SekkaProd20

# ================================================================
# SekkaProd v1.0 – Running on prod
# ---------------------------------------------------------------
# 
# ================================================================

from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
from pandas import DataFrame
import pandas as pd
import numpy as np
import talib.abstract as ta
import logging
from datetime import datetime
from typing import Optional


class SekkaProd20(IStrategy):
    timeframe = "1h"
    informative_timeframes = []  # Single timeframe only - no multi-timeframe
    process_only_new_candles = True
    can_short = False  # Long-only strategy

    use_exit_signal = True
    exit_profit_only = False
    ignore_buying_expiry = True
    position_adjustment_enable = True  # ✅ Ensure DCA works

    startup_candle_count = 20

    # TP_ON_RSI_THRESHOLD = 0.02
    # TP_NON_RSI_THRESHOLD = 0.05
    dca_threshold = DecimalParameter(0.01, 0.07, default=0.03, decimals=3, space="buy", optimize=True)
    rsi_threshold = IntParameter(25, 45, default=35, space="buy", optimize=True)

    DCA_STEP = 5
    vwap_gap = DecimalParameter(-0.03, -0.01, default=-0.01, decimals=3, space="buy", optimize=True)
    # RSI_TP = 60
    
    RSI_PERIOD = 14
    VWAP_WINDOW = 14
    LEVERAGE = 1

    minimal_roi = {}
    stoploss = -0.3
    
    #max_entry_position_adjustment = -1

    logger = logging.getLogger(__name__)
    _last_dca_stage = None

    ## -- TRAILING SETUP -- 
    # Enable Trailing Stop
    trailing_stop = True
    # 1. Start trailing ONLY when profit reaches 2% (0.02)
    trailing_stop_positive_offset = 0.02

    # 2. Once started, trail the price by 2% (0.02)
    #    (If price goes to +3%, stop moves to +1%)
    #    (If price goes to +5%, stop moves to +3%)
    trailing_stop_positive = 0.01
    # 3. Ensure it doesn't trail from the start (only after +3% profit)
    trailing_only_offset_is_reached = True

    # ------------------ Informative Pairs ------------------
    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = []
        #for pair in pairs:
            # Assume Futures pair 'BTC/USDT:USDT' -> Spot 'BTC/USDT'
            #if ":" in pair:
            #    spot_pair = pair.split(':')[0]
            #else:
            #    spot_pair = pair # Already spot?
         #   informative_pairs.append((spot_pair, self.timeframe, 'spot'))
        return informative_pairs

    # ------------------ Plot Config ------------------
    plot_config = {
        "main_plot": {
            "vwap_1h": {"color": "orange"},
            "ema_fast": {"color": "yellow"},
            "ema_slow": {"color": "blue"},
        },
        "subplots": {
            "RSI": {
                "rsi_default": {"color": "red"},
            },
            "MACD": {
                "macd": {"color": "blue"},
                "macdsignal": {"color": "orange"},
                "macdhist": {"color": "green", "type": "bar"},
            },
        },
    }

    # ------------------ Indicators ------------------
    def hlc3(self, df: DataFrame) -> pd.Series:
        return (df["high"] + df["low"] + df["close"]) / 3.0

    def compute_vwap(self, df: DataFrame, window: int) -> pd.Series:
        hlc3 = self.hlc3(df)
        pv = hlc3 * df["volume"]
        pv_sum = pv.rolling(window, min_periods=1).sum()
        vol_sum = df["volume"].rolling(window, min_periods=1).sum()
        vwap = (pv_sum / vol_sum.replace(0, np.nan)).ffill().fillna(df["close"])
        return vwap

    def populate_indicators(self, df: DataFrame, metadata: dict) -> DataFrame:
        # Single timeframe (1h) only - no multi-timeframe dependencies
        df["rsi_default"] = ta.RSI(df, timeperiod=self.RSI_PERIOD)
        df["vwap_1h"] = self.compute_vwap(df, self.VWAP_WINDOW)
        df["vwap_gap_1h"] = np.where(df["vwap_1h"] > 0, (df["close"] / df["vwap_1h"]) - 1.0, 0.0)
        # EMAs
        #df["ema_fast"] = ta.EMA(df, timeperiod=6)
        #df["ema_slow"] = ta.EMA(df, timeperiod=24)

        # MACD
        #macd = ta.MACD(df)
        #df["macd"] = macd["macd"]
        #df["macdsignal"] = macd["macdsignal"]
        #df["macdhist"] = macd["macdhist"]
        return df

    # Freqtrade 2025.10+ requires populate_entry_trend/populate_exit_trend
    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        df["enter_long"] = 0
        df.loc[
            (df["rsi_default"] <= self.rsi_threshold.value)
            & (df["vwap_gap_1h"] < self.vwap_gap.value),
            "enter_long",
        ] = 1
        return df

    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        df["exit_long"] = 0
        return df

    # ------------------ Custom Stake ------------------
    def custom_stake_amount(self, pair: str, current_time: pd.Timestamp, current_rate: float, **kwargs) -> float:
        balance = self.wallets.get_total_stake_amount()
        free = self.wallets.get_available_stake_amount()
        used = max(balance - free, 0)
        remaining = max(balance - used, 0)

        trade = kwargs.get("trade", None)
        stage = trade.nr_of_successful_entries if trade else 0
        
        # Total entries = DCA_STEP + 1 (Initial), since no cut loss, no need to add 1
        total_steps = self.DCA_STEP

        if stage == 0:
            stake = balance / total_steps
        else:
            # Remaining steps including this one
            remaining_steps = total_steps - stage
            if remaining_steps > 0:
                stake = remaining / remaining_steps
            else:
                stake = 0

        return float(max(min(stake, remaining), 0.0))

    # ------------------ DCA Logic ------------------
    def adjust_trade_position(self, trade, current_time, current_rate, current_profit, **kwargs):
        # self.logger.info(f"[{current_time}] {trade.pair} | DCA check stage={trade.nr_of_successful_entries}")
        if self._last_dca_stage is None:
            self._last_dca_stage = {}

        trade_id = getattr(trade, "id", None) or f"{trade.pair}_{getattr(trade, 'open_date', None)}"
        current_stage = trade.nr_of_successful_entries
        recorded = self._last_dca_stage.get(trade_id)

        if recorded is not None and recorded == current_stage:
            return 0
        
        # Stop if we reached max entries (Initial + DCA_STEP)
        if current_stage >= (self.DCA_STEP + 1):
            return 0

        avg_rate = trade.open_rate
        drop_ratio = (current_rate / avg_rate) - 1.0
        next_dca_trigger = -self.dca_threshold.value

        free_balance = self.wallets.get_available_stake_amount()
        est_stake = self.custom_stake_amount(trade.pair, current_time, current_rate, trade=trade)

        if drop_ratio <= next_dca_trigger and free_balance >= est_stake:
            next_stage = current_stage + 1
            tag = f"DCA_{next_stage}"
            self._last_dca_stage[trade_id] = current_stage

            # self.logger.info(
            #     f"[{current_time}] {trade.pair} | Triggering {tag} at {current_rate:.4f} "
            #     f"({drop_ratio*100:.2f}%) | Free={free_balance:.2f} Stake={est_stake:.2f}"
            # )
            trade.enter_tag = tag
            return est_stake  # ✅ FIXED: execute with actual stake

        return 0

    # ------------------ Exit Logic ------------------
    def custom_exit(self, pair: str, trade, current_time, current_rate, **kwargs):
        avg_price = trade.open_rate
        dca_stage = trade.nr_of_successful_entries
        rel = (current_rate / avg_price) - 1.0

        try:
            df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
            
            # Dynamic RSI Calculation (Repainting simulation)
            # 1. Get recent close prices
            closes = df['close'].values[-20:].copy() # Get last 20 candles
            
            # 2. Update the latest close with the CURRENT 5m price
            # This simulates how the 1h candle looks "right now"
            closes[-1] = current_rate
            
            # 3. Calculate RSI on this modified series
            import talib
            rsi_series = talib.RSI(closes, timeperiod=self.RSI_PERIOD)
            rsi_exit = rsi_series[-1]
            #rsi_exit = df.iloc[-1]["rsi_default"]
        except Exception:
            rsi_exit = 20
            
        #if rel >= self.TP_ON_RSI_THRESHOLD and rsi_exit >= self.RSI_TP: 
            #self.logger.info(f"[{current_time}] {pair} | TAKE_PROFIT reached +{rel*100:.2f}%")
        #    self._last_dca_stage.pop(f"{pair}_{trade.open_date}", None)
        #    return "TAKE_PROFIT"

        # Stop loss after all DCAs are used
        #if dca_stage >= (self.DCA_STEP + 1) and rel <= -self.DCA_THRESHOLD:
        #   self.logger.info(f"[{current_time}] {pair} | STOP_LOSS_AFTER_DCA triggered {rel*100:.2f}%")
        #   self._last_dca_stage.pop(f"{pair}_{trade.open_date}", None)
        #   return "STOP_LOSS_AFTER_DCA"

        return None

    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                 proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
                 **kwargs) -> float:
        return self.LEVERAGE