PYTHON SimpleStrategy.py

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
import numpy as np
import pandas as pd
from datetime import datetime, timedelta, timezone
from pandas import DataFrame
from typing import Dict, Optional, Union, Tuple

from freqtrade.strategy import (
    IStrategy,
    Trade,
    Order,
    PairLocks,
    informative,
    # Hyperopt Parameters
    BooleanParameter,
    CategoricalParameter,
    DecimalParameter,
    IntParameter,
    RealParameter,
    # timeframe helpers
    timeframe_to_minutes,
    timeframe_to_next_date,
    timeframe_to_prev_date,
    # Strategy helper functions
    merge_informative_pair,
    stoploss_from_absolute,
    stoploss_from_open,
    AnnotationType,
)

# --------------------------------
import talib.abstract as ta
from technical import qtpylib


class SimpleStrategy(IStrategy):
    """
    RSI crossover with trend filter and proper risk management.

    Key changes from original:
    - Trend filter (EMA50) — don't buy dips in a crash
    - Tight stoploss (-6%) — cut losers FAST instead of -22%
    - Trailing stop — locks in gains once profit > 5%
    - Partial exits — 25% at +4%, 25% at +8%

    The original -22% stoploss was the killer. One loss wiped out ~10 wins.
    With -6% stop vs +2-4% ROI, the risk/reward is ~1:0.5 which is still
    aggressive but the win rate should compensate.
    """
    INTERFACE_VERSION = 3
    timeframe = "5m"
    can_short: bool = False

    # Minimal ROI — disabled, using trailing stop + exit signals
    minimal_roi = {}

    # Tight stoploss — the most important fix
    stoploss = -0.06

    # Trailing stop: kicks in after +5%, then trails at 3%
    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.03
    trailing_stop_positive_offset = 0.05

    process_only_new_candles = True

    # Use exit signals for additional exits
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # Enable partial exits
    position_adjustment_enable = True
    max_entry_position_adjustment = 2

    startup_candle_count: int = 100

    # --- Hyperopt parameters ---
    buy_rsi = IntParameter(15, 45, default=30, space="buy", optimize=True)
    sell_rsi = IntParameter(60, 85, default=70, space="sell", optimize=True)

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

    order_time_in_force = {
        "entry": "GTC",
        "exit": "GTC",
    }

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Momentum
        dataframe["adx"] = ta.ADX(dataframe)
        dataframe["rsi"] = ta.RSI(dataframe)

        # Stochastic Fast
        stoch_fast = ta.STOCHF(dataframe)
        dataframe["fastd"] = stoch_fast["fastd"]
        dataframe["fastk"] = stoch_fast["fastk"]

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

        # MFI
        dataframe["mfi"] = ta.MFI(dataframe)

        # Bollinger Bands
        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_percent"] = (
            (dataframe["close"] - dataframe["bb_lowerband"])
            / (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
        )
        dataframe["bb_width"] = (
            (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
            / dataframe["bb_middleband"]
        )

        # Trend filter — key addition
        dataframe["ema20"] = ta.EMA(dataframe, timeperiod=20)
        dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50)

        # Volatility
        dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)

        # Other indicators from original
        dataframe["sar"] = ta.SAR(dataframe)
        dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
        hilbert = ta.HT_SINE(dataframe)
        dataframe["htsine"] = hilbert["sine"]
        dataframe["htleadsine"] = hilbert["leadsine"]

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                # RSI crosses above buy threshold
                (qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value))
                # Volume check
                & (dataframe["volume"] > 0)
                # TREND FILTER — price must be above EMA50 (uptrend)
                & (dataframe["close"] > dataframe["ema50"])
                # Not at the very top of BB (buy the dip)
                & (dataframe["bb_percent"] < 0.7)
            ),
            "enter_long",
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value))
                & (dataframe["volume"] > 0)
            ),
            "exit_long",
        ] = 1
        return dataframe

    def adjust_trade_position(self, trade: Trade, current_profit: float, **kwargs) -> Optional[Union[float, Tuple[float, str]]]:
        """
        Multi-tier partial exits:
        - Tier 1: Sell 25% at +4% profit
        - Tier 2: Sell 25% at +8% profit
        - Remaining 50% rides the trailing stop
        """
        if trade.is_short:
            return None

        if current_profit >= 0.04 and trade.nr_of_successful_exits == 0:
            return (-0.25, "TP1: 25% exit at 4%+ profit")

        if current_profit >= 0.08 and trade.nr_of_successful_exits == 1:
            return (-0.25, "TP2: 25% exit at 8%+ profit")

        return None