# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-michael-k8s-namespace/RobotradingBody.py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from freqtrade.strategy import BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# https://www.tradingview.com/script/zUaR3Vbb-robotrading-body/  
# translated for freqtrade: viksal1982  viktors.s@gmail.com
#  A timeframe of 4 hours to 1 day

class Github_DerSalvador_freqtrade_helm_chart__RobotradingBody__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    minimal_roi = {'0': 0.9}
    stoploss = -0.99
    for_mult = IntParameter(1, 20, default=3, space='entry', optimize=True)
    for_sma_length = IntParameter(20, 200, default=100, space='entry', optimize=True)
    trailing_stop = False
    timeframe = '4h'
    process_only_new_candles = False
    # These values can be overridden in the "ask_strategy" section in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 100
    # Optional order type mapping.
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False}
    # Optional order time in force.
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['body'] = (dataframe['close'] - dataframe['open']).abs()
        dataframe['body_sma'] = ta.SMA(dataframe['body'], timeperiod=int(self.for_sma_length.value)) * int(self.for_mult.value)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['open'] > dataframe['close']) & (dataframe['body'] > dataframe['body_sma']) & (dataframe['volume'] > 0), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['close'] > dataframe['open']) & (dataframe['volume'] > 0), 'exit'] = 1
        return dataframe