first commit
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Jérôme Delacotte
577
GodStraJD3_5_1.py
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577
GodStraJD3_5_1.py
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# GodStraNew Strategy
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy roi trailing sell --strategy GodStraNew
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# --- Do not remove these libs ---
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from datetime import timedelta, datetime
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from freqtrade import data
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from freqtrade.persistence import Trade
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from freqtrade.strategy.parameters import CategoricalParameter, DecimalParameter, IntParameter, BooleanParameter
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from numpy.lib import math
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from freqtrade.strategy.interface import IStrategy
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from pandas import DataFrame
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# --------------------------------
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# Add your lib to import here
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# TODO: talib is fast but have not more indicators
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from functools import reduce
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import numpy as np
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from random import shuffle
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operators = [
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"D", # Disabled gene
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">", # Indicator, bigger than cross indicator
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"<", # Indicator, smaller than cross indicator
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"=", # Indicator, equal with cross indicator
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"C", # Indicator, crossed the cross indicator
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"CA", # Indicator, crossed above the cross indicator
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"CB", # Indicator, crossed below the cross indicator
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">R", # Normalized indicator, bigger than real number
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"=R", # Normalized indicator, equal with real number
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"<R", # Normalized indicator, smaller than real number
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"/>R", # Normalized indicator devided to cross indicator, bigger than real number
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"/=R", # Normalized indicator devided to cross indicator, equal with real number
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"/<R", # Normalized indicator devided to cross indicator, smaller than real number
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"UT", # Indicator, is in UpTrend status
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"DT", # Indicator, is in DownTrend status
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"OT", # Indicator, is in Off trend status(RANGE)
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"CUT", # Indicator, Entered to UpTrend status
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"CDT", # Indicator, Entered to DownTrend status
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"COT" # Indicator, Entered to Off trend status(RANGE)
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]
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# number of candles to check up,don,off trend.
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TREND_CHECK_CANDLES = 8
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DECIMALS = 2
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buy_crossed_indicator0 = 'MINUS_DM-5'
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buy_crossed_indicator1 = 'DX-5'
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buy_crossed_indicator2 = 'LINEARREG-50'
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buy_indicator0 = 'MA-20'
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buy_indicator1 = 'STOCH-1-10'
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buy_indicator2 = 'CDLDRAGONFLYDOJI-5'
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buy_operator0 = "/<R"
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buy_operator1 = ">"
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buy_operator2 = "/<R"
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# buy_real_num0 = 0.1,
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# buy_real_num1 = 0.0,
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# buy_real_num2 = 0.5
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def normalize(df):
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df = (df-df.min())/(df.max()-df.min())
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return df
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def gene_calculator(dataframe, indicator):
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# Cuz Timeperiods not effect calculating CDL patterns recognations
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if 'CDL' in indicator:
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splited_indicator = indicator.split('-')
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splited_indicator[1] = "0"
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new_indicator = "-".join(splited_indicator)
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# print(indicator, new_indicator)
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indicator = new_indicator
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gene = indicator.split("-")
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gene_name = gene[0]
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gene_len = len(gene)
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if indicator in dataframe.keys():
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# print(f"{indicator}, calculated befoure")
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# print(len(dataframe.keys()))
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return dataframe[indicator]
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else:
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result = None
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# For Pattern Recognations
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if gene_len == 1:
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# print('gene_len == 1\t', indicator)
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result = getattr(ta, gene_name)(
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dataframe
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)
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return normalize(result)
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elif gene_len == 2:
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# print('gene_len == 2\t', indicator)
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gene_timeperiod = int(gene[1])
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result = getattr(ta, gene_name)(
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dataframe,
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timeperiod=gene_timeperiod,
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)
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return normalize(result)
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# For
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elif gene_len == 3:
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# print('gene_len == 3\t', indicator)
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gene_timeperiod = int(gene[2])
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gene_index = int(gene[1])
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result = getattr(ta, gene_name)(
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dataframe,
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timeperiod=gene_timeperiod,
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).iloc[:, gene_index]
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return normalize(result)
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# For trend operators(MA-5-SMA-4)
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elif gene_len == 4:
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# print('gene_len == 4\t', indicator)
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gene_timeperiod = int(gene[1])
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sharp_indicator = f'{gene_name}-{gene_timeperiod}'
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dataframe[sharp_indicator] = getattr(ta, gene_name)(
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dataframe,
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timeperiod=gene_timeperiod,
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)
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return normalize(ta.SMA(dataframe[sharp_indicator].fillna(0), TREND_CHECK_CANDLES))
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# For trend operators(STOCH-0-4-SMA-4)
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elif gene_len == 5:
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# print('gene_len == 5\t', indicator)
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gene_timeperiod = int(gene[2])
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gene_index = int(gene[1])
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sharp_indicator = f'{gene_name}-{gene_index}-{gene_timeperiod}'
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dataframe[sharp_indicator] = getattr(ta, gene_name)(
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dataframe,
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timeperiod=gene_timeperiod,
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).iloc[:, gene_index]
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return normalize(ta.SMA(dataframe[sharp_indicator].fillna(0), TREND_CHECK_CANDLES))
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def condition_generator(dataframe, operator, indicator, crossed_indicator, real_num):
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condition = (dataframe['volume'] > 10)
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# TODO : it ill callculated in populate indicators.
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dataframe[indicator] = gene_calculator(dataframe, indicator)
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dataframe[crossed_indicator] = gene_calculator(dataframe, crossed_indicator)
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indicator_trend_sma = f"{indicator}-SMA-{TREND_CHECK_CANDLES}"
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if operator in ["UT", "DT", "OT", "CUT", "CDT", "COT"]:
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dataframe[indicator_trend_sma] = gene_calculator(
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dataframe, indicator_trend_sma)
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if operator == ">":
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condition = (
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dataframe[indicator] > dataframe[crossed_indicator]
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)
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elif operator == "=":
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condition = (
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np.isclose(dataframe[indicator], dataframe[crossed_indicator])
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)
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elif operator == "<":
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condition = (
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dataframe[indicator] < dataframe[crossed_indicator]
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)
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elif operator == "C":
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condition = (
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(qtpylib.crossed_below(dataframe[indicator], dataframe[crossed_indicator])) |
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(qtpylib.crossed_above(
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dataframe[indicator], dataframe[crossed_indicator]))
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)
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elif operator == "CA":
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condition = (
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qtpylib.crossed_above(
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dataframe[indicator], dataframe[crossed_indicator])
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)
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elif operator == "CB":
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condition = (
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qtpylib.crossed_below(
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dataframe[indicator], dataframe[crossed_indicator])
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)
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elif operator == ">R":
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condition = (
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dataframe[indicator] > real_num
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)
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elif operator == "=R":
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condition = (
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np.isclose(dataframe[indicator], real_num)
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)
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elif operator == "<R":
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condition = (
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dataframe[indicator] < real_num
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)
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elif operator == "/>R":
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condition = (
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dataframe[indicator].div(dataframe[crossed_indicator]) > real_num
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)
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elif operator == "/=R":
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condition = (
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np.isclose(dataframe[indicator].div(
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dataframe[crossed_indicator]), real_num)
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)
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elif operator == "/<R":
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condition = (
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dataframe[indicator].div(dataframe[crossed_indicator]) < real_num
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)
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elif operator == "UT":
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condition = (
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dataframe[indicator] > dataframe[indicator_trend_sma]
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)
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elif operator == "DT":
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condition = (
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dataframe[indicator] < dataframe[indicator_trend_sma]
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)
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elif operator == "OT":
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condition = (
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np.isclose(dataframe[indicator], dataframe[indicator_trend_sma])
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)
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elif operator == "CUT":
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condition = (
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(
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qtpylib.crossed_above(
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dataframe[indicator],
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dataframe[indicator_trend_sma]
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)
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) &
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(
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dataframe[indicator] > dataframe[indicator_trend_sma]
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)
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)
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elif operator == "CDT":
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condition = (
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(
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qtpylib.crossed_below(
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dataframe[indicator],
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dataframe[indicator_trend_sma]
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)
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) &
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(
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dataframe[indicator] < dataframe[indicator_trend_sma]
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)
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)
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elif operator == "COT":
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condition = (
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(
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(
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qtpylib.crossed_below(
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dataframe[indicator],
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dataframe[indicator_trend_sma]
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)
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) |
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(
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qtpylib.crossed_above(
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dataframe[indicator],
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dataframe[indicator_trend_sma]
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)
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)
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) &
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(
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np.isclose(
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dataframe[indicator],
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dataframe[indicator_trend_sma]
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)
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)
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)
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return condition, dataframe
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class GodStraJD3_5_1(IStrategy):
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# #################### RESULTS PASTE PLACE ####################
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# ROI table:
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minimal_roi = {
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"0": 10,
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# "600": 0.12,
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# "1200": 0.08,
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# "2400": 0.06,
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# "3600": 0.04,
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# "7289": 0
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}
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# Stoploss:
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stoploss = -1
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# Buy hypers
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timeframe = '5m'
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# Trailing stoploss
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trailing_stop = False
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trailing_stop_positive = 0.15
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trailing_stop_positive_offset = 0.20
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trailing_only_offset_is_reached = True
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plot_config = {
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# Main plot indicators (Moving averages, ...)
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'main_plot': {
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'bb_lowerband': {'color': 'red'},
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'bb_upperband': {'color': 'green'},
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'sma100': {'color': 'blue'},
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'sma10': {'color': 'yellow'},
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'min': {'color': 'white'},
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'max': {'color': 'white'},
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'sma20': {'color': 'cyan'}
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},
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'subplots': {
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# Subplots - each dict defines one additional plot
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"BB": {
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'bb_width': {'color': 'white'},
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'bb_min': {'color': 'red'},
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},
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"Ind0": {
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buy_crossed_indicator0: {'color': 'green'},
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buy_indicator0: {'color': 'red'}
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},
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"Ind1": {
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buy_indicator1: {'color': 'yellow'},
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buy_crossed_indicator1: {'color': 'pink'}
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},
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"Ind2": {
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buy_indicator2: {'color': 'cyan'},
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buy_crossed_indicator2: {'color': 'blue'},
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},
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"Rsi": {
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'rsi': {'color': 'pink'},
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},
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# "rolling": {
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# 'bb_rolling': {'color': '#87e470'},
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# "bb_rolling_min": {'color': '#ac3e2a'}
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# },
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"percent": {
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"percent": {'color': 'green'},
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"percent3": {'color': 'blue'},
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"percent5": {'color': 'red'}
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}
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}
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}
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# #################### END OF RESULT PLACE ####################
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# TODO: Its not dry code!
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# Buy Hyperoptable Parameters/Spaces.
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buy_real_num0 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.1, space='buy')
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buy_real_num1 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.0, space='buy')
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buy_real_num2 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.5, space='buy')
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protection_max_allowed_dd = DecimalParameter(0, 1, decimals=DECIMALS, default=0.04, space='protection')
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protection_stop = IntParameter(0, 100, default=48, space='protection')
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protection_stoploss_stop = IntParameter(0, 100, default=48, space='protection')
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lookback = IntParameter(0, 200, default=48, space='protection')
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trade_limit = IntParameter(0, 10, default=2, space='protection')
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profit_no_change = BooleanParameter(default=True, space="buy")
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profit_sma10 = BooleanParameter(default=True, space="buy")
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profit_sma20 = BooleanParameter(default=True, space="buy")
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profit_quick_gain = BooleanParameter(default=True, space="buy")
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profit_quick_gain_3 = BooleanParameter(default=True, space="buy")
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profit_old_sma10 = BooleanParameter(default=True, space="buy")
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profit_very_old_sma10 = BooleanParameter(default=True, space="buy")
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profit_over_rsi = BooleanParameter(default=True, space="buy")
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profit_short_loss = BooleanParameter(default=True, space="buy")
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@property
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def protections(self):
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return [
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{
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"method": "CooldownPeriod",
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"stop_duration_candles": 10
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},
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{
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"method": "MaxDrawdown",
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"lookback_period_candles": self.lookback.value,
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"trade_limit": self.trade_limit.value,
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"stop_duration_candles": self.protection_stop.value,
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"max_allowed_drawdown": self.protection_max_allowed_dd.value,
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"only_per_pair": False
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},
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{
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"method": "StoplossGuard",
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"lookback_period_candles": 24,
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"trade_limit": 4,
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"stop_duration_candles": self.protection_stoploss_stop.value,
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"only_per_pair": False
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}
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]
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def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
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current_profit: float, **kwargs):
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = dataframe.iloc[-1].squeeze()
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previous_last_candle = dataframe.iloc[-2].squeeze()
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previous_5_candle = dataframe.iloc[-5].squeeze()
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if self.profit_no_change.value:
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if (current_profit > 0.005) & (last_candle['percent10'] < 0.001):
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return "no_change"
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#if (current_profit > 0.01) & (last_candle['rsi'] < 30):
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# return "small_rsi"
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if (current_profit >= 0.03) & (last_candle['percent3'] < 0) & ((current_time - trade.open_date_utc).seconds <= 3600):
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return "quick_gain_3"
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if (0.01 < current_profit < 0.03) & (last_candle['percent3'] < 0): #& ((current_time - trade.open_date_utc).seconds <= 3600)
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return "quick_gain"
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if self.profit_sma10.value:
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if (current_profit > 0.01) \
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& ((previous_5_candle['sma10'] > last_candle['sma10'] * 1.005) \
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| (last_candle['percent3'] < -0.01) | (last_candle['percent5'] < -0.01)) \
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& ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
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# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'sma10'
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if self.profit_sma20.value:
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if (current_profit > 0.005) & (last_candle['percent5'] < 0) \
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& ((current_time - trade.open_date_utc).seconds >= 3600) \
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& ((previous_last_candle['sma20'] > last_candle['sma20']) &
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((last_candle['percent10'] < 0) | (last_candle['percent20'] < 0))):
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# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'sma20'
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if self.profit_old_sma10.value:
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if (current_profit > 0) \
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& ((current_time - trade.open_date_utc).days >= 3) \
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& ((previous_5_candle['sma10'] > last_candle['sma10']) | (last_candle['percent3'] < -0.005) | (last_candle['percent5'] < -0.005)) \
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& ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
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# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'old_sma10'
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if self.profit_very_old_sma10.value:
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if (current_profit > -0.01) \
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& ((current_time - trade.open_date_utc).days >= 6) \
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& ((previous_5_candle['sma10'] > last_candle['sma10']) | (last_candle['percent3'] < 0) | (last_candle['percent5'] < 0)) \
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& ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
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# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'very_old_sma10'
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if self.profit_over_rsi.value:
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if (current_profit > 0) \
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& (last_candle['rsi'] > 88): #| (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
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# print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
||||
return 'over_rsi'
|
||||
|
||||
if self.profit_short_loss.value:
|
||||
if (current_profit > -0.01) & (previous_last_candle['percent10'] > 0.04) & (last_candle['percent'] < 0)\
|
||||
& ((current_time - trade.open_date_utc).days >= 1): #| (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
|
||||
# print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
||||
return 'short_lost'
|
||||
|
||||
# if (current_profit > 0) \
|
||||
# & (last_candle['rsi'] > 82) & (previous_last_candle['rsi'] > 75): #| (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
|
||||
# # print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
||||
# return 'over_rsi_2'
|
||||
|
||||
def informative_pairs(self):
|
||||
return []
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
dataframe[buy_crossed_indicator0] = gene_calculator(dataframe, buy_crossed_indicator0)
|
||||
dataframe[buy_crossed_indicator1] = gene_calculator(dataframe, buy_crossed_indicator1)
|
||||
dataframe[buy_crossed_indicator2] = gene_calculator(dataframe, buy_crossed_indicator2)
|
||||
dataframe[buy_indicator0] = gene_calculator(dataframe, buy_indicator0)
|
||||
dataframe[buy_indicator1] = gene_calculator(dataframe, buy_indicator1)
|
||||
dataframe[buy_indicator2] = gene_calculator(dataframe, buy_indicator2)
|
||||
|
||||
dataframe['profit'] = 0
|
||||
# RSI
|
||||
dataframe['rsi'] = ta.RSI(dataframe)
|
||||
dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
|
||||
dataframe['sma20'] = ta.SMA(dataframe, timeperiod=20)
|
||||
dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
|
||||
dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
|
||||
dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
|
||||
dataframe["percent5"] = dataframe["percent"].rolling(5).sum()
|
||||
dataframe["percent3"] = dataframe["percent"].rolling(3).sum()
|
||||
dataframe["percent10"] = dataframe["percent"].rolling(10).sum()
|
||||
dataframe["percent20"] = dataframe["percent"].rolling(20).sum()
|
||||
dataframe["percent50"] = dataframe["percent"].rolling(50).sum()
|
||||
|
||||
# if (dataframe["percent50"] < -0.03) & (dataframe['sma10'] > dataframe['sma10'].shift(2)):
|
||||
# dataframe["percent_ok"] = new dataframe()
|
||||
# else:
|
||||
# dataframe["percent_ok"] = 0
|
||||
|
||||
dataframe['min'] = ta.MIN(dataframe['close'], timeperiod=200)
|
||||
dataframe['min20'] = ta.MIN(dataframe['close'], timeperiod=20)
|
||||
dataframe["volume10"] = dataframe["volume"].rolling(10).mean()
|
||||
|
||||
dataframe['max'] = ta.MAX(dataframe['close'], timeperiod=200)
|
||||
dataframe['max_min'] = dataframe['max'] / dataframe['min']
|
||||
# Bollinger Bands
|
||||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
||||
dataframe['bb_lowerband'] = bollinger['lower']
|
||||
dataframe['bb_middleband'] = bollinger['mid']
|
||||
dataframe['bb_upperband'] = bollinger['upper']
|
||||
dataframe["bb_percent"] = (
|
||||
(dataframe["close"] - dataframe["bb_lowerband"]) /
|
||||
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
|
||||
)
|
||||
dataframe["bb_width"] = (
|
||||
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
|
||||
)
|
||||
dataframe['bb_min'] = ta.MIN(dataframe['bb_lowerband'], timeperiod=36)
|
||||
|
||||
# Bollinger Bands - Weighted (EMA based instead of SMA)
|
||||
weighted_bollinger = qtpylib.weighted_bollinger_bands(
|
||||
qtpylib.typical_price(dataframe), window=20, stds=2
|
||||
)
|
||||
dataframe["wbb_upperband"] = weighted_bollinger["upper"]
|
||||
dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
|
||||
dataframe["wbb_middleband"] = weighted_bollinger["mid"]
|
||||
dataframe["wbb_percent"] = (
|
||||
(dataframe["close"] - dataframe["wbb_lowerband"]) /
|
||||
(dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
|
||||
)
|
||||
dataframe["wbb_width"] = (
|
||||
(dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"]
|
||||
)
|
||||
|
||||
# # EMA - Exponential Moving Average
|
||||
# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
|
||||
return dataframe
|
||||
|
||||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
conditions = list()
|
||||
|
||||
condition, dataframe = condition_generator(
|
||||
dataframe,
|
||||
buy_operator0,
|
||||
buy_indicator0,
|
||||
buy_crossed_indicator0,
|
||||
self.buy_real_num0.value
|
||||
)
|
||||
conditions.append(condition)
|
||||
|
||||
# backup
|
||||
condition, dataframe = condition_generator(
|
||||
dataframe,
|
||||
buy_operator1,
|
||||
buy_indicator1,
|
||||
buy_crossed_indicator1,
|
||||
self.buy_real_num1.value
|
||||
)
|
||||
conditions.append(condition)
|
||||
|
||||
condition, dataframe = condition_generator(
|
||||
dataframe,
|
||||
buy_operator2,
|
||||
buy_indicator2,
|
||||
buy_crossed_indicator2,
|
||||
self.buy_real_num2.value
|
||||
)
|
||||
conditions.append(condition)
|
||||
|
||||
if conditions:
|
||||
dataframe.loc[
|
||||
(
|
||||
# reduce(lambda x, y: x & y, conditions)
|
||||
# & (dataframe['volume10'] * dataframe['close'] / 1000 >= 100)
|
||||
# # & (dataframe['percent5'] < 0.025)
|
||||
# # (dataframe['percent10'] < 0.04)
|
||||
# ) | (
|
||||
(dataframe['volume10'] * dataframe['close'] / 1000 >= 100)
|
||||
& ((dataframe['percent20'].shift(5) < -0.025) | (dataframe['percent20'].shift(10) < -0.025))
|
||||
& ((dataframe['percent5'] > 0.001) | (dataframe['open'] < dataframe['bb_lowerband']))
|
||||
& (dataframe['sma10'].shift(1) < dataframe['sma10'])
|
||||
# & (dataframe['open'].shift(50) > dataframe['open'])
|
||||
)
|
||||
#(dataframe['percent3'] > 0))
|
||||
,'buy']=1
|
||||
# print(len(dataframe.keys()))
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
return dataframe
|
||||
|
||||
Reference in New Issue
Block a user