import logging
from datetime import timedelta

import arrow
import talib.abstract as ta
from pandas import DataFrame, to_datetime

from freqtrade.exchange import get_ticker_history
from freqtrade.vendor.qtpylib.indicators import awesome_oscillator

logging.basicConfig(level=logging.DEBUG,
                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)


def parse_ticker_dataframe(ticker: list) -> DataFrame:
    """
    Analyses the trend for the given ticker history
    :param ticker: See exchange.get_ticker_history
    :return: DataFrame
    """
    columns = {'C': 'close', 'V': 'volume', 'O': 'open', 'H': 'high', 'L': 'low', 'T': 'date'}
    frame = DataFrame(ticker) \
        .drop('BV', 1) \
        .rename(columns=columns)
    frame['date'] = to_datetime(frame['date'], utc=True, infer_datetime_format=True)
    frame.sort_values('date', inplace=True)
    return frame


def populate_indicators(dataframe: DataFrame) -> DataFrame:
    """
    Adds several different TA indicators to the given DataFrame
    """
    dataframe['sar'] = ta.SAR(dataframe)
    dataframe['adx'] = ta.ADX(dataframe)
    stoch = ta.STOCHF(dataframe)
    dataframe['fastd'] = stoch['fastd']
    dataframe['fastk'] = stoch['fastk']
    dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
    dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
    dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
    dataframe['mfi'] = ta.MFI(dataframe)
    dataframe['cci'] = ta.CCI(dataframe)
    dataframe['rsi'] = ta.RSI(dataframe)
    dataframe['mom'] = ta.MOM(dataframe)
    dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
    dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
    dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
    dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
    dataframe['ao'] = awesome_oscillator(dataframe)
    macd = ta.MACD(dataframe)
    dataframe['macd'] = macd['macd']
    dataframe['macdsignal'] = macd['macdsignal']
    dataframe['macdhist'] = macd['macdhist']
    return dataframe


def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
    """
    Based on TA indicators, populates the buy trend for the given dataframe
    :param dataframe: DataFrame
    :return: DataFrame with buy column
    """
    dataframe.ix[
        (dataframe['close'] < dataframe['sma']) &
        (dataframe['tema'] <= dataframe['blower']) &
        (dataframe['mfi'] < 25) &
        (dataframe['fastd'] < 25) &
        (dataframe['adx'] > 30),
        'buy'] = 1
    dataframe.ix[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']

    return dataframe


def analyze_ticker(pair: str) -> DataFrame:
    """
    Get ticker data for given currency pair, push it to a DataFrame and
    add several TA indicators and buy signal to it
    :return DataFrame with ticker data and indicator data
    """
    data = get_ticker_history(pair)
    dataframe = parse_ticker_dataframe(data)

    if dataframe.empty:
        logger.warning('Empty dataframe for pair %s', pair)
        return dataframe

    dataframe = populate_indicators(dataframe)
    dataframe = populate_buy_trend(dataframe)
    return dataframe


def get_buy_signal(pair: str) -> bool:
    """
    Calculates a buy signal based several technical analysis indicators
    :param pair: pair in format BTC_ANT or BTC-ANT
    :return: True if pair is good for buying, False otherwise
    """
    dataframe = analyze_ticker(pair)

    if dataframe.empty:
        return False

    latest = dataframe.iloc[-1]

    # Check if dataframe is out of date
    signal_date = arrow.get(latest['date'])
    if signal_date < arrow.now() - timedelta(minutes=10):
        return False

    signal = latest['buy'] == 1
    logger.debug('buy_trigger: %s (pair=%s, signal=%s)', latest['date'], pair, signal)
    return signal