import os
from typing import Dict, Any
import strategies
from optimizer import run_optimized_backtest


class Optimizer:
    """Base optimizer wrapper tying a strategy to an optimization grid."""
    name = 'BaseOptimizer'
    strategy_cls = None
    optimize_kwargs: Dict[str, Any] = None

    @classmethod
    def run(cls, symbol):
        # pass optimizer name as report_tag so output files are unique per optimizer
        return run_optimized_backtest(symbol, strategy_cls=cls.strategy_cls, optimize_kwargs=cls.optimize_kwargs, report_tag=cls.name)


class RsiOptimizer(Optimizer):
    name = 'RsiOptimizer'
    strategy_cls = strategies.RsiStrategy
    def constraint(p):
        return p.rsi_upper > p.rsi_lower

    optimize_kwargs = dict(
        rsi_period=range(7, 30, 2),
        rsi_lower=range(20, 40, 5),
        rsi_upper=range(60, 80, 5),
        maximize='Return [%]',
        constraint=constraint,
        atr_period=range(10, 20, 2),
        stop_loss_atr_multiplier=[2.0, 2.5, 3.0, 3.5, 4.0],
    )


class CrossEmaOptimizer(Optimizer):
    name = 'CrossEmaOptimizer'
    strategy_cls = strategies.CrossEmaStrategy

    def constraint(p):
        # ensure long EMA period is greater than short EMA period
        return p.long_ema > p.short_ema

    optimize_kwargs = dict(
        short_ema=range(5, 20, 3),
        long_ema=range(20, 60, 5),
        maximize='Return [%]',
        constraint=constraint,
        atr_period=range(10, 20, 2),
        stop_loss_atr_multiplier=[2.0, 2.5, 3.0, 3.5],
        finalize_trades=False,  # run final backtest with best params
    )


def get_optimizer_by_name(name: str):
    mapping = {cls.name: cls for cls in (RsiOptimizer, CrossEmaOptimizer)}
    # allow passing class name as well
    if name in mapping:
        return mapping[name]
    # try attribute lookup in module
    cls = getattr(__import__('optimizers'), name, None)
    return cls


def run_optimizer(symbol: str, optimizer_name: str):
    cls = get_optimizer_by_name(optimizer_name)
    if cls is None:
        raise ValueError(f"Optimizer '{optimizer_name}' not found")
    return cls.run(symbol)