# source: https://raw.githubusercontent.com/azcoigreach/guru-skippyasurmuni/c46f2162c7df637c796e225cad8dce93c2f6c9ef/user_data/strategies/GuruSkippyasurmuni_strategy.py
# Guru Skippyasurmuni Strategy
# Author: AZcoigreach
# github: https://github.com/azcoigreach/

#             ___  _   _  ___  _   _                                                 
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#           | (_ || |_| ||   /| |_| |                                                
#            \___| \___/ |_|_\ \___/                                                 
#    ___  _  __ ___  ___  ___ __   __ _    ___  _   _  ___  __  __  _   _  _  _  ___ 
#   / __|| |/ /|_ _|| _ \| _ \\ \ / //_\  / __|| | | || _ \|  \/  || | | || \| ||_ _|
#   \__ \| ' <  | | |  _/|  _/ \ V // _ \ \__ \| |_| ||   /| |\/| || |_| || .` | | | 
#   |___/|_|\_\|___||_|  |_|    |_|/_/ \_\|___/ \___/ |_|_\|_|  |_| \___/ |_|\_||___|
#        

'''
Guru Skippyasurmuni - 
Even though Skippy is designed to be easily modified and hyperopted. However, Skippy has alread spent a lot of time thinking about the best way to trade crypto.
He has come to the conclusing - simple is better.  A couple basic indicators and some simple rules will yield the best results in every condition.

'''

# Hyperopting
'''
docker-compose run --rm freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell --strategy github_azcoigreach_guru_skippyasurmuni__GuruSkippyasurmuni_strategy__20220319_232240 -e 2000 --timerange=20220314- --eps
'''

# Backtesting - Powershell
'''
 $1 = 20220314 ; $2 = "day" ;  
 docker-compose run --rm freqtrade backtesting --datadir user_data/data/binanceus --config /freqtrade/user_data/SkippyGod_config.json --export trades  -s SkippyGod --fee 0.00075 --timerange=$1- --breakdown $2 --eps ; 
 docker-compose run --rm freqtrade plot-dataframe -s SkippyGod --config /freqtrade/user_data/SkippyGod_config.json -i 5m --timerange=$1- --indicators2 AROONOSC-5 RSI-5  ; 
 docker-compose run --rm freqtrade plot-profit -s SkippyGod --config /freqtrade/user_data/SkippyGod_config.json -i 5m --timerange=$1-
'''

# --- 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 datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
import technical.indicators as ftt
from freqtrade.exchange import timeframe_to_prev_date
import warnings
from pandas.core.common import SettingWithCopyWarning
warnings.simplefilter(action="ignore", category=SettingWithCopyWarning)
from random import shuffle

# Initiate genes splicing --> Source: MaBlue GodStrNew --> Target: Guru Skippyasurmuni strategy.

#  TODO: this gene is removed 'MAVP' cuz or error on periods
all_god_genes = {
    'Overlap Studies': {
        'BBANDS-0',             # Bollinger Bands
        'BBANDS-1',             # Bollinger Bands
        'BBANDS-2',             # Bollinger Bands
        'DEMA',                 # Double Exponential Moving Average
        'EMA',                  # Exponential Moving Average
        'HT_TRENDLINE',         # Hilbert Transform - Instantaneous Trendline
        'KAMA',                 # Kaufman Adaptive Moving Average
        'MA',                   # Moving average
        'MAMA-0',               # MESA Adaptive Moving Average
        'MAMA-1',               # MESA Adaptive Moving Average
        # TODO: Fix this
        # 'MAVP',               # Moving average with variable period
        'MIDPOINT',             # MidPoint over period
        'MIDPRICE',             # Midpoint Price over period
        'SAR',                  # Parabolic SAR
        'SAREXT',               # Parabolic SAR - Extended
        'SMA',                  # Simple Moving Average
        'T3',                   # Triple Exponential Moving Average (T3)
        'TEMA',                 # Triple Exponential Moving Average
        'TRIMA',                # Triangular Moving Average
        'WMA',                  # Weighted Moving Average
    },
    'Momentum Indicators': {
        'ADX',                  # Average Directional Movement Index
        'ADXR',                 # Average Directional Movement Index Rating
        'APO',                  # Absolute Price Oscillator
        'AROON-0',              # Aroon
        'AROON-1',              # Aroon
        'AROONOSC',             # Aroon Oscillator
        'BOP',                  # Balance Of Power
        'CCI',                  # Commodity Channel Index
        'CMO',                  # Chande Momentum Oscillator
        'DX',                   # Directional Movement Index
        'MACD-0',               # Moving Average Convergence/Divergence
        'MACD-1',               # Moving Average Convergence/Divergence
        'MACD-2',               # Moving Average Convergence/Divergence
        'MACDEXT-0',            # MACD with controllable MA type
        'MACDEXT-1',            # MACD with controllable MA type
        'MACDEXT-2',            # MACD with controllable MA type
        'MACDFIX-0',            # Moving Average Convergence/Divergence Fix 12/26
        'MACDFIX-1',            # Moving Average Convergence/Divergence Fix 12/26
        'MACDFIX-2',            # Moving Average Convergence/Divergence Fix 12/26
        'MFI',                  # Money Flow Index
        'MINUS_DI',             # Minus Directional Indicator
        'MINUS_DM',             # Minus Directional Movement
        'MOM',                  # Momentum
        'PLUS_DI',              # Plus Directional Indicator
        'PLUS_DM',              # Plus Directional Movement
        'PPO',                  # Percentage Price Oscillator
        'ROC',                  # Rate of change : ((price/prevPrice)-1)*100
        'ROCP',                 # Rate of change Percentage: (price-prevPrice)/prevPrice
        'ROCR',                 # Rate of change ratio: (price/prevPrice)
        'ROCR100',              # Rate of change ratio 100 scale: (price/prevPrice)*100
        'RSI',                  # Relative Strength Index
        'STOCH-0',              # Stochastic
        'STOCH-1',              # Stochastic
        'STOCHF-0',             # Stochastic Fast
        'STOCHF-1',             # Stochastic Fast
        'STOCHRSI-0',           # Stochastic Relative Strength Index
        'STOCHRSI-1',           # Stochastic Relative Strength Index
        'TRIX',                 # 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
        'ULTOSC',               # Ultimate Oscillator
        'WILLR',                # Williams' %R
    },
    'Volume Indicators': {
        'AD',                   # Chaikin A/D Line
        'ADOSC',                # Chaikin A/D Oscillator
        'OBV',                  # On Balance Volume
    },
    'Volatility Indicators': {
        'ATR',                  # Average True Range
        'NATR',                 # Normalized Average True Range
        'TRANGE',               # True Range
    },
    'Price Transform': {
        'AVGPRICE',             # Average Price
        'MEDPRICE',             # Median Price
        'TYPPRICE',             # Typical Price
        'WCLPRICE',             # Weighted Close Price
    },
    'Cycle Indicators': {
        'HT_DCPERIOD',          # Hilbert Transform - Dominant Cycle Period
        'HT_DCPHASE',           # Hilbert Transform - Dominant Cycle Phase
        'HT_PHASOR-0',          # Hilbert Transform - Phasor Components
        'HT_PHASOR-1',          # Hilbert Transform - Phasor Components
        'HT_SINE-0',            # Hilbert Transform - SineWave
        'HT_SINE-1',            # Hilbert Transform - SineWave
        'HT_TRENDMODE',         # Hilbert Transform - Trend vs Cycle Mode
    },
    'Pattern Recognition': {
        'CDL2CROWS',            # Two Crows
        'CDL3BLACKCROWS',       # Three Black Crows
        'CDL3INSIDE',           # Three Inside Up/Down
        'CDL3LINESTRIKE',       # Three-Line Strike
        'CDL3OUTSIDE',          # Three Outside Up/Down
        'CDL3STARSINSOUTH',     # Three Stars In The South
        'CDL3WHITESOLDIERS',    # Three Advancing White Soldiers
        'CDLABANDONEDBABY',     # Abandoned Baby
        'CDLADVANCEBLOCK',      # Advance Block
        'CDLBELTHOLD',          # Belt-hold
        'CDLBREAKAWAY',         # Breakaway
        'CDLCLOSINGMARUBOZU',   # Closing Marubozu
        'CDLCONCEALBABYSWALL',  # Concealing Baby Swallow
        'CDLCOUNTERATTACK',     # Counterattack
        'CDLDARKCLOUDCOVER',    # Dark Cloud Cover
        'CDLDOJI',              # Doji
        'CDLDOJISTAR',          # Doji Star
        'CDLDRAGONFLYDOJI',     # Dragonfly Doji
        'CDLENGULFING',         # Engulfing Pattern
        'CDLEVENINGDOJISTAR',   # Evening Doji Star
        'CDLEVENINGSTAR',       # Evening Star
        'CDLGAPSIDESIDEWHITE',  # Up/Down-gap side-by-side white lines
        'CDLGRAVESTONEDOJI',    # Gravestone Doji
        'CDLHAMMER',            # Hammer
        'CDLHANGINGMAN',        # Hanging Man
        'CDLHARAMI',            # Harami Pattern
        'CDLHARAMICROSS',       # Harami Cross Pattern
        'CDLHIGHWAVE',          # High-Wave Candle
        'CDLHIKKAKE',           # Hikkake Pattern
        'CDLHIKKAKEMOD',        # Modified Hikkake Pattern
        'CDLHOMINGPIGEON',      # Homing Pigeon
        'CDLIDENTICAL3CROWS',   # Identical Three Crows
        'CDLINNECK',            # In-Neck Pattern
        'CDLINVERTEDHAMMER',    # Inverted Hammer
        'CDLKICKING',           # Kicking
        'CDLKICKINGBYLENGTH',   # Kicking - bull/bear determined by the longer marubozu
        'CDLLADDERBOTTOM',      # Ladder Bottom
        'CDLLONGLEGGEDDOJI',    # Long Legged Doji
        'CDLLONGLINE',          # Long Line Candle
        'CDLMARUBOZU',          # Marubozu
        'CDLMATCHINGLOW',       # Matching Low
        'CDLMATHOLD',           # Mat Hold
        'CDLMORNINGDOJISTAR',   # Morning Doji Star
        'CDLMORNINGSTAR',       # Morning Star
        'CDLONNECK',            # On-Neck Pattern
        'CDLPIERCING',          # Piercing Pattern
        'CDLRICKSHAWMAN',       # Rickshaw Man
        'CDLRISEFALL3METHODS',  # Rising/Falling Three Methods
        'CDLSEPARATINGLINES',   # Separating Lines
        'CDLSHOOTINGSTAR',      # Shooting Star
        'CDLSHORTLINE',         # Short Line Candle
        'CDLSPINNINGTOP',       # Spinning Top
        'CDLSTALLEDPATTERN',    # Stalled Pattern
        'CDLSTICKSANDWICH',     # Stick Sandwich
        'CDLTAKURI',            # Takuri (Dragonfly Doji with very long lower shadow)
        'CDLTASUKIGAP',         # Tasuki Gap
        'CDLTHRUSTING',         # Thrusting Pattern
        'CDLTRISTAR',           # Tristar Pattern
        'CDLUNIQUE3RIVER',      # Unique 3 River
        'CDLUPSIDEGAP2CROWS',   # Upside Gap Two Crows
        'CDLXSIDEGAP3METHODS',  # Upside/Downside Gap Three Methods

    },
    'Statistic Functions': {
        'BETA',                 # Beta
        'CORREL',               # Pearson's Correlation Coefficient (r)
        'LINEARREG',            # Linear Regression
        'LINEARREG_ANGLE',      # Linear Regression Angle
        'LINEARREG_INTERCEPT',  # Linear Regression Intercept
        'LINEARREG_SLOPE',      # Linear Regression Slope
        'STDDEV',               # Standard Deviation
        'TSF',                  # Time Series Forecast
        'VAR',                  # Variance
    }

}
god_genes = set()
########################### SETTINGS ##############################
# RSI and an Aroon Oscillator are the only two metrics you need to catch the highs and lows

god_genes = {
    'RSI',                  # Relative Strength Index
    'AROONOSC',             # Aroon Oscillator
   }
# god_genes |= all_god_genes['Overlap Studies']
# god_genes |= all_god_genes['Momentum Indicators']
# god_genes |= all_god_genes['Volume Indicators']
# god_genes |= all_god_genes['Volatility Indicators']
# god_genes |= all_god_genes['Price Transform']
# god_genes |= all_god_genes['Cycle Indicators']
# god_genes |= all_god_genes['Pattern Recognition']
# god_genes |= all_god_genes['Statistic Functions']

timeperiods = [5, 6, 12, 15, 50, 55, 100, 110]
operators = [
    "D",  # Disabled gene
    ">",  # Indicator, bigger than cross indicator
    "<",  # Indicator, smaller than cross indicator
    "=",  # Indicator, equal with cross indicator
    "C",  # Indicator, crossed the cross indicator
    "CA",  # Indicator, crossed above the cross indicator
    "CB",  # Indicator, crossed below the cross indicator
    ">R",  # Normalized indicator, bigger than real number
    "=R",  # Normalized indicator, equal with real number
    "<R",  # Normalized indicator, smaller than real number
    "/>R",  # Normalized indicator devided to cross indicator, bigger than real number
    "/=R",  # Normalized indicator devided to cross indicator, equal with real number
    "/<R",  # Normalized indicator devided to cross indicator, smaller than real number
    "UT",  # Indicator, is in UpTrend status
    "DT",  # Indicator, is in DownTrend status
    "OT",  # Indicator, is in Off trend status(RANGE)
    "CUT",  # Indicator, Entered to UpTrend status
    "CDT",  # Indicator, Entered to DownTrend status
    "COT"  # Indicator, Entered to Off trend status(RANGE)
]
# number of candles to check up,don,off trend.
TREND_CHECK_CANDLES = 4
DECIMALS = 3
########################### END SETTINGS ##########################
# DATAFRAME = DataFrame()

god_genes = list(god_genes)
# #print('selected indicators for optimzatin: \n', god_genes)

god_genes_with_timeperiod = list()
for god_gene in god_genes:
    for timeperiod in timeperiods:
        god_genes_with_timeperiod.append(f'{god_gene}-{timeperiod}')

# Let give somethings to CatagoricalParam to Play with them
# When just one thing is inside catagorical lists
# TODO: its Not True Way :)
if len(god_genes) == 1:
    god_genes = god_genes*2
if len(timeperiods) == 1:
    timeperiods = timeperiods*2
if len(operators) == 1:
    operators = operators*2


def normalize(df):
    df = (df-df.min())/(df.max()-df.min())
    return df


def gene_calculator(dataframe, indicator):
    # Cuz Timeperiods not effect calculating CDL patterns recognations
    if 'CDL' in indicator:
        splited_indicator = indicator.split('-')
        splited_indicator[1] = "0"
        new_indicator = "-".join(splited_indicator)
        # #print(indicator, new_indicator)
        indicator = new_indicator

    gene = indicator.split("-")

    gene_name = gene[0]
    gene_len = len(gene)

    if indicator in dataframe.keys():
        # #print(f"{indicator}, calculated befoure")
        # #print(len(dataframe.keys()))
        return dataframe[indicator]
    else:
        result = None
        # For Pattern Recognations
        if gene_len == 1:
            # #print('gene_len == 1\t', indicator)
            result = getattr(ta, gene_name)(
                dataframe
            )
            return normalize(result)
        elif gene_len == 2:
            # #print('gene_len == 2\t', indicator)
            gene_timeperiod = int(gene[1])
            result = getattr(ta, gene_name)(
                dataframe,
                timeperiod=gene_timeperiod,
            )
            return normalize(result)
        # For
        elif gene_len == 3:
            # #print('gene_len == 3\t', indicator)
            gene_timeperiod = int(gene[2])
            gene_index = int(gene[1])
            result = getattr(ta, gene_name)(
                dataframe,
                timeperiod=gene_timeperiod,
            ).iloc[:, gene_index]
            return normalize(result)
        # For trend operators(MA-5-SMA-4)
        elif gene_len == 4:
            # #print('gene_len == 4\t', indicator)
            gene_timeperiod = int(gene[1])
            sharp_indicator = f'{gene_name}-{gene_timeperiod}'
            dataframe[sharp_indicator] = getattr(ta, gene_name)(
                dataframe,
                timeperiod=gene_timeperiod,
            )
            return normalize(ta.SMA(dataframe[sharp_indicator].fillna(0), TREND_CHECK_CANDLES))
        # For trend operators(STOCH-0-4-SMA-4)
        elif gene_len == 5:
            # #print('gene_len == 5\t', indicator)
            gene_timeperiod = int(gene[2])
            gene_index = int(gene[1])
            sharp_indicator = f'{gene_name}-{gene_index}-{gene_timeperiod}'
            dataframe[sharp_indicator] = getattr(ta, gene_name)(
                dataframe,
                timeperiod=gene_timeperiod,
            ).iloc[:, gene_index]
            return normalize(ta.SMA(dataframe[sharp_indicator].fillna(0), TREND_CHECK_CANDLES))


def condition_generator(dataframe, operator, indicator, crossed_indicator, real_num):

    condition = (dataframe['volume'] > 10)

    # TODO : it ill callculated in populate indicators.

    dataframe[indicator] = gene_calculator(dataframe, indicator)
    dataframe[crossed_indicator] = gene_calculator(dataframe, crossed_indicator)

    indicator_trend_sma = f"{indicator}-SMA-{TREND_CHECK_CANDLES}"
    if operator in ["UT", "DT", "OT", "CUT", "CDT", "COT"]:
        dataframe[indicator_trend_sma] = gene_calculator(dataframe, indicator_trend_sma)

    if operator == ">":
        condition = (
            dataframe[indicator] > dataframe[crossed_indicator]
        )
    elif operator == "=":
        condition = (
            np.isclose(dataframe[indicator], dataframe[crossed_indicator])
        )
    elif operator == "<":
        condition = (
            dataframe[indicator] < dataframe[crossed_indicator]
        )
    elif operator == "C":
        condition = (
            (qtpylib.crossed_below(dataframe[indicator], dataframe[crossed_indicator])) |
            (qtpylib.crossed_above(dataframe[indicator], dataframe[crossed_indicator]))
        )
    elif operator == "CA":
        condition = (
            qtpylib.crossed_above(dataframe[indicator], dataframe[crossed_indicator])
        )
    elif operator == "CB":
        condition = (
            qtpylib.crossed_below(
                dataframe[indicator], dataframe[crossed_indicator])
        )
    elif operator == ">R":
        condition = (
            dataframe[indicator] > real_num
        )
    elif operator == "=R":
        condition = (
            np.isclose(dataframe[indicator], real_num)
        )
    elif operator == "<R":
        condition = (
            dataframe[indicator] < real_num
        )
    elif operator == "/>R":
        condition = (
            dataframe[indicator].div(dataframe[crossed_indicator]) > real_num
        )
    elif operator == "/=R":
        condition = (
            np.isclose(dataframe[indicator].div(dataframe[crossed_indicator]), real_num)
        )
    elif operator == "/<R":
        condition = (
            dataframe[indicator].div(dataframe[crossed_indicator]) < real_num
        )
    elif operator == "UT":
        condition = (
            dataframe[indicator] > dataframe[indicator_trend_sma]
        )
    elif operator == "DT":
        condition = (
            dataframe[indicator] < dataframe[indicator_trend_sma]
        )
    elif operator == "OT":
        condition = (

            np.isclose(dataframe[indicator], dataframe[indicator_trend_sma])
        )
    elif operator == "CUT":
        condition = (
            (
                qtpylib.crossed_above(
                    dataframe[indicator],
                    dataframe[indicator_trend_sma]
                )
            ) &
            (
                dataframe[indicator] > dataframe[indicator_trend_sma]
            )
        )
    elif operator == "CDT":
        condition = (
            (
                qtpylib.crossed_below(
                    dataframe[indicator],
                    dataframe[indicator_trend_sma]
                )
            ) &
            (
                dataframe[indicator] < dataframe[indicator_trend_sma]
            )
        )
    elif operator == "COT":
        condition = (
            (
                (
                    qtpylib.crossed_below(
                        dataframe[indicator],
                        dataframe[indicator_trend_sma]
                    )
                ) |
                (
                    qtpylib.crossed_above(
                        dataframe[indicator],
                        dataframe[indicator_trend_sma]
                    )
                )
            ) &
            (
                np.isclose(
                    dataframe[indicator],
                    dataframe[indicator_trend_sma]
                )
            )
        )

    return condition, dataframe

class github_azcoigreach_guru_skippyasurmuni__GuruSkippyasurmuni_strategy__20220319_232240(IStrategy):
    INTERFACE_VERSION = 2

    # Variables
    DATESTAMP = 0    
    COUNT = 0
    custom_info = { }

    # Skippy's BUY and SELL conditions
    # Buy hyperspace params:
    buy_params = {
        "buy_crossed_indicator0": "STOCHRSI-0-15",
        "buy_crossed_indicator1": "MACDFIX-0-15",
        "buy_crossed_indicator2": "STOCHRSI-0-55",
        "buy_indicator0": "MACDFIX-0-15",
        "buy_indicator1": "RSI-5",
        "buy_indicator2": "AROONOSC-5",
        "buy_operator0": "D",
        "buy_operator1": "<R",
        "buy_operator2": "=R",
        "buy_real_num0": 0.35,
        "buy_real_num1": 0.30,
        "buy_real_num2": 0,
    }

        # Sell hyperspace params:
    sell_params = {
        "sell_crossed_indicator0": "STOCHRSI-0-15",
        "sell_crossed_indicator1": "MACDFIX-0-15",
        "sell_crossed_indicator2": "STOCHRSI-0-15",
        "sell_indicator0": "MACDFIX-0-15",
        "sell_indicator1": "RSI-5",
        "sell_indicator2": "AROONOSC-5",
        "sell_operator0": "D",
        "sell_operator1": ">R",
        "sell_operator2": "=R",
        "sell_real_num0": 0.65,
        "sell_real_num1": 0.9,
        "sell_real_num2": 1.0,
    }    

    # ROI table:
    minimal_roi = {
        "0": 1000.0
    }

    # Stoploss:
    stoploss = -1.0

    # 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_sell_signal = True
    sell_profit_only = True
    # sell_profit_offset = 0.01
    ignore_roi_if_buy_signal = False
    
    # Optimal timeframe for the strategy
    timeframe = '5m'
    # timeframe = '1m'
    process_only_new_candles = True

    plot_config = {
        'subplots': {
            "buy_1": {
                'buy_1': {'color': 'blue'}
            },
            "sell_1": {
                'sell_1': {'color': 'orange'}
            },
        }
    }

    # DCA config
    position_adjustment_enable = True
    max_entry_position_adjustment = 24

    # Protections
    @property
    def protections(self):
        return [
            {
            "method": "CooldownPeriod",
            "stop_duration_candles": 3
            }
        ]

    # TODO: Its not dry code!
    # Buy Hyperoptable Parameters/Spaces.
    buy_crossed_indicator0 = CategoricalParameter(
        god_genes_with_timeperiod, default="STOCHRSI-0-15", space='buy')
    buy_crossed_indicator1 = CategoricalParameter(
        god_genes_with_timeperiod, default="MACDFIX-0-15", space='buy')
    buy_crossed_indicator2 = CategoricalParameter(
        god_genes_with_timeperiod, default="STOCHRSI-0-55", space='buy')

    buy_indicator0 = CategoricalParameter(
        god_genes_with_timeperiod, default="MACDFIX-0-15", space='buy')
    buy_indicator1 = CategoricalParameter(
        god_genes_with_timeperiod, default="RSI-5", space='buy')
    buy_indicator2 = CategoricalParameter(
        god_genes_with_timeperiod, default="AROONOSC-5", space='buy')

    buy_operator0 = CategoricalParameter(operators, default="D", space='buy')
    buy_operator1 = CategoricalParameter(operators, default="<R", space='buy')
    buy_operator2 = CategoricalParameter(operators, default="=R", space='buy')

    buy_real_num0 = DecimalParameter(0, 1, decimals=DECIMALS,  default=0.35, space='buy')
    buy_real_num1 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.30, space='buy')
    buy_real_num2 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.00, space='buy')

    # Sell Hyperoptable Parameters/Spaces.
    sell_crossed_indicator0 = CategoricalParameter(
        god_genes_with_timeperiod, default="STOCHRSI-0-15", space='sell')
    sell_crossed_indicator1 = CategoricalParameter(
        god_genes_with_timeperiod, default="MACDFIX-0-15", space='sell')
    sell_crossed_indicator2 = CategoricalParameter(
        god_genes_with_timeperiod, default="STOCHRSI-0-55", space='sell')

    sell_indicator0 = CategoricalParameter(
        god_genes_with_timeperiod, default="MACDFIX-0-15", space='sell')
    sell_indicator1 = CategoricalParameter(
        god_genes_with_timeperiod, default="RSI-5", space='sell')
    sell_indicator2 = CategoricalParameter(
        god_genes_with_timeperiod, default="AROONOSC-5", space='sell')

    sell_operator0 = CategoricalParameter(operators, default="D", space='sell')
    sell_operator1 = CategoricalParameter(operators, default=">R", space='sell')
    sell_operator2 = CategoricalParameter(operators, default="=R", space='sell')

    sell_real_num0 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.65, space='sell')
    sell_real_num1 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.90, space='sell')
    sell_real_num2 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.00, space='sell')

    # Skippy learns from the best
    # DCA and Position Staking with help from Perkmeister on Discord

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

        # Check if the entry already exists
        pair = metadata['pair']

        if not pair in self.custom_info:
            # Create empty entry for this pair {DATESTAMP}
            self.custom_info[pair] = ['', 0] 

        # count = self.custom_info[pair][self.COUNT]

        # dataframe['buy_1'] = 0

        # # # iterate through dataframe, create buys and sells
        # row = 0
        # last_row = dataframe.tail(1).index.item()
        # while (row <= last_row):
                        
        #     if(count == 0):
        #         dataframe['buy'].iloc[row] = 1
        #     if(count > 0): 
        #         dataframe['buy_1'].iloc[row] = 1
        #     else:
        #         count += 1
            
        #     if(row == 0):
        #         self.custom_info[pair][self.COUNT] = count

        #     row += 1

        return dataframe

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
                           rate: float, time_in_force: str, sell_reason: str, current_profit: float,
                           **kwargs) -> bool:
        
        # Minimum 1% profit before Sell trigger
        if current_profit < 0.01:
                return False

        return True
    
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float, max_stake: float,
                            **kwargs) -> float:
        return proposed_stake

    # Skippy's has finally figured out how to stake positions
    def adjust_trade_position(self, trade: Trade, current_time: datetime,
                              current_rate: float, current_profit: float, min_stake: float,
                              max_stake: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)

        if(len(dataframe) < 1):
            return None

        last_candle = dataframe.iloc[-1].squeeze()

        if(self.custom_info[trade.pair][self.DATESTAMP] != last_candle['date']):
            # Trigger once per cnadle
            self.custom_info[trade.pair][self.DATESTAMP] = last_candle['date']

            # If current total profit is greater than value don't adjust.
            if current_profit > -0.01:
                return None

            # If last candle had 'buy' indicator adjust stake by original stake_amount
            if last_candle['buy'] > 0:
                filled_buys = trade.select_filled_orders('buy')
                try:
                    stake_amount = filled_buys[0].cost
                    return stake_amount
                except Exception as exception:
                    return None

        return None
    
    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = list()

        # Buy Condition 0
        buy_indicator = self.buy_indicator0.value
        buy_crossed_indicator = self.buy_crossed_indicator0.value
        buy_operator = self.buy_operator0.value
        buy_real_num = self.buy_real_num0.value
        condition, dataframe = condition_generator(
            dataframe,
            buy_operator,
            buy_indicator,
            buy_crossed_indicator,
            buy_real_num
        )
        conditions.append(condition)

        # Buy Condition 1
        buy_indicator = self.buy_indicator1.value
        buy_crossed_indicator = self.buy_crossed_indicator1.value
        buy_operator = self.buy_operator1.value
        buy_real_num = self.buy_real_num1.value

        condition, dataframe = condition_generator(
            dataframe,
            buy_operator,
            buy_indicator,
            buy_crossed_indicator,
            buy_real_num
        )
        conditions.append(condition)

        # Buy Condition 2
        buy_indicator = self.buy_indicator2.value
        buy_crossed_indicator = self.buy_crossed_indicator2.value
        buy_operator = self.buy_operator2.value
        buy_real_num = self.buy_real_num2.value
        condition, dataframe = condition_generator(
            dataframe,
            buy_operator,
            buy_indicator,
            buy_crossed_indicator,
            buy_real_num
        )
        conditions.append(condition)

        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 = list()
        
        # Sell Condition 0
        sell_indicator = self.sell_indicator0.value
        sell_crossed_indicator = self.sell_crossed_indicator0.value
        sell_operator = self.sell_operator0.value
        sell_real_num = self.sell_real_num0.value
        condition, dataframe = condition_generator(
            dataframe,
            sell_operator,
            sell_indicator,
            sell_crossed_indicator,
            sell_real_num
        )
        conditions.append(condition)

        # Sell Condition 1
        sell_indicator = self.sell_indicator1.value
        sell_crossed_indicator = self.sell_crossed_indicator1.value
        sell_operator = self.sell_operator1.value
        sell_real_num = self.sell_real_num1.value
        condition, dataframe = condition_generator(
            dataframe,
            sell_operator,
            sell_indicator,
            sell_crossed_indicator,
            sell_real_num
        )
        conditions.append(condition)

        # Sell Condition 2
        sell_indicator = self.sell_indicator2.value
        sell_crossed_indicator = self.sell_crossed_indicator2.value
        sell_operator = self.sell_operator2.value
        sell_real_num = self.sell_real_num2.value
        condition, dataframe = condition_generator(
            dataframe,
            sell_operator,
            sell_indicator,
            sell_crossed_indicator,
            sell_real_num
        )
        conditions.append(condition)

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

        return dataframe