SMAcrossover

# -*- coding: utf-8 -*-
"""
Sample trading strategies for Freqtrade.
Provides strategy templates and a manager for listing/selecting strategies.
"""

from pathlib import Path
from typing import Dict, List

STRATEGY_DIR = Path(__file__).parent.parent / "user_data" / "strategies"

STRATEGY_TEMPLATES: Dict[str, dict] = {
    "SMAcrossover": {
        "description": "Simple Moving Average crossover strategy using SMA 50/200",
        "timeframe": "1h",
        "indicators": ["SMA50", "SMA200"],
        "buy_logic": "SMA50 crosses above SMA200 (golden cross)",
        "sell_logic": "SMA50 crosses below SMA200 (death cross)",
        "code": '''
from freqtrade.strategy import IStrategy
import talib.abstract as ta
from pandas import DataFrame


class SMAcrossover(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = "1h"
    minimal_roi = {"0": 0.05, "60": 0.025, "120": 0}
    stoploss = -0.10
    trailing_stop = True
    trailing_stop_positive = 0.01

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["sma50"] = ta.SMA(dataframe, timeperiod=50)
        dataframe["sma200"] = ta.SMA(dataframe, timeperiod=200)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe["sma50"] > dataframe["sma200"]) &
            (dataframe["sma50"].shift(1) <= dataframe["sma200"].shift(1)),
            "enter_long"
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe["sma50"] < dataframe["sma200"]) &
            (dataframe["sma50"].shift(1) >= dataframe["sma200"].shift(1)),
            "exit_long"
        ] = 1
        return dataframe
''',
    },
    "RSIbounce": {
        "description": "RSI oversold bounce strategy with volume confirmation",
        "timeframe": "15m",
        "indicators": ["RSI14", "Volume SMA"],
        "buy_logic": "RSI < 30 and volume > 1.5x average",
        "sell_logic": "RSI > 70 or take-profit at 3%",
        "code": '''
from freqtrade.strategy import IStrategy
import talib.abstract as ta
from pandas import DataFrame


class RSIbounce(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = "15m"
    minimal_roi = {"0": 0.03, "30": 0.015, "60": 0}
    stoploss = -0.05

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["volume_sma"] = ta.SMA(dataframe["volume"], timeperiod=20)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe["rsi"] < 30) &
            (dataframe["volume"] > dataframe["volume_sma"] * 1.5),
            "enter_long"
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe["rsi"] > 70),
            "exit_long"
        ] = 1
        return dataframe
''',
    },
    "BollingerBreakout": {
        "description": "Bollinger Bands breakout with MACD confirmation",
        "timeframe": "1h",
        "indicators": ["Bollinger Bands", "MACD"],
        "buy_logic": "Price closes below lower band and MACD histogram turns positive",
        "sell_logic": "Price reaches upper band or MACD histogram turns negative",
        "code": '''
from freqtrade.strategy import IStrategy
import talib.abstract as ta
from pandas import DataFrame


class BollingerBreakout(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = "1h"
    minimal_roi = {"0": 0.04, "60": 0.02, "120": 0}
    stoploss = -0.08
    trailing_stop = True
    trailing_stop_positive = 0.015

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        bb = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
        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["macdsignal"] = macd["macdsignal"]
        dataframe["macdhist"] = macd["macdhist"]
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe["close"] < dataframe["bb_lower"]) &
            (dataframe["macdhist"] > 0) &
            (dataframe["macdhist"].shift(1) <= 0),
            "enter_long"
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe["close"] > dataframe["bb_upper"]) |
            ((dataframe["macdhist"] < 0) & (dataframe["macdhist"].shift(1) >= 0)),
            "exit_long"
        ] = 1
        return dataframe
''',
    },
}


def list_strategies() -> List[dict]:
    result = []
    for name, meta in STRATEGY_TEMPLATES.items():
        result.append({
            "name": name,
            "description": meta["description"],
            "timeframe": meta["timeframe"],
            "indicators": meta["indicators"],
        })
    return result


def get_strategy_code(name: str) -> str:
    template = STRATEGY_TEMPLATES.get(name)
    if not template:
        return ""
    return template["code"].strip()


def export_strategy(name: str, output_dir: Path = STRATEGY_DIR) -> bool:
    code = get_strategy_code(name)
    if not code:
        return False
    output_dir.mkdir(parents=True, exist_ok=True)
    target = output_dir / f"{name}.py"
    try:
        target.write_text(code, encoding="utf-8")
        return True
    except OSError:
        return False