Solipsis3

import numpy as np
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import arrow
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair
from typing import Dict, List, Optional, Tuple
from pandas import DataFrame, Series
from functools import reduce
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from cachetools import TTLCache
# Get rid of pandas warnings during backtesting
import pandas as pd
pd.options.mode.chained_assignment = None  # default='warn'
# Strategy specific imports, files must reside in same folder as strategy
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent))
import custom_indicators as cta
'\nSolipsis - By @werkkrew and @JimmyNixx\nThis strategy is an evolution of our previous framework "Schism" which can be found in this repository.  While Schism has been superceded by this\nstrategy there may still be valuable examples and ideas in it.\n\nWe ask for nothing in return except that if you make changes which bring you greater success than what has been provided, you share those ideas back to us\nand the rest of the community. Also, please don\'t nag us with a million questions and especially don\'t blame us if you lose a ton of money using this.\n\nWe take no responsibility for any success or failure you have using this strategy.\n\nApes together strong.\nThis is not financial advice.\nWe like the stock.\nWhere lambo?\n\n*************\nThis is a very advanced strategy.  It requires a lot of configuration, optimization, and understanding of how it works and what it does before use.\nIt **will not** work at all for you "out of the box".  If you download it and run an immediate backtest the odds are the results will be awful.\n\nPlease review the code, understand it, and attempt to do as much due diligence as you can before asking questions about it.\n*************\n\nFEATURES:\n    - Dynamic ROI\n        - Several options, initial idea was to ride trends past ROI in a similar way to trailing stoploss but using indicators.\n        - Fallback choices includes table, roc, atr, and others.  Has the ability to set ROI table values dynamically based on indicator math.\n    - Custom Stoploss\n        - Generally a vanilla implementation of Freqtrade custom stoploss but tries to do some clever things.  Uses indicator data. (Thanks @JoeSchr!)\n    - Dynamic informative indicators based on certain stake currences and whitelist contents.\n        - If BTC/STAKE is not in whitelist, make sure to use that for an informative.\n        - If your stake is BTC or ETH, use COIN/FIAT and BTC/FIAT as informatives.\n    - Ability to provide custom parameters on a per-pair or group of pairs basis, this includes entry/exit/minimal_roi/dynamic_roi/custom_stop settings, if one desired.\n    - Custom indicator file to keep primary strategy clean(ish).\n        - Most (but not all) of what is in there is taken from freqtrade/technical with some slight modification, removes dependenacy on that import and allows\n          for some customization without having to edit those files directly.\n    - Child strategy for stake specific settings and different settings for different instances, hoping to keep this strategy file relatively\n      clutter-free from the extensive options especially when using per-pair settings.\n\nSTRATEGY NOTES:\n    - If trading on a stablecoin or fiat stake (such as USD, EUR, USDT, etc.) is *highly recommended* that you remove BTC/STAKE\n      from your whitelist as this strategy performs much better on alts when using BTC as an informative but does not entry any BTC\n      itself.\n    - It is recommended to configure protections *if/as* you will use them in live and run *some* hyperopt/backtest with\n      "--enable-protections" as this strategy will hit a lot of stoplosses so the stoploss protection is helpful\n      to test. *However* - this option makes hyperopt very slow, so run your initial backtest/hyperopts without this\n      option. Once you settle on a baseline set of options, do some final optimizations with protections on.\n    - It is *not* recommended to use freqtrades built-in trailing stop, nor to hyperopt for that.\n    - It is *highly* recommended to hyperopt this with \'--spaces entry\' only and at least 1000 total epochs several times. There are\n      a lot of variables being hyperopted and it may take a lot of epochs to find the right settings.\n    - It is possible to hyperopt the custom stoploss and dynamic ROI settings, however a change to the freqtrade code is needed.  I have done\n      this in a fork on github and I use it personally, but this code will likely never get merged upstream so use with extreme caution.\n      (https://github.com/werkkrew/freqtrade/tree/hyperopt)\n    - Hyperopt Notes:\n        - Hyperopting entry/custom-stoploss/dynamic-roi together takes a LOT of repeat 1000 epoch runs to get optimal results.  There\n          are a ton of variables moving around and often times the reported best epoch is not desirable.\n        - Avoid hyperopt results with small avg. profit and avg. duration of < 60m (in my opinion.)\n        - I find the best results come from SharpeHyperOptLoss\n        - I personally re-run it until I find epochs with at least 0.5% avg profit and a 10:1 w/l ratio as my personal preference.\n    - It is *recommended* to leave this file untouched and do your configuration / optimizations from the child strategy Solipsis.py.\n\n    - Example of unique entry/exit params per pair/group of pairs:\n\n    custom_pair_params = [\n        {\n            \'pairs\': (\'ABC/XYZ\', \'DEF/XYZ\'),\n            \'entry_params\': {},\n            \'exit_params\': {},\n            \'minimal_roi\': {}\n        }\n    ]\n\nTODO:\n    - Continue to hunt for a better all around entry signal.\n    - Tweak ROI Trend Ride\n        - Adjust pullback to be more dynamic, seems to get out a tad bit early in many cases.\n        - Consider a way to identify very large/fast spikes when RMI has not yet reacted to stay in past ROI point.\n    - Further enchance and optimize custom stop loss\n        - Continue to evaluate good circumstances to bail and exit vs hold on for recovery\n        - Curent implementation seems to work pretty well but feel like there is room for improvement.\n    - Develop a PR to fully support hyperopting the custom_stoploss and dynamic_roi spaces?\n'

class Solipsis3(IStrategy):
    INTERFACE_VERSION = 3
    # Recommended for USD/USDT/etc.
    timeframe = '5m'
    inf_timeframe = '1h'
    entry_params = {'base-mp': 30, 'base-rmi-fast': 50, 'base-rmi-slow': 30, 'inf-guard': 'upper', 'inf-pct-adr-bot': 0.1, 'inf-pct-adr-top': 0.85, 'xbtc-base-rmi': 50, 'xbtc-inf-rmi': 20, 'xtra-base-fiat-rmi': 45, 'xtra-base-stake-rmi': 69, 'xtra-inf-stake-rmi': 27}
    exit_params = {}
    # Custom entry/exit parameters per pair
    custom_pair_params = []
    # Recommended on 5m timeframe
    minimal_roi = {'0': 0.01, '360': 0.005, '720': 0}  # enable dynamic roi which uses trennds and indicators to dynamically manipulate the roi table
    # factor for forumla of how far below peak profit to trigger exit
    # starting value for rmi-slow to be considered a positive trend
    # ending value
    # delay on growth
    # finish time of growth
    # if no trend, do what? (table, roc, atr, roc-table, atr-table)
    # minimum roi value to return in roc or atr mode
    dynamic_roi = {'enabled': True, 'profit-factor': 400, 'rmi-start': 30, 'rmi-end': 70, 'grow-delay': 0, 'grow-time': 720, 'fallback': 'table', 'min-roc-atr': 0.0075}
    use_custom_stoploss = True
    # Linear Decay Parameters
    # minutes to reach end, I find it works well to match this to the final ROI value
    # minutes to wait before decay starts
    # starting value: should be the same or smaller than initial stoploss
    # ending value
    # Profit and TA
    # diff between current and minimum profit to move stoploss up to min profit point
    # how far negative should current profit be before we consider moving it up based on cur/min or roc
    # value for roc to use for dynamic bailout
    # rmi-slow value to pause stoploss decay
    # set the stoploss to the atr offset below current price, or immediate
    # Positive Trailing
    # enable trailing once positive
    # trail after how far positive
    # how far behind to place the trail
    custom_stop = {'decay-time': 1080, 'decay-delay': 0, 'decay-start': -0.3, 'decay-end': -0.03, 'cur-min-diff': 0.03, 'cur-threshold': -0.02, 'roc-bail': -0.03, 'rmi-trend': 50, 'bail-how': 'immediate', 'pos-trail': False, 'pos-threshold': 0.005, 'pos-trail-dist': 0.015}
    stoploss = custom_stop['decay-start']
    # Recommended
    use_exit_signal = False
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    # Required
    startup_candle_count: int = 72
    process_only_new_candles = False
    # Strategy Specific Variable Storage
    custom_trade_info = {}
    custom_fiat = 'USD'  # Only relevant if stake is BTC or ETH
    custom_current_price_cache: TTLCache = TTLCache(maxsize=100, ttl=300)  # 5 minutes
    '\n    Informative Pair Definitions\n    '

    def informative_pairs(self):
        # add all whitelisted pairs on informative timeframe
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.inf_timeframe) for pair in pairs]
        # add extra informative pairs if the stake is BTC or ETH
        if self.config['stake_currency'] in ('BTC', 'ETH'):
            for pair in pairs:
                coin, stake = pair.split('/')
                coin_fiat = f'{coin}/{self.custom_fiat}'
                informative_pairs += [(coin_fiat, self.timeframe)]
            stake_fiat = f"{self.config['stake_currency']}/{self.custom_fiat}"
            informative_pairs += [(stake_fiat, self.timeframe)]
            informative_pairs += [(stake_fiat, self.inf_timeframe)]
        else:
            # if BTC/STAKE is not in whitelist, add it as an informative pair on both timeframes
            btc_stake = f"BTC/{self.config['stake_currency']}"
            if not btc_stake in pairs:
                informative_pairs += [(btc_stake, self.timeframe)]
                informative_pairs += [(btc_stake, self.inf_timeframe)]
        return informative_pairs
    '\n    Indicator Definitions\n    '

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Populate/update the trade data if there is any, set trades to false if not live/dry
        self.custom_trade_info[metadata['pair']] = self.populate_trades(metadata['pair'])
        # Base timeframe indicators
        dataframe['rmi-slow'] = cta.RMI(dataframe, length=21, mom=5)
        dataframe['rmi-fast'] = cta.RMI(dataframe, length=8, mom=4)
        # Indicators for ROI and Custom Stoploss
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=24)
        dataframe['roc'] = ta.ROC(dataframe, timeperiod=9)
        # Momentum Pinball: https://www.tradingview.com/script/fBpVB1ez-Momentum-Pinball-Indicator/
        dataframe['roc-mp'] = ta.ROC(dataframe, timeperiod=6)
        dataframe['mp'] = ta.RSI(dataframe['roc-mp'], timeperiod=6)
        # Trends, Peaks and Crosses
        dataframe['rmi-up'] = np.where(dataframe['rmi-slow'] >= dataframe['rmi-slow'].shift(), 1, 0)
        dataframe['rmi-dn'] = np.where(dataframe['rmi-slow'] <= dataframe['rmi-slow'].shift(), 1, 0)
        dataframe['rmi-up-trend'] = np.where(dataframe['rmi-up'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
        dataframe['rmi-dn-trend'] = np.where(dataframe['rmi-dn'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
        # Base pair informative timeframe indicators
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_timeframe)
        # Get the "average day range" between the 1d high and 3d low to set up guards
        informative['1d_high'] = informative['close'].rolling(24).max()
        informative['3d_low'] = informative['close'].rolling(72).min()
        informative['adr'] = informative['1d_high'] - informative['3d_low']
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True)
        # Other stake specific informative indicators
        # e.g if stake is BTC and current coin is XLM (pair: XLM/BTC)
        if self.config['stake_currency'] in ('BTC', 'ETH'):
            coin, stake = metadata['pair'].split('/')
            fiat = self.custom_fiat
            coin_fiat = f'{coin}/{fiat}'
            stake_fiat = f'{stake}/{fiat}'
            # Informative COIN/FIAT e.g. XLM/USD - Base Timeframe
            coin_fiat_tf = self.dp.get_pair_dataframe(pair=coin_fiat, timeframe=self.timeframe)
            dataframe[f'{fiat}_rmi'] = cta.RMI(coin_fiat_tf, length=21, mom=5)
            # Informative STAKE/FIAT e.g. BTC/USD - Base Timeframe
            stake_fiat_tf = self.dp.get_pair_dataframe(pair=stake_fiat, timeframe=self.timeframe)
            dataframe[f'{stake}_rmi'] = cta.RMI(stake_fiat_tf, length=21, mom=5)
            # Informative STAKE/FIAT e.g. BTC/USD - Informative Timeframe
            stake_fiat_inf_tf = self.dp.get_pair_dataframe(pair=stake_fiat, timeframe=self.inf_timeframe)
            stake_fiat_inf_tf[f'{stake}_rmi'] = cta.RMI(stake_fiat_inf_tf, length=48, mom=5)
            dataframe = merge_informative_pair(dataframe, stake_fiat_inf_tf, self.timeframe, self.inf_timeframe, ffill=True)
        else:
            # Informatives for BTC/STAKE if not in whitelist
            pairs = self.dp.current_whitelist()
            btc_stake = f"BTC/{self.config['stake_currency']}"
            if not btc_stake in pairs:
                # BTC/STAKE - Base Timeframe
                btc_stake_tf = self.dp.get_pair_dataframe(pair=btc_stake, timeframe=self.timeframe)
                dataframe['BTC_rmi'] = cta.RMI(btc_stake_tf, length=14, mom=3)
                # BTC/STAKE - Informative Timeframe
                btc_stake_inf_tf = self.dp.get_pair_dataframe(pair=btc_stake, timeframe=self.inf_timeframe)
                btc_stake_inf_tf['BTC_rmi'] = cta.RMI(btc_stake_inf_tf, length=48, mom=5)
                dataframe = merge_informative_pair(dataframe, btc_stake_inf_tf, self.timeframe, self.inf_timeframe, ffill=True)
        # Slam some indicators into the trade_info dict so we can dynamic roi and custom stoploss in backtest
        if self.dp.runmode.value in ('backtest', 'hyperopt'):
            self.custom_trade_info[metadata['pair']]['roc'] = dataframe[['date', 'roc']].copy().set_index('date')
            self.custom_trade_info[metadata['pair']]['atr'] = dataframe[['date', 'atr']].copy().set_index('date')
            self.custom_trade_info[metadata['pair']]['rmi-slow'] = dataframe[['date', 'rmi-slow']].copy().set_index('date')
            self.custom_trade_info[metadata['pair']]['rmi-up-trend'] = dataframe[['date', 'rmi-up-trend']].copy().set_index('date')
        return dataframe
    '\n    Buy Signal\n    '

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        params = self.get_pair_params(metadata['pair'], 'entry')
        conditions = []
        # Primary guards on informative timeframe to make sure we don't trade when market is peaked or bottomed out
        if params['inf-guard'] == 'upper' or params['inf-guard'] == 'both':
            conditions.append(dataframe['close'] <= dataframe[f'3d_low_{self.inf_timeframe}'] + params['inf-pct-adr-top'] * dataframe[f'adr_{self.inf_timeframe}'])
        if params['inf-guard'] == 'lower' or params['inf-guard'] == 'both':
            conditions.append(dataframe['close'] >= dataframe[f'3d_low_{self.inf_timeframe}'] + params['inf-pct-adr-bot'] * dataframe[f'adr_{self.inf_timeframe}'])
        # Base Timeframe
        conditions.append((dataframe['rmi-dn-trend'] == 1) & (dataframe['rmi-slow'] >= params['base-rmi-slow']) & (dataframe['rmi-fast'] <= params['base-rmi-fast']) & (dataframe['mp'] <= params['base-mp']))
        # Extra conditions for */BTC and */ETH stakes on additional informative pairs
        if self.config['stake_currency'] in ('BTC', 'ETH'):
            conditions.append((dataframe[f"{self.config['stake_currency']}_rmi"] < params['xtra-base-stake-rmi']) | (dataframe[f'{self.custom_fiat}_rmi'] > params['xtra-base-fiat-rmi']))
            conditions.append(dataframe[f"{self.config['stake_currency']}_rmi_{self.inf_timeframe}"] < params['xtra-inf-stake-rmi'])
        else:
            # Extra conditions for BTC/STAKE if not in whitelist
            pairs = self.dp.current_whitelist()
            btc_stake = f"BTC/{self.config['stake_currency']}"
            if not btc_stake in pairs:
                conditions.append((dataframe['BTC_rmi'] < params['xbtc-base-rmi']) & (dataframe[f'BTC_rmi_{self.inf_timeframe}'] > params['xbtc-inf-rmi']))
        conditions.append(dataframe['volume'].gt(0))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1
        return dataframe
    '\n    Sell Signal\n    '

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # params = self.get_pair_params(metadata['pair'], 'exit')
        dataframe['exit_long'] = 0
        return dataframe
    '\n    Custom Stoploss\n    '

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        params = self.get_pair_params(pair, 'custom_stop')
        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
        min_profit = trade.calc_profit_ratio(trade.min_rate)
        max_profit = trade.calc_profit_ratio(trade.max_rate)
        profit_diff = current_profit - min_profit
        decay_stoploss = cta.linear_growth(params['decay-start'], params['decay-end'], params['decay-delay'], params['decay-time'], trade_dur)
        # enable stoploss in positive profits after threshold to trail as specifed distance
        if params['pos-trail'] == True:
            if current_profit > params['pos-threshold']:
                return current_profit - params['pos-trail-dist']
        if self.config['runmode'].value in ('live', 'dry_run'):
            dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
            roc = dataframe['roc'].iat[-1]
            atr = dataframe['atr'].iat[-1]
            rmi_slow = dataframe['rmi-slow'].iat[-1]
        else:
            # If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!)
            roc = self.custom_trade_info[trade.pair]['roc'].loc[current_time]['roc']
            atr = self.custom_trade_info[trade.pair]['atr'].loc[current_time]['atr']
            rmi_slow = self.custom_trade_info[trade.pair]['rmi-slow'].loc[current_time]['rmi-slow']
        if current_profit < params['cur-threshold']:
            # Dynamic bailout based on rate of change
            if roc / 100 <= params['roc-bail']:
                if params['bail-how'] == 'atr':
                    return (current_rate - atr) / current_rate - 1
                elif params['bail-how'] == 'immediate':
                    return current_rate
                else:
                    return decay_stoploss
        # if we might be on a rebound, move the stoploss to the low point or keep it where it was
        if current_profit > min_profit or roc > 0 or rmi_slow >= params['rmi-trend']:
            if profit_diff > params['cur-min-diff'] and current_profit < 0:
                return min_profit
            return -1
        return decay_stoploss
    '\n    Freqtrade ROI Overload for dynamic ROI functionality\n    '

    def min_roi_reached_dynamic(self, trade: Trade, current_profit: float, current_time: datetime, trade_dur: int) -> Tuple[Optional[int], Optional[float]]:
        dynamic_roi = self.get_pair_params(trade.pair, 'dynamic_roi')
        minimal_roi = self.get_pair_params(trade.pair, 'minimal_roi')
        if not dynamic_roi or not minimal_roi:
            return (None, None)
        _, table_roi = self.min_roi_reached_entry(trade_dur, trade.pair)
        # see if we have the data we need to do this, otherwise fall back to the standard table
        if self.custom_trade_info and trade and (trade.pair in self.custom_trade_info):
            if self.config['runmode'].value in ('live', 'dry_run'):
                dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=trade.pair, timeframe=self.timeframe)
                roc = dataframe['roc'].iat[-1]
                atr = dataframe['atr'].iat[-1]
                rmi_slow = dataframe['rmi-slow'].iat[-1]
                rmi_trend = dataframe['rmi-up-trend'].iat[-1]
            else:
                # If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!)
                roc = self.custom_trade_info[trade.pair]['roc'].loc[current_time]['roc']
                atr = self.custom_trade_info[trade.pair]['atr'].loc[current_time]['atr']
                rmi_slow = self.custom_trade_info[trade.pair]['rmi-slow'].loc[current_time]['rmi-slow']
                rmi_trend = self.custom_trade_info[trade.pair]['rmi-up-trend'].loc[current_time]['rmi-up-trend']
            d = dynamic_roi
            open_rate = trade.open_rate
            max_profit = trade.calc_profit_ratio(trade.max_rate)
            atr_roi = max(d['min-roc-atr'], (open_rate + atr) / open_rate - 1)
            roc_roi = max(d['min-roc-atr'], roc / 100)
            # atr as the fallback (if > min-roc-atr)
            if d['fallback'] == 'atr':
                min_roi = atr_roi
            # roc as the fallback (if > min-roc-atr)
            elif d['fallback'] == 'roc':
                min_roi = roc_roi
            # atr or table as the fallback (whichever is larger)
            elif d['fallback'] == 'atr-table':
                min_roi = max(table_roi, atr_roi)
            # roc or table as the fallback (whichever is larger)
            elif d['fallback'] == 'roc-table':
                min_roi = max(table_roi, roc_roi)
            else:
                # default to table
                min_roi = table_roi
            rmi_grow = cta.linear_growth(d['rmi-start'], d['rmi-end'], d['grow-delay'], d['grow-time'], trade_dur)
            profit_factor = 1 - rmi_slow / d['profit-factor']
            pullback_buffer = max_profit * profit_factor
            # If we observe a strong upward trend and our current profit has not retreated from the peak by much, hold
            if rmi_trend == 1 and rmi_slow > rmi_grow:
                if current_profit < pullback_buffer and max_profit > min_roi:
                    # allow immediate bailout if we were above the ROI point and retreated below it
                    min_roi = current_profit / 2
                else:
                    min_roi = 100
        else:
            min_roi = table_roi
        return (trade_dur, min_roi)
    # Minor change to the usual method here to allow feeding the pair for per-pair settings

    def min_roi_reached_entry(self, trade_dur: int, pair: str='backtest') -> Tuple[Optional[int], Optional[float]]:
        minimal_roi = self.get_pair_params(pair, 'minimal_roi')
        roi_list = list(filter(lambda x: x <= trade_dur, minimal_roi.keys()))
        if not roi_list:
            return (None, None)
        roi_entry = max(roi_list)
        min_roi = minimal_roi[roi_entry]
        return (roi_entry, min_roi)
    # Change here to allow loading of the dynamic_roi settings

    def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
        if self.dynamic_roi and 'enabled' in self.dynamic_roi and self.dynamic_roi['enabled']:
            _, roi = self.min_roi_reached_dynamic(trade, current_profit, current_time, trade_dur)
        else:
            _, roi = self.min_roi_reached_entry(trade_dur, trade.pair)
        if roi is None:
            return False
        else:
            return current_profit > roi
    '\n    Trade Timeout Overloads\n    '

    def check_entry_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
        bid_strategy = self.config.get('bid_strategy', {})
        ob = self.dp.orderbook(pair, 1)
        current_price = ob[f"{bid_strategy['price_side']}s"][0][0]
        if current_price > order['price'] * 1.01:
            return True
        return False

    def check_exit_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
        ask_strategy = self.config.get('ask_strategy', {})
        ob = self.dp.orderbook(pair, 1)
        current_price = ob[f"{ask_strategy['price_side']}s"][0][0]
        if current_price < order['price'] * 0.99:
            return True
        return False
    '\n    Custom Methods\n    '
    # Get parameters for various settings on a per pair or group of pairs basis
    # This function can probably be simplified dramatically

    def get_pair_params(self, pair: str, params: str) -> Dict:
        entry_params, exit_params = (self.entry_params, self.exit_params)
        minimal_roi, dynamic_roi = (self.minimal_roi, self.dynamic_roi)
        custom_stop = self.custom_stop
        if self.custom_pair_params:
            # custom_params = next(item for item in self.custom_pair_params if pair in item['pairs'])
            for item in self.custom_pair_params:
                if 'pairs' in item and pair in item['pairs']:
                    custom_params = item
                    if 'entry_params' in custom_params:
                        entry_params = custom_params['entry_params']
                    if 'exit_params' in custom_params:
                        exit_params = custom_params['exit_params']
                    if 'minimal_roi' in custom_params:
                        custom_stop = custom_params['minimal_roi']
                    if 'custom_stop' in custom_params:
                        custom_stop = custom_params['custom_stop']
                    if 'dynamic_roi' in custom_params:
                        dynamic_roi = custom_params['dynamic_roi']
                    break
        if params == 'entry':
            return entry_params
        if params == 'exit':
            return exit_params
        if params == 'minimal_roi':
            return minimal_roi
        if params == 'custom_stop':
            return custom_stop
        if params == 'dynamic_roi':
            return dynamic_roi
        return False
    # Get the current price from the exchange (or local cache)

    def get_current_price(self, pair: str, refresh: bool) -> float:
        if not refresh:
            rate = self.custom_current_price_cache.get(pair)
            # Check if cache has been invalidated
            if rate:
                return rate
        ask_strategy = self.config.get('ask_strategy', {})
        if ask_strategy.get('use_order_book', False):
            ob = self.dp.orderbook(pair, 1)
            rate = ob[f"{ask_strategy['price_side']}s"][0][0]
        else:
            ticker = self.dp.ticker(pair)
            rate = ticker['last']
        self.custom_current_price_cache[pair] = rate
        return rate
    '\n    Stripped down version from Schism, meant only to update the price data a bit\n    more frequently than the default instead of getting all sorts of trade information\n    '

    def populate_trades(self, pair: str) -> dict:
        # Initialize the trades dict if it doesn't exist, persist it otherwise
        if not pair in self.custom_trade_info:
            self.custom_trade_info[pair] = {}
        # init the temp dicts and set the trade stuff to false
        trade_data = {}
        trade_data['active_trade'] = False
        # active trade stuff only works in live and dry, not backtest
        if self.config['runmode'].value in ('live', 'dry_run'):
            # find out if we have an open trade for this pair
            active_trade = Trade.get_trades([Trade.pair == pair, Trade.is_open.is_(True)]).all()
            # if so, get some information
            if active_trade:
                # get current price and update the min/max rate
                current_rate = self.get_current_price(pair, True)
                active_trade[0].adjust_min_max_rates(current_rate)
        return trade_data
# Sub-strategy with parameters specific to BTC stake

class Solipsis3_BTC(Solipsis3):
    timeframe = '15m'
    inf_timeframe = '1h'
    minimal_roi = {'0': 0.01, '720': 0.005, '1440': 0}
# Sub-strategy with parameters specific to ETH stake

class Solipsis3_ETH(Solipsis3):
    timeframe = '15m'
    inf_timeframe = '1h'
    minimal_roi = {'0': 0.01, '720': 0.005, '1440': 0}