DipBuyerStrategy

"""
Conservative 4h dip-buyer for small accounts.

Thesis: in ranging / mildly bearish altcoin markets, buying deep RSI dips
on a 4h chart while the weekly trend is still up captures mean reversion
bounces with fewer fee-eating trades than low-TF momentum.

Uses position_adjustment to average down once if price drops another 6%,
which improves average entry without increasing per-trade risk beyond budget.
"""

from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta


class DipBuyerStrategy(IStrategy):

    INTERFACE_VERSION = 3

    minimal_roi = {
        "0": 0.08,
        "720": 0.05,
        "1440": 0.03,
        "2880": 0.015,
    }

    stoploss = -0.12
    trailing_stop = True
    trailing_stop_positive = 0.025
    trailing_stop_positive_offset = 0.05
    trailing_only_offset_is_reached = True

    timeframe = "4h"
    process_only_new_candles = True
    startup_candle_count: int = 200

    position_adjustment_enable = True
    max_entry_position_adjustment = 1

    plot_config = {
        "main_plot": {
            "ema_50": {"color": "#f39c12"},
            "ema_200": {"color": "#e74c3c"},
            "bb_upper": {"color": "#3498db"},
            "bb_middle": {"color": "#95a5a6"},
            "bb_lower": {"color": "#3498db", "fill_to": "bb_upper", "fill_color": "rgba(52,152,219,0.08)"},
        },
        "subplots": {
            "RSI": {
                "rsi": {"color": "#9b59b6"},
                "rsi_slow": {"color": "#8e44ad"},
            },
            "MACD": {
                "macd": {"color": "#2ecc71"},
                "macd_signal": {"color": "#e67e22"},
                "macd_hist": {"color": "#7f8c8d", "type": "bar"},
            },
            "ADX": {
                "adx": {"color": "#16a085"},
            },
            "ATR": {
                "atr": {"color": "#c0392b"},
            },
        },
    }

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["rsi_slow"] = ta.RSI(dataframe, timeperiod=28)
        dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
        dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)

        bb = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.2, nbdevdn=2.2)
        dataframe["bb_upper"] = bb["upperband"]
        dataframe["bb_middle"] = bb["middleband"]
        dataframe["bb_lower"] = bb["lowerband"]

        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
        dataframe["macd"] = macd["macd"]
        dataframe["macd_signal"] = macd["macdsignal"]
        dataframe["macd_hist"] = macd["macdhist"]

        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
        dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
        dataframe["volume_sma"] = ta.SMA(dataframe["volume"], timeperiod=20)

        dataframe["low_10"] = dataframe["low"].rolling(10).min()
        dataframe["pct_from_low"] = (dataframe["close"] - dataframe["low_10"]) / dataframe["low_10"]

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["rsi"] < 30) &
                (dataframe["rsi_slow"] < 45) &
                (dataframe["close"] < dataframe["bb_lower"]) &
                (dataframe["close"] > dataframe["ema_200"] * 0.85) &
                (dataframe["volume"] > dataframe["volume_sma"] * 0.6) &
                (dataframe["volume"] > 0)
            ),
            ["enter_long", "enter_tag"]] = (1, "deep_dip")

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["rsi"] > 70) &
                (dataframe["close"] > dataframe["bb_middle"]) &
                (dataframe["volume"] > 0)
            ),
            ["exit_long", "exit_tag"]] = (1, "rsi_overbought")
        return dataframe

    def adjust_trade_position(self, trade, current_time, current_rate,
                              current_profit, min_stake, max_stake,
                              current_entry_rate, current_exit_rate,
                              current_entry_profit, current_exit_profit, **kwargs):
        if trade.nr_of_successful_entries >= 2:
            return None
        if current_profit > -0.06:
            return None
        filled_entries = trade.select_filled_orders(trade.entry_side)
        if not filled_entries:
            return None
        try:
            return filled_entries[0].stake_amount
        except Exception:
            return None