# strategies.py
from __future__ import annotations
import numpy as np
import pandas as pd

# ===== PRESET =====
BUY_TH  = 0.55
SELL_TH = -0.55
REQUIRE_TREND_CONFLUENCE = True   # EMA50 vs EMA200 musi się zgadzać z kierunkiem
USE_ADX_IN_FILTER        = True   # używaj ADX w filtrze kierunku
TREND_FILTER_ADX_MIN     = 18.0
MTF_CONFLICT_DAMP        = 0.6    # konflikt 1m vs 15m – osłab wynik

# ===== FILTRY DODATKOWE =====
SESSION_FILTER  = True
SESS_UTC_START  = 6               # handluj tylko 06:00–21:00 UTC (LON+NY)
SESS_UTC_END    = 21

VOL_FILTER      = True
MIN_ATR_PCT     = 0.0005          # min ATR/price (0.05%)
MAX_ATR_PCT     = 0.02            # max ATR/price (2%) – unikaj paniki
OVEREXT_COEF    = 1.2             # nie wchodź, jeśli |close-EMA20| > 1.2*ATR

def _resample_ohlc(df: pd.DataFrame, rule: str = "15T") -> pd.DataFrame:
    o = {"open":"first","high":"max","low":"min","close":"last"}
    return df[["open","high","low","close"]].resample(rule).agg(o).dropna(how="any")

def _safe(df: pd.DataFrame, col: str, default=None):
    return float(df[col].iloc[-1]) if col in df.columns else (float(default) if default is not None else None)

def _ema(series: pd.Series, span: int) -> float:
    return float(series.ewm(span=span, adjust=False).mean().iloc[-1])

def _atr_pct(df: pd.DataFrame, n: int = 14) -> float:
    if "atr" in df.columns:
        atr = float(df["atr"].iloc[-1])
    else:
        h, l, c = df["high"], df["low"], df["close"]
        prev_c = c.shift(1)
        tr = pd.concat([h - l, (h - prev_c).abs(), (l - prev_c).abs()], axis=1).max(axis=1)
        atr = float(tr.ewm(span=n, adjust=False).mean().iloc[-1])
    price = float(df["close"].iloc[-1])
    return 0.0 if price <= 0 else atr / price

def _in_session(df: pd.DataFrame) -> bool:
    ts = pd.Timestamp(df.index[-1])
    try:
        hour = ts.tz_convert("UTC").hour if ts.tzinfo else ts.hour
    except Exception:
        hour = ts.hour
    return SESS_UTC_START <= hour <= SESS_UTC_END

def _trend_score_1m(df: pd.DataFrame) -> float:
    s = 0.0
    ema20 = _safe(df, "ema20", _ema(df["close"], 20))
    ema50 = _safe(df, "ema50", _ema(df["close"], 50))
    s += 0.5 if ema20 > ema50 else -0.5

    macd = _safe(df, "macd", _ema(df["close"],12) - _ema(df["close"],26))
    macd_sig = _safe(df, "macd_sig", macd)
    s += 0.3 if macd > macd_sig else -0.3

    adx = _safe(df, "adx", None)
    if adx is not None:
        if adx >= 18: s += 0.2
        elif adx <= 12: s -= 0.1

    c  = float(df["close"].iloc[-1])
    st = _safe(df, "supertrend", c)
    s += 0.2 if c > st else -0.2

    up = _safe(df, "bb_up", c + 1e9); dn = _safe(df, "bb_dn", c - 1e9)
    if c > up: s += 0.2
    if c < dn: s -= 0.2

    rsi = _safe(df, "rsi14", 50.0)
    if rsi >= 70: s -= 0.2
    if rsi <= 30: s += 0.2
    return s

def _trend_score_15m(df_1m: pd.DataFrame) -> float:
    try:
        htf = _resample_ohlc(df_1m, "15T")
        if len(htf) < 30: return 0.0
        close = htf["close"]
        ema20 = close.ewm(span=20, adjust=False).mean().iloc[-1]
        ema50 = close.ewm(span=50, adjust=False).mean().iloc[-1]
        macd_line = close.ewm(span=12, adjust=False).mean().iloc[-1] - close.ewm(span=26, adjust=False).mean().iloc[-1]
        macd_sig  = pd.Series(close).ewm(span=9, adjust=False).mean().iloc[-1]
        s  = (0.6 if ema20 > ema50 else -0.6) + (0.4 if macd_line > macd_sig else -0.4)
        return float(s)
    except Exception:
        return 0.0

def _hysteresis_map(score: float) -> int:
    if score >= BUY_TH: return 1
    if score <= SELL_TH: return -1
    return 0

def get_signal(df: pd.DataFrame) -> int:
    if len(df) < 200:
        return 0

    # Filtry ex-ante
    if SESSION_FILTER and not _in_session(df):
        return 0
    atrp = _atr_pct(df)
    if VOL_FILTER and not (MIN_ATR_PCT <= atrp <= MAX_ATR_PCT):
        return 0

    s1  = _trend_score_1m(df)
    s15 = _trend_score_15m(df)
    score = 0.7 * s1 + 0.3 * s15
    if (s1 > 0 and s15 < 0) or (s1 < 0 and s15 > 0):
        score *= MTF_CONFLICT_DAMP
    sig = _hysteresis_map(score)

    # Nie handluj pod prąd + nie gonić świecy
    if sig != 0 and REQUIRE_TREND_CONFLUENCE:
        ema50  = _safe(df, "ema50", _ema(df["close"], 50))
        ema200 = _safe(df, "ema200", _ema(df["close"], 200))
        adx    = _safe(df, "adx", None)
        long_ok  = (ema50 > ema200) and (not USE_ADX_IN_FILTER or adx is None or adx >= TREND_FILTER_ADX_MIN)
        short_ok = (ema50 < ema200) and (not USE_ADX_IN_FILTER or adx is None or adx >= TREND_FILTER_ADX_MIN)
        if sig == 1 and not long_ok:  sig = 0
        if sig == -1 and not short_ok: sig = 0

        # za daleko od EMA20 -> poczekaj na pullback
        if sig != 0:
            ema20 = _safe(df, "ema20", _ema(df["close"], 20))
            c = float(df["close"].iloc[-1])
            # przy braku atr w df liczymy atrp już powyżej
            atr = atrp * c if c > 0 else 0.0
            if atr > 0.0 and abs(c - ema20) > OVEREXT_COEF * atr:
                sig = 0

    return int(sig)