import copy
import sys
import os

# Bulletproof pathing: Force Python to look in both the current folder AND the parent folder
_this_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, _this_dir)
sys.path.insert(0, os.path.dirname(_this_dir))

import pandas as pd
import numpy as np

from analytics import backtest, build_macro
from backtest import monte_carlo
from config import DEFAULT_CONFIG
from core_types import PortfolioState

# ─────────────────────────────────────────────
# 1. TIME-AGNOSTIC SCALING TESTS
# ─────────────────────────────────────────────
def test_trading_days_annualization_scaling():
    """
    Verifies that changing 'trading_days_per_year' correctly alters the 
    mathematical annualization of returns and volatility in the backtester.
    """
    # 100 days of synthetic returns at exactly 10 bps per day
    dates = pd.date_range("2023-01-01", periods=100, freq="D")
    returns_df = pd.DataFrame({'ASSET_A': np.repeat(0.001, 100)}, index=dates)
    spy_rets = pd.Series(np.repeat(0.001, 100), index=dates)
    
    weights = pd.Series({'ASSET_A': 1.0})
    capital = 10000.0
    rfr = 0.0
    spread_map = {'ASSET_A': 0.0}
    state = PortfolioState.empty(['ASSET_A'])
    betas = pd.Series({'ASSET_A': 1.0})
    
    # Run 1: US Equities (252 days)
    cfg_us = copy.deepcopy(DEFAULT_CONFIG)
    cfg_us['trading_days_per_year'] = 252
    cfg_us['transaction_cost'] = 0.0
    
    _, _, _, stats_us = backtest(returns_df, weights, capital, rfr, spy_rets, spread_map, cfg_us, state)
    
    # Run 2: Crypto (365 days)
    cfg_crypto = copy.deepcopy(DEFAULT_CONFIG)
    cfg_crypto['trading_days_per_year'] = 365
    cfg_crypto['transaction_cost'] = 0.0
    
    _, _, _, stats_crypto = backtest(returns_df, weights, capital, rfr, spy_rets, spread_map, cfg_crypto, state)
    
    # Assert: Because Crypto assumes a longer compounding year, its annualized return
    # extrapolated from the same 100-day sample MUST be mathematically higher.
    assert stats_crypto['ann_ret'] > stats_us['ann_ret']


def test_monte_carlo_path_horizon():
    """
    Verifies that the Monte Carlo engine dynamically extends its simulation 
    paths based on the global trading days configuration.
    """
    tickers = ["ASSET_A"]
    weights = pd.Series({'ASSET_A': 1.0})
    exp_rets = pd.Series({'ASSET_A': 0.10})
    cov_mat = pd.DataFrame([[0.04]], index=tickers, columns=tickers)
    
    # Run with Frankfurt calendar (256 days) for 2 years
    cfg_eu = copy.deepcopy(DEFAULT_CONFIG)
    cfg_eu['trading_days_per_year'] = 256
    cfg_eu['monte_carlo_years'] = 2.0
    
    _, stats = monte_carlo(weights, exp_rets, cov_mat, capital=10000.0, cfg=cfg_eu, seed=42)
    
    # Assert: 2 years * 256 days = 512 path points
    assert len(stats['dates']) == 512
    # Verify the paths array matches the length
    assert len(stats[50]) == 512


# ─────────────────────────────────────────────
# 2. DYNAMIC BENCHMARK TESTS
# ─────────────────────────────────────────────
def test_build_macro_dynamic_benchmarks():
    """
    Verifies that the macro-economic context builder correctly reads 
    dynamic benchmarks (like European VIX variants) instead of hardcoding US indexes.
    """
    cfg = copy.deepcopy(DEFAULT_CONFIG)
    # Switch to European benchmarks
    cfg['benchmarks'] = {
        "equity": "DAX",
        "volatility": "^V2TX", # Euro Stoxx 50 Volatility
        "risk_free": "^DE10Y"
    }
    
    # Mock data dictionaries matching the European tickers
    prices = {"^DE10Y": 2.50} 
    raw = {
        "DAX": pd.Series(np.linspace(10000, 15000, 250)), # Trending up
        "^V2TX": pd.Series([25.0, 30.0])
    }
    vix_raw = pd.Series([25.0, 35.0]) # Emulate a high volatility regime
    
    macro = build_macro(
        prices=prices, 
        raw=raw, 
        rfr=0.025, 
        display_df=pd.DataFrame(), 
        w_arr=np.array([]), 
        vix_raw=vix_raw, 
        cfg=cfg
    )
    
    # Assert: It should successfully read the ^V2TX proxy and flag it as high volatility (> 20)
    assert macro['vix_val'] == 35.0
    assert macro['vix_high'] is True
    
    # Assert: It should successfully read the DAX trend (which we mocked as a straight line up)
    assert macro['spy_trend'] == "BULL"