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stable/freqtrade/user_data/hyperopts/RuleNOTANDoptimizer.py

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Added sell_tag and buy/sell telegram performance functions
2021-10-12 21:02:28 +00:00
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
from functools import reduce
from typing import Any, Callable, Dict, List
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from skopt.space import Categorical, Dimension,Integer , Real # noqa
from freqtrade.optimize.space import SKDecimal
from freqtrade.optimize.hyperopt_interface import IHyperOpt
# --------------------------------
# Add your lib to import here
import talib.abstract as ta # noqa
import freqtrade.vendor.qtpylib.indicators as qtpylib
##PYCHARM
import sys
sys.path.append(r"/freqtrade/user_data/strategies")
# ##HYPEROPT
# import sys,os
# file_dir = os.path.dirname(__file__)
# sys.path.append(file_dir)
from z_buyer_mid_volatility import mid_volatility_buyer
from z_seller_mid_volatility import mid_volatility_seller
from z_COMMON_FUNCTIONS import MID_VOLATILITY
class RuleOptimizer15min(IHyperOpt):
"""
This is a sample hyperopt to inspire you.
Feel free to customize it.
More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
You should:
- Rename the class name to some unique name.
- Add any methods you want to build your hyperopt.
- Add any lib you need to build your hyperopt.
You must keep:
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
The methods roi_space, generate_roi_table and stoploss_space are not required
and are provided by default.
However, you may override them if you need the
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
This sample illustrates how to override these methods.
"""
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by hyperopt
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use
"""
conditions = []
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
##MAIN SELECTORS
#--------------------
##VOLATILITY
conditions.append(dataframe['vol_mid'] > 0 )
# conditions.append((dataframe['vol_low'] > 0) |(dataframe['vol_mid'] > 0) )
# conditions.append((dataframe['vol_high'] > 0) |(dataframe['vol_mid'] > 0) )
#--------------------
##PICKS TREND COMBO
conditions.append(
(dataframe['downtrend'] >= params['main_1_trend_strength'])
|#OR &
(dataframe['downtrendsmall'] >= params['main_2_trend_strength'])
)
##UPTREND
#conditions.append(dataframe['uptrend'] >= params['main_1_trend_strength'])
##DOWNTREND
#conditions.append(dataframe['downtrend'] >= params['main_1_trend_strength'])
##NOTREND
#conditions.append((dataframe['uptrend'] <1)&(dataframe['downtrend'] <1))
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
##ABOVE / BELOW THRESHOLDS
#RSI ABOVE
if 'include_sell_ab_9_rsi_above_value' in params and params['include_sell_ab_9_rsi_above_value']:
conditions.append(dataframe['rsi'] > params['sell_ab_9_rsi_above_value'])
#RSI RECENT PIT 5
if 'include_sell_ab_10_rsi_recent_pit_2_value' in params and params['include_sell_ab_10_rsi_recent_pit_2_value']:
conditions.append(dataframe['rsi'].rolling(2).min() < params['sell_ab_10_rsi_recent_pit_2_value'])
#RSI RECENT PIT 12
if 'include_sell_ab_11_rsi_recent_pit_4_value' in params and params['include_sell_ab_11_rsi_recent_pit_4_value']:
conditions.append(dataframe['rsi'].rolling(4).min() < params['sell_ab_11_rsi_recent_pit_4_value'])
#RSI5 BELOW
if 'include_sell_ab_12_rsi5_above_value' in params and params['include_sell_ab_12_rsi5_above_value']:
conditions.append(dataframe['rsi5'] > params['sell_ab_12_rsi5_above_value'])
#RSI50 BELOW
if 'include_sell_ab_13_rsi50_above_value' in params and params['include_sell_ab_13_rsi50_above_value']:
conditions.append(dataframe['rsi50'] > params['sell_ab_13_rsi50_above_value'])
#-----------------------
#ROC BELOW
if 'include_sell_ab_14_roc_above_value' in params and params['include_sell_ab_14_roc_above_value']:
conditions.append(dataframe['roc'] > (params['sell_ab_14_roc_above_value']/2))
#ROC50 BELOW
if 'include_sell_ab_15_roc50_above_value' in params and params['include_sell_ab_15_roc50_above_value']:
conditions.append(dataframe['roc50'] > (params['sell_ab_15_roc50_above_value']))
#ROC2 BELOW
if 'include_sell_ab_16_roc2_above_value' in params and params['include_sell_ab_16_roc2_above_value']:
conditions.append(dataframe['roc2'] > (params['sell_ab_16_roc2_above_value']/2))
#-----------------------
#PPO5 BELOW
if 'include_sell_ab_17_ppo5_above_value' in params and params['include_sell_ab_17_ppo5_above_value']:
conditions.append(dataframe['ppo5'] > (params['sell_ab_17_ppo5_above_value']/2))
#PPO10 BELOW
if 'include_sell_ab_18_ppo10_above_value' in params and params['include_sell_ab_18_ppo10_above_value']:
conditions.append(dataframe['ppo10'] > (params['sell_ab_18_ppo10_above_value']/2))
#PPO25 BELOW
if 'include_sell_ab_19_ppo25_above_value' in params and params['include_sell_ab_19_ppo25_above_value']:
conditions.append(dataframe['ppo25'] > (params['sell_ab_19_ppo25_above_value']/2))
#PPO50 BELOW
if 'include_sell_ab_20_ppo50_above_value' in params and params['include_sell_ab_20_ppo50_above_value']:
conditions.append(dataframe['ppo50'] > (params['sell_ab_20_ppo50_above_value']/2))
#PPO100 BELOW
if 'include_sell_ab_21_ppo100_above_value' in params and params['include_sell_ab_21_ppo100_above_value']:
conditions.append(dataframe['ppo100'] > (params['sell_ab_21_ppo100_above_value']))
#PPO200 BELOW
if 'include_sell_ab_22_ppo200_above_value' in params and params['include_sell_ab_22_ppo200_above_value']:
conditions.append(dataframe['ppo200'] > (params['sell_ab_22_ppo200_above_value']))
#PPO500 BELOW
if 'include_sell_ab_23_ppo500_above_value' in params and params['include_sell_ab_23_ppo500_above_value']:
conditions.append(dataframe['ppo500'] > (params['sell_ab_23_ppo500_above_value']*2))
##USE AT A LATER STEP
#convsmall BELOW
if 'include_sell_ab_24_convsmall_above_value' in params and params['include_sell_ab_24_convsmall_above_value']:
conditions.append(dataframe['convsmall'] > (params['sell_ab_24_convsmall_above_value']/2))
#convmedium BELOW
if 'include_sell_ab_25_convmedium_above_value' in params and params['include_sell_ab_25_convmedium_above_value']:
conditions.append(dataframe['convmedium'] >(params['sell_ab_25_convmedium_above_value']))
#convlarge BELOW
if 'include_sell_ab_26_convlarge_above_value' in params and params['include_sell_ab_26_convlarge_above_value']:
conditions.append(dataframe['convlarge'] > (params['sell_ab_26_convlarge_above_value']))
#convultra BELOW
if 'include_sell_ab_27_convultra_above_value' in params and params['include_sell_ab_27_convultra_above_value']:
conditions.append(dataframe['convultra'] > (params['sell_ab_27_convultra_above_value']/2))
#convdist BELOW
if 'include_sell_ab_28_convdist_above_value' in params and params['include_sell_ab_28_convdist_above_value']:
conditions.append(dataframe['convdist'] > (params['sell_ab_28_convdist_above_value']))
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
##SMA'S GOING DOWN
if 'sell_down_0a_sma3' in params and params['sell_down_0a_sma3']:
conditions.append((dataframe['sma3'].shift(1) >dataframe['sma3']))
if 'sell_down_0b_sma5' in params and params['sell_down_0b_sma5']:
conditions.append((dataframe['sma5'].shift(1) >dataframe['sma5']))
if 'sell_down_1_sma10' in params and params['sell_down_1_sma10']:
conditions.append((dataframe['sma10'].shift(1) >dataframe['sma10']))
if 'sell_down_2_sma25' in params and params['sell_down_2_sma25']:
conditions.append((dataframe['sma25'].shift(1) >dataframe['sma25']))
if 'sell_down_3_sma50' in params and params['sell_down_3_sma50']:
conditions.append((dataframe['sma50'].shift(2) >dataframe['sma50']))
if 'sell_down_4_sma100' in params and params['sell_down_4_sma100']:
conditions.append((dataframe['sma100'].shift(3) >dataframe['sma100']))
if 'sell_down_5_sma200' in params and params['sell_down_5_sma200']:
conditions.append((dataframe['sma200'].shift(4) >dataframe['sma200']))
if 'sell_down_6_sma400' in params and params['sell_down_6_sma400']:
conditions.append((dataframe['sma400'].shift(4) >dataframe['sma400']))
if 'sell_down_7_sma10k' in params and params['sell_down_7_sma10k']:
conditions.append((dataframe['sma10k'].shift(5) >dataframe['sma10k']))
# if 'sell_down_8_sma20k' in params and params['sell_down_8_sma20k']:
# conditions.append((dataframe['sma20k'].shift(5) >dataframe['sma20k']))
# if 'sell_down_9_sma30k' in params and params['sell_down_9_sma30k']:
# conditions.append((dataframe['sma30k'].shift(5) >dataframe['sma30k']))
if 'sell_down_10_convsmall' in params and params['sell_down_10_convsmall']:
conditions.append((dataframe['convsmall'].shift(2) >dataframe['convsmall']))
if 'sell_down_11_convmedium' in params and params['sell_down_11_convmedium']:
conditions.append((dataframe['convmedium'].shift(3) >dataframe['convmedium']))
if 'sell_down_12_convlarge' in params and params['sell_down_12_convlarge']:
conditions.append((dataframe['convlarge'].shift(4) >dataframe['convlarge']))
if 'sell_down_13_convultra' in params and params['sell_down_13_convultra']:
conditions.append((dataframe['convultra'].shift(4) >dataframe['convultra']))
if 'sell_down_14_convdist' in params and params['sell_down_14_convdist']:
conditions.append((dataframe['convdist'].shift(4) >dataframe['convdist']))
if 'sell_down_15_vol50' in params and params['sell_down_15_vol50']:
conditions.append((dataframe['vol50'].shift(2) >dataframe['vol50']))
if 'sell_down_16_vol100' in params and params['sell_down_16_vol100']:
conditions.append((dataframe['vol100'].shift(3) >dataframe['vol100']))
if 'sell_down_17_vol175' in params and params['sell_down_17_vol175']:
conditions.append((dataframe['vol175'].shift(4) >dataframe['vol175']))
if 'sell_down_18_vol250' in params and params['sell_down_18_vol250']:
conditions.append((dataframe['vol250'].shift(4) >dataframe['vol250']))
if 'sell_down_19_vol500' in params and params['sell_down_19_vol500']:
conditions.append((dataframe['vol500'].shift(4) >dataframe['vol500']))
if 'sell_down_20_vol1000' in params and params['sell_down_20_vol1000']:
conditions.append((dataframe['vol1000'].shift(4) >dataframe['vol1000']))
if 'sell_down_21_vol100mean' in params and params['sell_down_21_vol100mean']:
conditions.append((dataframe['vol100mean'].shift(4) >dataframe['vol100mean']))
if 'sell_down_22_vol250mean' in params and params['sell_down_22_vol250mean']:
conditions.append((dataframe['vol250mean'].shift(4) >dataframe['vol250mean']))
if 'up_20_conv3' in params and params['up_20_conv3']:
conditions.append(((dataframe['conv3'].shift(25) < dataframe['conv3'])&(dataframe['conv3'].shift(50) < dataframe['conv3'])))
if 'up_21_vol5' in params and params['up_21_vol5']:
conditions.append(((dataframe['vol5'].shift(25) < dataframe['vol5'])&(dataframe['vol5'].shift(50) < dataframe['vol5'])))
if 'up_22_vol5ultra' in params and params['up_22_vol5ultra']:
conditions.append(((dataframe['vol5ultra'].shift(25) < dataframe['vol5ultra'])&(dataframe['vol5ultra'].shift(50) < dataframe['vol5ultra'])))
if 'up_23_vol1ultra' in params and params['up_23_vol1ultra']:
conditions.append(((dataframe['vol1ultra'].shift(25) < dataframe['vol1ultra'])& (dataframe['vol1ultra'].shift(50) < dataframe['vol1ultra'])))
if 'up_24_vol1' in params and params['up_24_vol1']:
conditions.append(((dataframe['vol1'].shift(30) < dataframe['vol1'])&(dataframe['vol1'].shift(10) < dataframe['vol1'])))
if 'up_25_vol5inc24' in params and params['up_25_vol5inc24']:
conditions.append((dataframe['vol5inc24'].shift(50) < dataframe['vol5inc24']))
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
##ABOVE / BELOW SMAS 1 above/ 0 None / -1 below
#SMA10
conditions.append((dataframe['close'] > dataframe['sma10'])|(0.5 > params['ab_1_sma10']))
conditions.append((dataframe['close'] < dataframe['sma10'])|(-0.5 < params['ab_1_sma10']))
#SMA25
conditions.append((dataframe['close'] > dataframe['sma25'])|(0.5 > params['ab_2_sma25']))
conditions.append((dataframe['close'] < dataframe['sma25'])|(-0.5 < params['ab_2_sma25']))
#SMA50
conditions.append((dataframe['close'] > dataframe['sma50'])|(0.5 > params['ab_3_sma50']))
conditions.append((dataframe['close'] < dataframe['sma50'])|(-0.5 < params['ab_3_sma50']))
#SMA100
conditions.append((dataframe['close'] > dataframe['sma100'])|(0.5 > params['ab_4_sma100']))
conditions.append((dataframe['close'] < dataframe['sma100'])|(-0.5 < params['ab_4_sma100']))
#SMA100
conditions.append((dataframe['close'] > dataframe['sma200'])|(0.5 > params['ab_5_sma200']))
conditions.append((dataframe['close'] < dataframe['sma200'])|(-0.5 < params['ab_5_sma200']))
#SMA400
conditions.append((dataframe['close'] > dataframe['sma400'])|(0.5 > params['ab_6_sma400']))
conditions.append((dataframe['close'] < dataframe['sma400'])|(-0.5 < params['ab_6_sma400']))
#SMA10k
conditions.append((dataframe['close'] > dataframe['sma10k'])|(0.5 > params['ab_7_sma10k']))
conditions.append((dataframe['close'] < dataframe['sma10k'])|(-0.5 < params['ab_7_sma10k']))
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
##DOWNSWINGS / UPSWINGS PPO'S
#ppo5 UP OR DOWN (1 UP, 0 NOTHING, -1 DOWN)
conditions.append((dataframe['ppo5'].shift(2) <dataframe['ppo5'])|(0.5 > params['sell_swings_1_ppo5_up_or_down_bool']))
conditions.append((dataframe['ppo5'].shift(2) >dataframe['ppo5'])|(-0.5 < params['sell_swings_1_ppo5_up_or_down_bool']))
#ppo10
conditions.append((dataframe['ppo10'].shift(3) <dataframe['ppo10'])|(0.5 > params['sell_swings_2_ppo10_up_or_down_bool']))
conditions.append((dataframe['ppo10'].shift(3) >dataframe['ppo10'])|(-0.5 < params['sell_swings_2_ppo10_up_or_down_bool']))
#ppo25
#conditions.append((dataframe['ppo25'].shift(3) <dataframe['ppo25'])|(0.5 > params['sell_swings_3_ppo25_up_or_down_bool']))
conditions.append((dataframe['ppo25'].shift(3) >dataframe['ppo25'])|(-0.5 < params['sell_swings_3_ppo25_up_or_down_bool']))
#ppo50
#conditions.append((dataframe['ppo50'].shift(3 <dataframe['ppo50'])|(0.5 > params['sell_swings_4_ppo50_up_or_down_bool']))
conditions.append((dataframe['ppo50'].shift(3) >dataframe['ppo50'])|(-0.5 < params['sell_swings_4_ppo50_up_or_down_bool']))
#ppo100
#conditions.append((dataframe['ppo100'].shift(4) <dataframe['ppo100'])|(0.5 > params['sell_swings_5_ppo100_up_or_down_bool']))
conditions.append((dataframe['ppo100'].shift(4) >dataframe['ppo100'])|(-0.5 < params['sell_swings_5_ppo100_up_or_down_bool']))
#ppo200
#conditions.append((dataframe['ppo200'].shift(4) <dataframe['ppo200'])|(0.5 > params['sell_swings_6_ppo200_up_or_down_bool']))
conditions.append((dataframe['ppo200'].shift(4) >dataframe['ppo200'])|(-0.5 < params['sell_swings_6_ppo200_up_or_down_bool']))
#ppo500
#conditions.append((dataframe['ppo500'].shift(5) <dataframe['ppo500'])|(0.5 > params['sell_swings_7_ppo500_up_or_down_bool']))
conditions.append((dataframe['ppo500'].shift(5) >dataframe['ppo500'])|(-0.5 < params['sell_swings_7_ppo500_up_or_down_bool']))
#roc50
#conditions.append((dataframe['roc50'].shift(3) <dataframe['roc50'])|(0.5 > params['sell_swings_8_roc50_up_or_down_bool']))
conditions.append((dataframe['roc50'].shift(3) >dataframe['roc50'])|(-0.5 < params['sell_swings_8_roc50_up_or_down_bool']))
#roc10
#conditions.append((dataframe['roc10'].shift(2) <dataframe['roc10'])|(0.5 > params['sell_swings_9_roc10_up_or_down_bool']))
conditions.append((dataframe['roc10'].shift(2) >dataframe['roc10'])|(-0.5 < params['sell_swings_9_roc10_up_or_down_bool']))
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
##DISTANCES/ROC
##FOR MORE TOP SELLERS
#dist50 MORE THAN
if 'include_sell_dist_1_dist50_more_value' in params and params['include_sell_dist_1_dist50_more_value']:
conditions.append(dataframe['dist50'] > (params['sell_dist_1_dist50_more_value']))
#dist200 MORE THAN
if 'include_sell_dist_2_dist200_more_value' in params and params['include_sell_dist_2_dist200_more_value']:
conditions.append(dataframe['dist200'] > (params['sell_dist_2_dist200_more_value']))
#dist400 MORE THAN
if 'include_sell_dist_3_dist400_more_value' in params and params['include_sell_dist_3_dist400_more_value']:
conditions.append(dataframe['dist400'] > (params['sell_dist_3_dist400_more_value']))
#dist10k MORE THAN
if 'include_sell_dist_4_dist10k_more_value' in params and params['include_sell_dist_4_dist10k_more_value']:
conditions.append(dataframe['dist10k'] > (params['sell_dist_4_dist10k_more_value']))
##FOR MORE TOP SELLERS
#more =further from top bol up
#dist_upbol50 MORE THAN
if 'include_sell_dist_5_dist_upbol50_more_value' in params and params['include_sell_dist_5_dist_upbol50_more_value']:
conditions.append(dataframe['dist_upbol50'] > (params['sell_dist_5_dist_upbol50_more_value']/2))
#dist_upbol100 MORE THAN
if 'include_sell_dist_6_dist_upbol100_more_value' in params and params['include_sell_dist_6_dist_upbol100_more_value']:
conditions.append(dataframe['dist_upbol100'] > (params['sell_dist_6_dist_upbol100_more_value']/2))
##for bot bol prevent seller
# #less =closer to bot bol
#dist_upbol50 LESS THAN.
#if 'include_sell_dist_7_dist_lowbol50_more_value' in params and params['include_sell_dist_7_dist_lowbol50_more_value']:
# conditions.append(dataframe['dist_lowbol50'] > (params['sell_dist_7_dist_lowbol50_more_value']/2))
#dist_upbol100 LESS THAN
# if 'include_sell_dist_8_dist_lowbol100_more_value' in params and params['include_sell_dist_8_dist_lowbol100_more_value']:
# conditions.append(dataframe['dist_lowbol100'] > (params['sell_dist_8_dist_lowbol100_more_value']/2))
##others
#roc50sma LESS THAN
if 'include_sell_dist_7_roc50sma_less_value' in params and params['include_sell_dist_7_roc50sma_less_value']:
conditions.append(dataframe['roc50sma'] < (params['sell_dist_7_roc50sma_less_value'])*2)
#roc200sma LESS THAN
if 'include_sell_dist_8_roc200sma_less_value' in params and params['include_sell_dist_8_roc200sma_less_value']:
conditions.append(dataframe['roc200sma'] < (params['sell_dist_8_roc200sma_less_value'])*2)
##ENABLE TO BUY AWAY FROM HIGH
# #HIGH500 TO CLOSE MORE THAN
#if 'include_sell_dist_9_high100_more_value' in params and params['include_sell_dist_9_high100_more_value']:
# conditions.append((dataframe['high100']-dataframe['close']) > ((dataframe['high100']/100* (params['sell_dist_9_high100_more_value']))
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
# ##ENABLE PRODUCTION BUYS
# dataframe.loc[
# (add_production_buys_mid(dataframe)),
# 'buy'] = 1
#
dataframe.loc[
(~(reduce(lambda x, y: x & y, conditions)))&OPTIMIZED_RULE(dataframe,params),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return [
#-------------------------------------------------------------------------------------------------------
## CUSTOM RULE TRESHOLDS
# SKDecimal(5.0, 7.0,decimals=1, name='sell_trigger_0_roc_ab_value'),# 5 range 5-7 or 4-7
# SKDecimal(3.2, 4.5,decimals=1, name='sell_trigger_0_roc2_ab_value'),#3.8 range 3.2-4.5
# Integer(77, 82, name='sell_trigger_0_rsi_ab_value'),#79 range 82-77
# Integer(90, 95, name='sell_trigger_0_rsi5_ab_value'),#94 range 95-90
# Integer(63, 67, name='sell_trigger_0_rsi50_ab_value'),#66 range 67-63
#-------------------------------------------------------------------------------------------------------
##MAIN
Categorical([1, 2, 3], name='sell_main_1_trend_strength'), #BIG TREND STR
Categorical([1, 2, 3], name='sell_main_2_trend_strength'), #SMALL UPTREND STR
#Categorical([-1, 0, 1], name='sell_main_2_small_uptrend_downtrend'), #SMALL UPTREND ON/OFF 1 is on -1 is down
#-------------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------------
##INCLUDE/EXCLUDE RULES
Categorical([True, False], name='include_sell_ab_9_rsi_above_value'),
Categorical([True, False], name='include_sell_ab_10_rsi_recent_pit_2_value'),
Categorical([True, False], name='include_sell_ab_11_rsi_recent_pit_4_value'),
Categorical([True, False], name='include_sell_ab_12_rsi5_above_value'),
Categorical([True, False], name='include_sell_ab_13_rsi50_above_value'),
Categorical([True, False], name='include_sell_ab_14_roc_above_value'),
Categorical([True, False], name='include_sell_ab_15_roc50_above_value'),
Categorical([True, False], name='include_sell_ab_16_roc2_above_value'),
Categorical([True, False], name='include_sell_ab_17_ppo5_above_value'),
Categorical([True, False], name='include_sell_ab_18_ppo10_above_value'),
Categorical([True, False], name='include_sell_ab_19_ppo25_above_value'),
Categorical([True, False], name='include_sell_ab_20_ppo50_above_value'),
Categorical([True, False], name='include_sell_ab_21_ppo100_above_value'),
Categorical([True, False], name='include_sell_ab_22_ppo200_above_value'),
Categorical([True, False], name='include_sell_ab_23_ppo500_above_value'),
##USE AT A LATER STEP
Categorical([True, False], name='include_sell_ab_24_convsmall_above_value'),
Categorical([True, False], name='include_sell_ab_25_convmedium_above_value'),
Categorical([True, False], name='include_sell_ab_26_convlarge_above_value'),
Categorical([True, False], name='include_sell_ab_27_convultra_above_value'),
Categorical([True, False], name='include_sell_ab_28_convdist_above_value'),
Categorical([True, False], name='include_sell_dist_1_dist50_more_value'),
Categorical([True, False], name='include_sell_dist_2_dist200_more_value'),
Categorical([True, False], name='include_sell_dist_3_dist400_more_value'),
Categorical([True, False], name='include_sell_dist_4_dist10k_more_value'),
Categorical([True, False], name='include_sell_dist_5_dist_upbol50_more_value'),
Categorical([True, False], name='include_sell_dist_6_dist_upbol100_more_value'),
# FOR MORE DOWNTREND BUYS LIKELY
# Categorical([True, False], name='include_sell_dist_7_dist_lowbol50_more_value'),
# Categorical([True, False], name='include_sell_dist_8_dist_lowbol100_more_value'),
#MORE LIKE TRIGGERS
Categorical([True, False], name='include_sell_dist_7_roc50sma_less_value'),
Categorical([True, False], name='include_sell_dist_8_roc200sma_less_value'),
##below high 100
#Categorical([True, False], name='include_sell_dist_9_high100_more_value'),
#-------------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------------
##ABOVE/BELOW VALUES
Integer(35, 82, name='sell_ab_9_rsi_above_value'),
Integer(18, 35, name='sell_ab_10_rsi_recent_pit_2_value'),
Integer(18, 35, name='sell_ab_11_rsi_recent_pit_4_value'),
Integer(70, 91, name='sell_ab_12_rsi5_above_value'),
Integer(37, 60, name='sell_ab_13_rsi50_above_value'),
Integer(-4, 10, name='sell_ab_14_roc_above_value'),#/2
Integer(-2, 8, name='sell_ab_15_roc50_above_value'),
Integer(-4, 8, name='sell_ab_16_roc2_above_value'),#/2
#--------------------------------
##CHANGE DEPENDING WHAT TYPE OF SELL --> PEAK OR DOWTRENDS
Integer(-4, 6, name='sell_ab_17_ppo5_above_value'),#/2
Integer(-4, 6, name='sell_ab_18_ppo10_above_value'),#/2
Integer(-10, 8, name='sell_ab_19_ppo25_above_value'),#/2
Integer(-10, 8, name='sell_ab_20_ppo50_above_value'),#/2
Integer(-6, 6, name='sell_ab_21_ppo100_above_value'),
Integer(-6, 6, name='sell_ab_22_ppo200_above_value'),
Integer(-4, 5, name='sell_ab_23_ppo500_above_value'),#*2
# ##USE AT A LATER STEP
#
# Integer(-1, 6, name='sell_ab_24_convsmall_above_value'),#/2 # extreme 12
# Integer(-1, 4, name='sell_ab_25_convmedium_above_value'),# extreme 6
# Integer(-1, 7, name='sell_ab_26_convlarge_above_value'),# extreme 12
# Integer(-1, 8, name='sell_ab_27_convultra_above_value'),#/2# extreme 12
#
# Integer(-1, 6, name='sell_ab_28_convdist_above_value'), #very extreme not useful 10+
#-------------------------------------------------------------------------------------------------------
#SMA'S GOING DOWN
Categorical([True, False], name='sell_down_0a_sma3'),
Categorical([True, False], name='sell_down_0b_sma5'),
Categorical([True, False], name='sell_down_1_sma10'),
Categorical([True, False], name='sell_down_2_sma25'),
Categorical([True, False], name='sell_down_3_sma50'),
Categorical([True, False], name='sell_down_4_sma100'),
Categorical([True, False], name='sell_down_5_sma200'),
Categorical([True, False], name='sell_down_6_sma400'),
Categorical([True, False], name='sell_down_7_sma10k'),
# Categorical([True, False], name='sell_down_8_sma20k'),
# Categorical([True, False], name='sell_down_9_sma30k'),
Categorical([True, False], name='sell_down_10_convsmall'),
Categorical([True, False], name='sell_down_11_convmedium'),
Categorical([True, False], name='sell_down_12_convlarge'),
Categorical([True, False], name='sell_down_13_convultra'),
Categorical([True, False], name='sell_down_14_convdist'),
Categorical([True, False], name='sell_down_15_vol50'),
Categorical([True, False], name='sell_down_16_vol100'),
Categorical([True, False], name='sell_down_17_vol175'),
Categorical([True, False], name='sell_down_18_vol250'),
Categorical([True, False], name='sell_down_19_vol500'),
Categorical([True, False], name='sell_down_20_vol1000'),
Categorical([True, False], name='sell_down_21_vol100mean'),
Categorical([True, False], name='sell_down_22_vol250mean'),
#-------------------------------------------------------------------------------------------------------
##ABOVE/BELOW SMAS
Categorical([-1, 0, 1], name='sell_ab_1_sma10'),
Categorical([-1, 0, 1], name='sell_ab_2_sma25'),
Categorical([-1, 0, 1], name='sell_ab_3_sma50'),
Categorical([-1, 0, 1], name='sell_ab_4_sma100'),
Categorical([-1, 0, 1], name='sell_ab_5_sma200'),
Categorical([-1, 0, 1], name='sell_ab_6_sma400'),
Categorical([-1, 0, 1], name='sell_ab_7_sma10k'),
#-------------------------------------------------------------------------------------------------------
##DOWNSWINGS / UPSWINGS PPO'S
##UP OR DOWN (1 UP, 0 NOTHING, -1 DOWN)
Categorical([-1, 0, 1], name='sell_swings_1_ppo5_up_or_down_bool'),
Categorical([-1, 0, 1], name='sell_swings_2_ppo10_up_or_down_bool'),
Categorical([-1, 0], name='sell_swings_3_ppo25_up_or_down_bool'),
Categorical([-1, 0], name='sell_swings_4_ppo50_up_or_down_bool'),
Categorical([-1, 0], name='sell_swings_5_ppo100_up_or_down_bool'),
Categorical([-1, 0], name='sell_swings_6_ppo200_up_or_down_bool'),
Categorical([-1, 0], name='sell_swings_7_ppo500_up_or_down_bool'),
Categorical([-1, 0], name='sell_swings_8_roc50_up_or_down_bool'),
Categorical([-1, 0], name='sell_swings_9_roc10_up_or_down_bool'),
#-------------------------------------------------------------------------------------------------------
#DISTANCES
#FOR MORE TOP SELLERS
Integer(-6, 14, name='sell_dist_1_dist50_more_value'), #extreme, useless -4 ,30
Integer(-8, 20, name='sell_dist_2_dist200_more_value'), #extreme, useless -12-40
Integer(-15, 30, name='sell_dist_3_dist400_more_value'),
Integer(-15, 35, name='sell_dist_4_dist10k_more_value'),
#FOR MORE TOP SELLERS
Integer(-30, 25, name='sell_dist_5_dist_upbol50_more_value'),#/2
Integer(-30, 25, name='sell_dist_6_dist_upbol100_more_value'),#/2
#FOR MORE DOWNTREND BUYS LIKELY
# Integer(-8, 50, name='sell_dist_7_dist_lowbol50_more_value'),#/2 ##set to more, as in higher from lower boll
# Integer(-8, 50, name='sell_dist_8_dist_lowbol100_more_value'),#/2 ##set to more, as in higher from lower boll
# Integer(-70, 40, name='sell_dist_7_roc50sma_more_value'),#*2 ##fix less more
# Integer(-40, 12, name='sell_dist_8_roc200sma_more_value'),#*2
##below high 100
#Integer(0, 0, name='sell_dist_9_high100_more_value'),
#-------------------------------------------------------------------------------------------------------
]
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by hyperopt
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use
"""
# print(params)
conditions = []
# GUARDS AND TRENDS
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
##MAIN SELECTORS
#--------------------
##VOLATILITY
conditions.append(dataframe['vol_mid'] > 0 )
# conditions.append((dataframe['vol_low'] > 0) |(dataframe['vol_mid'] > 0) )
# conditions.append((dataframe['vol_high'] > 0) |(dataframe['vol_mid'] > 0) )
#--------------------
##PICKS TREND COMBO
conditions.append(
(dataframe['uptrend'] >= params['main_1_trend_strength'])
|#OR &
(dataframe['uptrendsmall'] >= params['main_2_trend_strength'])
)
##UPTREND
#conditions.append(dataframe['uptrend'] >= params['main_1_trend_strength'])
##DOWNTREND
#conditions.append(dataframe['downtrend'] >= params['main_1_trend_strength'])
##NOTREND
#conditions.append((dataframe['uptrend'] <1)&(dataframe['downtrend'] <1))
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
##ABOVE/BELOW VALUES
#RSI BELOW
if 'include_ab_9_rsi_below_value' in params and params['include_ab_9_rsi_below_value']:
conditions.append(dataframe['rsi'] < params['ab_9_rsi_below_value'])
#RSI RECENT PEAK 5
if 'include_ab_10_rsi_recent_peak_2_value' in params and params['include_ab_10_rsi_recent_peak_2_value']:
conditions.append(dataframe['rsi'].rolling(2).max() < params['ab_10_rsi_recent_peak_2_value'])
#RSI RECENT PEAK 12
if 'include_ab_11_rsi_recent_peak_4_value' in params and params['include_ab_11_rsi_recent_peak_4_value']:
conditions.append(dataframe['rsi'].rolling(4).max() < params['ab_11_rsi_recent_peak_4_value'])
#RSI5 BELOW
if 'include_ab_12_rsi5_below_value' in params and params['include_ab_12_rsi5_below_value']:
conditions.append(dataframe['rsi5'] < params['ab_12_rsi5_below_value'])
#RSI50 BELOW
if 'include_ab_13_rsi50_below_value' in params and params['include_ab_13_rsi50_below_value']:
conditions.append(dataframe['rsi50'] < params['ab_13_rsi50_below_value'])
#-----------------------
#ROC BELOW
if 'include_ab_14_roc_below_value' in params and params['include_ab_14_roc_below_value']:
conditions.append(dataframe['roc'] < (params['ab_14_roc_below_value']/2))
#ROC50 BELOW
if 'include_ab_15_roc50_below_value' in params and params['include_ab_15_roc50_below_value']:
conditions.append(dataframe['roc50'] < (params['ab_15_roc50_below_value']))
#ROC2 BELOW
if 'include_ab_16_roc2_below_value' in params and params['include_ab_16_roc2_below_value']:
conditions.append(dataframe['roc2'] < (params['ab_16_roc2_below_value']/2))
#-----------------------
#PPO5 BELOW
if 'include_ab_17_ppo5_below_value' in params and params['include_ab_17_ppo5_below_value']:
conditions.append(dataframe['ppo5'] < (params['ab_17_ppo5_below_value']/2))
#PPO10 BELOW
if 'include_ab_18_ppo10_below_value' in params and params['include_ab_18_ppo10_below_value']:
conditions.append(dataframe['ppo10'] < (params['ab_18_ppo10_below_value']/2))
#PPO25 BELOW
if 'include_ab_19_ppo25_below_value' in params and params['include_ab_19_ppo25_below_value']:
conditions.append(dataframe['ppo25'] < (params['ab_19_ppo25_below_value']/2))
#PPO50 BELOW
if 'include_ab_20_ppo50_below_value' in params and params['include_ab_20_ppo50_below_value']:
conditions.append(dataframe['ppo50'] < (params['ab_20_ppo50_below_value']/2))
#PPO100 BELOW
if 'include_ab_21_ppo100_below_value' in params and params['include_ab_21_ppo100_below_value']:
conditions.append(dataframe['ppo100'] < (params['ab_21_ppo100_below_value']))
#PPO200 BELOW
if 'include_ab_22_ppo200_below_value' in params and params['include_ab_22_ppo200_below_value']:
conditions.append(dataframe['ppo200'] < (params['ab_22_ppo200_below_value']))
#PPO500 BELOW
if 'include_ab_23_ppo500_below_value' in params and params['include_ab_23_ppo500_below_value']:
conditions.append(dataframe['ppo500'] < (params['ab_23_ppo500_below_value']*2))
##USE AT A LATER STEP
#convsmall BELOW
if 'include_ab_24_convsmall_below_value' in params and params['include_ab_24_convsmall_below_value']:
conditions.append(dataframe['convsmall'] < (params['ab_24_convsmall_below_value']/2))
#convmedium BELOW
if 'include_ab_25_convmedium_below_value' in params and params['include_ab_25_convmedium_below_value']:
conditions.append(dataframe['convmedium'] < (params['ab_25_convmedium_below_value']))
#convlarge BELOW
if 'include_ab_26_convlarge_below_value' in params and params['include_ab_26_convlarge_below_value']:
conditions.append(dataframe['convlarge'] < (params['ab_26_convlarge_below_value']))
#convultra BELOW
if 'include_ab_27_convultra_below_value' in params and params['include_ab_27_convultra_below_value']:
conditions.append(dataframe['convultra'] < (params['ab_27_convultra_below_value']/2))
#convdist BELOW
if 'include_ab_28_convdist_below_value' in params and params['include_ab_28_convdist_below_value']:
conditions.append(dataframe['convdist'] < (params['ab_28_convdist_below_value']))
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
##SMA'S GOING UP
if 'up_0a_sma3' in params and params['up_0a_sma3']:
conditions.append((dataframe['sma3'].shift(1) <dataframe['sma3']))
if 'up_0b_sma5' in params and params['up_0b_sma5']:
conditions.append((dataframe['sma5'].shift(1) <dataframe['sma5']))
if 'up_1_sma10' in params and params['up_1_sma10']:
conditions.append((dataframe['sma10'].shift(1) <dataframe['sma10']))
if 'up_2_sma25' in params and params['up_2_sma25']:
conditions.append((dataframe['sma25'].shift(1) <dataframe['sma25']))
if 'up_3_sma50' in params and params['up_3_sma50']:
conditions.append((dataframe['sma50'].shift(2) <dataframe['sma50']))
if 'up_4_sma100' in params and params['up_4_sma100']:
conditions.append((dataframe['sma100'].shift(3) <dataframe['sma100']))
if 'up_5_sma200' in params and params['up_5_sma200']:
conditions.append((dataframe['sma200'].shift(4) <dataframe['sma200']))
if 'up_6_sma400' in params and params['up_6_sma400']:
conditions.append((dataframe['sma400'].shift(4) <dataframe['sma400']))
if 'up_7_sma10k' in params and params['up_7_sma10k']:
conditions.append((dataframe['sma10k'].shift(5) <dataframe['sma10k']))
# if 'up_8_sma20k' in params and params['up_8_sma20k']:
# conditions.append((dataframe['sma20k'].shift(5) <dataframe['sma20k']))
# if 'up_9_sma30k' in params and params['up_9_sma30k']:
# conditions.append((dataframe['sma30k'].shift(5) <dataframe['sma30k']))
if 'up_10_convsmall' in params and params['up_10_convsmall']:
conditions.append((dataframe['convsmall'].shift(2) <dataframe['convsmall']))
if 'up_11_convmedium' in params and params['up_11_convmedium']:
conditions.append((dataframe['convmedium'].shift(3) <dataframe['convmedium']))
if 'up_12_convlarge' in params and params['up_12_convlarge']:
conditions.append((dataframe['convlarge'].shift(4) <dataframe['convlarge']))
if 'up_13_convultra' in params and params['up_13_convultra']:
conditions.append((dataframe['convultra'].shift(4) <dataframe['convultra']))
if 'up_14_convdist' in params and params['up_14_convdist']:
conditions.append((dataframe['convdist'].shift(4) <dataframe['convdist']))
if 'up_15_vol50' in params and params['up_15_vol50']:
conditions.append((dataframe['vol50'].shift(2) <dataframe['vol50']))
if 'up_16_vol100' in params and params['up_16_vol100']:
conditions.append((dataframe['vol100'].shift(3) <dataframe['vol100']))
if 'up_17_vol175' in params and params['up_17_vol175']:
conditions.append((dataframe['vol175'].shift(4) <dataframe['vol175']))
if 'up_18_vol250' in params and params['up_18_vol250']:
conditions.append((dataframe['vol250'].shift(4) <dataframe['vol250']))
if 'up_19_vol500' in params and params['up_19_vol500']:
conditions.append((dataframe['vol500'].shift(4) <dataframe['vol500']))
if 'up_20_vol1000' in params and params['up_20_vol1000']:
conditions.append((dataframe['vol1000'].shift(4) <dataframe['vol1000']))
if 'up_21_vol100mean' in params and params['up_21_vol100mean']:
conditions.append((dataframe['vol100mean'].shift(4) <dataframe['vol100mean']))
if 'up_22_vol250mean' in params and params['up_22_vol250mean']:
conditions.append((dataframe['vol250mean'].shift(4) <dataframe['vol250mean']))
if 'up_20_conv3' in params and params['up_20_conv3']:
conditions.append(((dataframe['conv3'].shift(25) < dataframe['conv3'])&(dataframe['conv3'].shift(50) < dataframe['conv3'])))
if 'up_21_vol5' in params and params['up_21_vol5']:
conditions.append(((dataframe['vol5'].shift(25) < dataframe['vol5'])&(dataframe['vol5'].shift(50) < dataframe['vol5'])))
if 'up_22_vol5ultra' in params and params['up_22_vol5ultra']:
conditions.append(((dataframe['vol5ultra'].shift(25) < dataframe['vol5ultra'])&(dataframe['vol5ultra'].shift(50) < dataframe['vol5ultra'])))
if 'up_23_vol1ultra' in params and params['up_23_vol1ultra']:
conditions.append(((dataframe['vol1ultra'].shift(25) < dataframe['vol1ultra'])& (dataframe['vol1ultra'].shift(50) < dataframe['vol1ultra'])))
if 'up_24_vol1' in params and params['up_24_vol1']:
conditions.append(((dataframe['vol1'].shift(30) < dataframe['vol1'])&(dataframe['vol1'].shift(10) < dataframe['vol1'])))
if 'up_25_vol5inc24' in params and params['up_25_vol5inc24']:
conditions.append((dataframe['vol5inc24'].shift(50) < dataframe['vol5inc24']))
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
##ABOVE / BELOW SMAS 1 above/ 0 None / -1 below
#SMA10
conditions.append((dataframe['close'] > dataframe['sma10'])|(0.5 > params['ab_1_sma10']))
conditions.append((dataframe['close'] < dataframe['sma10'])|(-0.5 < params['ab_1_sma10']))
#SMA25
conditions.append((dataframe['close'] > dataframe['sma25'])|(0.5 > params['ab_2_sma25']))
conditions.append((dataframe['close'] < dataframe['sma25'])|(-0.5 < params['ab_2_sma25']))
#SMA50
conditions.append((dataframe['close'] > dataframe['sma50'])|(0.5 > params['ab_3_sma50']))
conditions.append((dataframe['close'] < dataframe['sma50'])|(-0.5 < params['ab_3_sma50']))
#SMA100
conditions.append((dataframe['close'] > dataframe['sma100'])|(0.5 > params['ab_4_sma100']))
conditions.append((dataframe['close'] < dataframe['sma100'])|(-0.5 < params['ab_4_sma100']))
#SMA100
conditions.append((dataframe['close'] > dataframe['sma200'])|(0.5 > params['ab_5_sma200']))
conditions.append((dataframe['close'] < dataframe['sma200'])|(-0.5 < params['ab_5_sma200']))
#SMA400
conditions.append((dataframe['close'] > dataframe['sma400'])|(0.5 > params['ab_6_sma400']))
conditions.append((dataframe['close'] < dataframe['sma400'])|(-0.5 < params['ab_6_sma400']))
#SMA10k
conditions.append((dataframe['close'] > dataframe['sma10k'])|(0.5 > params['ab_7_sma10k']))
conditions.append((dataframe['close'] < dataframe['sma10k'])|(-0.5 < params['ab_7_sma10k']))
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
##DOWNSWINGS / UPSWINGS PPO'S
#ppo5 UP OR DOWN (1 UP, 0 NOTHING, -1 DOWN)
conditions.append((dataframe['ppo5'].shift(1) <dataframe['ppo5'])|(0.5 > params['swings_1_ppo5_up_or_down_bool']))
conditions.append((dataframe['ppo5'].shift(1) >dataframe['ppo5'])|(-0.5 < params['swings_1_ppo5_up_or_down_bool']))
#ppo10
conditions.append((dataframe['ppo10'].shift(1) <dataframe['ppo10'])|(0.5 > params['swings_2_ppo10_up_or_down_bool']))
conditions.append((dataframe['ppo10'].shift(1) >dataframe['ppo10'])|(-0.5 < params['swings_2_ppo10_up_or_down_bool']))
#ppo25
conditions.append((dataframe['ppo25'].shift(1) <dataframe['ppo25'])|(0.5 > params['swings_3_ppo25_up_or_down_bool']))
#conditions.append((dataframe['ppo25'].shift(1) >dataframe['ppo25'])|(-0.5 < params['swings_3_ppo25_up_or_down_bool']))
#ppo50
conditions.append((dataframe['ppo50'].shift(2) <dataframe['ppo50'])|(0.5 > params['swings_4_ppo50_up_or_down_bool']))
#conditions.append((dataframe['ppo50'].shift(2) >dataframe['ppo50'])|(-0.5 < params['swings_4_ppo50_up_or_down_bool']))
#ppo100
conditions.append((dataframe['ppo100'].shift(3) <dataframe['ppo100'])|(0.5 > params['swings_5_ppo100_up_or_down_bool']))
#conditions.append((dataframe['ppo100'].shift(3) >dataframe['ppo100'])|(-0.5 < params['swings_5_ppo100_up_or_down_bool']))
#ppo200
conditions.append((dataframe['ppo200'].shift(4) <dataframe['ppo200'])|(0.5 > params['swings_6_ppo200_up_or_down_bool']))
#conditions.append((dataframe['ppo200'].shift(4) >dataframe['ppo200'])|(-0.5 < params['swings_6_ppo200_up_or_down_bool']))
#ppo500
conditions.append((dataframe['ppo500'].shift(5) <dataframe['ppo500'])|(0.5 > params['swings_7_ppo500_up_or_down_bool']))
#conditions.append((dataframe['ppo500'].shift(5) >dataframe['ppo500'])|(-0.5 < params['swings_7_ppo500_up_or_down_bool']))
#roc50
conditions.append((dataframe['roc50'].shift(2) <dataframe['roc50'])|(0.5 > params['swings_8_roc50_up_or_down_bool']))
#conditions.append((dataframe['roc50'].shift(3) >dataframe['roc50'])|(-0.5 < params['swings_8_roc50_up_or_down_bool']))
#roc10
conditions.append((dataframe['roc10'].shift(1) <dataframe['roc10'])|(0.5 > params['swings_9_roc10_up_or_down_bool']))
#conditions.append((dataframe['roc10'].shift(2) >dataframe['roc10'])|(-0.5 < params['swings_9_roc10_up_or_down_bool']))
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
##DISTANCES/ROC
#dist50 LESS THAN
if 'include_dist_1_dist50_less_value' in params and params['include_dist_1_dist50_less_value']:
conditions.append(dataframe['dist50'] < (params['dist_1_dist50_less_value']))
#dist200 LESS THAN
if 'include_dist_2_dist200_less_value' in params and params['include_dist_2_dist200_less_value']:
conditions.append(dataframe['dist200'] < (params['dist_2_dist200_less_value']))
#dist400 LESS THAN
if 'include_dist_3_dist400_less_value' in params and params['include_dist_3_dist400_less_value']:
conditions.append(dataframe['dist400'] < (params['dist_3_dist400_less_value']))
#dist10k LESS THAN
if 'include_dist_4_dist10k_less_value' in params and params['include_dist_4_dist10k_less_value']:
conditions.append(dataframe['dist10k'] < (params['dist_4_dist10k_less_value']))
#less =further from top bol
#dist_upbol50 LESS THAN
if 'include_dist_5_dist_upbol50_less_value' in params and params['include_dist_5_dist_upbol50_less_value']:
conditions.append(dataframe['dist_upbol50'] < (params['dist_5_dist_upbol50_less_value']/2))
#dist_upbol100 LESS THAN
if 'include_dist_6_dist_upbol100_less_value' in params and params['include_dist_6_dist_upbol100_less_value']:
conditions.append(dataframe['dist_upbol100'] < (params['dist_6_dist_upbol100_less_value']/2))
# #less =closer to bot bol
# #dist_upbol50 LESS THAN
# if 'include_dist_7_dist_lowbol50_less_value' in params and params['include_dist_7_dist_lowbol50_less_value']:
# conditions.append(dataframe['dist_lowbol50'] < (params['dist_7_dist_lowbol50_less_value']/2))
# #dist_upbol100 LESS THAN
# if 'include_dist_8_dist_lowbol100_less_value' in params and params['include_dist_8_dist_lowbol100_less_value']:
# conditions.append(dataframe['dist_lowbol100'] < (params['dist_8_dist_lowbol100_less_value']/2))
#others
##roc50sma MORE THAN
if 'include_dist_7_roc50sma_less_value' in params and params['include_dist_7_roc50sma_less_value']:
conditions.append(dataframe['roc50sma'] < (params['dist_7_roc50sma_less_value']*2))
#roc200sma MORE THAN
if 'include_dist_8_roc200sma_less_value' in params and params['include_dist_8_roc200sma_less_value']:
conditions.append(dataframe['roc200sma'] < (params['dist_8_roc200sma_less_value']*2))
##ENABLE TO BUY AWAY FROM HIGH
# #HIGH500 TO CLOSE MORE THAN
#if 'include_dist_9_high100_more_value' in params and params['include_dist_9_high100_more_value']:
# conditions.append((dataframe['high100']-dataframe['close']) > ((dataframe['high100']/100* (params['dist_9_high100_more_value']))
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
# ##ENABLE SELLS ALWAYS ON OTHER VOLATILITYS
# dataframe.loc[
# ((dataframe['vol_low'] > 0) |(dataframe['vol_high'] > 0) ),
# 'sell'] = 1
# ##ENABLE PRODUCTION SELLS
# dataframe.loc[
# (add_production_sells_low(dataframe)),
# 'sell'] = 1
#
dataframe.loc[
(~(reduce(lambda x, y: x & y, conditions)))&OPTIMIZED_RULE(dataframe,params),
'sell'] = 1
return dataframe
return populate_sell_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters
"""
return [
#-------------------------------------------------------------------------------------------------------
## CUSTOM RULE TRESHOLDS
# SKDecimal(5.0, 7.0,decimals=1, name='sell_trigger_0_roc_ab_value'),# 5 range 5-7 or 4-7
# SKDecimal(3.2, 4.5,decimals=1, name='sell_trigger_0_roc2_ab_value'),#3.8 range 3.2-4.5
# Integer(77, 82, name='sell_trigger_0_rsi_ab_value'),#79 range 82-77
# Integer(90, 95, name='sell_trigger_0_rsi5_ab_value'),#94 range 95-90
# Integer(63, 67, name='sell_trigger_0_rsi50_ab_value'),#66 range 67-63
#-------------------------------------------------------------------------------------------------------
##MAIN
Categorical([1, 2, 3], name='main_1_trend_strength'), #UPTREND STR
Categorical([1, 2, 3], name='main_2_trend_strength'), #SMALL UPTREND STR
#Categorical([-1, 0, 1], name='main_2_small_uptrend_downtrend'), #SMALL UPTREND ON/OFF 1 is on -1 is down
#-------------------------------------------------------------------------------------------------------
##INCLUDE/EXCLUDE RULES
Categorical([True, False], name='include_ab_9_rsi_below_value'),
Categorical([True, False], name='include_ab_10_rsi_recent_peak_2_value'),
Categorical([True, False], name='include_ab_11_rsi_recent_peak_4_value'),
Categorical([True, False], name='include_ab_12_rsi5_below_value'),
Categorical([True, False], name='include_ab_13_rsi50_below_value'),
Categorical([True, False], name='include_ab_14_roc_below_value'),
Categorical([True, False], name='include_ab_15_roc50_below_value'),
Categorical([True, False], name='include_ab_16_roc2_below_value'),
Categorical([True, False], name='include_ab_17_ppo5_below_value'),
Categorical([True, False], name='include_ab_18_ppo10_below_value'),
Categorical([True, False], name='include_ab_19_ppo25_below_value'),
Categorical([True, False], name='include_ab_20_ppo50_below_value'),
Categorical([True, False], name='include_ab_21_ppo100_below_value'),
Categorical([True, False], name='include_ab_22_ppo200_below_value'),
Categorical([True, False], name='include_ab_23_ppo500_below_value'),
##USE AT A LATER STEP
Categorical([True, False], name='include_ab_24_convsmall_below_value'),
Categorical([True, False], name='include_ab_25_convmedium_below_value'),
Categorical([True, False], name='include_ab_26_convlarge_below_value'),
Categorical([True, False], name='include_ab_27_convultra_below_value'),
Categorical([True, False], name='include_ab_28_convdist_below_value'),
Categorical([True, False], name='include_dist_1_dist50_less_value'),
Categorical([True, False], name='include_dist_2_dist200_less_value'),
Categorical([True, False], name='include_dist_3_dist400_less_value'),
Categorical([True, False], name='include_dist_4_dist10k_less_value'),
Categorical([True, False], name='include_dist_5_dist_upbol50_less_value'),
Categorical([True, False], name='include_dist_6_dist_upbol100_less_value'),
# FOR MORE DOWNTREND BUYS LIKELY
# Categorical([True, False], name='include_dist_7_dist_lowbol50_less_value'),
# Categorical([True, False], name='include_dist_8_dist_lowbol100_less_value'),
#MORE LIKE TRIGGERS
Categorical([True, False], name='include_dist_7_roc50sma_less_value'),
Categorical([True, False], name='include_dist_8_roc200sma_less_value'),
##below high 100
#Categorical([True, False], name='include_dist_9_high100_more_value'),
#-------------------------------------------------------------------------------------------------------
##ABOVE/BELOW VALUES
Integer(35, 75, name='ab_9_rsi_below_value'),
Integer(60, 82, name='ab_10_rsi_recent_peak_2_value'),
Integer(60, 82, name='ab_11_rsi_recent_peak_4_value'),
Integer(40, 101, name='ab_12_rsi5_below_value'),
Integer(37, 73, name='ab_13_rsi50_below_value'),
Integer(-6, 10, name='ab_14_roc_below_value'),#/2
Integer(-8, 8, name='ab_15_roc50_below_value'),
Integer(-4, 6, name='ab_16_roc2_below_value'),#/2
#--------------------------------
Integer(-4, 4, name='ab_17_ppo5_below_value'),#/2
Integer(-5, 5, name='ab_18_ppo10_below_value'),#/2
Integer(-8, 10, name='ab_19_ppo25_below_value'),#/2
Integer(-6, 7, name='ab_20_ppo50_below_value'),#/2
Integer(-6, 7, name='ab_21_ppo100_below_value'),
Integer(-5, 7, name='ab_22_ppo200_below_value'),
Integer(-4, 4, name='ab_23_ppo500_below_value'),#*2
##USE AT A LATER STEP
Integer(1, 12, name='ab_24_convsmall_below_value'),#/2 #final
Integer(1, 6, name='ab_25_convmedium_below_value'),#final
Integer(1, 15, name='ab_26_convlarge_below_value'), #final
Integer(2, 12, name='ab_27_convultra_below_value'),#/2 #final
Integer(2, 30, name='ab_28_convdist_below_value'),
#-------------------------------------------------------------------------------------------------------
#SMA'S GOING UP
Categorical([True, False], name='up_0a_sma3'),
Categorical([True, False], name='up_0b_sma5'),
Categorical([True, False], name='up_1_sma10'),
Categorical([True, False], name='up_2_sma25'),
Categorical([True, False], name='up_3_sma50'),
Categorical([True, False], name='up_4_sma100'),
Categorical([True, False], name='up_5_sma200'),
Categorical([True, False], name='up_6_sma400'),
Categorical([True, False], name='up_7_sma10k'),
# Categorical([True, False], name='up_8_sma20k'),
# Categorical([True, False], name='up_9_sma30k'),
Categorical([True, False], name='up_10_convsmall'),
Categorical([True, False], name='up_11_convmedium'),
Categorical([True, False], name='up_12_convlarge'),
Categorical([True, False], name='up_13_convultra'),
Categorical([True, False], name='up_14_convdist'),
Categorical([True, False], name='up_15_vol50'),
Categorical([True, False], name='up_16_vol100'),
Categorical([True, False], name='up_17_vol175'),
Categorical([True, False], name='up_18_vol250'),
Categorical([True, False], name='up_19_vol500'),
Categorical([True, False], name='up_20_vol1000'),
Categorical([True, False], name='up_21_vol100mean'),
Categorical([True, False], name='up_22_vol250mean'),
#-------------------------------------------------------------------------------------------------------
##ABOVE/BELOW SMAS
Categorical([-1, 0, 1], name='ab_1_sma10'),
Categorical([-1, 0, 1], name='ab_2_sma25'),
Categorical([-1, 0, 1], name='ab_3_sma50'),
Categorical([-1, 0, 1], name='ab_4_sma100'),
Categorical([-1, 0, 1], name='ab_5_sma200'),
Categorical([-1, 0, 1], name='ab_6_sma400'),
Categorical([-1, 0, 1], name='ab_7_sma10k'),
#-------------------------------------------------------------------------------------------------------
##DOWNSWINGS / UPSWINGS PPO'S
##UP OR DOWN (1 UP, 0 NOTHING, -1 DOWN)
Categorical([-1, 0, 1], name='swings_1_ppo5_up_or_down_bool'), # -1 down, 1 up , 0 off
Categorical([-1, 0, 1],name='swings_2_ppo10_up_or_down_bool'),
Categorical([-1, 0, 1], name='swings_3_ppo25_up_or_down_bool'), #1 up , 0 off
Categorical([0, 1], name='swings_4_ppo50_up_or_down_bool'),
Categorical([0, 1], name='swings_5_ppo100_up_or_down_bool'),
Categorical([0, 1], name='swings_6_ppo200_up_or_down_bool'),
Categorical([ 0, 1],name='swings_7_ppo500_up_or_down_bool'),
Categorical([0, 1], name='swings_8_roc50_up_or_down_bool'),
Categorical([0, 1], name='swings_9_roc10_up_or_down_bool'),
#-------------------------------------------------------------------------------------------------------
##DISTANCES
Integer(-7, 14, name='dist_1_dist50_less_value'), ##extreme 8-30
Integer(-8, 25, name='dist_2_dist200_less_value'), ##extreme 12 -40
Integer(-12, 35, name='dist_3_dist400_less_value'),
Integer(-12, 40, name='dist_4_dist10k_less_value'),
Integer(-25, 30, name='dist_5_dist_upbol50_less_value'),#/2
Integer(-25, 30, name='dist_6_dist_upbol100_less_value'),#/2
# FOR MORE DOWNTREND BUYS LIKELY
# Integer(-6, 100, name='dist_7_dist_lowbol50_less_value'),#/2
# Integer(-6, 100, name='dist_8_dist_lowbol100_less_value'),#/2
##MORE LIKE TRIGGERS
# Integer(-40, 70, name='dist_7_roc50sma_less_value'),#*2 ##pretty extreme
# Integer(-12, 40, name='dist_8_roc200sma_less_value'),#*2
##below high 100
#Integer(0, 0, name='dist_9_high100_more_value'),
#-------------------------------------------------------------------------------------------------------
]
def OPTIMIZED_RULE(dataframe,params):
return(
(dataframe['sma100'] < dataframe['close'])
)
def add_production_buys_mid(dataframe):
return(
MID_VOLATILITY(dataframe)
&
mid_volatility_buyer(dataframe)
)
def add_production_sells_mid(dataframe):
return(
MID_VOLATILITY(dataframe)
&
mid_volatility_seller(dataframe)
)
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