# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/INSIDEUP.py
import copy
import logging
import pathlib
import rapidjson
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
import pandas as pd
import pandas_ta as pta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes
from freqtrade.exchange import timeframe_to_prev_date
from pandas import DataFrame, Series, concat
from functools import reduce
import math
from typing import Dict
from freqtrade.persistence import Trade
from datetime import datetime, timedelta
from technical.util import resample_to_interval, resampled_merge
from technical.indicators import RMI, zema, VIDYA, ichimoku
import time
import warnings
# Custom imports to fetch API data
import requests
import json

class Github_DerSalvador_freqtrade_helm_chart__INSIDEUP__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    # ROI table:
    minimal_roi = {'0': 0.237, '4195': 0.17, '7191': 0.053, '14695': 0}
    # Stoploss:
    stoploss = -0.99  # value loaded from strategy
    # Trailing stop:
    trailing_stop = True  # value loaded from strategy
    trailing_stop_positive = 0.011  # value loaded from strategy
    trailing_stop_positive_offset = 0.029  # value loaded from strategy
    trailing_only_offset_is_reached = True  # value loaded from strategy
    # Optimal timeframe for the strategy.
    timeframe = '1d'
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True
    # These values can be overridden in the "ask_strategy" section in the config.
    use_exit_signal = False
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Inputs:
        # # prices: ['open', 'high', 'low', 'close']
        # # Three Inside Up/Down: values [0, -100, 100]
        dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe)  # values [-100, 0, 100]
        # # MORNINGDOJISTAR: values [0, 100]
        dataframe['CDLMORNINGDOJISTAR'] = ta.CDLMORNINGDOJISTAR(dataframe)  # values [0, 100]
        # # Piercing Line: values [0, 100]
        dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe)  # values [0, 100]
        # # Three Black Crows: values [-100, 0, 100]
        dataframe['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(dataframe)  # values [-100, 0, 100]
        # RSI
        dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
        # ADX
        dataframe['adx'] = ta.ADX(dataframe)
        dataframe['slowadx'] = ta.ADX(dataframe, 35)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dateTime = datetime.now()
        # Check for downtrend movement
        # Check for patterns
        # the user should consider that a three inside up is significant
        #when it appears in a downtrend
        # Bullish
        dataframe.loc[((dataframe['close'] < dataframe['close'].shift(2)) | (dataframe['rsi_14'] < 50)) & (dataframe['adx'] > 13.0) & (dataframe['CDL3INSIDE'] >= 0).any(), ['entry', 'entry_tag']] = (1, 'entry_3_inside')
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        no exit signal
        """
        dataframe.loc[:, 'exit'] = 0
        return dataframe