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Devil has come to deceive the servants of God with his spells!

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Masoud Azizi
2021-05-18 22:19:56 +04:30
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# DevilStra Strategy
# ɐ̴̳̟̲̭̞̮̘̖̼̃̅͝ͅ⊥̸̢̢͈̝͇̹̩̫̿ǝ̸͎͕̞͘ᙠ̴̯̩̰̼͂̐͋͋̌̔́͝ɐ̵̢̡̛̥͕̠̟̠̹̠͑́͗̂̒͠͝͝⊥̸̨̦̖̺̰̫̠͕̭̼͐̎͂̐͒͝ɐ̷̨͔̰̹͓͂̅̂˥̷̜́͐̀̒̌̀̕n̴̨̦̙̰̺̗̏̓̑͐̈̌̅͛̿͝Ɔ̵̳̈̾̿̏͒̀ͅɐ̴̧̛̗͉̞̝̦̯̙̖͋̂͒̓̒͂̌͝ᴚ̸̮͕̗̳̹̈̀̋́̆̄̔͋̋͜ͅǝ̵̱͔̤͍̦͉̻̻̠̋̊͜͝ᗡ̸̡̻̗̮͉̈́͒̊͗̃̕ı̴̢̬̥̹̝̫̥̮̀̾͗͑͆́̈́̎̕͠ͅS̵̛͎̫̜̰̜̜̮̆ͅɐ̸̛̬͈̞͛̓̓͐̈̈́̑͗͝Ԁ̴̧̡̧̻̜̪̦͓̌̾͗̿͘͜ı̸̡̛͉̭̗͓͈̠͆̾͐̀S̶̤͕͕̗͖̗̩̠̲̘̍͐͆͗∀̸̨̡̺͍̆̿̈́̈́̌̔̄̚n̴̜͔̱̭̙̦̞͗͗̎̇̈́͌͠˥̷͚̩͙͖̆͑͒̐͋͝n̷̢̟͍̖̟̣̓̓̿̓̌̇͘⅁̷͍̳̺̯͇͇́͑̀̊͑͒͊͋̈͜ı̵͓̘̯̲̻͙̙͗͛̀̉͂̎̒̌̎͑Z̷̡͇͇̫͖̱̘̮͐̿͑͛̓̕ͅı̴̮̩̰͕̿Z̷̝̥̗̲͔͉̟̣̪͉̎
# 𝓓̶̤̳͇̮͗͆̍̀̎̄͠𝓮̵̢̦̗̬͕̯̼̏𝓿̶̬͚̞̤͛̅̎̔̏͝͠𝓲̴̩͔̱͂̈́͑͜𝓵̴̡̗͓̬̩̫̋͌ ̶̨̞͍̘͇̗̓̓̑͆͗̊͊̚𝔀̸̛̛̤̭͖͒̍͗͐͑̾̆̀𝓲̸̜͖̳̰̠̱̳̽̈́͑𝓵̶̼̲̳͙͎̺̈͘͜𝓵̴͖̾̔͆̆̅ ̶̞̦͎̅̇̈́̚𝓪̵̞̭́̓͐̓̉̓̈́̕𝓵̸͙͈̻̾͆̄͑͌͋̚͠𝔀̵̪̦̔𝓪̸̡̬̻̻̯͙̫̌̄̏̇̇𝔂̵̟̜̣̼̎𝓼̶̥̹̻͒̆̎́̀̅ ̷̮͖̱̮̹͉̂̃̆͑͠𝓼̴͕̱̤̥̖͚̻̇̍͗̐̐̅͘͘𝓽̸̧͔͚̹͂̾͋̎̀̚ͅ𝓻̴̫̦͇̦̩̮̊̊̎̇̈͘̕𝓸̶̹̭͈̓̈́̑̋͋̓̓͠𝓷̷̗͓͚̫̹̯̬̿̎̏̈̑́̉̚͘𝓰̵͎̗̣̻͍̪͇̙̝̭̌͝𝓮̸̧̆̊̉𝓻̸̛̝͚̪͕̥̝̠̤̉͒͆̇̑̄̕͝͠ ̵̨̼̮̲̗̰͉͈̏͝𝓽̷̡͍̘̓̅𝓱̶̨̢̦̼̰͍̣̝̘͑́̇̄̈́͌͊𝓪̴͇͍͂̏͠͝𝓷̵͚͖́ ̸̺͕̲̖̳̩̲̯̅̋̌̿́̇͒͝𝓰̵̛̘̥̠̫̭̦̪̻͚̱̒͌̿̿̆͌̈̓͆𝓸̶͖̠͖̂̊̍͒𝓭̶͈̈̋̃̄͆̉̃.̶̢̺͖̘͌̾
# ̵̧̨̛̤̤̪̘̺̩͎̅̀̒͋͆𝓑̸̠̔𝓾̵͓̑̓͒𝓽̸͕͍̮͠ ̴̡̛̦̹̝̯͕͈̖̌̎̀̉̈̀̓̂͠𝓲̷̨̰̥̭̣̣̪̹͖͓͂̊𝓽̸̢̛̲̙̏̑̄̓̎̊͂͘ ̶̧̳̼̥̝̞̺̗̺̱͊̈́̏͗͑𝓷̵̡͉͈̦̩̇̉̃̒͜͠𝓮̵͈̟̼̣̤͍͔̲͍̏͛̆͗̚͜͠𝓮̴̮̼̻̳̗̰͙̍̒̀͗͠ͅ𝓭̶̧͕̦̦̼̙̊̌̊́̊̐̎̔̍͝ͅ𝓼̵̧͊͊͂́́́͝͝ ̸͍͓̳̣̳̣̥̕𝓼̴̛̻̗͛͊͐͗𝓸̵̨̨̫̪̑̿̕̕𝓶̴̡̣̘̩̘̘͙̀̓̉͐̔̋̋𝓮̸̹̈́͆͆̀́̑ ̸̨̢̧̯̫̜̘̥̌̃͜𝓼̴̫̒͂̉͑̐͐̓̋𝓶̶̪̆̆͆͋̀́̇͐͌𝓪̴̞̪͓̱̰̬̣̆̔̍̾͋͆𝓵̷̡̛͉̝̯̤̈́̎́̍́̅𝓵̶̨̧͕̞̦̟̥̖͈̳͐̄̍̾̑̑ ̵͍̠̤̍𝓬̷͚͔̦̈̏͌̅͒̎̉͊͆̃𝓻̴̨̹͓̥̮͚̲̫̫̓̏𝓮̸̢̰̬̮͔̟̻̺͈̈́͋̓̍̌̈̈́̂̚̕𝓪̵̧̧͚̼̯̅̌͂́̂̑𝓽̴͙̪̙̽̓̔͆̈̏̍𝓾̸̢͉͈̰̪͖̱͍͈̿͗̿̍̊̚͜𝓻̵̧̛̞̱̰̗̪͝𝓼̸̹̘̥̟͈̤͙̯͓̖̀̅̅̌͌͒̎̕
# ̶̘̗̤͇͙̻́̈́͑̽̍ͅ(̵̨̳̞̗̻̰̐̾̍̍̐̿̃̀̀͗͜𝓵̷̡̭̮̣͙̬̱͉̓̈͗̐̓̂̄́𝓲̴͉̚͠𝓴̶͔̰̺̺͎͉͈͙̘̇͒̉̚̕𝓮̸̼̗̹̺̭̜͂́̇̊͛̑̌ ̶̢̩̝̪̝͙̲̮͕̗́̆̅̓̋̐̕͝𝓯̴̛̳͙̝̝̑̽̈͒̚𝓻̷̢͔̺̼̜̣̟̋𝓸̴̣̗̣̐͛̓̈́̚͘͘͝𝓰̷̜̜̌̍͐𝓼̸͍̰͐ ̴͕̖̖͇̝̯̩̩͍͋̿͜𝓪̴̛̙͙̼̪̯́̍̎̋̂̆̋̊𝓷̵̲̹̩̭̅𝓭̴̢̢͎̤̭̾͒̎̏͑̐͌͌̈́̕ ̵̤͙̓̌̽𝓮̴̨̨̧͖̞͉͎̜̱̅̃̈́̆̓̋͝𝓽̵̡̟̪͇͈̇̋̇͗̃̌̅̊𝓬̷͕͕̩̠̼̝͊̒̇̈́̒̈́͑.̵̨̙̫̼̱̞̣͕̋̌͠ͅ.̶̮̼̙̭͉̩̦͌͜.̴̨̱͍̺͍̦̠̣̰͑̓̈́͗͝)̴̨̤͕̖̪͛́͐̔̚ ̴̧̮̩̫͉̠̟̰̠̓́͋̓̽͛͆͝ͅ𝓽̶̨̰̰̱͒̈̊̆̃̓́͠𝓸̸̭̜̺͚͇̦͔̓̒͗͐͋͆͋͝
# ̷̨̭̦̳̘̹̱̦̾͌̊̉̾̕𝓜̷̳̗̗̫̘̗͚̝̟̙͐͗̌̂͝𝓪̸̩̲̱̟̗̮̠̄̈́̿̀̇̐͂̇𝓴̷̺̙̗̣̳̲̖̐̅̏𝓮̵̖͇̾ ̶̛͔͔̗̣̀̏̃͂̆̄͗͊𝓱̸̠̠̰̠̤̗͕͇̗̈́͐́̎̅͑̈́𝓲̸̡̛̯̥͈͈̟̠̪̺̈́̇̐̽̍𝓼̶̨̞̫̬͙̮̌̆̃̌̕ ̶̨̬̻̤̲͆͗̀̊͘͝𝓼̶̢̦̝͙̘̱̹̹͕̅͐͐̎𝓹̵̢̛̼͚͍̓̋̄̄̉̅͘͜𝓮̷̛͙̳͍̼̪̫̀͋͐͜𝓵̷̡͙̱̙̀͌͌͐́͑͛𝓵̶͓̮̖̾̌̈́́̈̊𝓼̵͇̼̥̝̰̰͎͆̿̉ ̴̤͔͓͓̅́̾͋̂͐̔̀̿̔͜𝓪̸̛͙̙̦̻̟̠̰͚̊̂͌͋̍̆̎͑͝𝓷̸̦̻̳̟͖̩͕̋̐ͅ𝓭̸͙̺̯̭̜͍̣̃ͅ ̶͓̰̘̖͇̯̺̺͇̈́̎̋̋̔̃̊̄͘̚ͅ𝓰̶̹̔𝓻̵̢̡̪̫̩̇̇𝓮̴̡̜͙̥̬͓̘̻̎̚𝓮̵̳̫̙̖̝͎̊̅̌̑̋̽͜͝𝓭̴̛̣̫̗̖̮̪͌͑̾͋͒̃̒̇ ̷̡͙̩͓̼̝͓̼̓̀̿𝓸̷͈̞͕͈̘͕̑͗̃𝓽̶̩͖͚͕̞̮̌̀̔̕𝓱̴̡̝͙̩͖̞̤̜͉̊͑͐͜𝓮̶̧͖͉͓̖̣͚̺͉̏́̾̐̂͒̚𝓻̶̧̯̬͙̗̗̫̬̬͑̾̚̚͜͝𝓼̷̗̠͈̝̞̫̰͂̒͐͗̀͘̚͝͝͝ͅ.̸̯̮̥̾̓̈́̕
# ̵̫͊̓̊̃̀̅𝓣̵͍̥̉̎͂̓̐͌𝓱̵̰͔͙̟͍̟̀̚𝓪̶͉̹̠̟̺͖͐̌̊̀̓͜͜ͅ𝓽̷̧̟͔̠̥̗̭̆͜ ̴̡̻̠̪̳̥̅͂̔̄͛̔̊ͅ𝓸̸͇̣̦̩̙͌̒͊̈́͊͒̅͛͠ͅ𝓷̷̺̦̊̊𝓵̷̧̪̮͕͎̭͈̯͍͚̒̈́̽̑͗͘͝͝𝔂̸̳͎͌̀͌̾͝ ̸̧̢͖̰̣̫̿͗̆̋̽̓͗̓̏͝𝓰̶͎̂̊̕𝓸̸̭̪͇̔̋͌𝓭̴̛͚̹͎͚̰̯̊̈͛̀͆́͑̃̚ ̸̭̲̳̦̲̄̇̈͂̆̔̆̕͘͜𝓬̷̝̒̒̔̎͘͝𝓪̴̫̪̥̼̖͕͎̃͜𝓷̵̗͓̽̒̑̂̈ ̵̧̩̫̮̩̈́͗̈̆͗̃͌̕͝͝𝓬̷̧̛̮̰̗̻̅̄͑̈𝓻̵̢̛̜̰͍͖̞̹͊͋̆̈𝓮̷̨̨̟̭̝̦̟̂͜𝓪̷̛̹͎̭̬͎̹͓͔̻͚̃̓͛́̿̾́͂̚𝓽̶͔͕̼̆̓͒͊̒͋͘𝓮̵̠̏̄͒͐͒̏̽͝ ̸̡̗̭̱͖͋̀͜𝓽̶̡̰̦͍͎̌͑̎͊𝓱̷͎̦̞̻͙͓̯́̃̐̉̉͐̃͋͐͑𝓮̴̝͍̃̄𝓶̸̪̏͜͠!̶̧͕̭͔̘̼̝̺̥̓͋͗ ̴̡͔̝̬̭̑͌̈́̅̽̀͂̍̕͜͝:̵͔̭͇̪̠̊ͅ𝓓̵̤̻͉͛͊͊͊̎
# Author: @Mablue (Masoud Azizi)
# github: https://github.com/mablue/
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell --strategy GodStraNew
# --- Do not remove these libs ---
import random
from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter, IntParameter
from numpy.lib import math
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
# Add your lib to import here
# TODO: talib is fast but have not more indicators
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from functools import reduce
import numpy as np
# TODO: this gene is removed 'MAVP' cuz or error on periods
########################### SETTINGS ##############################
# you can find exact value of is inside GodStra
TREND_CHECK_CANDLES = 4
# Set the pain range of devil(2~infinite)
PAIN_RANGE = 30000
# Add GodStra Generated Results As spells inside SPELLS.
# Set them unic phonemes like 'Zi' 'Gu' or 'Lu'!
# * Use below replacement on GodStra results to
# * Change God Generated Creatures to Spells:
# +--------------------------+----------------------+
# | GodStra Hyperopt Results | DevilStra Spells |
# +--------------------------+----------------------+
# | | "phonem" : { |
# | buy_params = { | "buy_params" : { |
# | ... | ... |
# | } | }, |
# | sell_params = { | "sell_params" : { |
# | ... | ... |
# | } | } |
# | | }, |
# +--------------------------+----------------------+
SPELLS = {
"Zi": {
"buy_params": {
"buy_crossed_indicator0": "BOP-4",
"buy_crossed_indicator1": "MACD-0-50",
"buy_crossed_indicator2": "DEMA-52",
"buy_indicator0": "MINUS_DI-50",
"buy_indicator1": "HT_TRENDMODE-50",
"buy_indicator2": "CORREL-128",
"buy_operator0": "/>R",
"buy_operator1": "CA",
"buy_operator2": "CDT",
"buy_real_num0": 0.1763,
"buy_real_num1": 0.6891,
"buy_real_num2": 0.0509,
},
"sell_params": {
"sell_crossed_indicator0": "WCLPRICE-52",
"sell_crossed_indicator1": "AROONOSC-15",
"sell_crossed_indicator2": "CDLRISEFALL3METHODS-52",
"sell_indicator0": "COS-50",
"sell_indicator1": "CDLCLOSINGMARUBOZU-30",
"sell_indicator2": "CDL2CROWS-130",
"sell_operator0": "DT",
"sell_operator1": ">R",
"sell_operator2": "/>R",
"sell_real_num0": 0.0678,
"sell_real_num1": 0.8698,
"sell_real_num2": 0.3917,
}
},
"Gu": {
"buy_params": {
"buy_crossed_indicator0": "SMA-20",
"buy_crossed_indicator1": "CDLLADDERBOTTOM-20",
"buy_crossed_indicator2": "OBV-50",
"buy_indicator0": "MAMA-1-50",
"buy_indicator1": "SUM-40",
"buy_indicator2": "VAR-30",
"buy_operator0": "<R",
"buy_operator1": "D",
"buy_operator2": "D",
"buy_real_num0": 0.2644,
"buy_real_num1": 0.0736,
"buy_real_num2": 0.8954,
},
"sell_params": {
"sell_crossed_indicator0": "CDLLADDERBOTTOM-50",
"sell_crossed_indicator1": "CDLHARAMICROSS-50",
"sell_crossed_indicator2": "CDLDARKCLOUDCOVER-30",
"sell_indicator0": "CDLLADDERBOTTOM-10",
"sell_indicator1": "MAMA-1-40",
"sell_indicator2": "OBV-30",
"sell_operator0": "UT",
"sell_operator1": ">R",
"sell_operator2": "CUT",
"sell_real_num0": 0.2707,
"sell_real_num1": 0.7987,
"sell_real_num2": 0.6891,
}
},
"Lu": {
"buy_params": {
"buy_crossed_indicator0": "HT_SINE-0-28",
"buy_crossed_indicator1": "ADD-130",
"buy_crossed_indicator2": "ADD-12",
"buy_indicator0": "ADD-28",
"buy_indicator1": "AVGPRICE-15",
"buy_indicator2": "AVGPRICE-12",
"buy_operator0": "DT",
"buy_operator1": "D",
"buy_operator2": "C",
"buy_real_num0": 0.3676,
"buy_real_num1": 0.4284,
"buy_real_num2": 0.372,
},
"sell_params": {
"sell_crossed_indicator0": "HT_SINE-0-5",
"sell_crossed_indicator1": "HT_SINE-0-4",
"sell_crossed_indicator2": "HT_SINE-0-28",
"sell_indicator0": "ADD-30",
"sell_indicator1": "AVGPRICE-28",
"sell_indicator2": "ADD-50",
"sell_operator0": "CUT",
"sell_operator1": "DT",
"sell_operator2": "=R",
"sell_real_num0": 0.3205,
"sell_real_num1": 0.2055,
"sell_real_num2": 0.8467,
}
},
"A": {
"buy_params": {
"buy_crossed_indicator0": "WMA-14",
"buy_crossed_indicator1": "MAMA-1-14",
"buy_crossed_indicator2": "CDLHIKKAKE-14",
"buy_indicator0": "T3-14",
"buy_indicator1": "BETA-14",
"buy_indicator2": "HT_PHASOR-1-14",
"buy_operator0": "/>R",
"buy_operator1": ">",
"buy_operator2": ">R",
"buy_real_num0": 0.0551,
"buy_real_num1": 0.3469,
"buy_real_num2": 0.3871,
},
"sell_params": {
"sell_crossed_indicator0": "HT_TRENDLINE-14",
"sell_crossed_indicator1": "LINEARREG-14",
"sell_crossed_indicator2": "STOCHRSI-1-14",
"sell_indicator0": "CDLDARKCLOUDCOVER-14",
"sell_indicator1": "AD-14",
"sell_indicator2": "CDLSTALLEDPATTERN-14",
"sell_operator0": "/=R",
"sell_operator1": "COT",
"sell_operator2": "OT",
"sell_real_num0": 0.3992,
"sell_real_num1": 0.7747,
"sell_real_num2": 0.7415,
}
},
"Si": {
"buy_params": {
"buy_crossed_indicator0": "MACDEXT-2-14",
"buy_crossed_indicator1": "CORREL-14",
"buy_crossed_indicator2": "CMO-14",
"buy_indicator0": "MA-14",
"buy_indicator1": "ADXR-14",
"buy_indicator2": "CDLMARUBOZU-14",
"buy_operator0": "<",
"buy_operator1": "/<R",
"buy_operator2": "<R",
"buy_real_num0": 0.7883,
"buy_real_num1": 0.8286,
"buy_real_num2": 0.6512,
},
"sell_params": {
"sell_crossed_indicator0": "AROON-1-14",
"sell_crossed_indicator1": "STOCHRSI-0-14",
"sell_crossed_indicator2": "SMA-14",
"sell_indicator0": "T3-14",
"sell_indicator1": "AROONOSC-14",
"sell_indicator2": "MIDPOINT-14",
"sell_operator0": "C",
"sell_operator1": "CA",
"sell_operator2": "CB",
"sell_real_num0": 0.372,
"sell_real_num1": 0.5948,
"sell_real_num2": 0.9872,
}
},
"Pa": {
"buy_params": {
"buy_crossed_indicator0": "AROON-0-60",
"buy_crossed_indicator1": "APO-60",
"buy_crossed_indicator2": "BBANDS-0-60",
"buy_indicator0": "WILLR-12",
"buy_indicator1": "AD-15",
"buy_indicator2": "MINUS_DI-12",
"buy_operator0": "D",
"buy_operator1": ">",
"buy_operator2": "CA",
"buy_real_num0": 0.2208,
"buy_real_num1": 0.1371,
"buy_real_num2": 0.6389,
},
"sell_params": {
"sell_crossed_indicator0": "MACDEXT-0-15",
"sell_crossed_indicator1": "BBANDS-2-15",
"sell_crossed_indicator2": "DEMA-15",
"sell_indicator0": "ULTOSC-15",
"sell_indicator1": "MIDPOINT-12",
"sell_indicator2": "PLUS_DI-12",
"sell_operator0": "<",
"sell_operator1": "DT",
"sell_operator2": "COT",
"sell_real_num0": 0.278,
"sell_real_num1": 0.0643,
"sell_real_num2": 0.7065,
}
},
"De": {
"buy_params": {
"buy_crossed_indicator0": "HT_DCPERIOD-12",
"buy_crossed_indicator1": "HT_PHASOR-0-12",
"buy_crossed_indicator2": "MACDFIX-1-15",
"buy_indicator0": "CMO-12",
"buy_indicator1": "TRIMA-12",
"buy_indicator2": "MACDEXT-0-15",
"buy_operator0": "<",
"buy_operator1": "D",
"buy_operator2": "<",
"buy_real_num0": 0.3924,
"buy_real_num1": 0.5546,
"buy_real_num2": 0.7648,
},
"sell_params": {
"sell_crossed_indicator0": "MACDFIX-1-15",
"sell_crossed_indicator1": "MACD-1-15",
"sell_crossed_indicator2": "WMA-15",
"sell_indicator0": "ROC-15",
"sell_indicator1": "MACD-2-15",
"sell_indicator2": "CCI-60",
"sell_operator0": "CA",
"sell_operator1": "<R",
"sell_operator2": "/<R",
"sell_real_num0": 0.4989,
"sell_real_num1": 0.4131,
"sell_real_num2": 0.8904,
}
},
"Ra": {
"buy_params": {
"buy_crossed_indicator0": "EMA-110",
"buy_crossed_indicator1": "SMA-5",
"buy_crossed_indicator2": "SMA-6",
"buy_indicator0": "SMA-6",
"buy_indicator1": "EMA-12",
"buy_indicator2": "EMA-5",
"buy_operator0": "D",
"buy_operator1": "<",
"buy_operator2": "/<R",
"buy_real_num0": 0.9814,
"buy_real_num1": 0.5528,
"buy_real_num2": 0.0541,
},
"sell_params": {
"sell_crossed_indicator0": "SMA-50",
"sell_crossed_indicator1": "EMA-12",
"sell_crossed_indicator2": "SMA-100",
"sell_indicator0": "EMA-110",
"sell_indicator1": "EMA-50",
"sell_indicator2": "EMA-15",
"sell_operator0": "<",
"sell_operator1": "COT",
"sell_operator2": "/=R",
"sell_real_num0": 0.3506,
"sell_real_num1": 0.8767,
"sell_real_num2": 0.0614,
}
},
"Cu": {
"buy_params": {
"buy_crossed_indicator0": "SMA-110",
"buy_crossed_indicator1": "SMA-110",
"buy_crossed_indicator2": "SMA-5",
"buy_indicator0": "SMA-110",
"buy_indicator1": "SMA-55",
"buy_indicator2": "SMA-15",
"buy_operator0": "<R",
"buy_operator1": "<",
"buy_operator2": "CA",
"buy_real_num0": 0.5,
"buy_real_num1": 0.7,
"buy_real_num2": 0.9,
},
"sell_params": {
"sell_crossed_indicator0": "SMA-55",
"sell_crossed_indicator1": "SMA-50",
"sell_crossed_indicator2": "SMA-100",
"sell_indicator0": "SMA-5",
"sell_indicator1": "SMA-50",
"sell_indicator2": "SMA-50",
"sell_operator0": "/=R",
"sell_operator1": "CUT",
"sell_operator2": "DT",
"sell_real_num0": 0.4,
"sell_real_num1": 0.2,
"sell_real_num2": 0.7,
}
}
}
######################### END SETTINGS ############################
def spell_finder(index, space):
return SPELLS[index][space+"_params"]
def normalize(df):
df = (df-df.min())/(df.max()-df.min())
return df
def gene_calculator(dataframe, indicator):
# Cuz Timeperiods not effect calculating CDL patterns recognations
if 'CDL' in indicator:
splited_indicator = indicator.split('-')
splited_indicator[1] = "0"
new_indicator = "-".join(splited_indicator)
# print(indicator, new_indicator)
indicator = new_indicator
gene = indicator.split("-")
gene_name = gene[0]
gene_len = len(gene)
if indicator in dataframe.keys():
# print(f"{indicator}, calculated befoure")
# print(len(dataframe.keys()))
return dataframe[indicator]
else:
result = None
# For Pattern Recognations
if gene_len == 1:
# print('gene_len == 1\t', indicator)
result = getattr(ta, gene_name)(
dataframe
)
return normalize(result)
elif gene_len == 2:
# print('gene_len == 2\t', indicator)
gene_timeperiod = int(gene[1])
result = getattr(ta, gene_name)(
dataframe,
timeperiod=gene_timeperiod,
)
return normalize(result)
# For
elif gene_len == 3:
# print('gene_len == 3\t', indicator)
gene_timeperiod = int(gene[2])
gene_index = int(gene[1])
result = getattr(ta, gene_name)(
dataframe,
timeperiod=gene_timeperiod,
).iloc[:, gene_index]
return normalize(result)
# For trend operators(MA-5-SMA-4)
elif gene_len == 4:
# print('gene_len == 4\t', indicator)
gene_timeperiod = int(gene[1])
sharp_indicator = f'{gene_name}-{gene_timeperiod}'
dataframe[sharp_indicator] = getattr(ta, gene_name)(
dataframe,
timeperiod=gene_timeperiod,
)
return normalize(ta.SMA(dataframe[sharp_indicator].fillna(0), TREND_CHECK_CANDLES))
# For trend operators(STOCH-0-4-SMA-4)
elif gene_len == 5:
# print('gene_len == 5\t', indicator)
gene_timeperiod = int(gene[2])
gene_index = int(gene[1])
sharp_indicator = f'{gene_name}-{gene_index}-{gene_timeperiod}'
dataframe[sharp_indicator] = getattr(ta, gene_name)(
dataframe,
timeperiod=gene_timeperiod,
).iloc[:, gene_index]
return normalize(ta.SMA(dataframe[sharp_indicator].fillna(0), TREND_CHECK_CANDLES))
def condition_generator(dataframe, operator, indicator, crossed_indicator, real_num):
condition = (dataframe['volume'] > 10)
# TODO : it ill callculated in populate indicators.
dataframe[indicator] = gene_calculator(dataframe, indicator)
dataframe[crossed_indicator] = gene_calculator(dataframe, crossed_indicator)
indicator_trend_sma = f"{indicator}-SMA-{TREND_CHECK_CANDLES}"
if operator in ["UT", "DT", "OT", "CUT", "CDT", "COT"]:
dataframe[indicator_trend_sma] = gene_calculator(dataframe, indicator_trend_sma)
if operator == ">":
condition = (
dataframe[indicator] > dataframe[crossed_indicator]
)
elif operator == "=":
condition = (
np.isclose(dataframe[indicator], dataframe[crossed_indicator])
)
elif operator == "<":
condition = (
dataframe[indicator] < dataframe[crossed_indicator]
)
elif operator == "C":
condition = (
(qtpylib.crossed_below(dataframe[indicator], dataframe[crossed_indicator])) |
(qtpylib.crossed_above(dataframe[indicator], dataframe[crossed_indicator]))
)
elif operator == "CA":
condition = (
qtpylib.crossed_above(dataframe[indicator], dataframe[crossed_indicator])
)
elif operator == "CB":
condition = (
qtpylib.crossed_below(
dataframe[indicator], dataframe[crossed_indicator])
)
elif operator == ">R":
condition = (
dataframe[indicator] > real_num
)
elif operator == "=R":
condition = (
np.isclose(dataframe[indicator], real_num)
)
elif operator == "<R":
condition = (
dataframe[indicator] < real_num
)
elif operator == "/>R":
condition = (
dataframe[indicator].div(dataframe[crossed_indicator]) > real_num
)
elif operator == "/=R":
condition = (
np.isclose(dataframe[indicator].div(dataframe[crossed_indicator]), real_num)
)
elif operator == "/<R":
condition = (
dataframe[indicator].div(dataframe[crossed_indicator]) < real_num
)
elif operator == "UT":
condition = (
dataframe[indicator] > dataframe[indicator_trend_sma]
)
elif operator == "DT":
condition = (
dataframe[indicator] < dataframe[indicator_trend_sma]
)
elif operator == "OT":
condition = (
np.isclose(dataframe[indicator], dataframe[indicator_trend_sma])
)
elif operator == "CUT":
condition = (
(
qtpylib.crossed_above(
dataframe[indicator],
dataframe[indicator_trend_sma]
)
) &
(
dataframe[indicator] > dataframe[indicator_trend_sma]
)
)
elif operator == "CDT":
condition = (
(
qtpylib.crossed_below(
dataframe[indicator],
dataframe[indicator_trend_sma]
)
) &
(
dataframe[indicator] < dataframe[indicator_trend_sma]
)
)
elif operator == "COT":
condition = (
(
(
qtpylib.crossed_below(
dataframe[indicator],
dataframe[indicator_trend_sma]
)
) |
(
qtpylib.crossed_above(
dataframe[indicator],
dataframe[indicator_trend_sma]
)
)
) &
(
np.isclose(
dataframe[indicator],
dataframe[indicator_trend_sma]
)
)
)
return condition, dataframe
class DevilStra(IStrategy):
# #################### RESULTS PASTE PLACE ####################
# * 11/1000: 123 trades. 80/0/43 Wins/Draws/Losses. Avg profit 18.06%. Median profit 7.08%. Total profit 0.02199610 BTC ( 22.00Σ%). Avg duration 22 days, 18:28:00 min. Objective: -36.39438
# Buy hyperspace params:
buy_params = {
"buy_spell": ('Lu', 'Lu', 'Si', 'Pa', 'Zi', 'Si', 'Gu', 'Zi', 'Si', 'Gu', 'Ra', 'Lu', 'Gu', 'A', 'Pa', 'A', 'Zi', 'De', 'Cu', 'Gu', 'Cu', 'Ra', 'Cu', 'Pa', 'Lu', 'Gu', 'Cu', 'Zi', 'Si', 'Zi', 'Cu', 'A', 'Ra', 'De', 'Si', 'Zi', 'A', 'Ra', 'Gu', 'De', 'De', 'Lu', 'Si', 'Gu', 'Gu', 'Pa', 'De', 'Lu', 'Si', 'Zi', 'Pa', 'Si', 'Gu', 'Zi', 'De', 'Cu', 'Gu', 'Si', 'Pa', 'Pa', 'Si', 'A', 'Zi', 'De', 'Ra', 'Si', 'Gu', 'Si', 'De', 'Gu', 'Pa', 'De', 'A', 'Si', 'De', 'Pa', 'Ra', 'Zi', 'De', 'Pa', 'Zi', 'Ra', 'A', 'Cu', 'Pa', 'Pa', 'Lu', 'Pa', 'De', 'Gu', 'Gu', 'Ra', 'Lu', 'De', 'Cu', 'Ra', 'Zi', 'Si', 'Ra', 'Lu', 'De', 'A', 'Zi', 'Cu', 'Gu', 'Pa', 'De', 'Ra', 'Pa', 'Pa', 'A', 'Si', 'Ra', 'Cu', 'De', 'Gu', 'Si', 'Gu', 'Zi', 'Cu', 'A', 'Lu', 'Lu', 'A', 'Si', 'Pa', 'Zi', 'Zi', 'Cu', 'Ra', 'Lu', 'Ra', 'De', 'De', 'A', 'Si', 'Pa', 'De', 'Lu', 'Pa', 'De', 'Ra', 'Ra', 'Pa', 'Pa', 'Gu', 'Gu', 'Ra', 'Gu', 'Ra', 'Pa', 'Lu', 'A', 'Zi', 'Gu', 'Lu', 'Lu', 'Zi', 'Gu', 'A', 'Ra', 'Gu', 'Zi', 'Si', 'A', 'Cu', 'Pa', 'De', 'Lu', 'Cu', 'A', 'Zi', 'A', 'Lu', 'Lu', 'Gu', 'Pa', 'Gu', 'A', 'Gu', 'Gu', 'Cu', 'Si', 'Lu', 'Zi', 'De', 'Gu', 'Zi', 'Si', 'Ra', 'De', 'Pa', 'Gu', 'Gu', 'Zi', 'Lu', 'Zi', 'Lu', 'Si', 'Zi', 'Gu', 'Si', 'Ra', 'Lu', 'Pa', 'A', 'A', 'Lu', 'Pa', 'De', 'De', 'Cu', 'Ra', 'Lu', 'De', 'Si', 'Cu', 'Si', 'Si', 'Zi', 'De', 'Si', 'Si', 'Cu', 'Gu', 'Si', 'Ra', 'Si', 'Cu', 'A', 'Zi', 'De', 'Gu', 'Pa', 'Lu', 'Cu', 'Cu', 'Pa', 'Cu', 'A', 'Si', 'Zi', 'Cu', 'Zi', 'Ra', 'Si', 'Lu', 'Lu', 'De', 'Cu', 'Cu', 'Lu', 'Pa', 'Ra', 'Cu', 'Lu', 'Ra', 'Zi', 'Gu', 'Ra', 'Cu', 'A', 'De', 'Cu', 'Ra', 'Ra', 'De', 'Zi', 'De', 'De', 'A', 'Pa', 'A', 'Gu', 'Lu', 'De', 'De', 'A', 'Pa', 'Gu', 'Gu', 'De', 'Gu', 'Cu', 'Zi', 'Si', 'Gu', 'Ra', 'Pa', 'Gu', 'Lu', 'A', 'Lu', 'A', 'Si', 'Si', 'Pa', 'Ra', 'Cu', 'Lu'),
}
# Sell hyperspace params:
sell_params = {
"sell_spell": ('A', 'Lu', 'Lu', 'Cu', 'De', 'Zi', 'Si', 'Lu', 'Cu', 'A', 'A', 'Pa', 'Si', 'Pa', 'Si', 'Zi', 'Si', 'Zi', 'Pa', 'Cu', 'Zi', 'A', 'De', 'Si', 'A', 'Gu', 'Gu', 'Pa', 'Lu', 'Ra', 'De', 'Gu', 'Pa', 'Gu', 'Ra', 'Gu', 'Gu', 'Zi', 'Lu', 'Gu', 'Ra', 'Si', 'Lu', 'Ra', 'Zi', 'De', 'Gu', 'Ra', 'Si', 'Ra', 'De', 'Si', 'Ra', 'Cu', 'Lu', 'Lu', 'Ra', 'Cu', 'A', 'De', 'Pa', 'Cu', 'Pa', 'Pa', 'A', 'Zi', 'Lu', 'Zi', 'Lu', 'Si', 'A', 'Lu', 'Zi', 'Pa', 'Gu', 'Ra', 'Zi', 'De', 'Cu', 'A', 'Si', 'Gu', 'De', 'Cu', 'De', 'Ra', 'Cu', 'Si', 'Gu', 'De', 'Cu', 'Pa', 'Si', 'Zi', 'Cu', 'Lu', 'Lu', 'Si', 'Cu', 'Zi', 'A', 'Pa', 'De', 'Zi', 'Si', 'Si', 'Cu', 'Pa', 'Zi', 'Si', 'Si', 'Lu', 'Pa', 'Lu', 'De', 'Si', 'Cu', 'Cu', 'Zi', 'Gu', 'Si', 'Ra', 'Pa', 'Gu', 'Gu', 'Lu', 'Pa', 'Cu', 'Zi', 'Pa', 'Si', 'Ra', 'A', 'Gu', 'Pa', 'A', 'A', 'A', 'A', 'Si', 'Cu', 'Gu', 'De', 'Pa', 'De', 'Cu', 'Zi', 'Pa', 'A', 'Cu', 'Zi', 'Gu', 'Gu', 'Lu', 'Zi', 'A', 'Gu', 'Pa', 'Lu', 'Si', 'Zi', 'A', 'A', 'Pa', 'Gu', 'Zi', 'De', 'Ra', 'De', 'Cu', 'Ra', 'Pa', 'Pa', 'Lu', 'Zi', 'Si', 'Gu', 'Zi', 'Ra', 'De', 'Cu', 'Zi', 'Cu', 'Gu', 'De', 'Lu', 'A', 'A', 'Si', 'De', 'Si', 'Zi', 'Gu', 'Si', 'Cu', 'Ra', 'Lu', 'Lu', 'De', 'Gu', 'Cu', 'Pa', 'Ra', 'Lu', 'Si', 'Ra', 'Gu', 'Cu', 'De', 'Si', 'Pa', 'Gu', 'Zi', 'Si', 'A', 'Cu', 'Lu', 'Si', 'Si', 'Zi', 'Si', 'Gu', 'A', 'Cu', 'A', 'Gu', 'A', 'Zi', 'Ra', 'Zi', 'Zi', 'Zi', 'Ra', 'Gu', 'De', 'Zi', 'Ra', 'Zi', 'A', 'De', 'De', 'Ra', 'A', 'Gu', 'De', 'Lu', 'Cu', 'Gu', 'Si', 'A', 'Cu', 'Lu', 'Cu', 'A', 'Zi', 'De', 'Gu', 'Si', 'Zi', 'Gu', 'Pa', 'Si', 'Gu', 'Lu', 'Cu', 'Gu', 'De', 'Cu', 'Cu', 'Ra', 'Gu', 'Pa', 'Cu', 'De', 'De', 'Zi', 'Pa', 'A', 'Cu', 'Zi', 'A', 'Zi', 'De', 'Lu', 'Cu', 'Cu', 'De', 'Cu', 'Ra', 'Ra', 'Zi', 'Si', 'Cu', 'Ra', 'De', 'Cu', 'Gu', 'Lu', 'Pa', 'Gu'),
}
# #################### END OF RESULT PLACE ####################
spell_pot = [
tuple(
random.choices(
list(SPELLS.keys()),
# TODO: k will be change to len(pairlist)
k=300
)
)for i in range(PAIN_RANGE)
]
buy_spell = CategoricalParameter(spell_pot, default=spell_pot[0], space='buy')
sell_spell = CategoricalParameter(spell_pot, default=spell_pot[0], space='sell')
# Stoploss:
stoploss = -1
# Buy hypers
timeframe = '1h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
pairs = self.dp.current_whitelist()
pair_index = pairs.index(metadata['pair'])
buy_params_index = self.buy_spell.value[pair_index]
params = spell_finder(buy_params_index, 'buy')
conditions = list()
# TODO: Its not dry code!
buy_indicator = params['buy_indicator0']
buy_crossed_indicator = params['buy_crossed_indicator0']
buy_operator = params['buy_operator0']
buy_real_num = params['buy_real_num0']
condition, dataframe = condition_generator(
dataframe,
buy_operator,
buy_indicator,
buy_crossed_indicator,
buy_real_num
)
conditions.append(condition)
# backup
buy_indicator = params['buy_indicator1']
buy_crossed_indicator = params['buy_crossed_indicator1']
buy_operator = params['buy_operator1']
buy_real_num = params['buy_real_num1']
condition, dataframe = condition_generator(
dataframe,
buy_operator,
buy_indicator,
buy_crossed_indicator,
buy_real_num
)
conditions.append(condition)
buy_indicator = params['buy_indicator2']
buy_crossed_indicator = params['buy_crossed_indicator2']
buy_operator = params['buy_operator2']
buy_real_num = params['buy_real_num2']
condition, dataframe = condition_generator(
dataframe,
buy_operator,
buy_indicator,
buy_crossed_indicator,
buy_real_num
)
conditions.append(condition)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
# print(len(dataframe.keys()))
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
pairs = self.dp.current_whitelist()
pair_index = pairs.index(metadata['pair'])
sell_params_index = self.sell_spell.value[pair_index]
params = spell_finder(sell_params_index, 'sell')
conditions = list()
# TODO: Its not dry code!
sell_indicator = params['sell_indicator0']
sell_crossed_indicator = params['sell_crossed_indicator0']
sell_operator = params['sell_operator0']
sell_real_num = params['sell_real_num0']
condition, dataframe = condition_generator(
dataframe,
sell_operator,
sell_indicator,
sell_crossed_indicator,
sell_real_num
)
conditions.append(condition)
sell_indicator = params['sell_indicator1']
sell_crossed_indicator = params['sell_crossed_indicator1']
sell_operator = params['sell_operator1']
sell_real_num = params['sell_real_num1']
condition, dataframe = condition_generator(
dataframe,
sell_operator,
sell_indicator,
sell_crossed_indicator,
sell_real_num
)
conditions.append(condition)
sell_indicator = params['sell_indicator2']
sell_crossed_indicator = params['sell_crossed_indicator2']
sell_operator = params['sell_operator2']
sell_real_num = params['sell_real_num2']
condition, dataframe = condition_generator(
dataframe,
sell_operator,
sell_indicator,
sell_crossed_indicator,
sell_real_num
)
conditions.append(condition)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell']=1
return dataframe