import logging import math 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_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 = pd.concat([data[pair].set_index('date').rename( {column: pair}, axis=1)[pair] for pair in data], axis=1) 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_minutes timeframe_minutes = timeframe_to_minutes(timeframe) # Resample to timeframe to make sure trades match candles _trades_sum = trades.resample(f'{timeframe_minutes}min', 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 def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date', value_col: str = 'profit_abs', starting_balance: float = 0, relative: bool = False ) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]: """ 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: Tuple (float, highdate, lowdate, highvalue, lowvalue, relative_drawdown) 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 ( abs(max_drawdown_df.loc[idxmin, 'drawdown']), high_date, low_date, high_val, low_val, 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 """ return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1 def calculate_expectancy(trades: pd.DataFrame) -> float: """ Calculate expectancy :param trades: DataFrame containing trades (requires columns close_date and profit_ratio) :return: expectancy """ if len(trades) == 0: return 0 expectancy = 1 profit_sum = trades.loc[trades['profit_abs'] > 0, 'profit_abs'].sum() loss_sum = abs(trades.loc[trades['profit_abs'] < 0, 'profit_abs'].sum()) nb_win_trades = len(trades.loc[trades['profit_abs'] > 0]) nb_loss_trades = len(trades.loc[trades['profit_abs'] < 0]) if (nb_win_trades > 0) and (nb_loss_trades > 0): average_win = profit_sum / nb_win_trades average_loss = loss_sum / nb_loss_trades risk_reward_ratio = average_win / average_loss winrate = nb_win_trades / len(trades) expectancy = ((1 + risk_reward_ratio) * winrate) - 1 elif nb_win_trades == 0: expectancy = 0 return expectancy def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: datetime) -> float: """ Calculate sortino :param trades: DataFrame containing trades (requires columns profit_ratio) :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_ratio"] days_period = (max_date - min_date).days if days_period == 0: return 0 # adding slippage of 0.1% per trade # total_profit = total_profit - 0.0005 expected_returns_mean = total_profit.sum() / days_period trades['downside_returns'] = 0 trades.loc[total_profit < 0, 'downside_returns'] = trades['profit_ratio'] down_stdev = np.std(trades['downside_returns']) if down_stdev != 0: 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) -> float: """ Calculate sharpe :param trades: DataFrame containing trades (requires columns close_date and profit_ratio) :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_ratio"] days_period = (max_date - min_date).days if days_period == 0: return 0 # adding slippage of 0.1% per trade # total_profit = total_profit - 0.0005 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) -> float: """ Calculate calmar :param trades: DataFrame containing trades (requires columns close_date and profit_ratio) :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_ratio"] days_period = (max_date - min_date).days # adding slippage of 0.1% per trade # total_profit = total_profit - 0.0005 expected_returns_mean = total_profit.sum() / days_period * 100 # calculate max drawdown try: _, _, _, _, _, max_drawdown = calculate_max_drawdown( trades, value_col="profit_abs" ) 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