stable/freqtrade/data/btanalysis.py

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"""
Helpers when analyzing backtest data
"""
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import logging
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from pathlib import Path
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from typing import Dict, Union, Tuple, Any
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import numpy as np
import pandas as pd
from datetime import timezone
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from freqtrade import persistence
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from freqtrade.misc import json_load
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from freqtrade.persistence import Trade
logger = logging.getLogger(__name__)
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# must align with columns in backtest.py
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BT_DATA_COLUMNS = ["pair", "profit_percent", "open_time", "close_time", "index", "duration",
"open_rate", "close_rate", "open_at_end", "sell_reason"]
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def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
"""
Load backtest statistics file.
:param filename: pathlib.Path object, or string pointing to the file.
:return: a dictionary containing the resulting file.
"""
if isinstance(filename, str):
filename = Path(filename)
if not filename.is_file():
raise ValueError(f"File {filename} does not exist.")
with filename.open() as file:
data = json_load(file)
return data
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def load_backtest_data(filename: Union[Path, str]) -> pd.DataFrame:
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"""
Load backtest data file.
:param filename: pathlib.Path object, or string pointing to the file.
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:return: a dataframe with the analysis results
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"""
if isinstance(filename, str):
filename = Path(filename)
if not filename.is_file():
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raise ValueError(f"File {filename} does not exist.")
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with filename.open() as file:
data = json_load(file)
df = pd.DataFrame(data, columns=BT_DATA_COLUMNS)
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df['open_time'] = pd.to_datetime(df['open_time'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['close_time'] = pd.to_datetime(df['close_time'],
unit='s',
utc=True,
infer_datetime_format=True
)
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df['profit'] = df['close_rate'] - df['open_rate']
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df = df.sort_values("open_time").reset_index(drop=True)
return df
def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataFrame:
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"""
Find overlapping trades by expanding each trade once per period it was open
and then counting overlaps.
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:param results: Results Dataframe - can be loaded
:param timeframe: Timeframe used for backtest
:return: dataframe with open-counts per time-period in timeframe
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"""
from freqtrade.exchange import timeframe_to_minutes
timeframe_min = timeframe_to_minutes(timeframe)
dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time,
freq=f"{timeframe_min}min"))
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for row in results[['open_time', 'close_time']].iterrows()]
deltas = [len(x) for x in dates]
dates = pd.Series(pd.concat(dates).values, name='date')
df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)
df2 = pd.concat([dates, df2], axis=1)
df2 = df2.set_index('date')
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
"""
df_final = analyze_trade_parallelism(results, timeframe)
return df_final[df_final['open_trades'] > max_open_trades]
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def load_trades_from_db(db_url: str) -> pd.DataFrame:
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"""
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Load trades from a DB (using dburl)
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:param db_url: Sqlite url (default format sqlite:///tradesv3.dry-run.sqlite)
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:return: Dataframe containing Trades
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"""
trades: pd.DataFrame = pd.DataFrame([], columns=BT_DATA_COLUMNS)
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persistence.init(db_url, clean_open_orders=False)
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columns = ["pair", "open_time", "close_time", "profit", "profit_percent",
"open_rate", "close_rate", "amount", "duration", "sell_reason",
"fee_open", "fee_close", "open_rate_requested", "close_rate_requested",
"stake_amount", "max_rate", "min_rate", "id", "exchange",
"stop_loss", "initial_stop_loss", "strategy", "timeframe"]
trades = pd.DataFrame([(t.pair,
t.open_date.replace(tzinfo=timezone.utc),
t.close_date.replace(tzinfo=timezone.utc) if t.close_date else None,
t.calc_profit(), t.calc_profit_ratio(),
t.open_rate, t.close_rate, t.amount,
(round((t.close_date.timestamp() - t.open_date.timestamp()) / 60, 2)
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if t.close_date else None),
t.sell_reason,
t.fee_open, t.fee_close,
t.open_rate_requested,
t.close_rate_requested,
t.stake_amount,
t.max_rate,
t.min_rate,
t.id, t.exchange,
t.stop_loss, t.initial_stop_loss,
t.strategy, t.timeframe
)
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for t in Trade.get_trades().all()],
columns=columns)
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return trades
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def load_trades(source: str, db_url: str, exportfilename: Path,
no_trades: bool = False) -> pd.DataFrame:
"""
Based on configuration option "trade_source":
* loads data from DB (using `db_url`)
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* loads data from backtestfile (using `exportfilename`)
: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
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:param no_trades: Skip using trades, only return backtesting data columns
:return: DataFrame containing trades
"""
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if no_trades:
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df = pd.DataFrame(columns=BT_DATA_COLUMNS)
return df
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if source == "DB":
return load_trades_from_db(db_url)
elif source == "file":
return load_backtest_data(exportfilename)
def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
date_index=False) -> pd.DataFrame:
"""
Compare trades and backtested pair DataFrames to get trades performed on backtested period
:return: the DataFrame of a trades of period
"""
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']
trades = trades.loc[(trades['open_time'] >= trades_start) &
(trades['close_time'] <= trades_stop)]
return trades
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def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close") -> float:
"""
Calculate market change based on "column".
Calculation is done by taking the first non-null and the last non-null element of each column
and calculating the pctchange as "(last - first) / first".
Then the results per pair are combined as mean.
:param data: Dict of Dataframes, dict key should be pair.
:param column: Column in the original dataframes to use
:return:
"""
tmp_means = []
for pair, df in data.items():
start = df[column].dropna().iloc[0]
end = df[column].dropna().iloc[-1]
tmp_means.append((end - start) / start)
return np.mean(tmp_means)
def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
column: str = "close") -> pd.DataFrame:
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"""
Combine multiple dataframes "column"
:param data: Dict of Dataframes, dict key should be pair.
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: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.
"""
df_comb = pd.concat([data[pair].set_index('date').rename(
{column: pair}, axis=1)[pair] for pair in data], axis=1)
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df_comb['mean'] = df_comb.mean(axis=1)
return df_comb
def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
timeframe: str) -> pd.DataFrame:
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"""
Adds a column `col_name` with the cumulative profit for the given trades array.
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:param df: DataFrame with date index
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:param trades: DataFrame containing trades (requires columns close_time and profit_percent)
:param col_name: Column name that will be assigned the results
:param timeframe: Timeframe used during the operations
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:return: Returns df with one additional column, col_name, containing the cumulative profit.
:raise: ValueError if trade-dataframe was found empty.
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"""
if len(trades) == 0:
raise ValueError("Trade dataframe empty.")
from freqtrade.exchange import timeframe_to_minutes
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timeframe_minutes = timeframe_to_minutes(timeframe)
# Resample to timeframe to make sure trades match candles
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_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_time'
)[['profit_percent']].sum()
df.loc[:, col_name] = _trades_sum.cumsum()
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# 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
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def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_time',
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value_col: str = 'profit_percent'
) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
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"""
Calculate max drawdown and the corresponding close dates
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:param trades: DataFrame containing trades (requires columns close_time and profit_percent)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_time')
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:param value_col: Column in DataFrame to use for values (defaults to 'profit_percent')
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:return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
:raise: ValueError if trade-dataframe was found empty.
"""
if len(trades) == 0:
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raise ValueError("Trade dataframe empty.")
profit_results = trades.sort_values(date_col).reset_index(drop=True)
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max_drawdown_df = pd.DataFrame()
max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
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max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
idxmin = max_drawdown_df['drawdown'].idxmin()
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if idxmin == 0:
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]
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return abs(min(max_drawdown_df['drawdown'])), high_date, low_date