multi_tf

import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy import (IStrategy, informative)
from pandas import DataFrame, Series
import talib.abstract as ta
import math
import pandas_ta as pta
# from finta import TA as fta
import logging
from logging import FATAL

logger = logging.getLogger(__name__)

# NOT TO BE USED FOR LIVE!!!!!!

class multi_tf (IStrategy):

    def version(self) -> str:
        return "v1"

    INTERFACE_VERSION = 3

    # ROI table:
    minimal_roi = {
        "0": 0.2
    }

    # Stoploss:
    stoploss = -0.1

    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.01
    trailing_only_offset_is_reached = True

    # Sell signal
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = False

    timeframe = '5m'

    process_only_new_candles = True
    startup_candle_count = 100

    # This method is not required.
    # def informative_pairs(self): ...

    # Define informative upper timeframe for each pair. Decorators can be stacked on same
    # method. Available in populate_indicators as 'rsi_30m' and 'rsi_1h'.
    @informative('30m')
    @informative('1h')
    def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        return dataframe

    # Define BTC/STAKE informative pair. Available in populate_indicators and other methods as
    # 'btc_rsi_1h'. Current stake currency should be specified as {stake} format variable
    # instead of hard-coding actual stake currency. Available in populate_indicators and other
    # methods as 'btc_usdt_rsi_1h' (when stake currency is USDT).
    @informative('1h', 'BTC/{stake}')
    def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        return dataframe

    # Define BTC/ETH informative pair. You must specify quote currency if it is different from
    # stake currency. Available in populate_indicators and other methods as 'eth_btc_rsi_1h'.
    @informative('1h', 'ETH/BTC')
    def populate_indicators_eth_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        return dataframe

    # Define BTC/STAKE informative pair. A custom formatter may be specified for formatting
    # column names. A callable `fmt(**kwargs) -> str` may be specified, to implement custom
    # formatting. Available in populate_indicators and other methods as 'rsi_fast_upper'.
    # Resulting column names: `BTC_rsi_fast_upper_1h`, `BTC_close_1h` ...
    @informative('1h', 'BTC/{stake}', 'BTC_{column}_{timeframe}')
    def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi_fast_upper'] = ta.RSI(dataframe, timeperiod=4)
        return dataframe

    # Define BTC/STAKE informative pair. A custom formatter may be specified for formatting
    # column names. A callable `fmt(**kwargs) -> str` may be specified, to implement custom
    # formatting. Available in populate_indicators and other methods as 'btc_rsi_super_fast_1h'.
    @informative('1h', 'BTC/{stake}', '{base}_{column}_{timeframe}')
    def populate_indicators_btc_1h_3(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi_super_fast'] = ta.RSI(dataframe, timeperiod=2)
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Strategy timeframe indicators for current pair.
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        # Informative pairs are available in this method.
        dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h']
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        stake = self.config['stake_currency'].lower()
        dataframe.loc[
            (
                (dataframe[f'btc_{stake}_rsi_1h'] < 35)
                &
                (dataframe['eth_btc_rsi_1h'] < 50)
                &
                (dataframe['BTC_rsi_fast_upper_1h'] < 40)
                &
                (dataframe['btc_rsi_super_fast_1h'] < 30)
                &
                (dataframe['rsi_30m'] < 40)
                &
                (dataframe['rsi_1h'] < 40)
                &
                (dataframe['rsi'] < 30)
                &
                (dataframe['rsi_less'] == True)
                &
                (dataframe['volume'] > 0)
            ),
            ['enter_long', 'enter_tag']] = (1, 'buy_signal_rsi')

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe.loc[
            (
                (dataframe['rsi'] > 70)
                &
                (dataframe['rsi_less'] == False)
                &
                (dataframe['volume'] > 0)
            ),
            ['exit_long', 'exit_tag']] = (1, 'exit_signal_rsi')

        return dataframe