仓位控制

等仓三次进场

1/3,1/3,1/3

import rqalpha
from rqalpha.api import *
import talib
def init(context):
    context.s1 = "000001.XSHE"
    context.SHORTPERIOD = 10
    context.LONGPERIOD = 30
    context.time = 0
def handle_bar(context, bar_dict):
    prices = history_bars(context.s1, context.LONGPERIOD+1, '1d', 'close')
    short_avg = talib.SMA(prices, context.SHORTPERIOD)
    long_avg = talib.SMA(prices, context.LONGPERIOD)
    cur_position = context.portfolio.positions[context.s1].quantity
    shares = context.portfolio.cash/bar_dict[context.s1].close
    if short_avg[-1] - long_avg[-1] < 0 and short_avg[-2] - long_avg[-2] > 0 and cur_position > 0:
        order_target_value(context.s1, 0)
        context.time=0
    if short_avg[-1] - long_avg[-1] > 0 and short_avg[-2] - long_avg[-2] < 0 and context.time==0:
        order_shares(context.s1, shares/3)
        context.time=1
    elif context.time == 1:
        order_shares(context.s1, shares/3)
        context.time=2
    elif context.time == 2:
        order_shares(context.s1, shares/3)
        context.time=3
config = {
  "base": {
    "start_date": "2000-06-01",
    "end_date": "2016-12-01",
    "accounts": {'stock': 1000000},
    "benchmark": "000300.XSHG"
#     "strategy_file_path": os.path.abspath(__file__)
  },
  "extra": {
    "log_level": "verbose",
  },
  "mod": {
    "sys_analyser": {
     "report_save_path": '.',
      "enabled": True,
      "plot": True
    }
  }
}
rqalpha.run_func(init=init, handle_bar=handle_bar, config=config)

5/3/2三次进场

import rqalpha
from rqalpha.api import *
import talib
def init(context):
    context.s1 = "000001.XSHE"
    context.SHORTPERIOD = 10
    context.LONGPERIOD = 30
    context.time = 0
def handle_bar(context, bar_dict):
    prices = history_bars(context.s1, context.LONGPERIOD+1, '1d', 'close')
    short_avg = talib.SMA(prices, context.SHORTPERIOD)
    long_avg = talib.SMA(prices, context.LONGPERIOD)
    cur_position = context.portfolio.positions[context.s1].quantity
    shares = context.portfolio.cash/bar_dict[context.s1].close
    if short_avg[-1] - long_avg[-1] < 0 and short_avg[-2] - long_avg[-2] > 0 and cur_position > 0:
        order_target_value(context.s1, 0)
        context.time=0
    if short_avg[-1] - long_avg[-1] > 0 and short_avg[-2] - long_avg[-2] < 0 and context.time==0:
        order_shares(context.s1, shares*0.5)
        context.time=1
    elif context.time == 1:
        order_shares(context.s1, shares*0.3)
        context.time=2
    elif context.time == 2:
        order_shares(context.s1, shares*0.2)
        context.time=3
config = {
  "base": {
    "start_date": "2000-06-01",
    "end_date": "2016-12-01",
    "accounts": {'stock': 1000000},
    "benchmark": "000300.XSHG"
#     "strategy_file_path": os.path.abspath(__file__)
  },
  "extra": {
    "log_level": "verbose",
  },
  "mod": {
    "sys_analyser": {
     "report_save_path": '.',
      "enabled": True,
      "plot": True
    }
  }
}
rqalpha.run_func(init=init, handle_bar=handle_bar, config=config)

三进三出

50/30/20/-50/-30/-20

import rqalpha
from rqalpha.api import *
from queue import Queue
import talib
def init(context):
    context.s1 = "000001.XSHE"
    context.SHORTPERIOD = 10
    context.LONGPERIOD = 30
    context.pos = [5000, 3000, 2000]
    context.queue = Queue()
    context.buy=0
    context.sell=0
def buy(context):
    prices = history_bars(context.s1, context.LONGPERIOD+1, '1d', 'close')
    short_avg = talib.SMA(prices, context.SHORTPERIOD)
    long_avg = talib.SMA(prices, context.LONGPERIOD)
    if short_avg[-1] - long_avg[-1] > 0 and short_avg[-2] - long_avg[-2] < 0:
        return True
def sell(context):
    prices = history_bars(context.s1, context.LONGPERIOD+1, '1d', 'close')
    short_avg = talib.SMA(prices, context.SHORTPERIOD)
    long_avg = talib.SMA(prices, context.LONGPERIOD)
    cur_position = context.portfolio.positions[context.s1].quantity
    if short_avg[-1] - long_avg[-1] < 0 and short_avg[-2] - long_avg[-2] > 0 and cur_position > 0:
        return True
def handle_bar(context, bar_dict):
    size = context.queue.qsize()
    if size == 0:
        if buy(context):
            context.buy=1
            volume = context.pos[size]
            ## order volume
            order_shares(context.s1, volume)
            context.queue.put(volume)
            return
    if context.buy:
        if sell(context):
            context.sell=1
            context.buy=0
        elif size < 3:
            volume = context.pos[size]
            ## order volume
            order_shares(context.s1, volume)
            context.queue.put(volume)
    if context.sell:
        if size > 0:
            volume = context.queue.get()
            ## sell volume
            order_shares(context.s1, -volume)
        else:
            context.sell=0
config = {
  "base": {
    "start_date": "2000-06-01",
    "end_date": "2016-12-01",
    "accounts": {'stock': 1000000},
    "benchmark": "000300.XSHG"
#     "strategy_file_path": os.path.abspath(__file__)
  },
  "extra": {
    "log_level": "verbose",
  },
  "mod": {
    "sys_analyser": {
     "report_save_path": '.',
      "enabled": True,
      "plot": True
    }
  }
}
rqalpha.run_func(init=init, handle_bar=handle_bar, config=config)

获利加仓

1. 在新的趋势信号中输入1个仓位。
2. 在n天之后只有在新高利润的情况下，才能进入另一个仓位。
3. 在添加了5次加仓之后，停止加仓。

平均成本 Averaging Down

亏损加仓来平均持有成本，这种策略胜率需要很高。

最优f: Investing and Reinvesting: Optimal f

$f=(p*r_w-q*r_l)/r_w$
$f=p-(1-p)/（r_w/r_l）$

r_w：赢赚比例
r_l： 输赔比例
p： 赢概率p
f： 投注比例