import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair
from pandas import DataFrame, Series
from functools import reduce
from datetime import datetime
from freqtrade.persistence import Trade
from technical.indicators import RMI
from statistics import mean
'\nTODO: \n    - Better entry signal.\n    - Potentially leverage an external data source?\n'

def bollinger_bands(stock_price, window_size, num_of_std):
    rolling_mean = stock_price.rolling(window=window_size).mean()
    rolling_std = stock_price.rolling(window=window_size).std()
    lower_band = rolling_mean - rolling_std * num_of_std
    return (np.nan_to_num(rolling_mean), np.nan_to_num(lower_band))

class Fakeentry(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = '5m'
    inf_timeframe = '1h'
    entry_params = {'bbdelta-close': 0.01697, 'bbdelta-tail': 0.85522, 'close-bblower': 0.01167, 'closedelta-close': 0.00513, 'rocr-1h': 0.54614, 'volume': 32}
    # ROI table:
    minimal_roi = {'0': 0.15, '5': 0.025, '10': 0.015, '30': 0.005}
    # Stoploss:
    stoploss = -0.085
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = True
    startup_candle_count: int = 168
    custom_trade_info = {}

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.inf_timeframe) for pair in pairs]
        return informative_pairs

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Misc. calculations regarding existing open positions (reset on every loop iteration)
        self.custom_trade_info[metadata['pair']] = trade_data = {}
        trade_data['active_trade'] = trade_data['other_trades'] = False
        if self.config['runmode'].value in ('live', 'dry_run'):
            active_trade = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True)]).all()
            other_trades = Trade.get_trades([Trade.pair != metadata['pair'], Trade.is_open.is_(True)]).all()
            if active_trade:
                current_rate = self.get_current_price(metadata['pair'])
                active_trade[0].adjust_min_max_rates(current_rate)
                trade_data['active_trade'] = True
                trade_data['current_profit'] = active_trade[0].calc_profit_ratio(current_rate)
                trade_data['peak_profit'] = active_trade[0].calc_profit_ratio(active_trade[0].max_rate)
            if other_trades:
                trade_data['other_trades'] = True
                total_other_profit = tuple((trade.calc_profit_ratio(self.get_current_price(trade.pair)) for trade in other_trades))
                trade_data['avg_other_profit'] = mean(total_other_profit)
        self.custom_trade_info[metadata['pair']] = trade_data
        # Set up Bollinger Bands
        mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2)
        dataframe['lower'] = lower
        dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb-lowerband'] = bollinger['lower']
        dataframe['bb-middleband'] = bollinger['mid']
        # Set up other indicators
        dataframe['volume-mean-slow'] = dataframe['volume'].rolling(window=24).mean()
        dataframe['rmi-slow'] = RMI(dataframe, length=20, mom=5)
        dataframe['rmi-fast'] = RMI(dataframe, length=9, mom=3)
        dataframe['rocr'] = ta.ROCR(dataframe, timeperiod=28)
        dataframe['ema-slow'] = ta.EMA(dataframe, timeperiod=50)
        # Trend Calculations
        dataframe['max'] = dataframe['high'].rolling(12).max()
        dataframe['min'] = dataframe['low'].rolling(12).min()
        dataframe['upper'] = np.where(dataframe['max'] > dataframe['max'].shift(), 1, 0)
        dataframe['lower'] = np.where(dataframe['min'] < dataframe['min'].shift(), 1, 0)
        dataframe['up_trend'] = np.where(dataframe['upper'].rolling(3, min_periods=1).sum() != 0, 1, 0)
        dataframe['dn_trend'] = np.where(dataframe['lower'].rolling(3, min_periods=1).sum() != 0, 1, 0)
        # Informative Pair Indicators
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_timeframe)
        informative['rocr'] = ta.ROCR(informative, timeperiod=168)
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        params = self.entry_params
        trade_data = self.custom_trade_info[metadata['pair']]
        conditions = []
        # Persist a entry signal for existing trades to make use of ignore_roi_if_entry_signal = True
        # when this entry signal is not present a exit can happen according to ROI table
        if trade_data['active_trade']:
            if trade_data['peak_profit'] > 0:
                conditions.append(trade_data['current_profit'] > trade_data['peak_profit'] * 0.8)
            conditions.append(dataframe['rmi-slow'] >= 60)
        else:
            # Normal entry triggers that apply to new trades we want to enter
            dataframe.loc[(dataframe['rocr_1h'] > params['rocr-1h']) & ((dataframe['lower'].shift() > 0) & (dataframe['bbdelta'] > dataframe['close'] * params['bbdelta-close']) & (dataframe['closedelta'] > dataframe['close'] * params['closedelta-close']) & (dataframe['tail'] < dataframe['bbdelta'] * params['bbdelta-tail']) & (dataframe['close'] < dataframe['lower'].shift()) & (dataframe['close'] <= dataframe['close'].shift()) | (dataframe['close'] < dataframe['ema-slow']) & (dataframe['close'] < params['close-bblower'] * dataframe['bb-lowerband']) & (dataframe['volume'] < dataframe['volume-mean-slow'].shift(1) * params['volume'])), 'fake_entry'] = 1
            conditions.append(dataframe['fake_entry'].shift(1).eq(1))
            conditions.append(dataframe['fake_entry'].eq(1))
        # applies to both new entrys and persisting entry signal
        conditions.append(dataframe['volume'].gt(0))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        trade_data = self.custom_trade_info[metadata['pair']]
        conditions = []
        # if we are in an active trade for this pair
        if trade_data['active_trade']:
            # if we are at a loss, consider what the trend looks and preempt the stoploss
            conditions.append((trade_data['current_profit'] < 0) & (trade_data['current_profit'] > self.stoploss) & (dataframe['dn_trend'] == 1) & (dataframe['rmi-fast'] < 50) & dataframe['volume'].gt(0))
            # if there are other open trades in addition to this one, consider the average profit 
            # across them all (not including this one), don't exit if entire market is down big and wait for recovery
            if trade_data['other_trades']:
                conditions.append(trade_data['avg_other_profit'] >= -0.005)
        else:
            # the bot comes through this loop even when there isn't an open trade to exit
            # so we pass an impossible condiiton here because we don't want a exit signal 
            # clogging up the charts and not having one leads the bot to crash
            conditions.append(dataframe['volume'].lt(0))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit'] = 1
        return dataframe
    '\n    Custom methods\n    '

    def get_current_price(self, pair: str) -> float:
        """
        # Using ticker seems significantly faster than orderbook.
        side = "asks"
        if (self.config['ask_strategy']['price_side'] == "bid"):
            side = "bids"
        
        ob = self.dp.orderbook(pair, 1)
        current_price = ob[side][0][0]
        """
        ticker = self.dp.ticker(pair)
        current_price = ticker['last']
        return current_price
    '\n    Price protection on trade entry and timeouts, built-in Freqtrade functionality\n    https://www.freqtrade.io/en/latest/strategy-advanced/\n    '

    def check_entry_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
        ob = self.dp.orderbook(pair, 1)
        current_price = ob['bids'][0][0]
        # Cancel entry order if price is more than 1% above the order.
        if current_price > order['price'] * 1.01:
            return True
        return False

    def check_exit_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
        ob = self.dp.orderbook(pair, 1)
        current_price = ob['asks'][0][0]
        # Cancel exit order if price is more than 1% below the order.
        if current_price < order['price'] * 0.99:
            return True
        return False

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool:
        ob = self.dp.orderbook(pair, 1)
        current_price = ob['asks'][0][0]
        # Cancel entry order if price is more than 1% above the order.
        if current_price > rate * 1.01:
            return False
        return True