# 克隆自聚宽文章：https://www.joinquant.com/post/52938
# 标题：QDII ETF/LOF 折溢价策略
# 作者：Gyro^.^
# 实际地址：https://www.joinquant.com/algorithm/index/edit?algorithmId=9608e6e01e60c8b9c1e0d8521b10882d

import pandas as pd
import datetime as dt
from jqdata import *

def initialize(context):
    # setting system
    log.set_level('order', 'error')
    set_option('use_real_price', True)
    set_option('avoid_future_data', True)
    # setting strategy
    run_daily(iUpdate, 'before_open')
    run_daily(iTrader, 'every_bar')
    run_daily(iReport, 'after_close')

def iUpdate(context):
    # parameters
    keys = ['标普', '美国', '纳指', '纳斯达克', '法国', '德国', \
            '日经', '亚太', '东南亚', '印度', '沙特',]
    black_list = ['562060.XSHG']
    # all funds
    dt_last = context.previous_date
    all_fund = get_all_securities('fund', dt_last)
    # onlist 1-month
    dt_1m = dt_last - dt.timedelta(days=30)
    funds = all_fund[all_fund.start_date < dt_1m].index.tolist()
    # filter, liquity
    hm = history(20, '1d', 'money', funds).mean()
    funds = hm[hm > 1e6].index.tolist()
    # key funds
    funds = [s for k in keys for s in funds if k in all_fund.display_name[s]]
    # black_list
    funds = [s for s in funds if s not in black_list]
    # net value
    value = get_extras('unit_net_value', funds, end_date=dt_last, count=2).iloc[0]
    # price
    price = history(1, '1d', 'close', funds).iloc[0]
    # low price
    r = price/value - 1.0
    r = r[r < 0.15].sort_values()
    # choice funds
    g.ratio = r
    log.info('funds', len(g.ratio))

def iTrader(context):
    # load data
    r = g.ratio
    position_size = 1.0/max(3, len(g.ratio)) * context.portfolio.total_value
    lm_value = 0.8*position_size
    hm_value = 1.3*position_size
    cash_size = 0.1 * context.portfolio.total_value
    cdata = get_current_data()
    # sell
    for s in context.portfolio.positions:
        if cdata[s].paused:
            continue
        if s not in r.index:
            log.info('sell', s, cdata[s].name)
            order_target(s, 0, MarketOrderStyle(0.99*cdata[s].last_price))
    # buy stocks
    for s in r.index:
        if context.portfolio.available_cash < cash_size:
            break
        if cdata[s].paused:
            continue
        if s not in context.portfolio.positions:
            if r[s] < 0:
                log.info('buy', s, cdata[s].name)
                order_target_value(s, position_size, MarketOrderStyle(1.01*cdata[s].last_price))
        elif context.portfolio.positions[s].value < lm_value:
            if r[s] < 0:
                log.info('balance+', s, cdata[s].name)
                order_target_value(s, position_size, MarketOrderStyle(1.01*cdata[s].last_price))
        elif context.portfolio.positions[s].value > hm_value:
            if r[s] > 0:
                log.info('balance-', s, cdata[s].name)
                order_target_value(s, position_size, MarketOrderStyle(0.99*cdata[s].last_price))

def iReport(context):
    # load data
    cdata = get_current_data()
    tvalue = context.portfolio.total_value
    # table of positions
    ptable = pd.DataFrame(columns=['amount', 'value', 'weight', 'name'])
    for s in context.portfolio.positions:
        ps = context.portfolio.positions[s]
        ptable.loc[s] = [ps.total_amount, int(ps.value), 100*ps.value/tvalue, cdata[s].name]
    ptable = ptable.sort_values(by='weight', ascending=False)
    # daily report
    pd.set_option('display.max_rows', None)
    log.info('  positions', len(ptable), '\n', ptable.head(10))
    log.info('  total value %.2f, cash %.2f', \
            context.portfolio.total_value/10000, context.portfolio.available_cash/10000)
# end