Canals

# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------

import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from freqtrade.strategy import DecimalParameter, IntParameter, BooleanParameter

rangeUpper = 60
rangeLower = 5

def EWO(dataframe, ema_length=5, ema2_length=35):
    df = dataframe.copy()
    ema1 = ta.EMA(df, timeperiod=ema_length)
    ema2 = ta.EMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df['close'] * 100
    return emadif

def valuewhen(dataframe, condition, source, occurrence):
    copy = dataframe.copy()
    copy['colFromIndex'] = copy.index
    copy = copy.sort_values(by=[condition, 'colFromIndex'], ascending=False).reset_index(drop=True)
    copy['valuewhen'] = np.where(copy[condition] > 0, copy[source].shift(-occurrence), copy[source])
    copy['barrsince'] = copy['colFromIndex'] - copy['colFromIndex'].shift(-occurrence)
    copy.loc[
        (
            (rangeLower <= copy['barrsince']) &
            (copy['barrsince']  <= rangeUpper)
        )
    , "in_range"] = 1
    copy['in_range'] = copy['in_range'].fillna(0)
    copy = copy.sort_values(by=['colFromIndex'], ascending=True).reset_index(drop=True)
    return copy['valuewhen'], copy['in_range'], copy['barrsince']


class Canals(IStrategy):
    INTERFACE_VERSION = 2

    # Buy hyperspace params:
    buy_params = {
    }
    # Sell hyperspace params:
    sell_params = {
    }

    # ROI table:
    minimal_roi = {
        "0": 0.05,
    }

    # Stoploss:
    stoploss = -0.08

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.005
    trailing_stop_positive_offset = 0.02
    trailing_only_offset_is_reached = True

    # Optimal timeframe for the strategy
    timeframe = '5m'

    use_custom_stoploss = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    osc = 'close'
    len = 14
    src = 'close'
    lbL = 14

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['RSI'] = ta.RSI(dataframe[self.src], self.len)
        dataframe['RSI'] = dataframe['RSI'].fillna(0)
        stoch = ta.STOCH(dataframe, fastk_period=10, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
        dataframe['slowk'] = stoch['slowk']
        dataframe['slowd'] = stoch['slowd']
        dataframe['osc'] = dataframe[self.osc]

        # plFound = na(pivotlow(osc, lbL, lbR)) ? false : true
        dataframe['min'] = dataframe['osc'].rolling(self.lbL).min()
        dataframe['prevMin'] = np.where(dataframe['min'] > dataframe['min'].shift(), dataframe['min'].shift(), dataframe['min'])
        dataframe.loc[
            (
                (dataframe['osc'] == dataframe['prevMin'])
            )
        , 'plFound'] = 1

        # phFound = na(pivothigh(osc, lbL, lbR)) ? false : true
        dataframe['max'] = dataframe['osc'].rolling(self.lbL).max()
        dataframe['prevMax'] = np.where(dataframe['max'] < dataframe['max'].shift(), dataframe['max'].shift(), dataframe['max'])
        dataframe.loc[
            (
                (dataframe['osc'] == dataframe['prevMax'])
            )
        , 'phFound'] = 1

        dataframe['valuewhen_plFound'], dataframe['inrange_plFound'], dataframe['barssince_plFound'] = valuewhen(dataframe,'plFound', 'close', 1)
        dataframe['valuewhen_phFound'], dataframe['inrange_phFound'], dataframe['barssince_phFound'] = valuewhen(dataframe, 'phFound', 'close', 1)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []

        conditions.append(
                (
                    (dataframe['bullCond'] > 0) &
                    (dataframe['volume'] > 0)
                )
            )

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions),
                'buy'
            ] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []

        conditions.append(
            (
                (dataframe['bearCond'] > 0) &
                (dataframe['volume'] > 0)
            )
        )

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions),
                'sell'
            ] = 1

        dataframe.to_csv('user_data/csvs/%s_%s.csv' % (self.__class__.__name__, metadata["pair"].replace("/", "_")))

        return dataframe