Obelisk_Ichimoku_Slow_v1_3

# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy, merge_informative_pair
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

# --------------------------------
import pandas as pd
import numpy as np
import technical.indicators as ftt
from freqtrade.exchange import timeframe_to_minutes

# Obelisk_Ichimoku_Slow v1.3 - 2021-04-20
#
# by Obelisk
# https://github.com/brookmiles/
#
# 1.3 increase return without too much additional drawdown
#  - add ema entry guards
#  - remove cloud top exit signal from 1.2
#
# The point of this strategy is to buy and hold an up trend as long as possible.
# If you are tempted to add ROI or trailing stops, you will need to make other modifications as well.
#
# This strategy can optionally be backtested at 5m or 1m to validate roi/trailing stop behaviour (there isn't any).
#
# WARNING
#
# Do not use stoploss_on_exchange or the bot may trigger emergencysell when it
# fails to place the stoploss.
#
# WARNING
#
# This strat will buy into ongoing trends, so pay attention to timing when you launch it.
# If the market is peaking then you may end up buying into trends that are just about to end.
#
#
# Contributions:
# JimmyNixx - SSL Channel confirmation
#
# Backtested with pairlist generated with:
#     "pairlists": [
#         {
#             "method": "VolumePairList",
#             "number_assets": 25,
#             "sort_key": "quoteVolume",
#             "refresh_period": 1800
#         },
#         {"method": "AgeFilter", "min_days_listed": 10},
#         {"method": "PrecisionFilter"},
#         {"method": "PriceFilter", "low_price_ratio": 0.001},
#         {
#             "method": "RangeStabilityFilter",
#             "lookback_days": 3,
#             "min_rate_of_change": 0.1,
#             "refresh_period": 1440
#         },
#     ],

def ssl_atr(dataframe, length = 7):
    df = dataframe.copy()
    df['smaHigh'] = df['high'].rolling(length).mean() + df['atr']
    df['smaLow'] = df['low'].rolling(length).mean() - df['atr']
    df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN))
    df['hlv'] = df['hlv'].ffill()
    df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
    df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])
    return df['sslDown'], df['sslUp']

class Obelisk_Ichimoku_Slow_v1_3(IStrategy):

    # Optimal timeframe for the strategy
    timeframe = '1h'

    # generate signals from the 1h timeframe
    informative_timeframe = '1h'

    # WARNING: ichimoku is a long indicator, if you remove or use a
    # shorter startup_candle_count your results will be unstable/invalid
    # for up to a week from the start of your backtest or dry/live run
    # (180 candles = 7.5 days)
    startup_candle_count = 180

    # NOTE: this strat only uses candle information, so processing between
    # new candles is a waste of resources as nothing will change
    process_only_new_candles = True

    minimal_roi = {
        "0": 10,
    }

    # WARNING setting a stoploss for this strategy doesn't make much sense, as it will buy
    # back into the trend at the next available opportunity, unless the trend has ended,
    # in which case it would sell anyway.

    # Stoploss:
    stoploss = -0.99


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

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

        displacement = 30
        ichimoku = ftt.ichimoku(dataframe,
            conversion_line_period=20,
            base_line_periods=60,
            laggin_span=120,
            displacement=displacement
            )

        dataframe['chikou_span'] = ichimoku['chikou_span']

        # cross indicators
        dataframe['tenkan_sen'] = ichimoku['tenkan_sen']
        dataframe['kijun_sen'] = ichimoku['kijun_sen']

        # cloud, green a > b, red a < b
        dataframe['senkou_a'] = ichimoku['senkou_span_a']
        dataframe['senkou_b'] = ichimoku['senkou_span_b']
        dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a']
        dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b']
        dataframe['cloud_green'] = ichimoku['cloud_green'] * 1
        dataframe['cloud_red'] = ichimoku['cloud_red'] * -1

        dataframe.loc[:, 'cloud_top'] = dataframe.loc[:, ['senkou_a', 'senkou_b']].max(axis=1)
        dataframe.loc[:, 'cloud_bottom'] = dataframe.loc[:, ['senkou_a', 'senkou_b']].min(axis=1)

        # DANGER ZONE START

        # NOTE: Not actually the future, present data that is normally shifted forward for display as the cloud
        dataframe['future_green'] = (dataframe['leading_senkou_span_a'] > dataframe['leading_senkou_span_b']).astype('int') * 2

        # The chikou_span is shifted into the past, so we need to be careful not to read the
        # current value.  But if we shift it forward again by displacement it should be safe to use.
        # We're effectively "looking back" at where it normally appears on the chart.
        dataframe['chikou_high'] = (
                (dataframe['chikou_span'] > dataframe['senkou_a']) &
                (dataframe['chikou_span'] > dataframe['senkou_b'])
            ).shift(displacement).fillna(0).astype('int')

        # DANGER ZONE END

        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
        dataframe['ema_ok'] = (
                (dataframe['close'] > dataframe['ema50'])
                & (dataframe['ema50'] > dataframe['ema200'])
            ).astype('int') * 2

        dataframe['efi_base'] = ((dataframe['close'] - dataframe['close'].shift()) * dataframe['volume'])
        dataframe['efi'] = ta.EMA(dataframe['efi_base'], 13)
        dataframe['efi_ok'] = (dataframe['efi'] > 0).astype('int')

        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        ssl_down, ssl_up = ssl_atr(dataframe, 10)
        dataframe['ssl_down'] = ssl_down
        dataframe['ssl_up'] = ssl_up
        dataframe['ssl_ok'] = (
                (ssl_up > ssl_down)
            ).astype('int') * 3

        dataframe['ichimoku_ok'] = (
                (dataframe['tenkan_sen'] > dataframe['kijun_sen'])
                & (dataframe['close'] > dataframe['cloud_top'])
                & (dataframe['future_green'] > 0)
                & (dataframe['chikou_high'] > 0)
            ).astype('int') * 4

        dataframe['entry_ok'] = (
                (dataframe['efi_ok'] > 0)
                & (dataframe['open'] < dataframe['ssl_up'])
                & (dataframe['close'] < dataframe['ssl_up'])
            ).astype('int') * 1

        dataframe['trend_pulse'] = (
                (dataframe['ichimoku_ok'] > 0)
                & (dataframe['ssl_ok'] > 0)
                & (dataframe['ema_ok'] > 0)
            ).astype('int') * 2

        dataframe['trend_over'] = (
                (dataframe['ssl_ok'] == 0)
            ).astype('int') * 1

        dataframe.loc[ (dataframe['trend_pulse'] > 0), 'trending'] = 3
        dataframe.loc[ (dataframe['trend_over'] > 0) , 'trending'] = 0
        dataframe['trending'].fillna(method='ffill', inplace=True)

        return dataframe

    def fast_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # none atm
        return dataframe

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

        # Obelisk_Ichimoku_Slow does not use trailing stop or roi and should be safe to backtest at 1h
        # if self.config['runmode'].value in ('backtest', 'hyperopt'):
        #     assert (timeframe_to_minutes(self.timeframe) <= 5), "Backtest this strategy in 5m or 1m timeframe."

        if self.timeframe == self.informative_timeframe:
            dataframe = self.slow_tf_indicators(dataframe, metadata)
        else:
            assert self.dp, "DataProvider is required for multiple timeframes."

            informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
            informative = self.slow_tf_indicators(informative.copy(), metadata)

            dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True)
            # don't overwrite the base dataframe's OHLCV information
            skip_columns = [(s + "_" + self.informative_timeframe) for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
            dataframe.rename(columns=lambda s: s.replace("_{}".format(self.informative_timeframe), "") if (not s in skip_columns) else s, inplace=True)

        dataframe = self.fast_tf_indicators(dataframe, metadata)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe['trending'] > 0)
            & (dataframe['entry_ok'] > 0)
            & (dataframe['date'].dt.minute == 0) # when backtesting at 5m/1m only set signal on the hour
        , 'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe['trending'] == 0)
            & (dataframe['date'].dt.minute == 0) # when backtesting at 5m/1m only set signal on the hour
            , 'sell'] = 1
        return dataframe

    plot_config = {
        # Main plot indicators (Moving averages, ...)
        'main_plot': {
            'senkou_a': {
                'color': 'green',
                'fill_to': 'senkou_b',
                'fill_label': 'Ichimoku Cloud',
                'fill_color': 'rgba(0,0,0,0.2)',
            },
            # plot senkou_b, too. Not only the area to it.
            'senkou_b': {
                'color': 'red',
            },
            'tenkan_sen': { 'color': 'blue' },
            'kijun_sen': { 'color': 'orange' },

            # 'chikou_span': { 'color': 'lightgreen' },

            # 'ssl_up': { 'color': 'green' },
            # 'ssl_down': { 'color': 'red' },

            # 'ema50': { 'color': 'violet' },
            # 'ema200': { 'color': 'magenta' },
        },
        'subplots': {
            "Trend": {
                'trend_pulse': {'color': 'blue'},
                'trending': {'color': 'orange'},
                'trend_over': {'color': 'red'},
            },
            "Signals": {
                'ichimoku_ok': {'color': 'green'},
                'ssl_ok': {'color': 'red'},
                'ema_ok': {'color': 'orange'},
                'entry_ok': {'color': 'blue'},
            },
        }
    }