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ClucFiatROI.py
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ClucFiatROI.py
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import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
from functools import reduce
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair
from datetime import datetime
from freqtrade.persistence import Trade
from pandas import DataFrame, Series
class ClucFiatROI(IStrategy):
# Buy hyperspace params:
buy_params = {
'bbdelta-close': 0.00642,
'bbdelta-tail': 0.75559,
'close-bblower': 0.01415,
'closedelta-close': 0.00883,
'fisher': -0.97101,
'volume': 18
}
# Sell hyperspace params:
sell_params = {
'sell-bbmiddle-close': 0.95153,
'sell-fisher': 0.60924
}
# ROI table:
minimal_roi = {
"0": 0.04354,
"5": 0.03734,
"8": 0.02569,
"10": 0.019,
"76": 0.01283,
"235": 0.007,
"415": 0
}
# Stoploss:
stoploss = -0.34299
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.01057
trailing_stop_positive_offset = 0.03668
trailing_only_offset_is_reached = True
"""
END HYPEROPT
"""
timeframe = '5m'
use_sell_signal = True
sell_profit_only = False
sell_profit_offset = 0.01
ignore_roi_if_buy_signal = True
startup_candle_count: int = 48
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Set Up Bollinger Bands
upper_bb1, mid_bb1, lower_bb1 = ta.BBANDS(dataframe['close'], timeperiod=40)
upper_bb2, mid_bb2, lower_bb2 = ta.BBANDS(qtpylib.typical_price(dataframe), timeperiod=20)
# only putting some bands into dataframe as the others are not used elsewhere in the strategy
dataframe['lower-bb1'] = lower_bb1
dataframe['lower-bb2'] = lower_bb2
dataframe['mid-bb2'] = mid_bb2
dataframe['bb1-delta'] = (mid_bb1 - dataframe['lower-bb1']).abs()
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
dataframe['ema_fast'] = ta.EMA(dataframe['close'], timeperiod=6)
dataframe['ema_slow'] = ta.EMA(dataframe['close'], timeperiod=48)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=24).mean()
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=9)
# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher-rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# These indicators are used to persist a buy signal in live trading only
# They dramatically slow backtesting down
if self.config['runmode'].value in ('live', 'dry_run'):
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
params = self.buy_params
active_trade = False
if self.config['runmode'].value in ('live', 'dry_run'):
active_trade = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True),]).all()
conditions = []
"""
If this is a fresh buy, apple additional conditions.
Idea is to leverage "ignore_roi_if_buy_signal = True" functionality by using certain
indicators for active trades while applying additional protections to new trades.
"""
if not active_trade:
conditions.append(
(
dataframe['fisher-rsi'].lt(params['fisher'])
) &
((
dataframe['bb1-delta'].gt(dataframe['close'] * params['bbdelta-close']) &
dataframe['closedelta'].gt(dataframe['close'] * params['closedelta-close']) &
dataframe['tail'].lt(dataframe['bb1-delta'] * params['bbdelta-tail']) &
dataframe['close'].lt(dataframe['lower-bb1'].shift()) &
dataframe['close'].le(dataframe['close'].shift())
) |
(
(dataframe['close'] < dataframe['ema_slow']) &
(dataframe['close'] < params['close-bblower'] * dataframe['lower-bb2']) &
(dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * params['volume']))
))
)
else:
conditions.append(dataframe['close'] > dataframe['close'].shift())
conditions.append(dataframe['close'] > dataframe['sar'])
conditions.append(dataframe['volume'].gt(0))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
params = self.sell_params
dataframe.loc[
((dataframe['close'] * params['sell-bbmiddle-close']) > dataframe['mid-bb2']) &
dataframe['ema_fast'].gt(dataframe['close']) &
dataframe['fisher-rsi'].gt(params['sell-fisher']) &
dataframe['volume'].gt(0)
,
'sell'
] = 1
return dataframe
"""
https://www.freqtrade.io/en/latest/strategy-advanced/
Custom Order Timeouts
"""
def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
ob = self.dp.orderbook(pair, 1)
current_price = ob['bids'][0][0]
# Cancel buy order if price is more than 1% above the order.
if current_price > order['price'] * 1.01:
return True
return False
def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
ob = self.dp.orderbook(pair, 1)
current_price = ob['asks'][0][0]
# Cancel sell order if price is more than 1% below the order.
if current_price < order['price'] * 0.99:
return True
return False