mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-09-20 09:31:12 +00:00
379 lines
14 KiB
Python
379 lines
14 KiB
Python
import logging
|
|
import math
|
|
from dataclasses import dataclass
|
|
from datetime import datetime
|
|
from typing import Dict, Tuple
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
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 float(np.mean(tmp_means))
|
|
|
|
|
|
def combine_dataframes_by_column(
|
|
data: Dict[str, pd.DataFrame], column: str = "close"
|
|
) -> pd.DataFrame:
|
|
"""
|
|
Combine multiple dataframes "column"
|
|
:param data: Dict of Dataframes, dict key should be pair.
|
|
:param column: Column in the original dataframes to use
|
|
:return: DataFrame with the column renamed to the dict key.
|
|
:raise: ValueError if no data is provided.
|
|
"""
|
|
if not data:
|
|
raise ValueError("No data provided.")
|
|
df_comb = pd.concat(
|
|
[data[pair].set_index("date").rename({column: pair}, axis=1)[pair] for pair in data], axis=1
|
|
)
|
|
return df_comb
|
|
|
|
|
|
def combined_dataframes_with_rel_mean(
|
|
data: Dict[str, pd.DataFrame], fromdt: datetime, todt: datetime, column: str = "close"
|
|
) -> pd.DataFrame:
|
|
"""
|
|
Combine multiple dataframes "column"
|
|
:param data: Dict of Dataframes, dict key should be pair.
|
|
: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.
|
|
:raise: ValueError if no data is provided.
|
|
"""
|
|
df_comb = combine_dataframes_by_column(data, column)
|
|
# Trim dataframes to the given timeframe
|
|
df_comb = df_comb.iloc[(df_comb.index >= fromdt) & (df_comb.index < todt)]
|
|
df_comb["count"] = df_comb.count(axis=1)
|
|
df_comb["mean"] = df_comb.mean(axis=1)
|
|
df_comb["rel_mean"] = df_comb["mean"].pct_change().fillna(0).cumsum()
|
|
return df_comb[["mean", "rel_mean", "count"]]
|
|
|
|
|
|
def combine_dataframes_with_mean(
|
|
data: Dict[str, pd.DataFrame], column: str = "close"
|
|
) -> pd.DataFrame:
|
|
"""
|
|
Combine multiple dataframes "column"
|
|
:param data: Dict of Dataframes, dict key should be pair.
|
|
: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.
|
|
:raise: ValueError if no data is provided.
|
|
"""
|
|
df_comb = combine_dataframes_by_column(data, column)
|
|
|
|
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:
|
|
"""
|
|
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_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.
|
|
:raise: ValueError if trade-dataframe was found empty.
|
|
"""
|
|
if len(trades) == 0:
|
|
raise ValueError("Trade dataframe empty.")
|
|
from freqtrade.exchange import timeframe_to_resample_freq
|
|
|
|
timeframe_freq = timeframe_to_resample_freq(timeframe)
|
|
# Resample to timeframe to make sure trades match candles
|
|
_trades_sum = trades.resample(timeframe_freq, on="close_date")[["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
|
|
df[col_name] = df[col_name].ffill()
|
|
return df
|
|
|
|
|
|
def _calc_drawdown_series(
|
|
profit_results: pd.DataFrame, *, date_col: str, value_col: str, starting_balance: float
|
|
) -> pd.DataFrame:
|
|
max_drawdown_df = pd.DataFrame()
|
|
max_drawdown_df["cumulative"] = profit_results[value_col].cumsum()
|
|
max_drawdown_df["high_value"] = max_drawdown_df["cumulative"].cummax()
|
|
max_drawdown_df["drawdown"] = max_drawdown_df["cumulative"] - max_drawdown_df["high_value"]
|
|
max_drawdown_df["date"] = profit_results.loc[:, date_col]
|
|
if starting_balance:
|
|
cumulative_balance = starting_balance + max_drawdown_df["cumulative"]
|
|
max_balance = starting_balance + max_drawdown_df["high_value"]
|
|
max_drawdown_df["drawdown_relative"] = (max_balance - cumulative_balance) / max_balance
|
|
else:
|
|
# NOTE: This is not completely accurate,
|
|
# but might good enough if starting_balance is not available
|
|
max_drawdown_df["drawdown_relative"] = (
|
|
max_drawdown_df["high_value"] - max_drawdown_df["cumulative"]
|
|
) / max_drawdown_df["high_value"]
|
|
return max_drawdown_df
|
|
|
|
|
|
def calculate_underwater(
|
|
trades: pd.DataFrame,
|
|
*,
|
|
date_col: str = "close_date",
|
|
value_col: str = "profit_ratio",
|
|
starting_balance: float = 0.0,
|
|
):
|
|
"""
|
|
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, 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:
|
|
raise ValueError("Trade dataframe empty.")
|
|
profit_results = trades.sort_values(date_col).reset_index(drop=True)
|
|
max_drawdown_df = _calc_drawdown_series(
|
|
profit_results, date_col=date_col, value_col=value_col, starting_balance=starting_balance
|
|
)
|
|
|
|
return max_drawdown_df
|
|
|
|
|
|
@dataclass()
|
|
class DrawDownResult:
|
|
drawdown_abs: float = 0.0
|
|
high_date: pd.Timestamp = None
|
|
low_date: pd.Timestamp = None
|
|
high_value: float = 0.0
|
|
low_value: float = 0.0
|
|
relative_account_drawdown: float = 0.0
|
|
|
|
|
|
def calculate_max_drawdown(
|
|
trades: pd.DataFrame,
|
|
*,
|
|
date_col: str = "close_date",
|
|
value_col: str = "profit_abs",
|
|
starting_balance: float = 0,
|
|
relative: bool = False,
|
|
) -> DrawDownResult:
|
|
"""
|
|
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_abs')
|
|
:param starting_balance: Portfolio starting balance - properly calculate relative drawdown.
|
|
:return: DrawDownResult object
|
|
with absolute max drawdown, high and low time and high and low value,
|
|
and the relative account drawdown
|
|
:raise: ValueError if trade-dataframe was found empty.
|
|
"""
|
|
if len(trades) == 0:
|
|
raise ValueError("Trade dataframe empty.")
|
|
profit_results = trades.sort_values(date_col).reset_index(drop=True)
|
|
max_drawdown_df = _calc_drawdown_series(
|
|
profit_results, date_col=date_col, value_col=value_col, starting_balance=starting_balance
|
|
)
|
|
|
|
idxmin = (
|
|
max_drawdown_df["drawdown_relative"].idxmax()
|
|
if relative
|
|
else max_drawdown_df["drawdown"].idxmin()
|
|
)
|
|
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]
|
|
high_val = max_drawdown_df.loc[
|
|
max_drawdown_df.iloc[:idxmin]["high_value"].idxmax(), "cumulative"
|
|
]
|
|
low_val = max_drawdown_df.loc[idxmin, "cumulative"]
|
|
max_drawdown_rel = max_drawdown_df.loc[idxmin, "drawdown_relative"]
|
|
|
|
return DrawDownResult(
|
|
drawdown_abs=abs(max_drawdown_df.loc[idxmin, "drawdown"]),
|
|
high_date=high_date,
|
|
low_date=low_date,
|
|
high_value=high_val,
|
|
low_value=low_val,
|
|
relative_account_drawdown=max_drawdown_rel,
|
|
)
|
|
|
|
|
|
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
|
|
|
|
|
|
def calculate_cagr(days_passed: int, starting_balance: float, final_balance: float) -> float:
|
|
"""
|
|
Calculate CAGR
|
|
:param days_passed: Days passed between start and ending balance
|
|
:param starting_balance: Starting balance
|
|
:param final_balance: Final balance to calculate CAGR against
|
|
:return: CAGR
|
|
"""
|
|
if final_balance < 0:
|
|
# With leveraged trades, final_balance can become negative.
|
|
return 0
|
|
return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1
|
|
|
|
|
|
def calculate_expectancy(trades: pd.DataFrame) -> Tuple[float, float]:
|
|
"""
|
|
Calculate expectancy
|
|
:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
|
|
:return: expectancy, expectancy_ratio
|
|
"""
|
|
|
|
expectancy = 0
|
|
expectancy_ratio = 100
|
|
|
|
if len(trades) > 0:
|
|
winning_trades = trades.loc[trades["profit_abs"] > 0]
|
|
losing_trades = trades.loc[trades["profit_abs"] < 0]
|
|
profit_sum = winning_trades["profit_abs"].sum()
|
|
loss_sum = abs(losing_trades["profit_abs"].sum())
|
|
nb_win_trades = len(winning_trades)
|
|
nb_loss_trades = len(losing_trades)
|
|
|
|
average_win = (profit_sum / nb_win_trades) if nb_win_trades > 0 else 0
|
|
average_loss = (loss_sum / nb_loss_trades) if nb_loss_trades > 0 else 0
|
|
winrate = nb_win_trades / len(trades)
|
|
loserate = nb_loss_trades / len(trades)
|
|
|
|
expectancy = (winrate * average_win) - (loserate * average_loss)
|
|
if average_loss > 0:
|
|
risk_reward_ratio = average_win / average_loss
|
|
expectancy_ratio = ((1 + risk_reward_ratio) * winrate) - 1
|
|
|
|
return expectancy, expectancy_ratio
|
|
|
|
|
|
def calculate_sortino(
|
|
trades: pd.DataFrame, min_date: datetime, max_date: datetime, starting_balance: float
|
|
) -> float:
|
|
"""
|
|
Calculate sortino
|
|
:param trades: DataFrame containing trades (requires columns profit_abs)
|
|
:return: sortino
|
|
"""
|
|
if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
|
|
return 0
|
|
|
|
total_profit = trades["profit_abs"] / starting_balance
|
|
days_period = max(1, (max_date - min_date).days)
|
|
|
|
expected_returns_mean = total_profit.sum() / days_period
|
|
|
|
down_stdev = np.std(trades.loc[trades["profit_abs"] < 0, "profit_abs"] / starting_balance)
|
|
|
|
if down_stdev != 0 and not np.isnan(down_stdev):
|
|
sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
|
|
else:
|
|
# Define high (negative) sortino ratio to be clear that this is NOT optimal.
|
|
sortino_ratio = -100
|
|
|
|
# print(expected_returns_mean, down_stdev, sortino_ratio)
|
|
return sortino_ratio
|
|
|
|
|
|
def calculate_sharpe(
|
|
trades: pd.DataFrame, min_date: datetime, max_date: datetime, starting_balance: float
|
|
) -> float:
|
|
"""
|
|
Calculate sharpe
|
|
:param trades: DataFrame containing trades (requires column profit_abs)
|
|
:return: sharpe
|
|
"""
|
|
if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
|
|
return 0
|
|
|
|
total_profit = trades["profit_abs"] / starting_balance
|
|
days_period = max(1, (max_date - min_date).days)
|
|
|
|
expected_returns_mean = total_profit.sum() / days_period
|
|
up_stdev = np.std(total_profit)
|
|
|
|
if up_stdev != 0:
|
|
sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
|
|
else:
|
|
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
|
|
sharp_ratio = -100
|
|
|
|
# print(expected_returns_mean, up_stdev, sharp_ratio)
|
|
return sharp_ratio
|
|
|
|
|
|
def calculate_calmar(
|
|
trades: pd.DataFrame, min_date: datetime, max_date: datetime, starting_balance: float
|
|
) -> float:
|
|
"""
|
|
Calculate calmar
|
|
:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
|
|
:return: calmar
|
|
"""
|
|
if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
|
|
return 0
|
|
|
|
total_profit = trades["profit_abs"].sum() / starting_balance
|
|
days_period = max(1, (max_date - min_date).days)
|
|
|
|
# adding slippage of 0.1% per trade
|
|
# total_profit = total_profit - 0.0005
|
|
expected_returns_mean = total_profit / days_period * 100
|
|
|
|
# calculate max drawdown
|
|
try:
|
|
drawdown = calculate_max_drawdown(
|
|
trades, value_col="profit_abs", starting_balance=starting_balance
|
|
)
|
|
max_drawdown = drawdown.relative_account_drawdown
|
|
except ValueError:
|
|
max_drawdown = 0
|
|
|
|
if max_drawdown != 0:
|
|
calmar_ratio = expected_returns_mean / max_drawdown * math.sqrt(365)
|
|
else:
|
|
# Define high (negative) calmar ratio to be clear that this is NOT optimal.
|
|
calmar_ratio = -100
|
|
|
|
# print(expected_returns_mean, max_drawdown, calmar_ratio)
|
|
return calmar_ratio
|