______ __ __ __ __ ______ __ __ __ __ __ ______ / \ / | _/ | / | / | / \ / \ / | / | / | / | / \ /$$$$$$ |$$ |____ / $$ | _______ $$ | /$$/ /$$$$$$ |$$ \ $$ | _$$ |_ $$ | $$ | _______ /$$$$$$ | _______ $$ | $$/ $$ \ $$$$ | / |$$ |/$$/ $$ ___$$ |$$$ \$$ | / $$ | $$ |__$$ | / |$$$ \$$ | / | $$ | $$$$$$$ | $$ | /$$$$$$$/ $$ $$< / $$< $$$$ $$ | $$$$$$/ $$ $$ |/$$$$$$$/ $$$$ $$ |/$$$$$$$/ $$ | __ $$ | $$ | $$ | $$ | $$$$$ \ _$$$$$ |$$ $$ $$ | $$ | __$$$$$$$$ |$$ | $$ $$ $$ |$$ \ $$ \__/ |$$ | $$ | _$
Timeframe
5m
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
No
ROI
0m: 24.3%, 46m: 3.5%, 127m: 1.5%, 237m: 0.0%
Interface Version
N/A
Startup Candles
200
Indicators
7
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from typing import Optional
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
import technical.indicators as ftt
import math
import logging
logger = logging.getLogger(__name__)
# @Rallipanos # changes by IcHiAT
def EWO(dataframe, ema_length=5, ema2_length=3):
df = dataframe.copy()
ema1 = ta.EMA(df, timeperiod=ema_length)
ema2 = ta.EMA(df, timeperiod=ema2_length)
emadif = (ema1 - ema2) / df['close'] * 100
return emadif
class Auto_EI_t4c0s(IStrategy):
'''
______ __ __ __ __ ______ __ __ __ __ __ ______
/ \ / | _/ | / | / | / \ / \ / | / | / | / | / \
/$$$$$$ |$$ |____ / $$ | _______ $$ | /$$/ /$$$$$$ |$$ \ $$ | _$$ |_ $$ | $$ | _______ /$$$$$$ | _______
$$ | $$/ $$ \ $$$$ | / |$$ |/$$/ $$ ___$$ |$$$ \$$ | / $$ | $$ |__$$ | / |$$$ \$$ | / |
$$ | $$$$$$$ | $$ | /$$$$$$$/ $$ $$< / $$< $$$$ $$ | $$$$$$/ $$ $$ |/$$$$$$$/ $$$$ $$ |/$$$$$$$/
$$ | __ $$ | $$ | $$ | $$ | $$$$$ \ _$$$$$ |$$ $$ $$ | $$ | __$$$$$$$$ |$$ | $$ $$ $$ |$$ \
$$ \__/ |$$ | $$ | _$$ |_ $$ \_____ $$ |$$ \ / \__$$ |$$ |$$$$ | $$ |/ | $$ |$$ \_____ $$ \$$$$ | $$$$$$ |
$$ $$/ $$ | $$ |/ $$ |$$ |$$ | $$ |$$ $$/ $$ | $$$ |______$$ $$/ $$ |$$ |$$ $$$/ / $$/
$$$$$$/ $$/ $$/ $$$$$$/ $$$$$$$/ $$/ $$/ $$$$$$/ $$/ $$// |$$$$/ $$/ $$$$$$$/ $$$$$$/ $$$$$$$/
$$$$$$/
'''
### 3-13-2023 ###
# WIP - V1
# No hyper-opt using just defaults from orig EI3 strat, you need to try and hyper-opt this file -
# Will suggest adding in an roi table.
###
# Buy hyperspace params:
buy_params = {
"atr_length": 23,
"b01": 0.5,
"b02": 0.5,
"b03": 0.5,
"b04": 0.5,
"b05": 0.5,
"b06": 0.5,
"base_nb_candles_buy": 17,
"buy_adx": 28,
"buy_ema_cofi": 0.969,
"buy_ewo_high": 5.687,
"buy_fastd": 23,
"buy_fastk": 30,
"fib_dn": 2.5,
"fib_up": 9.5,
"increment": 1.0007,
"lambo2_ema_14_factor": 0.967,
"lambo2_rsi_14_limit": 27,
"lambo2_rsi_4_limit": 32,
"low_offset": 0.995,
"mean_dn": 1.6,
"mean_up": 3.3,
"rsi_buy": 54,
"window": 30,
"x01": 4.2,
"x02": 1.3,
"x03": 1.2,
"x04": 1.3,
"x05": 4.2,
"z01": 2.1,
"zero_dn": 4.1,
"zero_up": 6.6,
}
# Sell hyperspace params:
sell_params = {
"base_nb_candles_sell": 14,
"day1": 1,
"day2": 4,
"day3": 5,
"day4": 8,
"fib_dns": 2.3,
"fib_ups": 1.6,
"high_offset": 1.012,
"high_offset_2": 1.014,
"mean_dns": 2.2,
"mean_ups": 5.4,
"moon": 0.004,
"s01": 0.5,
"s02": 0.5,
"s03": 0.5,
"s04": 0.5,
"s05": 0.5,
"s06": 0.5,
"unclog1": 0.15,
"unclog2": 0.16,
"unclog3": 0.12,
"unclog4": 0.18,
"y01": 3.6,
"y02": 3.4,
"y03": 2.6,
"zero_dns": 7.3,
"zero_ups": 1.8,
}
# ROI table:
minimal_roi = {
"0": 0.243,
"46": 0.035,
"127": 0.015,
"237": 0
}
@property
def protections(self):
return [
{
"method": "CooldownPeriod",
"stop_duration_candles": 5
},
{
"method": "MaxDrawdown",
"lookback_period_candles": 48,
"trade_limit": 20,
"stop_duration_candles": 4,
"max_allowed_drawdown": 0.2
},
{
"method": "StoplossGuard",
"lookback_period_candles": 24,
"trade_limit": 4,
"stop_duration_candles": 2,
"only_per_pair": False
},
{
"method": "LowProfitPairs",
"lookback_period_candles": 6,
"trade_limit": 2,
"stop_duration_candles": 60,
"required_profit": 0.02
},
{
"method": "LowProfitPairs",
"lookback_period_candles": 24,
"trade_limit": 4,
"stop_duration_candles": 2,
"required_profit": 0.01
}
]
# Stoploss - Unclog
stoploss = -0.99
moon = DecimalParameter(0.001, 0.004, default=0.35, decimals=3, space='sell', optimize=True)
unclog1 = DecimalParameter(0.1, 0.2, default=0.04, decimals=2, space='sell', optimize=True)
unclog2 = DecimalParameter(0.08, 0.2, default=0.04, decimals=2, space='sell', optimize=True)
unclog3 = DecimalParameter(0.10, 0.2, default=0.04, decimals=2, space='sell', optimize=True)
unclog4 = DecimalParameter(0.14, 0.2, default=0.04, decimals=2, space='sell', optimize=True)
day1 = IntParameter(1, 4, default=1, space='sell', optimize=True)
day2 = IntParameter(2, 5, default=2, space='sell', optimize=True)
day3 = IntParameter(3, 6, default=3, space='sell', optimize=True)
day4 = IntParameter(4, 10, default=4, space='sell', optimize=True)
# SMAOffset
base_nb_candles_buy = IntParameter(8, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True)
base_nb_candles_sell = IntParameter(8, 20, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
low_offset = DecimalParameter(0.985, 0.995, default=buy_params['low_offset'], space='buy', optimize=True)
high_offset = DecimalParameter(1.005, 1.015, default=sell_params['high_offset'], space='sell', optimize=True)
high_offset_2 = DecimalParameter(1.010, 1.020, default=sell_params['high_offset_2'], space='sell', optimize=True)
# lambo2
lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=True)
lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=True)
lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=True)
# Protection
fast_ewo = 50
slow_ewo = 200
locked_stoploss = {}
rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True)
window = IntParameter(12, 70, default=48, space='buy', optimize=True)
#cofi
is_optimize_cofi = True
buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97 , optimize = is_optimize_cofi)
buy_fastk = IntParameter(20, 30, default=20, optimize = is_optimize_cofi)
buy_fastd = IntParameter(20, 30, default=20, optimize = is_optimize_cofi)
buy_adx = IntParameter(20, 30, default=30, optimize = is_optimize_cofi)
buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize = is_optimize_cofi)
atr_length = IntParameter(10, 30, default=14, space='buy', optimize=True)
increment = DecimalParameter(low=1.0005, high=1.001, default=1.0007, decimals=4 ,space='buy', optimize=True, load=True)
### Buy Weight Mulitpliers ###
x01 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='buy', optimize=True)
x02 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='buy', optimize=True)
x03 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='buy', optimize=True)
x04 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='buy', optimize=True)
x05 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='buy', optimize=True)
### Sell Weight Mulitpliers ###
y01 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='sell', optimize=True)
y02 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='sell', optimize=True)
y03 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='sell', optimize=True)
### General Weight Mulitpliers ###
z01 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='buy', optimize=True)
z02 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='sell', optimize=True)
### Entry / Exit Thresholds ###
b01 = DecimalParameter(0.0, 1.10, default=0.5, decimals=2, space='buy', optimize=True)
b02 = DecimalParameter(0.0, 1.10, default=0.5, decimals=2, space='buy', optimize=True)
b03 = DecimalParameter(0.0, 1.10, default=0.5, decimals=2, space='buy', optimize=True)
b04 = DecimalParameter(0.0, 1.10, default=0.5, decimals=2, space='buy', optimize=True)
b05 = DecimalParameter(0.0, 1.10, default=0.5, decimals=2, space='buy', optimize=True)
b06 = DecimalParameter(0.0, 1.10, default=0.5, decimals=2, space='buy', optimize=True)
s01 = DecimalParameter(0.0, 1.10, default=0.5, decimals=2, space='sell', optimize=True)
s02 = DecimalParameter(0.0, 1.10, default=0.5, decimals=2, space='sell', optimize=True)
s03 = DecimalParameter(0.0, 1.10, default=0.5, decimals=2, space='sell', optimize=True)
s04 = DecimalParameter(0.0, 1.10, default=0.5, decimals=2, space='sell', optimize=True)
s05 = DecimalParameter(0.0, 1.10, default=0.5, decimals=2, space='sell', optimize=True)
s06 = DecimalParameter(0.0, 1.10, default=0.5, decimals=2, space='sell', optimize=True)
fib_dn = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space='buy', optimize=True)
mean_dn = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space='buy', optimize=True)
zero_dn = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space='buy', optimize=True)
zero_up = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space='buy', optimize=True)
mean_up = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space='buy', optimize=True)
fib_up = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space='buy', optimize=True)
fib_dns = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space='sell', optimize=True)
mean_dns = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space='sell', optimize=True)
zero_dns = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space='sell', optimize=True)
zero_ups = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space='sell', optimize=True)
mean_ups = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space='sell', optimize=True)
fib_ups = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space='sell', optimize=True)
use_custom_stoploss = True
process_only_new_candles = True
# Custom Entry
last_entry_price = None
### Trailing Stop ###
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
SLT1 = current_candle['move_mean']
SL1 = current_candle['move_mean'] * 0.5
SLT2 = current_candle['move_mean_x']
SL2 = current_candle['move_mean_x'] - current_candle['move_mean']
display_profit = current_profit * 100
slt1 = SLT1 * 100
sl1 = SL1 * 100
slt2 = SLT2 * 100
sl2 = SL2 * 100
# if len(self.locked_stoploss) > 0:
# print(self.locked_stoploss)
if current_candle['max_l'] != 0: # ignore stoploss if setting new highs
if pair not in self.locked_stoploss: # No locked stoploss for this pair yet
if SLT2 is not None and current_profit > SLT2:
self.locked_stoploss[pair] = SL2
self.dp.send_msg(f'*** {pair} *** Profit {display_profit:.3f}% - {slt2:.3f}%/{sl2:.3f}% activated')
logger.info(f'*** {pair} *** Profit {display_profit:.3f}% - {slt2:.3f}%/{sl2:.3f}% activated')
return SL2
elif SLT1 is not None and current_profit > SLT1:
self.locked_stoploss[pair] = SL1
self.dp.send_msg(f'*** {pair} *** Profit {display_profit:.3f}% - {slt1:.3f}%/{sl1:.3f}% activated')
logger.info(f'*** {pair} *** Profit {display_profit:.3f}% - {slt1:.3f}%/{sl1:.3f}% activated')
return SL1
else:
return self.stoploss
else: # Stoploss has been locked for this pair
self.dp.send_msg(f'*** {pair} *** Profit {display_profit:.3f}% stoploss locked at {self.locked_stoploss[pair]:.4f}')
logger.info(f'*** {pair} *** Profit {display_profit:.3f}% stoploss locked at {self.locked_stoploss[pair]:.4f}')
return self.locked_stoploss[pair]
if current_profit < -.01:
if pair in self.locked_stoploss:
del self.locked_stoploss[pair]
self.dp.send_msg(f'*** {pair} *** Stoploss reset.')
logger.info(f'*** {pair} *** Stoploss reset.')
return self.stoploss
def custom_entry_price(self, pair: str, trade: Optional['Trade'], current_time: datetime, proposed_rate: float,
entry_tag: Optional[str], side: str, **kwargs) -> float:
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
timeframe=self.timeframe)
entry_price = (dataframe['close'].iat[-1] + dataframe['open'].iat[-1] + proposed_rate + proposed_rate) / 4
logger.info(f"{pair} Using Entry Price: {entry_price} | close: {dataframe['close'].iat[-1]} open: {dataframe['open'].iat[-1]} proposed_rate: {proposed_rate}")
# Check if there is a stored last entry price and if it matches the proposed entry price
if self.last_entry_price is not None and abs(entry_price - self.last_entry_price) < 0.0001: # Tolerance for floating-point comparison
entry_price *= self.increment.value # Increment by 0.2%
logger.info(f"{pair} Incremented entry price: {entry_price} based on previous entry price : {self.last_entry_price}.")
# Update the last entry price
self.last_entry_price = entry_price
return entry_price
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, exit_reason: str,
current_time: datetime, **kwargs) -> bool:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
if exit_reason == 'roi' and (last_candle['max_l'] < 0.003):
return False
# Handle freak events
if exit_reason == 'roi' and trade.calc_profit_ratio(rate) < 0.003:
logger.info(f"{trade.pair} ROI is below 0")
self.dp.send_msg(f'{trade.pair} ROI is below 0')
return False
if exit_reason == 'partial_exit' and trade.calc_profit_ratio(rate) < 0:
logger.info(f"{trade.pair} partial exit is below 0")
self.dp.send_msg(f'{trade.pair} partial exit is below 0')
return False
if exit_reason == 'trailing_stop_loss' and trade.calc_profit_ratio(rate) < 0:
logger.info(f"{trade.pair} trailing stop price is below 0")
self.dp.send_msg(f'{trade.pair} trailing stop price is below 0')
return False
return True
def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
# Sell any positions at a loss if they are held for more than X days.
if current_profit < -self.unclog4.value and (current_time - trade.open_date_utc).days >= self.day4.value:
return 'unclog 4'
if current_profit < -self.unclog3.value and (current_time - trade.open_date_utc).days >= self.day3.value:
return 'unclog 3'
if current_profit < -self.unclog2.value and (current_time - trade.open_date_utc).days >= self.day2.value:
return 'unclog 2'
if current_profit < -self.unclog1.value and (current_time - trade.open_date_utc).days >= self.day1.value:
return 'unclog 1'
# Sell signal
use_exit_signal = True
exit_profit_only = True
exit_profit_offset = 0.01
ignore_roi_if_entry_signal = False
## Optional order time in force.
order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}
# Optimal timeframe for the strategy
timeframe = '5m'
position_adjustment_enable = False
process_only_new_candles = True
startup_candle_count = 200
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Calculate all ma_buy values
for val in self.base_nb_candles_buy.range:
dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
# Calculate all ma_sell values
for val in self.base_nb_candles_sell.range:
dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
dataframe['ma_lo'] = dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * (self.low_offset.value)
dataframe['ma_hi'] = dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * (self.high_offset.value)
dataframe['ma_hi_2'] = dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * (self.high_offset_2.value)
dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
# HMA-BUY SQUEEZE
dataframe['HMA_SQZ'] = (((dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] - dataframe['hma_50'])
/ dataframe[f'ma_buy_{self.base_nb_candles_buy.value}']) * 100)
dataframe['zero'] = 0
# Elliot
dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
dataframe.loc[dataframe['EWO'] > 0, "EWO_UP"] = dataframe['EWO']
dataframe.loc[dataframe['EWO'] < 0, "EWO_DN"] = dataframe['EWO']
dataframe['EWO_UP'].ffill()
dataframe['EWO_DN'].ffill()
dataframe['EWO_MEAN_UP'] = dataframe['EWO_UP'].mean()
dataframe['EWO_MEAN_DN'] = dataframe['EWO_DN'].mean()
dataframe['EWO_UP_FIB'] = dataframe['EWO_MEAN_UP'] * 1.618
dataframe['EWO_DN_FIB'] = dataframe['EWO_MEAN_DN'] * 1.618
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
#lambo2
dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14)
dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4)
dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
# Cofi
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['adx'] = ta.ADX(dataframe)
dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
dataframe['OHLC4'] = (dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4
# Check how far we are from min and max
dataframe['max'] = dataframe['OHLC4'].rolling(4).max() / dataframe['OHLC4'] - 1
dataframe['min'] = abs(dataframe['OHLC4'].rolling(4).min() / dataframe['OHLC4'] - 1)
dataframe['max_l'] = dataframe['OHLC4'].rolling(48).max() / dataframe['OHLC4'] - 1
dataframe['min_l'] = abs(dataframe['OHLC4'].rolling(48).min() / dataframe['OHLC4'] - 1)
dataframe['max_x'] = dataframe['OHLC4'].rolling(336).max() / dataframe['OHLC4'] - 1
dataframe['min_x'] = abs(dataframe['OHLC4'].rolling(336).min() / dataframe['OHLC4'] - 1)
# Apply rolling window operation to the 'OHLC4'column
rolling_window = dataframe['OHLC4'].rolling(self.window.value)
rolling_max = rolling_window.max()
rolling_min = rolling_window.min()
# Calculate the peak-to-peak value on the resulting rolling window data
ptp_value = rolling_window.apply(lambda x: np.ptp(x))
# Assign the calculated peak-to-peak value to the DataFrame column
dataframe['move'] = ptp_value / dataframe['OHLC4']
dataframe['move_mean'] = dataframe['move'].mean()
dataframe['move_mean_x'] = dataframe['move'].mean() * 1.6
dataframe['exit_mean'] = rolling_min * (1 + dataframe['move_mean'])
dataframe['exit_mean_x'] = rolling_min * (1 + dataframe['move_mean_x'])
dataframe['enter_mean'] = rolling_max * (1 - dataframe['move_mean'])
dataframe['enter_mean_x'] = rolling_max * (1 - dataframe['move_mean_x'])
dataframe['atr_pcnt'] = (ta.ATR(dataframe, timeperiod=5) / dataframe['OHLC4'])
# Apply rolling window operation to the 'OHLC4'column
rolling_window_x = dataframe['OHLC4'].rolling(200)
rolling_max_x = rolling_window_x.max()
rolling_min_x = rolling_window_x.min()
# Calculate the peak-to-peak value on the resulting rolling window data
ptp_value_x = rolling_window_x.apply(lambda x: np.ptp(x))
# Assign the calculated peak-to-peak value to the DataFrame column
dataframe['move_l'] = ptp_value_x / dataframe['OHLC4']
dataframe['move_mean_l'] = dataframe['move_l'].mean()
dataframe['move_mean_xl'] = dataframe['move_l'].mean() * 1.6
dataframe['exit_mean_l'] = rolling_min_x * (1 + dataframe['move_mean_l'])
dataframe['exit_mean_xl'] = rolling_min_x * (1 + dataframe['move_mean_xl'])
dataframe['enter_mean_l'] = rolling_max_x * (1 - dataframe['move_mean_l'])
dataframe['enter_mean_xL'] = rolling_max_x * (1 - dataframe['move_mean_xl'])
### Buying Weights & Signals ###
dataframe.loc[(dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)), 'buy0'] = 1
dataframe.loc[(dataframe['close'] > (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)), 'buy0'] = 0
dataframe.loc[(dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)), 'buy1'] = 1
dataframe.loc[(dataframe['rsi_4'] > int(self.lambo2_rsi_4_limit.value)), 'buy1'] = 0
dataframe.loc[(dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)), 'buy2'] = 1
dataframe.loc[(dataframe['rsi_14'] > int(self.lambo2_rsi_14_limit.value)), 'buy2'] = 0
dataframe.loc[(dataframe['atr_pcnt'] > dataframe['min_l']), 'buy3'] = 1
dataframe.loc[(dataframe['atr_pcnt'] < dataframe['min_l']), 'buy3'] = 0
dataframe.loc[(dataframe['rsi']<self.rsi_buy.value), 'buy4'] = 1
dataframe.loc[(dataframe['rsi']>self.rsi_buy.value), 'buy4'] = 0
dataframe['lambo_weight'] = (
(dataframe['buy0']+dataframe['buy1']+dataframe['buy2']+dataframe['buy3']+dataframe['buy4'])/5) * self.x01.value
dataframe.loc[(dataframe['rsi_fast'] < 35), 'buy10'] = 1
dataframe.loc[(dataframe['rsi_fast'] > 35), 'buy10'] = 0
dataframe.loc[(dataframe['close'] < dataframe['ma_lo']), 'buy11'] = 1
dataframe.loc[(dataframe['close'] > dataframe['ma_lo']), 'buy11'] = 0
dataframe.loc[(dataframe['close'] < dataframe['enter_mean_x']), 'buy12'] = 1
dataframe.loc[(dataframe['close'] > dataframe['enter_mean_x']), 'buy12'] = 0
dataframe.loc[(dataframe['close'].shift() < dataframe['enter_mean_x'].shift()), 'buy13'] = 1
dataframe.loc[(dataframe['close'].shift() > dataframe['enter_mean_x'].shift()), 'buy13'] = 0
dataframe.loc[(dataframe['rsi'] < self.rsi_buy.value), 'buy14'] = 1
dataframe.loc[(dataframe['rsi'] > self.rsi_buy.value), 'buy14'] = 0
dataframe.loc[(dataframe['atr_pcnt'] > dataframe['min']), 'buy15'] = 1
dataframe.loc[(dataframe['atr_pcnt'] < dataframe['min']), 'buy15'] = 0
dataframe.loc[(dataframe['EWO'] > dataframe['EWO_MEAN_UP']), 'buy16'] = 1
dataframe.loc[(dataframe['EWO'] < dataframe['EWO_MEAN_UP']), 'buy16'] = 0
dataframe['buy1ewo_weight'] = (
(dataframe['buy10']+dataframe['buy11']+dataframe['buy12']+dataframe['buy13']
+dataframe['buy14']+dataframe['buy15']+dataframe['buy16'])/7) * self.x02.value
dataframe.loc[(dataframe['rsi_fast'] < 35), 'buy20'] = 1
dataframe.loc[(dataframe['rsi_fast'] > 35), 'buy20'] = 0
dataframe.loc[(dataframe['close'] < dataframe['ma_lo']), 'buy21'] = 1
dataframe.loc[(dataframe['close'] > dataframe['ma_lo']), 'buy21'] = 0
dataframe.loc[(dataframe['EWO'] < dataframe['EWO_DN_FIB']), 'buy22'] = 1
dataframe.loc[(dataframe['EWO'] > dataframe['EWO_DN_FIB']), 'buy22'] = 0
dataframe.loc[(dataframe['atr_pcnt'] > dataframe['min']), 'buy23'] = 1
dataframe.loc[(dataframe['atr_pcnt'] < dataframe['min']), 'buy23'] = 0
dataframe['buy2ewo_weight'] = (
(dataframe['buy20']+dataframe['buy21']+dataframe['buy22']+dataframe['buy23'])/4) * self.x03.value
dataframe.loc[(dataframe['open'] < dataframe['ema_8'] * self.buy_ema_cofi.value), 'buy30'] = 1
dataframe.loc[(dataframe['open'] > dataframe['ema_8'] * self.buy_ema_cofi.value), 'buy30'] = 0
dataframe['buy31'] = 0
dataframe.loc[qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']), 'buy31'] = 1
dataframe.loc[(dataframe['fastk'] < self.buy_fastk.value), 'buy32'] = 1
dataframe.loc[(dataframe['fastk'] > self.buy_fastk.value), 'buy32'] = 0
dataframe.loc[(dataframe['fastd'] < self.buy_fastd.value), 'buy33'] = 1
dataframe.loc[(dataframe['fastd'] > self.buy_fastd.value), 'buy33'] = 0
dataframe.loc[(dataframe['adx'] > self.buy_adx.value), 'buy34'] = 1
dataframe.loc[(dataframe['adx'] < self.buy_adx.value), 'buy34'] = 0
dataframe.loc[(dataframe['EWO'] > dataframe['EWO_MEAN_UP']), 'buy35'] = 1
dataframe.loc[(dataframe['EWO'] < dataframe['EWO_MEAN_UP']), 'buy35'] = 0
dataframe.loc[(dataframe['atr_pcnt'] > dataframe['min']), 'buy36'] = 1
dataframe.loc[(dataframe['atr_pcnt'] < dataframe['min']), 'buy36'] = 0
dataframe['cofi_weight'] = (
(dataframe['buy30']+dataframe['buy31']+dataframe['buy32']+dataframe['buy33']+dataframe['buy34']+dataframe['buy35']+dataframe['buy36'])/7) * self.x04.value
dataframe['buy_weight'] = ta.SMA(((dataframe['lambo_weight'] + dataframe['buy1ewo_weight'] + dataframe['buy2ewo_weight'] + dataframe['cofi_weight']) / 4) * self.x05.value, timeperiod=5)
### General Indicators ###
dataframe.loc[(dataframe['fastk'] < self.buy_fastk.value), 'gen1'] = 1
dataframe.loc[(dataframe['fastk'] > self.buy_fastk.value), 'gen1'] = -1
dataframe.loc[(dataframe['fastd'] < self.buy_fastd.value), 'gen2'] = 1
dataframe.loc[(dataframe['fastd'] > self.buy_fastd.value), 'gen2'] = -1
dataframe.loc[(dataframe['adx'] > self.buy_adx.value), 'gen3'] = 1
dataframe.loc[(dataframe['adx'] < self.buy_adx.value), 'gen3'] = -1
dataframe['gen4'] = 0
dataframe.loc[qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']), 'gen4'] = 1
dataframe.loc[qtpylib.crossed_below(dataframe['fastk'], dataframe['fastd']), 'gen4'] = -1
# Distance from min and max summed for different ranges. Selling it will go negative.
dataframe['gen5'] = (dataframe['max']+dataframe['max_l']+dataframe['max_x']) - (dataframe['min']+dataframe['min_l']+dataframe['min_x'])
dataframe['gen6'] = 0
dataframe.loc[qtpylib.crossed_above(dataframe['ema_8'], dataframe['ema_14']), 'gen6'] = 1
dataframe.loc[qtpylib.crossed_below(dataframe['ema_8'], dataframe['ema_14']), 'gen6'] = -1
dataframe['general_weight'] = ((dataframe['gen1']+dataframe['gen2']+dataframe['gen3']+dataframe['gen4']+dataframe['gen5']+dataframe['gen6'])/6)
dataframe.loc[(dataframe['general_weight'] > 0), 'gen_buy'] = dataframe['general_weight'] * (1 + dataframe['move'])
dataframe.loc[(dataframe['general_weight'] < 0), 'gen_buy'] = 0
dataframe.loc[(dataframe['general_weight'] < 0), 'gen_sell'] = abs(dataframe['general_weight'] * (1 + dataframe['move']))
dataframe.loc[(dataframe['general_weight'] > 0), 'gen_sell'] = 0
### SELLING Weights & Signals ###
dataframe.loc[(dataframe['close'] > dataframe['hma_50']), 'sell0'] = 1
dataframe.loc[(dataframe['close'] < dataframe['hma_50']), 'sell0'] = 0
dataframe.loc[(dataframe['close'] > dataframe['ma_hi_2']), 'sell1'] = 1
dataframe.loc[(dataframe['close'] < dataframe['ma_hi_2']), 'sell1'] = 0
dataframe.loc[(dataframe['max_l'] != 0), 'sell2'] = 1
dataframe.loc[(dataframe['max_l'] == 0), 'sell2'] = 0
dataframe.loc[(dataframe['close'] > dataframe['exit_mean_x']), 'sell3'] = 1
dataframe.loc[(dataframe['close'] < dataframe['exit_mean_x']), 'sell3'] = 0
dataframe.loc[(dataframe['rsi'] > 50), 'sell4'] = 1
dataframe.loc[(dataframe['rsi'] < 50), 'sell4'] = 0
dataframe.loc[(dataframe['rsi_fast'] > dataframe['rsi_slow']), 'sell5'] = 1
dataframe.loc[(dataframe['rsi_fast'] < dataframe['rsi_slow']), 'sell5'] = 0
dataframe['hi2_weight'] = (
(dataframe['sell0']+dataframe['sell1']+dataframe['sell2']+dataframe['sell3']+dataframe['sell4']+dataframe['sell5'])/6) * self.y01.value
dataframe.loc[(dataframe['close'] < dataframe['hma_50']), 'sell10'] = 1
dataframe.loc[(dataframe['close'] > dataframe['hma_50']), 'sell10'] = 0
dataframe.loc[(dataframe['close'] > dataframe['ma_hi']), 'sell11'] = 1
dataframe.loc[(dataframe['close'] < dataframe['ma_hi']), 'sell11'] = 0
dataframe.loc[(dataframe['max_l'] != 0), 'sell12'] = 1
dataframe.loc[(dataframe['max_l'] == 0), 'sell12'] = 0
dataframe.loc[(dataframe['rsi_fast'] > dataframe['rsi_slow']), 'sell13'] = 1
dataframe.loc[(dataframe['rsi_fast'] < dataframe['rsi_slow']), 'sell13'] = 0
dataframe['hi_weight'] = (
(dataframe['sell10']+dataframe['sell11']+dataframe['sell12']+dataframe['sell13'])/4) * self.y02.value
dataframe['sell_weight'] = ta.SMA(((dataframe['hi_weight'] + dataframe['hi2_weight']) / 2) * self.y03.value, timeperiod=5)
dataframe['buy_decision'] = dataframe['buy_weight'] - dataframe['sell_weight']
dataframe['sell_decision'] = dataframe['sell_weight'] - dataframe['buy_weight']
dataframe['Gen Buy Above'] = 0
dataframe.loc[(dataframe['gen_buy'] > self.b01.value), 'Gen Buy Above'] = 1
dataframe.loc[(dataframe['gen_buy'] > self.b02.value), 'Gen Buy Above'] = 1
dataframe.loc[(dataframe['gen_buy'] > self.b03.value), 'Gen Buy Above'] = 1
dataframe.loc[(dataframe['gen_buy'] > self.b04.value), 'Gen Buy Above'] = 1
dataframe.loc[(dataframe['gen_buy'] > self.b05.value), 'Gen Buy Above'] = 1
dataframe.loc[(dataframe['gen_buy'] > self.b06.value), 'Gen Buy Above'] = 1
dataframe['Gen Sell Above'] = 0
dataframe.loc[(dataframe['gen_sell'] > self.s01.value), 'Gen Sell Above'] = 1
dataframe.loc[(dataframe['gen_sell'] > self.s02.value), 'Gen Sell Above'] = 1
dataframe.loc[(dataframe['gen_sell'] > self.s03.value), 'Gen Sell Above'] = 1
dataframe.loc[(dataframe['gen_sell'] > self.s04.value), 'Gen Sell Above'] = 1
dataframe.loc[(dataframe['gen_sell'] > self.s05.value), 'Gen Sell Above'] = 1
dataframe.loc[(dataframe['gen_sell'] > self.s06.value), 'Gen Sell Above'] = 1
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
ewo_fib_dn = (
(dataframe['EWO'] < dataframe['EWO_DN_FIB']) &
(dataframe['buy_decision'] > self.fib_dn.value) &
(dataframe['gen_buy'] > self.b01.value) &
(dataframe['volume'] > 0)
)
dataframe.loc[ewo_fib_dn, 'enter_long'] = 1
dataframe.loc[ewo_fib_dn, 'enter_tag'] = 'ewo_fib_dn'
ewo_mean_dn = (
(dataframe['EWO'] > dataframe['EWO_DN_FIB']) &
(dataframe['EWO'] < dataframe['EWO_MEAN_DN']) &
(dataframe['buy_decision'] > self.mean_dn.value) &
(dataframe['gen_buy'] > self.b02.value) &
(dataframe['volume'] > 0)
)
dataframe.loc[ewo_mean_dn, 'enter_long'] = 1
dataframe.loc[ewo_mean_dn, 'enter_tag'] = 'ewo_mean_dn'
ewo_zero_dn = (
(dataframe['EWO'] > dataframe['EWO_MEAN_DN']) &
(dataframe['EWO'] < 0) &
(dataframe['buy_decision'] > self.zero_dn.value) &
(dataframe['gen_buy'] > self.b03.value) &
(dataframe['volume'] > 0)
)
dataframe.loc[ewo_zero_dn, 'enter_long'] = 1
dataframe.loc[ewo_zero_dn, 'enter_tag'] = 'ewo_zero_dn'
ewo_fib_up = (
(dataframe['EWO'] > dataframe['EWO_UP_FIB']) &
(dataframe['buy_decision'] > self.fib_up.value) &
(dataframe['gen_buy'] > self.b04.value) &
(dataframe['volume'] > 0)
)
dataframe.loc[ewo_fib_up, 'enter_long'] = 1
dataframe.loc[ewo_fib_up, 'enter_tag'] = 'ewo_fib_up'
ewo_mean_up = (
(dataframe['EWO'] < dataframe['EWO_UP_FIB']) &
(dataframe['EWO'] > dataframe['EWO_MEAN_UP']) &
(dataframe['buy_decision'] > self.mean_up.value) &
(dataframe['gen_buy'] > self.b05.value) &
(dataframe['volume'] > 0)
)
dataframe.loc[ewo_mean_up, 'enter_long'] = 1
dataframe.loc[ewo_mean_up, 'enter_tag'] = 'ewo_mean_up'
ewo_zero_up = (
(dataframe['EWO'] < dataframe['EWO_MEAN_UP']) &
(dataframe['EWO'] > 0) &
(dataframe['buy_decision'] > self.zero_up.value) &
(dataframe['gen_buy'] > self.b06.value) &
(dataframe['volume'] > 0)
)
dataframe.loc[ewo_zero_dn, 'enter_long'] = 1
dataframe.loc[ewo_zero_dn, 'enter_tag'] = 'ewo_zero_dn'
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
ewo_fib_dns = (
(dataframe['EWO'] < dataframe['EWO_DN_FIB']) &
(dataframe['sell_decision'] > self.fib_dns.value) &
(dataframe['gen_sell'] > self.s01.value) &
(dataframe['volume'] > 0)
)
dataframe.loc[ewo_fib_dns, 'exit_long'] = 1
dataframe.loc[ewo_fib_dns, 'exit_tag'] = 'ewo_fib_dns'
ewo_mean_dns = (
(dataframe['EWO'] > dataframe['EWO_DN_FIB']) &
(dataframe['EWO'] < dataframe['EWO_MEAN_DN']) &
(dataframe['sell_decision'] > self.mean_dns.value) &
(dataframe['gen_sell'] > self.s02.value) &
(dataframe['volume'] > 0)
)
dataframe.loc[ewo_mean_dns, 'exit_long'] = 1
dataframe.loc[ewo_mean_dns, 'exit_tag'] = 'ewo_mean_dns'
ewo_zero_dns = (
(dataframe['EWO'] > dataframe['EWO_MEAN_DN']) &
(dataframe['EWO'] < 0) &
(dataframe['sell_decision'] > self.zero_dns.value) &
(dataframe['gen_sell'] > self.s03.value) &
(dataframe['volume'] > 0)
)
dataframe.loc[ewo_zero_dns, 'exit_long'] = 1
dataframe.loc[ewo_zero_dns, 'exit_tag'] = 'ewo_zero_dns'
ewo_fib_ups = (
(dataframe['EWO'] > dataframe['EWO_UP_FIB']) &
(dataframe['sell_decision'] > self.fib_ups.value) &
(dataframe['gen_sell'] > self.s04.value) &
(dataframe['volume'] > 0)
)
dataframe.loc[ewo_fib_ups, 'exit_long'] = 1
dataframe.loc[ewo_fib_ups, 'exit_tag'] = 'ewo_fib_ups'
ewo_mean_ups = (
(dataframe['EWO'] < dataframe['EWO_UP_FIB']) &
(dataframe['EWO'] > dataframe['EWO_MEAN_UP']) &
(dataframe['sell_decision'] > self.mean_ups.value) &
(dataframe['gen_sell'] > self.s05.value) &
(dataframe['volume'] > 0)
)
dataframe.loc[ewo_mean_ups, 'exit_long'] = 1
dataframe.loc[ewo_mean_ups, 'exit_tag'] = 'ewo_mean_ups'
ewo_zero_ups = (
(dataframe['EWO'] < dataframe['EWO_MEAN_UP']) &
(dataframe['EWO'] > 0) &
(dataframe['sell_decision'] > self.zero_ups.value) &
(dataframe['gen_sell'] > self.s06.value) &
(dataframe['volume'] > 0)
)
dataframe.loc[ewo_zero_dns, 'exit_long'] = 1
dataframe.loc[ewo_zero_dns, 'exit_tag'] = 'ewo_zero_dns'
return dataframe
def pct_change(a, b):
return (b - a) / a