# source: https://raw.githubusercontent.com/Roshan7869/algotrading/db07ceb71ac165a38fe200a86ae48586ed040f60/user_data/strategies/VectorOmni_MeanRevEV.py
# directory_url: https://github.com/Roshan7869/algotrading/blob/main/user_data/strategies/
# User: Roshan7869
# Repository: algotrading
# --------------------"""
Github_Roshan7869_algotrading__VectorOmni_MeanRevEV__20260817_183107 — Expected Value Mean Reversion Vector Strategy

Manipulates VectorStrategy's mean reversion pillar using ChromaDB EV framework:
  1. Expected Value (EV) calculation: EV = WR * R - LR * 1
  2. Multi-variable disequilibrium stacking:
     - Distance from 200 EMA (%)
     - Rate of change acceleration
     - RSI extreme + BB %b extreme
     - Volume climax (surge then drop)
  3. Right Side of V entry: wait for reversal candle after extreme
  4. Prior-bar trailing stop: trail to prior bar's high/low
  5. Dynamic sizing based on EV score

Source: Chart Fanatics "Expected Value Framework for Mean Reversion"
"""
from datetime import datetime
from typing import Optional
import numpy as np
import pandas as pd
from pandas import DataFrame

from freqtrade.strategy import IStrategy, Trade, DecimalParameter, IntParameter
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class Github_Roshan7869_algotrading__VectorOmni_MeanRevEV__20260817_183107(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = "1h"
    can_short = False

    minimal_roi = {"0": 0.10, "60": 0.06, "240": 0.04, "720": 0.02, "1440": 0.01}

    stoploss = -0.06
    trailing_stop = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.04
    trailing_only_offset_is_reached = True

    process_only_new_candles = True
    startup_candle_count = 200

    order_types = {"entry": "limit", "exit": "market", "stoploss": "market", "stoploss_on_exchange": False}
    order_time_in_force = {"entry": "GTC", "exit": "GTC"}

    bb_squeeze_threshold = DecimalParameter(0.02, 0.10, default=0.06, decimals=3, space="buy")
    rsi_oversold = IntParameter(25, 45, default=35, space="buy")
    rsi_overbought = IntParameter(55, 75, default=65, space="sell")
    volume_factor = DecimalParameter(1.0, 2.5, default=1.5, decimals=1, space="buy")
    ema_fast = IntParameter(8, 21, default=9, space="buy")
    ema_medium = IntParameter(20, 50, default=21, space="buy")
    bb_pctb_low = DecimalParameter(0.20, 0.50, default=0.30, space="buy")
    bb_pctb_high = DecimalParameter(0.50, 0.80, default=0.70, space="sell")
    min_confluence = IntParameter(1, 3, default=2, space="buy")
    ev_min_score = DecimalParameter(0.5, 3.0, default=1.0, decimals=1, space="buy")

    def leverage(self, pair, current_time, current_rate, proposed_leverage, max_leverage, entry_tag, side, **kwargs):
        return min(3, max_leverage)

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        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_pctb"] = ((dataframe["close"] - dataframe["bb_lowerband"])
                                / (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
                                ).replace([np.inf, -np.inf], 0.5).fillna(0.5)
        dataframe["bb_width"] = ((dataframe["bb_upperband"] - dataframe["bb_lowerband"])
                                 / dataframe["bb_middleband"]
                                 ).replace([np.inf, -np.inf], 0).fillna(0)

        bollinger_3sd = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
        dataframe["bb3_upper"] = bollinger_3sd["upper"]
        dataframe["bb3_lower"] = bollinger_3sd["lower"]

        dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=self.ema_fast.value)
        dataframe["ema_medium"] = ta.EMA(dataframe, timeperiod=self.ema_medium.value)
        dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["volume_mean"] = ta.SMA(dataframe["volume"], timeperiod=20)
        dataframe["volume_ratio"] = (dataframe["volume"] / dataframe["volume_mean"]
                                     ).replace([np.inf, -np.inf], 1).fillna(1)
        dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)

        typical_price = (dataframe["high"] + dataframe["low"] + dataframe["close"]) / 3
        dataframe["vwap"] = ((typical_price * dataframe["volume"]).rolling(20).sum()
                             / dataframe["volume"].rolling(20).sum()).bfill()

        dataframe["pivot_high"] = dataframe["high"].rolling(5, center=True).max()
        dataframe["pivot_low"] = dataframe["low"].rolling(5, center=True).min()
        dataframe["dist_to_resistance"] = ((dataframe["pivot_high"] - dataframe["close"]) / dataframe["atr"]).fillna(5)
        dataframe["dist_to_support"] = ((dataframe["close"] - dataframe["pivot_low"]) / dataframe["atr"]).fillna(5)

        # ── EV Framework: Disequilibrium Variables ──
        # 1. Distance from 200 EMA (normalized by ATR)
        dataframe["dist_from_ema200"] = (dataframe["close"] - dataframe["ema_200"]) / dataframe["atr"]
        # Extreme distance = stronger reversion signal
        dataframe["ema200_extreme_long"] = (dataframe["dist_from_ema200"] < -3.0).astype(int)
        dataframe["ema200_extreme_short"] = (dataframe["dist_from_ema200"] > 3.0).astype(int)

        # 2. Rate of change acceleration (3-period ROC)
        dataframe["roc_3"] = dataframe["close"].pct_change(3) * 100
        dataframe["roc_accel"] = dataframe["roc_3"].diff()
        # Waterfall pattern: accelerating decline
        dataframe["waterfall_long"] = (
            (dataframe["roc_3"] < -3.0) &
            (dataframe["roc_accel"] < -0.5)
        ).astype(int)
        dataframe["waterfall_short"] = (
            (dataframe["roc_3"] > 3.0) &
            (dataframe["roc_accel"] > 0.5)
        ).astype(int)

        # 3. Volume climax: surge then drop (climax = exhaustion)
        dataframe["vol_climax"] = (
            (dataframe["volume_ratio"] > 2.0) &
            (dataframe["volume_ratio"].shift(1) > dataframe["volume_ratio"])
        ).astype(int)

        # 4. Right Side of V detection: price reversed from extreme
        dataframe["right_side_v_long"] = (
            (dataframe["close"] > dataframe["open"]) &
            (dataframe["close"].shift(1) < dataframe["open"].shift(1)) &
            (dataframe["low"] < dataframe["low"].shift(1)) &
            (dataframe["close"] > dataframe["close"].shift(1))
        ).astype(int)
        dataframe["right_side_v_short"] = (
            (dataframe["close"] < dataframe["open"]) &
            (dataframe["close"].shift(1) > dataframe["open"].shift(1)) &
            (dataframe["high"] > dataframe["high"].shift(1)) &
            (dataframe["close"] < dataframe["close"].shift(1))
        ).astype(int)

        # 5. Combined EV score (higher = stronger reversion setup)
        dataframe["ev_long"] = (
            dataframe["ema200_extreme_long"] * 2.0 +
            dataframe["waterfall_long"] * 1.5 +
            dataframe["vol_climax"] * 1.0 +
            dataframe["right_side_v_long"] * 2.0 +
            ((dataframe["bb_pctb"] < 0.25) & (dataframe["rsi"] < 35)).astype(int) * 1.5
        )
        dataframe["ev_short"] = (
            dataframe["ema200_extreme_short"] * 2.0 +
            dataframe["waterfall_short"] * 1.5 +
            dataframe["vol_climax"] * 1.0 +
            dataframe["right_side_v_short"] * 2.0 +
            ((dataframe["bb_pctb"] > 0.75) & (dataframe["rsi"] > 65)).astype(int) * 1.5
        )

        # Prior bar high/low for trailing
        dataframe["prior_high"] = dataframe["high"].shift(1)
        dataframe["prior_low"] = dataframe["low"].shift(1)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        ev_ok_long = dataframe["ev_long"] >= self.ev_min_score.value
        ev_ok_short = dataframe["ev_short"] >= self.ev_min_score.value

        squeeze_breakout_long = (
            (dataframe["bb_width"] < self.bb_squeeze_threshold.value) &
            (dataframe["bb_width"].shift(1) < dataframe["bb_width"]) &
            (dataframe["close"] > dataframe["bb_middleband"]) &
            (dataframe["volume_ratio"] > self.volume_factor.value)
        )

        mean_reversion_long = (
            (dataframe["bb_pctb"] < self.bb_pctb_low.value) &
            (dataframe["close"] > dataframe["bb3_lower"]) &
            (dataframe["rsi"] < self.rsi_oversold.value) &
            (dataframe["close"] > dataframe["vwap"])
        )

        ema_alignment_long = (
            (dataframe["ema_fast"] > dataframe["ema_medium"]) &
            (dataframe["close"] > dataframe["ema_fast"]) &
            (dataframe["ema_medium"] > dataframe["ema_200"]) &
            (dataframe["rsi"] > 40) & (dataframe["rsi"] < 65)
        )

        expansion_long = (
            (dataframe["close"] > dataframe["bb3_upper"]) &
            (dataframe["close"].shift(1) <= dataframe["bb3_upper"].shift(1)) &
            (dataframe["volume_ratio"] > self.volume_factor.value) & (dataframe["rsi"] > 50)
        )

        key_level_long = (
            (dataframe["dist_to_support"] < 1.0) &
            (dataframe["close"] > dataframe["open"]) &
            (dataframe["volume_ratio"] > 1.2) & (dataframe["rsi"] > 35) & (dataframe["rsi"] < 65)
        )

        key_level_boost_long = (dataframe["dist_to_support"] < 0.5).astype(int)
        long_signals = [
            squeeze_breakout_long.astype(int), mean_reversion_long.astype(int),
            ema_alignment_long.astype(int), expansion_long.astype(int),
            key_level_long.astype(int),
        ]
        long_score = sum(long_signals) + key_level_boost_long

        dataframe.loc[
            (long_score >= self.min_confluence.value) &
            ev_ok_long &
            (dataframe["volume"] > 0),
            ["enter_long", "enter_tag"]
        ] = (1, "ev_meanrev_long")

        squeeze_breakout_short = (
            (dataframe["bb_width"] < self.bb_squeeze_threshold.value) &
            (dataframe["bb_width"].shift(1) < dataframe["bb_width"]) &
            (dataframe["close"] < dataframe["bb_middleband"]) &
            (dataframe["volume_ratio"] > self.volume_factor.value)
        )

        mean_reversion_short = (
            (dataframe["bb_pctb"] > self.bb_pctb_high.value) &
            (dataframe["close"] < dataframe["bb3_upper"]) &
            (dataframe["rsi"] > self.rsi_overbought.value) &
            (dataframe["close"] < dataframe["vwap"])
        )

        ema_alignment_short = (
            (dataframe["ema_fast"] < dataframe["ema_medium"]) &
            (dataframe["close"] < dataframe["ema_fast"]) &
            (dataframe["ema_medium"] < dataframe["ema_200"]) &
            (dataframe["rsi"] < 60) & (dataframe["rsi"] > 35)
        )

        expansion_short = (
            (dataframe["close"] < dataframe["bb3_lower"]) &
            (dataframe["close"].shift(1) >= dataframe["bb3_lower"].shift(1)) &
            (dataframe["volume_ratio"] > self.volume_factor.value) & (dataframe["rsi"] < 50)
        )

        key_level_short = (
            (dataframe["dist_to_resistance"] < 1.0) &
            (dataframe["close"] < dataframe["open"]) &
            (dataframe["volume_ratio"] > 1.2) & (dataframe["rsi"] < 65) & (dataframe["rsi"] > 35)
        )

        key_level_boost_short = (dataframe["dist_to_resistance"] < 0.5).astype(int)
        short_signals = [
            squeeze_breakout_short.astype(int), mean_reversion_short.astype(int),
            ema_alignment_short.astype(int), expansion_short.astype(int),
            key_level_short.astype(int),
        ]
        short_score = sum(short_signals) + key_level_boost_short

        dataframe.loc[
            (short_score >= self.min_confluence.value) &
            ev_ok_short &
            (dataframe["volume"] > 0),
            ["enter_short", "enter_tag"]
        ] = (1, "ev_meanrev_short")

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["bb_pctb"] > self.bb_pctb_high.value) |
                ((dataframe["rsi"] > self.rsi_overbought.value) &
                 (dataframe["close"] < dataframe["ema_fast"])) |
                (dataframe["bb_width"] > dataframe["bb_width"].rolling(10).mean() * 2.5)
            ) & (dataframe["volume"] > 0),
            ["exit_long", "exit_tag"]
        ] = (1, "ev_exit")

        dataframe.loc[
            (
                (dataframe["bb_pctb"] < self.bb_pctb_low.value) |
                ((dataframe["rsi"] < self.rsi_oversold.value) &
                 (dataframe["close"] > dataframe["ema_fast"])) |
                (dataframe["bb_width"] > dataframe["bb_width"].rolling(10).mean() * 2.5)
            ) & (dataframe["volume"] > 0),
            ["exit_short", "exit_tag"]
        ] = (1, "ev_exit")

        return dataframe

    def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
                    current_rate: float, current_profit: float, **kwargs) -> Optional[str]:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if len(dataframe) < 1:
            return None
        last = dataframe.iloc[-1]
        bb_pctb = last.get("bb_pctb", 0.5)

        if trade.is_short:
            if bb_pctb < 0.15:
                return "beacon_target_short"
        else:
            if bb_pctb > 0.85:
                return "beacon_target_long"

        return None