from datetime import datetime, timedelta

import numpy as np
import pandas as pd
# imports
import pytest
from gs_quant.timeseries.backtesting import basket_series

# function to test
# (Paste the basket_series and dependencies here, but for brevity, assume it's already imported as per your instructions.)

# Helper: Generate a simple price series for testing
def generate_series(start, n, step=1.0, name=None, missing_idx=None):
    """Generate a pd.Series of length n, starting at `start`, with increments of `step`."""
    idx = pd.date_range('2020-01-01', periods=n, freq='D')
    vals = np.array([start + i * step for i in range(n)], dtype=float)
    s = pd.Series(vals, index=idx, name=name)
    if missing_idx is not None:
        for i in missing_idx:
            s.iloc[i] = np.nan
    return s

# ========== BASIC TEST CASES ==========






def test_empty_series_list_raises():
    # Should raise error if series list is empty
    with pytest.raises(Exception):
        basket_series([]) # 2.38μs -> 2.32μs (2.98% faster)

def test_inconsistent_lengths_raises():
    # Series of different lengths should raise error
    s1 = generate_series(100, 5)
    s2 = generate_series(200, 4)
    with pytest.raises(Exception):
        basket_series([s1, s2])

def test_weights_length_mismatch_raises():
    # Weights list of wrong length should raise error
    s1 = generate_series(100, 5)
    s2 = generate_series(200, 5)
    with pytest.raises(Exception):
        basket_series([s1, s2], weights=[1]) # 3.89μs -> 3.91μs (0.537% slower)

def test_costs_length_mismatch_raises():
    # Costs list of wrong length should raise error
    s1 = generate_series(100, 5)
    s2 = generate_series(200, 5)
    with pytest.raises(Exception):
        basket_series([s1, s2], costs=[0.01]) # 4.16μs -> 4.09μs (1.54% faster)

def test_non_series_input_raises():
    # Non-pandas Series in input should raise error
    with pytest.raises(Exception):
        basket_series([1, 2, 3]) # 3.85μs -> 3.80μs (1.32% faster)


def test_all_nan_series_raises():
    # All-NaN series should raise or return all NaN
    s1 = pd.Series([np.nan]*5, index=pd.date_range('2020-01-01', periods=5))
    s2 = generate_series(200, 5)
    with pytest.raises(Exception):
        basket_series([s1, s2])










#------------------------------------------------
from datetime import datetime, timedelta

import numpy as np
import pandas as pd
# imports
import pytest
from gs_quant.timeseries.backtesting import basket_series

# function to test
# (copied from the prompt, with all dependencies satisfied for testing)

# --- Minimal stubs for dependencies ---
class MqTypeError(TypeError): pass
class MqValueError(ValueError): pass

def _create_enum(name, items):
    # Simple enum stub
    from enum import Enum
    return Enum(name, items)

def plot_function(fn):
    # No-op decorator for testing
    return fn

RebalFreq = _create_enum('RebalFreq', ['DAILY', 'WEEKLY', 'MONTHLY'])
ReturnType = _create_enum('ReturnType', ['EXCESS_RETURN'])
from gs_quant.timeseries.backtesting import basket_series

# --- Unit Tests ---

# Helper to create simple price series
def make_series(start, steps, step=1.0, freq='D', name=None):
    idx = pd.date_range('2020-01-01', periods=steps, freq=freq)
    data = np.array([start + i * step for i in range(steps)])
    return pd.Series(data, index=idx, name=name)

# 1. Basic Test Cases