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TRIWAVE.py
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from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
import talib.abstract as ta
from technical import qtpylib, pivots_points
import numpy as np
import logging
import pandas as pd
import pandas_ta as pta
import datetime
from datetime import datetime, timedelta, timezone
from typing import Optional
import talib.abstract as ta
from technical.util import resample_to_interval, resampled_merge
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter, RealParameter, merge_informative_pair)
from freqtrade.strategy import stoploss_from_open
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade
import technical.indicators as ftt
logger = logging.getLogger('freqtrade')
### Change log ###
# C.T. 3-9-23
# adding bull/bear detect of 1hr fast ewo
### Change log ###
def PC(dataframe, in1, in2):
df = dataframe.copy()
pc = ((in2-in1)/in1) * 100
return pc
class TRIWAVE(IStrategy):
### Strategy parameters ###
exit_profit_only = True ### No selling at a loss
use_custom_stoploss = True
trailing_stop = False # True
ignore_roi_if_entry_signal = True
use_exit_signal = True
stoploss = -0.25
# DCA Parameters
position_adjustment_enable = True
max_entry_position_adjustment = 0
max_dca_multiplier = 1
market_status = 0
minimal_roi = {
"0": 0.215,
}
### Hyperoptable parameters ###
# protections
cooldown_lookback = IntParameter(24, 48, default=46, space="protection", optimize=True)
stop_duration = IntParameter(12, 200, default=5, space="protection", optimize=True)
use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True)
# SMAOffset
filterlength = IntParameter(low=15, high=35, default=25, space='sell', optimize=True)
max_length = CategoricalParameter([24, 48, 72, 96, 144, 192, 240], default=48, space="buy", optimize=False)
# Buy Parameters
rsi_buy = IntParameter(55, 70, default=65, space='buy', optimize=True)
rsi_buy_safe = IntParameter(40, 55, default=50, space='buy', optimize=True)
rsi_ma_buypc = IntParameter(-5, 5, default=0, space='buy', optimize=True)
sma200_buy_pc = IntParameter(-5, 5, default=0, space='buy', optimize=True)
willr_buy = IntParameter(-50, -20, default=-50, space='buy', optimize=True)
auto_buy = IntParameter(5, 10, default=8, space='buy', optimize=True)
auto_buy_bearzzz = IntParameter(1, 15, default=2, space='buy', optimize=True)
# Buy Parameters
rsi_sell = IntParameter(55, 70, default=50, space='sell', optimize=True)
rsi_sell_safe = IntParameter(60, 80, default=70, space='sell', optimize=True)
rsi_ma_sellpc = IntParameter(-5, 5, default=0, space='sell', optimize=True)
sma200_sell_pc = IntParameter(-5, 5, default=0, space='sell', optimize=True)
willr_sell = IntParameter(-50, -20, default=-20, space='sell', optimize=True)
auto_sell = IntParameter(3, 10, default=4, space='sell', optimize=True)
### BTC and Pair EWO values
bull = DecimalParameter(-0.25, 0.25, default=0, space='buy',decimals=2, optimize=True)
estop = DecimalParameter(-0.5, 0, default=-0.5, space='sell',decimals=2, optimize=True)
### Buy Weight Mulitpliers ###
x1 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='buy', optimize=True)
x2 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='buy', optimize=True)
x3 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='buy', optimize=True)
x4 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='buy', optimize=True)
x5 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='buy', optimize=True)
x6 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='buy', optimize=True)
x7 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='buy', optimize=True)
x8 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='buy', optimize=True)
x9 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='buy', optimize=True)
x10 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='buy', optimize=True)
### Sell Weight Mulitpliers ###
y1 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='sell', optimize=True)
y2 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='sell', optimize=True)
y3 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='sell', optimize=True)
y4 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='sell', optimize=True)
y5 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='sell', optimize=True)
y6 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='sell', optimize=True)
y7 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='sell', optimize=True)
y8 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='sell', optimize=True)
y9 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='sell', optimize=True)
y10 = DecimalParameter(0.3, 5.0, default=1, decimals=1, space='sell', optimize=True)
#trailing stop loss optimiziation
tsl_target5 = DecimalParameter(low=0.25, high=0.4, decimals=1, default=0.3, space='sell', optimize=True, load=True)
ts5 = DecimalParameter(low=0.04, high=0.06, default=0.05, space='sell', optimize=True, load=True)
tsl_target4 = DecimalParameter(low=0.15, high=0.25, default=0.2, space='sell', optimize=True, load=True)
ts4 = DecimalParameter(low=0.03, high=0.05, default=0.045, space='sell', optimize=True, load=True)
tsl_target3 = DecimalParameter(low=0.08, high=0.15, default=0.15, space='sell', optimize=True, load=True)
ts3 = DecimalParameter(low=0.025, high=0.04, default=0.035, space='sell', optimize=True, load=True)
tsl_target2 = DecimalParameter(low=0.06, high=0.08, default=0.1, space='sell', optimize=True, load=True)
ts2 = DecimalParameter(low=0.015, high=0.03, default=0.02, space='sell', optimize=True, load=True)
tsl_target1 = DecimalParameter(low=0.04, high=0.06, default=0.06, space='sell', optimize=True, load=True)
ts1 = DecimalParameter(low=0.01, high=0.016, default=0.013, space='sell', optimize=True, load=True)
tsl_target0 = DecimalParameter(low=0.02, high=0.04, default=0.03, space='sell', optimize=True, load=True)
ts0 = DecimalParameter(low=0.008, high=0.015, default=0.01, space='sell', optimize=True, load=True)
## Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'ioc'
}
# Optimal timeframe for the strategy
timeframe = '15m'
informative_timeframe = '2h'
process_only_new_candles = True
startup_candle_count = 30
### protections ###
@property
def protections(self):
prot = []
prot.append({
"method": "CooldownPeriod",
"stop_duration_candles": self.cooldown_lookback.value
})
if self.use_stop_protection.value:
prot.append({
"method": "StoplossGuard",
"lookback_period_candles": 24 * 3,
"trade_limit": 2,
"stop_duration_candles": self.stop_duration.value,
"only_per_pair": False
})
return prot
def informative_pairs(self):
pairs = self.dp.current_whitelist()
pairs += ['BTC/USDT']
informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
return informative_pairs
def get_informative_indicators(self, metadata: dict):
dataframe = self.dp.get_pair_dataframe(
pair=metadata['pair'], timeframe=self.informative_timeframe)
return dataframe
### Dollar Cost Averaging ###
# This is called when placing the initial order (opening trade)
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: Optional[float], max_stake: float,
leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
# We need to leave most of the funds for possible further DCA orders
# This also applies to fixed stakes
return proposed_stake / self.max_dca_multiplier
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float,
min_stake: Optional[float], max_stake: float,
current_entry_rate: float, current_exit_rate: float,
current_entry_profit: float, current_exit_profit: float,
**kwargs) -> Optional[float]:
if current_profit > 0.10 and trade.nr_of_successful_exits == 0:
# Take half of the profit at +5%
return -(trade.stake_amount / 2)
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
for stop5 in self.tsl_target5.range:
if (current_profit > stop5):
for stop5a in self.ts5.range:
self.dp.send_msg(f'*** {pair} *** Profit: {current_profit} - lvl5 {stop5}/{stop5a} activated')
return stop5a
for stop4 in self.tsl_target4.range:
if (current_profit > stop4):
for stop4a in self.ts4.range:
self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl4 {stop4}/{stop4a} activated')
return stop4a
for stop3 in self.tsl_target3.range:
if (current_profit > stop3):
for stop3a in self.ts3.range:
self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl3 {stop3}/{stop3a} activated')
return stop3a
for stop2 in self.tsl_target2.range:
if (current_profit > stop2):
for stop2a in self.ts2.range:
self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl2 {stop2}/{stop2a} activated')
return stop2a
for stop1 in self.tsl_target1.range:
if (current_profit > stop1):
for stop1a in self.ts1.range:
self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl1 {stop1}/{stop1a} activated')
return stop1a
for stop0 in self.tsl_target0.range:
if (current_profit > stop0):
for stop0a in self.ts0.range:
self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl0 {stop0}/{stop0a} activated')
return stop0a
return self.stoploss
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if self.dp:
inf_tf = '2h'
pair = metadata['pair']
informative = self.dp.get_pair_dataframe(pair=pair, timeframe=inf_tf)
# RSI
informative['rsi_med'] = ta.RSI(informative)
informative['rsi_ma_med'] = ta.SMA(informative['rsi_med'], timeperiod=10)
# WaveTrend using OHLC4 or HA close - 3/21
ap = (0.25 * (informative['high'] + informative['low'] + informative["close"] + informative["open"]))
informative['esa_med'] = ta.EMA(ap, timeperiod = 3)
informative['d_med'] = ta.EMA(abs(ap - informative['esa_med']), timeperiod = 3)
informative['wave_ci_med'] = (ap-informative['esa_med']) / (0.015 * informative['d_med'])
informative['wave_t1_med'] = ta.EMA(informative['wave_ci_med'], timeperiod = 21)
informative['wave_t2_med'] = ta.SMA(informative['wave_t1_med'], timeperiod = 3)
informative['t1_pc_med'] = PC(informative, informative['wave_t1_med'], informative['wave_t1_med'].shift(1))
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
### 5m indicators ###
# WaveTrend using OHLC4 or HA close - 3/21
ap = (0.25 * (dataframe['high'] + dataframe['low'] + dataframe["close"] + dataframe["open"]))
dataframe['esa'] = ta.EMA(ap, timeperiod = 3)
dataframe['d'] = ta.EMA(abs(ap - dataframe['esa']), timeperiod = 3)
dataframe['wave_ci'] = (ap-dataframe['esa']) / (0.015 * dataframe['d'])
dataframe['wave_t1'] = ta.EMA(dataframe['wave_ci'], timeperiod = 21)
dataframe['wave_t2'] = ta.SMA(dataframe['wave_t1'], timeperiod = 3)
dataframe['t1_pc'] = PC(dataframe, dataframe['wave_t1'], dataframe['wave_t1'].shift(1))
# Filter ZEMA
for length in self.filterlength.range:
dataframe[f'ema_1{length}'] = ta.EMA(dataframe['close'], timeperiod=length)
dataframe[f'ema_2{length}'] = ta.EMA(dataframe[f'ema_1{length}'], timeperiod=length)
dataframe[f'ema_dif{length}'] = dataframe[f'ema_1{length}'] - dataframe[f'ema_2{length}']
dataframe[f'zema_{length}'] = dataframe[f'ema_1{length}'] + dataframe[f'ema_dif{length}']
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['rsi_ma'] = ta.SMA(dataframe['rsi'], timeperiod=10)
dataframe['rsi_ma_pcnt'] = PC(dataframe, dataframe['rsi_ma'], dataframe['rsi_ma'].shift(1))
# SMA
dataframe['200_SMA'] = ta.SMA(dataframe["close"], timeperiod = 200)
dataframe['200_SMAPC'] = PC(dataframe, dataframe['200_SMA'], dataframe['200_SMA'].shift(1) )
# Plot 0
dataframe['zero'] = 0
# Williams R%
dataframe['willr14'] = pta.willr(dataframe['high'], dataframe['low'], dataframe['close'])
dataframe['willr14PC'] = PC(dataframe, dataframe['willr14'], dataframe['willr14'].shift(1) )
for l in self.max_length.range:
dataframe['min'] = dataframe['open'].rolling(l).min()
dataframe['max'] = dataframe['close'].rolling(l).max()
# distance from the rolling max in percent
dataframe['from_200'] = ta.SMA(((((dataframe['close'] + dataframe['open']) / 2) - dataframe['200_SMA']) / dataframe['close']) * 100, timeperiod=5)
### Buying Weights ###
dataframe.loc[(dataframe['rsi']<self.rsi_buy.value), 'rsi_buy1'] = 1
dataframe.loc[(dataframe['rsi']>self.rsi_buy.value), 'rsi_buy1'] = -1
dataframe.loc[(dataframe['rsi']>dataframe['rsi_ma']), 'rsi_buy2'] = 1
dataframe.loc[(dataframe['rsi']<dataframe['rsi_ma']), 'rsi_buy2'] = -1
dataframe.loc[(dataframe['rsi_ma_pcnt']>self.rsi_ma_buypc.value), 'rsi_buy3'] = 1
dataframe.loc[(dataframe['rsi_ma_pcnt']<self.rsi_ma_buypc.value), 'rsi_buy3'] = -1
dataframe.loc[(dataframe['rsi']<self.rsi_buy_safe.value), 'rsi_buy4'] = 2
dataframe.loc[(dataframe['rsi']>self.rsi_buy_safe.value), 'rsi_buy4'] = 0
dataframe['rsi_weight'] = (
(dataframe['rsi_buy1']+dataframe['rsi_buy2']+dataframe['rsi_buy3']+dataframe['rsi_buy4'])/4) * self.x1.value
dataframe.loc[((dataframe['close'] > dataframe['200_SMA']) & (dataframe['200_SMAPC'] > self.sma200_buy_pc.value)), 'sma_buy1'] = 1
dataframe.loc[((dataframe['close'] < dataframe['200_SMA'])& (dataframe['200_SMAPC'] > self.sma200_buy_pc.value)), 'sma_buy1'] = 2
dataframe.loc[((dataframe['close'] > dataframe['200_SMA']) & (dataframe['200_SMAPC'] < self.sma200_buy_pc.value)), 'sma_buy1'] = -1
dataframe.loc[((dataframe['close'] < dataframe['200_SMA']) & (dataframe['200_SMAPC'] < self.sma200_buy_pc.value)), 'sma_buy1'] = -1
dataframe.loc[(dataframe['200_SMAPC'] > self.sma200_buy_pc.value), 'sma_buy2'] = 1
dataframe.loc[(dataframe['200_SMAPC'] < self.sma200_buy_pc.value), 'sma_buy2'] = -1
dataframe['200SMA_weight'] = ((dataframe['sma_buy1']+dataframe['sma_buy2'])/2) * self.x4.value
dataframe.loc[(dataframe['willr14'] < self.willr_buy.value), 'willr_buy1'] = 1
dataframe.loc[(dataframe['willr14'] > self.willr_buy.value), 'willr_buy1'] = -1
dataframe.loc[(dataframe['willr14'] > -80), 'willr_buy2'] = 1
dataframe.loc[(dataframe['willr14'] < -80), 'willr_buy2'] = -1
dataframe.loc[(dataframe['willr14PC'] > 0), 'willr_buy3'] = 1
dataframe.loc[(dataframe['willr14PC'] < 0), 'willr_buy3'] = -1
dataframe['willr_weight'] = ((dataframe['willr_buy1']+dataframe['willr_buy2']+dataframe['willr_buy3'])/3) * self.x5.value
dataframe['from_weight'] = -(dataframe['from_200'] * self.x10.value)
dataframe['auto_buy'] = dataframe[['rsi_weight', 'willr_weight', '200SMA_weight', 'from_weight']].sum(axis=1)
### SELLING ###
dataframe.loc[(dataframe['rsi']<self.rsi_sell.value), 'rsi_sell1'] = 1
dataframe.loc[(dataframe['rsi']>self.rsi_sell.value), 'rsi_sell1'] = -1
dataframe.loc[(dataframe['rsi']>dataframe['rsi_ma']), 'rsi_sell2'] = -1
dataframe.loc[(dataframe['rsi']<dataframe['rsi_ma']), 'rsi_sell2'] = 1
dataframe.loc[(dataframe['rsi_ma_pcnt']>self.rsi_ma_sellpc.value), 'rsi_sell3'] = -1
dataframe.loc[(dataframe['rsi_ma_pcnt']<self.rsi_ma_sellpc.value), 'rsi_sell3'] = 1
dataframe.loc[(dataframe['rsi']<self.rsi_sell_safe.value), 'rsi_sell4'] = -1
dataframe.loc[(dataframe['rsi']>self.rsi_sell_safe.value), 'rsi_sell4'] = 1
dataframe['rsi_weight_sell'] = (
(dataframe['rsi_sell1']+dataframe['rsi_sell2']+dataframe['rsi_sell3']+dataframe['rsi_sell4'])/4) * self.y1.value
dataframe.loc[((dataframe['close'] > dataframe['200_SMA']) & (dataframe['200_SMAPC'] > self.sma200_sell_pc.value)), 'sma_sell1'] = -1
dataframe.loc[((dataframe['close'] < dataframe['200_SMA'])& (dataframe['200_SMAPC'] > self.sma200_sell_pc.value)), 'sma_sell1'] = -2
dataframe.loc[((dataframe['close'] > dataframe['200_SMA']) & (dataframe['200_SMAPC'] < self.sma200_sell_pc.value)), 'sma_sell1'] = 2
dataframe.loc[((dataframe['close'] < dataframe['200_SMA']) & (dataframe['200_SMAPC'] < self.sma200_sell_pc.value)), 'sma_sell1'] = 1
dataframe.loc[(dataframe['200_SMAPC'] > self.sma200_sell_pc.value), 'sma_sell2'] = -1
dataframe.loc[(dataframe['200_SMAPC'] < self.sma200_sell_pc.value), 'sma_sell2'] = 1
dataframe['200SMA_weight_sell'] = ((dataframe['sma_sell1']+dataframe['sma_sell2'])/2) * self.y4.value
dataframe.loc[(dataframe['willr14'] < self.willr_sell.value), 'willr_sell1'] = -1
dataframe.loc[(dataframe['willr14'] > self.willr_sell.value), 'willr_sell1'] = 1
dataframe.loc[(dataframe['willr14'] > -10), 'willr_sell2'] = 1
dataframe.loc[(dataframe['willr14'] < -10), 'willr_sell2'] = -1
dataframe.loc[(dataframe['willr14PC'] > 0), 'willr_sell3'] = -1
dataframe.loc[(dataframe['willr14PC'] < 0), 'willr_sell3'] = 1
dataframe['willr_weight_sell'] = ((dataframe['willr_sell1']+dataframe['willr_sell2']+dataframe['willr_sell3'])/3) * self.y5.value
dataframe['from_weight_sell'] = (dataframe['from_200'] * self.y10.value)
dataframe['auto_sell'] = dataframe[['rsi_weight_sell', 'willr_weight_sell', '200SMA_weight_sell', 'from_weight_sell']].sum(axis=1)
dataframe['auto_buy_decision'] = ta.SMA((dataframe['auto_buy'] - dataframe['auto_sell']), timeperiod=2)
dataframe['auto_sell_decision'] = ta.SMA((dataframe['auto_sell'] - dataframe['auto_buy']), timeperiod=2)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
# (dataframe['auto_buy_decision'] >= self.auto_buy.value) &
(qtpylib.crossed_above(dataframe['auto_buy_decision'], self.auto_buy.value)) &
(dataframe['volume'] > 0)
),
['enter_long', 'enter_tag']] = (1, 'auto buy bullzzz')
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['auto_buy_decision'], (self.auto_buy.value + self.auto_buy_bearzzz.value))) &
(dataframe['volume'] > 0)
),
['enter_long', 'enter_tag']] = (1, 'auto buy bearzzz')
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['auto_sell_decision'] >= self.auto_sell.value) &
(dataframe['volume'] > 0)
),
['exit_long', 'exit_tag']] = (1, 'auto_sell')
return dataframe