
Refresh data, called with each cycle
"""
if helping_pairs:
self._exchange.refresh_latest_ohlcv(pairlist + helping_pairs)
else:
self._exchange.refresh_latest_ohlcv(pairlist)
@property
def available_pairs(self) -> ListPairsWithTimeframes:
"""
Return a list of tuples containing (pair, timeframe) for which data is currently cached.
Should be whitelist + open trades.
"""
return list(self._exchange._klines.keys())
def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
"""
Get candle (OHLCV) data for the given pair as DataFrame
Please use the `available_pairs` method to verify which pairs are currently cached.
:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
:param copy: copy dataframe before returning if True.
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['timeframe']),
copy=copy)
else:
return DataFrame()
def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
"""
Get stored historical candle (OHLCV) data
@@ -111,47 +88,27 @@ class DataProvider:
def get_analyzed_dataframe(self, pair: str, timeframe: str) -> Tuple[DataFrame, datetime]:
"""
Retrieve the analyzed dataframe. Returns the full dataframe in trade mode (live / dry),
and the last 1000 candles (up to the time evaluated at this moment) in all other modes.
: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)]
pair_key = (pair, timeframe)
if pair_key in self.__cached_pairs:
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
df, date = self.__cached_pairs[pair_key]
else:
df, date = self.__cached_pairs[pair_key]
if self.__slice_index is not None:
max_index = self.__slice_index
df = df.iloc[max(0, max_index - MAX_DATAFRAME_CANDLES):max_index]
return df, date
else:
return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
def market(self, pair: str) -> Optional[Dict[str, Any]]:
"""
Return market data for the pair
:param pair: Pair to get the data for
:return: Market data dict from ccxt or None if market info is not available for the pair
"""
return self._exchange.markets.get(pair)
def ticker(self, pair: str):
"""
Return last ticker data from exchange
:param pair: Pair to get the data for
:return: Ticker dict from exchange or empty dict if ticker is not available for the pair
"""
try:
return self._exchange.fetch_ticker(pair)
except ExchangeError:
return {}
def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
"""
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.fetch_l2_order_book(pair, maximum)
@property
def runmode(self) -> RunMode:
"""
@@ -170,6 +127,89 @@ class DataProvider:
"""
if self._pairlists:
return self._pairlists.whitelist
return self._pairlists.whitelist.copy()
else:
raise OperationalException("Dataprovider was not initialized with a pairlist provider.")
def clear_cache(self):
"""
Clear pair dataframe cache.
"""
self.__cached_pairs = {}
# Exchange functions
def refresh(self,
pairlist: ListPairsWithTimeframes,
helping_pairs: ListPairsWithTimeframes = None) -> None:
"""
Refresh data, called with each cycle
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
if helping_pairs:
self._exchange.refresh_latest_ohlcv(pairlist + helping_pairs)
else:
self._exchange.refresh_latest_ohlcv(pairlist)
@property
def available_pairs(self) -> ListPairsWithTimeframes:
"""
Return a list of tuples containing (pair, timeframe) for which data is currently cached.
Should be whitelist + open trades.
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
return list(self._exchange._klines.keys())
def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
"""
Get candle (OHLCV) data for the given pair as DataFrame
Please use the `available_pairs` method to verify which pairs are currently cached.
:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
:param copy: copy dataframe before returning if True.
Use False only for read-only operations (where the dataframe is not modified)
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
return self._exchange.klines((pair, timeframe or self._config['timeframe']),
copy=copy)
else:
return DataFrame()
def market(self, pair: str) -> Optional[Dict[str, Any]]:
"""
Return market data for the pair
:param pair: Pair to get the data for
:return: Market data dict from ccxt or None if market info is not available for the pair
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
return self._exchange.markets.get(pair)
def ticker(self, pair: str):
"""
Return last ticker data from exchange
:param pair: Pair to get the data for
:return: Ticker dict from exchange or empty dict if ticker is not available for the pair
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
try:
return self._exchange.fetch_ticker(pair)
except ExchangeError:
return {}
def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
"""
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.
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
return self._exchange.fetch_l2_order_book(pair, maximum)

2
freqtrade/data/history/hdf5datahandler.py
View File

@@ -89,7 +89,7 @@ class HDF5DataHandler(IDataHandler):
if timerange.starttype == 'date':
where.append(f"date >= Timestamp({timerange.startts * 1e9})")
if timerange.stoptype == 'date':
where.append(f"date < Timestamp({timerange.stopts * 1e9})")
where.append(f"date <= Timestamp({timerange.stopts * 1e9})")
pairdata = pd.read_hdf(filename, key=key, mode="r", where=where)

28
freqtrade/data/history/history_utils.py
View File

@@ -155,6 +155,7 @@ def _load_cached_data_for_updating(pair: str, timeframe: str, timerange: Optiona
def _download_pair_history(datadir: Path,
exchange: Exchange,
pair: str, *,
new_pairs_days: int = 30,
timeframe: str = '5m',
timerange: Optional[TimeRange] = None,
data_handler: IDataHandler = None) -> bool:
@@ -193,7 +194,7 @@ def _download_pair_history(datadir: Path,
timeframe=timeframe,
since_ms=since_ms if since_ms else
int(arrow.utcnow().shift(
days=-30).float_timestamp) * 1000
days=-new_pairs_days).float_timestamp) * 1000
)
# TODO: Maybe move parsing to exchange class (?)
new_dataframe = ohlcv_to_dataframe(new_data, timeframe, pair,
@@ -223,7 +224,8 @@ def _download_pair_history(datadir: Path,
def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes: List[str],
datadir: Path, timerange: Optional[TimeRange] = None,
erase: bool = False, data_format: str = None) -> List[str]:
new_pairs_days: int = 30, erase: bool = False,
data_format: str = None) -> List[str]:
"""
Refresh stored ohlcv data for backtesting and hyperopt operations.
Used by freqtrade download-data subcommand.
@@ -246,12 +248,14 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
logger.info(f'Downloading pair {pair}, interval {timeframe}.')
_download_pair_history(datadir=datadir, exchange=exchange,
pair=pair, timeframe=str(timeframe),
new_pairs_days=new_pairs_days,
timerange=timerange, data_handler=data_handler)
return pairs_not_available
def _download_trades_history(exchange: Exchange,
pair: str, *,
new_pairs_days: int = 30,
timerange: Optional[TimeRange] = None,
data_handler: IDataHandler
) -> bool:
@@ -261,9 +265,13 @@ def _download_trades_history(exchange: Exchange,
"""
try:
since = timerange.startts * 1000 if \
(timerange and timerange.starttype == 'date') else int(arrow.utcnow().shift(
days=-30).float_timestamp) * 1000
until = None
if (timerange and timerange.starttype == 'date'):
since = timerange.startts * 1000
if timerange.stoptype == 'date':
until = timerange.stopts * 1000
else:
since = int(arrow.utcnow().shift(days=-new_pairs_days).float_timestamp) * 1000
trades = data_handler.trades_load(pair)
@@ -291,6 +299,7 @@ def _download_trades_history(exchange: Exchange,
# Default since_ms to 30 days if nothing is given
new_trades = exchange.get_historic_trades(pair=pair,
since=since,
until=until,
from_id=from_id,
)
trades.extend(new_trades[1])
@@ -311,8 +320,8 @@ def _download_trades_history(exchange: Exchange,
def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir: Path,
timerange: TimeRange, erase: bool = False,
data_format: str = 'jsongz') -> List[str]:
timerange: TimeRange, new_pairs_days: int = 30,
erase: bool = False, data_format: str = 'jsongz') -> List[str]:
"""
Refresh stored trades data for backtesting and hyperopt operations.
Used by freqtrade download-data subcommand.
@@ -333,6 +342,7 @@ def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir:
logger.info(f'Downloading trades for pair {pair}.')
_download_trades_history(exchange=exchange,
pair=pair,
new_pairs_days=new_pairs_days,
timerange=timerange,
data_handler=data_handler)
return pairs_not_available
@@ -362,7 +372,7 @@ def convert_trades_to_ohlcv(pairs: List[str], timeframes: List[str],
logger.exception(f'Could not convert {pair} to OHLCV.')
def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
def get_timerange(data: Dict[str, DataFrame]) -> Tuple[datetime, datetime]:
"""
Get the maximum common timerange for the given backtest data.
@@ -370,7 +380,7 @@ def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]
:return: tuple containing min_date, max_date
"""
timeranges = [
(arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
(frame['date'].min().to_pydatetime(), frame['date'].max().to_pydatetime())
for frame in data.values()
]
return (min(timeranges, key=operator.itemgetter(0))[0],

53
freqtrade/edge/edge_positioning.py
View File

@@ -1,6 +1,8 @@
# pragma pylint: disable=W0603
""" Edge positioning package """
import logging
from collections import defaultdict
from copy import deepcopy
from typing import Any, Dict, List, NamedTuple
import arrow
@@ -11,9 +13,11 @@ from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT
from freqtrade.data.history import get_timerange, load_data, refresh_data
from freqtrade.enums import RunMode, SellType
from freqtrade.exceptions import OperationalException
from freqtrade.exchange.exchange import timeframe_to_seconds
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.strategy.interface import SellType
from freqtrade.strategy.interface import IStrategy
logger = logging.getLogger(__name__)
@@ -45,7 +49,7 @@ class Edge:
self.config = config
self.exchange = exchange
self.strategy = strategy
self.strategy: IStrategy = strategy
self.edge_config = self.config.get('edge', {})
self._cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
@@ -81,12 +85,16 @@ class Edge:
if config.get('fee'):
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee(symbol=expand_pairlist(
self.config['exchange']['pair_whitelist'], list(self.exchange.markets))[0])
try:
self.fee = self.exchange.get_fee(symbol=expand_pairlist(
self.config['exchange']['pair_whitelist'], list(self.exchange.markets))[0])
except IndexError:
self.fee = None
def calculate(self, pairs: List[str]) -> bool:
if self.fee is None and pairs:
self.fee = self.exchange.get_fee(pairs[0])
def calculate(self) -> bool:
pairs = expand_pairlist(self.config['exchange']['pair_whitelist'],
list(self.exchange.markets))
heartbeat = self.edge_config.get('process_throttle_secs')
if (self._last_updated > 0) and (
@@ -98,13 +106,33 @@ class Edge:
logger.info('Using local backtesting data (using whitelist in given config) ...')
if self._refresh_pairs:
timerange_startup = deepcopy(self._timerange)
timerange_startup.subtract_start(timeframe_to_seconds(
self.strategy.timeframe) * self.strategy.startup_candle_count)
refresh_data(
datadir=self.config['datadir'],
pairs=pairs,
exchange=self.exchange,
timeframe=self.strategy.timeframe,
timerange=self._timerange,
timerange=timerange_startup,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
# Download informative pairs too
res = defaultdict(list)
for p, t in self.strategy.informative_pairs():
res[t].append(p)
for timeframe, inf_pairs in res.items():
timerange_startup = deepcopy(self._timerange)
timerange_startup.subtract_start(timeframe_to_seconds(
timeframe) * self.strategy.startup_candle_count)
refresh_data(
datadir=self.config['datadir'],
pairs=inf_pairs,
exchange=self.exchange,
timeframe=timeframe,
timerange=timerange_startup,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
data = load_data(
datadir=self.config['datadir'],
@@ -120,8 +148,11 @@ class Edge:
self._cached_pairs = {}
logger.critical("No data found. Edge is stopped ...")
return False
# Fake run-mode to Edge
prior_rm = self.config['runmode']
self.config['runmode'] = RunMode.EDGE
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
self.config['runmode'] = prior_rm
# Print timeframe
min_date, max_date = get_timerange(preprocessed)
@@ -178,7 +209,7 @@ class Edge:
if pair in self._cached_pairs:
return self._cached_pairs[pair].stoploss
else:
logger.warning('tried to access stoploss of a non-existing pair, '
logger.warning(f'Tried to access stoploss of non-existing pair {pair}, '
'strategy stoploss is returned instead.')
return self.strategy.stoploss
@@ -209,7 +240,7 @@ class Edge:
return self._final_pairs
def accepted_pairs(self) -> list:
def accepted_pairs(self) -> List[Dict[str, Any]]:
"""
return a list of accepted pairs along with their winrate, expectancy and stoploss
"""

6
freqtrade/enums/__init__.py Normal file
View File

@@ -0,0 +1,6 @@
# flake8: noqa: F401
from freqtrade.enums.rpcmessagetype import RPCMessageType
from freqtrade.enums.runmode import NON_UTIL_MODES, OPTIMIZE_MODES, TRADING_MODES, RunMode
from freqtrade.enums.selltype import SellType
from freqtrade.enums.signaltype import SignalType
from freqtrade.enums.state import State

19
freqtrade/enums/rpcmessagetype.py Normal file
View File

@@ -0,0 +1,19 @@
from enum import Enum
class RPCMessageType(Enum):
STATUS = 'status'
WARNING = 'warning'
STARTUP = 'startup'
BUY = 'buy'
BUY_FILL = 'buy_fill'
BUY_CANCEL = 'buy_cancel'
SELL = 'sell'
SELL_FILL = 'sell_fill'
SELL_CANCEL = 'sell_cancel'
def __repr__(self):
return self.value
def __str__(self):
return self.value

17
freqtrade/state.py → freqtrade/enums/runmode.py
View File

@@ -1,23 +1,6 @@
# pragma pylint: disable=too-few-public-methods
"""
Bot state constant
"""
from enum import Enum
class State(Enum):
"""
Bot application states
"""
RUNNING = 1
STOPPED = 2
RELOAD_CONFIG = 3
def __str__(self):
return f"{self.name.lower()}"
class RunMode(Enum):
"""
Bot running mode (backtest, hyperopt, ...)

20
freqtrade/enums/selltype.py Normal file
View File

@@ -0,0 +1,20 @@
from enum import Enum
class SellType(Enum):
"""
Enum to distinguish between sell reasons
"""
ROI = "roi"
STOP_LOSS = "stop_loss"
STOPLOSS_ON_EXCHANGE = "stoploss_on_exchange"
TRAILING_STOP_LOSS = "trailing_stop_loss"
SELL_SIGNAL = "sell_signal"
FORCE_SELL = "force_sell"
EMERGENCY_SELL = "emergency_sell"
CUSTOM_SELL = "custom_sell"
NONE = ""
def __str__(self):
# explicitly convert to String to help with exporting data.
return self.value

9
freqtrade/enums/signaltype.py Normal file
View File

@@ -0,0 +1,9 @@
from enum import Enum
class SignalType(Enum):
"""
Enum to distinguish between buy and sell signals
"""
BUY = "buy"
SELL = "sell"

13
freqtrade/enums/state.py Normal file
View File

@@ -0,0 +1,13 @@
from enum import Enum
class State(Enum):
"""
Bot application states
"""
RUNNING = 1
STOPPED = 2
RELOAD_CONFIG = 3
def __str__(self):
return f"{self.name.lower()}"

7
freqtrade/exchange/__init__.py
View File

@@ -7,11 +7,14 @@ from freqtrade.exchange.bibox import Bibox
from freqtrade.exchange.binance import Binance
from freqtrade.exchange.bittrex import Bittrex
from freqtrade.exchange.bybit import Bybit
from freqtrade.exchange.coinbasepro import Coinbasepro
from freqtrade.exchange.exchange import (available_exchanges, ccxt_exchanges,
get_exchange_bad_reason, is_exchange_bad,
is_exchange_known_ccxt, is_exchange_officially_supported,
market_is_active, timeframe_to_minutes, timeframe_to_msecs,
timeframe_to_next_date, timeframe_to_prev_date,
timeframe_to_seconds)
timeframe_to_seconds, validate_exchange,
validate_exchanges)
from freqtrade.exchange.ftx import Ftx
from freqtrade.exchange.hitbtc import Hitbtc
from freqtrade.exchange.kraken import Kraken
from freqtrade.exchange.kucoin import Kucoin

2
freqtrade/exchange/binance.py
View File

@@ -52,7 +52,7 @@ class Binance(Exchange):
'In stoploss limit order, stop price should be more than limit price')
if self._config['dry_run']:
dry_order = self.dry_run_order(
dry_order = self.create_dry_run_order(
pair, ordertype, "sell", amount, stop_price)
return dry_order

10
freqtrade/exchange/bittrex.py
View File

@@ -12,12 +12,14 @@ class Bittrex(Exchange):
"""
Bittrex exchange class. Contains adjustments needed for Freqtrade to work
with this exchange.
Please note that this exchange is not included in the list of exchanges
officially supported by the Freqtrade development team. So some features
may still not work as expected.
"""
_ft_has: Dict = {
"ohlcv_candle_limit_per_timeframe": {
'1m': 1440,
'5m': 288,
'1h': 744,
'1d': 365,
},
"l2_limit_range": [1, 25, 500],
}

1
freqtrade/exchange/bybit.py
View File

@@ -18,7 +18,6 @@ class Bybit(Exchange):
may still not work as expected.
"""
# fetchCurrencies API point requires authentication for Bybit,
_ft_has: Dict = {
"ohlcv_candle_limit": 200,
}

23
freqtrade/exchange/coinbasepro.py Normal file
View File

@@ -0,0 +1,23 @@
""" CoinbasePro exchange subclass """
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Coinbasepro(Exchange):
"""
CoinbasePro exchange class. Contains adjustments needed for Freqtrade to work
with this exchange.
Please note that this exchange is not included in the list of exchanges
officially supported by the Freqtrade development team. So some features
may still not work as expected.
"""
_ft_has: Dict = {
"ohlcv_candle_limit": 300,
}

95
freqtrade/exchange/common.py
View File

@@ -18,78 +18,8 @@ BAD_EXCHANGES = {
"bitmex": "Various reasons.",
"bitstamp": "Does not provide history. "
"Details in https://github.com/freqtrade/freqtrade/issues/1983",
"hitbtc": "This API cannot be used with Freqtrade. "
"Use `hitbtc2` exchange id to access this exchange.",
"phemex": "Does not provide history. ",
"poloniex": "Does not provide fetch_order endpoint to fetch both open and closed orders.",
**dict.fromkeys([
'adara',
'anxpro',
'bigone',
'coinbase',
'coinexchange',
'coinmarketcap',
'lykke',
'xbtce',
], "Does not provide timeframes. ccxt fetchOHLCV: False"),
**dict.fromkeys([
'bcex',
'bit2c',
'bitbay',
'bitflyer',
'bitforex',
'bithumb',
'bitso',
'bitstamp1',
'bl3p',
'braziliex',
'btcbox',
'btcchina',
'btctradeim',
'btctradeua',
'bxinth',
'chilebit',
'coincheck',
'coinegg',
'coinfalcon',
'coinfloor',
'coingi',
'coinmate',
'coinone',
'coinspot',
'coolcoin',
'crypton',
'deribit',
'exmo',
'exx',
'flowbtc',
'foxbit',
'fybse',
# 'hitbtc',
'ice3x',
'independentreserve',
'indodax',
'itbit',
'lakebtc',
'latoken',
'liquid',
'livecoin',
'luno',
'mixcoins',
'negociecoins',
'nova',
'paymium',
'southxchange',
'stronghold',
'surbitcoin',
'therock',
'tidex',
'vaultoro',
'vbtc',
'virwox',
'yobit',
'zaif',
], "Does not provide timeframes. ccxt fetchOHLCV: emulated"),
}
MAP_EXCHANGE_CHILDCLASS = {
@@ -98,6 +28,29 @@ MAP_EXCHANGE_CHILDCLASS = {
}
EXCHANGE_HAS_REQUIRED = [
# Required / private
'fetchOrder',
'cancelOrder',
'createOrder',
# 'createLimitOrder', 'createMarketOrder',
'fetchBalance',
# Public endpoints
'loadMarkets',
'fetchOHLCV',
]
EXCHANGE_HAS_OPTIONAL = [
# Private
'fetchMyTrades', # Trades for order - fee detection
# Public
'fetchOrderBook', 'fetchL2OrderBook', 'fetchTicker', # OR for pricing
'fetchTickers', # For volumepairlist?
'fetchTrades', # Downloading trades data
]
def calculate_backoff(retrycount, max_retries):
"""
Calculate backoff
@@ -140,7 +93,7 @@ def retrier(_func=None, retries=API_RETRY_COUNT):
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):
if isinstance(ex, (DDosProtection, RetryableOrderError)):
# increasing backoff
backoff_delay = calculate_backoff(count + 1, retries)
logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")

1090
freqtrade/exchange/exchange.py
View File

File diff suppressed because it is too large Load Diff

31
freqtrade/exchange/ftx.py
View File

@@ -8,6 +8,7 @@ from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, Invali
OperationalException, TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import API_FETCH_ORDER_RETRY_COUNT, retrier
from freqtrade.misc import safe_value_fallback2
logger = logging.getLogger(__name__)
@@ -53,7 +54,7 @@ class Ftx(Exchange):
stop_price = self.price_to_precision(pair, stop_price)
if self._config['dry_run']:
dry_order = self.dry_run_order(
dry_order = self.create_dry_run_order(
pair, ordertype, "sell", amount, stop_price)
return dry_order
@@ -63,10 +64,11 @@ class Ftx(Exchange):
# set orderPrice to place limit order, otherwise it's a market order
params['orderPrice'] = limit_rate
params['stopPrice'] = stop_price
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)
amount=amount, params=params)
logger.info('stoploss order added for %s. '
'stop price: %s.', pair, stop_price)
return order
@@ -91,18 +93,24 @@ class Ftx(Exchange):
@retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
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
return self.fetch_dry_run_order(order_id)
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:
if order[0].get('status') == 'closed':
# Trigger order was triggered ...
real_order_id = order[0].get('info', {}).get('orderId')
order1 = self._api.fetch_order(real_order_id, pair)
# Fake type to stop - as this was really a stop order.
order1['id_stop'] = order1['id']
order1['id'] = order_id
order1['type'] = 'stop'
order1['status_stop'] = 'triggered'
return order1
return order[0]
else:
raise InvalidOrderException(f"Could not get stoploss order for id {order_id}")
@@ -134,3 +142,8 @@ class Ftx(Exchange):
f'Could not cancel order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
def get_order_id_conditional(self, order: Dict[str, Any]) -> str:
if order['type'] == 'stop':
return safe_value_fallback2(order, order, 'id_stop', 'id')
return order['id']

23
freqtrade/exchange/hitbtc.py Normal file
View File

@@ -0,0 +1,23 @@
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Hitbtc(Exchange):
"""
Hitbtc exchange class. Contains adjustments needed for Freqtrade to work
with this exchange.
Please note that this exchange is not included in the list of exchanges
officially supported by the Freqtrade development team. So some features
may still not work as expected.
"""
_ft_has: Dict = {
"ohlcv_candle_limit": 1000,
"ohlcv_params": {"sort": "DESC"}
}

4
freqtrade/exchange/kraken.py
View File

@@ -53,6 +53,8 @@ class Kraken(Exchange):
# x["side"], x["amount"],
) for x in orders]
for bal in balances:
if not isinstance(balances[bal], dict):
continue
balances[bal]['used'] = sum(order[1] for order in order_list if order[0] == bal)
balances[bal]['free'] = balances[bal]['total'] - balances[bal]['used']
@@ -92,7 +94,7 @@ class Kraken(Exchange):
stop_price = self.price_to_precision(pair, stop_price)
if self._config['dry_run']:
dry_order = self.dry_run_order(
dry_order = self.create_dry_run_order(
pair, ordertype, "sell", amount, stop_price)
return dry_order

24
freqtrade/exchange/kucoin.py Normal file
View File

@@ -0,0 +1,24 @@
""" Kucoin exchange subclass """
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Kucoin(Exchange):
"""
Kucoin exchange class. Contains adjustments needed for Freqtrade to work
with this exchange.
Please note that this exchange is not included in the list of exchanges
officially supported by the Freqtrade development team. So some features
may still not work as expected.
"""
_ft_has: Dict = {
"l2_limit_range": [20, 100],
"l2_limit_range_required": False,
}

301
freqtrade/freqtradebot.py
View File

@@ -10,13 +10,13 @@ from threading import Lock
from typing import Any, Dict, List, Optional
import arrow
from cachetools import TTLCache
from freqtrade import __version__, constants
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.enums import RPCMessageType, SellType, State
from freqtrade.exceptions import (DependencyException, ExchangeError, InsufficientFundsError,
InvalidOrderException, PricingError)
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
@@ -26,9 +26,8 @@ from freqtrade.persistence import Order, PairLocks, Trade, cleanup_db, init_db
from freqtrade.plugins.pairlistmanager import PairListManager
from freqtrade.plugins.protectionmanager import ProtectionManager
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.rpc import RPCManager, RPCMessageType
from freqtrade.state import State
from freqtrade.strategy.interface import IStrategy, SellType
from freqtrade.rpc import RPCManager
from freqtrade.strategy.interface import IStrategy, SellCheckTuple
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.wallets import Wallets
@@ -48,6 +47,7 @@ class FreqtradeBot(LoggingMixin):
:param config: configuration dict, you can use Configuration.get_config()
to get the config dict.
"""
self.active_pair_whitelist: List[str] = []
logger.info('Starting freqtrade %s', __version__)
@@ -57,12 +57,6 @@ class FreqtradeBot(LoggingMixin):
# Init objects
self.config = config
# Cache values for 1800 to avoid frequent polling of the exchange for prices
# Caching only applies to RPC methods, so prices for open trades are still
# refreshed once every iteration.
self._sell_rate_cache: TTLCache = TTLCache(maxsize=100, ttl=1800)
self._buy_rate_cache: TTLCache = TTLCache(maxsize=100, ttl=1800)
self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
# Check config consistency here since strategies can set certain options
@@ -76,12 +70,19 @@ class FreqtradeBot(LoggingMixin):
PairLocks.timeframe = self.config['timeframe']
self.protections = ProtectionManager(self.config)
# RPC runs in separate threads, can start handling external commands just after
# initialization, even before Freqtradebot has a chance to start its throttling,
# so anything in the Freqtradebot instance should be ready (initialized), including
# the initial state of the bot.
# Keep this at the end of this initialization method.
self.rpc: RPCManager = RPCManager(self)
self.pairlists = PairListManager(self.exchange, self.config)
self.dataprovider = DataProvider(self.config, self.exchange, self.pairlists)
self.protections = ProtectionManager(self.config)
# Attach Dataprovider to Strategy baseclass
IStrategy.dp = self.dataprovider
# Attach Wallets to Strategy baseclass
@@ -97,12 +98,6 @@ class FreqtradeBot(LoggingMixin):
initial_state = self.config.get('initial_state')
self.state = State[initial_state.upper()] if initial_state else State.STOPPED
# RPC runs in separate threads, can start handling external commands just after
# initialization, even before Freqtradebot has a chance to start its throttling,
# so anything in the Freqtradebot instance should be ready (initialized), including
# the initial state of the bot.
# Keep this at the end of this initialization method.
self.rpc: RPCManager = RPCManager(self)
# Protect sell-logic from forcesell and viceversa
self._sell_lock = Lock()
LoggingMixin.__init__(self, logger, timeframe_to_seconds(self.strategy.timeframe))
@@ -113,7 +108,7 @@ class FreqtradeBot(LoggingMixin):
via RPC about changes in the bot status.
"""
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'type': RPCMessageType.STATUS,
'status': msg
})
@@ -179,6 +174,7 @@ class FreqtradeBot(LoggingMixin):
# Without this, freqtrade my try to recreate stoploss_on_exchange orders
# while selling is in process, since telegram messages arrive in an different thread.
with self._sell_lock:
trades = Trade.get_open_trades()
# First process current opened trades (positions)
self.exit_positions(trades)
@@ -186,7 +182,7 @@ class FreqtradeBot(LoggingMixin):
if self.get_free_open_trades():
self.enter_positions()
Trade.session.flush()
Trade.commit()
def process_stopped(self) -> None:
"""
@@ -204,7 +200,7 @@ class FreqtradeBot(LoggingMixin):
if len(open_trades) != 0:
msg = {
'type': RPCMessageType.WARNING_NOTIFICATION,
'type': RPCMessageType.WARNING,
'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' "
@@ -224,7 +220,7 @@ class FreqtradeBot(LoggingMixin):
# Calculating Edge positioning
if self.edge:
self.edge.calculate()
self.edge.calculate(_whitelist)
_whitelist = self.edge.adjust(_whitelist)
if trades:
@@ -266,7 +262,7 @@ class FreqtradeBot(LoggingMixin):
def update_closed_trades_without_assigned_fees(self):
"""
Update closed trades without close fees assigned.
Only acts when Orders are in the database, otherwise the last orderid is unknown.
Only acts when Orders are in the database, otherwise the last order-id is unknown.
"""
if self.config['dry_run']:
# Updating open orders in dry-run does not make sense and will fail.
@@ -341,7 +337,7 @@ class FreqtradeBot(LoggingMixin):
# Assume this as the open order
trade.open_order_id = order.order_id
if fo:
logger.info(f"Found {order} for trade {trade}.jj")
logger.info(f"Found {order} for trade {trade}.")
self.update_trade_state(trade, order.order_id, fo,
stoploss_order=order.ft_order_side == 'stoploss')
@@ -377,7 +373,7 @@ class FreqtradeBot(LoggingMixin):
if lock:
self.log_once(f"Global pairlock active until "
f"{lock.lock_end_time.strftime(constants.DATETIME_PRINT_FORMAT)}. "
"Not creating new trades.", logger.info)
f"Not creating new trades, reason: {lock.reason}.", logger.info)
else:
self.log_once("Global pairlock active. Not creating new trades.", logger.info)
return trades_created
@@ -393,52 +389,6 @@ class FreqtradeBot(LoggingMixin):
return trades_created
def get_buy_rate(self, pair: str, refresh: bool) -> float:
"""
Calculates bid target between current ask price and last price
:param pair: Pair to get rate for
:param refresh: allow cached data
:return: float: Price
"""
if not refresh:
rate = self._buy_rate_cache.get(pair)
# Check if cache has been invalidated
if rate:
logger.debug(f"Using cached buy rate for {pair}.")
return rate
bid_strategy = self.config.get('bid_strategy', {})
if 'use_order_book' in bid_strategy and bid_strategy.get('use_order_book', False):
logger.info(
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.fetch_l2_order_book(pair, order_book_top)
logger.debug('order_book %s', order_book)
# top 1 = index 0
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)
ticker_rate = ticker[bid_strategy['price_side']]
if ticker['last'] and ticker_rate > ticker['last']:
balance = self.config['bid_strategy']['ask_last_balance']
ticker_rate = ticker_rate + balance * (ticker['last'] - ticker_rate)
used_rate = ticker_rate
self._buy_rate_cache[pair] = used_rate
return used_rate
def create_trade(self, pair: str) -> bool:
"""
Check the implemented trading strategy for buy signals.
@@ -456,7 +406,8 @@ class FreqtradeBot(LoggingMixin):
lock = PairLocks.get_pair_longest_lock(pair, nowtime)
if lock:
self.log_once(f"Pair {pair} is still locked until "
f"{lock.lock_end_time.strftime(constants.DATETIME_PRINT_FORMAT)}.",
f"{lock.lock_end_time.strftime(constants.DATETIME_PRINT_FORMAT)} "
f"due to {lock.reason}.",
logger.info)
else:
self.log_once(f"Pair {pair} is still locked.", logger.info)
@@ -472,25 +423,22 @@ class FreqtradeBot(LoggingMixin):
(buy, sell) = self.strategy.get_signal(pair, self.strategy.timeframe, analyzed_df)
if buy and not sell:
stake_amount = self.wallets.get_trade_stake_amount(pair, self.get_free_open_trades(),
self.edge)
stake_amount = self.wallets.get_trade_stake_amount(pair, self.edge)
if not stake_amount:
logger.debug(f"Stake amount is 0, ignoring possible trade for {pair}.")
return False
logger.info(f"Buy signal found: about create a new trade with stake_amount: "
logger.info(f"Buy signal found: about create a new trade for {pair} with stake_amount: "
f"{stake_amount} ...")
bid_check_dom = self.config.get('bid_strategy', {}).get('check_depth_of_market', {})
if ((bid_check_dom.get('enabled', False)) and
(bid_check_dom.get('bids_to_ask_delta', 0) > 0)):
if self._check_depth_of_market_buy(pair, bid_check_dom):
logger.info(f'Executing Buy for {pair}.')
return self.execute_buy(pair, stake_amount)
else:
return False
logger.info(f'Executing Buy for {pair}')
return self.execute_buy(pair, stake_amount)
else:
return False
@@ -519,7 +467,8 @@ class FreqtradeBot(LoggingMixin):
logger.info(f"Bids to asks delta for {pair} does not satisfy condition.")
return False
def execute_buy(self, pair: str, stake_amount: float, price: Optional[float] = None) -> bool:
def execute_buy(self, pair: str, stake_amount: float, price: Optional[float] = None,
forcebuy: bool = False) -> bool:
"""
Executes a limit buy for the given pair
:param pair: pair for which we want to create a LIMIT_BUY
@@ -531,7 +480,7 @@ class FreqtradeBot(LoggingMixin):
buy_limit_requested = price
else:
# Calculate price
buy_limit_requested = self.get_buy_rate(pair, True)
buy_limit_requested = self.exchange.get_buy_rate(pair, True)
if not buy_limit_requested:
raise PricingError('Could not determine buy price.')
@@ -547,9 +496,13 @@ class FreqtradeBot(LoggingMixin):
amount = stake_amount / buy_limit_requested
order_type = self.strategy.order_types['buy']
if forcebuy:
# Forcebuy can define a different ordertype
order_type = self.strategy.order_types.get('forcebuy', order_type)
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):
time_in_force=time_in_force, current_time=datetime.now(timezone.utc)):
logger.info(f"User requested abortion of buying {pair}")
return False
amount = self.exchange.amount_to_precision(pair, amount)
@@ -598,6 +551,7 @@ class FreqtradeBot(LoggingMixin):
pair=pair,
stake_amount=stake_amount,
amount=amount,
is_open=True,
amount_requested=amount_requested,
fee_open=fee,
fee_close=fee,
@@ -615,8 +569,8 @@ class FreqtradeBot(LoggingMixin):
if order_status == 'closed':
self.update_trade_state(trade, order_id, order)
Trade.session.add(trade)
Trade.session.flush()
Trade.query.session.add(trade)
Trade.commit()
# Updating wallets
self.wallets.update()
@@ -627,11 +581,11 @@ class FreqtradeBot(LoggingMixin):
def _notify_buy(self, trade: Trade, order_type: str) -> None:
"""
Sends rpc notification when a buy occured.
Sends rpc notification when a buy occurred.
"""
msg = {
'trade_id': trade.id,
'type': RPCMessageType.BUY_NOTIFICATION,
'type': RPCMessageType.BUY,
'exchange': self.exchange.name.capitalize(),
'pair': trade.pair,
'limit': trade.open_rate,
@@ -649,13 +603,13 @@ class FreqtradeBot(LoggingMixin):
def _notify_buy_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
"""
Sends rpc notification when a buy cancel occured.
Sends rpc notification when a buy cancel occurred.
"""
current_rate = self.get_buy_rate(trade.pair, False)
current_rate = self.exchange.get_buy_rate(trade.pair, False)
msg = {
'trade_id': trade.id,
'type': RPCMessageType.BUY_CANCEL_NOTIFICATION,
'type': RPCMessageType.BUY_CANCEL,
'exchange': self.exchange.name.capitalize(),
'pair': trade.pair,
'limit': trade.open_rate,
@@ -672,6 +626,21 @@ class FreqtradeBot(LoggingMixin):
# Send the message
self.rpc.send_msg(msg)
def _notify_buy_fill(self, trade: Trade) -> None:
msg = {
'trade_id': trade.id,
'type': RPCMessageType.BUY_FILL,
'exchange': self.exchange.name.capitalize(),
'pair': trade.pair,
'open_rate': trade.open_rate,
'stake_amount': trade.stake_amount,
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency', None),
'amount': trade.amount,
'open_date': trade.open_date,
}
self.rpc.send_msg(msg)
#
# SELL / exit positions / close trades logic and methods
#
@@ -687,6 +656,7 @@ class FreqtradeBot(LoggingMixin):
if (self.strategy.order_types.get('stoploss_on_exchange') and
self.handle_stoploss_on_exchange(trade)):
trades_closed += 1
Trade.commit()
continue
# Check if we can sell our current pair
if trade.open_order_id is None and trade.is_open and self.handle_trade(trade):
@@ -695,56 +665,12 @@ class FreqtradeBot(LoggingMixin):
except DependencyException as exception:
logger.warning('Unable to sell trade %s: %s', trade.pair, exception)
# Updating wallets if any trade occured
# Updating wallets if any trade occurred
if trades_closed:
self.wallets.update()
return trades_closed
def _order_book_gen(self, pair: str, side: str, order_book_max: int = 1,
order_book_min: int = 1):
"""
Helper generator to query orderbook in loop (used for early sell-order placing)
"""
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]
def get_sell_rate(self, pair: str, refresh: bool) -> float:
"""
Get sell rate - either using ticker bid or first bid based on orderbook
The orderbook portion is only used for rpc messaging, which would otherwise fail
for BitMex (has no bid/ask in fetch_ticker)
or remain static in any other case since it's not updating.
:param pair: Pair to get rate for
:param refresh: allow cached data
:return: Bid rate
"""
if not refresh:
rate = self._sell_rate_cache.get(pair)
# Check if cache has been invalidated
if rate:
logger.debug(f"Using cached sell rate for {pair}.")
return rate
ask_strategy = self.config.get('ask_strategy', {})
if ask_strategy.get('use_order_book', False):
# This code is only used for notifications, selling uses the generator directly
logger.info(
f"Getting price from order book {ask_strategy['price_side'].capitalize()} side."
)
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
def handle_trade(self, trade: Trade) -> bool:
"""
Sells the current pair if the threshold is reached and updates the trade record.
@@ -772,9 +698,9 @@ class FreqtradeBot(LoggingMixin):
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)
order_book = self.exchange._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):
try:
sell_rate = next(order_book)
@@ -787,14 +713,14 @@ class FreqtradeBot(LoggingMixin):
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
self.exchange._sell_rate_cache[trade.pair] = sell_rate
if self._check_and_execute_sell(trade, sell_rate, buy, sell):
return True
else:
logger.debug('checking sell')
sell_rate = self.get_sell_rate(trade.pair, True)
sell_rate = self.exchange.get_sell_rate(trade.pair, True)
if self._check_and_execute_sell(trade, sell_rate, buy, sell):
return True
@@ -826,7 +752,8 @@ class FreqtradeBot(LoggingMixin):
trade.stoploss_order_id = None
logger.error(f'Unable to place a stoploss order on exchange. {e}')
logger.warning('Selling the trade forcefully')
self.execute_sell(trade, trade.stop_loss, sell_reason=SellType.EMERGENCY_SELL)
self.execute_sell(trade, trade.stop_loss, sell_reason=SellCheckTuple(
sell_type=SellType.EMERGENCY_SELL))
except ExchangeError:
trade.stoploss_order_id = None
@@ -889,8 +816,13 @@ class FreqtradeBot(LoggingMixin):
logger.warning('Stoploss order was cancelled, but unable to recreate one.')
# Finally we check if stoploss on exchange should be moved up because of trailing.
if stoploss_order and (self.config.get('trailing_stop', False)
or self.config.get('use_custom_stoploss', False)):
# Triggered Orders are now real orders - so don't replace stoploss anymore
if (
stoploss_order
and stoploss_order.get('status_stop') != 'triggered'
and (self.config.get('trailing_stop', False)
or self.config.get('use_custom_stoploss', False))
):
# if trailing stoploss is enabled we check if stoploss value has changed
# in which case we cancel stoploss order and put another one with new
# value immediately
@@ -907,14 +839,15 @@ class FreqtradeBot(LoggingMixin):
:return: None
"""
if self.exchange.stoploss_adjust(trade.stop_loss, order):
# we check if the update is neccesary
# we check if the update is necessary
update_beat = self.strategy.order_types.get('stoploss_on_exchange_interval', 60)
if (datetime.utcnow() - trade.stoploss_last_update).total_seconds() >= update_beat:
# cancelling the current stoploss on exchange first
logger.info(f"Cancelling current stoploss on exchange for pair {trade.pair} "
f"(orderid:{order['id']}) in order to add another one ...")
try:
co = self.exchange.cancel_stoploss_order(order['id'], trade.pair)
co = self.exchange.cancel_stoploss_order_with_result(order['id'], trade.pair,
trade.amount)
trade.update_order(co)
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {order['id']} "
@@ -931,13 +864,13 @@ class FreqtradeBot(LoggingMixin):
Check and execute sell
"""
should_sell = self.strategy.should_sell(
trade, sell_rate, datetime.utcnow(), buy, sell,
trade, sell_rate, datetime.now(timezone.utc), buy, sell,
force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
)
if should_sell.sell_flag:
logger.info(f'Executing Sell for {trade.pair}. Reason: {should_sell.sell_type}')
self.execute_sell(trade, sell_rate, should_sell.sell_type)
self.execute_sell(trade, sell_rate, should_sell)
return True
return False
@@ -948,15 +881,16 @@ class FreqtradeBot(LoggingMixin):
timeout = self.config.get('unfilledtimeout', {}).get(side)
ordertime = arrow.get(order['datetime']).datetime
if timeout is not None:
timeout_threshold = arrow.utcnow().shift(minutes=-timeout).datetime
timeout_unit = self.config.get('unfilledtimeout', {}).get('unit', 'minutes')
timeout_kwargs = {timeout_unit: -timeout}
timeout_threshold = arrow.utcnow().shift(**timeout_kwargs).datetime
return (order['status'] == 'open' and order['side'] == side
and ordertime < timeout_threshold)
return False
def check_handle_timedout(self) -> None:
"""
Check if any orders are timed out and cancel if neccessary
Check if any orders are timed out and cancel if necessary
:param timeoutvalue: Number of minutes until order is considered timed out
:return: None
"""
@@ -1008,6 +942,7 @@ class FreqtradeBot(LoggingMixin):
elif order['side'] == 'sell':
self.handle_cancel_sell(trade, order, constants.CANCEL_REASON['ALL_CANCELLED'])
Trade.commit()
def handle_cancel_buy(self, trade: Trade, order: Dict, reason: str) -> bool:
"""
@@ -1017,13 +952,23 @@ class FreqtradeBot(LoggingMixin):
was_trade_fully_canceled = False
# Cancelled orders may have the status of 'canceled' or 'closed'
if order['status'] not in ('canceled', 'closed'):
if order['status'] not in ('cancelled', 'canceled', 'closed'):
filled_val = order.get('filled', 0.0) or 0.0
filled_stake = filled_val * trade.open_rate
minstake = self.exchange.get_min_pair_stake_amount(
trade.pair, trade.open_rate, self.strategy.stoploss)
if filled_val > 0 and filled_stake < minstake:
logger.warning(
f"Order {trade.open_order_id} for {trade.pair} not cancelled, "
f"as the filled amount of {filled_val} would result in an unsellable trade.")
return False
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'):
if corder.get('status') not in ('cancelled', 'canceled', 'closed'):
logger.warning(f"Order {trade.open_order_id} for {trade.pair} not cancelled.")
return False
else:
@@ -1126,16 +1071,16 @@ class FreqtradeBot(LoggingMixin):
raise DependencyException(
f"Not enough amount to sell. Trade-amount: {amount}, Wallet: {wallet_amount}")
def execute_sell(self, trade: Trade, limit: float, sell_reason: SellType) -> bool:
def execute_sell(self, trade: Trade, limit: float, sell_reason: SellCheckTuple) -> bool:
"""
Executes a limit sell for the given trade and limit
:param trade: Trade instance
:param limit: limit rate for the sell order
:param sellreason: Reason the sell was triggered
:param sell_reason: Reason the sell was triggered
:return: True if it succeeds (supported) False (not supported)
"""
sell_type = 'sell'
if sell_reason in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
if sell_reason.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
sell_type = 'stoploss'
# if stoploss is on exchange and we are on dry_run mode,
@@ -1147,22 +1092,28 @@ class FreqtradeBot(LoggingMixin):
# First cancelling stoploss on exchange ...
if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
try:
self.exchange.cancel_stoploss_order(trade.stoploss_order_id, trade.pair)
co = self.exchange.cancel_stoploss_order_with_result(trade.stoploss_order_id,
trade.pair, trade.amount)
trade.update_order(co)
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
order_type = self.strategy.order_types[sell_type]
if sell_reason == SellType.EMERGENCY_SELL:
if sell_reason.sell_type == SellType.EMERGENCY_SELL:
# Emergency sells (default to market!)
order_type = self.strategy.order_types.get("emergencysell", "market")
if sell_reason.sell_type == SellType.FORCE_SELL:
# Force sells (default to the sell_type defined in the strategy,
# but we allow this value to be changed)
order_type = self.strategy.order_types.get("forcesell", order_type)
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):
time_in_force=time_in_force, sell_reason=sell_reason.sell_reason,
current_time=datetime.now(timezone.utc)):
logger.info(f"User requested abortion of selling {trade.pair}")
return False
@@ -1183,14 +1134,15 @@ class FreqtradeBot(LoggingMixin):
trade.orders.append(order_obj)
trade.open_order_id = order['id']
trade.sell_order_status = ''
trade.close_rate_requested = limit
trade.sell_reason = sell_reason.value
trade.sell_reason = sell_reason.sell_reason
# In case of market sell orders the order can be closed immediately
if order.get('status', 'unknown') == 'closed':
self.update_trade_state(trade, trade.open_order_id, order)
Trade.session.flush()
Trade.commit()
# Lock pair for one candle to prevent immediate rebuys
# Lock pair for one candle to prevent immediate re-buys
self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
reason='Auto lock')
@@ -1198,19 +1150,20 @@ class FreqtradeBot(LoggingMixin):
return True
def _notify_sell(self, trade: Trade, order_type: str) -> None:
def _notify_sell(self, trade: Trade, order_type: str, fill: bool = False) -> None:
"""
Sends rpc notification when a sell occured.
Sends rpc notification when a sell occurred.
"""
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate)
# Use cached rates here - it was updated seconds ago.
current_rate = self.get_sell_rate(trade.pair, False)
current_rate = self.exchange.get_sell_rate(trade.pair, False) if not fill else None
profit_ratio = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_ratio > 0 else "loss"
msg = {
'type': RPCMessageType.SELL_NOTIFICATION,
'type': (RPCMessageType.SELL_FILL if fill
else RPCMessageType.SELL),
'trade_id': trade.id,
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
@@ -1219,6 +1172,7 @@ class FreqtradeBot(LoggingMixin):
'order_type': order_type,
'amount': trade.amount,
'open_rate': trade.open_rate,
'close_rate': trade.close_rate,
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_ratio': profit_ratio,
@@ -1239,7 +1193,7 @@ class FreqtradeBot(LoggingMixin):
def _notify_sell_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
"""
Sends rpc notification when a sell cancel occured.
Sends rpc notification when a sell cancel occurred.
"""
if trade.sell_order_status == reason:
return
@@ -1248,12 +1202,12 @@ class FreqtradeBot(LoggingMixin):
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate)
current_rate = self.get_sell_rate(trade.pair, False)
current_rate = self.exchange.get_sell_rate(trade.pair, False)
profit_ratio = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_ratio > 0 else "loss"
msg = {
'type': RPCMessageType.SELL_CANCEL_NOTIFICATION,
'type': RPCMessageType.SELL_CANCEL,
'trade_id': trade.id,
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
@@ -1292,7 +1246,7 @@ class FreqtradeBot(LoggingMixin):
Handles closing both buy and sell orders.
:param trade: Trade object of the trade we're analyzing
:param order_id: Order-id of the order we're analyzing
:param action_order: Already aquired order object
:param action_order: Already acquired order object
:return: True if order has been cancelled without being filled partially, False otherwise
"""
if not order_id:
@@ -1327,12 +1281,19 @@ class FreqtradeBot(LoggingMixin):
# Handling of this will happen in check_handle_timeout.
return True
trade.update(order)
Trade.commit()
# Updating wallets when order is closed
if not trade.is_open:
if not stoploss_order and not trade.open_order_id:
self._notify_sell(trade, '', True)
self.protections.stop_per_pair(trade.pair)
self.protections.global_stop()
self.wallets.update()
elif not trade.open_order_id:
# Buy fill
self._notify_buy_fill(trade)
return False
def apply_fee_conditional(self, trade: Trade, trade_base_currency: str,
@@ -1356,7 +1317,7 @@ class FreqtradeBot(LoggingMixin):
def get_real_amount(self, trade: Trade, order: Dict) -> float:
"""
Detect and update trade fee.
Calls trade.update_fee() uppon correct detection.
Calls trade.update_fee() upon correct detection.
Returns modified amount if the fee was taken from the destination currency.
Necessary for exchanges which charge fees in base currency (e.g. binance)
:return: identical (or new) amount for the trade
@@ -1389,8 +1350,8 @@ class FreqtradeBot(LoggingMixin):
"""
fee-detection fallback to Trades. Parses result of fetch_my_trades to get correct fee.
"""
trades = self.exchange.get_trades_for_order(order['id'], trade.pair,
trade.open_date)
trades = self.exchange.get_trades_for_order(self.exchange.get_order_id_conditional(order),
trade.pair, trade.open_date)
if len(trades) == 0:
logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)

15
freqtrade/misc.py
View File

@@ -6,7 +6,7 @@ import logging
import re
from datetime import datetime
from pathlib import Path
from typing import Any
from typing import Any, Iterator, List
from typing.io import IO
import rapidjson
@@ -81,7 +81,7 @@ def json_load(datafile: IO) -> Any:
"""
load data with rapidjson
Use this to have a consistent experience,
sete number_mode to "NM_NATIVE" for greatest speed
set number_mode to "NM_NATIVE" for greatest speed
"""
return rapidjson.load(datafile, number_mode=rapidjson.NM_NATIVE)
@@ -202,3 +202,14 @@ def render_template_with_fallback(templatefile: str, templatefallbackfile: str,
return render_template(templatefile, arguments)
except TemplateNotFound:
return render_template(templatefallbackfile, arguments)
def chunks(lst: List[Any], n: int) -> Iterator[List[Any]]:
"""
Split lst into chunks of the size n.
:param lst: list to split into chunks
:param n: number of max elements per chunk
:return: None
"""
for chunk in range(0, len(lst), n):
yield (lst[chunk:chunk + n])

263
freqtrade/optimize/backtesting.py
View File

@@ -15,19 +15,21 @@ from freqtrade.configuration import TimeRange, remove_credentials, validate_conf
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data import history
from freqtrade.data.btanalysis import trade_list_to_dataframe
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import OperationalException
from freqtrade.enums import SellType
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.mixins import LoggingMixin
from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
store_backtest_stats)
from freqtrade.persistence import PairLocks, Trade
from freqtrade.persistence import LocalTrade, PairLocks, Trade
from freqtrade.plugins.pairlistmanager import PairListManager
from freqtrade.plugins.protectionmanager import ProtectionManager
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
from freqtrade.strategy.interface import IStrategy, SellCheckTuple
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.wallets import Wallets
logger = logging.getLogger(__name__)
@@ -62,9 +64,7 @@ class Backtesting:
self.all_results: Dict[str, Dict] = {}
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = dataprovider
self.dataprovider = DataProvider(self.config, None)
if self.config.get('strategy_list', None):
for strat in list(self.config['strategy_list']):
@@ -95,7 +95,7 @@ class Backtesting:
"PrecisionFilter not allowed for backtesting multiple strategies."
)
dataprovider.add_pairlisthandler(self.pairlists)
self.dataprovider.add_pairlisthandler(self.pairlists)
self.pairlists.refresh_pairlist()
if len(self.pairlists.whitelist) == 0:
@@ -111,28 +111,33 @@ class Backtesting:
PairLocks.timeframe = self.config['timeframe']
PairLocks.use_db = False
PairLocks.reset_locks()
if self.config.get('enable_protections', False):
self.protections = ProtectionManager(self.config)
self.wallets = Wallets(self.config, self.exchange, log=False)
# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
# Load one (first) strategy
self._set_strategy(self.strategylist[0])
def __del__(self):
LoggingMixin.show_output = True
PairLocks.use_db = True
Trade.use_db = True
def _set_strategy(self, strategy):
def _set_strategy(self, strategy: IStrategy):
"""
Load strategy into backtesting
"""
self.strategy: IStrategy = strategy
strategy.dp = self.dataprovider
# Set stoploss_on_exchange to false for backtesting,
# since a "perfect" stoploss-sell is assumed anyway
# And the regular "stoploss" function would not apply to that case
self.strategy.order_types['stoploss_on_exchange'] = False
if self.config.get('enable_protections', False):
conf = self.config
if hasattr(strategy, 'protections'):
conf = deepcopy(conf)
conf['protections'] = strategy.protections
self.protections = ProtectionManager(conf)
def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
"""
@@ -156,7 +161,7 @@ class Backtesting:
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)..')
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),
@@ -171,10 +176,10 @@ class Backtesting:
PairLocks.use_db = False
PairLocks.timeframe = self.config['timeframe']
Trade.use_db = False
if enable_protections:
# Reset persisted data - used for protections only
PairLocks.reset_locks()
Trade.reset_trades()
PairLocks.reset_locks()
Trade.reset_trades()
self.rejected_trades = 0
self.dataprovider.clear_cache()
def _get_ohlcv_as_lists(self, processed: Dict[str, DataFrame]) -> Dict[str, Tuple]:
"""
@@ -188,8 +193,9 @@ class Backtesting:
data: Dict = {}
# Create dict with data
for pair, pair_data in processed.items():
pair_data.loc[:, 'buy'] = 0 # cleanup from previous run
pair_data.loc[:, 'sell'] = 0 # cleanup from previous run
if not pair_data.empty:
pair_data.loc[:, 'buy'] = 0 # cleanup if buy_signal is exist
pair_data.loc[:, 'sell'] = 0 # cleanup if sell_signal is exist
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
@@ -203,16 +209,22 @@ class Backtesting:
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
data[pair] = [x for x in df_analyzed.itertuples(index=False, name=None)]
data[pair] = df_analyzed.values.tolist()
return data
def _get_close_rate(self, sell_row: Tuple, trade: Trade, sell: SellCheckTuple,
def _get_close_rate(self, sell_row: Tuple, trade: LocalTrade, sell: SellCheckTuple,
trade_dur: int) -> float:
"""
Get close rate for backtesting result
"""
# Special handling if high or low hit STOP_LOSS or ROI
if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
if trade.stop_loss > sell_row[HIGH_IDX]:
# our stoploss was already higher than candle high,
# possibly due to a cancelled trade exit.
# sell at open price.
return sell_row[OPEN_IDX]
# Set close_rate to stoploss
return trade.stop_loss
elif sell.sell_type == (SellType.ROI):
@@ -238,7 +250,7 @@ class Backtesting:
# Use the maximum between close_rate and low as we
# cannot sell outside of a candle.
# Applies when a new ROI setting comes in place and the whole candle is above that.
return max(close_rate, sell_row[LOW_IDX])
return min(max(close_rate, sell_row[LOW_IDX]), sell_row[HIGH_IDX])
else:
# This should not be reached...
@@ -246,24 +258,67 @@ class Backtesting:
else:
return sell_row[OPEN_IDX]
def _get_sell_trade_entry(self, trade: Trade, sell_row: Tuple) -> Optional[Trade]:
def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], sell_row[DATE_IDX],
sell_row[BUY_IDX], sell_row[SELL_IDX],
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], # type: ignore
sell_row[DATE_IDX].to_pydatetime(), sell_row[BUY_IDX],
sell_row[SELL_IDX],
low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
if sell.sell_flag:
trade_dur = int((sell_row[DATE_IDX] - trade.open_date).total_seconds() // 60)
trade.close_date = sell_row[DATE_IDX].to_pydatetime()
trade.sell_reason = sell.sell_reason
trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
trade.close_date = sell_row[DATE_IDX]
trade.sell_reason = sell.sell_type
# Confirm trade exit:
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='limit', amount=trade.amount,
rate=closerate,
time_in_force=time_in_force,
sell_reason=sell.sell_reason,
current_time=sell_row[DATE_IDX].to_pydatetime()):
return None
trade.close(closerate, show_msg=False)
return trade
return None
def handle_left_open(self, open_trades: Dict[str, List[Trade]],
data: Dict[str, List[Tuple]]) -> List[Trade]:
def _enter_trade(self, pair: str, row: List) -> Optional[LocalTrade]:
try:
stake_amount = self.wallets.get_trade_stake_amount(pair, None)
except DependencyException:
return None
min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, row[OPEN_IDX], -0.05)
order_type = self.strategy.order_types['buy']
time_in_force = self.strategy.order_time_in_force['sell']
# Confirm trade entry:
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=stake_amount, rate=row[OPEN_IDX],
time_in_force=time_in_force, current_time=row[DATE_IDX].to_pydatetime()):
return None
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
# Enter trade
trade = LocalTrade(
pair=pair,
open_rate=row[OPEN_IDX],
open_date=row[DATE_IDX].to_pydatetime(),
stake_amount=stake_amount,
amount=round(stake_amount / row[OPEN_IDX], 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
exchange='backtesting',
)
return trade
return None
def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
"""
Handling of left open trades at the end of backtesting
"""
@@ -273,17 +328,28 @@ class Backtesting:
for trade in open_trades[pair]:
sell_row = data[pair][-1]
trade.close_date = sell_row[DATE_IDX]
trade.sell_reason = SellType.FORCE_SELL
trade.close_date = sell_row[DATE_IDX].to_pydatetime()
trade.sell_reason = SellType.FORCE_SELL.value
trade.close(sell_row[OPEN_IDX], show_msg=False)
trade.is_open = True
trades.append(trade)
LocalTrade.close_bt_trade(trade)
# Deepcopy object to have wallets update correctly
trade1 = deepcopy(trade)
trade1.is_open = True
trades.append(trade1)
return trades
def backtest(self, processed: Dict, stake_amount: float,
def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
# Always allow trades when max_open_trades is enabled.
if max_open_trades <= 0 or open_trade_count < max_open_trades:
return True
# Rejected trade
self.rejected_trades += 1
return False
def backtest(self, processed: Dict,
start_date: datetime, end_date: datetime,
max_open_trades: int = 0, position_stacking: bool = False,
enable_protections: bool = False) -> DataFrame:
enable_protections: bool = False) -> Dict[str, Any]:
"""
Implement backtesting functionality
@@ -292,7 +358,6 @@ class Backtesting:
Avoid extensive logging in this method and functions it calls.
:param processed: a processed dictionary with format {pair, data}
:param stake_amount: amount to use for each trade
:param start_date: backtesting timerange start datetime
:param end_date: backtesting timerange end datetime
:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
@@ -300,22 +365,22 @@ class Backtesting:
:param enable_protections: Should protections be enabled?
:return: DataFrame with trades (results of backtesting)
"""
logger.debug(f"Run backtest, stake_amount: {stake_amount}, "
f"start_date: {start_date}, end_date: {end_date}, "
f"max_open_trades: {max_open_trades}, position_stacking: {position_stacking}"
)
trades: List[Trade] = []
trades: List[LocalTrade] = []
self.prepare_backtest(enable_protections)
# Update dataprovider cache
for pair, dataframe in processed.items():
self.dataprovider._set_cached_df(pair, self.timeframe, dataframe)
# Use dict of lists with data for performance
# (looping lists is a lot faster than pandas DataFrames)
data: Dict = self._get_ohlcv_as_lists(processed)
# Indexes per pair, so some pairs are allowed to have a missing start.
indexes: Dict = {}
indexes: Dict = defaultdict(int)
tmp = start_date + timedelta(minutes=self.timeframe_min)
open_trades: Dict[str, List] = defaultdict(list)
open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
open_trade_count = 0
# Loop timerange and get candle for each pair at that point in time
@@ -323,11 +388,9 @@ class Backtesting:
open_trade_count_start = open_trade_count
for i, pair in enumerate(data):
if pair not in indexes:
indexes[pair] = 0
row_index = indexes[pair]
try:
row = data[pair][indexes[pair]]
row = data[pair][row_index]
except IndexError:
# missing Data for one pair at the end.
# Warnings for this are shown during data loading
@@ -336,38 +399,34 @@ class Backtesting:
# Waits until the time-counter reaches the start of the data for this pair.
if row[DATE_IDX] > tmp:
continue
indexes[pair] += 1
row_index += 1
self.dataprovider._set_dataframe_max_index(row_index)
indexes[pair] = row_index
# without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected
# don't open on the last row
if ((position_stacking or len(open_trades[pair]) == 0)
and (max_open_trades <= 0 or open_trade_count_start < max_open_trades)
and tmp != end_date
and row[BUY_IDX] == 1 and row[SELL_IDX] != 1
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])):
# Enter trade
trade = Trade(
pair=pair,
open_rate=row[OPEN_IDX],
open_date=row[DATE_IDX],
stake_amount=stake_amount,
amount=round(stake_amount / row[OPEN_IDX], 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
)
# TODO: hacky workaround to avoid opening > max_open_trades
# This emulates previous behaviour - not sure if this is correct
# Prevents buying if the trade-slot was freed in this candle
open_trade_count_start += 1
open_trade_count += 1
# logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
open_trades[pair].append(trade)
Trade.trades.append(trade)
if (
(position_stacking or len(open_trades[pair]) == 0)
and self.trade_slot_available(max_open_trades, open_trade_count_start)
and tmp != end_date
and row[BUY_IDX] == 1
and row[SELL_IDX] != 1
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])
):
trade = self._enter_trade(pair, row)
if trade:
# TODO: hacky workaround to avoid opening > max_open_trades
# This emulates previous behaviour - not sure if this is correct
# Prevents buying if the trade-slot was freed in this candle
open_trade_count_start += 1
open_trade_count += 1
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
open_trades[pair].append(trade)
LocalTrade.add_bt_trade(trade)
for trade in open_trades[pair]:
# since indexes has been incremented before, we need to go one step back to
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(trade, row)
# Sell occured
@@ -375,6 +434,8 @@ class Backtesting:
# logger.debug(f"{pair} - Backtesting sell {trade}")
open_trade_count -= 1
open_trades[pair].remove(trade)
LocalTrade.close_bt_trade(trade)
trades.append(trade_entry)
if enable_protections:
self.protections.stop_per_pair(pair, row[DATE_IDX])
@@ -384,8 +445,16 @@ class Backtesting:
tmp += timedelta(minutes=self.timeframe_min)
trades += self.handle_left_open(open_trades, data=data)
self.wallets.update()
return trade_list_to_dataframe(trades)
results = trade_list_to_dataframe(trades)
return {
'results': results,
'config': self.strategy.config,
'locks': PairLocks.get_all_locks(),
'rejected_signals': self.rejected_trades,
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
}
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
@@ -407,31 +476,32 @@ class Backtesting:
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = history.get_timerange(preprocessed)
preprocessed = trim_dataframes(preprocessed, timerange, self.required_startup)
if not preprocessed:
raise OperationalException(
"No data left after adjusting for startup candles.")
min_date, max_date = history.get_timerange(preprocessed)
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)..')
f'({(max_date - min_date).days} days).')
# Execute backtest and store results
results = self.backtest(
processed=preprocessed,
stake_amount=self.config['stake_amount'],
start_date=min_date.datetime,
end_date=max_date.datetime,
start_date=min_date,
end_date=max_date,
max_open_trades=max_open_trades,
position_stacking=self.config.get('position_stacking', False),
enable_protections=self.config.get('enable_protections', False),
)
backtest_end_time = datetime.now(timezone.utc)
self.all_results[self.strategy.get_strategy_name()] = {
'results': results,
'config': self.strategy.config,
'locks': PairLocks.locks,
results.update({
'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()),
}
})
self.all_results[self.strategy.get_strategy_name()] = results
return min_date, max_date
def start(self) -> None:
@@ -442,17 +512,16 @@ class Backtesting:
data: Dict[str, Any] = {}
data, timerange = self.load_bt_data()
logger.info("Dataload complete. Calculating indicators")
min_date = None
max_date = None
for strat in self.strategylist:
min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
if len(self.strategylist) > 0:
stats = generate_backtest_stats(data, self.all_results,
min_date=min_date, max_date=max_date)
stats = generate_backtest_stats(data, self.all_results,
min_date=min_date, max_date=max_date)
if self.config.get('export', False):
store_backtest_stats(self.config['exportfilename'], stats)
if self.config.get('export', False):
store_backtest_stats(self.config['exportfilename'], stats)
# Show backtest results
show_backtest_results(self.config, stats)
# Show backtest results
show_backtest_results(self.config, stats)

2
freqtrade/optimize/edge_cli.py
View File

@@ -44,7 +44,7 @@ class EdgeCli:
'timerange') is None else str(self.config.get('timerange')))
def start(self) -> None:
result = self.edge.calculate()
result = self.edge.calculate(self.config['exchange']['pair_whitelist'])
if result:
print('') # blank line for readability
print(generate_edge_table(self.edge._cached_pairs))

575
freqtrade/optimize/hyperopt.py
View File

@@ -4,38 +4,34 @@
This module contains the hyperopt logic
"""
import io
import locale
import logging
import random
import warnings
from collections import OrderedDict
from datetime import datetime
from datetime import datetime, timezone
from math import ceil
from operator import itemgetter
from pathlib import Path
from pprint import pformat
from typing import Any, Dict, List, Optional
import numpy as np
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, isna, json_normalize
from pandas import DataFrame
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.history import get_timerange
from freqtrade.exceptions import OperationalException
from freqtrade.misc import file_dump_json, plural, round_dict
from freqtrade.misc import file_dump_json, plural
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
from freqtrade.optimize.hyperopt_auto import HyperOptAuto
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.optimize.hyperopt_tools import HyperoptTools
from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
from freqtrade.strategy import IStrategy
# Suppress scikit-learn FutureWarnings from skopt
@@ -66,19 +62,33 @@ class Hyperopt:
hyperopt = Hyperopt(config)
hyperopt.start()
"""
custom_hyperopt: IHyperOpt
def __init__(self, config: Dict[str, Any]) -> None:
self.buy_space: List[Dimension] = []
self.sell_space: List[Dimension] = []
self.roi_space: List[Dimension] = []
self.stoploss_space: List[Dimension] = []
self.trailing_space: List[Dimension] = []
self.dimensions: List[Dimension] = []
self.config = config
self.backtesting = Backtesting(self.config)
self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
if not self.config.get('hyperopt'):
self.custom_hyperopt = HyperOptAuto(self.config)
else:
self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
self.backtesting._set_strategy(self.backtesting.strategylist[0])
self.custom_hyperopt.strategy = self.backtesting.strategy
self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
self.results_file = (self.config['user_data_dir'] /
'hyperopt_results' / f'hyperopt_results_{time_now}.pickle')
strategy = str(self.config['strategy'])
self.results_file: Path = (self.config['user_data_dir'] / 'hyperopt_results' /
f'strategy_{strategy}_{time_now}.fthypt')
self.data_pickle_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_tickerdata.pkl')
self.total_epochs = config.get('epochs', 0)
@@ -88,9 +98,7 @@ class Hyperopt:
self.clean_hyperopt()
self.num_epochs_saved = 0
# Previous evaluations
self.epochs: List = []
self.current_best_epoch: Optional[Dict[str, Any]] = None
# Populate functions here (hasattr is slow so should not be run during "regular" operations)
if hasattr(self.custom_hyperopt, 'populate_indicators'):
@@ -111,7 +119,7 @@ class Hyperopt:
self.max_open_trades = 0
self.position_stacking = self.config.get('position_stacking', False)
if self.has_space('sell'):
if HyperoptTools.has_space(self.config, 'sell'):
# Make sure use_sell_signal is enabled
if 'ask_strategy' not in self.config:
self.config['ask_strategy'] = {}
@@ -137,9 +145,7 @@ class Hyperopt:
logger.info(f"Removing `{p}`.")
p.unlink()
def _get_params_dict(self, raw_params: List[Any]) -> Dict:
dimensions: List[Dimension] = self.dimensions
def _get_params_dict(self, dimensions: List[Dimension], raw_params: List[Any]) -> Dict:
# Ensure the number of dimensions match
# the number of parameters in the list.
@@ -150,30 +156,30 @@ class Hyperopt:
# and the values are taken from the list of parameters.
return {d.name: v for d, v in zip(dimensions, raw_params)}
def _save_results(self) -> None:
def _save_result(self, epoch: Dict) -> None:
"""
Save hyperopt results to file
Store one line per epoch.
While not a valid json object - this allows appending easily.
:param epoch: result dictionary for this epoch.
"""
num_epochs = len(self.epochs)
if num_epochs > self.num_epochs_saved:
logger.debug(f"Saving {num_epochs} {plural(num_epochs, 'epoch')}.")
dump(self.epochs, self.results_file)
self.num_epochs_saved = num_epochs
logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
# Store hyperopt filename
latest_filename = Path.joinpath(self.results_file.parent, LAST_BT_RESULT_FN)
file_dump_json(latest_filename, {'latest_hyperopt': str(self.results_file.name)},
log=False)
def default_parser(x):
if isinstance(x, np.integer):
return int(x)
return str(x)
@staticmethod
def _read_results(results_file: Path) -> List:
"""
Read hyperopt results from file
"""
logger.info("Reading epochs from '%s'", results_file)
data = load(results_file)
return data
with self.results_file.open('a') as f:
rapidjson.dump(epoch, f, default=default_parser,
number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN)
f.write("\n")
self.num_epochs_saved += 1
logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
# Store hyperopt filename
latest_filename = Path.joinpath(self.results_file.parent, LAST_BT_RESULT_FN)
file_dump_json(latest_filename, {'latest_hyperopt': str(self.results_file.name)},
log=False)
def _get_params_details(self, params: Dict) -> Dict:
"""
@@ -181,118 +187,30 @@ class Hyperopt:
"""
result: Dict = {}
if self.has_space('buy'):
result['buy'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('buy')}
if self.has_space('sell'):
result['sell'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('sell')}
if self.has_space('roi'):
result['roi'] = self.custom_hyperopt.generate_roi_table(params)
if self.has_space('stoploss'):
result['stoploss'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('stoploss')}
if self.has_space('trailing'):
if HyperoptTools.has_space(self.config, 'buy'):
result['buy'] = {p.name: params.get(p.name) for p in self.buy_space}
if HyperoptTools.has_space(self.config, 'sell'):
result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
if HyperoptTools.has_space(self.config, 'roi'):
result['roi'] = {str(k): v for k, v in
self.custom_hyperopt.generate_roi_table(params).items()}
if HyperoptTools.has_space(self.config, 'stoploss'):
result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
if HyperoptTools.has_space(self.config, 'trailing'):
result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
return result
@staticmethod
def print_epoch_details(results, total_epochs: int, print_json: bool,
no_header: bool = False, header_str: str = None) -> None:
"""
Display details of the hyperopt result
"""
params = results.get('params_details', {})
# Default header string
if header_str is None:
header_str = "Best result"
if not no_header:
explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
print(f"\n{header_str}:\n\n{explanation_str}\n")
if print_json:
result_dict: Dict = {}
for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
Hyperopt._params_update_for_json(result_dict, params, s)
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
else:
Hyperopt._params_pretty_print(params, 'buy', "Buy hyperspace params:")
Hyperopt._params_pretty_print(params, 'sell', "Sell hyperspace params:")
Hyperopt._params_pretty_print(params, 'roi', "ROI table:")
Hyperopt._params_pretty_print(params, 'stoploss', "Stoploss:")
Hyperopt._params_pretty_print(params, 'trailing', "Trailing stop:")
@staticmethod
def _params_update_for_json(result_dict, params, space: str) -> None:
if space in params:
space_params = Hyperopt._space_params(params, space)
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
# in the dict.
result_dict['minimal_roi'] = OrderedDict(
(str(k), v) for k, v in space_params.items()
)
else: # 'stoploss', 'trailing'
result_dict.update(space_params)
@staticmethod
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':
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}"
elif space == 'trailing':
for k, v in space_params.items():
params_result += f'{k} = {v}\n'
else:
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:
d = params[space]
# Round floats to `r` digits after the decimal point if requested
return round_dict(d, r) if r else d
@staticmethod
def is_best_loss(results, current_best_loss: float) -> bool:
return results['loss'] < current_best_loss
def print_results(self, results) -> None:
"""
Log results if it is better than any previous evaluation
TODO: this should be moved to HyperoptTools too
"""
is_best = results['is_best']
if self.print_all or is_best:
print(
self.get_result_table(
HyperoptTools.get_result_table(
self.config, results, self.total_epochs,
self.print_all, self.print_colorized,
self.hyperopt_table_header
@@ -300,229 +218,58 @@ class Hyperopt:
)
self.hyperopt_table_header = 2
@staticmethod
def _format_explanation_string(results, total_epochs) -> str:
return (("*" if results['is_initial_point'] else " ") +
f"{results['current_epoch']:5d}/{total_epochs}: " +
f"{results['results_explanation']} " +
f"Objective: {results['loss']:.5f}")
@staticmethod
def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
print_colorized: bool, remove_header: int) -> str:
def init_spaces(self):
"""
Log result table
Assign the dimensions in the hyperoptimization space.
"""
if not results:
return ''
tabulate.PRESERVE_WHITESPACE = True
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', ' Win Draw Loss', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'Objective',
'is_initial_point', 'is_best']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* '
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str)
trials['Epoch'] = trials['Epoch'].apply(
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
)
trials['Profit'] = trials.apply(
lambda x: '{:,.8f} {} {}'.format(
x['Total profit'], config['stake_currency'],
'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
).rjust(25+len(config['stake_currency']))
if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
axis=1
)
trials = trials.drop(columns=['Total profit'])
if print_colorized:
for i in range(len(trials)):
if trials.loc[i]['is_profit']:
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
str(trials.loc[i][j]), Fore.RESET)
if trials.loc[i]['is_best'] and highlight_best:
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
str(trials.loc[i][j]), Style.RESET_ALL)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
if remove_header > 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='orgtbl',
headers='keys', stralign="right"
)
table = table.split("\n", remove_header)[remove_header]
elif remove_header < 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
table = "\n".join(table.split("\n")[0:remove_header])
else:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
return table
@staticmethod
def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
csv_file: str) -> None:
"""
Log result to csv-file
"""
if not results:
return
# Verification for overwrite
if Path(csv_file).is_file():
logger.error(f"CSV file already exists: {csv_file}")
return
try:
io.open(csv_file, 'w+').close()
except IOError:
logger.error(f"Failed to create CSV file: {csv_file}")
return
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
trials['Stake currency'] = config['stake_currency']
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']
param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
trials = trials[base_metrics + param_metrics]
base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
param_columns = list(results[0]['params_dict'].keys())
trials.columns = base_columns + param_columns
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '*'
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Epoch'] = trials['Epoch'].astype(str)
trials['Trades'] = trials['Trades'].astype(str)
trials['Total profit'] = trials['Total profit'].apply(
lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
)
trials['Profit'] = trials['Profit'].apply(
lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
logger.info(f"CSV file created: {csv_file}")
def has_space(self, space: str) -> bool:
"""
Tell if the space value is contained in the configuration
"""
# The 'trailing' space is not included in the 'default' set of spaces
if space == 'trailing':
return any(s in self.config['spaces'] for s in [space, 'all'])
else:
return any(s in self.config['spaces'] for s in [space, 'all', 'default'])
def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
"""
Return the dimensions in the hyperoptimization space.
:param space: Defines hyperspace to return dimensions for.
If None, then the self.has_space() will be used to return dimensions
for all hyperspaces used.
"""
spaces: List[Dimension] = []
if space == 'buy' or (space is None and self.has_space('buy')):
if HyperoptTools.has_space(self.config, 'buy'):
logger.debug("Hyperopt has 'buy' space")
spaces += self.custom_hyperopt.indicator_space()
self.buy_space = self.custom_hyperopt.indicator_space()
if space == 'sell' or (space is None and self.has_space('sell')):
if HyperoptTools.has_space(self.config, 'sell'):
logger.debug("Hyperopt has 'sell' space")
spaces += self.custom_hyperopt.sell_indicator_space()
self.sell_space = self.custom_hyperopt.sell_indicator_space()
if space == 'roi' or (space is None and self.has_space('roi')):
if HyperoptTools.has_space(self.config, 'roi'):
logger.debug("Hyperopt has 'roi' space")
spaces += self.custom_hyperopt.roi_space()
self.roi_space = self.custom_hyperopt.roi_space()
if space == 'stoploss' or (space is None and self.has_space('stoploss')):
if HyperoptTools.has_space(self.config, 'stoploss'):
logger.debug("Hyperopt has 'stoploss' space")
spaces += self.custom_hyperopt.stoploss_space()
self.stoploss_space = self.custom_hyperopt.stoploss_space()
if space == 'trailing' or (space is None and self.has_space('trailing')):
if HyperoptTools.has_space(self.config, 'trailing'):
logger.debug("Hyperopt has 'trailing' space")
spaces += self.custom_hyperopt.trailing_space()
return spaces
self.trailing_space = self.custom_hyperopt.trailing_space()
self.dimensions = (self.buy_space + self.sell_space + self.roi_space +
self.stoploss_space + self.trailing_space)
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
"""
Used Optimize function. Called once per epoch to optimize whatever is configured.
Keep this function as optimized as possible!
"""
params_dict = self._get_params_dict(raw_params)
params_details = self._get_params_details(params_dict)
backtest_start_time = datetime.now(timezone.utc)
params_dict = self._get_params_dict(self.dimensions, raw_params)
if self.has_space('roi'):
# Apply parameters
if HyperoptTools.has_space(self.config, 'roi'):
self.backtesting.strategy.minimal_roi = ( # type: ignore
self.custom_hyperopt.generate_roi_table(params_dict))
if self.has_space('buy'):
if HyperoptTools.has_space(self.config, 'buy'):
self.backtesting.strategy.advise_buy = ( # type: ignore
self.custom_hyperopt.buy_strategy_generator(params_dict))
if self.has_space('sell'):
if HyperoptTools.has_space(self.config, 'sell'):
self.backtesting.strategy.advise_sell = ( # type: ignore
self.custom_hyperopt.sell_strategy_generator(params_dict))
if self.has_space('stoploss'):
if HyperoptTools.has_space(self.config, 'stoploss'):
self.backtesting.strategy.stoploss = params_dict['stoploss']
if self.has_space('trailing'):
if HyperoptTools.has_space(self.config, 'trailing'):
d = self.custom_hyperopt.generate_trailing_params(params_dict)
self.backtesting.strategy.trailing_stop = d['trailing_stop']
self.backtesting.strategy.trailing_stop_positive = d['trailing_stop_positive']
@@ -531,30 +278,42 @@ class Hyperopt:
self.backtesting.strategy.trailing_only_offset_is_reached = \
d['trailing_only_offset_is_reached']
processed = load(self.data_pickle_file)
min_date, max_date = get_timerange(processed)
backtesting_results = self.backtesting.backtest(
with self.data_pickle_file.open('rb') as f:
processed = load(f, mmap_mode='r')
bt_results = self.backtesting.backtest(
processed=processed,
stake_amount=self.config['stake_amount'],
start_date=min_date.datetime,
end_date=max_date.datetime,
start_date=self.min_date,
end_date=self.max_date,
max_open_trades=self.max_open_trades,
position_stacking=self.position_stacking,
enable_protections=self.config.get('enable_protections', False),
)
return self._get_results_dict(backtesting_results, min_date, max_date,
params_dict, params_details)
backtest_end_time = datetime.now(timezone.utc)
bt_results.update({
'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()),
})
return self._get_results_dict(bt_results, self.min_date, self.max_date,
params_dict,
processed=processed)
def _get_results_dict(self, backtesting_results, min_date, max_date,
params_dict, params_details):
results_metrics = self._calculate_results_metrics(backtesting_results)
results_explanation = self._format_results_explanation_string(results_metrics)
params_dict, processed: Dict[str, DataFrame]
) -> Dict[str, Any]:
params_details = self._get_params_details(params_dict)
trade_count = results_metrics['trade_count']
total_profit = results_metrics['total_profit']
strat_stats = generate_strategy_stats(
processed, self.backtesting.strategy.get_strategy_name(),
backtesting_results, min_date, max_date, market_change=0
)
results_explanation = HyperoptTools.format_results_explanation_string(
strat_stats, self.config['stake_currency'])
not_optimized = self.backtesting.strategy.get_params_dict()
trade_count = strat_stats['total_trades']
total_profit = strat_stats['profit_total']
# If this evaluation contains too short amount of trades to be
# interesting -- consider it as 'bad' (assigned max. loss value)
@@ -562,49 +321,20 @@ class Hyperopt:
# path. We do not want to optimize 'hodl' strategies.
loss: float = MAX_LOSS
if trade_count >= self.config['hyperopt_min_trades']:
loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count,
min_date=min_date.datetime, max_date=max_date.datetime)
loss = self.calculate_loss(results=backtesting_results['results'],
trade_count=trade_count,
min_date=min_date, max_date=max_date,
config=self.config, processed=processed)
return {
'loss': loss,
'params_dict': params_dict,
'params_details': params_details,
'results_metrics': results_metrics,
'params_not_optimized': not_optimized,
'results_metrics': strat_stats,
'results_explanation': results_explanation,
'total_profit': total_profit,
}
def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
wins = len(backtesting_results[backtesting_results['profit_ratio'] > 0])
draws = len(backtesting_results[backtesting_results['profit_ratio'] == 0])
losses = len(backtesting_results[backtesting_results['profit_ratio'] < 0])
return {
'trade_count': len(backtesting_results.index),
'wins': wins,
'draws': draws,
'losses': losses,
'winsdrawslosses': f"{wins:>4} {draws:>4} {losses:>4}",
'avg_profit': backtesting_results['profit_ratio'].mean() * 100.0,
'median_profit': backtesting_results['profit_ratio'].median() * 100.0,
'total_profit': backtesting_results['profit_abs'].sum(),
'profit': backtesting_results['profit_ratio'].sum() * 100.0,
'duration': backtesting_results['trade_duration'].mean(),
}
def _format_results_explanation_string(self, results_metrics: Dict) -> str:
"""
Return the formatted results explanation in a string
"""
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."
).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
return Optimizer(
dimensions,
@@ -620,56 +350,47 @@ class Hyperopt:
return parallel(delayed(
wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
@staticmethod
def load_previous_results(results_file: Path) -> List:
"""
Load data for epochs from the file if we have one
"""
epochs: List = []
if results_file.is_file() and results_file.stat().st_size > 0:
epochs = Hyperopt._read_results(results_file)
# Detection of some old format, without 'is_best' field saved
if epochs[0].get('is_best') is None:
raise OperationalException(
"The file with Hyperopt results is incompatible with this version "
"of Freqtrade and cannot be loaded.")
logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
return epochs
def _set_random_state(self, random_state: Optional[int]) -> int:
return random_state or random.randint(1, 2**16 - 1)
def prepare_hyperopt_data(self) -> None:
data, timerange = self.backtesting.load_bt_data()
logger.info("Dataload complete. Calculating indicators")
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup)
self.min_date, self.max_date = get_timerange(processed)
logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(self.max_date - self.min_date).days} days)..')
dump(processed, self.data_pickle_file)
def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
logger.info(f"Using optimizer random state: {self.random_state}")
self.hyperopt_table_header = -1
data, timerange = self.backtesting.load_bt_data()
# Initialize spaces ...
self.init_spaces()
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = get_timerange(preprocessed)
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)
self.prepare_hyperopt_data()
# We don't need exchange instance anymore while running hyperopt
self.backtesting.exchange = None # type: ignore
self.backtesting.exchange.close()
self.backtesting.exchange._api = None # type: ignore
self.backtesting.exchange._api_async = None # type: ignore
# self.backtesting.exchange = None # type: ignore
self.backtesting.pairlists = None # type: ignore
self.backtesting.strategy.dp = None # type: ignore
IStrategy.dp = None # type: ignore
cpus = cpu_count()
logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
config_jobs = self.config.get('hyperopt_jobs', -1)
logger.info(f'Number of parallel jobs set as: {config_jobs}')
self.dimensions: List[Dimension] = self.hyperopt_space()
self.opt = self.get_optimizer(self.dimensions, config_jobs)
if self.print_colorized:
@@ -725,7 +446,7 @@ class Hyperopt:
logger.debug(f"Optimizer epoch evaluated: {val}")
is_best = self.is_best_loss(val, self.current_best_loss)
is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
@@ -735,25 +456,21 @@ class Hyperopt:
if is_best:
self.current_best_loss = val['loss']
self.epochs.append(val)
self.current_best_epoch = val
# Save results after each best epoch and every 100 epochs
if is_best or current % 100 == 0:
self._save_results()
self._save_result(val)
pbar.update(current)
except KeyboardInterrupt:
print('User interrupted..')
self._save_results()
logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
if self.epochs:
sorted_epochs = sorted(self.epochs, key=itemgetter('loss'))
best_epoch = sorted_epochs[0]
self.print_epoch_details(best_epoch, self.total_epochs, self.print_json)
if self.current_best_epoch:
HyperoptTools.show_epoch_details(self.current_best_epoch, self.total_epochs,
self.print_json)
else:
# This is printed when Ctrl+C is pressed quickly, before first epochs have
# a chance to be evaluated.

89
freqtrade/optimize/hyperopt_auto.py Normal file
View File

@@ -0,0 +1,89 @@
"""
HyperOptAuto class.
This module implements a convenience auto-hyperopt class, which can be used together with strategies
that implement IHyperStrategy interface.
"""
from contextlib import suppress
from typing import Any, Callable, Dict, List
from pandas import DataFrame
with suppress(ImportError):
from skopt.space import Dimension
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class HyperOptAuto(IHyperOpt):
"""
This class delegates functionality to Strategy(IHyperStrategy) and Strategy.HyperOpt classes.
Most of the time Strategy.HyperOpt class would only implement indicator_space and
sell_indicator_space methods, but other hyperopt methods can be overridden as well.
"""
def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
def populate_buy_trend(dataframe: DataFrame, metadata: dict):
for attr_name, attr in self.strategy.enumerate_parameters('buy'):
if attr.optimize:
# noinspection PyProtectedMember
attr.value = params[attr_name]
return self.strategy.populate_buy_trend(dataframe, metadata)
return populate_buy_trend
def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
def populate_sell_trend(dataframe: DataFrame, metadata: dict):
for attr_name, attr in self.strategy.enumerate_parameters('sell'):
if attr.optimize:
# noinspection PyProtectedMember
attr.value = params[attr_name]
return self.strategy.populate_sell_trend(dataframe, metadata)
return populate_sell_trend
def _get_func(self, name) -> Callable:
"""
Return a function defined in Strategy.HyperOpt class, or one defined in super() class.
:param name: function name.
:return: a requested function.
"""
hyperopt_cls = getattr(self.strategy, 'HyperOpt', None)
default_func = getattr(super(), name)
if hyperopt_cls:
return getattr(hyperopt_cls, name, default_func)
else:
return default_func
def _generate_indicator_space(self, category):
for attr_name, attr in self.strategy.enumerate_parameters(category):
if attr.optimize:
yield attr.get_space(attr_name)
def _get_indicator_space(self, category, fallback_method_name):
indicator_space = list(self._generate_indicator_space(category))
if len(indicator_space) > 0:
return indicator_space
else:
return self._get_func(fallback_method_name)()
def indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('buy', 'indicator_space')
def sell_indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('sell', 'sell_indicator_space')
def generate_roi_table(self, params: Dict) -> Dict[int, float]:
return self._get_func('generate_roi_table')(params)
def roi_space(self) -> List['Dimension']:
return self._get_func('roi_space')()
def stoploss_space(self) -> List['Dimension']:
return self._get_func('stoploss_space')()
def generate_trailing_params(self, params: Dict) -> Dict:
return self._get_func('generate_trailing_params')(params)
def trailing_space(self) -> List['Dimension']:
return self._get_func('trailing_space')()

59
freqtrade/optimize/hyperopt_interface.py
View File

@@ -7,11 +7,13 @@ import math
from abc import ABC
from typing import Any, Callable, Dict, List
from skopt.space import Categorical, Dimension, Integer, Real
from skopt.space import Categorical, Dimension, Integer
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import round_dict
from freqtrade.optimize.space import SKDecimal
from freqtrade.strategy import IStrategy
logger = logging.getLogger(__name__)
@@ -30,10 +32,11 @@ class IHyperOpt(ABC):
Defines the mandatory structure must follow any custom hyperopt
Class attributes you can use:
ticker_interval -> int: value of the ticker interval to use for the strategy
timeframe -> int: value of the timeframe to use for the strategy
"""
ticker_interval: str # DEPRECATED
timeframe: str
strategy: IStrategy
def __init__(self, config: dict) -> None:
self.config = config
@@ -42,36 +45,31 @@ class IHyperOpt(ABC):
IHyperOpt.ticker_interval = str(config['timeframe']) # DEPRECATED
IHyperOpt.timeframe = str(config['timeframe'])
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
"""
Create a buy strategy generator.
"""
raise OperationalException(_format_exception_message('buy_strategy_generator', 'buy'))
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
"""
Create a sell strategy generator.
"""
raise OperationalException(_format_exception_message('sell_strategy_generator', 'sell'))
@staticmethod
def indicator_space() -> List[Dimension]:
def indicator_space(self) -> List[Dimension]:
"""
Create an indicator space.
"""
raise OperationalException(_format_exception_message('indicator_space', 'buy'))
@staticmethod
def sell_indicator_space() -> List[Dimension]:
def sell_indicator_space(self) -> List[Dimension]:
"""
Create a sell indicator space.
"""
raise OperationalException(_format_exception_message('sell_indicator_space', 'sell'))
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
def generate_roi_table(self, params: Dict) -> Dict[int, float]:
"""
Create a ROI table.
@@ -86,8 +84,7 @@ class IHyperOpt(ABC):
return roi_table
@staticmethod
def roi_space() -> List[Dimension]:
def roi_space(self) -> List[Dimension]:
"""
Create a ROI space.
@@ -95,7 +92,7 @@ class IHyperOpt(ABC):
This method implements adaptive roi hyperspace with varied
ranges for parameters which automatically adapts to the
ticker interval used.
timeframe used.
It's used by Freqtrade by default, if no custom roi_space method is defined.
"""
@@ -107,7 +104,7 @@ class IHyperOpt(ABC):
roi_t_alpha = 1.0
roi_p_alpha = 1.0
timeframe_min = timeframe_to_minutes(IHyperOpt.ticker_interval)
timeframe_min = timeframe_to_minutes(self.timeframe)
# We define here limits for the ROI space parameters automagically adapted to the
# timeframe used by the bot:
@@ -117,7 +114,7 @@ class IHyperOpt(ABC):
# * 'roi_p' (limits for the ROI value steps) components are scaled logarithmically.
#
# The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
# method for the 5m ticker interval.
# method for the 5m timeframe.
roi_t_scale = timeframe_min / 5
roi_p_scale = math.log1p(timeframe_min) / math.log1p(5)
roi_limits = {
@@ -143,7 +140,7 @@ class IHyperOpt(ABC):
'roi_p2': roi_limits['roi_p2_min'],
'roi_p3': roi_limits['roi_p3_min'],
}
logger.info(f"Min roi table: {round_dict(IHyperOpt.generate_roi_table(p), 5)}")
logger.info(f"Min roi table: {round_dict(self.generate_roi_table(p), 3)}")
p = {
'roi_t1': roi_limits['roi_t1_max'],
'roi_t2': roi_limits['roi_t2_max'],
@@ -152,19 +149,21 @@ class IHyperOpt(ABC):
'roi_p2': roi_limits['roi_p2_max'],
'roi_p3': roi_limits['roi_p3_max'],
}
logger.info(f"Max roi table: {round_dict(IHyperOpt.generate_roi_table(p), 5)}")
logger.info(f"Max roi table: {round_dict(self.generate_roi_table(p), 3)}")
return [
Integer(roi_limits['roi_t1_min'], roi_limits['roi_t1_max'], name='roi_t1'),
Integer(roi_limits['roi_t2_min'], roi_limits['roi_t2_max'], name='roi_t2'),
Integer(roi_limits['roi_t3_min'], roi_limits['roi_t3_max'], name='roi_t3'),
Real(roi_limits['roi_p1_min'], roi_limits['roi_p1_max'], name='roi_p1'),
Real(roi_limits['roi_p2_min'], roi_limits['roi_p2_max'], name='roi_p2'),
Real(roi_limits['roi_p3_min'], roi_limits['roi_p3_max'], name='roi_p3'),
SKDecimal(roi_limits['roi_p1_min'], roi_limits['roi_p1_max'], decimals=3,
name='roi_p1'),
SKDecimal(roi_limits['roi_p2_min'], roi_limits['roi_p2_max'], decimals=3,
name='roi_p2'),
SKDecimal(roi_limits['roi_p3_min'], roi_limits['roi_p3_max'], decimals=3,
name='roi_p3'),
]
@staticmethod
def stoploss_space() -> List[Dimension]:
def stoploss_space(self) -> List[Dimension]:
"""
Create a stoploss space.
@@ -172,11 +171,10 @@ class IHyperOpt(ABC):
You may override it in your custom Hyperopt class.
"""
return [
Real(-0.35, -0.02, name='stoploss'),
SKDecimal(-0.35, -0.02, decimals=3, name='stoploss'),
]
@staticmethod
def generate_trailing_params(params: Dict) -> Dict:
def generate_trailing_params(self, params: Dict) -> Dict:
"""
Create dict with trailing stop parameters.
"""
@@ -188,8 +186,7 @@ class IHyperOpt(ABC):
'trailing_only_offset_is_reached': params['trailing_only_offset_is_reached'],
}
@staticmethod
def trailing_space() -> List[Dimension]:
def trailing_space(self) -> List[Dimension]:
"""
Create a trailing stoploss space.
@@ -204,14 +201,14 @@ class IHyperOpt(ABC):
# other 'trailing' hyperspace parameters.
Categorical([True], name='trailing_stop'),
Real(0.01, 0.35, name='trailing_stop_positive'),
SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
# so this intermediate parameter is used as the value of the difference between
# them. The value of the 'trailing_stop_positive_offset' is constructed in the
# generate_trailing_params() method.
# This is similar to the hyperspace dimensions used for constructing the ROI tables.
Real(0.001, 0.1, name='trailing_stop_positive_offset_p1'),
SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
Categorical([True, False], name='trailing_only_offset_is_reached'),
]

5
freqtrade/optimize/hyperopt_loss_interface.py
View File

@@ -5,6 +5,7 @@ This module defines the interface for the loss-function for hyperopt
from abc import ABC, abstractmethod
from datetime import datetime
from typing import Dict
from pandas import DataFrame
@@ -19,7 +20,9 @@ class IHyperOptLoss(ABC):
@staticmethod
@abstractmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime, *args, **kwargs) -> float:
min_date: datetime, max_date: datetime,
config: Dict, processed: Dict[str, DataFrame],
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results
"""

18
freqtrade/optimize/hyperopt_loss_onlyprofit.py
View File

@@ -9,23 +9,11 @@ from pandas import DataFrame
from freqtrade.optimize.hyperopt import IHyperOptLoss
# This is assumed to be expected avg profit * expected trade count.
# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
# expected max profit = 3.85
#
# Note, this is ratio. 3.85 stated above means 385Σ%, 3.0 means 300Σ%.
#
# In this implementation it's only used in calculation of the resulting value
# of the objective function as a normalization coefficient and does not
# represent any limit for profits as in the Freqtrade legacy default loss function.
EXPECTED_MAX_PROFIT = 3.0
class OnlyProfitHyperOptLoss(IHyperOptLoss):
"""
Defines the loss function for hyperopt.
This implementation takes only profit into account.
This implementation takes only absolute profit into account, not looking at any other indicator.
"""
@staticmethod
@@ -34,5 +22,5 @@ class OnlyProfitHyperOptLoss(IHyperOptLoss):
"""
Objective function, returns smaller number for better results.
"""
total_profit = results['profit_ratio'].sum()
return 1 - total_profit / EXPECTED_MAX_PROFIT
total_profit = results['profit_abs'].sum()
return -1 * total_profit

410
freqtrade/optimize/hyperopt_tools.py Normal file
View File

@@ -0,0 +1,410 @@
import io
import logging
from pathlib import Path
from typing import Any, Dict, List
import rapidjson
import tabulate
from colorama import Fore, Style
from pandas import isna, json_normalize
from freqtrade.exceptions import OperationalException
from freqtrade.misc import round_coin_value, round_dict
logger = logging.getLogger(__name__)
class HyperoptTools():
@staticmethod
def has_space(config: Dict[str, Any], space: str) -> bool:
"""
Tell if the space value is contained in the configuration
"""
# The 'trailing' space is not included in the 'default' set of spaces
if space == 'trailing':
return any(s in config['spaces'] for s in [space, 'all'])
else:
return any(s in config['spaces'] for s in [space, 'all', 'default'])
@staticmethod
def _read_results_pickle(results_file: Path) -> List:
"""
Read hyperopt results from pickle file
LEGACY method - new files are written as json and cannot be read with this method.
"""
from joblib import load
logger.info(f"Reading pickled epochs from '{results_file}'")
data = load(results_file)
return data
@staticmethod
def _read_results(results_file: Path) -> List:
"""
Read hyperopt results from file
"""
import rapidjson
logger.info(f"Reading epochs from '{results_file}'")
with results_file.open('r') as f:
data = [rapidjson.loads(line) for line in f]
return data
@staticmethod
def load_previous_results(results_file: Path) -> List:
"""
Load data for epochs from the file if we have one
"""
epochs: List = []
if results_file.is_file() and results_file.stat().st_size > 0:
if results_file.suffix == '.pickle':
epochs = HyperoptTools._read_results_pickle(results_file)
else:
epochs = HyperoptTools._read_results(results_file)
# Detection of some old format, without 'is_best' field saved
if epochs[0].get('is_best') is None:
raise OperationalException(
"The file with HyperoptTools results is incompatible with this version "
"of Freqtrade and cannot be loaded.")
logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
return epochs
@staticmethod
def show_epoch_details(results, total_epochs: int, print_json: bool,
no_header: bool = False, header_str: str = None) -> None:
"""
Display details of the hyperopt result
"""
params = results.get('params_details', {})
non_optimized = results.get('params_not_optimized', {})
# Default header string
if header_str is None:
header_str = "Best result"
if not no_header:
explanation_str = HyperoptTools._format_explanation_string(results, total_epochs)
print(f"\n{header_str}:\n\n{explanation_str}\n")
if print_json:
result_dict: Dict = {}
for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
HyperoptTools._params_update_for_json(result_dict, params, s)
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
else:
HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:",
non_optimized)
HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:",
non_optimized)
HyperoptTools._params_pretty_print(params, 'roi', "ROI table:")
HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:")
HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:")
@staticmethod
def _params_update_for_json(result_dict, params, space: str) -> None:
if space in params:
space_params = HyperoptTools._space_params(params, space)
if space in ['buy', 'sell']:
result_dict.setdefault('params', {}).update(space_params)
elif space == 'roi':
# Convert keys in min_roi dict to strings because
# rapidjson cannot dump dicts with integer keys...
result_dict['minimal_roi'] = {str(k): v for k, v in space_params.items()}
else: # 'stoploss', 'trailing'
result_dict.update(space_params)
@staticmethod
def _params_pretty_print(params, space: str, header: str, non_optimized={}) -> None:
if space in params or space in non_optimized:
space_params = HyperoptTools._space_params(params, space, 5)
result = f"\n# {header}\n"
if space == 'stoploss':
result += f"stoploss = {space_params.get('stoploss')}"
elif space == 'roi':
minimal_roi_result = rapidjson.dumps({
str(k): v for k, v in space_params.items()
}, default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
result += f"minimal_roi = {minimal_roi_result}"
elif space == 'trailing':
for k, v in space_params.items():
result += f'{k} = {v}\n'
else:
no_params = HyperoptTools._space_params(non_optimized, space, 5)
result += f"{space}_params = {HyperoptTools._pprint(space_params, no_params)}"
result = result.replace("\n", "\n ")
print(result)
@staticmethod
def _space_params(params, space: str, r: int = None) -> Dict:
d = params.get(space)
if d:
# Round floats to `r` digits after the decimal point if requested
return round_dict(d, r) if r else d
return {}
@staticmethod
def _pprint(params, non_optimized, indent: int = 4):
"""
Pretty-print hyperopt results (based on 2 dicts - with add. comment)
"""
p = params.copy()
p.update(non_optimized)
result = '{\n'
for k, param in p.items():
result += " " * indent + f'"{k}": '
result += f'"{param}",' if isinstance(param, str) else f'{param},'
if k in non_optimized:
result += " # value loaded from strategy"
result += "\n"
result += '}'
return result
@staticmethod
def is_best_loss(results, current_best_loss: float) -> bool:
return bool(results['loss'] < current_best_loss)
@staticmethod
def format_results_explanation_string(results_metrics: Dict, stake_currency: str) -> str:
"""
Return the formatted results explanation in a string
"""
return (f"{results_metrics['total_trades']:6d} trades. "
f"{results_metrics['wins']}/{results_metrics['draws']}"
f"/{results_metrics['losses']} Wins/Draws/Losses. "
f"Avg profit {results_metrics['profit_mean'] * 100: 6.2f}%. "
f"Median profit {results_metrics['profit_median'] * 100: 6.2f}%. "
f"Total profit {results_metrics['profit_total_abs']: 11.8f} {stake_currency} "
f"({results_metrics['profit_total'] * 100: 7.2f}%). "
f"Avg duration {results_metrics['holding_avg']} min."
)
@staticmethod
def _format_explanation_string(results, total_epochs) -> str:
return (("*" if results['is_initial_point'] else " ") +
f"{results['current_epoch']:5d}/{total_epochs}: " +
f"{results['results_explanation']} " +
f"Objective: {results['loss']:.5f}")
@staticmethod
def prepare_trials_columns(trials, legacy_mode: bool, has_drawdown: bool) -> str:
trials['Best'] = ''
if 'results_metrics.winsdrawslosses' not in trials.columns:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.winsdrawslosses'] = 'N/A'
if not has_drawdown:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.max_drawdown_abs'] = None
trials['results_metrics.max_drawdown'] = None
if not legacy_mode:
# New mode, using backtest result for metrics
trials['results_metrics.winsdrawslosses'] = trials.apply(
lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
f"{x['results_metrics.losses']:>4}", axis=1)
trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
'results_metrics.winsdrawslosses',
'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
'results_metrics.profit_total', 'results_metrics.holding_avg',
'results_metrics.max_drawdown', 'results_metrics.max_drawdown_abs',
'loss', 'is_initial_point', 'is_best']]
else:
# Legacy mode
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', 'results_metrics.max_drawdown',
'results_metrics.max_drawdown_abs', 'loss', 'is_initial_point',
'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'Max Drawdown',
'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best']
return trials
@staticmethod
def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
print_colorized: bool, remove_header: int) -> str:
"""
Log result table
"""
if not results:
return ''
tabulate.PRESERVE_WHITESPACE = True
trials = json_normalize(results, max_level=1)
legacy_mode = 'results_metrics.total_trades' not in trials
has_drawdown = 'results_metrics.max_drawdown_abs' in trials.columns
trials = HyperoptTools.prepare_trials_columns(trials, legacy_mode, has_drawdown)
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* '
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str)
perc_multi = 1 if legacy_mode else 100
trials['Epoch'] = trials['Epoch'].apply(
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: f'{x * perc_multi:,.2f}%'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
if not isna(x) else "--".rjust(7, ' ')
)
trials['Objective'] = trials['Objective'].apply(
lambda x: f'{x:,.5f}'.rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
)
stake_currency = config['stake_currency']
if has_drawdown:
trials['Max Drawdown'] = trials.apply(
lambda x: '{} {}'.format(
round_coin_value(x['max_drawdown_abs'], stake_currency),
'({:,.2f}%)'.format(x['Max Drawdown'] * perc_multi).rjust(10, ' ')
).rjust(25 + len(stake_currency))
if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
axis=1
)
else:
trials = trials.drop(columns=['Max Drawdown'])
trials = trials.drop(columns=['max_drawdown_abs'])
trials['Profit'] = trials.apply(
lambda x: '{} {}'.format(
round_coin_value(x['Total profit'], stake_currency),
'({:,.2f}%)'.format(x['Profit'] * perc_multi).rjust(10, ' ')
).rjust(25+len(stake_currency))
if x['Total profit'] != 0.0 else '--'.rjust(25+len(stake_currency)),
axis=1
)
trials = trials.drop(columns=['Total profit'])
if print_colorized:
for i in range(len(trials)):
if trials.loc[i]['is_profit']:
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
str(trials.loc[i][j]), Fore.RESET)
if trials.loc[i]['is_best'] and highlight_best:
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
str(trials.loc[i][j]), Style.RESET_ALL)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
if remove_header > 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='orgtbl',
headers='keys', stralign="right"
)
table = table.split("\n", remove_header)[remove_header]
elif remove_header < 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
table = "\n".join(table.split("\n")[0:remove_header])
else:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
return table
@staticmethod
def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
csv_file: str) -> None:
"""
Log result to csv-file
"""
if not results:
return
# Verification for overwrite
if Path(csv_file).is_file():
logger.error(f"CSV file already exists: {csv_file}")
return
try:
io.open(csv_file, 'w+').close()
except IOError:
logger.error(f"Failed to create CSV file: {csv_file}")
return
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
trials['Stake currency'] = config['stake_currency']
if 'results_metrics.total_trades' in trials:
base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
'results_metrics.profit_mean', 'results_metrics.profit_median',
'results_metrics.profit_total',
'Stake currency',
'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
'loss', 'is_initial_point', 'is_best']
perc_multi = 100
else:
perc_multi = 1
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.median_profit',
'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']
param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
trials = trials[base_metrics + param_metrics]
base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Median profit', 'Total profit',
'Stake currency', 'Profit', 'Avg duration', 'Objective',
'is_initial_point', 'is_best']
param_columns = list(results[0]['params_dict'].keys())
trials.columns = base_columns + param_columns
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '*'
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Epoch'] = trials['Epoch'].astype(str)
trials['Trades'] = trials['Trades'].astype(str)
trials['Median profit'] = trials['Median profit'] * perc_multi
trials['Total profit'] = trials['Total profit'].apply(
lambda x: f'{x:,.8f}' if x != 0.0 else ""
)
trials['Profit'] = trials['Profit'].apply(
lambda x: f'{x:,.2f}' if not isna(x) else ""
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: f'{x * perc_multi:,.2f}%' if not isna(x) else ""
)
if perc_multi == 1:
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: f'{x:,.1f} m' if isinstance(
x, float) else f"{x.total_seconds() // 60:,.1f} m" if not isna(x) else ""
)
trials['Objective'] = trials['Objective'].apply(
lambda x: f'{x:,.5f}' if x != 100000 else ""
)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
logger.info(f"CSV file created: {csv_file}")

506
freqtrade/optimize/optimize_reports.py
View File

@@ -3,13 +3,13 @@ from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, List, Union
from arrow import Arrow
from numpy import int64
from pandas import DataFrame
from tabulate import tabulate
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data.btanalysis import calculate_market_change, calculate_max_drawdown
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT
from freqtrade.data.btanalysis import (calculate_csum, calculate_market_change,
calculate_max_drawdown)
from freqtrade.misc import decimals_per_coin, file_dump_json, round_coin_value
@@ -43,7 +43,7 @@ def _get_line_floatfmt(stake_currency: str) -> List[str]:
Generate floatformat (goes in line with _generate_result_line())
"""
return ['s', 'd', '.2f', '.2f', f'.{decimals_per_coin(stake_currency)}f',
'.2f', 'd', 'd', 'd', 'd']
'.2f', 'd', 's', 's']
def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
@@ -52,15 +52,26 @@ def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
"""
return [first_column, 'Buys', 'Avg Profit %', 'Cum Profit %',
f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
'Wins', 'Draws', 'Losses']
'Win Draw Loss Win%']
def _generate_result_line(result: DataFrame, max_open_trades: int, first_column: str) -> Dict:
def _generate_wins_draws_losses(wins, draws, losses):
if wins > 0 and losses == 0:
wl_ratio = '100'
elif wins == 0:
wl_ratio = '0'
else:
wl_ratio = f'{100.0 / (wins + draws + losses) * wins:.1f}' if losses > 0 else '100'
return f'{wins:>4} {draws:>4} {losses:>4} {wl_ratio:>4}'
def _generate_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict:
"""
Generate one result dict, with "first_column" as key.
"""
profit_sum = result['profit_ratio'].sum()
profit_total = profit_sum / max_open_trades
# (end-capital - starting capital) / starting capital
profit_total = result['profit_abs'].sum() / starting_balance
return {
'key': first_column,
@@ -87,13 +98,13 @@ def _generate_result_line(result: DataFrame, max_open_trades: int, first_column:
}
def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, starting_balance: 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 starting_balance: Starting balance
:param results: Dataframe containing the backtest results
:param skip_nan: Print "left open" open trades
:return: List of Dicts containing the metrics per pair
@@ -106,10 +117,13 @@ def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, max_open_t
if skip_nan and result['profit_abs'].isnull().all():
continue
tabular_data.append(_generate_result_line(result, max_open_trades, pair))
tabular_data.append(_generate_result_line(result, starting_balance, pair))
# Sort by total profit %:
tabular_data = sorted(tabular_data, key=lambda k: k['profit_total_abs'], reverse=True)
# Append Total
tabular_data.append(_generate_result_line(results, max_open_trades, 'TOTAL'))
tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL'))
return tabular_data
@@ -131,7 +145,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
tabular_data.append(
{
'sell_reason': reason.value,
'sell_reason': reason,
'trades': count,
'wins': len(result[result['profit_abs'] > 0]),
'draws': len(result[result['profit_abs'] == 0]),
@@ -148,7 +162,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
return tabular_data
def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
"""
Generate summary per strategy
:param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
@@ -158,8 +172,19 @@ def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
tabular_data = []
for strategy, results in all_results.items():
tabular_data.append(_generate_result_line(
results['results'], results['config']['max_open_trades'], strategy)
results['results'], results['config']['dry_run_wallet'], strategy)
)
try:
max_drawdown_per, _, _, _, _ = calculate_max_drawdown(results['results'],
value_col='profit_ratio')
max_drawdown_abs, _, _, _, _ = calculate_max_drawdown(results['results'],
value_col='profit_abs')
except ValueError:
max_drawdown_per = 0
max_drawdown_abs = 0
tabular_data[-1]['max_drawdown_per'] = round(max_drawdown_per * 100, 2)
tabular_data[-1]['max_drawdown_abs'] = \
round_coin_value(max_drawdown_abs, results['config']['stake_currency'], False)
return tabular_data
@@ -189,43 +214,221 @@ def generate_edge_table(results: dict) -> str:
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
""" Generate overall trade statistics """
if len(results) == 0:
return {
'wins': 0,
'losses': 0,
'draws': 0,
'holding_avg': timedelta(),
'winner_holding_avg': timedelta(),
'loser_holding_avg': timedelta(),
}
winning_trades = results.loc[results['profit_ratio'] > 0]
draw_trades = results.loc[results['profit_ratio'] == 0]
losing_trades = results.loc[results['profit_ratio'] < 0]
zero_duration_trades = len(results.loc[(results['trade_duration'] == 0) &
(results['sell_reason'] == 'trailing_stop_loss')])
holding_avg = (timedelta(minutes=round(results['trade_duration'].mean()))
if not results.empty else timedelta())
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())
return {
'wins': len(winning_trades),
'losses': len(losing_trades),
'draws': len(draw_trades),
'holding_avg': holding_avg,
'holding_avg_s': holding_avg.total_seconds(),
'winner_holding_avg': winner_holding_avg,
'winner_holding_avg_s': winner_holding_avg.total_seconds(),
'loser_holding_avg': loser_holding_avg,
'loser_holding_avg_s': loser_holding_avg.total_seconds(),
'zero_duration_trades': zero_duration_trades,
}
def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
""" Generate daily statistics """
if len(results) == 0:
return {
'backtest_best_day': 0,
'backtest_worst_day': 0,
'backtest_best_day_abs': 0,
'backtest_worst_day_abs': 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_ratio'].sum()
daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum()
daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10)
worst_rel = min(daily_profit_rel)
best_rel = max(daily_profit_rel)
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_ratio'] > 0]
losing_trades = results.loc[results['profit_ratio'] < 0]
return {
'backtest_best_day': best,
'backtest_worst_day': worst,
'backtest_best_day': best_rel,
'backtest_worst_day': worst_rel,
'backtest_best_day_abs': best,
'backtest_worst_day_abs': 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_strategy_stats(btdata: Dict[str, DataFrame],
strategy: str,
content: Dict[str, Any],
min_date: datetime, max_date: datetime,
market_change: float
) -> Dict[str, Any]:
"""
:param btdata: Backtest data
:param strategy: Strategy name
:param content: Backtest result data in the format:
{'results: results, 'config: config}}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:param market_change: float indicating the market change
:return: Dictionary containing results per strategy and a stratgy summary.
"""
results: Dict[str, DataFrame] = content['results']
if not isinstance(results, DataFrame):
return {}
config = content['config']
max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
starting_balance = config['dry_run_wallet']
stake_currency = config['stake_currency']
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
starting_balance=starting_balance,
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,
starting_balance=starting_balance,
results=results.loc[results['is_open']],
skip_nan=True)
daily_stats = generate_daily_stats(results)
trade_stats = generate_trading_stats(results)
best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
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'),
'locks': [lock.to_json() for lock in content['locks']],
'best_pair': best_pair,
'worst_pair': worst_pair,
'results_per_pair': pair_results,
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
'total_trades': len(results),
'total_volume': float(results['stake_amount'].sum()),
'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
'profit_median': results['profit_ratio'].median() if len(results) > 0 else 0,
'profit_total': results['profit_abs'].sum() / starting_balance,
'profit_total_abs': results['profit_abs'].sum(),
'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_start_ts': int(min_date.timestamp() * 1000),
'backtest_end': max_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_end_ts': int(max_date.timestamp() * 1000),
'backtest_days': backtest_days,
'backtest_run_start_ts': content['backtest_start_time'],
'backtest_run_end_ts': content['backtest_end_time'],
'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'],
'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
'starting_balance': starting_balance,
'dry_run_wallet': starting_balance,
'final_balance': content['final_balance'],
'rejected_signals': content['rejected_signals'],
'max_open_trades': max_open_trades,
'max_open_trades_setting': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
'timeframe': config['timeframe'],
'timerange': config.get('timerange', ''),
'enable_protections': config.get('enable_protections', False),
'strategy_name': strategy,
# Parameters relevant for backtesting
'stoploss': config['stoploss'],
'trailing_stop': config.get('trailing_stop', False),
'trailing_stop_positive': config.get('trailing_stop_positive'),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0),
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
'use_custom_stoploss': config.get('use_custom_stoploss', False),
'minimal_roi': config['minimal_roi'],
'use_sell_signal': config['ask_strategy']['use_sell_signal'],
'sell_profit_only': config['ask_strategy']['sell_profit_only'],
'sell_profit_offset': config['ask_strategy']['sell_profit_offset'],
'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
**daily_stats,
**trade_stats
}
try:
max_drawdown, _, _, _, _ = calculate_max_drawdown(
results, value_col='profit_ratio')
drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
results, value_col='profit_abs')
strat_stats.update({
'max_drawdown': max_drawdown,
'max_drawdown_abs': drawdown_abs,
'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT),
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
'drawdown_end': drawdown_end.strftime(DATETIME_PRINT_FORMAT),
'drawdown_end_ts': drawdown_end.timestamp() * 1000,
'max_drawdown_low': low_val,
'max_drawdown_high': high_val,
})
csum_min, csum_max = calculate_csum(results, starting_balance)
strat_stats.update({
'csum_min': csum_min,
'csum_max': csum_max
})
except ValueError:
strat_stats.update({
'max_drawdown': 0.0,
'max_drawdown_abs': 0.0,
'max_drawdown_low': 0.0,
'max_drawdown_high': 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,
'csum_min': 0,
'csum_max': 0
})
return strat_stats
def generate_backtest_stats(btdata: Dict[str, DataFrame],
all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
min_date: Arrow, max_date: Arrow
min_date: datetime, max_date: datetime
) -> Dict[str, Any]:
"""
:param btdata: Backtest data
@@ -233,107 +436,17 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
{ Strategy: {'results: results, 'config: config}}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:return:
Dictionary containing results per strategy and a stratgy summary.
:return: Dictionary containing results per strategy and a stratgy summary.
"""
result: Dict[str, Any] = {'strategy': {}}
market_change = calculate_market_change(btdata, 'close')
for strategy, content in all_results.items():
results: Dict[str, DataFrame] = content['results']
if not isinstance(results, DataFrame):
continue
config = content['config']
max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
stake_currency = config['stake_currency']
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['is_open']],
skip_nan=True)
daily_stats = generate_daily_stats(results)
best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
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'),
'locks': [lock.to_json() for lock in content['locks']],
'best_pair': best_pair,
'worst_pair': worst_pair,
'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_ratio'].mean() if len(results) > 0 else 0,
'profit_total': results['profit_ratio'].sum() / max_open_trades,
'profit_total_abs': results['profit_abs'].sum(),
'backtest_start': min_date.datetime,
'backtest_start_ts': min_date.int_timestamp * 1000,
'backtest_end': max_date.datetime,
'backtest_end_ts': max_date.int_timestamp * 1000,
'backtest_days': backtest_days,
'backtest_run_start_ts': content['backtest_start_time'],
'backtest_run_end_ts': content['backtest_end_time'],
'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': max_open_trades,
'max_open_trades_setting': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
'timeframe': config['timeframe'],
'timerange': config.get('timerange', ''),
'enable_protections': config.get('enable_protections', False),
'strategy_name': strategy,
# Parameters relevant for backtesting
'stoploss': config['stoploss'],
'trailing_stop': config.get('trailing_stop', False),
'trailing_stop_positive': config.get('trailing_stop_positive'),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0),
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
'use_custom_stoploss': config.get('use_custom_stoploss', False),
'minimal_roi': config['minimal_roi'],
'use_sell_signal': config['ask_strategy']['use_sell_signal'],
'sell_profit_only': config['ask_strategy']['sell_profit_only'],
'sell_profit_offset': config['ask_strategy']['sell_profit_offset'],
'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
**daily_stats,
}
strat_stats = generate_strategy_stats(btdata, strategy, content,
min_date, max_date, market_change=market_change)
result['strategy'][strategy] = strat_stats
try:
max_drawdown, drawdown_start, drawdown_end = calculate_max_drawdown(
results, value_col='profit_ratio')
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(all_results=all_results)
strategy_results = generate_strategy_comparison(all_results=all_results)
result['strategy_comparison'] = strategy_results
@@ -356,7 +469,8 @@ def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: st
floatfmt = _get_line_floatfmt(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']
t['profit_total_pct'], t['duration_avg'],
_generate_wins_draws_losses(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,
@@ -373,9 +487,7 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
headers = [
'Sell Reason',
'Sells',
'Wins',
'Draws',
'Losses',
'Win Draws Loss Win%',
'Avg Profit %',
'Cum Profit %',
f'Tot Profit {stake_currency}',
@@ -383,7 +495,8 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
]
output = [[
t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'],
t['sell_reason'], t['trades'],
_generate_wins_draws_losses(t['wins'], t['draws'], t['losses']),
t['profit_mean_pct'], t['profit_sum_pct'],
round_coin_value(t['profit_total_abs'], stake_currency, False),
t['profit_total_pct'],
@@ -401,11 +514,22 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
"""
floatfmt = _get_line_floatfmt(stake_currency)
headers = _get_line_header('Strategy', stake_currency)
# _get_line_header() is also used for per-pair summary. Per-pair drawdown is mostly useless
# therefore we slip this column in only for strategy summary here.
headers.append('Drawdown')
# Align drawdown string on the center two space separator.
drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
dd_pad_abs = max([len(t['max_drawdown_abs']) for t in strategy_results])
dd_pad_per = max([len(dd) for dd in drawdown])
drawdown = [f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency} {dd:>{dd_pad_per}}%'
for t, dd in zip(strategy_results, drawdown)]
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]
t['profit_total_pct'], t['duration_avg'],
_generate_wins_draws_losses(t['wins'], t['draws'], t['losses']), drawdown]
for t, drawdown in zip(strategy_results, drawdown)]
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
@@ -415,14 +539,36 @@ def text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0:
best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio'])
worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio'])
# Newly added fields should be ignored if they are missing in strat_results. hyperopt-show
# command stores these results and newer version of freqtrade must be able to handle old
# results with missing new fields.
zero_duration_trades = '--'
if 'zero_duration_trades' in strat_results:
zero_duration_trades_per = \
100.0 / strat_results['total_trades'] * strat_results['zero_duration_trades']
zero_duration_trades = f'{zero_duration_trades_per:.2f}% ' \
f'({strat_results["zero_duration_trades"]})'
metrics = [
('Backtesting from', strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
('Backtesting to', strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
('Backtesting from', strat_results['backtest_start']),
('Backtesting to', strat_results['backtest_end']),
('Max open trades', strat_results['max_open_trades']),
('', ''), # Empty line to improve readability
('Total trades', strat_results['total_trades']),
('Total Profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
('Starting balance', round_coin_value(strat_results['starting_balance'],
strat_results['stake_currency'])),
('Final balance', round_coin_value(strat_results['final_balance'],
strat_results['stake_currency'])),
('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
strat_results['stake_currency'])),
('Total profit %', f"{round(strat_results['profit_total'] * 100, 2):}%"),
('Trades per day', strat_results['trades_per_day']),
('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'],
strat_results['stake_currency'])),
('Total trade volume', round_coin_value(strat_results['total_volume'],
strat_results['stake_currency'])),
('', ''), # Empty line to improve readability
('Best Pair', f"{strat_results['best_pair']['key']} "
f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
@@ -432,55 +578,87 @@ def text_table_add_metrics(strat_results: Dict) -> str:
('Worst trade', f"{worst_trade['pair']} "
f"{round(worst_trade['profit_ratio'] * 100, 2)}%"),
('Best day', f"{round(strat_results['backtest_best_day'] * 100, 2)}%"),
('Worst day', f"{round(strat_results['backtest_worst_day'] * 100, 2)}%"),
('Best day', round_coin_value(strat_results['backtest_best_day_abs'],
strat_results['stake_currency'])),
('Worst day', round_coin_value(strat_results['backtest_worst_day_abs'],
strat_results['stake_currency'])),
('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']}"),
('Zero Duration Trades', zero_duration_trades),
('Rejected Buy signals', strat_results.get('rejected_signals', 'N/A')),
('', ''), # 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)),
('Min balance', round_coin_value(strat_results['csum_min'],
strat_results['stake_currency'])),
('Max balance', round_coin_value(strat_results['csum_max'],
strat_results['stake_currency'])),
('Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
('Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
strat_results['stake_currency'])),
('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],
strat_results['stake_currency'])),
('Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
strat_results['stake_currency'])),
('Drawdown Start', strat_results['drawdown_start']),
('Drawdown End', strat_results['drawdown_end']),
('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
]
return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
else:
return ''
start_balance = round_coin_value(strat_results['starting_balance'],
strat_results['stake_currency'])
stake_amount = round_coin_value(
strat_results['stake_amount'], strat_results['stake_currency']
) if strat_results['stake_amount'] != UNLIMITED_STAKE_AMOUNT else 'unlimited'
message = ("No trades made. "
f"Your starting balance was {start_balance}, "
f"and your stake was {stake_amount}."
)
return message
def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str):
"""
Print results for one strategy
"""
# Print results
print(f"Result for strategy {strategy}")
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 = 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 = 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)
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()
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 = 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 = 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 = 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)
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()
show_backtest_result(strategy, results, stake_currency)
if len(backtest_stats['strategy']) > 1:
# Print Strategy summary table

4
freqtrade/optimize/space/__init__.py Normal file
View File

@@ -0,0 +1,4 @@
# flake8: noqa: F401
from skopt.space import Categorical, Dimension, Integer, Real
from .decimalspace import SKDecimal

33
freqtrade/optimize/space/decimalspace.py Normal file
View File

@@ -0,0 +1,33 @@
import numpy as np
from skopt.space import Integer
class SKDecimal(Integer):
def __init__(self, low, high, decimals=3, prior="uniform", base=10, transform=None,
name=None, dtype=np.int64):
self.decimals = decimals
_low = int(low * pow(10, self.decimals))
_high = int(high * pow(10, self.decimals))
# trunc to precision to avoid points out of space
self.low_orig = round(_low * pow(0.1, self.decimals), self.decimals)
self.high_orig = round(_high * pow(0.1, self.decimals), self.decimals)
super().__init__(_low, _high, prior, base, transform, name, dtype)
def __repr__(self):
return "Decimal(low={}, high={}, decimals={}, prior='{}', transform='{}')".format(
self.low_orig, self.high_orig, self.decimals, self.prior, self.transform_)
def __contains__(self, point):
if isinstance(point, list):
point = np.array(point)
return self.low_orig <= point <= self.high_orig
def transform(self, Xt):
aa = [int(x * pow(10, self.decimals)) for x in Xt]
return super().transform(aa)
def inverse_transform(self, Xt):
res = super().inverse_transform(Xt)
return [round(x * pow(0.1, self.decimals), self.decimals) for x in res]

3
freqtrade/persistence/__init__.py
View File

@@ -1,4 +1,5 @@
# flake8: noqa: F401
from freqtrade.persistence.models import Order, Trade, clean_dry_run_db, cleanup_db, init_db
from freqtrade.persistence.models import (LocalTrade, Order, Trade, clean_dry_run_db, cleanup_db,
init_db)
from freqtrade.persistence.pairlock_middleware import PairLocks

57
freqtrade/persistence/migrations.py
View File

@@ -1,7 +1,7 @@
import logging
from typing import List
from sqlalchemy import inspect
from sqlalchemy import inspect, text
logger = logging.getLogger(__name__)
@@ -62,15 +62,17 @@ def migrate_trades_table(decl_base, inspector, engine, table_back_name: str, col
amount_requested = get_column_def(cols, 'amount_requested', 'amount')
# Schema migration necessary
engine.execute(f"alter table trades rename to {table_back_name}")
# drop indexes on backup table
for index in inspector.get_indexes(table_back_name):
engine.execute(f"drop index {index['name']}")
with engine.begin() as connection:
connection.execute(text(f"alter table trades rename to {table_back_name}"))
# drop indexes on backup table
for index in inspector.get_indexes(table_back_name):
connection.execute(text(f"drop index {index['name']}"))
# let SQLAlchemy create the schema as required
decl_base.metadata.create_all(engine)
# Copy data back - following the correct schema
engine.execute(f"""insert into trades
with engine.begin() as connection:
connection.execute(text(f"""insert into trades
(id, exchange, pair, is_open,
fee_open, fee_open_cost, fee_open_currency,
fee_close, fee_close_cost, fee_open_currency, open_rate,
@@ -104,11 +106,12 @@ def migrate_trades_table(decl_base, inspector, engine, table_back_name: str, col
{strategy} strategy, {timeframe} timeframe,
{open_trade_value} open_trade_value, {close_profit_abs} close_profit_abs
from {table_back_name}
""")
"""))
def migrate_open_orders_to_trades(engine):
engine.execute("""
with engine.begin() as connection:
connection.execute(text("""
insert into orders (ft_trade_id, ft_pair, order_id, ft_order_side, ft_is_open)
select id ft_trade_id, pair ft_pair, open_order_id,
case when close_rate_requested is null then 'buy'
@@ -120,7 +123,30 @@ def migrate_open_orders_to_trades(engine):
'stoploss' ft_order_side, 1 ft_is_open
from trades
where stoploss_order_id is not null
""")
"""))
def migrate_orders_table(decl_base, inspector, engine, table_back_name: str, cols: List):
# Schema migration necessary
with engine.begin() as connection:
connection.execute(text(f"alter table orders rename to {table_back_name}"))
# drop indexes on backup table
for index in inspector.get_indexes(table_back_name):
connection.execute(text(f"drop index {index['name']}"))
# let SQLAlchemy create the schema as required
decl_base.metadata.create_all(engine)
with engine.begin() as connection:
connection.execute(text(f"""
insert into orders ( id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
status, symbol, order_type, side, price, amount, filled, average, remaining, cost,
order_date, order_filled_date, order_update_date)
select id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
status, symbol, order_type, side, price, amount, filled, null average, remaining, cost,
order_date, order_filled_date, order_update_date
from {table_back_name}
"""))
def check_migrate(engine, decl_base, previous_tables) -> None:
@@ -141,10 +167,15 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
inspector = inspect(engine)
cols = inspector.get_columns('trades')
if 'orders' not in previous_tables:
if 'orders' not in previous_tables and 'trades' in previous_tables:
logger.info('Moving open orders to Orders table.')
migrate_open_orders_to_trades(engine)
else:
pass
# Empty for now - as there is only one iteration of the orders table so far.
# table_back_name = get_backup_name(tabs, 'orders_bak')
cols_order = inspector.get_columns('orders')
if not has_column(cols_order, 'average'):
tabs = get_table_names_for_table(inspector, 'orders')
# Empty for now - as there is only one iteration of the orders table so far.
table_back_name = get_backup_name(tabs, 'orders_bak')
migrate_orders_table(decl_base, inspector, engine, table_back_name, cols)

547
freqtrade/persistence/models.py
View File

@@ -6,18 +6,15 @@ from datetime import datetime, timezone
from decimal import Decimal
from typing import Any, Dict, List, Optional
import arrow
from sqlalchemy import (Boolean, Column, DateTime, Float, ForeignKey, Integer, String,
create_engine, desc, func, inspect)
from sqlalchemy.exc import NoSuchModuleError
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import Query, relationship
from sqlalchemy.orm.scoping import scoped_session
from sqlalchemy.orm.session import sessionmaker
from sqlalchemy.orm import Query, declarative_base, relationship, scoped_session, sessionmaker
from sqlalchemy.pool import StaticPool
from sqlalchemy.sql.schema import UniqueConstraint
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.enums import SellType
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.misc import safe_value_fallback
from freqtrade.persistence.migrations import check_migrate
@@ -42,16 +39,18 @@ def init_db(db_url: str, clean_open_orders: bool = False) -> None:
"""
kwargs = {}
# Take care of thread ownership if in-memory db
if db_url == 'sqlite://':
kwargs.update({
'connect_args': {'check_same_thread': False},
'poolclass': StaticPool,
'echo': False,
})
# Take care of thread ownership
if db_url.startswith('sqlite://'):
kwargs.update({
'connect_args': {'check_same_thread': False},
})
try:
engine = create_engine(db_url, **kwargs)
engine = create_engine(db_url, future=True, **kwargs)
except NoSuchModuleError:
raise OperationalException(f"Given value for db_url: '{db_url}' "
f"is no valid database URL! (See {_SQL_DOCS_URL})")
@@ -59,13 +58,10 @@ def init_db(db_url: str, clean_open_orders: bool = False) -> None:
# https://docs.sqlalchemy.org/en/13/orm/contextual.html#thread-local-scope
# Scoped sessions proxy requests to the appropriate thread-local session.
# We should use the scoped_session object - not a seperately initialized version
Trade.session = scoped_session(sessionmaker(bind=engine, autoflush=True, autocommit=True))
Trade.query = Trade.session.query_property()
# Copy session attributes to order object too
Order.session = Trade.session
Order.query = Order.session.query_property()
PairLock.session = Trade.session
PairLock.query = PairLock.session.query_property()
Trade._session = scoped_session(sessionmaker(bind=engine, autoflush=True))
Trade.query = Trade._session.query_property()
Order.query = Trade._session.query_property()
PairLock.query = Trade._session.query_property()
previous_tables = inspect(engine).get_table_names()
_DECL_BASE.metadata.create_all(engine)
@@ -81,7 +77,7 @@ def cleanup_db() -> None:
Flushes all pending operations to disk.
:return: None
"""
Trade.session.flush()
Trade.commit()
def clean_dry_run_db() -> None:
@@ -93,6 +89,7 @@ def clean_dry_run_db() -> None:
# Check we are updating only a dry_run order not a prod one
if 'dry_run' in trade.open_order_id:
trade.open_order_id = None
Trade.commit()
class Order(_DECL_BASE):
@@ -116,16 +113,17 @@ class Order(_DECL_BASE):
trade = relationship("Trade", back_populates="orders")
ft_order_side = Column(String, nullable=False)
ft_pair = Column(String, nullable=False)
ft_order_side = Column(String(25), nullable=False)
ft_pair = Column(String(25), nullable=False)
ft_is_open = Column(Boolean, nullable=False, default=True, index=True)
order_id = Column(String, nullable=False, index=True)
status = Column(String, nullable=True)
symbol = Column(String, nullable=True)
order_type = Column(String, nullable=True)
side = Column(String, nullable=True)
order_id = Column(String(255), nullable=False, index=True)
status = Column(String(255), nullable=True)
symbol = Column(String(25), nullable=True)
order_type = Column(String(50), nullable=True)
side = Column(String(25), nullable=True)
price = Column(Float, nullable=True)
average = Column(Float, nullable=True)
amount = Column(Float, nullable=True)
filled = Column(Float, nullable=True)
remaining = Column(Float, nullable=True)
@@ -154,6 +152,7 @@ class Order(_DECL_BASE):
self.price = order.get('price', self.price)
self.amount = order.get('amount', self.amount)
self.filled = order.get('filled', self.filled)
self.average = order.get('average', self.average)
self.remaining = order.get('remaining', self.remaining)
self.cost = order.get('cost', self.cost)
if 'timestamp' in order and order['timestamp'] is not None:
@@ -163,8 +162,8 @@ class Order(_DECL_BASE):
if self.status in ('closed', 'canceled', 'cancelled'):
self.ft_is_open = False
if order.get('filled', 0) > 0:
self.order_filled_date = arrow.utcnow().datetime
self.order_update_date = arrow.utcnow().datetime
self.order_filled_date = datetime.now(timezone.utc)
self.order_update_date = datetime.now(timezone.utc)
@staticmethod
def update_orders(orders: List['Order'], order: Dict[str, Any]):
@@ -179,6 +178,7 @@ class Order(_DECL_BASE):
if filtered_orders:
oobj = filtered_orders[0]
oobj.update_from_ccxt_object(order)
Order.query.session.commit()
else:
logger.warning(f"Did not find order for {order}.")
@@ -199,67 +199,69 @@ class Order(_DECL_BASE):
return Order.query.filter(Order.ft_is_open.is_(True)).all()
class Trade(_DECL_BASE):
class LocalTrade():
"""
Trade database model.
Also handles updating and querying trades
Used in backtesting - must be aligned to Trade model!
"""
__tablename__ = 'trades'
use_db: bool = True
use_db: bool = False
# Trades container for backtesting
trades: List['Trade'] = []
trades: List['LocalTrade'] = []
trades_open: List['LocalTrade'] = []
total_profit: float = 0
id = Column(Integer, primary_key=True)
id: int = 0
orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan")
orders: List[Order] = []
exchange = Column(String, nullable=False)
pair = Column(String, nullable=False, index=True)
is_open = Column(Boolean, nullable=False, default=True, index=True)
fee_open = Column(Float, nullable=False, default=0.0)
fee_open_cost = Column(Float, nullable=True)
fee_open_currency = Column(String, nullable=True)
fee_close = Column(Float, nullable=False, default=0.0)
fee_close_cost = Column(Float, nullable=True)
fee_close_currency = Column(String, nullable=True)
open_rate = Column(Float)
open_rate_requested = Column(Float)
exchange: str = ''
pair: str = ''
is_open: bool = True
fee_open: float = 0.0
fee_open_cost: Optional[float] = None
fee_open_currency: str = ''
fee_close: float = 0.0
fee_close_cost: Optional[float] = None
fee_close_currency: str = ''
open_rate: float = 0.0
open_rate_requested: Optional[float] = None
# open_trade_value - calculated via _calc_open_trade_value
open_trade_value = Column(Float)
close_rate = Column(Float)
close_rate_requested = Column(Float)
close_profit = Column(Float)
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)
open_trade_value: float = 0.0
close_rate: Optional[float] = None
close_rate_requested: Optional[float] = None
close_profit: Optional[float] = None
close_profit_abs: Optional[float] = None
stake_amount: float = 0.0
amount: float = 0.0
amount_requested: Optional[float] = None
open_date: datetime
close_date: Optional[datetime] = None
open_order_id: Optional[str] = None
# absolute value of the stop loss
stop_loss = Column(Float, nullable=True, default=0.0)
stop_loss: float = 0.0
# percentage value of the stop loss
stop_loss_pct = Column(Float, nullable=True)
stop_loss_pct: float = 0.0
# absolute value of the initial stop loss
initial_stop_loss = Column(Float, nullable=True, default=0.0)
initial_stop_loss: float = 0.0
# percentage value of the initial stop loss
initial_stop_loss_pct = Column(Float, nullable=True)
initial_stop_loss_pct: float = 0.0
# stoploss order id which is on exchange
stoploss_order_id = Column(String, nullable=True, index=True)
stoploss_order_id: Optional[str] = None
# last update time of the stoploss order on exchange
stoploss_last_update = Column(DateTime, nullable=True)
stoploss_last_update: Optional[datetime] = None
# absolute value of the highest reached price
max_rate = Column(Float, nullable=True, default=0.0)
max_rate: float = 0.0
# Lowest price reached
min_rate = Column(Float, nullable=True)
sell_reason = Column(String, nullable=True)
sell_order_status = Column(String, nullable=True)
strategy = Column(String, nullable=True)
timeframe = Column(Integer, nullable=True)
min_rate: float = 0.0
sell_reason: str = ''
sell_order_status: str = ''
strategy: str = ''
timeframe: Optional[int] = None
def __init__(self, **kwargs):
super().__init__(**kwargs)
for key in kwargs:
setattr(self, key, kwargs[key])
self.recalc_open_trade_value()
def __repr__(self):
@@ -268,6 +270,14 @@ class Trade(_DECL_BASE):
return (f'Trade(id={self.id}, pair={self.pair}, amount={self.amount:.8f}, '
f'open_rate={self.open_rate:.8f}, open_since={open_since})')
@property
def open_date_utc(self):
return self.open_date.replace(tzinfo=timezone.utc)
@property
def close_date_utc(self):
return self.close_date.replace(tzinfo=timezone.utc)
def to_json(self) -> Dict[str, Any]:
return {
'trade_id': self.id,
@@ -287,15 +297,12 @@ class Trade(_DECL_BASE):
'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(DATETIME_PRINT_FORMAT),
'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_value': round(self.open_trade_value, 8),
'close_date_hum': (arrow.get(self.close_date).humanize()
if self.close_date else None),
'close_date': (self.close_date.strftime(DATETIME_PRINT_FORMAT)
if self.close_date else None),
'close_timestamp': int(self.close_date.replace(
@@ -306,9 +313,9 @@ class Trade(_DECL_BASE):
'close_profit_pct': round(self.close_profit * 100, 2) if self.close_profit else None,
'close_profit_abs': self.close_profit_abs, # Deprecated
'trade_duration_s': (int((self.close_date - self.open_date).total_seconds())
'trade_duration_s': (int((self.close_date_utc - self.open_date_utc).total_seconds())
if self.close_date else None),
'trade_duration': (int((self.close_date - self.open_date).total_seconds() // 60)
'trade_duration': (int((self.close_date_utc - self.open_date_utc).total_seconds() // 60)
if self.close_date else None),
'profit_ratio': self.close_profit,
@@ -341,8 +348,9 @@ class Trade(_DECL_BASE):
"""
Resets all trades. Only active for backtesting mode.
"""
if not Trade.use_db:
Trade.trades = []
LocalTrade.trades = []
LocalTrade.trades_open = []
LocalTrade.total_profit = 0
def adjust_min_max_rates(self, current_price: float) -> None:
"""
@@ -410,8 +418,8 @@ class Trade(_DECL_BASE):
if order_type in ('market', 'limit') and order['side'] == 'buy':
# Update open rate and actual amount
self.open_rate = Decimal(safe_value_fallback(order, 'average', 'price'))
self.amount = Decimal(safe_value_fallback(order, 'filled', 'amount'))
self.open_rate = float(safe_value_fallback(order, 'average', 'price'))
self.amount = float(safe_value_fallback(order, 'filled', 'amount'))
self.recalc_open_trade_value()
if self.is_open:
logger.info(f'{order_type.upper()}_BUY has been fulfilled for {self}.')
@@ -423,19 +431,20 @@ class Trade(_DECL_BASE):
elif order_type in ('stop_loss_limit', 'stop-loss', 'stop-loss-limit', 'stop'):
self.stoploss_order_id = None
self.close_rate_requested = self.stop_loss
self.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value
if self.is_open:
logger.info(f'{order_type.upper()} is hit for {self}.')
self.close(order['average'])
self.close(safe_value_fallback(order, 'average', 'price'))
else:
raise ValueError(f'Unknown order type: {order_type}')
cleanup_db()
Trade.commit()
def close(self, rate: float, *, show_msg: bool = True) -> None:
"""
Sets close_rate to the given rate, calculates total profit
and marks trade as closed
"""
self.close_rate = Decimal(rate)
self.close_rate = rate
self.close_profit = self.calc_profit_ratio()
self.close_profit_abs = self.calc_profit()
self.close_date = self.close_date or datetime.utcnow()
@@ -480,14 +489,6 @@ class Trade(_DECL_BASE):
def update_order(self, order: Dict) -> None:
Order.update_orders(self.orders, order)
def delete(self) -> None:
for order in self.orders:
Order.session.delete(order)
Trade.session.delete(self)
Trade.session.flush()
def _calc_open_trade_value(self) -> float:
"""
Calculate the open_rate including open_fee.
@@ -517,7 +518,7 @@ class Trade(_DECL_BASE):
if rate is None and not self.close_rate:
return 0.0
sell_trade = Decimal(self.amount) * Decimal(rate or self.close_rate)
sell_trade = Decimal(self.amount) * Decimal(rate or self.close_rate) # type: ignore
fees = sell_trade * Decimal(fee or self.fee_close)
return float(sell_trade - fees)
@@ -551,6 +552,8 @@ class Trade(_DECL_BASE):
rate=(rate or self.close_rate),
fee=(fee or self.fee_close)
)
if self.open_trade_value == 0.0:
return 0.0
profit_ratio = (close_trade_value / self.open_trade_value) - 1
return float(f"{profit_ratio:.8f}")
@@ -569,27 +572,10 @@ class Trade(_DECL_BASE):
else:
return None
@staticmethod
def get_trades(trade_filter=None) -> Query:
"""
Helper function to query Trades using filters.
:param trade_filter: Optional filter to apply to trades
Can be either a Filter object, or a List of filters
e.g. `(trade_filter=[Trade.id == trade_id, Trade.is_open.is_(True),])`
e.g. `(trade_filter=Trade.id == trade_id)`
:return: unsorted query object
"""
if trade_filter is not None:
if not isinstance(trade_filter, list):
trade_filter = [trade_filter]
return Trade.query.filter(*trade_filter)
else:
return Trade.query
@staticmethod
def get_trades_proxy(*, pair: str = None, is_open: bool = None,
open_date: datetime = None, close_date: datetime = None,
) -> List['Trade']:
) -> List['LocalTrade']:
"""
Helper function to query Trades.
Returns a List of trades, filtered on the parameters given.
@@ -598,110 +584,47 @@ class Trade(_DECL_BASE):
:return: unsorted List[Trade]
"""
if Trade.use_db:
trade_filter = []
if pair:
trade_filter.append(Trade.pair == pair)
if open_date:
trade_filter.append(Trade.open_date > open_date)
if close_date:
trade_filter.append(Trade.close_date > close_date)
if is_open is not None:
trade_filter.append(Trade.is_open.is_(is_open))
return Trade.get_trades(trade_filter).all()
# Offline mode - without database
if is_open is not None:
if is_open:
sel_trades = LocalTrade.trades_open
else:
sel_trades = LocalTrade.trades
else:
# Offline mode - without database
sel_trades = [trade for trade in Trade.trades]
if pair:
sel_trades = [trade for trade in sel_trades if trade.pair == pair]
if open_date:
sel_trades = [trade for trade in sel_trades if trade.open_date > open_date]
if close_date:
sel_trades = [trade for trade in sel_trades if trade.close_date
and trade.close_date > close_date]
if is_open is not None:
sel_trades = [trade for trade in sel_trades if trade.is_open == is_open]
return sel_trades
# Not used during backtesting, but might be used by a strategy
sel_trades = list(LocalTrade.trades + LocalTrade.trades_open)
if pair:
sel_trades = [trade for trade in sel_trades if trade.pair == pair]
if open_date:
sel_trades = [trade for trade in sel_trades if trade.open_date > open_date]
if close_date:
sel_trades = [trade for trade in sel_trades if trade.close_date
and trade.close_date > close_date]
return sel_trades
@staticmethod
def close_bt_trade(trade):
LocalTrade.trades_open.remove(trade)
LocalTrade.trades.append(trade)
LocalTrade.total_profit += trade.close_profit_abs
@staticmethod
def add_bt_trade(trade):
if trade.is_open:
LocalTrade.trades_open.append(trade)
else:
LocalTrade.trades.append(trade)
@staticmethod
def get_open_trades() -> List[Any]:
"""
Query trades from persistence layer
"""
return Trade.get_trades(Trade.is_open.is_(True)).all()
@staticmethod
def get_open_order_trades():
"""
Returns all open trades
"""
return Trade.get_trades(Trade.open_order_id.isnot(None)).all()
@staticmethod
def get_open_trades_without_assigned_fees():
"""
Returns all open trades which don't have open fees set correctly
"""
return Trade.get_trades([Trade.fee_open_currency.is_(None),
Trade.orders.any(),
Trade.is_open.is_(True),
]).all()
@staticmethod
def get_sold_trades_without_assigned_fees():
"""
Returns all closed trades which don't have fees set correctly
"""
return Trade.get_trades([Trade.fee_close_currency.is_(None),
Trade.orders.any(),
Trade.is_open.is_(False),
]).all()
@staticmethod
def total_open_trades_stakes() -> float:
"""
Calculates total invested amount in open trades
in stake currency
"""
total_open_stake_amount = Trade.session.query(func.sum(Trade.stake_amount))\
.filter(Trade.is_open.is_(True))\
.scalar()
return total_open_stake_amount or 0
@staticmethod
def get_overall_performance() -> List[Dict[str, Any]]:
"""
Returns List of dicts containing all Trades, including profit and trade count
"""
pair_rates = Trade.session.query(
Trade.pair,
func.sum(Trade.close_profit).label('profit_sum'),
func.count(Trade.pair).label('count')
).filter(Trade.is_open.is_(False))\
.group_by(Trade.pair) \
.order_by(desc('profit_sum')) \
.all()
return [
{
'pair': pair,
'profit': rate,
'count': count
}
for pair, rate, count in pair_rates
]
@staticmethod
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')
).filter(Trade.is_open.is_(False)) \
.group_by(Trade.pair) \
.order_by(desc('profit_sum')).first()
return best_pair
return Trade.get_trades_proxy(is_open=True)
@staticmethod
def stoploss_reinitialization(desired_stoploss):
@@ -723,6 +646,215 @@ class Trade(_DECL_BASE):
logger.info(f"New stoploss: {trade.stop_loss}.")
class Trade(_DECL_BASE, LocalTrade):
"""
Trade database model.
Also handles updating and querying trades
Note: Fields must be aligned with LocalTrade class
"""
__tablename__ = 'trades'
use_db: bool = True
id = Column(Integer, primary_key=True)
orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan")
exchange = Column(String(25), nullable=False)
pair = Column(String(25), nullable=False, index=True)
is_open = Column(Boolean, nullable=False, default=True, index=True)
fee_open = Column(Float, nullable=False, default=0.0)
fee_open_cost = Column(Float, nullable=True)
fee_open_currency = Column(String(25), nullable=True)
fee_close = Column(Float, nullable=False, default=0.0)
fee_close_cost = Column(Float, nullable=True)
fee_close_currency = Column(String(25), nullable=True)
open_rate = Column(Float)
open_rate_requested = Column(Float)
# open_trade_value - calculated via _calc_open_trade_value
open_trade_value = Column(Float)
close_rate = Column(Float)
close_rate_requested = Column(Float)
close_profit = Column(Float)
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(255))
# absolute value of the stop loss
stop_loss = Column(Float, nullable=True, default=0.0)
# percentage value of the stop loss
stop_loss_pct = Column(Float, nullable=True)
# absolute value of the initial stop loss
initial_stop_loss = Column(Float, nullable=True, default=0.0)
# percentage value of the initial stop loss
initial_stop_loss_pct = Column(Float, nullable=True)
# stoploss order id which is on exchange
stoploss_order_id = Column(String(255), nullable=True, index=True)
# last update time of the stoploss order on exchange
stoploss_last_update = Column(DateTime, nullable=True)
# absolute value of the highest reached price
max_rate = Column(Float, nullable=True, default=0.0)
# Lowest price reached
min_rate = Column(Float, nullable=True)
sell_reason = Column(String(100), nullable=True)
sell_order_status = Column(String(100), nullable=True)
strategy = Column(String(100), nullable=True)
timeframe = Column(Integer, nullable=True)
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.recalc_open_trade_value()
def delete(self) -> None:
for order in self.orders:
Order.query.session.delete(order)
Trade.query.session.delete(self)
Trade.commit()
@staticmethod
def commit():
Trade.query.session.commit()
@staticmethod
def get_trades_proxy(*, pair: str = None, is_open: bool = None,
open_date: datetime = None, close_date: datetime = None,
) -> List['LocalTrade']:
"""
Helper function to query Trades.j
Returns a List of trades, filtered on the parameters given.
In live mode, converts the filter to a database query and returns all rows
In Backtest mode, uses filters on Trade.trades to get the result.
:return: unsorted List[Trade]
"""
if Trade.use_db:
trade_filter = []
if pair:
trade_filter.append(Trade.pair == pair)
if open_date:
trade_filter.append(Trade.open_date > open_date)
if close_date:
trade_filter.append(Trade.close_date > close_date)
if is_open is not None:
trade_filter.append(Trade.is_open.is_(is_open))
return Trade.get_trades(trade_filter).all()
else:
return LocalTrade.get_trades_proxy(
pair=pair, is_open=is_open,
open_date=open_date,
close_date=close_date
)
@staticmethod
def get_trades(trade_filter=None) -> Query:
"""
Helper function to query Trades using filters.
NOTE: Not supported in Backtesting.
:param trade_filter: Optional filter to apply to trades
Can be either a Filter object, or a List of filters
e.g. `(trade_filter=[Trade.id == trade_id, Trade.is_open.is_(True),])`
e.g. `(trade_filter=Trade.id == trade_id)`
:return: unsorted query object
"""
if not Trade.use_db:
raise NotImplementedError('`Trade.get_trades()` not supported in backtesting mode.')
if trade_filter is not None:
if not isinstance(trade_filter, list):
trade_filter = [trade_filter]
return Trade.query.filter(*trade_filter)
else:
return Trade.query
@staticmethod
def get_open_order_trades():
"""
Returns all open trades
NOTE: Not supported in Backtesting.
"""
return Trade.get_trades(Trade.open_order_id.isnot(None)).all()
@staticmethod
def get_open_trades_without_assigned_fees():
"""
Returns all open trades which don't have open fees set correctly
NOTE: Not supported in Backtesting.
"""
return Trade.get_trades([Trade.fee_open_currency.is_(None),
Trade.orders.any(),
Trade.is_open.is_(True),
]).all()
@staticmethod
def get_sold_trades_without_assigned_fees():
"""
Returns all closed trades which don't have fees set correctly
NOTE: Not supported in Backtesting.
"""
return Trade.get_trades([Trade.fee_close_currency.is_(None),
Trade.orders.any(),
Trade.is_open.is_(False),
]).all()
@staticmethod
def total_open_trades_stakes() -> float:
"""
Calculates total invested amount in open trades
in stake currency
"""
if Trade.use_db:
total_open_stake_amount = Trade.query.with_entities(
func.sum(Trade.stake_amount)).filter(Trade.is_open.is_(True)).scalar()
else:
total_open_stake_amount = sum(
t.stake_amount for t in LocalTrade.get_trades_proxy(is_open=True))
return total_open_stake_amount or 0
@staticmethod
def get_overall_performance() -> List[Dict[str, Any]]:
"""
Returns List of dicts containing all Trades, including profit and trade count
NOTE: Not supported in Backtesting.
"""
pair_rates = Trade.query.with_entities(
Trade.pair,
func.sum(Trade.close_profit).label('profit_sum'),
func.sum(Trade.close_profit_abs).label('profit_sum_abs'),
func.count(Trade.pair).label('count')
).filter(Trade.is_open.is_(False))\
.group_by(Trade.pair) \
.order_by(desc('profit_sum_abs')) \
.all()
return [
{
'pair': pair,
'profit': profit,
'profit_abs': profit_abs,
'count': count
}
for pair, profit, profit_abs, count in pair_rates
]
@staticmethod
def get_best_pair():
"""
Get best pair with closed trade.
NOTE: Not supported in Backtesting.
:returns: Tuple containing (pair, profit_sum)
"""
best_pair = Trade.query.with_entities(
Trade.pair, func.sum(Trade.close_profit).label('profit_sum')
).filter(Trade.is_open.is_(False)) \
.group_by(Trade.pair) \
.order_by(desc('profit_sum')).first()
return best_pair
class PairLock(_DECL_BASE):
"""
Pair Locks database model.
@@ -731,8 +863,8 @@ class PairLock(_DECL_BASE):
id = Column(Integer, primary_key=True)
pair = Column(String, nullable=False, index=True)
reason = Column(String, nullable=True)
pair = Column(String(25), nullable=False, index=True)
reason = Column(String(255), nullable=True)
# Time the pair was locked (start time)
lock_time = Column(DateTime, nullable=False)
# Time until the pair is locked (end time)
@@ -765,6 +897,7 @@ class PairLock(_DECL_BASE):
def to_json(self) -> Dict[str, Any]:
return {
'id': self.id,
'pair': self.pair,
'lock_time': self.lock_time.strftime(DATETIME_PRINT_FORMAT),
'lock_timestamp': int(self.lock_time.replace(tzinfo=timezone.utc).timestamp() * 1000),

14
freqtrade/persistence/pairlock_middleware.py
View File

@@ -48,8 +48,8 @@ class PairLocks():
active=True
)
if PairLocks.use_db:
PairLock.session.add(lock)
PairLock.session.flush()
PairLock.query.session.add(lock)
PairLock.query.session.commit()
else:
PairLocks.locks.append(lock)
@@ -99,7 +99,7 @@ class PairLocks():
for lock in locks:
lock.active = False
if PairLocks.use_db:
PairLock.session.flush()
PairLock.query.session.commit()
@staticmethod
def is_global_lock(now: Optional[datetime] = None) -> bool:
@@ -123,3 +123,11 @@ class PairLocks():
now = datetime.now(timezone.utc)
return len(PairLocks.get_pair_locks(pair, now)) > 0 or PairLocks.is_global_lock(now)
@staticmethod
def get_all_locks() -> List[PairLock]:
if PairLocks.use_db:
return PairLock.query.all()
else:
return PairLocks.locks

59
freqtrade/plot/plotting.py
View File

@@ -47,7 +47,7 @@ def init_plotscript(config, markets: List, startup_candles: int = 0):
data = load_data(
datadir=config.get('datadir'),
pairs=pairs,
timeframe=config.get('timeframe', '5m'),
timeframe=config['timeframe'],
timerange=timerange,
startup_candles=startup_candles,
data_format=config.get('dataformat_ohlcv', 'json'),
@@ -56,7 +56,7 @@ def init_plotscript(config, markets: List, startup_candles: int = 0):
if startup_candles and data:
min_date, max_date = get_timerange(data)
logger.info(f"Loading data from {min_date} to {max_date}")
timerange.adjust_start_if_necessary(timeframe_to_seconds(config.get('timeframe', '5m')),
timerange.adjust_start_if_necessary(timeframe_to_seconds(config['timeframe']),
startup_candles, min_date)
no_trades = False
@@ -77,7 +77,8 @@ def init_plotscript(config, markets: List, startup_candles: int = 0):
)
except ValueError as e:
raise OperationalException(e) from e
trades = trim_dataframe(trades, timerange, 'open_date')
if not trades.empty:
trades = trim_dataframe(trades, timerange, 'open_date')
return {"ohlcv": data,
"trades": trades,
@@ -95,20 +96,34 @@ def add_indicators(fig, row, indicators: Dict[str, Dict], data: pd.DataFrame) ->
Dict key must correspond to dataframe column.
:param data: candlestick DataFrame
"""
plot_kinds = {
'scatter': go.Scatter,
'bar': go.Bar,
}
for indicator, conf in indicators.items():
logger.debug(f"indicator {indicator} with config {conf}")
if indicator in data:
kwargs = {'x': data['date'],
'y': data[indicator].values,
'mode': 'lines',
'name': indicator
}
if 'color' in conf:
kwargs.update({'line': {'color': conf['color']}})
scatter = go.Scatter(
**kwargs
)
fig.add_trace(scatter, row, 1)
plot_type = conf.get('type', 'scatter')
color = conf.get('color')
if plot_type == 'bar':
kwargs.update({'marker_color': color or 'DarkSlateGrey',
'marker_line_color': color or 'DarkSlateGrey'})
else:
if color:
kwargs.update({'line': {'color': color}})
kwargs['mode'] = 'lines'
if plot_type != 'scatter':
logger.warning(f'Indicator {indicator} has unknown plot trace kind {plot_type}'
f', assuming "scatter".')
kwargs.update(conf.get('plotly', {}))
trace = plot_kinds[plot_type](**kwargs)
fig.add_trace(trace, row, 1)
else:
logger.info(
'Indicator "%s" ignored. Reason: This indicator is not found '
@@ -145,7 +160,7 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
Add scatter points indicating max drawdown
"""
try:
max_drawdown, highdate, lowdate = calculate_max_drawdown(trades)
max_drawdown, highdate, lowdate, _, _ = calculate_max_drawdown(trades)
drawdown = go.Scatter(
x=[highdate, lowdate],
@@ -441,7 +456,7 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
trades: pd.DataFrame, timeframe: str) -> go.Figure:
trades: pd.DataFrame, timeframe: str, stake_currency: str) -> go.Figure:
# Combine close-values for all pairs, rename columns to "pair"
df_comb = combine_dataframes_with_mean(data, "close")
@@ -466,8 +481,8 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
subplot_titles=["AVG Close Price", "Combined Profit", "Profit per pair"])
fig['layout'].update(title="Freqtrade Profit plot")
fig['layout']['yaxis1'].update(title='Price')
fig['layout']['yaxis2'].update(title='Profit')
fig['layout']['yaxis3'].update(title='Profit')
fig['layout']['yaxis2'].update(title=f'Profit {stake_currency}')
fig['layout']['yaxis3'].update(title=f'Profit {stake_currency}')
fig['layout']['xaxis']['rangeslider'].update(visible=False)
fig.add_trace(avgclose, 1, 1)
@@ -540,8 +555,11 @@ def load_and_plot_trades(config: Dict[str, Any]):
df_analyzed = strategy.analyze_ticker(data, {'pair': pair})
df_analyzed = trim_dataframe(df_analyzed, timerange)
trades_pair = trades.loc[trades['pair'] == pair]
trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
if not trades.empty:
trades_pair = trades.loc[trades['pair'] == pair]
trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
else:
trades_pair = trades
fig = generate_candlestick_graph(
pair=pair,
@@ -565,6 +583,9 @@ def plot_profit(config: Dict[str, Any]) -> None:
But should be somewhat proportional, and therefor useful
in helping out to find a good algorithm.
"""
if 'timeframe' not in config:
raise OperationalException('Timeframe must be set in either config or via --timeframe.')
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
plot_elements = init_plotscript(config, list(exchange.markets))
trades = plot_elements['trades']
@@ -581,6 +602,8 @@ def plot_profit(config: Dict[str, Any]) -> None:
# 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('timeframe', '5m'))
trades, config['timeframe'],
config.get('stake_currency', ''))
store_plot_file(fig, filename='freqtrade-profit-plot.html',
directory=config['user_data_dir'] / 'plot', auto_open=True)
directory=config['user_data_dir'] / 'plot',
auto_open=config.get('plot_auto_open', False))

10
freqtrade/plugins/pairlist/AgeFilter.py
View File

@@ -30,10 +30,10 @@ class AgeFilter(IPairList):
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:
if self._min_days_listed > exchange.ohlcv_candle_limit('1d'):
raise OperationalException("AgeFilter requires min_days_listed to not exceed "
"exchange max request size "
f"({exchange.ohlcv_candle_limit})")
f"({exchange.ohlcv_candle_limit('1d')})")
@property
def needstickers(self) -> bool:
@@ -71,14 +71,14 @@ class AgeFilter(IPairList):
daily_candles = candles[(p, '1d')] if (p, '1d') in candles else None
if not self._validate_pair_loc(p, daily_candles):
pairlist.remove(p)
logger.info(f"Validated {len(pairlist)} pairs.")
self.log_once(f"Validated {len(pairlist)} pairs.", logger.info)
return pairlist
def _validate_pair_loc(self, pair: str, daily_candles: Optional[DataFrame]) -> bool:
"""
Validate age for the ticker
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
# Check symbol in cache
@@ -86,7 +86,7 @@ class AgeFilter(IPairList):
return True
if daily_candles is not None:
if len(daily_candles) > self._min_days_listed:
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[pair] = int(arrow.utcnow().float_timestamp) * 1000

13
freqtrade/plugins/pairlist/IPairList.py
View File

@@ -7,7 +7,7 @@ from copy import deepcopy
from typing import Any, Dict, List
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import market_is_active
from freqtrade.exchange import Exchange, market_is_active
from freqtrade.mixins import LoggingMixin
@@ -16,7 +16,7 @@ logger = logging.getLogger(__name__)
class IPairList(LoggingMixin, ABC):
def __init__(self, exchange, pairlistmanager,
def __init__(self, exchange: Exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
"""
@@ -28,7 +28,7 @@ class IPairList(LoggingMixin, ABC):
"""
self._enabled = True
self._exchange = exchange
self._exchange: Exchange = exchange
self._pairlistmanager = pairlistmanager
self._config = config
self._pairlistconfig = pairlistconfig
@@ -68,12 +68,12 @@ class IPairList(LoggingMixin, ABC):
filter_pairlist() method.
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
: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]:
def gen_pairlist(self, tickers: Dict) -> List[str]:
"""
Generate the pairlist.
@@ -84,8 +84,7 @@ class IPairList(LoggingMixin, ABC):
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()).
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: List of pairs
"""
raise OperationalException("This Pairlist Handler should not be used "

14
freqtrade/plugins/pairlist/PerformanceFilter.py
View File

@@ -2,7 +2,7 @@
Performance pair list filter
"""
import logging
from typing import Any, Dict, List
from typing import Dict, List
import pandas as pd
@@ -15,11 +15,6 @@ logger = logging.getLogger(__name__)
class PerformanceFilter(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)
@property
def needstickers(self) -> bool:
"""
@@ -44,7 +39,12 @@ class PerformanceFilter(IPairList):
:return: new allowlist
"""
# Get the trading performance for pairs from database
performance = pd.DataFrame(Trade.get_overall_performance())
try:
performance = pd.DataFrame(Trade.get_overall_performance())
except AttributeError:
# Performancefilter does not work in backtesting.
self.log_once("PerformanceFilter is not available in this mode.", logger.warning)
return pairlist
# Skip performance-based sorting if no performance data is available
if len(performance) == 0:

2
freqtrade/plugins/pairlist/PrecisionFilter.py
View File

@@ -48,7 +48,7 @@ class PrecisionFilter(IPairList):
Check if pair has enough room to add a stoploss to avoid "unsellable" buys of very
low value pairs.
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
stop_price = ticker['ask'] * self._stoploss

40
freqtrade/plugins/pairlist/PriceFilter.py
View File

@@ -27,9 +27,13 @@ class PriceFilter(IPairList):
self._max_price = pairlistconfig.get('max_price', 0)
if self._max_price < 0:
raise OperationalException("PriceFilter requires max_price to be >= 0")
self._max_value = pairlistconfig.get('max_value', 0)
if self._max_value < 0:
raise OperationalException("PriceFilter requires max_value to be >= 0")
self._enabled = ((self._low_price_ratio > 0) or
(self._min_price > 0) or
(self._max_price > 0))
(self._max_price > 0) or
(self._max_value > 0))
@property
def needstickers(self) -> bool:
@@ -51,6 +55,8 @@ class PriceFilter(IPairList):
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}")
if self._max_value != 0:
active_price_filters.append(f"Value above {self._max_value:.8f}")
if len(active_price_filters):
return f"{self.name} - Filtering pairs priced {' or '.join(active_price_filters)}."
@@ -61,10 +67,10 @@ class PriceFilter(IPairList):
"""
Check if if one price-step (pip) is > than a certain barrier.
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
if ticker['last'] is None or ticker['last'] == 0:
if ticker.get('last', None) is None or ticker.get('last') == 0:
self.log_once(f"Removed {pair} from whitelist, because "
"ticker['last'] is empty (Usually no trade in the last 24h).",
logger.info)
@@ -79,6 +85,32 @@ class PriceFilter(IPairList):
f"because 1 unit is {changeperc * 100:.3f}%", logger.info)
return False
# Perform low_amount check
if self._max_value != 0:
price = ticker['last']
market = self._exchange.markets[pair]
limits = market['limits']
if ('amount' in limits and 'min' in limits['amount']
and limits['amount']['min'] is not None):
min_amount = limits['amount']['min']
min_precision = market['precision']['amount']
min_value = min_amount * price
if self._exchange.precisionMode == 4:
# tick size
next_value = (min_amount + min_precision) * price
else:
# Decimal places
min_precision = pow(0.1, min_precision)
next_value = (min_amount + min_precision) * price
diff = next_value - min_value
if diff > self._max_value:
self.log_once(f"Removed {pair} from whitelist, "
f"because min value change of {diff} > {self._max_value}.",
logger.info)
return False
# Perform min_price check.
if self._min_price != 0:
if ticker['last'] < self._min_price:
@@ -89,7 +121,7 @@ class PriceFilter(IPairList):
# Perform max_price check.
if self._max_price != 0:
if ticker['last'] > self._max_price:
self.log_once(f"Removed {ticker['symbol']} from whitelist, "
self.log_once(f"Removed {pair} from whitelist, "
f"because last price > {self._max_price:.8f}", logger.info)
return False

2
freqtrade/plugins/pairlist/SpreadFilter.py
View File

@@ -40,7 +40,7 @@ class SpreadFilter(IPairList):
"""
Validate spread for the ticker
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
if 'bid' in ticker and 'ask' in ticker and ticker['ask']:

5
freqtrade/plugins/pairlist/StaticPairList.py
View File

@@ -42,11 +42,10 @@ class StaticPairList(IPairList):
"""
return f"{self.name}"
def gen_pairlist(self, cached_pairlist: List[str], tickers: Dict) -> List[str]:
def gen_pairlist(self, tickers: Dict) -> List[str]:
"""
Generate the pairlist
:param cached_pairlist: Previously generated pairlist (cached)
:param tickers: Tickers (from exchange.get_tickers()).
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: List of pairs
"""
if self._allow_inactive:

121
freqtrade/plugins/pairlist/VolatilityFilter.py Normal file
View File

@@ -0,0 +1,121 @@
"""
Volatility pairlist filter
"""
import logging
import sys
from copy import deepcopy
from typing import Any, Dict, List, Optional
import arrow
import numpy as np
from cachetools.ttl import TTLCache
from pandas import DataFrame
from freqtrade.exceptions import OperationalException
from freqtrade.misc import plural
from freqtrade.plugins.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)
class VolatilityFilter(IPairList):
'''
Filters pairs by volatility
'''
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._days = pairlistconfig.get('lookback_days', 10)
self._min_volatility = pairlistconfig.get('min_volatility', 0)
self._max_volatility = pairlistconfig.get('max_volatility', sys.maxsize)
self._refresh_period = pairlistconfig.get('refresh_period', 1440)
self._pair_cache: TTLCache = TTLCache(maxsize=1000, ttl=self._refresh_period)
if self._days < 1:
raise OperationalException("VolatilityFilter requires lookback_days to be >= 1")
if self._days > exchange.ohlcv_candle_limit('1d'):
raise OperationalException("VolatilityFilter requires lookback_days to not "
"exceed exchange max request size "
f"({exchange.ohlcv_candle_limit('1d')})")
@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 False
def short_desc(self) -> str:
"""
Short whitelist method description - used for startup-messages
"""
return (f"{self.name} - Filtering pairs with volatility range "
f"{self._min_volatility}-{self._max_volatility} "
f" the last {self._days} {plural(self._days, 'day')}.")
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Validate trading range
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new allowlist
"""
needed_pairs = [(p, '1d') for p in pairlist if p not in self._pair_cache]
since_ms = int(arrow.utcnow()
.floor('day')
.shift(days=-self._days - 1)
.float_timestamp) * 1000
# Get all candles
candles = {}
if needed_pairs:
candles = self._exchange.refresh_latest_ohlcv(needed_pairs, since_ms=since_ms,
cache=False)
if self._enabled:
for p in deepcopy(pairlist):
daily_candles = candles[(p, '1d')] if (p, '1d') in candles else None
if not self._validate_pair_loc(p, daily_candles):
pairlist.remove(p)
return pairlist
def _validate_pair_loc(self, pair: str, daily_candles: Optional[DataFrame]) -> bool:
"""
Validate trading range
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
# Check symbol in cache
cached_res = self._pair_cache.get(pair, None)
if cached_res is not None:
return cached_res
result = False
if daily_candles is not None and not daily_candles.empty:
returns = (np.log(daily_candles.close / daily_candles.close.shift(-1)))
returns.fillna(0, inplace=True)
volatility_series = returns.rolling(window=self._days).std()*np.sqrt(self._days)
volatility_avg = volatility_series.mean()
if self._min_volatility <= volatility_avg <= self._max_volatility:
result = True
else:
self.log_once(f"Removed {pair} from whitelist, because volatility "
f"over {self._days} {plural(self._days, 'day')} "
f"is: {volatility_avg:.3f} "
f"which is not in the configured range of "
f"{self._min_volatility}-{self._max_volatility}.",
logger.info)
result = False
self._pair_cache[pair] = result
return result

24
freqtrade/plugins/pairlist/VolumePairList.py
View File

@@ -4,9 +4,10 @@ Volume PairList provider
Provides dynamic pair list based on trade volumes
"""
import logging
from datetime import datetime
from typing import Any, Dict, List
from cachetools.ttl import TTLCache
from freqtrade.exceptions import OperationalException
from freqtrade.plugins.pairlist.IPairList import IPairList
@@ -33,7 +34,8 @@ class VolumePairList(IPairList):
self._number_pairs = self._pairlistconfig['number_assets']
self._sort_key = self._pairlistconfig.get('sort_key', 'quoteVolume')
self._min_value = self._pairlistconfig.get('min_value', 0)
self.refresh_period = self._pairlistconfig.get('refresh_period', 1800)
self._refresh_period = self._pairlistconfig.get('refresh_period', 1800)
self._pair_cache: TTLCache = TTLCache(maxsize=1, ttl=self._refresh_period)
if not self._exchange.exchange_has('fetchTickers'):
raise OperationalException(
@@ -63,17 +65,19 @@ 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]:
def gen_pairlist(self, tickers: Dict) -> List[str]:
"""
Generate the pairlist
:param cached_pairlist: Previously generated pairlist (cached)
:param tickers: Tickers (from exchange.get_tickers()).
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
: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())
pairlist = self._pair_cache.get('pairlist')
if pairlist:
# Item found - no refresh necessary
return pairlist
else:
# Use fresh pairlist
# Check if pair quote currency equals to the stake currency.
@@ -82,9 +86,9 @@ class VolumePairList(IPairList):
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
pairlist = self.filter_pairlist(pairlist, tickers)
self._pair_cache['pairlist'] = pairlist
return pairlist

13
freqtrade/plugins/pairlist/rangestabilityfilter.py
View File

@@ -28,14 +28,14 @@ class RangeStabilityFilter(IPairList):
self._min_rate_of_change = pairlistconfig.get('min_rate_of_change', 0.01)
self._refresh_period = pairlistconfig.get('refresh_period', 1440)
self._pair_cache: TTLCache = TTLCache(maxsize=100, ttl=self._refresh_period)
self._pair_cache: TTLCache = TTLCache(maxsize=1000, ttl=self._refresh_period)
if self._days < 1:
raise OperationalException("RangeStabilityFilter requires lookback_days to be >= 1")
if self._days > exchange.ohlcv_candle_limit:
if self._days > exchange.ohlcv_candle_limit('1d'):
raise OperationalException("RangeStabilityFilter requires lookback_days to not "
"exceed exchange max request size "
f"({exchange.ohlcv_candle_limit})")
f"({exchange.ohlcv_candle_limit('1d')})")
@property
def needstickers(self) -> bool:
@@ -83,12 +83,13 @@ class RangeStabilityFilter(IPairList):
"""
Validate trading range
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.load_markets()
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
# Check symbol in cache
if pair in self._pair_cache:
return self._pair_cache[pair]
cached_res = self._pair_cache.get(pair, None)
if cached_res is not None:
return cached_res
result = False
if daily_candles is not None and not daily_candles.empty:

20
freqtrade/plugins/pairlistmanager.py
View File

@@ -3,7 +3,7 @@ PairList manager class
"""
import logging
from copy import deepcopy
from typing import Any, Dict, List
from typing import Dict, List
from cachetools import TTLCache, cached
@@ -79,11 +79,8 @@ class PairListManager():
if self._tickers_needed:
tickers = self._get_cached_tickers()
# 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)
pairlist = self._pairlist_handlers[0].gen_pairlist(tickers)
# Process all Pairlist Handlers in the chain
for pairlist_handler in self._pairlist_handlers:
@@ -95,19 +92,6 @@ class PairListManager():
self._whitelist = pairlist
def _prepare_whitelist(self, pairlist: List[str], tickers: Dict[str, Any]) -> List[str]:
"""
Prepare sanitized pairlist for Pairlist Handlers that use tickers data - remove
pairs that do not have ticker available
"""
if self._tickers_needed:
# Copy list since we're modifying this list
for p in deepcopy(pairlist):
if p not in tickers:
pairlist.remove(p)
return pairlist
def verify_blacklist(self, pairlist: List[str], logmethod) -> List[str]:
"""
Verify and remove items from pairlist - returning a filtered pairlist.

7
freqtrade/plugins/protections/cooldown_period.py
View File

@@ -1,7 +1,6 @@
import logging
from datetime import datetime, timedelta
from typing import Any, Dict
from freqtrade.persistence import Trade
from freqtrade.plugins.protections import IProtection, ProtectionReturn
@@ -15,9 +14,6 @@ class CooldownPeriod(IProtection):
has_global_stop: bool = False
has_local_stop: bool = True
def __init__(self, config: Dict[str, Any], protection_config: Dict[str, Any]) -> None:
super().__init__(config, protection_config)
def _reason(self) -> str:
"""
LockReason to use
@@ -44,7 +40,8 @@ class CooldownPeriod(IProtection):
trades = Trade.get_trades_proxy(pair=pair, is_open=False, close_date=look_back_until)
if trades:
# Get latest trade
trade = sorted(trades, key=lambda t: t.close_date)[-1]
# Ignore type error as we know we only get closed trades.
trade = sorted(trades, key=lambda t: t.close_date)[-1] # type: ignore
self.log_once(f"Cooldown for {pair} for {self.stop_duration_str}.", logger.info)
until = self.calculate_lock_end([trade], self._stop_duration)

6
freqtrade/plugins/protections/iprotection.py
View File

@@ -7,7 +7,7 @@ from typing import Any, Dict, List, Optional, Tuple
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import plural
from freqtrade.mixins import LoggingMixin
from freqtrade.persistence import Trade
from freqtrade.persistence import LocalTrade
logger = logging.getLogger(__name__)
@@ -93,11 +93,11 @@ class IProtection(LoggingMixin, ABC):
"""
@staticmethod
def calculate_lock_end(trades: List[Trade], stop_minutes: int) -> datetime:
def calculate_lock_end(trades: List[LocalTrade], stop_minutes: int) -> datetime:
"""
Get lock end time
"""
max_date: datetime = max([trade.close_date for trade in trades])
max_date: datetime = max([trade.close_date for trade in trades if trade.close_date])
# comming from Database, tzinfo is not set.
if max_date.tzinfo is None:
max_date = max_date.replace(tzinfo=timezone.utc)

2
freqtrade/plugins/protections/low_profit_pairs.py
View File

@@ -53,7 +53,7 @@ class LowProfitPairs(IProtection):
# Not enough trades in the relevant period
return False, None, None
profit = sum(trade.close_profit for trade in trades)
profit = sum(trade.close_profit for trade in trades if trade.close_profit)
if profit < self._required_profit:
self.log_once(
f"Trading for {pair} stopped due to {profit:.2f} < {self._required_profit} "

4
freqtrade/plugins/protections/max_drawdown_protection.py
View File

@@ -55,13 +55,13 @@ class MaxDrawdown(IProtection):
# Drawdown is always positive
try:
drawdown, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
drawdown, _, _, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
except ValueError:
return False, None, None
if drawdown > self._max_allowed_drawdown:
self.log_once(
f"Trading stopped due to Max Drawdown {drawdown:.2f} < {self._max_allowed_drawdown}"
f"Trading stopped due to Max Drawdown {drawdown:.2f} > {self._max_allowed_drawdown}"
f" within {self.lookback_period_str}.", logger.info)
until = self.calculate_lock_end(trades, self._stop_duration)

16
freqtrade/plugins/protections/stoploss_guard.py
View File

@@ -3,9 +3,9 @@ import logging
from datetime import datetime, timedelta
from typing import Any, Dict
from freqtrade.enums import SellType
from freqtrade.persistence import Trade
from freqtrade.plugins.protections import IProtection, ProtectionReturn
from freqtrade.strategy.interface import SellType
logger = logging.getLogger(__name__)
@@ -56,15 +56,15 @@ class StoplossGuard(IProtection):
trades = [trade for trade in trades1 if (str(trade.sell_reason) in (
SellType.TRAILING_STOP_LOSS.value, SellType.STOP_LOSS.value,
SellType.STOPLOSS_ON_EXCHANGE.value)
and trade.close_profit < 0)]
and trade.close_profit and trade.close_profit < 0)]
if len(trades) > self._trade_limit:
self.log_once(f"Trading stopped due to {self._trade_limit} "
f"stoplosses within {self._lookback_period} minutes.", logger.info)
until = self.calculate_lock_end(trades, self._stop_duration)
return True, until, self._reason()
if len(trades) < self._trade_limit:
return False, None, None
return False, None, None
self.log_once(f"Trading stopped due to {self._trade_limit} "
f"stoplosses within {self._lookback_period} minutes.", logger.info)
until = self.calculate_lock_end(trades, self._stop_duration)
return True, until, self._reason()
def global_stop(self, date_now: datetime) -> ProtectionReturn:
"""

2
freqtrade/resolvers/iresolver.py
View File

@@ -61,7 +61,7 @@ class IResolver:
module = importlib.util.module_from_spec(spec)
try:
spec.loader.exec_module(module) # type: ignore # importlib does not use typehints
except (ModuleNotFoundError, SyntaxError, ImportError) as err:
except (ModuleNotFoundError, SyntaxError, ImportError, NameError) as err:
# Catch errors in case a specific module is not installed
logger.warning(f"Could not import {module_path} due to '{err}'")
if enum_failed:

9
freqtrade/resolvers/strategy_resolver.py
View File

@@ -6,7 +6,6 @@ This module load custom strategies
import logging
import tempfile
from base64 import urlsafe_b64decode
from collections import OrderedDict
from inspect import getfullargspec
from pathlib import Path
from typing import Any, Dict, Optional
@@ -139,7 +138,7 @@ class StrategyResolver(IResolver):
# Sort and apply type conversions
if hasattr(strategy, 'minimal_roi'):
strategy.minimal_roi = OrderedDict(sorted(
strategy.minimal_roi = dict(sorted(
{int(key): value for (key, value) in strategy.minimal_roi.items()}.items(),
key=lambda t: t[0]))
if hasattr(strategy, 'stoploss'):
@@ -196,9 +195,9 @@ class StrategyResolver(IResolver):
strategy._populate_fun_len = len(getfullargspec(strategy.populate_indicators).args)
strategy._buy_fun_len = len(getfullargspec(strategy.populate_buy_trend).args)
strategy._sell_fun_len = len(getfullargspec(strategy.populate_sell_trend).args)
if any([x == 2 for x in [strategy._populate_fun_len,
strategy._buy_fun_len,
strategy._sell_fun_len]]):
if any(x == 2 for x in [strategy._populate_fun_len,
strategy._buy_fun_len,
strategy._sell_fun_len]):
strategy.INTERFACE_VERSION = 1
return strategy

2
freqtrade/rpc/__init__.py
View File

@@ -1,3 +1,3 @@
# flake8: noqa: F401
from .rpc import RPC, RPCException, RPCHandler, RPCMessageType
from .rpc import RPC, RPCException, RPCHandler
from .rpc_manager import RPCManager

26
freqtrade/rpc/api_server/api_schemas.py
View File

@@ -1,5 +1,5 @@
from datetime import date, datetime
from typing import Any, Dict, List, Optional, TypeVar, Union
from typing import Any, Dict, List, Optional, Union
from pydantic import BaseModel
@@ -57,19 +57,18 @@ class Count(BaseModel):
class PerformanceEntry(BaseModel):
pair: str
profit: float
profit_abs: float
count: int
class Profit(BaseModel):
profit_closed_coin: float
profit_closed_percent: float
profit_closed_percent_mean: float
profit_closed_ratio_mean: float
profit_closed_percent_sum: float
profit_closed_ratio_sum: float
profit_closed_fiat: float
profit_all_coin: float
profit_all_percent: float
profit_all_percent_mean: float
profit_all_ratio_mean: float
profit_all_percent_sum: float
@@ -113,7 +112,7 @@ class Daily(BaseModel):
class ShowConfig(BaseModel):
dry_run: str
dry_run: bool
stake_currency: str
stake_amount: Union[float, str]
max_open_trades: int
@@ -153,13 +152,11 @@ class TradeSchema(BaseModel):
fee_close: Optional[float]
fee_close_cost: Optional[float]
fee_close_currency: Optional[str]
open_date_hum: str
open_date: str
open_timestamp: int
open_rate: float
open_rate_requested: Optional[float]
open_trade_value: float
close_date_hum: Optional[str]
close_date: Optional[str]
close_timestamp: Optional[int]
close_rate: Optional[float]
@@ -170,6 +167,7 @@ class TradeSchema(BaseModel):
profit_ratio: Optional[float]
profit_pct: Optional[float]
profit_abs: Optional[float]
profit_fiat: Optional[float]
sell_reason: Optional[str]
sell_order_status: Optional[str]
stop_loss_abs: Optional[float]
@@ -192,7 +190,6 @@ class OpenTradeSchema(TradeSchema):
stoploss_current_dist_ratio: Optional[float]
stoploss_entry_dist: Optional[float]
stoploss_entry_dist_ratio: Optional[float]
base_currency: str
current_profit: float
current_profit_abs: float
current_profit_pct: float
@@ -203,12 +200,15 @@ class OpenTradeSchema(TradeSchema):
class TradeResponse(BaseModel):
trades: List[TradeSchema]
trades_count: int
total_trades: int
ForceBuyResponse = TypeVar('ForceBuyResponse', TradeSchema, StatusMsg)
class ForceBuyResponse(BaseModel):
__root__: Union[TradeSchema, StatusMsg]
class LockModel(BaseModel):
id: int
active: bool
lock_end_time: str
lock_end_timestamp: int
@@ -223,6 +223,11 @@ class Locks(BaseModel):
locks: List[LockModel]
class DeleteLockRequest(BaseModel):
pair: Optional[str]
lockid: Optional[int]
class Logs(BaseModel):
log_count: int
logs: List[List]
@@ -264,10 +269,11 @@ class DeleteTrade(BaseModel):
class PlotConfig_(BaseModel):
main_plot: Dict[str, Any]
subplots: Optional[Dict[str, Any]]
subplots: Dict[str, Any]
PlotConfig = TypeVar('PlotConfig', PlotConfig_, Dict)
class PlotConfig(BaseModel):
__root__: Union[PlotConfig_, Dict]
class StrategyListResponse(BaseModel):

48
freqtrade/rpc/api_server/api_v1.py
View File

@@ -11,13 +11,13 @@ from freqtrade.data.history import get_datahandler
from freqtrade.exceptions import OperationalException
from freqtrade.rpc import RPC
from freqtrade.rpc.api_server.api_schemas import (AvailablePairs, Balances, BlacklistPayload,
BlacklistResponse, Count, Daily, DeleteTrade,
ForceBuyPayload, ForceBuyResponse,
ForceSellPayload, Locks, Logs, OpenTradeSchema,
PairHistory, PerformanceEntry, Ping, PlotConfig,
Profit, ResultMsg, ShowConfig, Stats, StatusMsg,
StrategyListResponse, StrategyResponse,
TradeResponse, Version, WhitelistResponse)
BlacklistResponse, Count, Daily,
DeleteLockRequest, DeleteTrade, ForceBuyPayload,
ForceBuyResponse, ForceSellPayload, Locks, Logs,
OpenTradeSchema, PairHistory, PerformanceEntry,
Ping, PlotConfig, Profit, ResultMsg, ShowConfig,
Stats, StatusMsg, StrategyListResponse,
StrategyResponse, Version, WhitelistResponse)
from freqtrade.rpc.api_server.deps import get_config, get_rpc, get_rpc_optional
from freqtrade.rpc.rpc import RPCException
@@ -82,9 +82,19 @@ def status(rpc: RPC = Depends(get_rpc)):
return []
@router.get('/trades', response_model=TradeResponse, tags=['info', 'trading'])
def trades(limit: int = 0, rpc: RPC = Depends(get_rpc)):
return rpc._rpc_trade_history(limit)
# Using the responsemodel here will cause a ~100% increase in response time (from 1s to 2s)
# on big databases. Correct response model: response_model=TradeResponse,
@router.get('/trades', tags=['info', 'trading'])
def trades(limit: int = 500, offset: int = 0, rpc: RPC = Depends(get_rpc)):
return rpc._rpc_trade_history(limit, offset=offset, order_by_id=True)
@router.get('/trade/{tradeid}', response_model=OpenTradeSchema, tags=['info', 'trading'])
def trade(tradeid: int = 0, rpc: RPC = Depends(get_rpc)):
try:
return rpc._rpc_trade_status([tradeid])[0]
except (RPCException, KeyError):
raise HTTPException(status_code=404, detail='Trade not found.')
@router.delete('/trades/{tradeid}', response_model=DeleteTrade, tags=['info', 'trading'])
@@ -111,9 +121,9 @@ def forcebuy(payload: ForceBuyPayload, rpc: RPC = Depends(get_rpc)):
trade = rpc._rpc_forcebuy(payload.pair, payload.price)
if trade:
return trade.to_json()
return ForceBuyResponse.parse_obj(trade.to_json())
else:
return {"status": f"Error buying pair {payload.pair}."}
return ForceBuyResponse.parse_obj({"status": f"Error buying pair {payload.pair}."})
@router.post('/forcesell', response_model=ResultMsg, tags=['trading'])
@@ -136,11 +146,21 @@ def whitelist(rpc: RPC = Depends(get_rpc)):
return rpc._rpc_whitelist()
@router.get('/locks', response_model=Locks, tags=['info'])
@router.get('/locks', response_model=Locks, tags=['info', 'locks'])
def locks(rpc: RPC = Depends(get_rpc)):
return rpc._rpc_locks()
@router.delete('/locks/{lockid}', response_model=Locks, tags=['info', 'locks'])
def delete_lock(lockid: int, rpc: RPC = Depends(get_rpc)):
return rpc._rpc_delete_lock(lockid=lockid)
@router.post('/locks/delete', response_model=Locks, tags=['info', 'locks'])
def delete_lock_pair(payload: DeleteLockRequest, rpc: RPC = Depends(get_rpc)):
return rpc._rpc_delete_lock(lockid=payload.lockid, pair=payload.pair)
@router.get('/logs', response_model=Logs, tags=['info'])
def logs(limit: Optional[int] = None, rpc: RPC = Depends(get_rpc)):
return rpc._rpc_get_logs(limit)
@@ -183,7 +203,7 @@ def pair_history(pair: str, timeframe: str, timerange: str, strategy: str,
@router.get('/plot_config', response_model=PlotConfig, tags=['candle data'])
def plot_config(rpc: RPC = Depends(get_rpc)):
return rpc._rpc_plot_config()
return PlotConfig.parse_obj(rpc._rpc_plot_config())
@router.get('/strategies', response_model=StrategyListResponse, tags=['strategy'])

27
freqtrade/rpc/api_server/uvicorn_threaded.py
View File

@@ -8,12 +8,33 @@ import uvicorn
class UvicornServer(uvicorn.Server):
"""
Multithreaded server - as found in https://github.com/encode/uvicorn/issues/742
Removed install_signal_handlers() override based on changes from this commit:
https://github.com/encode/uvicorn/commit/ce2ef45a9109df8eae038c0ec323eb63d644cbc6
Cannot rely on asyncio.get_event_loop() to create new event loop because of this check:
https://github.com/python/cpython/blob/4d7f11e05731f67fd2c07ec2972c6cb9861d52be/Lib/asyncio/events.py#L638
Fix by overriding run() and forcing creation of new event loop if uvloop is available
"""
def install_signal_handlers(self):
def run(self, sockets=None):
import asyncio
"""
In the parent implementation, this starts the thread, therefore we must patch it away here.
Parent implementation calls self.config.setup_event_loop(),
but we need to create uvloop event loop manually
"""
pass
try:
import uvloop # noqa
except ImportError: # pragma: no cover
from uvicorn.loops.asyncio import asyncio_setup
asyncio_setup()
else:
asyncio.set_event_loop(uvloop.new_event_loop())
loop = asyncio.get_event_loop()
loop.run_until_complete(self.serve(sockets=sockets))
@contextlib.contextmanager
def run_in_thread(self):

7
freqtrade/rpc/api_server/web_ui.py
View File

@@ -10,7 +10,12 @@ router_ui = APIRouter()
@router_ui.get('/favicon.ico', include_in_schema=False)
async def favicon():
return FileResponse(Path(__file__).parent / 'ui/favicon.ico')
return FileResponse(str(Path(__file__).parent / 'ui/favicon.ico'))
@router_ui.get('/fallback_file.html', include_in_schema=False)
async def fallback():
return FileResponse(str(Path(__file__).parent / 'ui/fallback_file.html'))
@router_ui.get('/{rest_of_path:path}', include_in_schema=False)

13
freqtrade/rpc/api_server/webserver.py
View File

@@ -2,6 +2,7 @@ import logging
from ipaddress import IPv4Address
from typing import Any, Dict
import rapidjson
import uvicorn
from fastapi import Depends, FastAPI
from fastapi.middleware.cors import CORSMiddleware
@@ -14,6 +15,17 @@ from freqtrade.rpc.rpc import RPC, RPCException, RPCHandler
logger = logging.getLogger(__name__)
class FTJSONResponse(JSONResponse):
media_type = "application/json"
def render(self, content: Any) -> bytes:
"""
Use rapidjson for responses
Handles NaN and Inf / -Inf in a javascript way by default.
"""
return rapidjson.dumps(content).encode("utf-8")
class ApiServer(RPCHandler):
_rpc: RPC
@@ -32,6 +44,7 @@ class ApiServer(RPCHandler):
self.app = FastAPI(title="Freqtrade API",
docs_url='/docs' if api_config.get('enable_openapi', False) else None,
redoc_url=None,
default_response_class=FTJSONResponse,
)
self.configure_app(self.app, self._config)

130
freqtrade/rpc/fiat_convert.py
View File

@@ -3,11 +3,13 @@ Module that define classes to convert Crypto-currency to FIAT
e.g BTC to USD
"""
import datetime
import logging
import time
from typing import Dict, List
from typing import Dict
from cachetools.ttl import TTLCache
from pycoingecko import CoinGeckoAPI
from requests.exceptions import RequestException
from freqtrade.constants import SUPPORTED_FIAT
@@ -15,51 +17,6 @@ from freqtrade.constants import SUPPORTED_FIAT
logger = logging.getLogger(__name__)
class CryptoFiat:
"""
Object to describe what is the price of Crypto-currency in a FIAT
"""
# Constants
CACHE_DURATION = 6 * 60 * 60 # 6 hours
def __init__(self, crypto_symbol: str, fiat_symbol: str, price: float) -> None:
"""
Create an object that will contains the price for a crypto-currency in fiat
:param crypto_symbol: Crypto-currency you want to convert (e.g BTC)
:param fiat_symbol: FIAT currency you want to convert to (e.g USD)
:param price: Price in FIAT
"""
# Public attributes
self.crypto_symbol = None
self.fiat_symbol = None
self.price = 0.0
# Private attributes
self._expiration = 0.0
self.crypto_symbol = crypto_symbol.lower()
self.fiat_symbol = fiat_symbol.lower()
self.set_price(price=price)
def set_price(self, price: float) -> None:
"""
Set the price of the Crypto-currency in FIAT and set the expiration time
:param price: Price of the current Crypto currency in the fiat
:return: None
"""
self.price = price
self._expiration = time.time() + self.CACHE_DURATION
def is_expired(self) -> bool:
"""
Return if the current price is still valid or needs to be refreshed
:return: bool, true the price is expired and needs to be refreshed, false the price is
still valid
"""
return self._expiration - time.time() <= 0
class CryptoToFiatConverter:
"""
Main class to initiate Crypto to FIAT.
@@ -70,6 +27,7 @@ class CryptoToFiatConverter:
_coingekko: CoinGeckoAPI = None
_cryptomap: Dict = {}
_backoff: float = 0.0
def __new__(cls):
"""
@@ -84,14 +42,29 @@ class CryptoToFiatConverter:
return CryptoToFiatConverter.__instance
def __init__(self) -> None:
self._pairs: List[CryptoFiat] = []
# Timeout: 6h
self._pair_price: TTLCache = TTLCache(maxsize=500, ttl=6 * 60 * 60)
self._load_cryptomap()
def _load_cryptomap(self) -> None:
try:
coinlistings = self._coingekko.get_coins_list()
# Create mapping table from synbol to coingekko_id
# Create mapping table from symbol to coingekko_id
self._cryptomap = {x['symbol']: x['id'] for x in coinlistings}
except RequestException as request_exception:
if "429" in str(request_exception):
logger.warning(
"Too many requests for Coingecko API, backing off and trying again later.")
# Set backoff timestamp to 60 seconds in the future
self._backoff = datetime.datetime.now().timestamp() + 60
return
# If the request is not a 429 error we want to raise the normal error
logger.error(
"Could not load FIAT Cryptocurrency map for the following problem: {}".format(
request_exception
)
)
except (Exception) as exception:
logger.error(
f"Could not load FIAT Cryptocurrency map for the following problem: {exception}")
@@ -118,49 +91,31 @@ class CryptoToFiatConverter:
"""
crypto_symbol = crypto_symbol.lower()
fiat_symbol = fiat_symbol.lower()
inverse = False
if crypto_symbol == 'usd':
# usd corresponds to "uniswap-state-dollar" for coingecko.
# We'll therefore need to "swap" the currencies
logger.info(f"reversing Rates {crypto_symbol}, {fiat_symbol}")
crypto_symbol = fiat_symbol
fiat_symbol = 'usd'
inverse = True
symbol = f"{crypto_symbol}/{fiat_symbol}"
# Check if the fiat convertion you want is supported
if not self._is_supported_fiat(fiat=fiat_symbol):
raise ValueError(f'The fiat {fiat_symbol} is not supported.')
# Get the pair that interest us and return the price in fiat
for pair in self._pairs:
if pair.crypto_symbol == crypto_symbol and pair.fiat_symbol == fiat_symbol:
# If the price is expired we refresh it, avoid to call the API all the time
if pair.is_expired():
pair.set_price(
price=self._find_price(
crypto_symbol=pair.crypto_symbol,
fiat_symbol=pair.fiat_symbol
)
)
price = self._pair_price.get(symbol, None)
# return the last price we have for this pair
return pair.price
# The pair does not exist, so we create it and return the price
return self._add_pair(
crypto_symbol=crypto_symbol,
fiat_symbol=fiat_symbol,
price=self._find_price(
if not price:
price = self._find_price(
crypto_symbol=crypto_symbol,
fiat_symbol=fiat_symbol
)
)
def _add_pair(self, crypto_symbol: str, fiat_symbol: str, price: float) -> float:
"""
:param crypto_symbol: Crypto-currency you want to convert (e.g BTC)
:param fiat_symbol: FIAT currency you want to convert to (e.g USD)
:return: price in FIAT
"""
self._pairs.append(
CryptoFiat(
crypto_symbol=crypto_symbol,
fiat_symbol=fiat_symbol,
price=price
)
)
if inverse and price != 0.0:
price = 1 / price
self._pair_price[symbol] = price
return price
@@ -188,6 +143,15 @@ class CryptoToFiatConverter:
if crypto_symbol == fiat_symbol:
return 1.0
if self._cryptomap == {}:
if self._backoff <= datetime.datetime.now().timestamp():
self._load_cryptomap()
# return 0.0 if we still dont have data to check, no reason to proceed
if self._cryptomap == {}:
return 0.0
else:
return 0.0
if crypto_symbol not in self._cryptomap:
# return 0 for unsupported stake currencies (fiat-convert should not break the bot)
logger.warning("unsupported crypto-symbol %s - returning 0.0", crypto_symbol)

117
freqtrade/rpc/rpc.py
View File

@@ -3,8 +3,7 @@ This module contains class to define a RPC communications
"""
import logging
from abc import abstractmethod
from datetime import date, datetime, timedelta
from enum import Enum
from datetime import date, datetime, timedelta, timezone
from math import isnan
from typing import Any, Dict, List, Optional, Tuple, Union
@@ -15,36 +14,21 @@ from pandas import DataFrame
from freqtrade.configuration.timerange import TimeRange
from freqtrade.constants import CANCEL_REASON, DATETIME_PRINT_FORMAT
from freqtrade.data.history import load_data
from freqtrade.enums import SellType, State
from freqtrade.exceptions import ExchangeError, PricingError
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_msecs
from freqtrade.loggers import bufferHandler
from freqtrade.misc import shorten_date
from freqtrade.persistence import PairLocks, Trade
from freqtrade.persistence.models import PairLock
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
from freqtrade.state import State
from freqtrade.strategy.interface import SellType
from freqtrade.strategy.interface import SellCheckTuple
logger = logging.getLogger(__name__)
class RPCMessageType(Enum):
STATUS_NOTIFICATION = 'status'
WARNING_NOTIFICATION = 'warning'
STARTUP_NOTIFICATION = 'startup'
BUY_NOTIFICATION = 'buy'
BUY_CANCEL_NOTIFICATION = 'buy_cancel'
SELL_NOTIFICATION = 'sell'
SELL_CANCEL_NOTIFICATION = 'sell_cancel'
def __repr__(self):
return self.value
def __str__(self):
return self.value
class RPCException(Exception):
"""
Should be raised with a rpc-formatted message in an _rpc_* method
@@ -166,12 +150,24 @@ class RPC:
if trade.open_order_id:
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 (ExchangeError, PricingError):
current_rate = NAN
if trade.is_open:
try:
current_rate = self._freqtrade.exchange.get_sell_rate(trade.pair, False)
except (ExchangeError, PricingError):
current_rate = NAN
else:
current_rate = trade.close_rate
current_profit = trade.calc_profit_ratio(current_rate)
current_profit_abs = trade.calc_profit(current_rate)
current_profit_fiat: Optional[float] = None
# Calculate fiat profit
if self._fiat_converter:
current_profit_fiat = self._fiat_converter.convert_amount(
current_profit_abs,
self._freqtrade.config['stake_currency'],
self._freqtrade.config['fiat_display_currency']
)
# 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)
@@ -190,6 +186,7 @@ class RPC:
profit_ratio=current_profit,
profit_pct=round(current_profit * 100, 2),
profit_abs=current_profit_abs,
profit_fiat=current_profit_fiat,
stoploss_current_dist=stoploss_current_dist,
stoploss_current_dist_ratio=round(stoploss_current_dist_ratio, 8),
@@ -204,16 +201,17 @@ class RPC:
return results
def _rpc_status_table(self, stake_currency: str,
fiat_display_currency: str) -> Tuple[List, List]:
fiat_display_currency: str) -> Tuple[List, List, float]:
trades = Trade.get_open_trades()
if not trades:
raise RPCException('no active trade')
else:
trades_list = []
fiat_profit_sum = NAN
for trade in trades:
# calculate profit and send message to user
try:
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
current_rate = self._freqtrade.exchange.get_sell_rate(trade.pair, False)
except (PricingError, ExchangeError):
current_rate = NAN
trade_percent = (100 * trade.calc_profit_ratio(current_rate))
@@ -227,6 +225,8 @@ class RPC:
)
if fiat_profit and not isnan(fiat_profit):
profit_str += f" ({fiat_profit:.2f})"
fiat_profit_sum = fiat_profit if isnan(fiat_profit_sum) \
else fiat_profit_sum + fiat_profit
trades_list.append([
trade.id,
trade.pair + ('*' if (trade.open_order_id is not None
@@ -240,7 +240,7 @@ class RPC:
profitcol += " (" + fiat_display_currency + ")"
columns = ['ID', 'Pair', 'Since', profitcol]
return trades_list, columns
return trades_list, columns, fiat_profit_sum
def _rpc_daily_profit(
self, timescale: int,
@@ -284,19 +284,22 @@ class RPC:
'data': data
}
def _rpc_trade_history(self, limit: int) -> Dict:
def _rpc_trade_history(self, limit: int, offset: int = 0, order_by_id: bool = False) -> Dict:
""" Returns the X last trades """
if limit > 0:
order_by = Trade.id if order_by_id else Trade.close_date.desc()
if limit:
trades = Trade.get_trades([Trade.is_open.is_(False)]).order_by(
Trade.id.desc()).limit(limit)
order_by).limit(limit).offset(offset)
else:
trades = Trade.get_trades([Trade.is_open.is_(False)]).order_by(Trade.id.desc()).all()
trades = Trade.get_trades([Trade.is_open.is_(False)]).order_by(
Trade.close_date.desc()).all()
output = [trade.to_json() for trade in trades]
return {
"trades": output,
"trades_count": len(output)
"trades_count": len(output),
"total_trades": Trade.get_trades([Trade.is_open.is_(False)]).count(),
}
def _rpc_stats(self) -> Dict[str, Any]:
@@ -333,9 +336,10 @@ class RPC:
return {'sell_reasons': sell_reasons, 'durations': durations}
def _rpc_trade_statistics(
self, stake_currency: str, fiat_display_currency: str) -> Dict[str, Any]:
self, stake_currency: str, fiat_display_currency: str,
start_date: datetime = datetime.fromtimestamp(0)) -> Dict[str, Any]:
""" Returns cumulative profit statistics """
trades = Trade.get_trades().order_by(Trade.id).all()
trades = Trade.get_trades([Trade.open_date >= start_date]).order_by(Trade.id).all()
profit_all_coin = []
profit_all_ratio = []
@@ -364,7 +368,7 @@ class RPC:
else:
# Get current rate
try:
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
current_rate = self._freqtrade.exchange.get_sell_rate(trade.pair, False)
except (PricingError, ExchangeError):
current_rate = NAN
profit_ratio = trade.calc_profit_ratio(rate=current_rate)
@@ -401,14 +405,12 @@ class RPC:
num = float(len(durations) or 1)
return {
'profit_closed_coin': profit_closed_coin_sum,
'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': 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),
@@ -432,7 +434,7 @@ class RPC:
output = []
total = 0.0
try:
tickers = self._freqtrade.exchange.get_tickers()
tickers = self._freqtrade.exchange.get_tickers(cached=True)
except (ExchangeError):
raise RPCException('Error getting current tickers.')
@@ -536,8 +538,9 @@ class RPC:
if not fully_canceled:
# Get current rate and execute sell
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
self._freqtrade.execute_sell(trade, current_rate, SellType.FORCE_SELL)
current_rate = self._freqtrade.exchange.get_sell_rate(trade.pair, False)
sell_reason = SellCheckTuple(sell_type=SellType.FORCE_SELL)
self._freqtrade.execute_sell(trade, current_rate, sell_reason)
# ---- EOF def _exec_forcesell ----
if self._freqtrade.state != State.RUNNING:
@@ -548,7 +551,7 @@ class RPC:
# Execute sell for all open orders
for trade in Trade.get_open_trades():
_exec_forcesell(trade)
Trade.session.flush()
Trade.commit()
self._freqtrade.wallets.update()
return {'result': 'Created sell orders for all open trades.'}
@@ -561,7 +564,7 @@ class RPC:
raise RPCException('invalid argument')
_exec_forcesell(trade)
Trade.session.flush()
Trade.commit()
self._freqtrade.wallets.update()
return {'result': f'Created sell order for trade {trade_id}.'}
@@ -590,11 +593,11 @@ class RPC:
raise RPCException(f'position for {pair} already open - id: {trade.id}')
# gen stake amount
stakeamount = self._freqtrade.wallets.get_trade_stake_amount(
pair, self._freqtrade.get_free_open_trades())
stakeamount = self._freqtrade.wallets.get_trade_stake_amount(pair)
# execute buy
if self._freqtrade.execute_buy(pair, stakeamount, price):
if self._freqtrade.execute_buy(pair, stakeamount, price, forcebuy=True):
Trade.commit()
trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first()
return trade
else:
@@ -663,7 +666,7 @@ class RPC:
}
def _rpc_locks(self) -> Dict[str, Any]:
""" Returns the current locks"""
""" Returns the current locks """
locks = PairLocks.get_pair_locks(None)
return {
@@ -671,6 +674,24 @@ class RPC:
'locks': [lock.to_json() for lock in locks]
}
def _rpc_delete_lock(self, lockid: Optional[int] = None,
pair: Optional[str] = None) -> Dict[str, Any]:
""" Delete specific lock(s) """
locks = []
if pair:
locks = PairLocks.get_pair_locks(pair)
if lockid:
locks = PairLock.query.filter(PairLock.id == lockid).all()
for lock in locks:
lock.active = False
lock.lock_end_time = datetime.now(timezone.utc)
PairLock.query.session.commit()
return self._rpc_locks()
def _rpc_whitelist(self) -> Dict:
""" Returns the currently active whitelist"""
res = {'method': self._freqtrade.pairlists.name_list,
@@ -809,5 +830,7 @@ class RPC:
df_analyzed, arrow.Arrow.utcnow().datetime)
def _rpc_plot_config(self) -> Dict[str, Any]:
if (self._freqtrade.strategy.plot_config and
'subplots' not in self._freqtrade.strategy.plot_config):
self._freqtrade.strategy.plot_config['subplots'] = {}
return self._freqtrade.strategy.plot_config

11
freqtrade/rpc/rpc_manager.py
View File

@@ -4,7 +4,8 @@ This module contains class to manage RPC communications (Telegram, Slack, ...)
import logging
from typing import Any, Dict, List
from freqtrade.rpc import RPC, RPCHandler, RPCMessageType
from freqtrade.enums import RPCMessageType
from freqtrade.rpc import RPC, RPCHandler
logger = logging.getLogger(__name__)
@@ -67,7 +68,7 @@ class RPCManager:
def startup_messages(self, config: Dict[str, Any], pairlist, protections) -> None:
if config['dry_run']:
self.send_msg({
'type': RPCMessageType.WARNING_NOTIFICATION,
'type': RPCMessageType.WARNING,
'status': 'Dry run is enabled. All trades are simulated.'
})
stake_currency = config['stake_currency']
@@ -79,7 +80,7 @@ class RPCManager:
exchange_name = config['exchange']['name']
strategy_name = config.get('strategy', '')
self.send_msg({
'type': RPCMessageType.STARTUP_NOTIFICATION,
'type': RPCMessageType.STARTUP,
'status': f'*Exchange:* `{exchange_name}`\n'
f'*Stake per trade:* `{stake_amount} {stake_currency}`\n'
f'*Minimum ROI:* `{minimal_roi}`\n'
@@ -88,13 +89,13 @@ class RPCManager:
f'*Strategy:* `{strategy_name}`'
})
self.send_msg({
'type': RPCMessageType.STARTUP_NOTIFICATION,
'type': RPCMessageType.STARTUP,
'status': f'Searching for {stake_currency} pairs to buy and sell '
f'based on {pairlist.short_desc()}'
})
if len(protections.name_list) > 0:
prots = '\n'.join([p for prot in protections.short_desc() for k, p in prot.items()])
self.send_msg({
'type': RPCMessageType.STARTUP_NOTIFICATION,
'type': RPCMessageType.STARTUP,
'status': f'Using Protections: \n{prots}'
})

446
freqtrade/rpc/telegram.py
View File

@@ -5,21 +5,27 @@ This module manage Telegram communication
"""
import json
import logging
from datetime import timedelta, datetime
import re
from datetime import date, datetime, timedelta
from html import escape
from itertools import chain
from typing import Any, Callable, Dict, List, Union
from math import isnan
from typing import Any, Callable, Dict, List, Union, cast
import arrow
from tabulate import tabulate
from telegram import KeyboardButton, ParseMode, ReplyKeyboardMarkup, Update, InlineKeyboardButton, InlineKeyboardMarkup
from telegram.error import NetworkError, TelegramError
from telegram.ext import CallbackContext, CommandHandler, Updater, CallbackQueryHandler
from telegram import (InlineKeyboardButton, InlineKeyboardMarkup, KeyboardButton, ParseMode,
ReplyKeyboardMarkup, Update)
from telegram.error import BadRequest, NetworkError, TelegramError
from telegram.ext import CallbackContext, CallbackQueryHandler, CommandHandler, Updater
from telegram.utils.helpers import escape_markdown
from freqtrade.__init__ import __version__
from freqtrade.constants import DUST_PER_COIN
from freqtrade.enums import RPCMessageType
from freqtrade.exceptions import OperationalException
from freqtrade.misc import round_coin_value
from freqtrade.rpc import RPC, RPCException, RPCHandler, RPCMessageType
from freqtrade.misc import chunks, round_coin_value
from freqtrade.rpc import RPC, RPCException, RPCHandler
logger = logging.getLogger(__name__)
@@ -45,7 +51,7 @@ def authorized_only(command_handler: Callable[..., None]) -> Callable[..., Any]:
cchat_id = int(update.callback_query.message.chat.id)
else:
cchat_id = int(update.message.chat_id)
chat_id = int(self._config['telegram']['chat_id'])
if cchat_id != chat_id:
logger.info(
@@ -89,7 +95,7 @@ class Telegram(RPCHandler):
Validates the keyboard configuration from telegram config
section.
"""
self._keyboard: List[List[Union[str, KeyboardButton]]] = [
self._keyboard: List[List[Union[str, KeyboardButton, InlineKeyboardButton]]] = [
['/daily', '/profit', '/balance'],
['/status', '/status table', '/performance'],
['/count', '/start', '/stop', '/help']
@@ -99,23 +105,27 @@ class Telegram(RPCHandler):
# TODO: DRY! - its not good to list all valid cmds here. But otherwise
# this needs refacoring of the whole telegram module (same
# problem in _help()).
valid_keys: List[str] = ['/start', '/stop', '/status', '/status table',
'/trades', '/profit', '/performance', '/daily',
'/stats', '/count', '/locks', '/balance',
'/stopbuy', '/reload_config', '/show_config',
'/logs', '/whitelist', '/blacklist', '/edge',
'/help', '/version']
valid_keys: List[str] = [r'/start$', r'/stop$', r'/status$', r'/status table$',
r'/trades$', r'/performance$', r'/daily$', r'/daily \d+$',
r'/profit$', r'/profit \d+',
r'/stats$', r'/count$', r'/locks$', r'/balance$',
r'/stopbuy$', r'/reload_config$', r'/show_config$',
r'/logs$', r'/whitelist$', r'/blacklist$', r'/edge$',
r'/forcebuy$', r'/help$', r'/version$']
# Create keys for generation
valid_keys_print = [k.replace('$', '') for k in valid_keys]
# custom keyboard specified in config.json
cust_keyboard = self._config['telegram'].get('keyboard', [])
if cust_keyboard:
combined = "(" + ")|(".join(valid_keys) + ")"
# check for valid shortcuts
invalid_keys = [b for b in chain.from_iterable(cust_keyboard)
if b not in valid_keys]
if not re.match(combined, b)]
if len(invalid_keys):
err_msg = ('config.telegram.keyboard: Invalid commands for '
f'custom Telegram keyboard: {invalid_keys}'
f'\nvalid commands are: {valid_keys}')
f'\nvalid commands are: {valid_keys_print}')
raise OperationalException(err_msg)
else:
self._keyboard = cust_keyboard
@@ -147,6 +157,7 @@ class Telegram(RPCHandler):
CommandHandler('daily', self._daily),
CommandHandler('count', self._count),
CommandHandler('locks', self._locks),
CommandHandler(['unlock', 'delete_locks'], self._delete_locks),
CommandHandler(['reload_config', 'reload_conf'], self._reload_config),
CommandHandler(['show_config', 'show_conf'], self._show_config),
CommandHandler('stopbuy', self._stopbuy),
@@ -163,7 +174,8 @@ class Telegram(RPCHandler):
CallbackQueryHandler(self._profit, pattern='update_profit'),
CallbackQueryHandler(self._balance, pattern='update_balance'),
CallbackQueryHandler(self._performance, pattern='update_performance'),
CallbackQueryHandler(self._count, pattern='update_count')
CallbackQueryHandler(self._count, pattern='update_count'),
CallbackQueryHandler(self._forcebuy_inline),
]
for handle in handles:
self._updater.dispatcher.add_handler(handle)
@@ -172,10 +184,10 @@ class Telegram(RPCHandler):
self._updater.dispatcher.add_handler(handle)
self._updater.start_polling(
clean=True,
bootstrap_retries=-1,
timeout=30,
read_latency=60,
drop_pending_updates=True,
)
logger.info(
'rpc.telegram is listening for following commands: %s',
@@ -189,79 +201,111 @@ class Telegram(RPCHandler):
"""
self._updater.stop()
def _format_buy_msg(self, msg: Dict[str, Any]) -> str:
if self._rpc._fiat_converter:
msg['stake_amount_fiat'] = self._rpc._fiat_converter.convert_amount(
msg['stake_amount'], msg['stake_currency'], msg['fiat_currency'])
else:
msg['stake_amount_fiat'] = 0
message = (f"\N{LARGE BLUE CIRCLE} *{msg['exchange']}:* Buying {msg['pair']}"
f" (#{msg['trade_id']})\n"
f"*Amount:* `{msg['amount']:.8f}`\n"
f"*Open Rate:* `{msg['limit']:.8f}`\n"
f"*Current Rate:* `{msg['current_rate']:.8f}`\n"
f"*Total:* `({round_coin_value(msg['stake_amount'], msg['stake_currency'])}")
if msg.get('fiat_currency', None):
message += f", {round_coin_value(msg['stake_amount_fiat'], msg['fiat_currency'])}"
message += ")`"
return message
def _format_sell_msg(self, msg: Dict[str, Any]) -> str:
msg['amount'] = round(msg['amount'], 8)
msg['profit_percent'] = round(msg['profit_ratio'] * 100, 2)
msg['duration'] = msg['close_date'].replace(
microsecond=0) - msg['open_date'].replace(microsecond=0)
msg['duration_min'] = msg['duration'].total_seconds() / 60
msg['emoji'] = self._get_sell_emoji(msg)
# 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._rpc._fiat_converter):
msg['profit_fiat'] = self._rpc._fiat_converter.convert_amount(
msg['profit_amount'], msg['stake_currency'], msg['fiat_currency'])
msg['profit_extra'] = (' ({gain}: {profit_amount:.8f} {stake_currency}'
' / {profit_fiat:.3f} {fiat_currency})').format(**msg)
else:
msg['profit_extra'] = ''
message = ("{emoji} *{exchange}:* Selling {pair} (#{trade_id})\n"
"*Profit:* `{profit_percent:.2f}%{profit_extra}`\n"
"*Sell Reason:* `{sell_reason}`\n"
"*Duration:* `{duration} ({duration_min:.1f} min)`\n"
"*Amount:* `{amount:.8f}`\n"
"*Open Rate:* `{open_rate:.8f}`\n"
"*Current Rate:* `{current_rate:.8f}`\n"
"*Close Rate:* `{limit:.8f}`").format(**msg)
return message
def send_msg(self, msg: Dict[str, Any]) -> None:
""" Send a message to telegram channel """
noti = self._config['telegram'].get('notification_settings', {}
).get(str(msg['type']), 'on')
default_noti = 'on'
msg_type = msg['type']
noti = ''
if msg_type == RPCMessageType.SELL:
sell_noti = self._config['telegram'] \
.get('notification_settings', {}).get(str(msg_type), {})
# For backward compatibility sell still can be string
if isinstance(sell_noti, str):
noti = sell_noti
else:
noti = sell_noti.get(str(msg['sell_reason']), default_noti)
else:
noti = self._config['telegram'] \
.get('notification_settings', {}).get(str(msg_type), default_noti)
if noti == 'off':
logger.info(f"Notification '{msg['type']}' not sent.")
logger.info(f"Notification '{msg_type}' not sent.")
# Notification disabled
return
if msg['type'] == RPCMessageType.BUY_NOTIFICATION:
if self._rpc._fiat_converter:
msg['stake_amount_fiat'] = self._rpc._fiat_converter.convert_amount(
msg['stake_amount'], msg['stake_currency'], msg['fiat_currency'])
else:
msg['stake_amount_fiat'] = 0
if msg_type == RPCMessageType.BUY:
message = self._format_buy_msg(msg)
message = (f"\N{LARGE BLUE CIRCLE} *{msg['exchange']}:* Buying {msg['pair']}\n"
f"*Amount:* `{msg['amount']:.8f}`\n"
f"*Open Rate:* `{msg['limit']:.8f}`\n"
f"*Current Rate:* `{msg['current_rate']:.8f}`\n"
f"*Total:* `({round_coin_value(msg['stake_amount'], msg['stake_currency'])}")
if msg.get('fiat_currency', None):
message += f", {round_coin_value(msg['stake_amount_fiat'], msg['fiat_currency'])}"
message += ")`"
elif msg['type'] == RPCMessageType.BUY_CANCEL_NOTIFICATION:
elif msg_type in (RPCMessageType.BUY_CANCEL, RPCMessageType.SELL_CANCEL):
msg['message_side'] = 'buy' if msg_type == RPCMessageType.BUY_CANCEL else 'sell'
message = ("\N{WARNING SIGN} *{exchange}:* "
"Cancelling open buy Order for {pair}. Reason: {reason}.".format(**msg))
"Cancelling open {message_side} Order for {pair} (#{trade_id}). "
"Reason: {reason}.".format(**msg))
elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
msg['amount'] = round(msg['amount'], 8)
msg['profit_percent'] = round(msg['profit_ratio'] * 100, 2)
msg['duration'] = msg['close_date'].replace(
microsecond=0) - msg['open_date'].replace(microsecond=0)
msg['duration_min'] = msg['duration'].total_seconds() / 60
elif msg_type == RPCMessageType.BUY_FILL:
message = ("\N{LARGE CIRCLE} *{exchange}:* "
"Buy order for {pair} (#{trade_id}) filled "
"for {open_rate}.".format(**msg))
elif msg_type == RPCMessageType.SELL_FILL:
message = ("\N{LARGE CIRCLE} *{exchange}:* "
"Sell order for {pair} (#{trade_id}) filled "
"for {close_rate}.".format(**msg))
elif msg_type == RPCMessageType.SELL:
message = self._format_sell_msg(msg)
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"
"*Close Rate:* `{limit:.8f}`\n"
"*Sell Reason:* `{sell_reason}`\n"
"*Duration:* `{duration} ({duration_min:.1f} min)`\n"
"*Profit:* `{profit_percent:.2f}%`").format(**msg)
# 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._rpc._fiat_converter):
msg['profit_fiat'] = self._rpc._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 = ("\N{WARNING SIGN} *{exchange}:* Cancelling Open Sell Order "
"for {pair}. Reason: {reason}").format(**msg)
elif msg['type'] == RPCMessageType.STATUS_NOTIFICATION:
elif msg_type == RPCMessageType.STATUS:
message = '*Status:* `{status}`'.format(**msg)
elif msg['type'] == RPCMessageType.WARNING_NOTIFICATION:
elif msg_type == RPCMessageType.WARNING:
message = '\N{WARNING SIGN} *Warning:* `{status}`'.format(**msg)
elif msg['type'] == RPCMessageType.STARTUP_NOTIFICATION:
elif msg_type == RPCMessageType.STARTUP:
message = '{status}'.format(**msg)
else:
raise NotImplementedError('Unknown message type: {}'.format(msg['type']))
raise NotImplementedError('Unknown message type: {}'.format(msg_type))
self._send_msg(message, disable_notification=(noti == 'silent'))
@@ -305,6 +349,7 @@ class Telegram(RPCHandler):
messages = []
for r in results:
r['open_date_hum'] = arrow.get(r['open_date']).humanize()
lines = [
"*Trade ID:* `{trade_id}` `(since {open_date_hum})`",
"*Current Pair:* {pair}",
@@ -351,15 +396,41 @@ class Telegram(RPCHandler):
:return: None
"""
try:
statlist, head = self._rpc._rpc_status_table(
self._config['stake_currency'], self._config.get('fiat_display_currency', ''))
fiat_currency = self._config.get('fiat_display_currency', '')
statlist, head, fiat_profit_sum = self._rpc._rpc_status_table(
self._config['stake_currency'], fiat_currency)
message = tabulate(statlist, headers=head, tablefmt='simple')
if(update.callback_query):
query = update.callback_query
self._update_msg(chat_id=query.message.chat_id, message_id=query.message.message_id, msg=f"<pre>{message}</pre>", parse_mode=ParseMode.HTML, callback_path="update_status_table", reload_able=True)
else:
self._send_msg(f"<pre>{message}</pre>", reload_able=True, callback_path="update_status_table", parse_mode=ParseMode.HTML)
show_total = not isnan(fiat_profit_sum) and len(statlist) > 1
max_trades_per_msg = 50
"""
Calculate the number of messages of 50 trades per message
0.99 is used to make sure that there are no extra (empty) messages
As an example with 50 trades, there will be int(50/50 + 0.99) = 1 message
"""
messages_count = max(int(len(statlist) / max_trades_per_msg + 0.99), 1)
for i in range(0, messages_count):
trades = statlist[i * max_trades_per_msg:(i + 1) * max_trades_per_msg]
if show_total and i == messages_count - 1:
# append total line
trades.append(["Total", "", "", f"{fiat_profit_sum:.2f} {fiat_currency}"])
message = tabulate(trades,
headers=head,
tablefmt='simple')
if show_total and i == messages_count - 1:
# insert separators line between Total
lines = message.split("\n")
message = "\n".join(lines[:-1] + [lines[1]] + [lines[-1]])
if(messages_count == 1 and update.callback_query):
query = update.callback_query
self._update_msg(chat_id=query.message.chat_id,
message_id=query.message.message_id,
msg=f"<pre>{message}</pre>",
parse_mode=ParseMode.HTML,
callback_path="update_status_table", reload_able=True)
else:
self._send_msg(f"<pre>{message}</pre>", reload_able=True,
callback_path="update_status_table", parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e))
@@ -399,9 +470,12 @@ class Telegram(RPCHandler):
message = f'<b>Daily Profit over the last {timescale} days</b>:\n<pre>{stats_tab}</pre>'
if(update.callback_query):
query = update.callback_query
self._update_msg(chat_id=query.message.chat_id, message_id=query.message.message_id, msg=message, parse_mode=ParseMode.HTML, callback_path="update_daily", reload_able=True)
self._update_msg(chat_id=query.message.chat_id, message_id=query.message.message_id,
msg=message, parse_mode=ParseMode.HTML,
callback_path="update_daily", reload_able=True)
else:
self._send_msg(msg=message, parse_mode=ParseMode.HTML, callback_path="update_daily", reload_able=True)
self._send_msg(msg=message, parse_mode=ParseMode.HTML, callback_path="update_daily",
reload_able=True)
except RPCException as e:
self._send_msg(str(e))
@@ -417,9 +491,20 @@ class Telegram(RPCHandler):
stake_cur = self._config['stake_currency']
fiat_disp_cur = self._config.get('fiat_display_currency', '')
start_date = datetime.fromtimestamp(0)
timescale = None
try:
if context.args:
timescale = int(context.args[0])
today_start = datetime.combine(date.today(), datetime.min.time())
start_date = today_start - timedelta(days=timescale)
except (TypeError, ValueError, IndexError):
pass
stats = self._rpc._rpc_trade_statistics(
stake_cur,
fiat_disp_cur)
fiat_disp_cur,
start_date)
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']
@@ -447,22 +532,25 @@ class Telegram(RPCHandler):
else:
markdown_msg = "`No closed trade` \n"
markdown_msg += (f"*ROI:* All trades\n"
f"∙ `{round_coin_value(profit_all_coin, stake_cur)} "
f"({profit_all_percent_mean:.2f}%) "
f"({profit_all_percent_sum} \N{GREEK CAPITAL LETTER SIGMA}%)`\n"
f"∙ `{round_coin_value(profit_all_fiat, 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']}`"
)
markdown_msg += (
f"*ROI:* All trades\n"
f"∙ `{round_coin_value(profit_all_coin, stake_cur)} "
f"({profit_all_percent_mean:.2f}%) "
f"({profit_all_percent_sum} \N{GREEK CAPITAL LETTER SIGMA}%)`\n"
f"∙ `{round_coin_value(profit_all_fiat, fiat_disp_cur)}`\n"
f"*Total Trade Count:* `{trade_count}`\n"
f"*{'First Trade opened' if not timescale else 'Showing Profit since'}:* "
f"`{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}%`")
if(update.callback_query):
query = update.callback_query
self._update_msg(chat_id=query.message.chat_id, message_id=query.message.message_id, msg=markdown_msg, callback_path="update_profit", reload_able=True)
self._update_msg(chat_id=query.message.chat_id, message_id=query.message.message_id,
msg=markdown_msg, callback_path="update_profit", reload_able=True)
else:
self._send_msg(msg=markdown_msg, callback_path="update_profit", reload_able=True)
@@ -515,6 +603,10 @@ class Telegram(RPCHandler):
result = self._rpc._rpc_balance(self._config['stake_currency'],
self._config.get('fiat_display_currency', ''))
balance_dust_level = self._config['telegram'].get('balance_dust_level', 0.0)
if not balance_dust_level:
balance_dust_level = DUST_PER_COIN.get(self._config['stake_currency'], 1.0)
output = ''
if self._config['dry_run']:
output += (
@@ -524,7 +616,7 @@ class Telegram(RPCHandler):
f"`{self._config['dry_run_wallet']}` {self._config['stake_currency']}.\n"
)
for curr in result['currencies']:
if curr['est_stake'] > 0.0001:
if curr['est_stake'] > balance_dust_level:
curr_output = (
f"*{curr['currency']}:*\n"
f"\t`Available: {curr['free']:.8f}`\n"
@@ -533,7 +625,8 @@ class Telegram(RPCHandler):
f"\t`Est. {curr['stake']}: "
f"{round_coin_value(curr['est_stake'], curr['stake'], False)}`\n")
else:
curr_output = f"*{curr['currency']}:* not showing <1$ amount \n"
curr_output = (f"*{curr['currency']}:* not showing <{balance_dust_level} "
f"{curr['stake']} amount \n")
# Handle overflowing messsage length
if len(output + curr_output) >= MAX_TELEGRAM_MESSAGE_LENGTH:
@@ -548,7 +641,8 @@ class Telegram(RPCHandler):
f"{round_coin_value(result['value'], result['symbol'], False)}`\n")
if(update.callback_query):
query = update.callback_query
self._update_msg(chat_id=query.message.chat_id, message_id=query.message.message_id, msg=output, callback_path="update_balance", reload_able=True)
self._update_msg(chat_id=query.message.chat_id, message_id=query.message.message_id,
msg=output, callback_path="update_balance", reload_able=True)
else:
self._send_msg(msg=output, callback_path="update_balance", reload_able=True)
except RPCException as e:
@@ -623,6 +717,25 @@ class Telegram(RPCHandler):
except RPCException as e:
self._send_msg(str(e))
def _forcebuy_action(self, pair, price=None):
try:
self._rpc._rpc_forcebuy(pair, price)
except RPCException as e:
self._send_msg(str(e))
def _forcebuy_inline(self, update: Update, _: CallbackContext) -> None:
if update.callback_query:
query = update.callback_query
pair = query.data
query.answer()
query.edit_message_text(text=f"Force Buying: {pair}")
self._forcebuy_action(pair)
@staticmethod
def _layout_inline_keyboard(buttons: List[InlineKeyboardButton],
cols=3) -> List[List[InlineKeyboardButton]]:
return [buttons[i:i + cols] for i in range(0, len(buttons), cols)]
@authorized_only
def _forcebuy(self, update: Update, context: CallbackContext) -> None:
"""
@@ -635,10 +748,13 @@ class Telegram(RPCHandler):
if context.args:
pair = context.args[0]
price = float(context.args[1]) if len(context.args) > 1 else None
try:
self._rpc._rpc_forcebuy(pair, price)
except RPCException as e:
self._send_msg(str(e))
self._forcebuy_action(pair, price)
else:
whitelist = self._rpc._rpc_whitelist()['whitelist']
pairs = [InlineKeyboardButton(text=pair, callback_data=pair) for pair in whitelist]
self._send_msg(msg="Which pair?",
keyboard=self._layout_inline_keyboard(pairs))
@authorized_only
def _trades(self, update: Update, context: CallbackContext) -> None:
@@ -659,13 +775,13 @@ class Telegram(RPCHandler):
nrecent
)
trades_tab = tabulate(
[[arrow.get(trade['open_date']).humanize(),
trade['pair'],
[[arrow.get(trade['close_date']).humanize(),
trade['pair'] + " (#" + str(trade['trade_id']) + ")",
f"{(100 * trade['close_profit']):.2f}% ({trade['close_profit_abs']})"]
for trade in trades['trades']],
headers=[
'Open Date',
'Pair',
'Close Date',
'Pair (ID)',
f'Profit ({stake_cur})',
],
tablefmt='simple')
@@ -708,18 +824,30 @@ class Telegram(RPCHandler):
"""
try:
trades = self._rpc._rpc_performance()
stats = '\n'.join('{index}.\t<code>{pair}\t{profit:.2f}% ({count})</code>'.format(
index=i + 1,
pair=trade['pair'],
profit=trade['profit'],
count=trade['count']
) for i, trade in enumerate(trades))
message = '<b>Performance:</b>\n{}'.format(stats)
if(update.callback_query):
output = "<b>Performance:</b>\n"
sent_messages = 0
for i, trade in enumerate(trades):
stat_line = (
f"{i+1}.\t <code>{trade['pair']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit']:.2f}%) "
f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_TELEGRAM_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
sent_messages += 1
else:
output += stat_line
if(sent_messages == 0 and update.callback_query):
query = update.callback_query
self._update_msg(chat_id=query.message.chat_id, message_id=query.message.message_id, msg=message, parse_mode=ParseMode.HTML, callback_path="update_performance", reload_able=True)
self._update_msg(chat_id=query.message.chat_id, message_id=query.message.message_id,
msg=output, parse_mode=ParseMode.HTML,
callback_path="update_performance", reload_able=True)
else:
self._send_msg(msg=message, parse_mode=ParseMode.HTML, callback_path="update_performance", reload_able=True)
self._send_msg(msg=output, parse_mode=ParseMode.HTML,
callback_path="update_performance", reload_able=True)
except RPCException as e:
self._send_msg(str(e))
@@ -741,9 +869,12 @@ class Telegram(RPCHandler):
logger.debug(message)
if(update.callback_query):
query = update.callback_query
self._update_msg(chat_id=query.message.chat_id, message_id=query.message.message_id, msg=message, parse_mode=ParseMode.HTML, callback_path="update_count", reload_able=True)
self._update_msg(chat_id=query.message.chat_id, message_id=query.message.message_id,
msg=message, parse_mode=ParseMode.HTML,
callback_path="update_count", reload_able=True)
else:
self._send_msg(msg=message, parse_mode=ParseMode.HTML, callback_path="update_count", reload_able=True)
self._send_msg(msg=message, parse_mode=ParseMode.HTML,
callback_path="update_count", reload_able=True)
except RPCException as e:
self._send_msg(str(e))
@@ -753,19 +884,39 @@ class Telegram(RPCHandler):
Handler for /locks.
Returns the currently active locks
"""
try:
locks = self._rpc._rpc_locks()
rpc_locks = self._rpc._rpc_locks()
if not rpc_locks['locks']:
self._send_msg('No active locks.', parse_mode=ParseMode.HTML)
for locks in chunks(rpc_locks['locks'], 25):
message = tabulate([[
lock['id'],
lock['pair'],
lock['lock_end_time'],
lock['reason']] for lock in locks['locks']],
headers=['Pair', 'Until', 'Reason'],
lock['reason']] for lock in locks],
headers=['ID', 'Pair', 'Until', 'Reason'],
tablefmt='simple')
message = "<pre>{}</pre>".format(message)
message = f"<pre>{escape(message)}</pre>"
logger.debug(message)
self._send_msg(message, parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _delete_locks(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /delete_locks.
Returns the currently active locks
"""
arg = context.args[0] if context.args and len(context.args) > 0 else None
lockid = None
pair = None
if arg:
try:
lockid = int(arg)
except ValueError:
pair = arg
self._rpc._rpc_delete_lock(lockid=lockid, pair=pair)
self._locks(update, context)
@authorized_only
def _whitelist(self, update: Update, context: CallbackContext) -> None:
@@ -847,9 +998,17 @@ class Telegram(RPCHandler):
"""
try:
edge_pairs = self._rpc._rpc_edge()
edge_pairs_tab = tabulate(edge_pairs, headers='keys', tablefmt='simple')
message = f'<b>Edge only validated following pairs:</b>\n<pre>{edge_pairs_tab}</pre>'
self._send_msg(message, parse_mode=ParseMode.HTML)
if not edge_pairs:
message = '<b>Edge only validated following pairs:</b>'
self._send_msg(message, parse_mode=ParseMode.HTML)
for chunk in chunks(edge_pairs, 25):
edge_pairs_tab = tabulate(chunk, headers='keys', tablefmt='simple')
message = (f'<b>Edge only validated following pairs:</b>\n'
f'<pre>{edge_pairs_tab}</pre>')
self._send_msg(message, parse_mode=ParseMode.HTML)
except RPCException as e:
self._send_msg(str(e))
@@ -873,7 +1032,8 @@ class Telegram(RPCHandler):
" `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"
"*/profit [<n>]:* `Lists cumulative profit from all finished trades, "
"over the last n days`\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 ''}"
@@ -884,6 +1044,7 @@ class Telegram(RPCHandler):
"Avg. holding durationsfor buys and sells.`\n"
"*/count:* `Show number of active trades compared to allowed number of trades`\n"
"*/locks:* `Show currently locked pairs`\n"
"*/unlock <pair|id>:* `Unlock this Pair (or this lock id if it's numeric)`\n"
"*/balance:* `Show account balance per currency`\n"
"*/stopbuy:* `Stops buying, but handles open trades gracefully` \n"
"*/reload_config:* `Reload configuration file` \n"
@@ -945,12 +1106,15 @@ class Telegram(RPCHandler):
f"*Current state:* `{val['state']}`"
)
def _update_msg(self, chat_id: str, message_id: str, msg: str, callback_path: str = "", reload_able: bool = False, parse_mode: str = ParseMode.MARKDOWN) -> None:
def _update_msg(self, chat_id: str, message_id: str, msg: str, callback_path: str = "",
reload_able: bool = False, parse_mode: str = ParseMode.MARKDOWN) -> None:
if reload_able:
reply_markup = InlineKeyboardMarkup([[InlineKeyboardButton("Refresh", callback_data=callback_path)]])
reply_markup = InlineKeyboardMarkup([
[InlineKeyboardButton("Refresh", callback_data=callback_path)],
])
else:
reply_markup = InlineKeyboardMarkup([[]])
msg+="\nUpdated: {}".format(datetime.now().ctime())
msg += "\nUpdated: {}".format(datetime.now().ctime())
try:
try:
self._updater.bot.edit_message_text(
@@ -974,7 +1138,11 @@ class Telegram(RPCHandler):
telegram_err.message
)
def _send_msg(self, msg: str, parse_mode: str = ParseMode.MARKDOWN, disable_notification: bool = False, callback_path: str = "", reload_able: bool = False) -> None:
def _send_msg(self, msg: str, parse_mode: str = ParseMode.MARKDOWN,
disable_notification: bool = False,
keyboard: List[List[Union[str, KeyboardButton, InlineKeyboardButton]]] = None,
callback_path: str = "",
reload_able: bool = False) -> None:
"""
Send given markdown message
:param msg: message
@@ -982,10 +1150,14 @@ class Telegram(RPCHandler):
:param parse_mode: telegram parse mode
:return: None
"""
if reload_able and self._config['telegram'].get('reload',True):
reply_markup = InlineKeyboardMarkup([[InlineKeyboardButton("Refresh", callback_data=callback_path)]])
if reload_able and self._config['telegram'].get('reload', True):
reply_markup = InlineKeyboardMarkup([
[InlineKeyboardButton("Refresh", callback_data=callback_path)]])
else:
reply_markup = ReplyKeyboardMarkup(self._keyboard, resize_keyboard=True)
if keyboard is not None:
reply_markup = InlineKeyboardMarkup(keyboard, resize_keyboard=True)
else:
reply_markup = ReplyKeyboardMarkup(self._keyboard, resize_keyboard=True)
try:
try:
self._updater.bot.send_message(

36
freqtrade/rpc/webhook.py
View File

@@ -6,7 +6,8 @@ from typing import Any, Dict
from requests import RequestException, post
from freqtrade.rpc import RPC, RPCHandler, RPCMessageType
from freqtrade.enums import RPCMessageType
from freqtrade.rpc import RPC, RPCHandler
logger = logging.getLogger(__name__)
@@ -28,6 +29,12 @@ class Webhook(RPCHandler):
self._url = self._config['webhook']['url']
self._format = self._config['webhook'].get('format', 'form')
if self._format != 'form' and self._format != 'json':
raise NotImplementedError('Unknown webhook format `{}`, possible values are '
'`form` (default) and `json`'.format(self._format))
def cleanup(self) -> None:
"""
Cleanup pending module resources.
@@ -39,17 +46,21 @@ class Webhook(RPCHandler):
""" Send a message to telegram channel """
try:
if msg['type'] == RPCMessageType.BUY_NOTIFICATION:
if msg['type'] == RPCMessageType.BUY:
valuedict = self._config['webhook'].get('webhookbuy', None)
elif msg['type'] == RPCMessageType.BUY_CANCEL_NOTIFICATION:
elif msg['type'] == RPCMessageType.BUY_CANCEL:
valuedict = self._config['webhook'].get('webhookbuycancel', None)
elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
elif msg['type'] == RPCMessageType.BUY_FILL:
valuedict = self._config['webhook'].get('webhookbuyfill', None)
elif msg['type'] == RPCMessageType.SELL:
valuedict = self._config['webhook'].get('webhooksell', None)
elif msg['type'] == RPCMessageType.SELL_CANCEL_NOTIFICATION:
elif msg['type'] == RPCMessageType.SELL_FILL:
valuedict = self._config['webhook'].get('webhooksellfill', None)
elif msg['type'] == RPCMessageType.SELL_CANCEL:
valuedict = self._config['webhook'].get('webhooksellcancel', None)
elif msg['type'] in (RPCMessageType.STATUS_NOTIFICATION,
RPCMessageType.STARTUP_NOTIFICATION,
RPCMessageType.WARNING_NOTIFICATION):
elif msg['type'] in (RPCMessageType.STATUS,
RPCMessageType.STARTUP,
RPCMessageType.WARNING):
valuedict = self._config['webhook'].get('webhookstatus', None)
else:
raise NotImplementedError('Unknown message type: {}'.format(msg['type']))
@@ -66,7 +77,14 @@ class Webhook(RPCHandler):
def _send_msg(self, payload: dict) -> None:
"""do the actual call to the webhook"""
if self._format == 'form':
kwargs = {'data': payload}
elif self._format == 'json':
kwargs = {'json': payload}
else:
raise NotImplementedError('Unknown format: {}'.format(self._format))
try:
post(self._url, data=payload)
post(self._url, **kwargs)
except RequestException as exc:
logger.warning("Could not call webhook url. Exception: %s", exc)

4
freqtrade/strategy/__init__.py
View File

@@ -1,5 +1,7 @@
# flake8: noqa: F401
from freqtrade.exchange import (timeframe_to_minutes, timeframe_to_msecs, timeframe_to_next_date,
timeframe_to_prev_date, timeframe_to_seconds)
from freqtrade.strategy.hyper import (CategoricalParameter, DecimalParameter, IntParameter,
RealParameter)
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.strategy_helper import merge_informative_pair
from freqtrade.strategy.strategy_helper import merge_informative_pair, stoploss_from_open

349
freqtrade/strategy/hyper.py Normal file
View File

@@ -0,0 +1,349 @@
"""
IHyperStrategy interface, hyperoptable Parameter class.
This module defines a base class for auto-hyperoptable strategies.
"""
import logging
from abc import ABC, abstractmethod
from contextlib import suppress
from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union
from freqtrade.optimize.hyperopt_tools import HyperoptTools
with suppress(ImportError):
from skopt.space import Integer, Real, Categorical
from freqtrade.optimize.space import SKDecimal
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
class BaseParameter(ABC):
"""
Defines a parameter that can be optimized by hyperopt.
"""
category: Optional[str]
default: Any
value: Any
in_space: bool = False
name: str
def __init__(self, *, default: Any, space: Optional[str] = None,
optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable parameter.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter field
name is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.(Integer|Real|Categorical).
"""
if 'name' in kwargs:
raise OperationalException(
'Name is determined by parameter field name and can not be specified manually.')
self.category = space
self._space_params = kwargs
self.value = default
self.optimize = optimize
self.load = load
def __repr__(self):
return f'{self.__class__.__name__}({self.value})'
@abstractmethod
def get_space(self, name: str) -> Union['Integer', 'Real', 'SKDecimal', 'Categorical']:
"""
Get-space - will be used by Hyperopt to get the hyperopt Space
"""
class NumericParameter(BaseParameter):
""" Internal parameter used for Numeric purposes """
float_or_int = Union[int, float]
default: float_or_int
value: float_or_int
def __init__(self, low: Union[float_or_int, Sequence[float_or_int]],
high: Optional[float_or_int] = None, *, default: float_or_int,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable numeric parameter.
Cannot be instantiated, but provides the validation for other numeric parameters
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none of entire range is passed first parameter.
:param default: A default value.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.*.
"""
if high is not None and isinstance(low, Sequence):
raise OperationalException(f'{self.__class__.__name__} space invalid.')
if high is None or isinstance(low, Sequence):
if not isinstance(low, Sequence) or len(low) != 2:
raise OperationalException(f'{self.__class__.__name__} space must be [low, high]')
self.low, self.high = low
else:
self.low = low
self.high = high
super().__init__(default=default, space=space, optimize=optimize,
load=load, **kwargs)
class IntParameter(NumericParameter):
default: int
value: int
def __init__(self, low: Union[int, Sequence[int]], high: Optional[int] = None, *, default: int,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable integer parameter.
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none of entire range is passed first parameter.
:param default: A default value.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Integer.
"""
super().__init__(low=low, high=high, default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'Integer':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return Integer(low=self.low, high=self.high, name=name, **self._space_params)
@property
def range(self):
"""
Get each value in this space as list.
Returns a List from low to high (inclusive) in Hyperopt mode.
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
calculating 100ds of indicators.
"""
if self.in_space and self.optimize:
# Scikit-optimize ranges are "inclusive", while python's "range" is exclusive
return range(self.low, self.high + 1)
else:
return range(self.value, self.value + 1)
class RealParameter(NumericParameter):
default: float
value: float
def __init__(self, low: Union[float, Sequence[float]], high: Optional[float] = None, *,
default: float, space: Optional[str] = None, optimize: bool = True,
load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable floating point parameter with unlimited precision.
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none if entire range is passed first parameter.
:param default: A default value.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Real.
"""
super().__init__(low=low, high=high, default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'Real':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return Real(low=self.low, high=self.high, name=name, **self._space_params)
class DecimalParameter(NumericParameter):
default: float
value: float
def __init__(self, low: Union[float, Sequence[float]], high: Optional[float] = None, *,
default: float, decimals: int = 3, space: Optional[str] = None,
optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable decimal parameter with a limited precision.
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none if entire range is passed first parameter.
:param default: A default value.
:param decimals: A number of decimals after floating point to be included in testing.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Integer.
"""
self._decimals = decimals
default = round(default, self._decimals)
super().__init__(low=low, high=high, default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'SKDecimal':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return SKDecimal(low=self.low, high=self.high, decimals=self._decimals, name=name,
**self._space_params)
class CategoricalParameter(BaseParameter):
default: Any
value: Any
opt_range: Sequence[Any]
def __init__(self, categories: Sequence[Any], *, default: Optional[Any] = None,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable parameter.
:param categories: Optimization space, [a, b, ...].
:param default: A default value. If not specified, first item from specified space will be
used.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter field
name is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Categorical.
"""
if len(categories) < 2:
raise OperationalException(
'CategoricalParameter space must be [a, b, ...] (at least two parameters)')
self.opt_range = categories
super().__init__(default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'Categorical':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return Categorical(self.opt_range, name=name, **self._space_params)
class HyperStrategyMixin(object):
"""
A helper base class which allows HyperOptAuto class to reuse implementations of of buy/sell
strategy logic.
"""
def __init__(self, config: Dict[str, Any], *args, **kwargs):
"""
Initialize hyperoptable strategy mixin.
"""
self.config = config
self.ft_buy_params: List[BaseParameter] = []
self.ft_sell_params: List[BaseParameter] = []
self._load_hyper_params(config.get('runmode') == RunMode.HYPEROPT)
def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, BaseParameter]]:
"""
Find all optimizeable parameters and return (name, attr) iterator.
:param category:
:return:
"""
if category not in ('buy', 'sell', None):
raise OperationalException('Category must be one of: "buy", "sell", None.')
if category is None:
params = self.ft_buy_params + self.ft_sell_params
else:
params = getattr(self, f"ft_{category}_params")
for par in params:
yield par.name, par
@classmethod
def detect_parameters(cls, category: str) -> Iterator[Tuple[str, BaseParameter]]:
""" Detect all parameters for 'category' """
for attr_name in dir(cls):
if not attr_name.startswith('__'): # Ignore internals, not strictly necessary.
attr = getattr(cls, attr_name)
if issubclass(attr.__class__, BaseParameter):
if (attr_name.startswith(category + '_')
and attr.category is not None and attr.category != category):
raise OperationalException(
f'Inconclusive parameter name {attr_name}, category: {attr.category}.')
if (category == attr.category or
(attr_name.startswith(category + '_') and attr.category is None)):
yield attr_name, attr
@classmethod
def detect_all_parameters(cls) -> Dict:
""" Detect all parameters and return them as a list"""
params: Dict = {
'buy': list(cls.detect_parameters('buy')),
'sell': list(cls.detect_parameters('sell')),
}
params.update({
'count': len(params['buy'] + params['sell'])
})
return params
def _load_hyper_params(self, hyperopt: bool = False) -> None:
"""
Load Hyperoptable parameters
"""
self._load_params(getattr(self, 'buy_params', None), 'buy', hyperopt)
self._load_params(getattr(self, 'sell_params', None), 'sell', hyperopt)
def _load_params(self, params: dict, space: str, hyperopt: bool = False) -> None:
"""
Set optimizeable parameter values.
:param params: Dictionary with new parameter values.
"""
if not params:
logger.info(f"No params for {space} found, using default values.")
param_container: List[BaseParameter] = getattr(self, f"ft_{space}_params")
for attr_name, attr in self.detect_parameters(space):
attr.name = attr_name
attr.in_space = hyperopt and HyperoptTools.has_space(self.config, space)
if not attr.category:
attr.category = space
param_container.append(attr)
if params and attr_name in params:
if attr.load:
attr.value = params[attr_name]
logger.info(f'Strategy Parameter: {attr_name} = {attr.value}')
else:
logger.warning(f'Parameter "{attr_name}" exists, but is disabled. '
f'Default value "{attr.value}" used.')
else:
logger.info(f'Strategy Parameter(default): {attr_name} = {attr.value}')
def get_params_dict(self):
"""
Returns list of Parameters that are not part of the current optimize job
"""
params = {
'buy': {},
'sell': {}
}
for name, p in self.enumerate_parameters():
if not p.optimize or not p.in_space:
params[p.category][name] = p.value
return params

137
freqtrade/strategy/interface.py
View File

@@ -6,60 +6,44 @@ import logging
import warnings
from abc import ABC, abstractmethod
from datetime import datetime, timedelta, timezone
from enum import Enum
from typing import Dict, List, NamedTuple, Optional, Tuple
from typing import Dict, List, Optional, Tuple, Union
import arrow
from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import SellType, SignalType
from freqtrade.exceptions import OperationalException, StrategyError
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.exchange.exchange import timeframe_to_next_date
from freqtrade.persistence import PairLocks, Trade
from freqtrade.strategy.hyper import HyperStrategyMixin
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.wallets import Wallets
logger = logging.getLogger(__name__)
CUSTOM_SELL_MAX_LENGTH = 64
class SignalType(Enum):
"""
Enum to distinguish between buy and sell signals
"""
BUY = "buy"
SELL = "sell"
class SellType(Enum):
"""
Enum to distinguish between sell reasons
"""
ROI = "roi"
STOP_LOSS = "stop_loss"
STOPLOSS_ON_EXCHANGE = "stoploss_on_exchange"
TRAILING_STOP_LOSS = "trailing_stop_loss"
SELL_SIGNAL = "sell_signal"
FORCE_SELL = "force_sell"
EMERGENCY_SELL = "emergency_sell"
NONE = ""
def __str__(self):
# explicitly convert to String to help with exporting data.
return self.value
class SellCheckTuple(NamedTuple):
class SellCheckTuple(object):
"""
NamedTuple for Sell type + reason
"""
sell_flag: bool
sell_type: SellType
sell_reason: str = ''
def __init__(self, sell_type: SellType, sell_reason: str = ''):
self.sell_type = sell_type
self.sell_reason = sell_reason or sell_type.value
@property
def sell_flag(self):
return self.sell_type != SellType.NONE
class IStrategy(ABC):
class IStrategy(ABC, HyperStrategyMixin):
"""
Interface for freqtrade strategies
Defines the mandatory structure must follow any custom strategies
@@ -140,6 +124,7 @@ class IStrategy(ABC):
self.config = config
# Dict to determine if analysis is necessary
self._last_candle_seen_per_pair: Dict[str, datetime] = {}
super().__init__(config)
@abstractmethod
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -149,6 +134,7 @@ class IStrategy(ABC):
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
return dataframe
@abstractmethod
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -158,6 +144,7 @@ class IStrategy(ABC):
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
return dataframe
@abstractmethod
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -167,6 +154,7 @@ class IStrategy(ABC):
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with sell column
"""
return dataframe
def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
"""
@@ -214,7 +202,7 @@ class IStrategy(ABC):
pass
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, **kwargs) -> bool:
time_in_force: str, current_time: datetime, **kwargs) -> bool:
"""
Called right before placing a buy order.
Timing for this function is critical, so avoid doing heavy computations or
@@ -229,6 +217,7 @@ class IStrategy(ABC):
: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 current_time: datetime object, containing the current datetime
: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
@@ -236,7 +225,8 @@ class IStrategy(ABC):
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:
rate: float, time_in_force: str, sell_reason: str,
current_time: datetime, **kwargs) -> bool:
"""
Called right before placing a regular sell order.
Timing for this function is critical, so avoid doing heavy computations or
@@ -255,6 +245,7 @@ class IStrategy(ABC):
: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 current_time: datetime object, containing the current datetime
: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
@@ -283,6 +274,30 @@ class IStrategy(ABC):
"""
return self.stoploss
def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
"""
Custom sell signal logic indicating that specified position should be sold. Returning a
string or True from this method is equal to setting sell signal on a candle at specified
time. This method is not called when sell signal is set.
This method should be overridden to create sell signals that depend on trade parameters. For
example you could implement a stoploss relative to candle when trade was opened, or a custom
1:2 risk-reward ROI.
Custom sell reason max length is 64. Exceeding this limit will raise OperationalException.
:param pair: Pair that's currently analyzed
:param trade: trade object.
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in ask_strategy.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return: To execute sell, return a string with custom sell reason or True. Otherwise return
None or False.
"""
return None
def informative_pairs(self) -> ListPairsWithTimeframes:
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
@@ -529,12 +544,33 @@ class IStrategy(ABC):
and self.min_roi_reached(trade=trade, current_profit=current_profit,
current_time=date))
sell_signal = SellType.NONE
custom_reason = ''
# use provided rate in backtesting, not high/low.
current_rate = rate
current_profit = trade.calc_profit_ratio(current_rate)
if (ask_strategy.get('sell_profit_only', False)
and current_profit <= ask_strategy.get('sell_profit_offset', 0)):
# sell_profit_only and profit doesn't reach the offset - ignore sell signal
sell_signal = False
else:
sell_signal = sell and not buy and ask_strategy.get('use_sell_signal', True)
pass
elif ask_strategy.get('use_sell_signal', True) and not buy:
if sell:
sell_signal = SellType.SELL_SIGNAL
else:
custom_reason = strategy_safe_wrapper(self.custom_sell, default_retval=False)(
pair=trade.pair, trade=trade, current_time=date, current_rate=current_rate,
current_profit=current_profit)
if custom_reason:
sell_signal = SellType.CUSTOM_SELL
if isinstance(custom_reason, str):
if len(custom_reason) > CUSTOM_SELL_MAX_LENGTH:
logger.warning(f'Custom sell reason returned from custom_sell is too '
f'long and was trimmed to {CUSTOM_SELL_MAX_LENGTH} '
f'characters.')
custom_reason = custom_reason[:CUSTOM_SELL_MAX_LENGTH]
else:
custom_reason = None
# TODO: return here if sell-signal should be favored over ROI
# Start evaluations
@@ -543,24 +579,23 @@ class IStrategy(ABC):
# Sell-signal
# Stoploss
if roi_reached and stoplossflag.sell_type != SellType.STOP_LOSS:
logger.debug(f"{trade.pair} - Required profit reached. sell_flag=True, "
f"sell_type=SellType.ROI")
return SellCheckTuple(sell_flag=True, sell_type=SellType.ROI)
logger.debug(f"{trade.pair} - Required profit reached. sell_type=SellType.ROI")
return SellCheckTuple(sell_type=SellType.ROI)
if sell_signal:
logger.debug(f"{trade.pair} - Sell signal received. sell_flag=True, "
f"sell_type=SellType.SELL_SIGNAL")
return SellCheckTuple(sell_flag=True, sell_type=SellType.SELL_SIGNAL)
if sell_signal != SellType.NONE:
logger.debug(f"{trade.pair} - Sell signal received. "
f"sell_type=SellType.{sell_signal.name}" +
(f", custom_reason={custom_reason}" if custom_reason else ""))
return SellCheckTuple(sell_type=sell_signal, sell_reason=custom_reason)
if stoplossflag.sell_flag:
logger.debug(f"{trade.pair} - Stoploss hit. sell_flag=True, "
f"sell_type={stoplossflag.sell_type}")
logger.debug(f"{trade.pair} - Stoploss hit. sell_type={stoplossflag.sell_type}")
return stoplossflag
# This one is noisy, commented out...
# logger.debug(f"{trade.pair} - No sell signal. sell_flag=False")
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
# logger.debug(f"{trade.pair} - No sell signal.")
return SellCheckTuple(sell_type=SellType.NONE)
def stop_loss_reached(self, current_rate: float, trade: Trade,
current_time: datetime, current_profit: float,
@@ -624,9 +659,9 @@ class IStrategy(ABC):
logger.debug(f"{trade.pair} - Trailing stop saved "
f"{trade.stop_loss - trade.initial_stop_loss:.6f}")
return SellCheckTuple(sell_flag=True, sell_type=sell_type)
return SellCheckTuple(sell_type=sell_type)
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
return SellCheckTuple(sell_type=SellType.NONE)
def min_roi_reached_entry(self, trade_dur: int) -> Tuple[Optional[int], Optional[float]]:
"""
@@ -649,7 +684,7 @@ class IStrategy(ABC):
:return: True if bot should sell at current rate
"""
# Check if time matches and current rate is above threshold
trade_dur = int((current_time.timestamp() - trade.open_date.timestamp()) // 60)
trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
_, roi = self.min_roi_reached_entry(trade_dur)
if roi is None:
return False
@@ -659,7 +694,7 @@ class IStrategy(ABC):
def ohlcvdata_to_dataframe(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
"""
Populates indicators for given candle (OHLCV) data (for multiple pairs)
Does not run advice_buy or advise_sell!
Does not run advise_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

27
freqtrade/strategy/strategy_helper.py
View File

@@ -56,3 +56,30 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
dataframe = dataframe.ffill()
return dataframe
def stoploss_from_open(open_relative_stop: float, current_profit: float) -> float:
"""
Given the current profit, and a desired stop loss value relative to the open price,
return a stop loss value that is relative to the current price, and which can be
returned from `custom_stoploss`.
The requested stop can be positive for a stop above the open price, or negative for
a stop below the open price. The return value is always >= 0.
Returns 0 if the resulting stop price would be above the current price.
:param open_relative_stop: Desired stop loss percentage relative to open price
:param current_profit: The current profit percentage
:return: Positive stop loss value relative to current price
"""
# formula is undefined for current_profit -1, return maximum value
if current_profit == -1:
return 1
stoploss = 1-((1+open_relative_stop)/(1+current_profit))
# negative stoploss values indicate the requested stop price is higher than the current price
return max(stoploss, 0.0)

16
freqtrade/templates/base_config.json.j2
View File

@@ -9,7 +9,8 @@
"cancel_open_orders_on_exit": false,
"unfilledtimeout": {
"buy": 10,
"sell": 30
"sell": 30,
"unit": "minutes"
},
"bid_strategy": {
"price_side": "bid",
@@ -54,14 +55,15 @@
"chat_id": "{{ telegram_chat_id }}"
},
"api_server": {
"enabled": false,
"listen_ip_address": "127.0.0.1",
"enabled": {{ api_server | lower }},
"listen_ip_address": "{{ api_server_listen_addr | default("127.0.0.1", true) }}",
"listen_port": 8080,
"verbosity": "info",
"jwt_secret_key": "somethingrandom",
"verbosity": "error",
"enable_openapi": false,
"jwt_secret_key": "{{ api_server_jwt_key }}",
"CORS_origins": [],
"username": "",
"password": ""
"username": "{{ api_server_username }}",
"password": "{{ api_server_password }}"
},
"bot_name": "freqtrade",
"initial_state": "running",

23
freqtrade/templates/base_hyperopt.py.j2
View File

@@ -39,6 +39,15 @@ class {{ hyperopt }}(IHyperOpt):
https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
"""
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching buy strategy parameters.
"""
return [
{{ buy_space | indent(12) }}
]
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
@@ -79,12 +88,12 @@ class {{ hyperopt }}(IHyperOpt):
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching buy strategy parameters.
Define your Hyperopt space for searching sell strategy parameters.
"""
return [
{{ buy_space | indent(12) }}
{{ sell_space | indent(12) }}
]
@staticmethod
@@ -126,11 +135,3 @@ class {{ hyperopt }}(IHyperOpt):
return populate_sell_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters.
"""
return [
{{ sell_space | indent(12) }}
]

9
freqtrade/templates/base_strategy.py.j2
View File

@@ -1,4 +1,5 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# --- Do not remove these libs ---
import numpy as np # noqa
@@ -6,6 +7,7 @@ import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
# --------------------------------
# Add your lib to import here
@@ -16,7 +18,7 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
class {{ strategy }}(IStrategy):
"""
This is a strategy template to get you started.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
More information in https://www.freqtrade.io/en/latest/strategy-customization/
You can:
:return: a Dataframe with all mandatory indicators for the strategies
@@ -26,8 +28,9 @@ class {{ strategy }}(IStrategy):
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
- the methods: populate_indicators, populate_buy_trend, populate_sell_trend
You should keep:
- timeframe, minimal_roi, stoploss, trailing_*
"""
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.

94
freqtrade/templates/sample_hyperopt.py
View File

@@ -45,6 +45,23 @@ class SampleHyperOpt(IHyperOpt):
https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
"""
@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 buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
@@ -92,20 +109,22 @@ class SampleHyperOpt(IHyperOpt):
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching buy strategy parameters.
Define your Hyperopt space for searching sell 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')
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')
]
@staticmethod
@@ -153,56 +172,3 @@ class SampleHyperOpt(IHyperOpt):
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

111
freqtrade/templates/sample_hyperopt_advanced.py
View File

@@ -7,7 +7,7 @@ from typing import Any, Callable, Dict, List
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from skopt.space import Categorical, Dimension, Integer, Real # noqa
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real # noqa
from freqtrade.optimize.hyperopt_interface import IHyperOpt
@@ -60,6 +60,23 @@ class AdvancedSampleHyperOpt(IHyperOpt):
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
@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 buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
@@ -106,20 +123,22 @@ class AdvancedSampleHyperOpt(IHyperOpt):
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
Define your Hyperopt space for searching sell 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')
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')
]
@staticmethod
@@ -168,25 +187,6 @@ class AdvancedSampleHyperOpt(IHyperOpt):
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')
]
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
@@ -223,9 +223,9 @@ class AdvancedSampleHyperOpt(IHyperOpt):
Integer(10, 120, name='roi_t1'),
Integer(10, 60, name='roi_t2'),
Integer(10, 40, name='roi_t3'),
Real(0.01, 0.04, name='roi_p1'),
Real(0.01, 0.07, name='roi_p2'),
Real(0.01, 0.20, name='roi_p3'),
SKDecimal(0.01, 0.04, decimals=3, name='roi_p1'),
SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
]
@staticmethod
@@ -237,7 +237,7 @@ class AdvancedSampleHyperOpt(IHyperOpt):
'stoploss' optimization hyperspace.
"""
return [
Real(-0.35, -0.02, name='stoploss'),
SKDecimal(-0.35, -0.02, decimals=3, name='stoploss'),
]
@staticmethod
@@ -256,51 +256,14 @@ class AdvancedSampleHyperOpt(IHyperOpt):
# other 'trailing' hyperspace parameters.
Categorical([True], name='trailing_stop'),
Real(0.01, 0.35, name='trailing_stop_positive'),
SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
# so this intermediate parameter is used as the value of the difference between
# them. The value of the 'trailing_stop_positive_offset' is constructed in the
# generate_trailing_params() method.
# This is similar to the hyperspace dimensions used for constructing the ROI tables.
Real(0.001, 0.1, name='trailing_stop_positive_offset_p1'),
SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
Categorical([True, False], name='trailing_only_offset_is_reached'),
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators.
Can be a copy of the corresponding method from the strategy,
or will be loaded from the strategy.
Must align to populate_indicators used (either from this File, or from the strategy)
Only used when --spaces does not include buy
"""
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.
Can be a copy of the corresponding method from the strategy,
or will be loaded from the strategy.
Must align to populate_indicators used (either from this File, or from the strategy)
Only used when --spaces does not include sell
"""
dataframe.loc[
(
(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
)) &
(dataframe['fastd'] > 54)
),
'sell'] = 1
return dataframe

2
freqtrade/templates/sample_hyperopt_loss.py
View File

@@ -1,5 +1,6 @@
from datetime import datetime
from math import exp
from typing import Dict
from pandas import DataFrame
@@ -35,6 +36,7 @@ class SampleHyperOptLoss(IHyperOptLoss):
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
config: Dict, processed: Dict[str, DataFrame],
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results

25
freqtrade/templates/sample_strategy.py
View File

@@ -1,11 +1,13 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
# --------------------------------
# Add your lib to import here
@@ -27,8 +29,9 @@ class SampleStrategy(IStrategy):
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
- the methods: populate_indicators, populate_buy_trend, populate_sell_trend
You should keep:
- timeframe, minimal_roi, stoploss, trailing_*
"""
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
@@ -52,7 +55,11 @@ class SampleStrategy(IStrategy):
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Optimal ticker interval for the strategy.
# Hyperoptable parameters
buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
# Optimal timeframe for the strategy.
timeframe = '5m'
# Run "populate_indicators()" only for new candle.
@@ -322,7 +329,7 @@ class SampleStrategy(IStrategy):
"""
# first check if dataprovider is available
if self.dp:
if self.dp.runmode in ('live', 'dry_run'):
if self.dp.runmode.value in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
@@ -339,7 +346,8 @@ class SampleStrategy(IStrategy):
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
# Signal: RSI crosses above 30
(qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) &
(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard: tema below BB middle
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising
(dataframe['volume'] > 0) # Make sure Volume is not 0
@@ -353,11 +361,12 @@ class SampleStrategy(IStrategy):
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
:return: DataFrame with sell column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
# Signal: RSI crosses above 70
(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) &
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling
(dataframe['volume'] > 0) # Make sure Volume is not 0

28
freqtrade/templates/strategy_analysis_example.ipynb
View File

@@ -40,7 +40,7 @@
"# Location of the data\n",
"data_location = Path(config['user_data_dir'], 'data', 'binance')\n",
"# Pair to analyze - Only use one pair here\n",
"pair = \"BTC_USDT\""
"pair = \"BTC/USDT\""
]
},
{
@@ -54,7 +54,9 @@
"\n",
"candles = load_pair_history(datadir=data_location,\n",
" timeframe=config[\"timeframe\"],\n",
" pair=pair)\n",
" pair=pair,\n",
" data_format = \"hdf5\",\n",
" )\n",
"\n",
"# Confirm success\n",
"print(\"Loaded \" + str(len(candles)) + f\" rows of data for {pair} from {data_location}\")\n",
@@ -280,6 +282,28 @@
"graph.show(renderer=\"browser\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot average profit per trade as distribution graph"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import plotly.figure_factory as ff\n",
"\n",
"hist_data = [trades.profit_ratio]\n",
"group_labels = ['profit_ratio'] # name of the dataset\n",
"\n",
"fig = ff.create_distplot(hist_data, group_labels,bin_size=0.01)\n",
"fig.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},

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