from typing import Callable
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
from quantstats.stats import skew, sortino
from utils.metrics import probabilistic_sharpe_ratio, sharpe_ratio
from utils.helpers import get_first_valid_return_index
import pandas as pd
from data_loader.types import ForwardReturnSeries, ySeries
from training.types import Stats, WeightsSeries


def backtest(
    returns: pd.Series, signal: pd.Series, transaction_cost: float
) -> pd.Series:
    delta_pos = signal.diff(1).abs().fillna(0.0)
    costs = transaction_cost * delta_pos
    return (signal * returns) - costs


def __preprocess(
    forward_returns: ForwardReturnSeries,
    y_pred: pd.Series,
    y_true: pd.Series,
    discretize_func: Callable,
    transaction_costs: float,
) -> pd.DataFrame:
    y_pred.name = "y_pred"
    forward_returns.name = "forward_returns"
    df = pd.concat([y_pred, forward_returns], axis=1).dropna()

    # make sure that we evaluate binary/three-way predictions even if the model is a regression
    df["sign_pred"] = df.y_pred.apply(discretize_func)
    df["sign_true"] = y_true

    df["result"] = backtest(df.forward_returns, df.y_pred, transaction_costs)

    return df


def evaluate_predictions(
    forward_returns: ForwardReturnSeries,
    y_pred: WeightsSeries,
    y_true: ySeries,
    discretize_func: Callable,
    labels: list[int],
    transaction_costs: float,
) -> Stats:
    # ignore the predictions until we see a non-zero returns (and definitely skip the first initial_window_size)
    evaluate_from = max(
        get_first_valid_return_index(forward_returns),
        get_first_valid_return_index(y_pred),
    )

    forward_returns = pd.Series(forward_returns[evaluate_from:])
    y_pred = pd.Series(y_pred[evaluate_from:])

    df = __preprocess(
        forward_returns, y_pred, y_true, discretize_func, transaction_costs
    )

    scorecard = dict()

    def count_non_zero(series: pd.Series) -> int:
        return len(series[series != 0])

    no_of_samples = count_non_zero(df.y_pred)
    scorecard["no_of_samples"] = no_of_samples
    sharpe = sharpe_ratio(df.result + 1e-20)
    scorecard["sharpe"] = sharpe
    benchmark_sharpe = sharpe_ratio(df.forward_returns)
    scorecard["benchmark_sharpe"] = benchmark_sharpe
    scorecard["prob_sharpe"] = probabilistic_sharpe_ratio(
        sharpe, benchmark_sharpe, no_of_samples
    )
    scorecard["sortino"] = sortino(df.result)
    scorecard["skew"] = skew(df.result)

    avg_type = "weighted" if len(labels) == 2 else "macro"

    scorecard["accuracy"] = accuracy_score(df.sign_true, df.sign_pred) * 100
    scorecard["recall"] = recall_score(
        df.sign_true, df.sign_pred, labels=labels, average=avg_type
    )
    scorecard["precision"] = precision_score(
        df.sign_true, df.sign_pred, labels=labels, average=avg_type
    )
    scorecard["f1_score"] = f1_score(
        df.sign_true, df.sign_pred, labels=labels, average=avg_type
    )
    scorecard["edge"] = df.result.mean()
    scorecard["noise"] = df.y_pred.diff().abs().mean()
    scorecard["edge_to_noise"] = scorecard["edge"] / (scorecard["noise"] + 0.00001)

    for index, row in df.sign_true.value_counts().iteritems():
        scorecard["sign_true_ratio_" + str(index)] = row / len(df.sign_true)

    for index, row in df.sign_pred.value_counts().iteritems():
        scorecard["sign_pred_ratio_" + str(index)] = row / len(df.sign_pred)

    scorecard = {k: round(float(v), 3) for k, v in scorecard.items()}
    return scorecard