2019-03-07 20:20:32 +00:00
|
|
|
"""
|
|
|
|
Helpers when analyzing backtest data
|
|
|
|
"""
|
2019-06-16 08:41:05 +00:00
|
|
|
import logging
|
2019-03-07 20:20:32 +00:00
|
|
|
from pathlib import Path
|
2021-01-23 19:49:49 +00:00
|
|
|
from typing import Any, Dict, List, Optional, Tuple, Union
|
2019-03-07 20:20:32 +00:00
|
|
|
|
|
|
|
import numpy as np
|
|
|
|
import pandas as pd
|
|
|
|
|
2020-06-28 07:27:19 +00:00
|
|
|
from freqtrade.constants import LAST_BT_RESULT_FN
|
2019-03-07 20:20:32 +00:00
|
|
|
from freqtrade.misc import json_load
|
2021-02-20 19:22:00 +00:00
|
|
|
from freqtrade.persistence import LocalTrade, Trade, init_db
|
2019-06-16 08:41:05 +00:00
|
|
|
|
2020-09-28 17:39:41 +00:00
|
|
|
|
2019-06-16 08:41:05 +00:00
|
|
|
logger = logging.getLogger(__name__)
|
2019-03-07 20:20:32 +00:00
|
|
|
|
2021-01-23 19:49:49 +00:00
|
|
|
# Old format - maybe remove?
|
|
|
|
BT_DATA_COLUMNS_OLD = ["pair", "profit_percent", "open_date", "close_date", "index",
|
|
|
|
"trade_duration", "open_rate", "close_rate", "open_at_end", "sell_reason"]
|
|
|
|
|
2021-08-16 12:16:24 +00:00
|
|
|
# Mid-term format, created by BacktestResult Named Tuple
|
2021-01-23 19:49:49 +00:00
|
|
|
BT_DATA_COLUMNS_MID = ['pair', 'profit_percent', 'open_date', 'close_date', 'trade_duration',
|
|
|
|
'open_rate', 'close_rate', 'open_at_end', 'sell_reason', 'fee_open',
|
|
|
|
'fee_close', 'amount', 'profit_abs', 'profit_ratio']
|
|
|
|
|
|
|
|
# Newest format
|
|
|
|
BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
|
2021-01-24 08:56:27 +00:00
|
|
|
'open_rate', 'close_rate',
|
2021-01-23 19:49:49 +00:00
|
|
|
'fee_open', 'fee_close', 'trade_duration',
|
|
|
|
'profit_ratio', 'profit_abs', 'sell_reason',
|
|
|
|
'initial_stop_loss_abs', 'initial_stop_loss_ratio', 'stop_loss_abs',
|
2021-11-21 08:51:16 +00:00
|
|
|
'stop_loss_ratio', 'min_rate', 'max_rate', 'is_open', 'enter_tag']
|
2021-09-25 17:31:06 +00:00
|
|
|
# TODO-lev: usage of the above might need compatibility code (buy_tag, is_short?, ...?)
|
2019-03-07 20:23:53 +00:00
|
|
|
|
2019-03-07 20:20:32 +00:00
|
|
|
|
2020-09-27 14:50:22 +00:00
|
|
|
def get_latest_optimize_filename(directory: Union[Path, str], variant: str) -> str:
|
2020-06-26 07:34:18 +00:00
|
|
|
"""
|
|
|
|
Get latest backtest export based on '.last_result.json'.
|
|
|
|
:param directory: Directory to search for last result
|
2020-09-27 14:50:22 +00:00
|
|
|
:param variant: 'backtest' or 'hyperopt' - the method to return
|
2020-06-26 07:34:18 +00:00
|
|
|
:return: string containing the filename of the latest backtest result
|
|
|
|
:raises: ValueError in the following cases:
|
|
|
|
* Directory does not exist
|
|
|
|
* `directory/.last_result.json` does not exist
|
|
|
|
* `directory/.last_result.json` has the wrong content
|
|
|
|
"""
|
|
|
|
if isinstance(directory, str):
|
|
|
|
directory = Path(directory)
|
|
|
|
if not directory.is_dir():
|
2020-06-27 04:46:54 +00:00
|
|
|
raise ValueError(f"Directory '{directory}' does not exist.")
|
2020-06-28 07:27:19 +00:00
|
|
|
filename = directory / LAST_BT_RESULT_FN
|
2020-06-26 07:34:18 +00:00
|
|
|
|
|
|
|
if not filename.is_file():
|
2020-06-28 07:45:23 +00:00
|
|
|
raise ValueError(
|
|
|
|
f"Directory '{directory}' does not seem to contain backtest statistics yet.")
|
2020-06-26 07:34:18 +00:00
|
|
|
|
|
|
|
with filename.open() as file:
|
|
|
|
data = json_load(file)
|
|
|
|
|
2020-09-27 14:50:22 +00:00
|
|
|
if f'latest_{variant}' not in data:
|
2020-06-28 07:27:19 +00:00
|
|
|
raise ValueError(f"Invalid '{LAST_BT_RESULT_FN}' format.")
|
2020-06-26 07:34:18 +00:00
|
|
|
|
2020-09-27 14:50:22 +00:00
|
|
|
return data[f'latest_{variant}']
|
|
|
|
|
|
|
|
|
|
|
|
def get_latest_backtest_filename(directory: Union[Path, str]) -> str:
|
|
|
|
"""
|
|
|
|
Get latest backtest export based on '.last_result.json'.
|
|
|
|
:param directory: Directory to search for last result
|
|
|
|
:return: string containing the filename of the latest backtest result
|
|
|
|
:raises: ValueError in the following cases:
|
|
|
|
* Directory does not exist
|
|
|
|
* `directory/.last_result.json` does not exist
|
|
|
|
* `directory/.last_result.json` has the wrong content
|
|
|
|
"""
|
|
|
|
return get_latest_optimize_filename(directory, 'backtest')
|
|
|
|
|
|
|
|
|
|
|
|
def get_latest_hyperopt_filename(directory: Union[Path, str]) -> str:
|
|
|
|
"""
|
|
|
|
Get latest hyperopt export based on '.last_result.json'.
|
|
|
|
:param directory: Directory to search for last result
|
|
|
|
:return: string containing the filename of the latest hyperopt result
|
|
|
|
:raises: ValueError in the following cases:
|
|
|
|
* Directory does not exist
|
|
|
|
* `directory/.last_result.json` does not exist
|
|
|
|
* `directory/.last_result.json` has the wrong content
|
|
|
|
"""
|
|
|
|
try:
|
|
|
|
return get_latest_optimize_filename(directory, 'hyperopt')
|
|
|
|
except ValueError:
|
|
|
|
# Return default (legacy) pickle filename
|
|
|
|
return 'hyperopt_results.pickle'
|
|
|
|
|
|
|
|
|
2020-09-27 15:00:23 +00:00
|
|
|
def get_latest_hyperopt_file(directory: Union[Path, str], predef_filename: str = None) -> Path:
|
2020-09-27 14:50:22 +00:00
|
|
|
"""
|
|
|
|
Get latest hyperopt export based on '.last_result.json'.
|
|
|
|
:param directory: Directory to search for last result
|
|
|
|
:return: string containing the filename of the latest hyperopt result
|
|
|
|
:raises: ValueError in the following cases:
|
|
|
|
* Directory does not exist
|
|
|
|
* `directory/.last_result.json` does not exist
|
|
|
|
* `directory/.last_result.json` has the wrong content
|
|
|
|
"""
|
2020-09-27 17:40:55 +00:00
|
|
|
if isinstance(directory, str):
|
|
|
|
directory = Path(directory)
|
2020-09-27 15:00:23 +00:00
|
|
|
if predef_filename:
|
|
|
|
return directory / predef_filename
|
2020-09-27 14:50:22 +00:00
|
|
|
return directory / get_latest_hyperopt_filename(directory)
|
2020-06-26 07:34:18 +00:00
|
|
|
|
|
|
|
|
2020-06-26 05:46:59 +00:00
|
|
|
def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
|
|
|
|
"""
|
|
|
|
Load backtest statistics file.
|
|
|
|
:param filename: pathlib.Path object, or string pointing to the file.
|
|
|
|
:return: a dictionary containing the resulting file.
|
|
|
|
"""
|
|
|
|
if isinstance(filename, str):
|
|
|
|
filename = Path(filename)
|
2020-06-28 07:45:23 +00:00
|
|
|
if filename.is_dir():
|
2020-06-28 07:51:49 +00:00
|
|
|
filename = filename / get_latest_backtest_filename(filename)
|
2020-06-26 05:46:59 +00:00
|
|
|
if not filename.is_file():
|
|
|
|
raise ValueError(f"File {filename} does not exist.")
|
2020-06-28 07:45:23 +00:00
|
|
|
logger.info(f"Loading backtest result from {filename}")
|
2020-06-26 05:46:59 +00:00
|
|
|
with filename.open() as file:
|
|
|
|
data = json_load(file)
|
|
|
|
|
|
|
|
return data
|
|
|
|
|
|
|
|
|
2020-06-27 07:56:37 +00:00
|
|
|
def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = None) -> pd.DataFrame:
|
2019-03-07 20:20:32 +00:00
|
|
|
"""
|
|
|
|
Load backtest data file.
|
2020-07-03 05:20:43 +00:00
|
|
|
:param filename: pathlib.Path object, or string pointing to a file or directory
|
2020-06-27 07:56:37 +00:00
|
|
|
:param strategy: Strategy to load - mainly relevant for multi-strategy backtests
|
|
|
|
Can also serve as protection to load the correct result.
|
2019-06-23 20:10:37 +00:00
|
|
|
:return: a dataframe with the analysis results
|
2020-06-27 05:15:33 +00:00
|
|
|
:raise: ValueError if loading goes wrong.
|
2019-03-07 20:20:32 +00:00
|
|
|
"""
|
2020-06-27 05:15:33 +00:00
|
|
|
data = load_backtest_stats(filename)
|
|
|
|
if not isinstance(data, list):
|
2020-06-27 07:56:37 +00:00
|
|
|
# new, nested format
|
2020-06-27 05:15:33 +00:00
|
|
|
if 'strategy' not in data:
|
2020-06-27 07:56:37 +00:00
|
|
|
raise ValueError("Unknown dataformat.")
|
|
|
|
|
|
|
|
if not strategy:
|
|
|
|
if len(data['strategy']) == 1:
|
|
|
|
strategy = list(data['strategy'].keys())[0]
|
|
|
|
else:
|
|
|
|
raise ValueError("Detected backtest result with more than one strategy. "
|
|
|
|
"Please specify a strategy.")
|
|
|
|
|
|
|
|
if strategy not in data['strategy']:
|
|
|
|
raise ValueError(f"Strategy {strategy} not available in the backtest result.")
|
|
|
|
|
2020-06-27 05:15:33 +00:00
|
|
|
data = data['strategy'][strategy]['trades']
|
|
|
|
df = pd.DataFrame(data)
|
2021-05-22 15:03:16 +00:00
|
|
|
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
|
|
|
|
)
|
2020-06-27 05:15:33 +00:00
|
|
|
else:
|
|
|
|
# old format - only with lists.
|
2021-01-23 19:49:49 +00:00
|
|
|
df = pd.DataFrame(data, columns=BT_DATA_COLUMNS_OLD)
|
2021-05-22 15:03:16 +00:00
|
|
|
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)
|
2019-03-07 20:20:32 +00:00
|
|
|
return df
|
|
|
|
|
|
|
|
|
2019-10-30 13:07:23 +00:00
|
|
|
def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataFrame:
|
2019-03-07 20:20:32 +00:00
|
|
|
"""
|
|
|
|
Find overlapping trades by expanding each trade once per period it was open
|
2019-10-30 12:35:55 +00:00
|
|
|
and then counting overlaps.
|
2019-03-07 20:20:32 +00:00
|
|
|
:param results: Results Dataframe - can be loaded
|
2019-10-30 12:35:55 +00:00
|
|
|
:param timeframe: Timeframe used for backtest
|
|
|
|
:return: dataframe with open-counts per time-period in timeframe
|
2019-03-07 20:20:32 +00:00
|
|
|
"""
|
2019-10-30 12:35:55 +00:00
|
|
|
from freqtrade.exchange import timeframe_to_minutes
|
|
|
|
timeframe_min = timeframe_to_minutes(timeframe)
|
2020-06-26 07:19:44 +00:00
|
|
|
dates = [pd.Series(pd.date_range(row[1]['open_date'], row[1]['close_date'],
|
2019-10-30 13:07:23 +00:00
|
|
|
freq=f"{timeframe_min}min"))
|
2020-06-26 07:19:44 +00:00
|
|
|
for row in results[['open_date', 'close_date']].iterrows()]
|
2019-03-07 20:20:32 +00:00
|
|
|
deltas = [len(x) for x in dates]
|
|
|
|
dates = pd.Series(pd.concat(dates).values, name='date')
|
|
|
|
df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)
|
|
|
|
|
|
|
|
df2 = pd.concat([dates, df2], axis=1)
|
|
|
|
df2 = df2.set_index('date')
|
2019-10-30 12:35:55 +00:00
|
|
|
df_final = df2.resample(f"{timeframe_min}min")[['pair']].count()
|
|
|
|
df_final = df_final.rename({'pair': 'open_trades'}, axis=1)
|
|
|
|
return df_final
|
|
|
|
|
|
|
|
|
|
|
|
def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
|
|
|
|
max_open_trades: int) -> pd.DataFrame:
|
|
|
|
"""
|
|
|
|
Find overlapping trades by expanding each trade once per period it was open
|
|
|
|
and then counting overlaps
|
|
|
|
:param results: Results Dataframe - can be loaded
|
|
|
|
:param timeframe: Frequency used for the backtest
|
|
|
|
:param max_open_trades: parameter max_open_trades used during backtest run
|
|
|
|
:return: dataframe with open-counts per time-period in freq
|
|
|
|
"""
|
2019-10-30 13:07:23 +00:00
|
|
|
df_final = analyze_trade_parallelism(results, timeframe)
|
2019-10-30 12:35:55 +00:00
|
|
|
return df_final[df_final['open_trades'] > max_open_trades]
|
2019-06-16 08:41:05 +00:00
|
|
|
|
|
|
|
|
2021-02-20 19:22:00 +00:00
|
|
|
def trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
|
2021-01-23 19:49:49 +00:00
|
|
|
"""
|
|
|
|
Convert list of Trade objects to pandas Dataframe
|
|
|
|
:param trades: List of trade objects
|
|
|
|
:return: Dataframe with BT_DATA_COLUMNS
|
|
|
|
"""
|
|
|
|
df = pd.DataFrame.from_records([t.to_json() for t in trades], columns=BT_DATA_COLUMNS)
|
|
|
|
if len(df) > 0:
|
|
|
|
df.loc[:, 'close_date'] = pd.to_datetime(df['close_date'], utc=True)
|
|
|
|
df.loc[:, 'open_date'] = pd.to_datetime(df['open_date'], utc=True)
|
2021-01-24 08:56:27 +00:00
|
|
|
df.loc[:, 'close_rate'] = df['close_rate'].astype('float64')
|
2021-01-23 19:49:49 +00:00
|
|
|
return df
|
|
|
|
|
|
|
|
|
2020-06-27 07:56:37 +00:00
|
|
|
def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataFrame:
|
2019-06-16 08:41:05 +00:00
|
|
|
"""
|
2019-06-22 13:45:20 +00:00
|
|
|
Load trades from a DB (using dburl)
|
2019-06-16 08:41:05 +00:00
|
|
|
:param db_url: Sqlite url (default format sqlite:///tradesv3.dry-run.sqlite)
|
2020-06-27 07:56:37 +00:00
|
|
|
:param strategy: Strategy to load - mainly relevant for multi-strategy backtests
|
|
|
|
Can also serve as protection to load the correct result.
|
2019-06-23 20:10:37 +00:00
|
|
|
:return: Dataframe containing Trades
|
2019-06-16 08:41:05 +00:00
|
|
|
"""
|
2020-10-16 05:39:12 +00:00
|
|
|
init_db(db_url, clean_open_orders=False)
|
2019-06-22 14:20:41 +00:00
|
|
|
|
2020-06-27 07:56:37 +00:00
|
|
|
filters = []
|
|
|
|
if strategy:
|
2020-08-18 13:20:37 +00:00
|
|
|
filters.append(Trade.strategy == strategy)
|
2021-01-23 19:49:49 +00:00
|
|
|
trades = trade_list_to_dataframe(Trade.get_trades(filters).all())
|
2019-06-16 08:41:05 +00:00
|
|
|
|
|
|
|
return trades
|
2019-06-16 09:12:19 +00:00
|
|
|
|
|
|
|
|
2020-03-18 10:42:42 +00:00
|
|
|
def load_trades(source: str, db_url: str, exportfilename: Path,
|
2020-06-27 07:56:37 +00:00
|
|
|
no_trades: bool = False, strategy: Optional[str] = None) -> pd.DataFrame:
|
2019-06-29 18:50:31 +00:00
|
|
|
"""
|
2020-08-26 18:52:09 +00:00
|
|
|
Based on configuration option 'trade_source':
|
2019-06-29 18:50:31 +00:00
|
|
|
* loads data from DB (using `db_url`)
|
2019-07-03 04:26:39 +00:00
|
|
|
* loads data from backtestfile (using `exportfilename`)
|
2020-03-15 08:39:45 +00:00
|
|
|
:param source: "DB" or "file" - specify source to load from
|
|
|
|
:param db_url: sqlalchemy formatted url to a database
|
|
|
|
:param exportfilename: Json file generated by backtesting
|
2020-03-15 20:20:32 +00:00
|
|
|
:param no_trades: Skip using trades, only return backtesting data columns
|
2020-03-15 08:39:45 +00:00
|
|
|
:return: DataFrame containing trades
|
2019-06-29 18:50:31 +00:00
|
|
|
"""
|
2020-03-15 20:20:32 +00:00
|
|
|
if no_trades:
|
2020-03-14 21:15:03 +00:00
|
|
|
df = pd.DataFrame(columns=BT_DATA_COLUMNS)
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|
|
|
return df
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|
|
|
|
2019-08-22 18:17:36 +00:00
|
|
|
if source == "DB":
|
|
|
|
return load_trades_from_db(db_url)
|
|
|
|
elif source == "file":
|
2020-06-27 07:56:37 +00:00
|
|
|
return load_backtest_data(exportfilename, strategy)
|
2019-06-29 18:50:31 +00:00
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|
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|
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|
2020-04-06 13:49:59 +00:00
|
|
|
def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
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|
|
|
date_index=False) -> pd.DataFrame:
|
2019-06-16 09:12:19 +00:00
|
|
|
"""
|
|
|
|
Compare trades and backtested pair DataFrames to get trades performed on backtested period
|
|
|
|
:return: the DataFrame of a trades of period
|
|
|
|
"""
|
2020-04-06 13:49:59 +00:00
|
|
|
if date_index:
|
|
|
|
trades_start = dataframe.index[0]
|
|
|
|
trades_stop = dataframe.index[-1]
|
|
|
|
else:
|
|
|
|
trades_start = dataframe.iloc[0]['date']
|
|
|
|
trades_stop = dataframe.iloc[-1]['date']
|
2020-06-26 07:19:44 +00:00
|
|
|
trades = trades.loc[(trades['open_date'] >= trades_start) &
|
|
|
|
(trades['close_date'] <= trades_stop)]
|
2019-06-16 09:12:19 +00:00
|
|
|
return trades
|
2019-06-29 14:57:04 +00:00
|
|
|
|
|
|
|
|
2020-06-25 18:39:55 +00:00
|
|
|
def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close") -> float:
|
|
|
|
"""
|
|
|
|
Calculate market change based on "column".
|
|
|
|
Calculation is done by taking the first non-null and the last non-null element of each column
|
|
|
|
and calculating the pctchange as "(last - first) / first".
|
|
|
|
Then the results per pair are combined as mean.
|
|
|
|
|
|
|
|
:param data: Dict of Dataframes, dict key should be pair.
|
|
|
|
:param column: Column in the original dataframes to use
|
|
|
|
:return:
|
|
|
|
"""
|
|
|
|
tmp_means = []
|
|
|
|
for pair, df in data.items():
|
|
|
|
start = df[column].dropna().iloc[0]
|
|
|
|
end = df[column].dropna().iloc[-1]
|
|
|
|
tmp_means.append((end - start) / start)
|
|
|
|
|
2021-01-31 10:21:23 +00:00
|
|
|
return float(np.mean(tmp_means))
|
2020-06-25 18:39:55 +00:00
|
|
|
|
|
|
|
|
2020-03-08 10:35:31 +00:00
|
|
|
def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
|
|
|
|
column: str = "close") -> pd.DataFrame:
|
2019-06-30 08:04:43 +00:00
|
|
|
"""
|
|
|
|
Combine multiple dataframes "column"
|
2020-03-08 10:35:31 +00:00
|
|
|
:param data: Dict of Dataframes, dict key should be pair.
|
2019-06-30 08:04:43 +00:00
|
|
|
:param column: Column in the original dataframes to use
|
|
|
|
:return: DataFrame with the column renamed to the dict key, and a column
|
|
|
|
named mean, containing the mean of all pairs.
|
|
|
|
"""
|
2020-03-08 10:35:31 +00:00
|
|
|
df_comb = pd.concat([data[pair].set_index('date').rename(
|
|
|
|
{column: pair}, axis=1)[pair] for pair in data], axis=1)
|
2019-06-30 08:04:43 +00:00
|
|
|
|
|
|
|
df_comb['mean'] = df_comb.mean(axis=1)
|
|
|
|
|
|
|
|
return df_comb
|
|
|
|
|
|
|
|
|
2019-10-28 13:24:12 +00:00
|
|
|
def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
|
|
|
|
timeframe: str) -> pd.DataFrame:
|
2019-06-29 14:57:04 +00:00
|
|
|
"""
|
|
|
|
Adds a column `col_name` with the cumulative profit for the given trades array.
|
2019-06-29 15:19:42 +00:00
|
|
|
:param df: DataFrame with date index
|
2021-04-25 08:10:09 +00:00
|
|
|
:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
|
2019-10-28 13:24:12 +00:00
|
|
|
:param col_name: Column name that will be assigned the results
|
|
|
|
:param timeframe: Timeframe used during the operations
|
2019-06-29 15:19:42 +00:00
|
|
|
:return: Returns df with one additional column, col_name, containing the cumulative profit.
|
2020-05-21 05:12:23 +00:00
|
|
|
:raise: ValueError if trade-dataframe was found empty.
|
2019-06-29 14:57:04 +00:00
|
|
|
"""
|
2020-05-21 05:12:23 +00:00
|
|
|
if len(trades) == 0:
|
|
|
|
raise ValueError("Trade dataframe empty.")
|
2019-10-28 13:24:12 +00:00
|
|
|
from freqtrade.exchange import timeframe_to_minutes
|
2019-11-02 19:34:39 +00:00
|
|
|
timeframe_minutes = timeframe_to_minutes(timeframe)
|
|
|
|
# Resample to timeframe to make sure trades match candles
|
2020-06-26 07:19:44 +00:00
|
|
|
_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_date'
|
2021-04-25 08:10:09 +00:00
|
|
|
)[['profit_abs']].sum()
|
|
|
|
df.loc[:, col_name] = _trades_sum['profit_abs'].cumsum()
|
2019-06-29 14:57:04 +00:00
|
|
|
# Set first value to 0
|
|
|
|
df.loc[df.iloc[0].name, col_name] = 0
|
|
|
|
# FFill to get continuous
|
|
|
|
df[col_name] = df[col_name].ffill()
|
|
|
|
return df
|
2020-03-03 06:13:11 +00:00
|
|
|
|
|
|
|
|
2020-06-26 07:19:44 +00:00
|
|
|
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
|
2021-01-29 18:06:46 +00:00
|
|
|
value_col: str = 'profit_ratio'
|
2021-02-14 18:30:17 +00:00
|
|
|
) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]:
|
2020-03-03 06:13:11 +00:00
|
|
|
"""
|
|
|
|
Calculate max drawdown and the corresponding close dates
|
2021-01-29 18:06:46 +00:00
|
|
|
:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
|
2020-06-26 07:19:44 +00:00
|
|
|
:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
|
2021-01-29 18:06:46 +00:00
|
|
|
:param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
|
2021-02-14 18:30:17 +00:00
|
|
|
:return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
|
|
|
|
high and low time and high and low value.
|
2020-03-03 06:13:11 +00:00
|
|
|
:raise: ValueError if trade-dataframe was found empty.
|
|
|
|
"""
|
|
|
|
if len(trades) == 0:
|
2020-03-03 06:20:41 +00:00
|
|
|
raise ValueError("Trade dataframe empty.")
|
2020-04-14 06:02:42 +00:00
|
|
|
profit_results = trades.sort_values(date_col).reset_index(drop=True)
|
2020-03-03 06:13:11 +00:00
|
|
|
max_drawdown_df = pd.DataFrame()
|
2020-03-03 06:23:38 +00:00
|
|
|
max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
|
2020-03-03 06:13:11 +00:00
|
|
|
max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
|
|
|
|
max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
|
2020-03-03 06:23:38 +00:00
|
|
|
|
2020-04-05 12:29:40 +00:00
|
|
|
idxmin = max_drawdown_df['drawdown'].idxmin()
|
2020-04-05 12:43:01 +00:00
|
|
|
if idxmin == 0:
|
|
|
|
raise ValueError("No losing trade, therefore no drawdown.")
|
2020-04-05 12:29:40 +00:00
|
|
|
high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
|
|
|
|
low_date = profit_results.loc[idxmin, date_col]
|
2021-02-14 18:30:17 +00:00
|
|
|
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
|
2020-12-24 21:17:24 +00:00
|
|
|
|
|
|
|
|
2021-02-16 19:12:59 +00:00
|
|
|
def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:
|
2020-12-24 21:17:24 +00:00
|
|
|
"""
|
|
|
|
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)
|
2021-02-16 19:12:59 +00:00
|
|
|
:param starting_balance: Add starting balance to results, to show the wallets high / low points
|
2020-12-24 21:17:24 +00:00
|
|
|
: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()
|
2021-02-16 19:12:59 +00:00
|
|
|
csum_min = csum_df['sum'].min() + starting_balance
|
|
|
|
csum_max = csum_df['sum'].max() + starting_balance
|
2020-12-24 21:17:24 +00:00
|
|
|
|
|
|
|
return csum_min, csum_max
|