每日调仓

并且融合以下三个策略的优点：

1，将基准回报率碾压成直线的小市值股票策略

小市值公司股票带来超高收益，但它们往往涨幅大跌幅也大，所以在此基础进行优化。

2，将动量带入价值投资的考虑动量的价值投资策略

将该策略中的动量因素加入本策略，经过对比回测显示，该思路进一步强化了小市值股票的收益率。

3，经典的Dual Thrust策略

考虑大盘走势选择性空仓是这个策略的要点之一，把大盘的空仓判定加入到小盘股策略中来，即前一日市场跌幅若大于3%，第二日空仓。

欢迎各种调整！应该还有不少提升空间。

源代码

import pandas as pd
import numpy as np

def init(context):
    context.s1 = '000001.XSHG'
    context.max_num_stocks = 40
    context.days = 0
    context.period_days = 1
    context.relative_strength_6m = {}

def period_passed(context): 
    return context.days % context.period_days == 0

def before_trading(context): 
    context.days += 1
    if not period_passed(context):
        return

    dofilter(context)
    update_universe(context.fundamental_df.columns.values)

def dofilter(context):

    fundamental_df = get_fundamentals(
        query(fundamentals.eod_derivative_indicator.market_cap)
        .order_by(fundamentals.eod_derivative_indicator.market_cap.asc())
        .limit(context.max_num_stocks)
    )

    #Update context
    context.stocks = [stock for stock in fundamental_df]
    context.fundamental_df = fundamental_df

def rebalance(context, bar_dict):

    for stock in context.portfolio.positions:
        if stock not in context.fundamental_df:
            order_target_percent(stock, 0)

    context.stocks = [stock for stock in context.stocks
                      if stock in bar_dict and bar_dict[stock].is_trading and context.relative_strength_6m[stock] <-0.5]

    if len(context.stocks) == 0:
        return

    weight = 1.0/len(context.stocks)

    for stock in context.stocks:
        order_target_percent(stock, weight)

def handle_bar(context, bar_dict):

    his = history(10, '1d', 'close')['000001.XSHG']

    if period_passed(context):
        if his[9]/his[8]< 0.97:
            if len(context.portfolio.positions)>0:
                for stock in context.portfolio.positions.keys():
                    order_target_percent(stock, 0)
            return

    if not period_passed(context):
        return

    compute_relative_strength(context,bar_dict)
    rebalance(context, bar_dict)

def compute_relative_strength(context,bar_dict):

    prices = history (150, '1d', 'close')

    #过去六个月的价格变化率
    pct_change = (prices.ix[149] - prices.ix[19]) / prices.ix[19]
    #print(prices.ix[19])
    #print(pct_change)
    priceofbase = history (150, '1d', 'close')[context.s1]
    pct_changeforbase = (priceofbase.ix[149] - priceofbase.ix[19]) / priceofbase.ix[19]
    pct_change = pct_change - pct_changeforbase
    print(pct_change.index)
    print(bar_dict)
    if pct_changeforbase != 0:
        pct_change = pct_change / abs(pct_changeforbase)
    context.relative_strength_6m = pct_change