freqtrade_origin/freqtrade/optimize/analysis/lookahead_helpers.py

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import logging
import time
from pathlib import Path
from typing import Any, Union
import pandas as pd
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from rich.text import Text
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
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from freqtrade.optimize.analysis.lookahead import LookaheadAnalysis
from freqtrade.resolvers import StrategyResolver
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from freqtrade.util import print_rich_table
logger = logging.getLogger(__name__)
class LookaheadAnalysisSubFunctions:
@staticmethod
def text_table_lookahead_analysis_instances(
config: dict[str, Any],
lookahead_instances: list[LookaheadAnalysis],
caption: Union[str, None] = None,
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):
headers = [
"filename",
"strategy",
"has_bias",
"total_signals",
"biased_entry_signals",
"biased_exit_signals",
"biased_indicators",
]
data = []
for inst in lookahead_instances:
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if config["minimum_trade_amount"] > inst.current_analysis.total_signals:
data.append(
[
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inst.strategy_obj["location"].parts[-1],
inst.strategy_obj["name"],
"too few trades caught "
f"({inst.current_analysis.total_signals}/{config['minimum_trade_amount']})."
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f"Test failed.",
]
)
elif inst.failed_bias_check:
data.append(
[
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inst.strategy_obj["location"].parts[-1],
inst.strategy_obj["name"],
"error while checking",
]
)
else:
data.append(
[
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inst.strategy_obj["location"].parts[-1],
inst.strategy_obj["name"],
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Text("Yes", style="bold red")
if inst.current_analysis.has_bias
else Text("No", style="bold green"),
inst.current_analysis.total_signals,
inst.current_analysis.false_entry_signals,
inst.current_analysis.false_exit_signals,
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", ".join(inst.current_analysis.false_indicators),
]
)
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print_rich_table(
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data, headers, summary="Lookahead Analysis", table_kwargs={"caption": caption}
)
return data
@staticmethod
def export_to_csv(config: dict[str, Any], lookahead_analysis: list[LookaheadAnalysis]):
def add_or_update_row(df, row_data):
if (
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(df["filename"] == row_data["filename"]) & (df["strategy"] == row_data["strategy"])
).any():
# Update existing row
pd_series = pd.DataFrame([row_data])
df.loc[
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(df["filename"] == row_data["filename"])
& (df["strategy"] == row_data["strategy"])
] = pd_series
else:
# Add new row
df = pd.concat([df, pd.DataFrame([row_data], columns=df.columns)])
return df
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if Path(config["lookahead_analysis_exportfilename"]).exists():
# Read CSV file into a pandas dataframe
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csv_df = pd.read_csv(config["lookahead_analysis_exportfilename"])
else:
# Create a new empty DataFrame with the desired column names and set the index
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csv_df = pd.DataFrame(
columns=[
"filename",
"strategy",
"has_bias",
"total_signals",
"biased_entry_signals",
"biased_exit_signals",
"biased_indicators",
],
index=None,
)
for inst in lookahead_analysis:
# only update if
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if (
inst.current_analysis.total_signals > config["minimum_trade_amount"]
and inst.failed_bias_check is not True
):
new_row_data = {
"filename": inst.strategy_obj["location"].parts[-1],
"strategy": inst.strategy_obj["name"],
"has_bias": inst.current_analysis.has_bias,
"total_signals": int(inst.current_analysis.total_signals),
"biased_entry_signals": int(inst.current_analysis.false_entry_signals),
"biased_exit_signals": int(inst.current_analysis.false_exit_signals),
"biased_indicators": ",".join(inst.current_analysis.false_indicators),
}
csv_df = add_or_update_row(csv_df, new_row_data)
# Fill NaN values with a default value (e.g., 0)
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csv_df["total_signals"] = csv_df["total_signals"].astype(int).fillna(0)
csv_df["biased_entry_signals"] = csv_df["biased_entry_signals"].astype(int).fillna(0)
csv_df["biased_exit_signals"] = csv_df["biased_exit_signals"].astype(int).fillna(0)
# Convert columns to integers
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csv_df["total_signals"] = csv_df["total_signals"].astype(int)
csv_df["biased_entry_signals"] = csv_df["biased_entry_signals"].astype(int)
csv_df["biased_exit_signals"] = csv_df["biased_exit_signals"].astype(int)
logger.info(f"saving {config['lookahead_analysis_exportfilename']}")
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csv_df.to_csv(config["lookahead_analysis_exportfilename"], index=False)
@staticmethod
def calculate_config_overrides(config: Config):
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if config.get("enable_protections", False):
# if protections are used globally, they can produce false positives.
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config["enable_protections"] = False
logger.info(
"Protections were enabled. "
"Disabling protections now "
"since they could otherwise produce false positives."
)
if config["targeted_trade_amount"] < config["minimum_trade_amount"]:
# this combo doesn't make any sense.
raise OperationalException(
"Targeted trade amount can't be smaller than minimum trade amount."
)
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if len(config["pairs"]) > config.get("max_open_trades", 0):
logger.info(
"Max_open_trades were less than amount of pairs "
"or defined in the strategy. "
"Set max_open_trades to amount of pairs "
"just to avoid false positives."
)
config["max_open_trades"] = len(config["pairs"])
min_dry_run_wallet = 1000000000
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if config["dry_run_wallet"] < min_dry_run_wallet:
logger.info(
"Dry run wallet was not set to 1 billion, pushing it up there "
"just to avoid false positives"
)
config["dry_run_wallet"] = min_dry_run_wallet
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if "timerange" not in config:
# setting a timerange is enforced here
raise OperationalException(
"Please set a timerange. "
"Usually a few months are enough depending on your needs and strategy."
)
# fix stake_amount to 10k.
# in a combination with a wallet size of 1 billion it should always be able to trade
# no matter if they use custom_stake_amount as a small percentage of wallet size
# or fixate custom_stake_amount to a certain value.
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logger.info("fixing stake_amount to 10k")
config["stake_amount"] = 10000
# enforce cache to be 'none', shift it to 'none' if not already
# (since the default value is 'day')
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if config.get("backtest_cache") is None:
config["backtest_cache"] = "none"
elif config["backtest_cache"] != "none":
logger.info(
f"backtest_cache = "
f"{config['backtest_cache']} detected. "
f"Inside lookahead-analysis it is enforced to be 'none'. "
f"Changed it to 'none'"
)
config["backtest_cache"] = "none"
return config
@staticmethod
def initialize_single_lookahead_analysis(config: Config, strategy_obj: dict[str, Any]):
logger.info(f"Bias test of {Path(strategy_obj['location']).name} started.")
start = time.perf_counter()
current_instance = LookaheadAnalysis(config, strategy_obj)
current_instance.start()
elapsed = time.perf_counter() - start
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logger.info(
f"Checking look ahead bias via backtests "
f"of {Path(strategy_obj['location']).name} "
f"took {elapsed:.0f} seconds."
)
return current_instance
@staticmethod
def start(config: Config):
config = LookaheadAnalysisSubFunctions.calculate_config_overrides(config)
strategy_objs = StrategyResolver.search_all_objects(
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config, enum_failed=False, recursive=config.get("recursive_strategy_search", False)
)
lookaheadAnalysis_instances = []
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# unify --strategy and --strategy-list to one list
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if not (strategy_list := config.get("strategy_list", [])):
if config.get("strategy") is None:
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raise OperationalException(
"No Strategy specified. Please specify a strategy via --strategy or "
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"--strategy-list"
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)
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strategy_list = [config["strategy"]]
# check if strategies can be properly loaded, only check them if they can be.
for strat in strategy_list:
for strategy_obj in strategy_objs:
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if strategy_obj["name"] == strat and strategy_obj not in strategy_list:
lookaheadAnalysis_instances.append(
LookaheadAnalysisSubFunctions.initialize_single_lookahead_analysis(
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config, strategy_obj
)
)
break
# report the results
if lookaheadAnalysis_instances:
caption: Union[str, None] = None
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if any(
[
any(
[
indicator.startswith("&")
for indicator in inst.current_analysis.false_indicators
]
)
for inst in lookaheadAnalysis_instances
]
):
caption = (
"Any indicators in 'biased_indicators' which are used within "
"set_freqai_targets() can be ignored."
)
LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances(
config, lookaheadAnalysis_instances, caption=caption
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)
if config.get("lookahead_analysis_exportfilename") is not None:
LookaheadAnalysisSubFunctions.export_to_csv(config, lookaheadAnalysis_instances)
else:
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logger.error(
"There were no strategies specified neither through "
"--strategy nor through "
"--strategy-list "
"or timeframe was not specified."
)