mirror of
https://github.com/freqtrade/freqtrade.git
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351 lines
14 KiB
Python
351 lines
14 KiB
Python
import logging
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import math
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from datetime import datetime
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from typing import Dict, Tuple
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import numpy as np
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import pandas as pd
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logger = logging.getLogger(__name__)
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def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close") -> float:
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"""
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Calculate market change based on "column".
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Calculation is done by taking the first non-null and the last non-null element of each column
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and calculating the pctchange as "(last - first) / first".
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Then the results per pair are combined as mean.
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:param data: Dict of Dataframes, dict key should be pair.
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:param column: Column in the original dataframes to use
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:return:
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"""
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tmp_means = []
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for pair, df in data.items():
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start = df[column].dropna().iloc[0]
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end = df[column].dropna().iloc[-1]
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tmp_means.append((end - start) / start)
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return float(np.mean(tmp_means))
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def combine_dataframes_by_column(
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data: Dict[str, pd.DataFrame], column: str = "close") -> pd.DataFrame:
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"""
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Combine multiple dataframes "column"
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:param data: Dict of Dataframes, dict key should be pair.
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:param column: Column in the original dataframes to use
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:return: DataFrame with the column renamed to the dict key.
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:raise: ValueError if no data is provided.
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"""
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if not data:
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raise ValueError("No data provided.")
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df_comb = pd.concat([data[pair].set_index('date').rename(
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{column: pair}, axis=1)[pair] for pair in data], axis=1)
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return df_comb
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def combined_dataframes_with_rel_mean(
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data: Dict[str, pd.DataFrame], column: str = "close") -> pd.DataFrame:
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"""
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Combine multiple dataframes "column"
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:param data: Dict of Dataframes, dict key should be pair.
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:param column: Column in the original dataframes to use
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:return: DataFrame with the column renamed to the dict key, and a column
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named mean, containing the mean of all pairs.
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:raise: ValueError if no data is provided.
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"""
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df_comb = combine_dataframes_by_column(data, column)
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df_comb['count'] = df_comb.count(axis=1)
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df_comb['mean'] = df_comb.mean(axis=1)
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df_comb['rel_mean'] = df_comb['mean'].pct_change().fillna(0)
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return df_comb[['mean', 'rel_mean', 'count']]
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def combine_dataframes_with_mean(
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data: Dict[str, pd.DataFrame], column: str = "close") -> pd.DataFrame:
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"""
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Combine multiple dataframes "column"
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:param data: Dict of Dataframes, dict key should be pair.
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:param column: Column in the original dataframes to use
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:return: DataFrame with the column renamed to the dict key, and a column
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named mean, containing the mean of all pairs.
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:raise: ValueError if no data is provided.
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"""
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df_comb = combine_dataframes_by_column(data, column)
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df_comb['mean'] = df_comb.mean(axis=1)
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return df_comb
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def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
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timeframe: str) -> pd.DataFrame:
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"""
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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_date and profit_abs)
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:param col_name: Column name that will be assigned the results
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:param timeframe: Timeframe used during the operations
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:return: Returns df with one additional column, col_name, containing the cumulative profit.
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:raise: ValueError if trade-dataframe was found empty.
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"""
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if len(trades) == 0:
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raise ValueError("Trade dataframe empty.")
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from freqtrade.exchange import timeframe_to_resample_freq
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timeframe_freq = timeframe_to_resample_freq(timeframe)
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# Resample to timeframe to make sure trades match candles
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_trades_sum = trades.resample(timeframe_freq, on='close_date'
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)[['profit_abs']].sum()
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df.loc[:, col_name] = _trades_sum['profit_abs'].cumsum()
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# Set first value to 0
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df.loc[df.iloc[0].name, col_name] = 0
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# FFill to get continuous
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df[col_name] = df[col_name].ffill()
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return df
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def _calc_drawdown_series(profit_results: pd.DataFrame, *, date_col: str, value_col: str,
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starting_balance: float) -> pd.DataFrame:
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max_drawdown_df = pd.DataFrame()
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max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
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max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
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max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
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max_drawdown_df['date'] = profit_results.loc[:, date_col]
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if starting_balance:
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cumulative_balance = starting_balance + max_drawdown_df['cumulative']
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max_balance = starting_balance + max_drawdown_df['high_value']
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max_drawdown_df['drawdown_relative'] = ((max_balance - cumulative_balance) / max_balance)
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else:
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# NOTE: This is not completely accurate,
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# but might good enough if starting_balance is not available
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max_drawdown_df['drawdown_relative'] = (
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(max_drawdown_df['high_value'] - max_drawdown_df['cumulative'])
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/ max_drawdown_df['high_value'])
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return max_drawdown_df
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def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
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value_col: str = 'profit_ratio', starting_balance: float = 0.0
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):
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"""
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Calculate max drawdown and the corresponding close dates
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:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
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:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
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:param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
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:return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
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high and low time and high and low value.
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:raise: ValueError if trade-dataframe was found empty.
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"""
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if len(trades) == 0:
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raise ValueError("Trade dataframe empty.")
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profit_results = trades.sort_values(date_col).reset_index(drop=True)
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max_drawdown_df = _calc_drawdown_series(
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profit_results,
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date_col=date_col,
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value_col=value_col,
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starting_balance=starting_balance)
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return max_drawdown_df
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def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
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value_col: str = 'profit_abs', starting_balance: float = 0,
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relative: bool = False
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) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]:
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"""
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Calculate max drawdown and the corresponding close dates
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:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
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:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
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:param value_col: Column in DataFrame to use for values (defaults to 'profit_abs')
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:param starting_balance: Portfolio starting balance - properly calculate relative drawdown.
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:return: Tuple (float, highdate, lowdate, highvalue, lowvalue, relative_drawdown)
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with absolute max drawdown, high and low time and high and low value,
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and the relative account drawdown
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:raise: ValueError if trade-dataframe was found empty.
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"""
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if len(trades) == 0:
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raise ValueError("Trade dataframe empty.")
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profit_results = trades.sort_values(date_col).reset_index(drop=True)
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max_drawdown_df = _calc_drawdown_series(
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profit_results,
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date_col=date_col,
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value_col=value_col,
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starting_balance=starting_balance
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)
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idxmin = (
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max_drawdown_df['drawdown_relative'].idxmax()
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if relative else max_drawdown_df['drawdown'].idxmin()
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)
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if idxmin == 0:
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raise ValueError("No losing trade, therefore no drawdown.")
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high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
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low_date = profit_results.loc[idxmin, date_col]
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high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
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['high_value'].idxmax(), 'cumulative']
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low_val = max_drawdown_df.loc[idxmin, 'cumulative']
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max_drawdown_rel = max_drawdown_df.loc[idxmin, 'drawdown_relative']
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return (
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abs(max_drawdown_df.loc[idxmin, 'drawdown']),
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high_date,
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low_date,
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high_val,
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low_val,
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max_drawdown_rel
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)
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def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:
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"""
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Calculate min/max cumsum of trades, to show if the wallet/stake amount ratio is sane
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:param trades: DataFrame containing trades (requires columns close_date and profit_percent)
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:param starting_balance: Add starting balance to results, to show the wallets high / low points
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:return: Tuple (float, float) with cumsum of profit_abs
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:raise: ValueError if trade-dataframe was found empty.
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"""
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if len(trades) == 0:
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raise ValueError("Trade dataframe empty.")
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csum_df = pd.DataFrame()
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csum_df['sum'] = trades['profit_abs'].cumsum()
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csum_min = csum_df['sum'].min() + starting_balance
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csum_max = csum_df['sum'].max() + starting_balance
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return csum_min, csum_max
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def calculate_cagr(days_passed: int, starting_balance: float, final_balance: float) -> float:
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"""
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Calculate CAGR
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:param days_passed: Days passed between start and ending balance
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:param starting_balance: Starting balance
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:param final_balance: Final balance to calculate CAGR against
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:return: CAGR
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"""
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if final_balance < 0:
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# With leveraged trades, final_balance can become negative.
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return 0
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return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1
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def calculate_expectancy(trades: pd.DataFrame) -> Tuple[float, float]:
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"""
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Calculate expectancy
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:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
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:return: expectancy, expectancy_ratio
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"""
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expectancy = 0
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expectancy_ratio = 100
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if len(trades) > 0:
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winning_trades = trades.loc[trades['profit_abs'] > 0]
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losing_trades = trades.loc[trades['profit_abs'] < 0]
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profit_sum = winning_trades['profit_abs'].sum()
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loss_sum = abs(losing_trades['profit_abs'].sum())
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nb_win_trades = len(winning_trades)
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nb_loss_trades = len(losing_trades)
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average_win = (profit_sum / nb_win_trades) if nb_win_trades > 0 else 0
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average_loss = (loss_sum / nb_loss_trades) if nb_loss_trades > 0 else 0
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winrate = (nb_win_trades / len(trades))
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loserate = (nb_loss_trades / len(trades))
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expectancy = (winrate * average_win) - (loserate * average_loss)
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if (average_loss > 0):
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risk_reward_ratio = average_win / average_loss
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expectancy_ratio = ((1 + risk_reward_ratio) * winrate) - 1
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return expectancy, expectancy_ratio
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def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
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starting_balance: float) -> float:
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"""
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Calculate sortino
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:param trades: DataFrame containing trades (requires columns profit_abs)
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:return: sortino
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"""
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if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
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return 0
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total_profit = trades['profit_abs'] / starting_balance
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days_period = max(1, (max_date - min_date).days)
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expected_returns_mean = total_profit.sum() / days_period
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down_stdev = np.std(trades.loc[trades['profit_abs'] < 0, 'profit_abs'] / starting_balance)
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if down_stdev != 0 and not np.isnan(down_stdev):
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sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
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else:
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# Define high (negative) sortino ratio to be clear that this is NOT optimal.
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sortino_ratio = -100
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# print(expected_returns_mean, down_stdev, sortino_ratio)
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return sortino_ratio
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def calculate_sharpe(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
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starting_balance: float) -> float:
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"""
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Calculate sharpe
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:param trades: DataFrame containing trades (requires column profit_abs)
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:return: sharpe
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"""
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if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
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return 0
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total_profit = trades['profit_abs'] / starting_balance
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days_period = max(1, (max_date - min_date).days)
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expected_returns_mean = total_profit.sum() / days_period
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up_stdev = np.std(total_profit)
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if up_stdev != 0:
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sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
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else:
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# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
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sharp_ratio = -100
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# print(expected_returns_mean, up_stdev, sharp_ratio)
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return sharp_ratio
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def calculate_calmar(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
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starting_balance: float) -> float:
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"""
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Calculate calmar
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:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
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:return: calmar
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"""
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if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
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return 0
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total_profit = trades['profit_abs'].sum() / starting_balance
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days_period = max(1, (max_date - min_date).days)
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# adding slippage of 0.1% per trade
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# total_profit = total_profit - 0.0005
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expected_returns_mean = total_profit / days_period * 100
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# calculate max drawdown
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try:
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_, _, _, _, _, max_drawdown = calculate_max_drawdown(
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trades, value_col="profit_abs", starting_balance=starting_balance
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)
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except ValueError:
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max_drawdown = 0
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if max_drawdown != 0:
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calmar_ratio = expected_returns_mean / max_drawdown * math.sqrt(365)
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else:
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# Define high (negative) calmar ratio to be clear that this is NOT optimal.
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calmar_ratio = -100
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# print(expected_returns_mean, max_drawdown, calmar_ratio)
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return calmar_ratio
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