import logging
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
                          ) -> 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]
    return max_drawdown_df


def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
                         value_col: str = 'profit_ratio'
                         ):
    """
    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)

    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
                           ) -> 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)

    idxmin = 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 = 0.0
    if high_val + starting_balance != 0:
        max_drawdown_rel = (high_val - low_val) / (high_val + starting_balance)

    return (
        abs(min(max_drawdown_df['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