chore: split BTAnalyais to metrics
This commit is contained in:
Matthias
parent
2acb68e6e2
commit
c6c569b772
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@ -5,7 +5,7 @@ import logging
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from copy import copy
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple, Union
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from typing import Any, Dict, List, Optional, Union
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import numpy as np
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import pandas as pd
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@ -400,168 +400,3 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
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trades = trades.loc[(trades['open_date'] >= trades_start) &
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(trades['close_date'] <= trades_stop)]
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return trades
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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_with_mean(data: Dict[str, pd.DataFrame],
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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 = 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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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_minutes
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timeframe_minutes = timeframe_to_minutes(timeframe)
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# Resample to timeframe to make sure trades match candles
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_trades_sum = trades.resample(f'{timeframe_minutes}min', 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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) -> 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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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'
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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(profit_results, date_col=date_col, value_col=value_col)
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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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) -> 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(profit_results, date_col=date_col, value_col=value_col)
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idxmin = max_drawdown_df['drawdown'].idxmin()
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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 = 0.0
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if high_val + starting_balance != 0:
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max_drawdown_rel = (high_val - low_val) / (high_val + starting_balance)
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return (
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abs(min(max_drawdown_df['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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return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1
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@ -0,0 +1,173 @@
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import logging
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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_with_mean(data: Dict[str, pd.DataFrame],
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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 = 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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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_minutes
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timeframe_minutes = timeframe_to_minutes(timeframe)
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# Resample to timeframe to make sure trades match candles
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_trades_sum = trades.resample(f'{timeframe_minutes}min', 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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) -> 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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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'
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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(profit_results, date_col=date_col, value_col=value_col)
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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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) -> 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(profit_results, date_col=date_col, value_col=value_col)
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idxmin = max_drawdown_df['drawdown'].idxmin()
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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 = 0.0
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if high_val + starting_balance != 0:
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max_drawdown_rel = (high_val - low_val) / (high_val + starting_balance)
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return (
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abs(min(max_drawdown_df['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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return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1
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@ -10,7 +10,7 @@ from typing import Any, Dict
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from pandas import DataFrame
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from freqtrade.data.btanalysis import calculate_max_drawdown
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from freqtrade.data.metrics import calculate_max_drawdown
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from freqtrade.optimize.hyperopt import IHyperOptLoss
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@ -8,7 +8,7 @@ from datetime import datetime
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from pandas import DataFrame
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from freqtrade.data.btanalysis import calculate_max_drawdown
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from freqtrade.data.metrics import calculate_max_drawdown
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from freqtrade.optimize.hyperopt import IHyperOptLoss
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@ -9,7 +9,7 @@ individual needs.
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"""
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from pandas import DataFrame
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from freqtrade.data.btanalysis import calculate_max_drawdown
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from freqtrade.data.metrics import calculate_max_drawdown
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from freqtrade.optimize.hyperopt import IHyperOptLoss
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@ -9,8 +9,8 @@ from pandas import DataFrame, to_datetime
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from tabulate import tabulate
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from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT
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from freqtrade.data.btanalysis import (calculate_cagr, calculate_csum, calculate_market_change,
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calculate_max_drawdown)
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from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
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calculate_max_drawdown)
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from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value
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from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
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@ -5,12 +5,13 @@ from typing import Any, Dict, List, Optional
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import pandas as pd
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from freqtrade.configuration import TimeRange
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from freqtrade.data.btanalysis import (analyze_trade_parallelism, calculate_max_drawdown,
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calculate_underwater, combine_dataframes_with_mean,
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create_cum_profit, extract_trades_of_period, load_trades)
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from freqtrade.data.btanalysis import (analyze_trade_parallelism, extract_trades_of_period,
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load_trades)
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from freqtrade.data.converter import trim_dataframe
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.data.history import get_timerange, load_data
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from freqtrade.data.metrics import (calculate_max_drawdown, calculate_underwater,
|
||||
combine_dataframes_with_mean, create_cum_profit)
|
||||
from freqtrade.enums import CandleType
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange import timeframe_to_prev_date, timeframe_to_seconds
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ from typing import Any, Dict
|
|||
|
||||
import pandas as pd
|
||||
|
||||
from freqtrade.data.btanalysis import calculate_max_drawdown
|
||||
from freqtrade.data.metrics import calculate_max_drawdown
|
||||
from freqtrade.persistence import Trade
|
||||
from freqtrade.plugins.protections import IProtection, ProtectionReturn
|
||||
|
||||
|
|
|
|||
|
|
@ -8,14 +8,14 @@ from pandas import DataFrame, DateOffset, Timestamp, to_datetime
|
|||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.constants import LAST_BT_RESULT_FN
|
||||
from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, analyze_trade_parallelism, calculate_cagr,
|
||||
calculate_csum, calculate_market_change,
|
||||
calculate_max_drawdown, calculate_underwater,
|
||||
combine_dataframes_with_mean, create_cum_profit,
|
||||
from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, analyze_trade_parallelism,
|
||||
extract_trades_of_period, get_latest_backtest_filename,
|
||||
get_latest_hyperopt_file, load_backtest_data,
|
||||
load_backtest_metadata, load_trades, load_trades_from_db)
|
||||
from freqtrade.data.history import load_data, load_pair_history
|
||||
from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
|
||||
calculate_max_drawdown, calculate_underwater,
|
||||
combine_dataframes_with_mean, create_cum_profit)
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from tests.conftest import CURRENT_TEST_STRATEGY, create_mock_trades
|
||||
from tests.conftest_trades import MOCK_TRADE_COUNT
|
||||
|
|
|
|||
|
|
@ -10,7 +10,8 @@ from plotly.subplots import make_subplots
|
|||
from freqtrade.commands import start_plot_dataframe, start_plot_profit
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.data import history
|
||||
from freqtrade.data.btanalysis import create_cum_profit, load_backtest_data
|
||||
from freqtrade.data.btanalysis import load_backtest_data
|
||||
from freqtrade.data.metrics import create_cum_profit
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.plot.plotting import (add_areas, add_indicators, add_profit, create_plotconfig,
|
||||
generate_candlestick_graph, generate_plot_filename,
|
||||
|
|
|
|||
Loading…
Reference in New Issue