"""Example 14: Bollinger Band Mean Reversion Strategy.

Demonstrates a classic mean reversion strategy using Bollinger Bands:
- Buy when price touches the lower band (oversold)
- Sell when price touches the upper band (overbought)
- Include stop-loss for risk management

Usage:
    python examples/14_bollinger_strategy.py

⚠️  RISK DISCLOSURE — TEACHING EXAMPLE ONLY
    This script is provided for educational purposes to demonstrate the
    EasyQuant backtesting framework.  Do NOT deploy this strategy to live
    trading without thorough out-of-sample validation.  Past backtest
    performance does NOT guarantee future results.  All strategies should
    be tested across multiple time periods and market regimes before any
    real capital is committed.
"""

import os
from eqlib import *
from eqlib import utils


# ============================================================
# Strategy parameters
# ============================================================

SECURITY = "601088"       # China Shenhua 中国神华
PERIOD = 20               # Bollinger Band period
STD_DEV = 2.0             # Standard deviation multiplier
STOP_LOSS_PCT = 0.08      # Stop loss at 8% below entry


# ============================================================
# Strategy code
# ============================================================

def initialize(context):
    """Strategy initialization."""
    set_benchmark("000300.XSHG")
    set_order_cost(OrderCost(
        open_tax=0,
        close_tax=0.001,
        open_commission=0.0003,
        close_commission=0.0003,
        close_today_commission=0,
        min_commission=5,
    ))

    context.universe = [SECURITY]
    run_daily(market_open, time="every_bar")

    log.info("Bollinger Band init: %s, period=%d, std=%.1f" % (
        SECURITY, PERIOD, STD_DEV))


def market_open(context):
    """Daily trading logic: mean reversion on Bollinger Bands."""
    security = SECURITY

    # Fetch price history (need enough bars for Bollinger calculation)
    bars_needed = PERIOD + 10
    close_data = attribute_history(security, bars_needed, "1d", ["close"])
    if close_data.empty or len(close_data) < PERIOD:
        return

    close_prices = close_data["close"]
    current_price = close_prices.iloc[-1]

    # Calculate Bollinger Bands
    upper, mid, lower = utils.boll(close_prices, period=PERIOD, num_std=STD_DEV)

    # === Stop-loss check ===
    if security in context.portfolio.positions:
        position = context.portfolio.positions[security]
        loss_pct = (current_price - position.avg_cost) / position.avg_cost
        if loss_pct < -STOP_LOSS_PCT:
            order_target(security, 0)
            log.info("Stop-loss SELL: %s @ %.3f, loss=%.1f%%" % (
                security, current_price, loss_pct * 100))
            return

        # === Take-profit: price reaches upper band ===
        if current_price > upper.iloc[-1] and position.amount > 0:
            order_target(security, 0)
            log.info("Upper band SELL: %s @ %.3f, upper=%.3f" % (
                security, current_price, upper.iloc[-1]))
            return

    # === Buy signal: price drops below lower band (oversold) ===
    if current_price < lower.iloc[-1]:
        if security not in context.portfolio.positions or \
           context.portfolio.positions[security].amount == 0:
            order_value(security, context.portfolio.available_cash)
            log.info("Lower band BUY: %s @ %.3f, lower=%.3f" % (
                security, current_price, lower.iloc[-1]))

    # Record for analysis
    record(
        price=current_price,
        upper=upper.iloc[-1],
        mid=mid.iloc[-1],
        lower=lower.iloc[-1],
    )


# ============================================================
# Run backtest
# ============================================================

if __name__ == "__main__":
    print("=" * 60)
    print("Bollinger Band Mean Reversion Strategy")
    print("=" * 60)

    os.makedirs("reports", exist_ok=True)

    result = run_strategy(
        initialize_func=initialize,
        start_date="2024-01-01",
        end_date="2025-01-01",
        starting_cash=100000,
        benchmark="000300.XSHG",
        securities=["601088"],
        report_dir="reports",
        use_local=True,
    )