986 lines
44 KiB
Python
986 lines
44 KiB
Python
# 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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import time
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import calendar
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from freqtrade.loggers import setup_logging
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from freqtrade.strategy.strategy_helper import merge_informative_pair
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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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# TODO: this gene is removed 'MAVP' cuz or error on periods
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all_god_genes = {
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'Overlap Studies': {
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'BBANDS-0', # Bollinger Bands
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'BBANDS-1', # Bollinger Bands
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'BBANDS-2', # Bollinger Bands
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'DEMA', # Double Exponential Moving Average
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'EMA', # Exponential Moving Average
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'HT_TRENDLINE', # Hilbert Transform - Instantaneous Trendline
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'KAMA', # Kaufman Adaptive Moving Average
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'MA', # Moving average
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'MAMA-0', # MESA Adaptive Moving Average
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'MAMA-1', # MESA Adaptive Moving Average
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# TODO: Fix this
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# 'MAVP', # Moving average with variable period
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'MIDPOINT', # MidPoint over period
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'MIDPRICE', # Midpoint Price over period
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'SAR', # Parabolic SAR
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'SAREXT', # Parabolic SAR - Extended
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'SMA', # Simple Moving Average
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'T3', # Triple Exponential Moving Average (T3)
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'TEMA', # Triple Exponential Moving Average
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'TRIMA', # Triangular Moving Average
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'WMA', # Weighted Moving Average
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},
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'Momentum Indicators': {
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'ADX', # Average Directional Movement Index
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'ADXR', # Average Directional Movement Index Rating
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'APO', # Absolute Price Oscillator
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'AROON-0', # Aroon
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'AROON-1', # Aroon
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'AROONOSC', # Aroon Oscillator
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'BOP', # Balance Of Power
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'CCI', # Commodity Channel Index
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'CMO', # Chande Momentum Oscillator
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'DX', # Directional Movement Index
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'MACD-0', # Moving Average Convergence/Divergence
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'MACD-1', # Moving Average Convergence/Divergence
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'MACD-2', # Moving Average Convergence/Divergence
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'MACDEXT-0', # MACD with controllable MA type
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'MACDEXT-1', # MACD with controllable MA type
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'MACDEXT-2', # MACD with controllable MA type
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'MACDFIX-0', # Moving Average Convergence/Divergence Fix 12/26
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'MACDFIX-1', # Moving Average Convergence/Divergence Fix 12/26
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'MACDFIX-2', # Moving Average Convergence/Divergence Fix 12/26
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'MFI', # Money Flow Index
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'MINUS_DI', # Minus Directional Indicator
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'MINUS_DM', # Minus Directional Movement
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'MOM', # Momentum
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'PLUS_DI', # Plus Directional Indicator
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'PLUS_DM', # Plus Directional Movement
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'PPO', # Percentage Price Oscillator
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'ROC', # Rate of change : ((price/prevPrice)-1)*100
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# Rate of change Percentage: (price-prevPrice)/prevPrice
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'ROCP',
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'ROCR', # Rate of change ratio: (price/prevPrice)
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# Rate of change ratio 100 scale: (price/prevPrice)*100
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'ROCR100',
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'RSI', # Relative Strength Index
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'STOCH-0', # Stochastic
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'STOCH-1', # Stochastic
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'STOCHF-0', # Stochastic Fast
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'STOCHF-1', # Stochastic Fast
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'STOCHRSI-0', # Stochastic Relative Strength Index
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'STOCHRSI-1', # Stochastic Relative Strength Index
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# 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
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'TRIX',
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'ULTOSC', # Ultimate Oscillator
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'WILLR', # Williams' %R
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},
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'Volume Indicators': {
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'AD', # Chaikin A/D Line
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'ADOSC', # Chaikin A/D Oscillator
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'OBV', # On Balance Volume
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},
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'Volatility Indicators': {
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'ATR', # Average True Range
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'NATR', # Normalized Average True Range
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'TRANGE', # True Range
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},
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'Price Transform': {
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'AVGPRICE', # Average Price
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'MEDPRICE', # Median Price
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'TYPPRICE', # Typical Price
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'WCLPRICE', # Weighted Close Price
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},
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'Cycle Indicators': {
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'HT_DCPERIOD', # Hilbert Transform - Dominant Cycle Period
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'HT_DCPHASE', # Hilbert Transform - Dominant Cycle Phase
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'HT_PHASOR-0', # Hilbert Transform - Phasor Components
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'HT_PHASOR-1', # Hilbert Transform - Phasor Components
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'HT_SINE-0', # Hilbert Transform - SineWave
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'HT_SINE-1', # Hilbert Transform - SineWave
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'HT_TRENDMODE', # Hilbert Transform - Trend vs Cycle Mode
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},
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'Pattern Recognition': {
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'CDL2CROWS', # Two Crows
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'CDL3BLACKCROWS', # Three Black Crows
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'CDL3INSIDE', # Three Inside Up/Down
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'CDL3LINESTRIKE', # Three-Line Strike
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'CDL3OUTSIDE', # Three Outside Up/Down
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'CDL3STARSINSOUTH', # Three Stars In The South
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'CDL3WHITESOLDIERS', # Three Advancing White Soldiers
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'CDLABANDONEDBABY', # Abandoned Baby
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'CDLADVANCEBLOCK', # Advance Block
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'CDLBELTHOLD', # Belt-hold
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'CDLBREAKAWAY', # Breakaway
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'CDLCLOSINGMARUBOZU', # Closing Marubozu
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'CDLCONCEALBABYSWALL', # Concealing Baby Swallow
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'CDLCOUNTERATTACK', # Counterattack
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'CDLDARKCLOUDCOVER', # Dark Cloud Cover
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'CDLDOJI', # Doji
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'CDLDOJISTAR', # Doji Star
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'CDLDRAGONFLYDOJI', # Dragonfly Doji
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'CDLENGULFING', # Engulfing Pattern
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'CDLEVENINGDOJISTAR', # Evening Doji Star
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'CDLEVENINGSTAR', # Evening Star
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'CDLGAPSIDESIDEWHITE', # Up/Down-gap side-by-side white lines
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'CDLGRAVESTONEDOJI', # Gravestone Doji
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'CDLHAMMER', # Hammer
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'CDLHANGINGMAN', # Hanging Man
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'CDLHARAMI', # Harami Pattern
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'CDLHARAMICROSS', # Harami Cross Pattern
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'CDLHIGHWAVE', # High-Wave Candle
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'CDLHIKKAKE', # Hikkake Pattern
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'CDLHIKKAKEMOD', # Modified Hikkake Pattern
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'CDLHOMINGPIGEON', # Homing Pigeon
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'CDLIDENTICAL3CROWS', # Identical Three Crows
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'CDLINNECK', # In-Neck Pattern
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'CDLINVERTEDHAMMER', # Inverted Hammer
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'CDLKICKING', # Kicking
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'CDLKICKINGBYLENGTH', # Kicking - bull/bear determined by the longer marubozu
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'CDLLADDERBOTTOM', # Ladder Bottom
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'CDLLONGLEGGEDDOJI', # Long Legged Doji
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'CDLLONGLINE', # Long Line Candle
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'CDLMARUBOZU', # Marubozu
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'CDLMATCHINGLOW', # Matching Low
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'CDLMATHOLD', # Mat Hold
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'CDLMORNINGDOJISTAR', # Morning Doji Star
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'CDLMORNINGSTAR', # Morning Star
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'CDLONNECK', # On-Neck Pattern
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'CDLPIERCING', # Piercing Pattern
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'CDLRICKSHAWMAN', # Rickshaw Man
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'CDLRISEFALL3METHODS', # Rising/Falling Three Methods
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'CDLSEPARATINGLINES', # Separating Lines
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'CDLSHOOTINGSTAR', # Shooting Star
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'CDLSHORTLINE', # Short Line Candle
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'CDLSPINNINGTOP', # Spinning Top
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'CDLSTALLEDPATTERN', # Stalled Pattern
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'CDLSTICKSANDWICH', # Stick Sandwich
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# Takuri (Dragonfly Doji with very long lower shadow)
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'CDLTAKURI',
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'CDLTASUKIGAP', # Tasuki Gap
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'CDLTHRUSTING', # Thrusting Pattern
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'CDLTRISTAR', # Tristar Pattern
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'CDLUNIQUE3RIVER', # Unique 3 River
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'CDLUPSIDEGAP2CROWS', # Upside Gap Two Crows
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'CDLXSIDEGAP3METHODS', # Upside/Downside Gap Three Methods
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},
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'Statistic Functions': {
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'BETA', # Beta
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'CORREL', # Pearson's Correlation Coefficient (r)
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'LINEARREG', # Linear Regression
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'LINEARREG_ANGLE', # Linear Regression Angle
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'LINEARREG_INTERCEPT', # Linear Regression Intercept
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'LINEARREG_SLOPE', # Linear Regression Slope
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'STDDEV', # Standard Deviation
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'TSF', # Time Series Forecast
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'VAR', # Variance
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}
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}
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god_genes = set()
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########################### SETTINGS ##############################
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# god_genes = {'SMA'}
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god_genes |= all_god_genes['Overlap Studies']
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god_genes |= all_god_genes['Momentum Indicators']
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god_genes |= all_god_genes['Volume Indicators']
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god_genes |= all_god_genes['Volatility Indicators']
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god_genes |= all_god_genes['Price Transform']
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god_genes |= all_god_genes['Cycle Indicators']
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god_genes |= all_god_genes['Pattern Recognition']
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god_genes |= all_god_genes['Statistic Functions']
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#timeperiods = [5, 6, 12, 15, 50, 55, 100, 110]
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timeperiods = [5, 10, 20, 50, 100]
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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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########################### END SETTINGS ##########################
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# DATAFRAME = DataFrame()
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god_genes = list(god_genes)
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# print('selected indicators for optimzatin: \n', god_genes)
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god_genes_with_timeperiod = list()
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for god_gene in god_genes:
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for timeperiod in timeperiods:
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# print(f'{god_gene}-{timeperiod}')
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god_genes_with_timeperiod.append(f'{god_gene}-{timeperiod}')
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# Let give somethings to CatagoricalParam to Play with them
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# When just one thing is inside catagorical lists
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# TODO: its Not True Way :)
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if len(god_genes) == 1:
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god_genes = god_genes*2
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if len(timeperiods) == 1:
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timeperiods = timeperiods*2
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if len(operators) == 1:
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operators = operators*2
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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_4(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,
|
|
# "600": 0.12,
|
|
# "1200": 0.08,
|
|
# "2400": 0.06,
|
|
# "3600": 0.04,
|
|
# "7289": 0
|
|
}
|
|
|
|
# Stoploss:
|
|
stoploss = -1
|
|
# Buy hypers
|
|
timeframe = '5m'
|
|
|
|
# Trailing stoploss
|
|
trailing_stop = False
|
|
trailing_stop_positive = 0.15
|
|
trailing_stop_positive_offset = 0.20
|
|
trailing_only_offset_is_reached = True
|
|
|
|
plot_config = {
|
|
# Main plot indicators (Moving averages, ...)
|
|
'main_plot': {
|
|
'bb_lowerband': {'color': 'red'},
|
|
'bb_upperband': {'color': 'green'},
|
|
'sma100': {'color': 'blue'},
|
|
'sma10': {'color': 'yellow'},
|
|
# 'min': {'color': 'white'},
|
|
# 'max': {'color': 'white'},
|
|
'sma20': {'color': 'cyan'}
|
|
},
|
|
'subplots': {
|
|
# Subplots - each dict defines one additional plot
|
|
"BB": {
|
|
'bb_width': {'color': 'white'},
|
|
'bb_min': {'color': 'red'},
|
|
},
|
|
"Ind": {
|
|
"PLUS_DM-20": {'color': 'green'},
|
|
"CDLTAKURI-5": {'color': 'blue'}
|
|
},
|
|
# "Ind2": {
|
|
# 'MINUS_DM-5': {'color': 'green'},
|
|
# 'DX-5': {'color': 'blue'},
|
|
# 'LINEARREG-50': {'color': 'red'}
|
|
# },
|
|
# "Profit": {
|
|
# 'profit': {'color': 'pink'},
|
|
# },
|
|
"Rsi": {
|
|
'rsi': {'color': 'pink'},
|
|
},
|
|
# "rolling": {
|
|
# 'bb_rolling': {'color': '#87e470'},
|
|
# "bb_rolling_min": {'color': '#ac3e2a'}
|
|
# },
|
|
"percent": {
|
|
"percent": {'color': 'green'},
|
|
"percent3": {'color': 'blue'},
|
|
"percent5": {'color': 'red'}
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
# #################### END OF RESULT PLACE ####################
|
|
|
|
profit_no_change = False
|
|
profit_old_sma10 = False
|
|
profit_over_rsi = True
|
|
profit_quick_gain = True
|
|
profit_quick_gain_3 = True
|
|
profit_quick_lost = False
|
|
profit_short_loss = False
|
|
profit_sma10 = True
|
|
profit_sma20 = True
|
|
profit_very_old_sma10 = False
|
|
|
|
# TODO: Its not dry code!
|
|
# Buy Hyperoptable Parameters/Spaces.
|
|
buy_crossed_indicator0 = CategoricalParameter(god_genes_with_timeperiod, default="ADD-20", space='buy')
|
|
buy_crossed_indicator1 = CategoricalParameter(god_genes_with_timeperiod, default="ASIN-6", space='buy')
|
|
buy_crossed_indicator2 = CategoricalParameter(god_genes_with_timeperiod, default="CDLEVENINGSTAR-50", space='buy')
|
|
|
|
buy_indicator0 = CategoricalParameter(god_genes_with_timeperiod, default="SMA-100", space='buy')
|
|
buy_indicator1 = CategoricalParameter(god_genes_with_timeperiod, default="WILLR-50", space='buy')
|
|
buy_indicator2 = CategoricalParameter(god_genes_with_timeperiod, default="CDLHANGINGMAN-20", space='buy')
|
|
|
|
buy_operator0 = CategoricalParameter(operators, default="/<R", space='buy')
|
|
buy_operator1 = CategoricalParameter(operators, default="<R", space='buy')
|
|
buy_operator2 = CategoricalParameter(operators, default="CB", space='buy')
|
|
|
|
buy_real_num0 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.89009, space='buy')
|
|
buy_real_num1 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.56953, space='buy')
|
|
buy_real_num2 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.38365, space='buy')
|
|
|
|
# buy_shift_bb_lowerband = IntParameter(0, 10, default=2, space='buy')
|
|
|
|
# Sell Hyperoptable Parameters/Spaces.
|
|
# sell_crossed_indicator0 = CategoricalParameter(god_genes_with_timeperiod, default="CDLSHOOTINGSTAR-150", space='sell')
|
|
# sell_crossed_indicator1 = CategoricalParameter(god_genes_with_timeperiod, default="MAMA-1-100", space='sell')
|
|
# sell_crossed_indicator2 = CategoricalParameter(god_genes_with_timeperiod, default="CDLMATHOLD-6", space='sell')
|
|
#
|
|
# sell_indicator0 = CategoricalParameter(god_genes_with_timeperiod, default="CDLUPSIDEGAP2CROWS-5", space='sell')
|
|
# sell_indicator1 = CategoricalParameter(god_genes_with_timeperiod, default="CDLHARAMICROSS-150", space='sell')
|
|
# sell_indicator2 = CategoricalParameter(god_genes_with_timeperiod, default="CDL2CROWS-5", space='sell')
|
|
#
|
|
# sell_operator0 = CategoricalParameter(operators, default="<R", space='sell')
|
|
# sell_operator1 = CategoricalParameter(operators, default="D", space='sell')
|
|
# sell_operator2 = CategoricalParameter(operators, default="/>R", space='sell')
|
|
#
|
|
# sell_real_num0 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.09731, space='sell')
|
|
# sell_real_num1 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.81657, space='sell')
|
|
# sell_real_num2 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.87267, space='sell')
|
|
|
|
protection_max_allowed_dd = DecimalParameter(0, 1, decimals=DECIMALS, default=0.04, space='protection')
|
|
protection_stop = IntParameter(0, 100, default=48, space='protection')
|
|
protection_stoploss_stop = IntParameter(0, 100, default=48, space='protection')
|
|
lookback = IntParameter(0, 200, default=48, space='protection')
|
|
trade_limit = IntParameter(0, 10, default=2, space='protection')
|
|
|
|
# buy_cond1_num0 = DecimalParameter(0, 10, decimals=DECIMALS, default=1, space='buy')
|
|
#buy_cond1_num1 = DecimalParameter(0, 10, decimals=DECIMALS, default=2, space='buy')
|
|
# buy_bbwidth_num0 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.035, space='buy')
|
|
#buy_bbwidth_num1 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.055, space='buy')
|
|
|
|
@property
|
|
def protections(self):
|
|
return [
|
|
{
|
|
"method": "CooldownPeriod",
|
|
"stop_duration_candles": 10
|
|
},
|
|
{
|
|
"method": "MaxDrawdown",
|
|
"lookback_period_candles": self.lookback.value,
|
|
"trade_limit": self.trade_limit.value,
|
|
"stop_duration_candles": self.protection_stop.value,
|
|
"max_allowed_drawdown": self.protection_max_allowed_dd.value,
|
|
"only_per_pair": False
|
|
},
|
|
{
|
|
"method": "StoplossGuard",
|
|
"lookback_period_candles": 24,
|
|
"trade_limit": 4,
|
|
"stop_duration_candles": self.protection_stoploss_stop.value,
|
|
"only_per_pair": False
|
|
},
|
|
# {
|
|
# "method": "StoplossGuard",
|
|
# "lookback_period_candles": 24,
|
|
# "trade_limit": 4,
|
|
# "stop_duration_candles": 2,
|
|
# "only_per_pair": False
|
|
# },
|
|
# {
|
|
# "method": "LowProfitPairs",
|
|
# "lookback_period_candles": 6,
|
|
# "trade_limit": 2,
|
|
# "stop_duration_candles": 60,
|
|
# "required_profit": 0.02
|
|
# },
|
|
# {
|
|
# "method": "LowProfitPairs",
|
|
# "lookback_period_candles": 24,
|
|
# "trade_limit": 4,
|
|
# "stop_duration_candles": 2,
|
|
# "required_profit": 0.01
|
|
# }
|
|
]
|
|
|
|
# def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
|
|
# proposed_stake: float, min_stake: float, max_stake: float,
|
|
# **kwargs) -> float:
|
|
#
|
|
# dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
|
|
# current_candle = dataframe.iloc[-1].squeeze()
|
|
#
|
|
# # print("proposed_stake=", proposed_stake, " max_stake=", max_stake)
|
|
# if current_candle['bb_width'] > 0.065:
|
|
# # print("use more stake", pair, " ", proposed_stake * 2)
|
|
# return min(max_stake, proposed_stake * 2)
|
|
#
|
|
# if current_candle['bb_width'] > 0.045:
|
|
# # print("use more stake", pair, " ", proposed_stake * 1.5)
|
|
# return min(max_stake, proposed_stake * 1.5)
|
|
#
|
|
# # if current_candle['bb_width'] < 0.020:
|
|
# # print("use less stake", pair, " ", proposed_stake / 2)
|
|
# # return min(max_stake, proposed_stake / 2)
|
|
# # if self.config['stake_amount'] == 'unlimited':
|
|
# # # Use entire available wallet during favorable conditions when in compounding mode.
|
|
# # return max_stake
|
|
# # else:
|
|
# # # Compound profits during favorable conditions instead of using a static stake.
|
|
# # return self.wallets.get_total_stake_amount() / self.config['max_open_trades']
|
|
#
|
|
# # Use default stake amount.
|
|
# return proposed_stake
|
|
|
|
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
|
|
current_profit: float, **kwargs):
|
|
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
|
last_candle = dataframe.iloc[-1].squeeze()
|
|
previous_last_candle = dataframe.iloc[-2].squeeze()
|
|
previous_5_candle = dataframe.iloc[-5].squeeze()
|
|
|
|
# if (0 < current_profit) & ((current_time - trade.open_date_utc).seconds > 3600) \
|
|
# & (last_candle['percent10'] < 0.001):
|
|
# return 'small_profit'
|
|
#
|
|
# if (current_profit > 0.01) \
|
|
# & ((previous_last_candle['sma10'] - last_candle['sma10']) / previous_last_candle['sma10'] > 0.003):
|
|
# # print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
|
# return 'sma10_quick'
|
|
|
|
if (current_profit >= -0.01) & ((current_time - trade.open_date_utc).days >= 5)\
|
|
& ((current_time - trade.open_date_utc).days < 10)\
|
|
& (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
|
|
return "too_old_0.01"
|
|
if (current_profit >= -0.02) & ((current_time - trade.open_date_utc).days >= 10)\
|
|
& ((current_time - trade.open_date_utc).days < 15) \
|
|
& (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
|
|
return "too_old_0.02"
|
|
if (current_profit >= -0.03) & ((current_time - trade.open_date_utc).days >= 15) \
|
|
& (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
|
|
return "too_old_0.03"
|
|
|
|
if self.profit_quick_lost:
|
|
if (current_profit >= 0) & (last_candle['percent3'] < -0.015):
|
|
return "quick_lost"
|
|
|
|
if self.profit_no_change:
|
|
if (current_profit > 0.005) & (last_candle['percent10'] < 0.001) & (last_candle['percent5'] < 0) & ((current_time - trade.open_date_utc).seconds >= 3600):
|
|
return "no_change"
|
|
|
|
if (current_profit > 0.01) & (last_candle['rsi'] < 30):
|
|
return "small_rsi"
|
|
if self.profit_quick_gain_3:
|
|
if (current_profit >= 0.03) & (last_candle['percent3'] < 0) & ((current_time - trade.open_date_utc).seconds <= 3600):
|
|
return "quick_gain_3"
|
|
if self.profit_quick_gain:
|
|
if (0.01 < current_profit < 0.03) & (last_candle['percent3'] < 0) & ((current_time - trade.open_date_utc).seconds <= 3600):
|
|
return "quick_gain"
|
|
|
|
# if (current_profit > self.sell_percent.value) & (last_candle['percent3'] < - self.sell_percent3.value) \
|
|
# & ((current_time - trade.open_date_utc).seconds <= 300 * self.sell_candels.value):
|
|
# return "quick_gain_param"
|
|
|
|
if self.profit_sma10:
|
|
if (current_profit > 0.01) \
|
|
& ((previous_5_candle['sma10'] > last_candle['sma10']) \
|
|
| (last_candle['percent3'] < -0.01) | (last_candle['percent5'] < -0.01)) \
|
|
& ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
|
|
# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
|
return 'sma10'
|
|
|
|
if self.profit_sma20:
|
|
if (current_profit > 0.005) \
|
|
& (previous_last_candle['sma10'] > last_candle['sma10']) \
|
|
& ((current_time - trade.open_date_utc).seconds >= 3600) \
|
|
& ((previous_last_candle['sma20'] > last_candle['sma20']) &
|
|
((last_candle['percent5'] < 0) | (last_candle['percent10'] < 0) | (last_candle['percent20'] < 0))):
|
|
# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
|
return 'sma20'
|
|
|
|
# if self.profit_old_sma10:
|
|
# if (current_profit > 0) \
|
|
# & ((current_time - trade.open_date_utc).days >= 3) \
|
|
# & ((previous_5_candle['sma10'] > last_candle['sma10']) | (last_candle['percent3'] < -0.005) | (last_candle['percent5'] < -0.005)) \
|
|
# & ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
|
|
# # print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
|
# return 'old_sma10'
|
|
# if self.profit_very_old_sma10:
|
|
# if (current_profit > -0.01) \
|
|
# & ((current_time - trade.open_date_utc).days >= 6) \
|
|
# & ((previous_5_candle['sma10'] > last_candle['sma10']) | (last_candle['percent3'] < 0) | (last_candle['percent5'] < 0)) \
|
|
# & ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
|
|
# # print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
|
# return 'very_old_sma10'
|
|
|
|
if self.profit_over_rsi:
|
|
if (current_profit > 0) & (previous_last_candle['rsi'] > 88): # & (last_candle['percent'] < 0): #| (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
|
|
# print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
|
return 'over_rsi'
|
|
|
|
if (current_profit > 0) & (previous_last_candle['rsi'] > 82) & (last_candle['percent'] < -0.02): #| (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
|
|
# print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
|
return 'over_rsi_2'
|
|
|
|
if self.profit_short_loss:
|
|
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):
|
|
# get access to all pairs available in whitelist.
|
|
pairs = self.dp.current_whitelist()
|
|
# Assign tf to each pair so they can be downloaded and cached for strategy.
|
|
informative_pairs = [(pair, "4h") for pair in pairs]
|
|
#informative_pairs += [(pair, '1d') for pair in pairs]
|
|
#informative_pairs += [(pair, '1w') for pair in pairs]
|
|
|
|
# Optionally Add additional "static" pairs
|
|
# informative_pairs += [("ETH/USDT", "5m"), ("BTC/TUSD", "15m")]
|
|
|
|
return informative_pairs
|
|
|
|
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
# MACD
|
|
# macd = ta.MACD(dataframe)
|
|
# dataframe['macd'] = macd['macd']
|
|
# dataframe['macdsignal'] = macd['macdsignal']
|
|
# dataframe['macdhist'] = macd['macdhist']
|
|
|
|
# # # Plus Directional Indicator / Movement
|
|
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
|
|
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
|
|
#
|
|
# # Minus Directional Indicator / Movement
|
|
# dataframe['adx'] = ta.ADX(dataframe)
|
|
dataframe[self.buy_indicator0.value] = gene_calculator(dataframe, self.buy_indicator0.value)
|
|
dataframe[self.buy_crossed_indicator0.value] = gene_calculator(dataframe, self.buy_crossed_indicator0.value)
|
|
dataframe[self.buy_indicator1.value] = gene_calculator(dataframe, self.buy_indicator1.value)
|
|
dataframe[self.buy_crossed_indicator1.value] = gene_calculator(dataframe, self.buy_crossed_indicator1.value)
|
|
# dataframe[self.buy_indicator2.value] = gene_calculator(dataframe, self.buy_indicator2.value)
|
|
# dataframe[self.buy_crossed_indicator2.value] = gene_calculator(dataframe, self.buy_crossed_indicator2.value)
|
|
|
|
# dataframe['MINUS_DM-5'] = ta.MINUS_DM(dataframe, timeperiod=5)
|
|
# dataframe['LINEARREG-50'] = ta.LINEARREG(dataframe, timeperiod=50)
|
|
# dataframe['MA-20'] = ta.MA(dataframe, timeperiod=20)
|
|
# stoch = ta.STOCH(dataframe, timeperiod=10)
|
|
# # print(stoch)
|
|
# dataframe['STOCH-1-10'] = stoch['slowd']
|
|
# dataframe['CDLDRAGONFLYDOJI-5'] = ta.CDLDRAGONFLYDOJI(dataframe, timeperiod=5)
|
|
|
|
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
|
|
|
|
# dataframe['min'] = ta.MIN(dataframe)
|
|
# dataframe['max'] = ta.MAX(dataframe)
|
|
|
|
# # Aroon, Aroon Oscillator
|
|
# aroon = ta.AROON(dataframe)
|
|
# dataframe['aroonup'] = aroon['aroonup']
|
|
# dataframe['aroondown'] = aroon['aroondown']
|
|
# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
|
|
|
|
dataframe['profit'] = 0
|
|
# RSI
|
|
dataframe['rsi'] = ta.RSI(dataframe)
|
|
|
|
# # EMA - Exponential Moving Average
|
|
# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
|
|
# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
|
|
# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
|
|
# dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
|
|
# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
|
|
# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
|
|
|
|
# # SMA - Simple Moving Average
|
|
# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
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|
# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
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|
dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
|
|
dataframe['sma20'] = ta.SMA(dataframe, timeperiod=20)
|
|
dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
|
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dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
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# dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
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# dataframe['sma200_95'] = ta.SMA(dataframe, timeperiod=200) * 0.95
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# dataframe['sma200_98'] = ta.SMA(dataframe, timeperiod=200) * 0.98
|
|
# dataframe['sma500'] = ta.SMA(dataframe, timeperiod=500)
|
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# dataframe['sma500_90'] = ta.SMA(dataframe, timeperiod=500) * 0.9
|
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# dataframe['sma500_95'] = ta.SMA(dataframe, timeperiod=500) * 0.95
|
|
# dataframe['sma500_20'] = ta.SMA(dataframe, timeperiod=500) * 0.2
|
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dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
|
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# dataframe["sum_percent"] = dataframe["sum_percent"].shift(1) + dataframe["percent"]
|
|
dataframe["percent5"] = dataframe["percent"].rolling(5).sum()
|
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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()
|
|
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)
|
|
|
|
# # INFORMATIVE PAIRS
|
|
|
|
# Get the informative pair
|
|
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="4h")
|
|
|
|
informative["percent"] = (informative["close"] - informative["open"]) / informative["open"]
|
|
informative["percent5"] = informative["percent"].rolling(5).sum()
|
|
informative["percent3"] = informative["percent"].rolling(3).sum()
|
|
# print('informative', metadata['pair'], informative.tail(1))
|
|
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "4h", ffill=True)
|
|
|
|
# informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d')
|
|
# informative[buy_crossed_indicator0] = gene_calculator(informative, buy_crossed_indicator0)
|
|
# informative[buy_indicator0] = gene_calculator(informative, buy_indicator0)
|
|
# informative["cond1"] = informative[buy_indicator0].div(informative[buy_crossed_indicator0])
|
|
# informative["percent"] = (informative["close"] - informative["open"]) / informative["open"]
|
|
# informative["percent5"] = informative["percent"].rolling(5).sum()
|
|
# informative["percent3"] = informative["percent"].rolling(3).sum()
|
|
# dataframe = merge_informative_pair(dataframe, informative, self.timeframe, '1d', ffill=True)
|
|
|
|
# informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1w')
|
|
# informative[buy_crossed_indicator0] = gene_calculator(informative, buy_crossed_indicator0)
|
|
# informative[buy_indicator0] = gene_calculator(informative, buy_indicator0)
|
|
# informative["cond1"] = informative[buy_indicator0].div(informative[buy_crossed_indicator0])
|
|
# informative["percent"] = (informative["close"] - informative["open"]) / informative["open"]
|
|
# informative["percent5"] = informative["percent"].rolling(5).sum()
|
|
# informative["percent3"] = informative["percent"].rolling(3).sum()
|
|
# dataframe = merge_informative_pair(dataframe, informative, self.timeframe, '1w', ffill=True)
|
|
|
|
return dataframe
|
|
|
|
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
|
|
conditions = list()
|
|
|
|
# TODO: Its not dry code!
|
|
buy_indicator = self.buy_indicator0.value
|
|
buy_crossed_indicator = self.buy_crossed_indicator0.value
|
|
buy_operator = self.buy_operator0.value
|
|
buy_real_num = self.buy_real_num0.value
|
|
condition, dataframe = condition_generator(
|
|
dataframe,
|
|
buy_operator,
|
|
buy_indicator,
|
|
buy_crossed_indicator,
|
|
buy_real_num
|
|
)
|
|
conditions.append(condition)
|
|
# backup
|
|
buy_indicator = self.buy_indicator1.value
|
|
buy_crossed_indicator = self.buy_crossed_indicator1.value
|
|
buy_operator = self.buy_operator1.value
|
|
buy_real_num = self.buy_real_num1.value
|
|
|
|
condition, dataframe = condition_generator(
|
|
dataframe,
|
|
buy_operator,
|
|
buy_indicator,
|
|
buy_crossed_indicator,
|
|
buy_real_num
|
|
)
|
|
conditions.append(condition)
|
|
|
|
buy_indicator = self.buy_indicator2.value
|
|
buy_crossed_indicator = self.buy_crossed_indicator2.value
|
|
buy_operator = self.buy_operator2.value
|
|
buy_real_num = self.buy_real_num2.value
|
|
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)
|
|
# & (dataframe['percent_4h'] > 0)
|
|
& (dataframe['percent3_4h'] <= 0)
|
|
),
|
|
'buy']=1
|
|
# print(len(dataframe.keys()))
|
|
|
|
return dataframe
|
|
|
|
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
|
|
return dataframe
|
|
|