# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/PumpDetector.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 IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import math
'\n    https://fr.tradingview.com/script/vDX9m7PJ-L2-KDJ-with-Whale-Pump-Detector/\n    translated for freqtrade: viksal1982  viktors.s@gmail.com\n'

def xsa(dataframe, source, len, wei):
    df = dataframe.copy().fillna(0)

    def calc_xsa(dfr, init=0):
        global calc_sumf_value
        global calc_src_value
        global calc_out_value
        if init == 1:
            calc_sumf_value = [0.0] * len
            calc_src_value = [0.0] * len
            calc_out_value = [0.0] * len
            return
        calc_src_value.pop(0)
        calc_src_value.append(dfr[source])
        sumf_val = calc_sumf_value[-1] - calc_src_value[0]
        ma_val = sumf_val / len
        out_val = (calc_src_value[-1] * wei + calc_out_value[-1] * (len - wei)) / len
        calc_sumf_value.pop(0)
        calc_sumf_value.append(sumf_val)
        calc_out_value.pop(0)
        calc_out_value.append(out_val)
        return out_val
    calc_xsa(None, init=1)
    df['retxsa'] = df.apply(calc_xsa, axis=1)
    return df['retxsa']

class Github_DerSalvador_freqtrade_helm_chart__PumpDetector__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    stoploss = -0.99
    trailing_stop = False
    # Optimal timeframe for the strategy.
    timeframe = '5m'
    # Run "populate_indicators()" only for new candle.
    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 = 30
    # 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'}
    # Main plot indicators (Moving averages, ...)
    # Subplots - each dict defines one additional plot
    plot_config = {'main_plot': {}, 'subplots': {'XSA': {'j': {'color': 'blue'}, 'k': {'color': 'orange'}}}}

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        n1 = 18
        m1 = 4
        m2 = 4
        dataframe['var1'] = dataframe['low'].shift(1)
        dataframe['var1_abs'] = (dataframe['low'] - dataframe['var1']).abs()
        dataframe['var1_max'] = np.where(dataframe['low'] - dataframe['var1'] > 0, dataframe['low'] - dataframe['var1'], 0)
        dataframe['var2_test'] = xsa(dataframe, source='var1_abs', len=3, wei=1)
        dataframe['var2'] = xsa(dataframe, source='var1_abs', len=3, wei=1) / xsa(dataframe, source='var1_max', len=3, wei=1) * 100
        dataframe['var2_10'] = dataframe['var2'] * 10
        dataframe['var3'] = ta.EMA(dataframe['var2_10'], timeperiod=3)
        dataframe['var4'] = dataframe['low'].rolling(38).min()
        dataframe['var5'] = dataframe['var3'].rolling(38).max()
        dataframe['var6'] = 1
        dataframe['var7_data'] = np.where(dataframe['low'] <= dataframe['var4'], (dataframe['var3'] + dataframe['var5'] * 2) / 2, 0)
        dataframe['var7'] = ta.EMA(dataframe['var7_data'], timeperiod=3) / 618 * dataframe['var3']
        dataframe['var8'] = (dataframe['close'] - dataframe['low'].rolling(21).min()) / (dataframe['high'].rolling(21).max() - dataframe['low'].rolling(21).min()) * 100
        dataframe['var9'] = xsa(dataframe, source='var8', len=13, wei=8)
        dataframe['rsv'] = (dataframe['close'] - dataframe['low'].rolling(n1).min()) / (dataframe['high'].rolling(n1).max() - dataframe['low'].rolling(n1).min()) * 100
        dataframe['k'] = xsa(dataframe, source='rsv', len=m1, wei=1)
        dataframe['d'] = xsa(dataframe, source='k', len=m2, wei=1)
        dataframe['j'] = 3 * dataframe['k'] - 2 * dataframe['d']
        # dataframe.to_csv('test.csv')
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # Make sure Volume is not 0
        dataframe.loc[qtpylib.crossed_above(dataframe['j'], 0) & (dataframe['volume'] > 0), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # Make sure Volume is not 0
        dataframe.loc[(qtpylib.crossed_above(dataframe['j'], 90) | (qtpylib.crossed_below(dataframe['j'], dataframe['k']) & dataframe['j'] > 50)) & (dataframe['volume'] > 0), 'exit'] = 1
        return dataframe