201 lines
8.2 KiB
Python
201 lines
8.2 KiB
Python
# GodStra Strategy Hyperopt
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# IMPORTANT: INSTALL TA BEFOUR RUN:
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# :~$ pip install ta
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# freqtrade hyperopt --hyperopt GodStraHo --hyperopt-loss SharpeHyperOptLossDaily --spaces all --strategy GodStra --config config.json -e 100
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# --- Do not remove these libs ---
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from functools import reduce
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from typing import Any, Callable, Dict, List
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import numpy as np # noqa
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import pandas as pd # noqa
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from pandas import DataFrame
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from skopt.space import Categorical, Dimension, Integer, Real # noqa
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from freqtrade.optimize.hyperopt_interface import IHyperOpt
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# --------------------------------
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# Add your lib to import here
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# import talib.abstract as ta # noqa
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import talib.abstract as ta
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from ta import add_all_ta_features
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from ta.utils import dropna
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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# this is your trading strategy DNA Size
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# you can change it and see the results...
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DNA_SIZE = 1
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GodGenes = ["open", "high", "low", "close", "volume", "volume_adi", "volume_obv",
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"volume_cmf", "volume_fi", "volume_mfi", "volume_em", "volume_sma_em", "volume_vpt",
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"volume_nvi", "volume_vwap", "volatility_atr", "volatility_bbm", "volatility_bbh",
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"volatility_bbl", "volatility_bbw", "volatility_bbp", "volatility_bbhi",
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"volatility_bbli", "volatility_kcc", "volatility_kch", "volatility_kcl",
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"volatility_kcw", "volatility_kcp", "volatility_kchi", "volatility_kcli",
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"volatility_dcl", "volatility_dch", "volatility_dcm", "volatility_dcw",
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"volatility_dcp", "volatility_ui", "trend_macd", "trend_macd_signal",
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"trend_macd_diff", "trend_sma_fast", "trend_sma_slow", "trend_ema_fast",
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"trend_ema_slow", "trend_adx", "trend_adx_pos", "trend_adx_neg", "trend_vortex_ind_pos",
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"trend_vortex_ind_neg", "trend_vortex_ind_diff", "trend_trix",
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"trend_mass_index", "trend_cci", "trend_dpo", "trend_kst",
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"trend_kst_sig", "trend_kst_diff", "trend_ichimoku_conv",
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"trend_ichimoku_base", "trend_ichimoku_a", "trend_ichimoku_b",
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"trend_visual_ichimoku_a", "trend_visual_ichimoku_b", "trend_aroon_up",
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"trend_aroon_down", "trend_aroon_ind", "trend_psar_up", "trend_psar_down",
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"trend_psar_up_indicator", "trend_psar_down_indicator", "trend_stc",
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"momentum_rsi", "momentum_stoch_rsi", "momentum_stoch_rsi_k",
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"momentum_stoch_rsi_d", "momentum_tsi", "momentum_uo", "momentum_stoch",
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"momentum_stoch_signal", "momentum_wr", "momentum_ao", "momentum_kama",
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"momentum_roc", "momentum_ppo", "momentum_ppo_signal", "momentum_ppo_hist",
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"others_dr", "others_dlr", "others_cr"]
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class GodStraHo(IHyperOpt):
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@staticmethod
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def indicator_space() -> List[Dimension]:
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"""
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Define your Hyperopt space for searching buy strategy parameters.
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"""
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gene = list()
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for i in range(DNA_SIZE):
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gene.append(Categorical(GodGenes, name=f'buy-indicator-{i}'))
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gene.append(Categorical(GodGenes, name=f'buy-cross-{i}'))
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gene.append(Integer(-1, 101, name=f'buy-int-{i}'))
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gene.append(Real(-1.1, 1.1, name=f'buy-real-{i}'))
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# Operations
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# CA: Crossed Above, CB: Crossed Below,
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# I: Integer, R: Real, D: Disabled
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gene.append(Categorical(["D", ">", "<", "=", "CA", "CB",
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">I", "=I", "<I", ">R", "=R", "<R"], name=f'buy-oper-{i}'))
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return gene
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@staticmethod
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def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Define the buy strategy parameters to be used by Hyperopt.
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"""
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def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Buy strategy Hyperopt will build and use.
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"""
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conditions = []
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# GUARDS AND TRENDS
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for i in range(DNA_SIZE):
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OPR = params[f'buy-oper-{i}']
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IND = params[f'buy-indicator-{i}']
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CRS = params[f'buy-cross-{i}']
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INT = params[f'buy-int-{i}']
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REAL = params[f'buy-real-{i}']
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DFIND = dataframe[IND]
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DFCRS = dataframe[CRS]
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if OPR == ">":
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conditions.append(DFIND > DFCRS)
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elif OPR == "=":
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conditions.append(np.isclose(DFIND, DFCRS))
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elif OPR == "<":
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conditions.append(DFIND < DFCRS)
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elif OPR == "CA":
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conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
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elif OPR == "CB":
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conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
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elif OPR == ">I":
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conditions.append(DFIND > INT)
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elif OPR == "=I":
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conditions.append(DFIND == INT)
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elif OPR == "<I":
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conditions.append(DFIND < INT)
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elif OPR == ">R":
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conditions.append(DFIND > REAL)
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elif OPR == "=R":
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conditions.append(np.isclose(DFIND, REAL))
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elif OPR == "<R":
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conditions.append(DFIND < REAL)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy'] = 1
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return dataframe
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return populate_buy_trend
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@ staticmethod
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def sell_indicator_space() -> List[Dimension]:
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"""
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Define your Hyperopt space for searching sell strategy parameters.
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"""
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gene = list()
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for i in range(DNA_SIZE):
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gene.append(Categorical(GodGenes, name=f'sell-indicator-{i}'))
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gene.append(Categorical(GodGenes, name=f'sell-cross-{i}'))
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gene.append(Integer(-1, 101, name=f'sell-int-{i}'))
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gene.append(Real(-0.01, 1.01, name=f'sell-real-{i}'))
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# Operations
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# CA: Crossed Above, CB: Crossed Below,
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# I: Integer, R: Real, D: Disabled
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gene.append(Categorical(["D", ">", "<", "=", "CA", "CB",
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">I", "=I", "<I", ">R", "=R", "<R"], name=f'sell-oper-{i}'))
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return gene
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@ staticmethod
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def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Define the sell strategy parameters to be used by Hyperopt.
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"""
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def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Sell strategy Hyperopt will build and use.
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"""
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conditions = []
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# GUARDS AND TRENDS
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for i in range(DNA_SIZE):
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OPR = params[f'sell-oper-{i}']
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IND = params[f'sell-indicator-{i}']
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CRS = params[f'sell-cross-{i}']
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INT = params[f'sell-int-{i}']
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REAL = params[f'sell-real-{i}']
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DFIND = dataframe[IND]
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DFCRS = dataframe[CRS]
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if OPR == ">":
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conditions.append(DFIND > DFCRS)
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elif OPR == "=":
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conditions.append(np.isclose(DFIND, DFCRS))
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elif OPR == "<":
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conditions.append(DFIND < DFCRS)
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elif OPR == "CA":
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conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
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elif OPR == "CB":
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conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
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elif OPR == ">I":
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conditions.append(DFIND > INT)
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elif OPR == "=I":
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conditions.append(DFIND == INT)
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elif OPR == "<I":
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conditions.append(DFIND < INT)
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elif OPR == ">R":
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conditions.append(DFIND > REAL)
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elif OPR == "=R":
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conditions.append(np.isclose(DFIND, REAL))
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elif OPR == "<R":
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conditions.append(DFIND < REAL)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'sell']=1
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return dataframe
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return populate_sell_trend
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