Timeframe
15m
Direction
Long Only
Stoploss
-9.0%
Trailing Stop
Yes
ROI
0m: 50.0%, 60m: 45.0%, 120m: 40.0%, 180m: 30.0%
Interface Version
N/A
Startup Candles
N/A
Indicators
5
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
import logging
import numpy as np
import pandas as pd
from technical import qtpylib, pivots_points
from pandas import DataFrame
from datetime import datetime, timezone
from typing import Optional
from functools import reduce
import talib.abstract as ta
import pandas_ta as pta
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter, RealParameter, merge_informative_pair)
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade
logger = logging.getLogger(__name__)
class thetank4TV(IStrategy):
### Strategy parameters ###
exit_profit_only = True ### No selling at a loss
use_custom_stoploss = True
trailing_stop = True
position_adjustment_enable = True
ignore_roi_if_entry_signal = True
use_exit_signal = True
stoploss = -0.09
startup_candle_count: int = 30
timeframe = '15m'
# DCA Parameters
position_adjustment_enable = True
max_entry_position_adjustment = 0
max_dca_multiplier = 1
minimal_roi = {
"12000": 0.01,
"2400": 0.10,
"300": 0.15,
"180": 0.30,
"120":0.40,
"60": 0.45,
"0": 0.50
}
### Hyperoptable parameters ###
# entry optizimation
max_epa = CategoricalParameter([0, 1], default=0, space="buy", optimize=True)
# protections
cooldown_lookback = IntParameter(4, 48, default=16, space="protection", optimize=True)
stop_duration = IntParameter(5, 96, default=5, space="protection", optimize=True)
use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True)
# indicators
wavelength = IntParameter(low=3, high=10, default=8, space='buy', optimize=True)
crosslength = IntParameter(low=3, high=10, default=3, space='sell', optimize=True)
filterlength = IntParameter(low=15, high=35, default=25, space='buy', optimize=True)
# trading
buy_rsi = IntParameter(low=20, high=35, default=25, space='buy', optimize=True, load=True)
buy_rsi_bear = IntParameter(low=40, high=55, default=45, space='buy', optimize=True, load=True)
buy_rsi_bull = IntParameter(low=40, high=75, default=65, space='buy', optimize=True, load=True)
buy_wt_bear = IntParameter(low=20, high=55, default=45, space='buy', optimize=True, load=True)
buy_wt_bull = IntParameter(low=30, high=75, default=65, space='buy', optimize=True, load=True)
sell_rsi = IntParameter(low=50, high=80, default=55, space='sell', optimize=True, load=True)
# dca level optimization
dca1 = DecimalParameter(low=0.03, high=0.08, decimals=2, default=0.05, space='buy', optimize=True, load=True)
# dca2 = DecimalParameter(low=0.08, high=0.15, decimals=2, default=0.10, space='buy', optimize=True, load=True)
# dca3 = DecimalParameter(low=0.15, high=0.25, decimals=2, default=0.15, space='buy', optimize=True, load=True)
#trailing stop loss optimiziation
tsl_target5 = DecimalParameter(low=0.2, 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=0.18, high=0.3, 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=0.10, 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=0.07, high=0.12, 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=0.05, high=0.06, default=0.07, 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=0.05, default=0.03, space='sell', optimize=True, load=True)
ts0 = DecimalParameter(low=0.008, high=0.015, default=0.013, space='sell', optimize=True, load=True)
### 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": 1,
"stop_duration_candles": self.stop_duration.value,
"only_per_pair": False
})
return prot
### 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:
# if self.max_epa.value == 0:
# self.max_dca_multiplier = 1
# elif self.max_epa.value == 1:
# self.max_dca_multiplier = 2
# elif self.max_epa.value == 2:
# self.max_dca_multiplier = 3
# else:
# self.max_dca_multiplier = 4
# 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]:
"""
Custom trade adjustment logic, returning the stake amount that a trade should be
increased or decreased.
This means extra buy or sell orders with additional fees.
Only called when `position_adjustment_enable` is set to True.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns None
:param trade: trade object.
:param current_time: datetime object, containing the current datetime
:param current_rate: Current buy rate.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param min_stake: Minimal stake size allowed by exchange (for both entries and exits)
:param max_stake: Maximum stake allowed (either through balance, or by exchange limits).
:param current_entry_rate: Current rate using entry pricing.
:param current_exit_rate: Current rate using exit pricing.
:param current_entry_profit: Current profit using entry pricing.
:param current_exit_profit: Current profit using exit pricing.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: Stake amount to adjust your trade,
Positive values to increase position, Negative values to decrease position.
Return None for no action.
"""
if current_profit > 0.10 and trade.nr_of_successful_exits == 0:
# Take half of the profit at +5%
return -(trade.stake_amount / 2)
if current_profit > -(self.dca1.value) and trade.nr_of_successful_entries == 1:
return None
# if current_profit > -(self.dca2.value) and trade.nr_of_successful_entries == 2:
# return None
# if current_profit > -(self.dca3.value) and trade.nr_of_successful_entries == 3:
# return None
# Obtain pair dataframe (just to show how to access it)
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
filled_entries = trade.select_filled_orders(trade.entry_side)
count_of_entries = trade.nr_of_successful_entries
# Allow up to 3 additional increasingly larger buys (4 in total)
# Initial buy is 1x
# If that falls to -2.5% profit, we buy more,
# If that falls down to -5% again, we buy 1.5x more
# If that falls once again down to -5%, we buy more
# Total stake for this trade would be 1 + 1.5 + 2 + 2.5 = 7x of the initial allowed stake.
# That is why max_dca_multiplier is 7
# Hope you have a deep wallet!
try:
# This returns first order stake size
stake_amount = filled_entries[0].cost
# This then calculates current safety order size
if count_of_entries == 1:
stake_amount = stake_amount * 1
elif count_of_entries == 2:
stake_amount = stake_amount * 1
elif count_of_entries == 3:
stake_amount = stake_amount * 1
else:
stake_amount = stake_amount
return stake_amount
except Exception as exception:
return None
return None
### Trailing Stop ###
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:
return stop5a
for stop4 in self.tsl_target4.range:
if (current_profit > stop4):
for stop4a in self.ts4.range:
return stop4a
for stop3 in self.tsl_target3.range:
if (current_profit > stop3):
for stop3a in self.ts3.range:
return stop3a
for stop2 in self.tsl_target2.range:
if (current_profit > stop2):
for stop2a in self.ts2.range:
return stop2a
for stop1 in self.tsl_target1.range:
if (current_profit > stop1):
for stop1a in self.ts1.range:
return stop1a
for stop0 in self.tsl_target0.range:
if (current_profit > stop0):
for stop0a in self.ts0.range:
return stop0a
return self.stoploss
### NORMAL INDICATORS ###
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Pivot Points
pivots = pivots_points.pivots_points(dataframe)
dataframe['pivot'] = pivots['pivot']
dataframe['s1'] = pivots['s1']
dataframe['r1'] = pivots['r1']
dataframe['s2'] = pivots['s2']
dataframe['r2'] = pivots['r2']
dataframe['s3'] = pivots['s3']
dataframe['r3'] = pivots['r3']
dataframe['r3-dif'] = (dataframe['r3'] - dataframe['r2']) / 4
dataframe['r2.25'] = dataframe['r2'] + dataframe['r3-dif']
dataframe['r2.50'] = dataframe['r2'] + (dataframe['r3-dif'] * 2)
dataframe['r2.75'] = dataframe['r2'] + (dataframe['r3-dif'] * 3)
# 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)
dataframe['rsi_ma'] = ta.SMA(dataframe['rsi'], timeperiod=7)
# WaveTrend using OHLC4 or HA close - 3/21
ap = (0.25 * (dataframe['high'] + dataframe['low'] + dataframe["close"] + dataframe["open"]))
for wave in self.wavelength.range:
dataframe[f'esa{wave}'] = ta.EMA(ap, timeperiod = wave)
dataframe[f'd{wave}'] = ta.EMA(abs(ap - dataframe[f'esa{wave}']), timeperiod = wave)
dataframe[f'wave_ci{wave}'] = (ap-dataframe[f'esa{wave}']) / (0.015 * dataframe[f'd{wave}'])
dataframe[f'wave_t1{wave}'] = ta.EMA(dataframe[f'wave_ci{wave}'], timeperiod = 21)
for cross in self.crosslength.range:
dataframe[f'wave_t2{cross}_{wave}'] = ta.SMA(dataframe[f'wave_t1{wave}'], timeperiod = cross)
# SMA
dataframe['200_SMA'] = ta.SMA(dataframe["close"], timeperiod = 200)
dataframe['30_SMA'] = ta.SMA(dataframe["close"], timeperiod = 30)
dataframe['8_SMA'] = ta.SMA(dataframe["close"], timeperiod = 8)
# TTM Squeeze
ttm_Squeeze = pta.squeeze(high = dataframe['high'], low = dataframe['low'], close = dataframe["close"], lazybear = True)
dataframe['ttm_Squeeze'] = ttm_Squeeze['SQZ_20_2.0_20_1.5_LB']
dataframe['ttm_ema'] = ta.EMA(dataframe['ttm_Squeeze'], timeperiod = 4)
dataframe['squeeze_ON'] = ttm_Squeeze['SQZ_ON']
dataframe['squeeze_OFF'] = ttm_Squeeze['SQZ_OFF']
dataframe['NO_squeeze'] = ttm_Squeeze['SQZ_NO']
# Calculate the percentage change between the high and open prices for each 5-minute candle
dataframe['perc_change'] = (dataframe['high'] / dataframe['open'] - 1) * 100
# Create a custom indicator that checks if any of the past 100 5-minute candles' high price is 3% or more above the open price
dataframe['candle_3perc_100'] = dataframe['perc_change'].rolling(200).apply(lambda x: np.where(x >= 3, 1, 0).sum()).shift()
# Create a custom indicator that checks if the price has gone up 10% or more over the last hundred candles
dataframe['candle_10perc_100'] = dataframe['close'].pct_change(periods=50).shift()
# Calculate the percentage of the current candle's range where the close price is
dataframe['close_percentage'] = (dataframe['close'] - dataframe['low']) / (dataframe['high'] - dataframe['low'])
dataframe['body_size'] = abs(dataframe['open'] - dataframe['close'])
dataframe['range_size'] = dataframe['high'] - dataframe['low']
dataframe['body_range_ratio'] = dataframe['body_size'] / dataframe['range_size']
dataframe['upper_wick_size'] = dataframe['high'] - dataframe[['open', 'close']].max(axis=1)
dataframe['upper_wick_range_ratio'] = dataframe['upper_wick_size'] / dataframe['range_size']
lookback_period = 10
dataframe['max_high'] = dataframe['high'].rolling(lookback_period).max()
dataframe['min_low'] = dataframe['low'].rolling(lookback_period).min()
dataframe['close_position'] = (dataframe['close'] - dataframe['min_low']) / (dataframe['max_high'] - dataframe['min_low'])
dataframe['current_candle_perc_change'] = (dataframe['high'] / dataframe['open'] - 1) * 100
# if self.dp.runmode.value in ('live', 'dry_run'):
# ticker = self.dp.ticker(metadata['pair'])
# dataframe['last_price'] = ticker['last']
# dataframe['volume24h'] = ticker['quoteVolume']
# dataframe['vwap'] = ticker['vwap']
# # Trading pair: {'symbol': 'KAVA/USDT', 'timestamp': 1675451904532, 'datetime': '2023-02-03T19:18:24.532Z',
# 'high': 1.0652, 'low': 1.0031, 'bid': 1.0382, 'bidVolume': None, 'ask': 1.0395, 'askVolume': None,
# 'vwap': 1.035200204815235, 'open': 1.0558, 'close': 1.0403, 'last': 1.0403, 'previousClose': None, 'change': -0.0155,
# 'percentage': -1.46, 'average': 1.04130553, 'baseVolume': 457210.7641, 'quoteVolume': 473304.67664005,
# 'info':
# {'time': 1675451904532, 'symbol': 'KAVA-USDT', 'buy': '1.0382', 'sell': '1.0395', 'changeRate': '-0.0146', 'changePrice': '-0.0155', 'high': '1.0652', 'low': '1.0031', 'vol': '457210.7641', 'volValue': '473304.67664005', 'last': '1.0403', 'averagePrice': '1.04130553', 'takerFeeRate': '0.001', 'makerFeeRate': '0.001', 'takerCoefficient': '1', 'makerCoefficient': '1'}}, self.max_epa.value: 1, self.max_dca_multiplier: 2
# if (dataframe['30_SMA'].iloc[-1] > dataframe['200_SMA'].iloc[-1]
# and dataframe['30_SMA'].iloc[-1] > dataframe['30_SMA'].iloc[-2]
# and dataframe['200_SMA'].iloc[-1] > dataframe['200_SMA'].iloc[-2]).all():
# self.max_epa.value = 1
# elif (dataframe['30_SMA'].iloc[-1] > dataframe['200_SMA'].iloc[-1]
# and dataframe['30_SMA'].iloc[-1] > dataframe['30_SMA'].iloc[-2].all()):
# self.max_epa.value = 1
# elif (dataframe['30_SMA'].iloc[-1] < dataframe['200_SMA'].iloc[-1]
# and dataframe['30_SMA'].iloc[-1] > dataframe['30_SMA'].iloc[-2].all()):
# self.max_epa.value = 2
# else:
# self.max_epa.value = 2
# # print(f"Trading Pair: {dataframe.name}")
# print(f"self.max_epa.value: {self.max_epa.value}")
# print(f"self.max_dca_multiplier: {self.max_dca_multiplier}")
# print(f"Trading pair: {ticker['symbol']}, max_epa: {self.max_epa.value}, dca_multiplier: {self.max_dca_multiplier}")
return dataframe
### ENTRY CONDITIONS ###
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
# df.loc[
# (
# # Signal: RSI crosses above 30
# (df['close'] > df[f'zema_{self.filterlength.value}'])&
# (df['close'] < df['r2']) &
# (df['squeeze_ON'] == 1)&
# (df['rsi_ma'] > df['rsi_ma'].shift(1)) &
# (df['200_SMA'] < df['200_SMA'].shift(1)) &
# (df['rsi'] < self.buy_rsi_bear.value) &
# (df[f'wave_t1{self.wavelength.value}'] < self.buy_wt_bear.value)&
# (df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # Guard: Wave 1 is raising
# (qtpylib.crossed_above(df[f'wave_t1{self.wavelength.value}'], df[f'wave_t2{self.crosslength.value}_{self.wavelength.value}'])) &
# (df['volume'] > 0) # Make sure Volume is not 0
# ),
# ['enter_long', 'enter_tag']] = (1, 'ttm_Squeeze/WT - bear')
# df.loc[
# (
# (df['close'] > df[f'zema_{self.filterlength.value}'])&
# (df['close'] < df['r2']) &
# (df['squeeze_ON'] == 1)&
# (df['rsi_ma'] > df['rsi_ma'].shift(1)) &
# (df['rsi'] < self.buy_rsi_bear.value) &
# (df['200_SMA'] < df['200_SMA'].shift(1)) &
# (df[f'wave_t1{self.wavelength.value}'] < self.buy_wt_bear.value)&
# (df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # Guard: Wave 1 is raising
# (df[f'wave_t1{self.wavelength.value}'].shift(2) > df[f'wave_t1{self.wavelength.value}'].shift(1)) &
# (df['volume'] > 0) # Make sure Volume is not 0
# ),
# ['enter_long', 'enter_tag']] = (1, 'ttm_Squeeze/WTT - bear')
# df.loc[
# (
# (df['close'] > df[f'zema_{self.filterlength.value}'])&
# (df['close'] < df['r2']) &
# (df['rsi_ma'] > df['rsi_ma'].shift(1)) &
# (df['rsi'] < self.buy_rsi_bear.value) &
# (df['200_SMA'] < df['200_SMA'].shift(1)) &
# (df[f'wave_t1{self.wavelength.value}'] < self.buy_wt_bear.value)&
# (df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # Guard: Wave 1 is raising
# (df[f'wave_t1{self.wavelength.value}'].shift(2) > df[f'wave_t1{self.wavelength.value}'].shift(1)) &
# (df['volume'] > 0) # Make sure Volume is not 0
# ),
# ['enter_long', 'enter_tag']] = (1, 'WT transition - bear')
# df.loc[
# (
# (df['close'] > df[f'zema_{self.filterlength.value}'])&
# (df['close'] < df['r2']) &
# (df['200_SMA'] < df['200_SMA'].shift(1)) &
# (df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # Guard: Wave 1 is raising
# (df[f'wave_t1{self.wavelength.value}'] < self.buy_wt_bear.value) &
# (df[f'wave_t1{self.wavelength.value}'].shift(2) > df[f'wave_t1{self.wavelength.value}'].shift(1)) &
# (df['volume'] > 0) # Make sure Volume is not 0
# ),
# ['enter_long', 'enter_tag']] = (1, 'WT - bear')
# df.loc[
# (
# (df['close'] > df[f'zema_{self.filterlength.value}'])&
# (df['close'] < df['r2']) &
# (df['squeeze_ON'] == 1)&
# (df['rsi_ma'] > df['rsi_ma'].shift(1)) &
# (df['rsi'] < self.buy_rsi_bull.value) &
# (df['200_SMA'] > df['200_SMA'].shift(1)) &
# (df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # Guard: Wave 1 is raising
# (qtpylib.crossed_above(df[f'wave_t1{self.wavelength.value}'], df[f'wave_t2{self.crosslength.value}_{self.wavelength.value}'])) &
# (df['volume'] > 0) # Make sure Volume is not 0
# ),
# ['enter_long', 'enter_tag']] = (1, 'ttm_Squeeze/WT - bull')
# df.loc[
# (
# (df['close'] > df[f'zema_{self.filterlength.value}'])&
# (df['close'] < df['s1']) &
# (df['squeeze_ON'] == 1)&
# (df['rsi_ma'] > df['rsi_ma'].shift(1)) &
# (df['rsi'] < self.buy_rsi_bull.value) &
# (df['200_SMA'] > df['200_SMA'].shift(1)) &
# (df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) &
# (df[f'wave_t1{self.wavelength.value}'] < self.buy_wt_bull.value) &
# (df[f'wave_t1{self.wavelength.value}'].shift(2) > df[f'wave_t1{self.wavelength.value}'].shift(1)) &
# # Check if no candle high price rose more than 5% in the past 50 candles
# (df['candle_3perc_100'] == 0) &
# # Check if the price has not gone up 10% or more over the last hundred candles
# (df['candle_10perc_100'] < 0.5) &
# # Check if the high price of the current candle is not 3% or more above the open price
# (df['current_candle_perc_change'] < 0.75) &
# (df['volume'] > 0) # Make sure Volume is not 0
# ),
# ['enter_long', 'enter_tag']] = (1, 'ttm_Squeeze/WTT - bull')
# df.loc[
# (
# (df['close'] > df[f'zema_{self.filterlength.value}'])&
# (df['close'] < df['s1']) &
# (df['rsi_ma'] > df['rsi_ma'].shift(1)) &
# (df['rsi'] < self.buy_rsi_bull.value) &
# (df['200_SMA'] > df['200_SMA'].shift(1)) &
# (df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # Guard: Wave 1 is raising
# (df[f'wave_t1{self.wavelength.value}'] < self.buy_wt_bull.value) &
# (df[f'wave_t1{self.wavelength.value}'].shift(2) > df[f'wave_t1{self.wavelength.value}'].shift(1)) &
# # Check if no candle high price rose more than 5% in the past 50 candles
# (df['candle_3perc_100'] == 0) &
# # Check if the price has not gone up 10% or more over the last hundred candles
# (df['candle_10perc_100'] < 0.5) &
# # Check if the high price of the current candle is not 3% or more above the open price
# (df['current_candle_perc_change'] < 0.75) &
# (df['volume'] > 0) # Make sure Volume is not 0
# ),
# ['enter_long', 'enter_tag']] = (1, 'WT transition - bull')
df.loc[
(
(df['close'] > df[f'zema_{self.filterlength.value}'])&
(df['close'] < df['s1']) &
(df['200_SMA'] > df['200_SMA'].shift(1)) &
(df[f'wave_t1{self.wavelength.value}'] > df[f'wave_t1{self.wavelength.value}'].shift(1)) & # Guard: Wave 1 is raising
(df[f'wave_t1{self.wavelength.value}'] < self.buy_wt_bull.value) &
(df[f'wave_t1{self.wavelength.value}'].shift(2) > df[f'wave_t1{self.wavelength.value}'].shift(1)) &
# Check if no candle high price rose more than 5% in the past 50 candles
(df['candle_3perc_100'] == 0) &
# Check if the price has not gone up 10% or more over the last hundred candles
(df['candle_10perc_100'] < 0.5) &
# Check if the high price of the current candle is not 3% or more above the open price
(df['current_candle_perc_change'] < 0.75) &
(df['volume'] > 0) # Make sure Volume is not 0
),
['enter_long', 'enter_tag']] = (1, 'WT - bull')
# df.loc[
# (
# (df['close'] > df[f'zema_{self.filterlength.value}'])&
# (df['rsi'] > self.buy_rsi.value) &
# (df['rsi'] < self.buy_rsi_bear.value) &
# (df['200_SMA'] < df['200_SMA'].shift(1)) &
# (qtpylib.crossed_above(df['rsi'], df['rsi_ma'])) &
# # Check if no candle high price rose more than 5% in the past 50 candles
# (df['candle_3perc_100'] == 0) &
# # Check if the price has not gone up 10% or more over the last hundred candles
# (df['candle_10perc_100'] < 0.5) &
# # Check if the high price of the current candle is not 3% or more above the open price
# (df['current_candle_perc_change'] < 0.75) &
# (df['volume'] > 0) # Make sure Volume is not 0
# ),
# ['enter_long', 'enter_tag']] = (1, 'RSI-XO bear')
# df.loc[
# (
# (df['rsi'] > self.buy_rsi.value) &
# (df['rsi'] < self.buy_rsi_bull.value) &
# (df['30_SMA'] > df['200_SMA']) &
# (df['200_SMA'] > df['200_SMA'].shift(1)) &
# (qtpylib.crossed_above(df['rsi'], df['rsi_ma'])) &
# # Check if no candle high price rose more than 5% in the past 50 candles
# (df['candle_3perc_100'] == 0) &
# # Check if the price has not gone up 10% or more over the last hundred candles
# (df['candle_10perc_100'] < 0.5) &
# # Check if the high price of the current candle is not 3% or more above the open price
# (df['current_candle_perc_change'] < 0.75) &
# (df['volume'] > 0) # Make sure Volume is not 0
# ),
# ['enter_long', 'enter_tag']] = (1, 'RSI-XO bull')
return df
### EXIT CONDITIONS ###
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
df.loc[
(
(qtpylib.crossed_below(df[f'zema_{self.filterlength.value}'], df['r3'])) &
(df['volume'] > 0) # Make sure Volume is not 0
),
['exit_long', 'exit_tag']] = (1, 'R3 - XO')
df.loc[
(
(qtpylib.crossed_below(df[f'zema_{self.filterlength.value}'], df['r2.75'])) &
(df['volume'] > 0) # Make sure Volume is not 0
),
['exit_long', 'exit_tag']] = (1, 'R2.75 - XO')
df.loc[
(
(qtpylib.crossed_below(df[f'zema_{self.filterlength.value}'], df['r2.50'])) &
(df['volume'] > 0) # Make sure Volume is not 0
),
['exit_long', 'exit_tag']] = (1, 'R2.5 - XO')
df.loc[
(
(qtpylib.crossed_below(df[f'zema_{self.filterlength.value}'], df['r2.25'])) &
(df['volume'] > 0) # Make sure Volume is not 0
),
['exit_long', 'exit_tag']] = (1, 'R2.25 - XO')
df.loc[
(
(qtpylib.crossed_below(df[f'zema_{self.filterlength.value}'], df['r2'])) &
(df['volume'] > 0) # Make sure Volume is not 0
),
['exit_long', 'exit_tag']] = (1, 'R2 - XO')
return df