# source: https://raw.githubusercontent.com/jcd1991/market-behavior-lab/cb6338f0c58af52bee6c7e745276ece8434c33b7/user_data/strategies/VolatilityManagedTrendCash.py
# directory_url: https://github.com/jcd1991/market-behavior-lab/blob/main/user_data/strategies/
# User: jcd1991
# Repository: market-behavior-lab
# --------------------"""Time-series trend lane that stays in cash during unstable volatility."""

from __future__ import annotations

import json
from pathlib import Path

import numpy as np
import pandas as pd
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy
from research_strategy_helpers import execution_cost_floor, quote_volume_features


class Github_jcd1991_market_behavior_lab__VolatilityManagedTrendCash__20260925_234614(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = "1h"
    can_short = True
    process_only_new_candles = True
    startup_candle_count = 240
    minimal_roi = {"0": 100.0}
    stoploss = -0.12

    buy_vmt_fast = IntParameter(12, 72, default=24, space="buy", optimize=True)
    buy_vmt_slow = IntParameter(96, 360, default=168, space="buy", optimize=True)
    buy_vmt_min_score = DecimalParameter(0.20, 1.00, default=0.50, decimals=2, space="buy", optimize=True)
    buy_vmt_max_vol = DecimalParameter(0.40, 2.50, default=1.20, decimals=2, space="buy", optimize=True)
    buy_vmt_target_vol = DecimalParameter(0.10, 0.60, default=0.25, decimals=2, space="buy", optimize=True)
    buy_vmt_min_edge = DecimalParameter(0.001, 0.050, default=0.005, decimals=3, space="buy", optimize=True)
    buy_vmt_min_liq_ratio = DecimalParameter(0.25, 2.00, default=0.50, decimals=2, space="buy", optimize=True)
    buy_vmt_min_quote_volume = IntParameter(10_000, 10_000_000, default=100_000, space="buy", optimize=False)

    def load_params_from_file(self) -> dict:
        """Share the frozen research parameters with the spot subclass.

        Freqtrade resolves the parameter JSON from this module's filename,
        while both spot and futures variants live in this module. The saved
        research file is intentionally labeled for the spot validation lane;
        accepting both class names keeps the frozen values reproducible for
        the corresponding futures comparison without silently accepting an
        unrelated parameter file.
        """
        filename = Path(__file__).with_suffix(".json")
        if not filename.is_file():
            return {}
        params = json.loads(filename.read_text(encoding="utf-8"))
        allowed = {"Github_jcd1991_market_behavior_lab__VolatilityManagedTrendCash__20260925_234614", "Github_jcd1991_market_behavior_lab__VolatilityManagedTrendCash__20260925_234614Spot"}
        if params.get("strategy_name") not in allowed:
            raise ValueError(f"invalid parameter file strategy: {params.get('strategy_name')!r}")
        return params

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        close = pd.to_numeric(dataframe["close"], errors="coerce")
        fast = int(self.buy_vmt_fast.value)
        slow = int(self.buy_vmt_slow.value)
        returns = close.pct_change()
        vol = returns.rolling(48, min_periods=24).std() * np.sqrt(24 * 365)
        ema_fast = close.ewm(span=fast, adjust=False, min_periods=fast).mean()
        ema_slow = close.ewm(span=slow, adjust=False, min_periods=slow).mean()
        normalized_slope = (ema_fast / ema_slow - 1.0) / vol.replace(0, np.nan)
        momentum = close.pct_change(fast) / vol.replace(0, np.nan)
        dataframe["vmt_score"] = (np.sign(normalized_slope) + np.sign(momentum)) * 0.5
        dataframe["vmt_vol"] = vol
        dataframe["vmt_scale"] = (float(self.buy_vmt_target_vol.value) / vol.replace(0, np.nan)).clip(0.20, 1.25).fillna(0.20)
        dataframe["vmt_risk_on"] = vol.le(float(self.buy_vmt_max_vol.value))
        dataframe["vmt_expected_move"] = close.pct_change(fast).abs()
        dataframe = quote_volume_features(dataframe, "vmt")
        dataframe["vmt_cost_floor"] = execution_cost_floor(self.config)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        liquid = (
            dataframe["vmt_quote_volume"].ge(float(self.buy_vmt_min_quote_volume.value))
            & dataframe["vmt_volume_ratio"].ge(float(self.buy_vmt_min_liq_ratio.value))
        )
        edge = dataframe["vmt_expected_move"].ge(
            dataframe["vmt_cost_floor"] + float(self.buy_vmt_min_edge.value)
        )
        active = dataframe["vmt_risk_on"] & liquid & edge
        long = active & dataframe["vmt_score"].ge(float(self.buy_vmt_min_score.value))
        short = active & dataframe["vmt_score"].le(-float(self.buy_vmt_min_score.value))
        dataframe["enter_long"] = long.astype(int)
        dataframe["enter_short"] = short.astype(int)
        dataframe["enter_tag"] = np.where(long, "vmt_trend_long", np.where(short, "vmt_trend_short", ""))
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["exit_long"] = (~dataframe["vmt_risk_on"] | (dataframe["vmt_score"] < 0)).astype(int)
        dataframe["exit_short"] = (~dataframe["vmt_risk_on"] | (dataframe["vmt_score"] > 0)).astype(int)
        dataframe["exit_tag"] = "vmt_cash_or_flip"
        return dataframe

    def custom_stake_amount(self, pair, current_time, current_rate, proposed_stake, min_stake, max_stake, leverage, entry_tag, side, **kwargs):
        try:
            frame, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
            scale = float(frame.iloc[-1].get("vmt_scale", 0.20)) if frame is not None and not frame.empty else 0.20
        except Exception:
            scale = 0.20
        return min(float(max_stake), max(float(min_stake or 0), float(proposed_stake) * scale))


class Github_jcd1991_market_behavior_lab__VolatilityManagedTrendCash__20260925_234614Spot(Github_jcd1991_market_behavior_lab__VolatilityManagedTrendCash__20260925_234614):
    """Long-only spot validation variant with the same defensive cash state."""

    can_short = False

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = super().populate_entry_trend(dataframe, metadata)
        dataframe["enter_short"] = 0
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = super().populate_exit_trend(dataframe, metadata)
        dataframe["exit_short"] = 0
        return dataframe