Full Code of froggleston/cryptofrog-strategies for AI

Repository: froggleston/cryptofrog-strategies
Branch: main
Commit: bebdf6341548
Files: 7
Total size: 55.4 KB

Directory structure:
gitextract__iqrbgj2/

├── .gitignore
├── CryptoFrog.py
├── LICENSE
├── README.md
├── cryptofrog.config.json
├── custom_indicators.py
└── live_plotting.ipynb

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================================================
FILE: CryptoFrog.py
================================================
from typing import Dict, List, Optional, Tuple
from datetime import datetime, timedelta
from cachetools import TTLCache

## I hope you know what these are already
from pandas import DataFrame
import numpy as np

## Indicator libs
import talib.abstract as ta
from finta import TA as fta

## FT stuffs
from freqtrade.strategy import IStrategy, merge_informative_pair, stoploss_from_open, IntParameter, DecimalParameter, CategoricalParameter
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.persistence import Trade
from skopt.space import Dimension

class CryptoFrog(IStrategy):

    # ROI table - this strat REALLY benefits from roi and trailing hyperopt:
    minimal_roi = {
        "0": 0.213,
        "39": 0.103,
        "96": 0.037,
        "166": 0
    }

    # Stoploss:
    stoploss = -0.085

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.047
    trailing_only_offset_is_reached = False
    
    use_custom_stoploss = True
    custom_stop = {
        # Linear Decay Parameters
        'decay-time': 166,       # minutes to reach end, I find it works well to match this to the final ROI value - default 1080
        'decay-delay': 0,         # minutes to wait before decay starts
        'decay-start': -0.085, # -0.32118, # -0.07163,     # starting value: should be the same or smaller than initial stoploss - default -0.30
        'decay-end': -0.02,       # ending value - default -0.03
        # Profit and TA  
        'cur-min-diff': 0.03,     # diff between current and minimum profit to move stoploss up to min profit point
        'cur-threshold': -0.02,   # how far negative should current profit be before we consider moving it up based on cur/min or roc
        'roc-bail': -0.03,        # value for roc to use for dynamic bailout
        'rmi-trend': 50,          # rmi-slow value to pause stoploss decay
        'bail-how': 'immediate',  # set the stoploss to the atr offset below current price, or immediate
        # Positive Trailing
        'pos-trail': True,        # enable trailing once positive  
        'pos-threshold': 0.005,   # trail after how far positive
        'pos-trail-dist': 0.015   # how far behind to place the trail
    }

    # Dynamic ROI
    droi_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'any'], default='any', space='sell', optimize=True)
    droi_pullback = CategoricalParameter([True, False], default=True, space='sell', optimize=True)
    droi_pullback_amount = DecimalParameter(0.005, 0.02, default=0.005, space='sell')
    droi_pullback_respect_table = CategoricalParameter([True, False], default=False, space='sell', optimize=True)    
    
    # Custom Stoploss
    cstp_threshold = DecimalParameter(-0.05, 0, default=-0.03, space='sell')
    cstp_bail_how = CategoricalParameter(['roc', 'time', 'any'], default='roc', space='sell', optimize=True)
    cstp_bail_roc = DecimalParameter(-0.05, -0.01, default=-0.03, space='sell')
    cstp_bail_time = IntParameter(720, 1440, default=720, space='sell')    
    
    stoploss = custom_stop['decay-start']    

    custom_trade_info = {}
    custom_current_price_cache: TTLCache = TTLCache(maxsize=100, ttl=300) # 5 minutes
        
    # run "populate_indicators" only for new candle
    process_only_new_candles = False

    # Experimental settings (configuration will overide these if set)
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    use_dynamic_roi = True    
    
    timeframe = '5m'
    informative_timeframe = '1h'

    # Optional order type mapping
    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }
    
    plot_config = {
        'main_plot': {
            'Smooth_HA_H': {'color': 'orange'},
            'Smooth_HA_L': {'color': 'yellow'},
        },
        'subplots': {
            "StochRSI": {
                'srsi_k': {'color': 'blue'},
                'srsi_d': {'color': 'red'},
            },
            "MFI": {
                'mfi': {'color': 'green'},
            },
            "BBEXP": {
                'bbw_expansion': {'color': 'orange'},
            },
            "FAST": {
                'fastd': {'color': 'red'},
                'fastk': {'color': 'blue'},
            },
            "SQZMI": {
                'sqzmi': {'color': 'lightgreen'},
            },
            "VFI": {
                'vfi': {'color': 'lightblue'},
            },
            "DMI": {
                'dmi_plus': {'color': 'orange'},
                'dmi_minus': {'color': 'yellow'},
            },
            "EMACO": {
                'emac_1h': {'color': 'red'},
                'emao_1h': {'color': 'blue'},
            },
        }
    }

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        #pairs.append("BTC/USDT")
        #pairs.append("ETH/USDT")
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
        return informative_pairs

    ## smoothed Heiken Ashi
    def HA(self, dataframe, smoothing=None):
        df = dataframe.copy()

        df['HA_Close']=(df['open'] + df['high'] + df['low'] + df['close'])/4

        df.reset_index(inplace=True)

        ha_open = [ (df['open'][0] + df['close'][0]) / 2 ]
        [ ha_open.append((ha_open[i] + df['HA_Close'].values[i]) / 2) for i in range(0, len(df)-1) ]
        df['HA_Open'] = ha_open

        df.set_index('index', inplace=True)

        df['HA_High']=df[['HA_Open','HA_Close','high']].max(axis=1)
        df['HA_Low']=df[['HA_Open','HA_Close','low']].min(axis=1)

        if smoothing is not None:
            sml = abs(int(smoothing))
            if sml > 0:
                df['Smooth_HA_O']=ta.EMA(df['HA_Open'], sml)
                df['Smooth_HA_C']=ta.EMA(df['HA_Close'], sml)
                df['Smooth_HA_H']=ta.EMA(df['HA_High'], sml)
                df['Smooth_HA_L']=ta.EMA(df['HA_Low'], sml)
                
        return df
    
    def hansen_HA(self, informative_df, period=6):
        dataframe = informative_df.copy()
        
        dataframe['hhclose']=(dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4
        dataframe['hhopen']= ((dataframe['open'].shift(2) + dataframe['close'].shift(2))/ 2) #it is not the same as real heikin ashi since I found that this is better.
        dataframe['hhhigh']=dataframe[['open','close','high']].max(axis=1)
        dataframe['hhlow']=dataframe[['open','close','low']].min(axis=1)

        dataframe['emac'] = ta.SMA(dataframe['hhclose'], timeperiod=period) #to smooth out the data and thus less noise.
        dataframe['emao'] = ta.SMA(dataframe['hhopen'], timeperiod=period)
        
        return {'emac': dataframe['emac'], 'emao': dataframe['emao']}
    
    ## detect BB width expansion to indicate possible volatility
    def bbw_expansion(self, bbw_rolling, mult=1.1):
        bbw = list(bbw_rolling)

        m = 0.0
        for i in range(len(bbw)-1):
            if bbw[i] > m:
                m = bbw[i]

        if (bbw[-1] > (m * mult)):
            return 1
        return 0

    ## do_indicator style a la Obelisk strategies
    def do_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Stoch fast - mainly due to 5m timeframes
        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']        
        
        #StochRSI for double checking things
        period = 14
        smoothD = 3
        SmoothK = 3
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        stochrsi  = (dataframe['rsi'] - dataframe['rsi'].rolling(period).min()) / (dataframe['rsi'].rolling(period).max() - dataframe['rsi'].rolling(period).min())
        dataframe['srsi_k'] = stochrsi.rolling(SmoothK).mean() * 100
        dataframe['srsi_d'] = dataframe['srsi_k'].rolling(smoothD).mean()

        # Bollinger Bands because obviously
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        
        # SAR Parabol - probably don't need this
        dataframe['sar'] = ta.SAR(dataframe)
        
        ## confirm wideboi variance signal with bbw expansion
        dataframe["bb_width"] = ((dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"])
        dataframe['bbw_expansion'] = dataframe['bb_width'].rolling(window=4).apply(self.bbw_expansion)

        # confirm entry and exit on smoothed HA
        dataframe = self.HA(dataframe, 4)

        # thanks to Hansen_Khornelius for this idea that I apply to the 1hr informative
        # https://github.com/hansen1015/freqtrade_strategy
        hansencalc = self.hansen_HA(dataframe, 6)
        dataframe['emac'] = hansencalc['emac']
        dataframe['emao'] = hansencalc['emao']
        
        # money flow index (MFI) for in/outflow of money, like RSI adjusted for vol
        dataframe['mfi'] = fta.MFI(dataframe)
        
        ## sqzmi to detect quiet periods
        dataframe['sqzmi'] = fta.SQZMI(dataframe) #, MA=hansencalc['emac'])
        
        # Volume Flow Indicator (MFI) for volume based on the direction of price movement
        dataframe['vfi'] = fta.VFI(dataframe, period=14)
        
        dmi = fta.DMI(dataframe, period=14)
        dataframe['dmi_plus'] = dmi['DI+']
        dataframe['dmi_minus'] = dmi['DI-']
        dataframe['adx'] = fta.ADX(dataframe, period=14)
        
        ## for stoploss - all from Solipsis4
        ## simple ATR and ROC for stoploss
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        dataframe['roc'] = ta.ROC(dataframe, timeperiod=9)        
        dataframe['rmi'] = RMI(dataframe, length=24, mom=5)
        ssldown, sslup = SSLChannels_ATR(dataframe, length=21)
        dataframe['sroc'] = SROC(dataframe, roclen=21, emalen=13, smooth=21)
        dataframe['ssl-dir'] = np.where(sslup > ssldown,'up','down')        
        dataframe['rmi-up'] = np.where(dataframe['rmi'] >= dataframe['rmi'].shift(),1,0)      
        dataframe['rmi-up-trend'] = np.where(dataframe['rmi-up'].rolling(5).sum() >= 3,1,0) 
        dataframe['candle-up'] = np.where(dataframe['close'] >= dataframe['close'].shift(),1,0)
        dataframe['candle-up-trend'] = np.where(dataframe['candle-up'].rolling(5).sum() >= 3,1,0)        
        
        return dataframe

    ## stolen from Obelisk's Ichi strat code and backtest blog post, and Solipsis4
    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'])
        
        if self.config['runmode'].value in ('backtest', 'hyperopt'):
            assert (timeframe_to_minutes(self.timeframe) <= 30), "Backtest this strategy in 5m or 1m timeframe."

        if self.timeframe == self.informative_timeframe:
            dataframe = self.do_indicators(dataframe, metadata)
        else:
            if not self.dp:
                return dataframe

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

            informative = self.do_indicators(informative.copy(), metadata)
            
            dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True)
            
            skip_columns = [(s + "_" + self.informative_timeframe) for s in ['date', 'open', 'high', 'low', 'close', 'volume', 'emac', 'emao']]
            dataframe.rename(columns=lambda s: s.replace("_{}".format(self.informative_timeframe), "") if (not s in skip_columns) else s, inplace=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']]['sroc'] = dataframe[['date', 'sroc']].copy().set_index('date')
            self.custom_trade_info[metadata['pair']]['ssl-dir'] = dataframe[['date', 'ssl-dir']].copy().set_index('date')
            self.custom_trade_info[metadata['pair']]['rmi-up-trend'] = dataframe[['date', 'rmi-up-trend']].copy().set_index('date')
            self.custom_trade_info[metadata['pair']]['candle-up-trend'] = dataframe[['date', 'candle-up-trend']].copy().set_index('date')            
            
        return dataframe

    ## cryptofrog signals
    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (
                    ## close ALWAYS needs to be lower than the heiken low at 5m
                    (dataframe['close'] < dataframe['Smooth_HA_L'])
                    &
                    ## Hansen's HA EMA at informative timeframe
                    (dataframe['emac_1h'] < dataframe['emao_1h'])
                )
                &
                (
                    (
                        ## potential uptick incoming so buy
                        (dataframe['bbw_expansion'] == 1) & (dataframe['sqzmi'] == False)
                        &
                        (
                            (dataframe['mfi'] < 20)
                            |
                            (dataframe['dmi_minus'] > 30)
                        )
                    )
                    |
                    (
                        # this tries to find extra buys in undersold regions
                        (dataframe['close'] < dataframe['sar'])
                        &
                        ((dataframe['srsi_d'] >= dataframe['srsi_k']) & (dataframe['srsi_d'] < 30))
                        &
                        ((dataframe['fastd'] > dataframe['fastk']) & (dataframe['fastd'] < 23))
                        &
                        (dataframe['mfi'] < 30)
                    )
                    |
                    (
                        # find smaller temporary dips in sideways
                        (
                            ((dataframe['dmi_minus'] > 30) & qtpylib.crossed_above(dataframe['dmi_minus'], dataframe['dmi_plus']))
                            &
                            (dataframe['close'] < dataframe['bb_lowerband'])
                        )
                        |
                        (
                            ## if nothing else is making a buy signal
                            ## just throw in any old SQZMI shit based fastd
                            ## this needs work!
                            (dataframe['sqzmi'] == True)
                            &
                            ((dataframe['fastd'] > dataframe['fastk']) & (dataframe['fastd'] < 20))
                        )
                    )
                    ## volume sanity checks
                    &
                    (dataframe['vfi'] < 0.0)                    
                    &
                    (dataframe['volume'] > 0)                    
                )
            ),
            'buy'] = 1

        return dataframe
    
    ## more going on here
    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (
                    ## close ALWAYS needs to be higher than the heiken high at 5m
                    (dataframe['close'] > dataframe['Smooth_HA_H'])
                    &
                    ## Hansen's HA EMA at informative timeframe
                    (dataframe['emac_1h'] > dataframe['emao_1h'])
                )
                &
                (
                    ## try to find oversold regions with a corresponding BB expansion
                    (
                        (dataframe['bbw_expansion'] == 1)
                        &
                        (
                            (dataframe['mfi'] > 80)
                            |
                            (dataframe['dmi_plus'] > 30)
                        )
                    )
                    ## volume sanity checks
                    &
                    (dataframe['vfi'] > 0.0)
                    &
                    (dataframe['volume'] > 0)                    
                )
            ),
            'sell'] = 1
        return dataframe

    """
    Everything from here completely stolen from the godly work of @werkkrew
    
    Custom Stoploss 
    """ 
    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)

        if self.config['runmode'].value in ('live', 'dry_run'):
            dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
            sroc = dataframe['sroc'].iat[-1]
        # If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!)
        else:
            sroc = self.custom_trade_info[trade.pair]['sroc'].loc[current_time]['sroc']

        if current_profit < self.cstp_threshold.value:
            if self.cstp_bail_how.value == 'roc' or self.cstp_bail_how.value == 'any':
                # Dynamic bailout based on rate of change
                if (sroc/100) <= self.cstp_bail_roc.value:
                    return 0.001
            if self.cstp_bail_how.value == 'time' or self.cstp_bail_how.value == 'any':
                # Dynamic bailout based on time
                if trade_dur > self.cstp_bail_time.value:
                    return 0.001
                   
        return 1

    """
    Freqtrade ROI Overload for dynamic ROI functionality
    """
    def min_roi_reached_dynamic(self, trade: Trade, current_profit: float, current_time: datetime, trade_dur: int) -> Tuple[Optional[int], Optional[float]]:

        minimal_roi = self.minimal_roi
        _, table_roi = self.min_roi_reached_entry(trade_dur)

        # 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)
                rmi_trend = dataframe['rmi-up-trend'].iat[-1]
                candle_trend = dataframe['candle-up-trend'].iat[-1]
                ssl_dir = dataframe['ssl-dir'].iat[-1]
            # If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!)
            else:
                rmi_trend = self.custom_trade_info[trade.pair]['rmi-up-trend'].loc[current_time]['rmi-up-trend']
                candle_trend = self.custom_trade_info[trade.pair]['candle-up-trend'].loc[current_time]['candle-up-trend']
                ssl_dir = self.custom_trade_info[trade.pair]['ssl-dir'].loc[current_time]['ssl-dir']

            min_roi = table_roi
            max_profit = trade.calc_profit_ratio(trade.max_rate)
            pullback_value = (max_profit - self.droi_pullback_amount.value)
            in_trend = False

            if self.droi_trend_type.value == 'rmi' or self.droi_trend_type.value == 'any':
                if rmi_trend == 1:
                    in_trend = True
            if self.droi_trend_type.value == 'ssl' or self.droi_trend_type.value == 'any':
                if ssl_dir == 'up':
                    in_trend = True
            if self.droi_trend_type.value == 'candle' or self.droi_trend_type.value == 'any':
                if candle_trend == 1:
                    in_trend = True

            # Force the ROI value high if in trend
            if (in_trend == True):
                min_roi = 100
                # If pullback is enabled, allow to sell if a pullback from peak has happened regardless of trend
                if self.droi_pullback.value == True and (current_profit < pullback_value):
                    if self.droi_pullback_respect_table.value == True:
                        min_roi = table_roi
                    else:
                        min_roi = current_profit / 2

        else:
            min_roi = table_roi

        return trade_dur, 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.use_dynamic_roi:
            _, roi = self.min_roi_reached_dynamic(trade, current_profit, current_time, trade_dur)
        else:
            _, roi = self.min_roi_reached_entry(trade_dur)
        if roi is None:
            return False
        else:
            return current_profit > roi    
    
    # 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    
    
    """
    Stripped down version from Schism, meant only to update the price data a bit
    more frequently than the default instead of getting all sorts of trade information
    """
    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

    # nested hyperopt class
    class HyperOpt:

        # defining as dummy, so that no error is thrown about missing
        # sell indicator space when hyperopting for all spaces
        @staticmethod
        def indicator_space() -> List[Dimension]:
            return []

## goddamnit

def RMI(dataframe, *, length=20, mom=5):
    """
    Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L912
    """
    df = dataframe.copy()

    df['maxup'] = (df['close'] - df['close'].shift(mom)).clip(lower=0)
    df['maxdown'] = (df['close'].shift(mom) - df['close']).clip(lower=0)

    df.fillna(0, inplace=True)

    df["emaInc"] = ta.EMA(df, price='maxup', timeperiod=length)
    df["emaDec"] = ta.EMA(df, price='maxdown', timeperiod=length)

    df['RMI'] = np.where(df['emaDec'] == 0, 0, 100 - 100 / (1 + df["emaInc"] / df["emaDec"]))

    return df["RMI"]

def SSLChannels_ATR(dataframe, length=7):
    """
    SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/
    Credit to @JimmyNixx for python
    """
    df = dataframe.copy()

    df['ATR'] = ta.ATR(df, timeperiod=14)
    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']

def SROC(dataframe, roclen=21, emalen=13, smooth=21):
    df = dataframe.copy()

    roc = ta.ROC(df, timeperiod=roclen)
    ema = ta.EMA(df, timeperiod=emalen)
    sroc = ta.ROC(ema, timeperiod=smooth)

    return sroc


================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# cryptofrog-strategies
CryptoFrog - My First Strategy for freqtrade

**_DO NOT USE THIS FOR LIVE TRADING_**

# "Release" Notes

- 2021-04-26: The informatives branch now includes a big refactor to include new KAMA and Madrid Squeeze code. Hyperopting now in the main strategy. I'll pull this into main whenever I feel it's ready.
- 2021-04-20: You'll need the latest freqtrade develop branch otherwise you might see weird "supersell" results in your backtraces. Head to the freqtrade discord for more info.

Heavily borrowing ideas from:

- https://github.com/werkkrew/freqtrade-strategies : Amazing work on Solipsis that influenced my general framework and custom_stoploss
- https://github.com/brookmiles/freqtrade-stuff : Great Ichi based strat from Obelisk
- https://github.com/hansen1015/freqtrade_strategy/blob/main/heikin.py : Using the smoothed Heiken Ashi on the CryptoFrog 1hr informative timeframe

# Things to Know

- Fairly conservative strategy focusing on fewer buys and longer holds to find large peaks.
- Designed to trade altcoins against stablecoins, and I've used USDT intentionally to gain relative stability within BTC/ETH dump cycles
- Hyperopting is now available for most of the key indicator thresholds.
- Protections need to be enabled. I've included a basic template config - hit me up on the freqtrade discord for any info but no surprises expected really
- Included a live_plotting.ipynb notebook that can be used to immediately and easily view backtest results

# TODO

- Better buy signals
- Better informative pair work looking for BTC/ETH trends
- More testing

# Preprequisites

You'll need:
- Python 3.7+
- Jupyter Notebook for the live_plotting.ipynb
- finta
- TA-Lib (I run my bot on a Raspberry Pi 400, so you'll need to build TA-Lib as per the Freqtrade docs if you're doing the same)
- Pandas
- Numpy
- Pandas-TA indicator library
- ~~Solipsis_v4 custom_indicators.py (now included in this repo - thanks for the go-ahead @werkkrew)~~


================================================
FILE: cryptofrog.config.json
================================================
{
    "max_open_trades": -1,
    "stake_currency": "USDT",
    "stake_amount": 150,
    "tradable_balance_ratio": 0.99,
    "fiat_display_currency": "GBP",
    "dry_run": true,
    "dry_run_wallet": 1500,
    "unfilledtimeout": {
        "buy": 20,
        "sell": 40
    },
    "bid_strategy": {
        "price_side" : "ask",
        "ask_last_balance": 0.0,
        "use_order_book": true,
        "order_book_top": 1,
        "check_depth_of_market": {
            "enabled": true,
            "bids_to_ask_delta": 1
        }
    },
    "ask_strategy": {
        "price_side" : "bid",
        "use_order_book": true,
        "order_book_min": 1,
        "order_book_max": 1,
    },
    "exchange": {
        "name": "",
        "sandbox": false,
        "key": "",
        "secret": "",
        "ccxt_config": {"enableRateLimit": true},
        "ccxt_async_config": {
            "enableRateLimit": false,
            "rateLimit": 500
        },
        "pair_whitelist":[],
        "pair_blacklist": [
            "GBP/USDT", "EUR/USDT", "BUSD/USDT", "USDC/USDT"
        ]
    },

    "pairlists": [
    {
        "method": "StaticPairList"
    },
//    {
//        "method": "VolumePairList",
//        "number_assets": 80,
//        "sort_key": "quoteVolume",
//        "refresh_period": 300
//    },

    {"method": "AgeFilter", "min_days_listed": 30},
//    {"method": "PrecisionFilter"},
    {"method": "PriceFilter", "low_price_ratio": 0.01},
    {"method": "SpreadFilter", "max_spread_ratio": 0.003},
    {
        "method": "RangeStabilityFilter",
        "lookback_days": 3,
        "min_rate_of_change": 0.1,
        "refresh_period": 360
    },
    ],

    "protections": [
    {
        "method": "CooldownPeriod",
        "stop_duration_candles": 1
    },
    {
        "method": "StoplossGuard",
        "lookback_period_candles": 6,
        "trade_limit": 2,
        "stop_duration_candles": 1440,
        "only_per_pair": true
    },
//    {
//        "method": "LowProfitPairs",
//        "lookback_period_candles": 3,
//        "trade_limit": 2,
//        "stop_duration_candles": 4,
//        "required_profit": 0.015
//    },
//    {
//        "method": "LowProfitPairs",
//        "lookback_period_candles": 24,
//        "trade_limit": 3,
//        "stop_duration_candles": 12,
//        "required_profit": 0.01
//    }
    ],
    "edge": {
        "enabled": false,
        "process_throttle_secs": 3600,
        "calculate_since_number_of_days": 10,
        "allowed_risk": 0.02,
        "stoploss_range_min": -0.01,
        "stoploss_range_max": -0.3,
        "stoploss_range_step": -0.01,
        "minimum_winrate": 0.60,
        "minimum_expectancy": 0.20,
        "min_trade_number": 10,
        "max_trade_duration_minute": 1440,
        "remove_pumps": false
    },
    "api_server": {
        "enabled": false,
        "listen_ip_address": "127.0.0.1",
        "listen_port": 8080,
        "verbosity": "error",
        "enable_openapi": false,
        "jwt_secret_key": "",
        "CORS_origins": [],
        "username": "",
        "password": ""
    },
    "telegram": {
        "enabled": false,
        "token": "",
        "chat_id": ""
    },
    "initial_state": "running",
    "forcebuy_enable": false,
    "internals": {
        "process_throttle_secs": 5
    },
    "db_url": "sqlite:///cryptofrog.sqlite",
    "user_data_dir" : "user_data",
    "strategy": "CryptoFrog",
    "strategy_path": "user_data/strategies"
}


================================================
FILE: custom_indicators.py
================================================
"""
Solipsis Custom Indicators and Maths
"""
import numpy as np
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

from pandas import DataFrame, Series


"""
Misc. Helper Functions
"""
def same_length(bigger, shorter):
    return np.concatenate((np.full((bigger.shape[0] - shorter.shape[0]), np.nan), shorter))

"""
Maths
"""
def linear_growth(start: float, end: float, start_time: int, end_time: int, trade_time: int) -> float:
    """
    Simple linear growth function. Grows from start to end after end_time minutes (starts after start_time minutes)
    """
    time = max(0, trade_time - start_time)
    rate = (end - start) / (end_time - start_time)

    return min(end, start + (rate * time))

"""
TA Indicators
"""

def zema(dataframe, period, field='close'):
    """
    Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/overlap_studies.py#L79
    Modified slightly to use ta.EMA instead of technical ema
    """
    df = dataframe.copy()

    df['ema1'] = ta.EMA(df[field], timeperiod=period)
    df['ema2'] = ta.EMA(df['ema1'], timeperiod=period)
    df['d'] = df['ema1'] - df['ema2']
    df['zema'] = df['ema1'] + df['d']

    return df['zema']

def RMI(dataframe, *, length=20, mom=5):
    """
    Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L912
    """
    df = dataframe.copy()

    df['maxup'] = (df['close'] - df['close'].shift(mom)).clip(lower=0)
    df['maxdown'] = (df['close'].shift(mom) - df['close']).clip(lower=0)

    df.fillna(0, inplace=True)

    df["emaInc"] = ta.EMA(df, price='maxup', timeperiod=length)
    df["emaDec"] = ta.EMA(df, price='maxdown', timeperiod=length)

    df['RMI'] = np.where(df['emaDec'] == 0, 0, 100 - 100 / (1 + df["emaInc"] / df["emaDec"]))

    return df["RMI"]

def mastreak(dataframe: DataFrame, period: int = 4, field='close') -> Series:
    """
    MA Streak
    Port of: https://www.tradingview.com/script/Yq1z7cIv-MA-Streak-Can-Show-When-a-Run-Is-Getting-Long-in-the-Tooth/
    """    
    df = dataframe.copy()

    avgval = zema(df, period, field)

    arr = np.diff(avgval)
    pos = np.clip(arr, 0, 1).astype(bool).cumsum()
    neg = np.clip(arr, -1, 0).astype(bool).cumsum()
    streak = np.where(arr >= 0, pos - np.maximum.accumulate(np.where(arr <= 0, pos, 0)),
                    -neg + np.maximum.accumulate(np.where(arr >= 0, neg, 0)))

    res = same_length(df['close'], streak)

    return res

def pcc(dataframe: DataFrame, period: int = 20, mult: int = 2):
    """
    Percent Change Channel
    PCC is like KC unless it uses percentage changes in price to set channel distance.
    https://www.tradingview.com/script/6wwAWXA1-MA-Streak-Change-Channel/
    """
    df = dataframe.copy()

    df['previous_close'] = df['close'].shift()

    df['close_change'] = (df['close'] - df['previous_close']) / df['previous_close'] * 100
    df['high_change'] = (df['high'] - df['close']) / df['close'] * 100
    df['low_change'] = (df['low'] - df['close']) / df['close'] * 100

    df['delta'] = df['high_change'] - df['low_change']

    mid = zema(df, period, 'close_change')
    rangema = zema(df, period, 'delta')

    upper = mid + rangema * mult
    lower = mid - rangema * mult

    return upper, rangema, lower

def SSLChannels(dataframe, length=10, mode='sma'):
    """
    Source: https://www.tradingview.com/script/xzIoaIJC-SSL-channel/
    Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L1025
    Usage:
        dataframe['sslDown'], dataframe['sslUp'] = SSLChannels(dataframe, 10)
    """
    if mode not in ('sma'):
        raise ValueError(f"Mode {mode} not supported yet")

    df = dataframe.copy()

    if mode == 'sma':
        df['smaHigh'] = df['high'].rolling(length).mean()
        df['smaLow'] = df['low'].rolling(length).mean()

    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']

def SSLChannels_ATR(dataframe, length=7):
    """
    SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/
    Credit to @JimmyNixx for python
    """
    df = dataframe.copy()

    df['ATR'] = ta.ATR(df, timeperiod=14)
    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']

def WaveTrend(dataframe, chlen=10, avg=21, smalen=4):
    """
    WaveTrend Ocillator by LazyBear
    https://www.tradingview.com/script/2KE8wTuF-Indicator-WaveTrend-Oscillator-WT/
    """
    df = dataframe.copy()

    df['hlc3'] = (df['high'] + df['low'] + df['close']) / 3
    df['esa'] = ta.EMA(df['hlc3'], timeperiod=chlen)
    df['d'] = ta.EMA((df['hlc3'] - df['esa']).abs(), timeperiod=chlen)
    df['ci'] = (df['hlc3'] - df['esa']) / (0.015 * df['d'])
    df['tci'] = ta.EMA(df['ci'], timeperiod=avg)

    df['wt1'] = df['tci']
    df['wt2'] = ta.SMA(df['wt1'], timeperiod=smalen)
    df['wt1-wt2'] = df['wt1'] - df['wt2']

    return df['wt1'], df['wt2']

def T3(dataframe, length=5):
    """
    T3 Average by HPotter on Tradingview
    https://www.tradingview.com/script/qzoC9H1I-T3-Average/
    """
    df = dataframe.copy()

    df['xe1'] = ta.EMA(df['close'], timeperiod=length)
    df['xe2'] = ta.EMA(df['xe1'], timeperiod=length)
    df['xe3'] = ta.EMA(df['xe2'], timeperiod=length)
    df['xe4'] = ta.EMA(df['xe3'], timeperiod=length)
    df['xe5'] = ta.EMA(df['xe4'], timeperiod=length)
    df['xe6'] = ta.EMA(df['xe5'], timeperiod=length)
    b = 0.7
    c1 = -b*b*b
    c2 = 3*b*b+3*b*b*b
    c3 = -6*b*b-3*b-3*b*b*b
    c4 = 1+3*b+b*b*b+3*b*b
    df['T3Average'] = c1 * df['xe6'] + c2 * df['xe5'] + c3 * df['xe4'] + c4 * df['xe3']

    return df['T3Average']


def SROC(dataframe, roclen=21, emalen=13, smooth=21):
    df = dataframe.copy()

    roc = ta.ROC(df, timeperiod=roclen)
    ema = ta.EMA(df, timeperiod=emalen)
    sroc = ta.ROC(ema, timeperiod=smooth)

    return sroc

================================================
FILE: live_plotting.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "difficult-binding",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json, os\n",
    "from pathlib import Path\n",
    "\n",
    "from freqtrade.configuration import Configuration\n",
    "from freqtrade.data.btanalysis import load_trades_from_db, load_backtest_data, load_backtest_stats\n",
    "from freqtrade.data.history import load_pair_history\n",
    "from freqtrade.data.dataprovider import DataProvider\n",
    "from freqtrade.plugins.pairlistmanager import PairListManager\n",
    "from freqtrade.exceptions import ExchangeError, OperationalException\n",
    "from freqtrade.exchange import Exchange\n",
    "from freqtrade.resolvers import ExchangeResolver, StrategyResolver\n",
    "from freqtrade.state import RunMode\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "from collections import deque\n",
    "\n",
    "import nest_asyncio\n",
    "nest_asyncio.apply()\n",
    "\n",
    "configs=[\"cryptofrog.config.json\"]\n",
    "\n",
    "ft_config = Configuration.from_files(files=configs)\n",
    "ft_exchange = ExchangeResolver.load_exchange(ft_config['exchange']['name'], config=ft_config, validate=True)\n",
    "ft_pairlists = PairListManager(ft_exchange, ft_config)\n",
    "ft_dataprovider = DataProvider(ft_config, ft_exchange, ft_pairlists)\n",
    "\n",
    "data_location = Path(ft_config['user_data_dir'], 'data', 'binance')\n",
    "backtest_dir = Path(ft_config['user_data_dir'], 'backtest_results')\n",
    "\n",
    "# Load strategy using values set above\n",
    "strategy = StrategyResolver.load_strategy(ft_config)\n",
    "strategy.dp = ft_dataprovider\n",
    "\n",
    "# Generate buy/sell signals using strategy\n",
    "timeframe = \"5m\"\n",
    "\n",
    "backtest = False\n",
    "\n",
    "if ft_config[\"timeframe\"] is not None:\n",
    "    timeframe = ft_config[\"timeframe\"]\n",
    "    print(\"Using config timeframe:\" , timeframe)\n",
    "elif strategy.timeframe is not None:\n",
    "    timeframe = strategy.timeframe\n",
    "    print(\"Using strategy timeframe:\" , timeframe)\n",
    "else:\n",
    "    print(\"Using default timeframe:\" , timeframe)\n",
    "\n",
    "def do_analysis(pair, strategy, timeframe, only_backtest=False, hist_and_backtest=True):\n",
    "    if only_backtest is True:\n",
    "        print(\"Loading backtest data...\")\n",
    "        trades = load_backtest_data(backtest_dir)\n",
    "    else:\n",
    "        print(\"Loading historic data...\")\n",
    "        candles = load_pair_history(datadir=data_location,\n",
    "                                    timeframe=timeframe,\n",
    "                                    pair=pair,\n",
    "                                    data_format = \"json\",\n",
    "                                    )\n",
    "\n",
    "        # Confirm success\n",
    "        print(\"Loaded \" + str(len(candles)) + f\" rows of data for {pair} from {data_location}\")\n",
    "        df = strategy.analyze_ticker(candles, {'pair': pair})\n",
    "        \n",
    "        if hist_and_backtest is True:\n",
    "            print(\"Loading backtest trades data...\")\n",
    "            trades = load_backtest_data(backtest_dir)            \n",
    "        else:\n",
    "            # Fetch trades from database\n",
    "            print(\"Loading DB trades data...\")\n",
    "            trades = load_trades_from_db(ft_config['db_url'])\n",
    "\n",
    "        print(f\"Generated {df['buy'].sum()} buy / {df['sell'].sum()} sell signals\")\n",
    "        data = df.set_index('date', drop=False)\n",
    "        # print(data.info())\n",
    "        # print(data.tail())\n",
    "        # print(data[\"buy\"].dropna())\n",
    "        return (data, trades)\n",
    "    \n",
    "    return (pd.Dataframe(), trades)\n",
    "\n",
    "def do_plot(pair, data, trades, plot_config=None):\n",
    "    from freqtrade.plot.plotting import generate_candlestick_graph\n",
    "    import plotly.offline as pyo\n",
    "\n",
    "    # Filter trades to one pair\n",
    "    trades_red = trades.loc[trades['pair'] == pair].copy()\n",
    "\n",
    "    # Limit graph period to your BT timerange\n",
    "    data_red = data['2021-04-01':'2021-04-20']\n",
    "\n",
    "    plotconf = strategy.plot_config\n",
    "    if plot_config is not None:\n",
    "        plotconf = plot_config\n",
    "    \n",
    "    # Generate candlestick graph\n",
    "    graph = generate_candlestick_graph(pair=pair,\n",
    "                                       data=data_red,\n",
    "                                       trades=trades_red,\n",
    "                                       plot_config=plotconf\n",
    "                                       )\n",
    "\n",
    "    pyo.iplot(graph, show_link = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "effective-vintage",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# example inline plot config to override strat\n",
    "plot_config = {\n",
    "    'main_plot': {\n",
    "    },\n",
    "    'subplots': {\n",
    "        'STR' :{\n",
    "            'mastreak': {'color': 'black'},\n",
    "        },\n",
    "        'KAMA' :{\n",
    "            'kama': {'color': 'red'},\n",
    "        },\n",
    "        'RMI' :{\n",
    "            'rmi': {'color': 'blue'},\n",
    "        },\n",
    "        'MP' :{\n",
    "            'mp': {'color': 'green'},\n",
    "        },        \n",
    "    }\n",
    "}\n",
    "\n",
    "## set the pairlist you want to plot\n",
    "pairlist = [\"WIN/USDT\", \"SC/USDT\"]\n",
    "\n",
    "## don't do this for more than a couple of pairs and for a few days otherwise Slowness Will Occur\n",
    "for i in pairlist:\n",
    "    (data, trades) = do_analysis(i, strategy, timeframe)\n",
    "    do_plot(i, data, trades) #, plot_config=plot_config)"
   ]
  }
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