"""
Day 4：规模因子单因子选股策略
100天经典策略学习计划

测试因子：总市值、流通市值、小盘效应(ln市值)
方法：选市值最小的N只股票，等权持有
股票池：沪深300
调仓频率：每月

使用方法：修改 FACTOR_NAME 切换测试不同因子
回测建议：2020-01-01 至 2026-02-01，初始资金100万
"""


# ========== 切换因子 ==========
# 可选: 'market_cap', 'circulating_cap', 'ln_market_cap'
FACTOR_NAME = 'market_cap'
# ==============================


def initialize(context):
    set_params()
    set_backtest()
    run_monthly(rebalance, monthday=1, time='09:31')


def set_params():
    g.stock_pool = '000300.XSHG'  # 沪深300
    g.stock_num = 20              # 持仓数量
    g.factor = FACTOR_NAME


def set_backtest():
    set_benchmark('000300.XSHG')
    set_option('use_real_price', True)
    set_slippage(FixedSlippage(0.02))
    set_commission(PerTrade(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
    log.set_level('order', 'error')


def rebalance(context):
    stocks = get_index_stocks(g.stock_pool)
    stocks = filter_stocks(stocks)

    df = get_factor_data(stocks, context)

    if df is None or df.empty:
        return

    # 规模因子：越小越好（ascending=True）
    df = df.sort_values(g.factor, ascending=True)
    target = df['code'].head(g.stock_num).tolist()

    log.info(f'[{g.factor}] 选出{len(target)}只，前3: {target[:3]}')

    adjust_portfolio(context, target)


def get_factor_data(stocks, context):
    """获取因子数据"""
    import numpy as np

    if g.factor == 'market_cap':
        # 总市值（聚宽单位：亿元）
        q = query(
            valuation.code,
            valuation.market_cap
        ).filter(
            valuation.code.in_(stocks),
            valuation.market_cap > 0
        )
        df = get_fundamentals(q, date=context.current_dt)
        return df

    elif g.factor == 'circulating_cap':
        # 流通市值
        q = query(
            valuation.code,
            valuation.circulating_market_cap
        ).filter(
            valuation.code.in_(stocks),
            valuation.circulating_market_cap > 0
        )
        df = get_fundamentals(q, date=context.current_dt)
        if not df.empty:
            df = df.rename(columns={'circulating_market_cap': 'circulating_cap'})
        return df

    elif g.factor == 'ln_market_cap':
        # 对数市值（消除量纲差异）
        q = query(
            valuation.code,
            valuation.market_cap
        ).filter(
            valuation.code.in_(stocks),
            valuation.market_cap > 0
        )
        df = get_fundamentals(q, date=context.current_dt)
        if not df.empty:
            df['ln_market_cap'] = np.log(df['market_cap'])
        return df


def filter_stocks(stocks):
    """过滤ST和停牌"""
    current_data = get_current_data()
    return [s for s in stocks
            if not current_data[s].is_st
            and not current_data[s].paused]


def adjust_portfolio(context, target):
    """调仓：先卖后买"""
    for stock in list(context.portfolio.positions):
        if stock not in target:
            order_target(stock, 0)

    if target:
        per_value = context.portfolio.total_value * 0.95 / len(target)
        for stock in target:
            order_target_value(stock, per_value)