strategy_updater:
removed args_common_optimize for strategy-updater backtest_lookahead_bias_checker: added args and cli-options for minimum and target trade amounts fixed code according to best-practice coding requests of matthias (CamelCase etc)
This commit is contained in:
hippocritical
parent
efefcb240b
commit
7bd55971dc
@ -116,9 +116,10 @@ NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list
|
||||
|
||||
NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-strategy"]
|
||||
|
||||
ARGS_STRATEGY_UPDATER = ARGS_COMMON_OPTIMIZE + ["strategy_list"]
|
||||
ARGS_STRATEGY_UPDATER = ["strategy_list"]
|
||||
|
||||
ARGS_BACKTEST_LOOKAHEAD_BIAS_CHECKER = ARGS_BACKTEST
|
||||
ARGS_BACKTEST_LOOKAHEAD_BIAS_CHECKER = ARGS_BACKTEST + ["minimum_trade_amount",
|
||||
"targeted_trade_amount"]
|
||||
|
||||
|
||||
# + ["target_trades", "minimum_trades",
|
||||
@ -461,7 +462,7 @@ class Arguments:
|
||||
|
||||
# Add backtest lookahead bias checker subcommand
|
||||
backtest_lookahead_bias_checker_cmd = \
|
||||
subparsers.add_parser('backtest_lookahead_bias_checker',
|
||||
subparsers.add_parser('backtest-lookahead-bias-checker',
|
||||
help="checks for potential look ahead bias",
|
||||
parents=[_common_parser])
|
||||
backtest_lookahead_bias_checker_cmd.set_defaults(func=start_backtest_lookahead_bias_checker)
|
||||
|
||||
@ -675,4 +675,18 @@ AVAILABLE_CLI_OPTIONS = {
|
||||
help='Run backtest with ready models.',
|
||||
action='store_true'
|
||||
),
|
||||
"minimum_trade_amount": Arg(
|
||||
'--minimum-trade-amount',
|
||||
help='set INT minimum trade amount',
|
||||
type=check_int_positive,
|
||||
metavar='INT',
|
||||
default=10,
|
||||
),
|
||||
"targeted_trade_amount": Arg(
|
||||
'--targeted-trade-amount',
|
||||
help='set INT targeted trade amount',
|
||||
type=check_int_positive,
|
||||
metavar='INT',
|
||||
default=20,
|
||||
)
|
||||
}
|
||||
|
||||
@ -7,7 +7,7 @@ from typing import Any, Dict
|
||||
from freqtrade.configuration import setup_utils_configuration
|
||||
from freqtrade.enums import RunMode
|
||||
from freqtrade.resolvers import StrategyResolver
|
||||
from freqtrade.strategy.backtest_lookahead_bias_checker import backtest_lookahead_bias_checker
|
||||
from freqtrade.strategy.backtest_lookahead_bias_checker import BacktestLookaheadBiasChecker
|
||||
from freqtrade.strategy.strategyupdater import StrategyUpdater
|
||||
|
||||
|
||||
@ -67,9 +67,16 @@ def start_backtest_lookahead_bias_checker(args: Dict[str, Any]) -> None:
|
||||
"""
|
||||
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
|
||||
|
||||
if args['targeted_trade_amount'] < args['minimum_trade_amount']:
|
||||
# add logic that tells the user to check the configuration
|
||||
# since this combo doesn't make any sense.
|
||||
pass
|
||||
|
||||
strategy_objs = StrategyResolver.search_all_objects(
|
||||
config, enum_failed=False, recursive=config.get('recursive_strategy_search', False))
|
||||
|
||||
bias_checker_instances = []
|
||||
|
||||
filtered_strategy_objs = []
|
||||
if 'strategy_list' in args and args['strategy_list'] is not None:
|
||||
for args_strategy in args['strategy_list']:
|
||||
@ -80,21 +87,29 @@ def start_backtest_lookahead_bias_checker(args: Dict[str, Any]) -> None:
|
||||
break
|
||||
|
||||
for filtered_strategy_obj in filtered_strategy_objs:
|
||||
initialize_single_lookahead_bias_checker(filtered_strategy_obj, config)
|
||||
bias_checker_instances = initialize_single_lookahead_bias_checker(
|
||||
filtered_strategy_obj, config, args)
|
||||
else:
|
||||
processed_locations = set()
|
||||
for strategy_obj in strategy_objs:
|
||||
if strategy_obj['location'] not in processed_locations:
|
||||
processed_locations.add(strategy_obj['location'])
|
||||
initialize_single_lookahead_bias_checker(strategy_obj, config)
|
||||
bias_checker_instances = initialize_single_lookahead_bias_checker(
|
||||
strategy_obj, config, args)
|
||||
create_result_list(bias_checker_instances)
|
||||
|
||||
|
||||
def initialize_single_lookahead_bias_checker(strategy_obj, config):
|
||||
def create_result_list(bias_checker_instances):
|
||||
pass
|
||||
|
||||
|
||||
def initialize_single_lookahead_bias_checker(strategy_obj, config, args):
|
||||
# try:
|
||||
print(f"Bias test of {Path(strategy_obj['location']).name} started.")
|
||||
instance_backtest_lookahead_bias_checker = backtest_lookahead_bias_checker()
|
||||
instance_backtest_lookahead_bias_checker = BacktestLookaheadBiasChecker()
|
||||
start = time.perf_counter()
|
||||
instance_backtest_lookahead_bias_checker.start(config, strategy_obj)
|
||||
current_instance = instance_backtest_lookahead_bias_checker.start(config, strategy_obj, args)
|
||||
elapsed = time.perf_counter() - start
|
||||
print(f"checking look ahead bias via backtests of {Path(strategy_obj['location']).name} "
|
||||
f"took {elapsed:.1f} seconds.")
|
||||
return current_instance
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
|
||||
import copy
|
||||
from copy import deepcopy
|
||||
from datetime import datetime, timedelta, timezone
|
||||
|
||||
@ -10,8 +10,8 @@ from freqtrade.exchange import timeframe_to_minutes
|
||||
from freqtrade.optimize.backtesting import Backtesting
|
||||
|
||||
|
||||
class backtest_lookahead_bias_checker:
|
||||
class varHolder:
|
||||
class BacktestLookaheadBiasChecker:
|
||||
class VarHolder:
|
||||
timerange: TimeRange
|
||||
data: pandas.DataFrame
|
||||
indicators: pandas.DataFrame
|
||||
@ -21,7 +21,7 @@ class backtest_lookahead_bias_checker:
|
||||
to_dt: datetime
|
||||
compared_dt: datetime
|
||||
|
||||
class analysis:
|
||||
class Analysis:
|
||||
def __init__(self):
|
||||
self.total_signals = 0
|
||||
self.false_entry_signals = 0
|
||||
@ -37,24 +37,24 @@ class backtest_lookahead_bias_checker:
|
||||
has_bias: bool
|
||||
|
||||
def __init__(self):
|
||||
self.strategy_obj
|
||||
self.current_analysis
|
||||
self.config
|
||||
self.full_varHolder
|
||||
self.entry_varholder
|
||||
self.exit_varholder
|
||||
self.backtesting
|
||||
self.signals_to_check: int = 20
|
||||
self.current_analysis
|
||||
self.full_varHolder.from_dt
|
||||
self.full_varHolder.to_dt
|
||||
self.strategy_obj = None
|
||||
self.current_analysis = None
|
||||
self.local_config = None
|
||||
self.full_varHolder = None
|
||||
self.entry_varHolder = None
|
||||
self.exit_varHolder = None
|
||||
self.backtesting = None
|
||||
self.current_analysis = None
|
||||
self.minimum_trade_amount = None
|
||||
self.targeted_trade_amount = None
|
||||
|
||||
@staticmethod
|
||||
def dt_to_timestamp(dt):
|
||||
timestamp = int(dt.replace(tzinfo=timezone.utc).timestamp())
|
||||
return timestamp
|
||||
|
||||
def get_result(self, backtesting, processed):
|
||||
@staticmethod
|
||||
def get_result(backtesting, processed):
|
||||
min_date, max_date = get_timerange(processed)
|
||||
|
||||
result = backtesting.backtest(
|
||||
@ -64,6 +64,24 @@ class backtest_lookahead_bias_checker:
|
||||
)
|
||||
return result
|
||||
|
||||
@staticmethod
|
||||
def report_signal(result, column_name, checked_timestamp):
|
||||
df = result['results']
|
||||
row_count = df[column_name].shape[0]
|
||||
|
||||
if row_count == 0:
|
||||
return False
|
||||
else:
|
||||
|
||||
df_cut = df[(df[column_name] == checked_timestamp)]
|
||||
if df_cut[column_name].shape[0] == 0:
|
||||
# print("did NOT find the same signal in column " + column_name +
|
||||
# " at timestamp " + str(checked_timestamp))
|
||||
return False
|
||||
else:
|
||||
return True
|
||||
return False
|
||||
|
||||
# analyzes two data frames with processed indicators and shows differences between them.
|
||||
def analyze_indicators(self, full_vars, cut_vars, current_pair):
|
||||
# extract dataframes
|
||||
@ -87,12 +105,11 @@ class backtest_lookahead_bias_checker:
|
||||
for col_name, values in compare_df.items():
|
||||
col_idx = compare_df.columns.get_loc(col_name)
|
||||
compare_df_row = compare_df.iloc[0]
|
||||
# compare_df now is comprised of tuples with [1] having either 'self' or 'other'
|
||||
# compare_df now comprises tuples with [1] having either 'self' or 'other'
|
||||
if 'other' in col_name[1]:
|
||||
continue
|
||||
self_value = compare_df_row[col_idx]
|
||||
other_value = compare_df_row[col_idx + 1]
|
||||
other_value = compare_df_row[col_idx + 1]
|
||||
|
||||
# output differences
|
||||
if self_value != other_value:
|
||||
@ -101,90 +118,62 @@ class backtest_lookahead_bias_checker:
|
||||
self.current_analysis.false_indicators.append(col_name[0])
|
||||
print(f"=> found look ahead bias in indicator {col_name[0]}. " +
|
||||
f"{str(self_value)} != {str(other_value)}")
|
||||
# return compare_df
|
||||
|
||||
def report_signal(self, result, column_name, checked_timestamp):
|
||||
df = result['results']
|
||||
row_count = df[column_name].shape[0]
|
||||
|
||||
if row_count == 0:
|
||||
return False
|
||||
else:
|
||||
|
||||
df_cut = df[(df[column_name] == checked_timestamp)]
|
||||
if df_cut[column_name].shape[0] == 0:
|
||||
# print("did NOT find the same signal in column " + column_name +
|
||||
# " at timestamp " + str(checked_timestamp))
|
||||
return False
|
||||
else:
|
||||
return True
|
||||
return False
|
||||
|
||||
def prepare_data(self, varholder, var_pairs):
|
||||
self.config['timerange'] = \
|
||||
str(int(self.dt_to_timestamp(varholder.from_dt))) + "-" + \
|
||||
str(int(self.dt_to_timestamp(varholder.to_dt)))
|
||||
self.backtesting = Backtesting(self.config)
|
||||
def prepare_data(self, varHolder, pairs_to_load):
|
||||
prepare_data_config = copy.deepcopy(self.local_config)
|
||||
prepare_data_config['timerange'] = (str(self.dt_to_timestamp(varHolder.from_dt)) + "-" +
|
||||
str(self.dt_to_timestamp(varHolder.to_dt)))
|
||||
prepare_data_config['pairs'] = pairs_to_load
|
||||
self.backtesting = Backtesting(prepare_data_config)
|
||||
self.backtesting._set_strategy(self.backtesting.strategylist[0])
|
||||
varholder.data, varholder.timerange = self.backtesting.load_bt_data()
|
||||
varholder.indicators = self.backtesting.strategy.advise_all_indicators(varholder.data)
|
||||
varholder.result = self.get_result(self.backtesting, varholder.indicators)
|
||||
varHolder.data, varHolder.timerange = self.backtesting.load_bt_data()
|
||||
varHolder.indicators = self.backtesting.strategy.advise_all_indicators(varHolder.data)
|
||||
varHolder.result = self.get_result(self.backtesting, varHolder.indicators)
|
||||
|
||||
def start(self, config, strategy_obj: dict) -> None:
|
||||
self.strategy_obj = strategy_obj
|
||||
self.config = config
|
||||
self.current_analysis = backtest_lookahead_bias_checker.analysis()
|
||||
def update_output_file(self):
|
||||
pass
|
||||
|
||||
max_try_signals: int = 3
|
||||
found_signals: int = 0
|
||||
continue_with_strategy = True
|
||||
def start(self, config, strategy_obj: dict, args) -> None:
|
||||
|
||||
# first we need to get the necessary entry/exit signals
|
||||
# so we start by 14 days and increase in 1 month steps
|
||||
# until we have the desired trade amount.
|
||||
for try_buysignals in range(max_try_signals): # range(3) = 0..2
|
||||
# re-initialize backtesting-variable
|
||||
self.full_varHolder = backtest_lookahead_bias_checker.varHolder()
|
||||
# deepcopy so we can change the pairs for the 2ndary runs
|
||||
# and not worry about another strategy to check after.
|
||||
self.local_config = deepcopy(config)
|
||||
self.local_config['strategy_list'] = [strategy_obj['name']]
|
||||
self.current_analysis = BacktestLookaheadBiasChecker.Analysis()
|
||||
self.minimum_trade_amount = args['minimum_trade_amount']
|
||||
self.targeted_trade_amount = args['targeted_trade_amount']
|
||||
|
||||
# define datetimes in human readable format
|
||||
self.full_varHolder.from_dt = datetime(2022, 9, 1)
|
||||
self.full_varHolder.to_dt = datetime(2022, 9, 15) + timedelta(days=30 * try_buysignals)
|
||||
# first make a single backtest
|
||||
self.full_varHolder = BacktestLookaheadBiasChecker.VarHolder()
|
||||
|
||||
self.prepare_data(self.full_varHolder, self.config['pairs'])
|
||||
|
||||
found_signals = self.full_varHolder.result['results'].shape[0] + 1
|
||||
if try_buysignals == max_try_signals - 1:
|
||||
if found_signals < self.signals_to_check / 2:
|
||||
print(f"... only found {str(int(found_signals / 2))} "
|
||||
f"buy signals for {self.strategy_obj['name']}. "
|
||||
f"Cancelling...")
|
||||
continue_with_strategy = False
|
||||
# define datetime in human-readable format
|
||||
parsed_timerange = TimeRange.parse_timerange(config['timerange'])
|
||||
if (parsed_timerange is not None and
|
||||
parsed_timerange.startdt is not None and
|
||||
parsed_timerange.stopdt is not None):
|
||||
self.full_varHolder.from_dt = parsed_timerange.startdt
|
||||
self.full_varHolder.to_dt = parsed_timerange.stopdt
|
||||
else:
|
||||
print(
|
||||
f"Found {str(found_signals)} buy signals. "
|
||||
f"Going with max {str(self.signals_to_check)} "
|
||||
f" buy signals in the full timerange from "
|
||||
f"{str(self.full_varHolder.from_dt)} to {str(self.full_varHolder.to_dt)}")
|
||||
break
|
||||
elif found_signals < self.signals_to_check:
|
||||
print(
|
||||
f"Only found {str(found_signals)} buy signals in the full timerange from "
|
||||
f"{str(self.full_varHolder.from_dt)} to "
|
||||
f"{str(self.full_varHolder.to_dt)}. "
|
||||
f"will increase timerange trying to get at least "
|
||||
f"{str(self.signals_to_check)} signals.")
|
||||
else:
|
||||
print(
|
||||
f"Found {str(found_signals)} buy signals, more than necessary. "
|
||||
f"Reducing to {str(self.signals_to_check)} "
|
||||
f"checked buy signals in the full timerange from "
|
||||
f"{str(self.full_varHolder.from_dt)} to {str(self.full_varHolder.to_dt)}")
|
||||
break
|
||||
if not continue_with_strategy:
|
||||
print("Parsing of parsed_timerange failed. exiting!")
|
||||
return
|
||||
|
||||
self.prepare_data(self.full_varHolder, self.local_config['pairs'])
|
||||
|
||||
found_signals: int = self.full_varHolder.result['results'].shape[0] + 1
|
||||
if found_signals >= self.targeted_trade_amount:
|
||||
print(f"Found {found_signals} trades, calculating {self.targeted_trade_amount} trades.")
|
||||
elif self.targeted_trade_amount >= found_signals >= self.minimum_trade_amount:
|
||||
print(f"Only found {found_signals} trades. Calculating all available trades.")
|
||||
else:
|
||||
print(f"found {found_signals} trades "
|
||||
f"which is less than minimum_trade_amount {self.minimum_trade_amount}. "
|
||||
f"Cancelling this backtest lookahead bias test.")
|
||||
return
|
||||
|
||||
# now we loop through all entry signals
|
||||
# starting from the same datetime to avoid miss-reports of bias
|
||||
for idx, result_row in self.full_varHolder.result['results'].iterrows():
|
||||
if self.current_analysis.total_signals == self.signals_to_check:
|
||||
if self.current_analysis.total_signals == self.targeted_trade_amount:
|
||||
break
|
||||
|
||||
# if force-sold, ignore this signal since here it will unconditionally exit.
|
||||
@ -193,49 +182,45 @@ class backtest_lookahead_bias_checker:
|
||||
|
||||
self.current_analysis.total_signals += 1
|
||||
|
||||
self.entry_varholder = backtest_lookahead_bias_checker.varHolder()
|
||||
self.exit_varholder = backtest_lookahead_bias_checker.varHolder()
|
||||
|
||||
self.entry_varholder.from_dt = self.full_varHolder.from_dt # result_row['open_date']
|
||||
self.entry_varholder.compared_dt = result_row['open_date']
|
||||
self.entry_varHolder = BacktestLookaheadBiasChecker.VarHolder()
|
||||
self.exit_varHolder = BacktestLookaheadBiasChecker.VarHolder()
|
||||
|
||||
self.entry_varHolder.from_dt = self.full_varHolder.from_dt
|
||||
self.entry_varHolder.compared_dt = result_row['open_date']
|
||||
# to_dt needs +1 candle since it won't buy on the last candle
|
||||
self.entry_varholder.to_dt = result_row['open_date'] + \
|
||||
timedelta(minutes=timeframe_to_minutes(self.config['timeframe']) * 2)
|
||||
self.entry_varHolder.to_dt = (result_row['open_date'] +
|
||||
timedelta(minutes=timeframe_to_minutes(
|
||||
self.local_config['timeframe'])))
|
||||
|
||||
self.prepare_data(self.entry_varholder, [result_row['pair']])
|
||||
self.prepare_data(self.entry_varHolder, [result_row['pair']])
|
||||
|
||||
# ---
|
||||
# print("analyzing the sell signal")
|
||||
# to_dt needs +1 candle since it will always sell all trades on the last candle
|
||||
self.exit_varholder.from_dt = self.full_varHolder.from_dt # result_row['open_date']
|
||||
self.exit_varholder.to_dt = \
|
||||
result_row['close_date'] + \
|
||||
timedelta(minutes=timeframe_to_minutes(self.config['timeframe']))
|
||||
self.exit_varholder.compared_dt = result_row['close_date']
|
||||
# to_dt needs +1 candle since it will always exit/force-exit trades on the last candle
|
||||
self.exit_varHolder.from_dt = self.full_varHolder.from_dt
|
||||
self.exit_varHolder.to_dt = (result_row['close_date'] +
|
||||
timedelta(minutes=timeframe_to_minutes(
|
||||
self.local_config['timeframe'])))
|
||||
self.exit_varHolder.compared_dt = result_row['close_date']
|
||||
|
||||
self.prepare_data(self.exit_varholder, [result_row['pair']])
|
||||
self.prepare_data(self.exit_varHolder, [result_row['pair']])
|
||||
|
||||
# register if buy signal is broken
|
||||
if not self.report_signal(
|
||||
self.entry_varholder.result,
|
||||
"open_date", self.entry_varholder.compared_dt):
|
||||
self.entry_varHolder.result, "open_date", self.entry_varHolder.compared_dt):
|
||||
self.current_analysis.false_entry_signals += 1
|
||||
|
||||
# register if buy or sell signal is broken
|
||||
if not self.report_signal(self.entry_varholder.result,
|
||||
"open_date", self.entry_varholder.compared_dt) \
|
||||
or not self.report_signal(self.exit_varholder.result,
|
||||
"close_date", self.exit_varholder.compared_dt):
|
||||
if not self.report_signal(
|
||||
self.exit_varHolder.result, "close_date", self.exit_varHolder.compared_dt):
|
||||
self.current_analysis.false_exit_signals += 1
|
||||
|
||||
self.analyze_indicators(self.full_varHolder, self.entry_varholder, result_row['pair'])
|
||||
self.analyze_indicators(self.full_varHolder, self.exit_varholder, result_row['pair'])
|
||||
# check if the indicators themselves contain biased data
|
||||
self.analyze_indicators(self.full_varHolder, self.entry_varHolder, result_row['pair'])
|
||||
self.analyze_indicators(self.full_varHolder, self.exit_varHolder, result_row['pair'])
|
||||
|
||||
if self.current_analysis.false_entry_signals > 0 or \
|
||||
self.current_analysis.false_exit_signals > 0 or \
|
||||
len(self.current_analysis.false_indicators) > 0:
|
||||
print(" => " + self.strategy_obj['name'] + ": bias detected!")
|
||||
if (self.current_analysis.false_entry_signals > 0 or
|
||||
self.current_analysis.false_exit_signals > 0 or
|
||||
len(self.current_analysis.false_indicators) > 0):
|
||||
print(" => " + self.local_config['strategy_list'][0] + ": bias detected!")
|
||||
self.current_analysis.has_bias = True
|
||||
else:
|
||||
print(self.strategy_obj['name'] + ": no bias detected")
|
||||
print(self.local_config['strategy_list'][0] + ": no bias detected")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user