金融量化分析策略展示

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我是靠谱客的博主欢喜蜗牛，这篇文章主要介绍金融量化分析策略展示，现在分享给大家，希望可以做个参考。

在聚宽网实现回测：https://www.joinquant.com/algorithm/index/edit?algorithmId=a5d6decffd9805f8f7896e431ca619c4

还有米筐、优矿来进行回测。

一、双均线策略

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import jqdata

p1 = 5
p2 = 60


def initialize(context):
    set_benchmark('000300.XSHG')
    set_option('use_real_price', True)
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
    g.security = ['601318.XSHG']
    
def handle_data(context, data):
    for stock in g.security:
        hist = attribute_history(stock, count=p2)
        ma5 = hist['close'][-5:].mean()
        ma60 = hist['close'].mean()
        
        if ma5 > ma60 and stock not in context.portfolio.positions:
            order = order_value(stock, context.portfolio.available_cash)
            if order:
                print('Buying: %dat Price: %.2f' % (order.amount, order.price))
        elif ma5 < ma60 and stock in context.portfolio.positions:
            order = order_target_value(stock, 0)
            if order:
                print('Selling: %d at Price: %.2f' % (order.amount, order.price))
    record(ma5=ma5, ma60=ma60)

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二、因子选股

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# 导入函数库
import jqdata

# 初始化函数，设定基准等等
def initialize(context):
    set_benchmark('000300.XSHG')
    set_option('use_real_price', True)
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
    g.security = get_index_stocks('000300.XSHG')
    g.q = query(valuation).filter(valuation.code.in_(g.security))
    g.N = 10
    run_monthly(handle, 1)

def handle(context):
    df = get_fundamentals(g.q)
    df = df.sort(columns='market_cap')
    df = df[:g.N]
    tohold = df['code'].values
    
    for stock in context.portfolio.positions:
        if stock not in tohold:
            #卖出
            order_target(stock, 0)
            
    tobuy = [stock for stock in tohold if stock not in context.portfolio.positions]
    
    cash = context.portfolio.available_cash
    n = len(tobuy)
    #买入
    for stock in tobuy:
        order_value(stock, int(cash/n))

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三、均值回归

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import jqdata
import math
import numpy as np
import pandas as pd

def initialize(context):
    set_option('use_real_price', True)
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
    
    g.benchmark = '000300.XSHG'
    set_benchmark(g.benchmark)
    
    g.ma_days = 30
    g.stock_num = 10
    
    run_monthly(handle, 1)
    #run_monthly(handle, 11)
    
def handle(context):
    tohold = get_hold_list(context)
    for stock in context.portfolio.positions:
        if stock not in tohold:
            order_target_value(stock, 0)
    
    tobuy = [stock for stock in tohold if stock not in context.portfolio.positions]
    
    if len(tobuy)>0:
        cash = context.portfolio.available_cash
        cash_every_stock = cash / len(tobuy)
        
        for stock in tobuy:
            order_value(stock, cash_every_stock)

def get_hold_list(context):
    stock_pool = get_index_stocks(g.benchmark)
    stock_score = pd.Series(index=stock_pool)
    for stock in stock_pool:
        df = attribute_history(stock, g.ma_days, '1d', ['close'])
        ma = df.mean()[0]
        current_price = get_current_data()[stock].day_open
        ratio = (ma - current_price) / ma
        stock_score[stock] = ratio
    return stock_score.nlargest(g.stock_num).index.values

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import jqdata
import math
import numpy as np
import pandas as pd

def initialize(context):
    set_option('use_real_price', True)
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
    
    g.benchmark = '000300.XSHG'
    set_benchmark(g.benchmark)
    
    g.ma_days = 30
    g.stock_num = 10
    
    run_monthly(handle, 1)
    #run_monthly(handle, 11)
    
def handle(context):
    tohold = get_hold_list(context)
    for stock in context.portfolio.positions:
        if stock not in tohold:
            order_target_value(stock, 0)
    
    tobuy = [stock for stock in tohold if stock not in context.portfolio.positions]
    
    if len(tobuy)>0:
        cash = context.portfolio.available_cash
        cash_every_stock = cash / len(tobuy)
        
        for stock in tobuy:
            order_value(stock, cash_every_stock)

def get_hold_list(context):
    stock_pool = get_index_stocks(g.benchmark)
    stock_score = pd.Series(index=stock_pool)
    for stock in stock_pool:
        df = attribute_history(stock, g.ma_days, '1d', ['close'])
        ma = df.mean()[0]
        current_price = get_current_data()[stock].day_open
        ratio = (ma - current_price) / ma
        stock_score[stock] = ratio
    return stock_score.nlargest(g.stock_num).index.values

View Code

四、布林带策略

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# 导入函数库
import jqdata

def initialize(context):
    set_option('use_real_price', True)
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
    set_benchmark('000300.XSHG')
    
    g.security = ['600036.XSHG']
    
    g.N = 2
    g.ma_days = 20
    
def handle_data(context, data):
    for stock in g.security:
        df = attribute_history(stock, g.ma_days)
        middle = df['close'].mean()
        upper = middle + g.N * df['close'].std()
        lower = middle - g.N * df['close'].std()
        
        p = get_current_data()[stock].day_open
        # 如果价格突破阻力线
        if p >= upper and stock in context.portfolio.positions:
            order_target(stock, 0)
    
    cash = context.portfolio.available_cash / len(g.security)
    
    for stock in g.security:
        df = attribute_history(stock, g.ma_days)
        middle = df['close'].mean()
        upper = middle + g.N * df['close'].std()
        lower = middle - g.N * df['close'].std()
        
        p = get_current_data()[stock].day_open
        # 如果价格跌破支撑线
        
        if p <= lower and stock not in context.portfolio.positions:
            order_target(stock, cash)

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# 导入函数库
import jqdata

def initialize(context):
    set_option('use_real_price', True)
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
    set_benchmark('000300.XSHG')
    
    g.security = ['600036.XSHG']
    
    g.N = 2
    g.ma_days = 20
    
def handle_data(context, data):
    for stock in g.security:
        df = attribute_history(stock, g.ma_days)
        middle = df['close'].mean()
        upper = middle + g.N * df['close'].std()
        lower = middle - g.N * df['close'].std()
        
        p = get_current_data()[stock].day_open
        # 如果价格突破阻力线
        if p >= upper and stock in context.portfolio.positions:
            order_target(stock, 0)
    
    cash = context.portfolio.available_cash / len(g.security)
    
    for stock in g.security:
        df = attribute_history(stock, g.ma_days)
        middle = df['close'].mean()
        upper = middle + g.N * df['close'].std()
        lower = middle - g.N * df['close'].std()
        
        p = get_current_data()[stock].day_open
        # 如果价格跌破支撑线
        
        if p <= lower and stock not in context.portfolio.positions:
            order_target(stock, cash)

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五、PEG策略

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def initialize(context):
    set_option('use_real_price', True)
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
    set_benchmark('000300.XSHG')
    
    g.security = get_index_stocks('000300.XSHG')
    g.N = 20
    run_monthly(handle, 1)
    
def handle(context):
    df = get_fundamentals(query(valuation.code, valuation.pe_ratio, indicator.inc_net_profit_year_on_year).filter(valuation.code.in_(g.security)))
    df = df[(df['pe_ratio']>0) & (df['inc_net_profit_year_on_year']>0)]
    df['PEG'] = df['pe_ratio'] / df['inc_net_profit_year_on_year'] / 100
    df = df.sort(columns='PEG')[:g.N]
    tohold = df['code'].values
    
    for stock in context.portfolio.positions:
        if stock not in tohold:
            order_target_value(stock, 0)
    
    tobuy = [stock for stock in tohold if stock not in context.portfolio.positions]
    
    if len(tobuy)>0:
        print('Buying')
        cash = context.portfolio.available_cash
        cash_every_stock = cash / len(tobuy)
        
        for stock in tobuy:
            order_value(stock, cash_every_stock)

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def initialize(context):
    set_option('use_real_price', True)
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
    set_benchmark('000300.XSHG')
    
    g.security = get_index_stocks('000300.XSHG')
    g.N = 20
    run_monthly(handle, 1)
    
def handle(context):
    df = get_fundamentals(query(valuation.code, valuation.pe_ratio, indicator.inc_net_profit_year_on_year).filter(valuation.code.in_(g.security)))
    df = df[(df['pe_ratio']>0) & (df['inc_net_profit_year_on_year']>0)]
    df['PEG'] = df['pe_ratio'] / df['inc_net_profit_year_on_year'] / 100
    df = df.sort(columns='PEG')[:g.N]
    tohold = df['code'].values
    
    for stock in context.portfolio.positions:
        if stock not in tohold:
            order_target_value(stock, 0)
    
    tobuy = [stock for stock in tohold if stock not in context.portfolio.positions]
    
    if len(tobuy)>0:
        print('Buying')
        cash = context.portfolio.available_cash
        cash_every_stock = cash / len(tobuy)
        
        for stock in tobuy:
            order_value(stock, cash_every_stock)

View Code

六、羊驼交易法则-表现最优入池

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def initialize(context):
    
    set_option('use_real_price', True)
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
    
    set_benchmark('000300.XSHG')
    
    g.security = get_index_stocks('000300.XSHG')
    g.period = 3
    g.N = 10
    g.M = 2
    print("init")
    run_monthly(handle, 10)
    
    stocks = get_sorted_stocks(context, g.security)[:g.N]
    cash = context.portfolio.available_cash / len(stocks)
    for stock in stocks:
        order_value(stock, cash)
    
    
def get_sorted_stocks(context, stocks):
    df = history(g.period, field='close', security_list=stocks).T
    df['ret'] = (df.iloc[:,-1] - df.iloc[:,0]) / df.iloc[:,0]
    df = df.sort(columns='ret', ascending=False)
    return df.index.values
    
def handle(context):
    stocks = get_sorted_stocks(context, context.portfolio.positions.keys())
    
    for stock in stocks[-g.M:]:
        order_target(stock, 0)
        
    stocks = get_sorted_stocks(context, g.security)
    
    cash = context.portfolio.available_cash / g.M
    
    for stock in stocks:
        if len(context.portfolio.positions) >= g.N:
            break
        if stock not in context.portfolio.positions:
            order_target(stock, cash)

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def initialize(context):
    
    set_option('use_real_price', True)
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
    
    set_benchmark('000300.XSHG')
    
    g.security = get_index_stocks('000300.XSHG')
    g.period = 3
    g.N = 10
    g.M = 2
    print("init")
    run_monthly(handle, 10)
    
    stocks = get_sorted_stocks(context, g.security)[:g.N]
    cash = context.portfolio.available_cash / len(stocks)
    for stock in stocks:
        order_value(stock, cash)
    
    
def get_sorted_stocks(context, stocks):
    df = history(g.period, field='close', security_list=stocks).T
    df['ret'] = (df.iloc[:,-1] - df.iloc[:,0]) / df.iloc[:,0]
    df = df.sort(columns='ret', ascending=False)
    return df.index.values
    
def handle(context):
    stocks = get_sorted_stocks(context, context.portfolio.positions.keys())
    
    for stock in stocks[-g.M:]:
        order_target(stock, 0)
        
    stocks = get_sorted_stocks(context, g.security)
    
    cash = context.portfolio.available_cash / g.M
    
    for stock in stocks:
        if len(context.portfolio.positions) >= g.N:
            break
        if stock not in context.portfolio.positions:
            order_target(stock, cash)

View Code

七、海龟交易法则

可参考：https://www.joinquant.com/post/1401?f=study&m=algorithm

复制代码

import jqdata
import math
import numpy as np
import pandas as pd
from collections import deque

def initialize(context):
    
    set_option('use_real_price', True)
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')

g.security = '000060.XSHE'
    set_benchmark(g.security)
    g.in_day = 20
    g.out_day = 10
    g.today_units = 0
    g.current_units = 0
    g.N=deque(maxlen=19)
    g.current_N = 0
    g.last_buy_price = 0
    
    price = attribute_history(g.security, g.N.maxlen*2+1, '1d', ('high', 'low', 'close'))
    
    for i in range(g.N.maxlen+1, g.N.maxlen*2+1):
        li = []
        for j in range(i-19,i+1):
            a = price['high'][j]-price['low'][j]
            b = abs(price['high'][j]-price['close'][j-1])
            c = abs(price['low'][j]-price['close'][j-1])
            li.append(max(a,b,c))
        current_N = np.array(li).mean()
        g.N.append(current_N)
        
    
def before_trading_start(context):
    g.current_N = cal_N()
    g.today_units = 0

def handle_data(context, data):
    dt = context.current_dt
    current_price = data[g.security].price #上一分钟价格
    value = context.portfolio.total_value
    cash = context.portfolio.available_cash
    
    unit = math.floor(value * 0.01 / g.current_N)
    
        
    if g.current_units == 0:
        buy(current_price, cash, unit)
    else:
        if stop_loss(current_price):
            return
        if sell(current_price):
            return
        addin(current_price, cash, unit)
    
def cal_N():
    # if len(g.N) < g.N.maxlen:
    #     price = attribute_history(g.security, g.N.maxlen+2, '1d', ('high', 'low', 'close'))
    #     li = []
    #     for i in range(1, g.N.maxlen+2):
    #         a = price['high'][i]-price['low'][i]
    #         b = abs(price['high'][i]-price['close'][i-1])
    #         c = abs(price['low'][i]-price['close'][i-1])
    #         li.append(max(a,b,c))
    #     current_N = np.array(li).mean()
    # else:
    price = attribute_history(g.security, 2, '1d', ('high', 'low', 'close'))
    a = price['high'][1]-price['low'][1]
    b = abs(price['high'][1]-price['close'][0])
    c = abs(price['low'][1]-price['close'][0])
    current_N = (max(a,b,c) + np.array(g.N).sum())/(g.N.maxlen+1)
    g.N.append(current_N)
    return current_N
    
def buy(current_price, cash, unit):
    price = attribute_history(g.security, g.in_day, '1d', ('close',))
    if current_price > max(price['close']):
        shares = cash / current_price
        if shares >= unit:
            print("buying %d" % unit)
            o = order(g.security, unit)
            g.last_buy_price = o.price
            g.current_units += 1
            g.today_units += 1
            return True
    return False

def addin(current_price, cash, unit):
    if current_price >= g.last_buy_price + 0.5 * g.current_N:
        shares = cash / current_price
        if shares >= unit:
            print("adding %d" % unit)
            o = order(g.security, unit)
            g.last_buy_price = o.price
            g.current_units += 1
            g.today_units += 1
            return True
    return False
            
def sell(current_price):
    price = attribute_history(g.security, g.out_day, '1d', ('close',))
    if current_price < min(price['close']):
        print("selling")
        order_target(g.security, 0)
        g.current_units = g.today_units
        return True
    return False
        
def stop_loss(current_price):
    if current_price < g.last_buy_price - 2 * g.current_N:
        print("stop loss")
        order_target(g.security, 0)
        g.current_units = g.today_units
        return True
    return False

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import jqdata
import math
import numpy as np
import pandas as pd
from collections import deque

def initialize(context):
    
    set_option('use_real_price', True)
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')

    g.security = '000060.XSHE'
    set_benchmark(g.security)
    g.in_day = 20
    g.out_day = 10
    g.today_units = 0
    g.current_units = 0
    g.N=deque(maxlen=19)
    g.current_N = 0
    g.last_buy_price = 0
    
    price = attribute_history(g.security, g.N.maxlen*2+1, '1d', ('high', 'low', 'close'))
    
    for i in range(g.N.maxlen+1, g.N.maxlen*2+1):
        li = []
        for j in range(i-19,i+1):
            a = price['high'][j]-price['low'][j]
            b = abs(price['high'][j]-price['close'][j-1])
            c = abs(price['low'][j]-price['close'][j-1])
            li.append(max(a,b,c))
        current_N = np.array(li).mean()
        g.N.append(current_N)
        
    
def before_trading_start(context):
    g.current_N = cal_N()
    g.today_units = 0

    
def handle_data(context, data):
    dt = context.current_dt
    current_price = data[g.security].price #上一分钟价格
    value = context.portfolio.total_value
    cash = context.portfolio.available_cash
    
    unit = math.floor(value * 0.01 / g.current_N)
    
        
    if g.current_units == 0:
        buy(current_price, cash, unit)
    else:
        if stop_loss(current_price):
            return
        if sell(current_price):
            return
        addin(current_price, cash, unit)
    
def cal_N():
    # if len(g.N) < g.N.maxlen:
    #     price = attribute_history(g.security, g.N.maxlen+2, '1d', ('high', 'low', 'close'))
    #     li = []
    #     for i in range(1, g.N.maxlen+2):
    #         a = price['high'][i]-price['low'][i]
    #         b = abs(price['high'][i]-price['close'][i-1])
    #         c = abs(price['low'][i]-price['close'][i-1])
    #         li.append(max(a,b,c))
    #     current_N = np.array(li).mean()
    # else:
    price = attribute_history(g.security, 2, '1d', ('high', 'low', 'close'))
    a = price['high'][1]-price['low'][1]
    b = abs(price['high'][1]-price['close'][0])
    c = abs(price['low'][1]-price['close'][0])
    current_N = (max(a,b,c) + np.array(g.N).sum())/(g.N.maxlen+1)
    g.N.append(current_N)
    return current_N
    
def buy(current_price, cash, unit):
    price = attribute_history(g.security, g.in_day, '1d', ('close',))
    if current_price > max(price['close']):
        shares = cash / current_price
        if shares >= unit:
            print("buying %d" % unit)
            o = order(g.security, unit)
            g.last_buy_price = o.price
            g.current_units += 1
            g.today_units += 1
            return True
    return False
            

def addin(current_price, cash, unit):
    if current_price >= g.last_buy_price + 0.5 * g.current_N:
        shares = cash / current_price
        if shares >= unit:
            print("adding %d" % unit)
            o = order(g.security, unit)
            g.last_buy_price = o.price
            g.current_units += 1
            g.today_units += 1
            return True
    return False
            
def sell(current_price):
    price = attribute_history(g.security, g.out_day, '1d', ('close',))
    if current_price < min(price['close']):
        print("selling")
        order_target(g.security, 0)
        g.current_units = g.today_units
        return True
    return False
        
def stop_loss(current_price):
    if current_price < g.last_buy_price - 2 * g.current_N:
        print("stop loss")
        order_target(g.security, 0)
        g.current_units = g.today_units
        return True
    return False

View Code

八、鳄鱼交易法则

复制代码

# 导入函数库
import jqdata
import numpy as np

# 初始化函数，设定基准等等
def initialize(context):
    set_option('use_real_price', True)
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
    set_benchmark('000300.XSHG')
    
    g.up_price = {} #向上碎形最高价
    g.low_price = {} #向下碎形最低价
    g.up_fractal_exists = {} #判断有效向上碎形
    g.down_fractal_exists = {} #判断有效向下碎形
    g.AO_index = {} #存放连续的AO指标数据
    g.cal_AC_index = {} #计算AC指标中转存储
    g.AC_index = {} #存放连续的AC指标数据
    g.amount = {} #满仓仓位
    g.stock = get_index_stocks('000300.XSHG')
    g.buy_stock = []
    g.month = context.current_dt.month
    run_monthly(select_universe,1,'open')
    
#重置全局变量
def reset_global():
    g.up_price = {} #向上碎形最高价
    g.low_price = {} #向下碎形最低价
    g.up_fractal_exists = {} #判断有效向上碎形
    g.down_fractal_exists = {} #判断有效向下碎形
    g.AO_index = {} #存放连续的AO指标数据
    g.cal_AC_index = {} #计算AC指标中转存储
    g.AC_index = {} #存放连续的AC指标数据
    g.amount = {} #满仓仓位
    g.buy_stock = []

def initial_stock_global(stock):
    g.up_price[stock] = 0
    g.low_price[stock] = 0
    g.up_fractal_exists[stock] = False
    g.down_fractal_exists[stock] = False #判断有效向下碎形
    g.AO_index[stock] = [0] #存放连续的AO指标数据
    g.cal_AC_index[stock] = [0]  #计算AC指标中转存储
    g.AC_index[stock] = [0] #存放连续的AC指标数据
    g.amount[stock] = 0 #满仓仓位

#轮换选股后清空持仓
def reset_position(context):
    for stock in g.buy_stock:
        order_target(stock,0)
        log.info("sell %s for reset position"%stock)

#选股
def select_universe(context):
    #每三个月操作一次
    month = context.current_dt.month
    if month%6 != g.month%6:
        return
    #清空全局变量
    reset_position(context)
    reset_global()
    hist = history(30,'1d','close',g.stock,df = False)
    for stock in g.stock:
        if is_sleeping_alligator(stock,hist,20):
            g.buy_stock.append(stock)
            #初始化该股票全局变量
            initial_stock_global(stock)
    print g.buy_stock
    return None
    
#睡着的鳄鱼
def is_sleeping_alligator(stock,hist,nday):
    for i in range(nday):
        if is_struggle(stock,hist,i) == False:
            return False
    return True

#均线纠缠，BRG三线非常接近
def is_struggle(stock,hist,delta):
    blue_line = hist[stock][-21-delta:-8-delta].mean()
    red_line = hist[stock][-13-delta:-5-delta].mean()
    green_line = hist[stock][-8-delta:-3-delta].mean()
    if abs(blue_line/red_line-1)<0.02 and abs(red_line/green_line-1)<0.02:
        return True
    else:
        return False
        
#判断 向上 或 向下 碎形
def is_fractal(stock,direction):
    hist = attribute_history(stock, 5, fields=[direction])
    if direction == 'high':
        if np.all(hist.iloc[:2] < hist.iloc[2]) and np.all(hist.iloc[3:] < hist.iloc[2]):
            g.up_price[stock] = hist.iloc[2].values
            return True
    elif direction == 'low':
        if np.all(hist.iloc[:2] > hist.iloc[2]) and np.all(hist.iloc[3:] > hist.iloc[2]):
            g.low_price[stock] = hist.iloc[2].values
            return True
    return False
    
#通过比较碎形与红线位置，判断碎形是否有效
def is_effective_fractal(stock, direction):
    if is_fractal(stock,direction):
        hist = attribute_history(stock, 11)
        red_line = hist['close'][:-3].mean()
        close_price = hist['close'][-1]
        if direction == 'high':
            if close_price > red_line:
                g.up_fractal_exists[stock] = True
            else:
                g.up_fractal_exists[stock] = False
        elif direction == 'low':
            if close_price < red_line:
                g.down_fractal_exists[stock] = True
            else:
                g.down_fractal_exists[stock] = False

#N日内最高价格的N日线
def nday_high_point(stock,n):
    hist = history(2*n,'1d','high',[stock],df = False)[stock]
    high_point = []
    for i in range(n):
        high_point.append(max(hist[-5-i:-1-i]))
    return np.array(high_point).mean()

#N日内最低价格的N日线
def nday_low_point(stock,n):
    hist = history(2*n,'1d','low',[stock],df = False)[stock]
    low_point = []
    for i in range(n):
        low_point.append(max(hist[-5-i:-1-i]))
    return np.array(low_point).mean()

#AO=5日内（最高-最低）/2的5日移动平均-34日内（最高-最低）/2的34日移动平均
def AO_index(stock):
    g.AO_index[stock].append(nday_high_point(stock,5)/2 + nday_low_point(stock,5)/2
                      - nday_high_point(stock,34)/2 - nday_low_point(stock,34)/2)
    return None

#AO-AO的5日平均值的5日平均
def AC_index(stock):
    AO_index(stock)
    if len(g.AO_index[stock]) >= 5:
        g.cal_AC_index[stock].append(g.AO_index[stock][-1] - np.array(g.AO_index[stock][-5:]).mean())
        if len(g.cal_AC_index[stock]) >=5:
            g.AC_index[stock].append(np.array(g.cal_AC_index[stock][-5:]).mean())

#判断序列n日上行
def is_up_going(alist,n):
    if len(alist) < n:
        return False
    for i in range(n-1):
        if alist[-(1+i)] <= alist[-(2+i)]:
            return False
    return True

#判断序列n日下行
def is_down_going(alist,n):
    if len(alist) < n:
        return False
    for i in range(n-1):
        if alist[-(1+i)] >= alist[-(2+i)]:
            return False
    return True

#碎形被突破
def active_fractal(stock,direction):
    close_price = history(1,'1d','close',[stock],df=False)[stock][0]
    if direction == 'up' and close_price > g.up_price[stock]:
        return True
    elif direction == 'down' and close_price < g.low_price[stock]:
        return True
    return False

#进场，初始仓位
def set_initial_position(stock,context):
    close_price = history(1,'1d','close',[stock],df=False)[stock][0]
    g.amount[stock] = context.portfolio.cash/close_price/len(g.buy_stock)*3
    order(stock, g.amount[stock])
    log.info("buying %s 股数为 %s"%(stock,g.amount[stock]))
    g.down_fractal_exists[stock] = False

#卖出
def sell_all_stock(stock,context):
    order_target(stock,0)
    log.info("selling %s"%stock)
    g.up_fractal_exists[stock] = False

#加仓
def adjust_position(stock,context,position):
    order(stock,g.amount[stock]*position)
    log.info("adjust position buying %s 股数为 %s"%(stock,g.amount[stock]*position))

# 计算股票前n日收益率
def security_return(days,security_code):
    hist1 = attribute_history(security_code, days + 1, '1d', 'close',df=False)
    security_returns = (hist1['close'][-1]-hist1['close'][0])/hist1['close'][0]
    return security_returns

# 止损，根据前n日收益率
def conduct_nday_stoploss(context,security_code,days,bench):
    if  security_return(days,security_code)<= bench:
        for stock in g.buy_stock:
            order_target_value(stock,0)
            log.info("Sell %s for stoploss" %stock)
        return True
    else:
        return False

# 计算股票累计收益率（从建仓至今）
def security_accumulate_return(context,data,stock):
    current_price = data[stock].price
    cost = context.portfolio.positions[stock].avg_cost
    if cost != 0:
        return (current_price-cost)/cost
    else:
        return None

# 个股止损，根据累计收益
def conduct_accumulate_stoploss(context,data,stock,bench):
    if security_accumulate_return(context,data,stock) != None
    and security_accumulate_return(context,data,stock) < bench:
        order_target_value(stock,0)
        log.info("Sell %s for stoploss" %stock)
        return True
    else:
        return False

# 个股止盈，根据累计收益
def conduct_accumulate_stopwin(context,data,stock,bench):
    if security_accumulate_return(context,data,stock) != None
    and security_accumulate_return(context,data,stock) > bench:
        order_target_value(stock,0)
        log.info("Sell %s for stopwin" %stock)
        return True
    else:
        return False

def handle_data(context,data):
    #大盘止损
    if conduct_nday_stoploss(context,'000300.XSHG',3,-0.03):
        return
    for stock in g.buy_stock:
        #个股止损
        if conduct_accumulate_stopwin(context,data,stock,0.3)
        or conduct_accumulate_stoploss(context,data,stock,-0.1):
            return
        #计算AO，AC指标
        AC_index(stock)
        #空仓时，寻找机会入场
        if context.portfolio.positions[stock].amount == 0:
            #计算向上碎形
            is_effective_fractal(stock,'high')
            #有效向上碎形存在，并被突破，买入
            if g.up_fractal_exists and active_fractal(stock,'up'):
                close_price = history(5, '1d', 'close', [stock],df = False)
                if is_up_going(g.AO_index[stock],5)
                and is_up_going(g.AC_index[stock],3)
                and is_up_going(close_price[stock],2):
                    set_initial_position(stock,context)
        #有持仓时，加仓或离场
        else:
            #计算向下碎形
            is_effective_fractal(stock,'low')
            #出场条件1：有效向下碎形存在，并被突破，卖出
            if g.down_fractal_exists and active_fractal(stock,'down'):
                sell_all_stock(stock,context)
                return

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# 导入函数库
import jqdata
import numpy as np

# 初始化函数，设定基准等等
def initialize(context):
    set_option('use_real_price', True)
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
    set_benchmark('000300.XSHG')
    
    g.up_price = {} #向上碎形最高价
    g.low_price = {} #向下碎形最低价
    g.up_fractal_exists = {} #判断有效向上碎形
    g.down_fractal_exists = {} #判断有效向下碎形
    g.AO_index = {} #存放连续的AO指标数据
    g.cal_AC_index = {} #计算AC指标中转存储
    g.AC_index = {} #存放连续的AC指标数据
    g.amount = {} #满仓仓位
    g.stock = get_index_stocks('000300.XSHG')
    g.buy_stock = []
    g.month = context.current_dt.month
    run_monthly(select_universe,1,'open')
    
#重置全局变量
def reset_global():
    g.up_price = {} #向上碎形最高价
    g.low_price = {} #向下碎形最低价
    g.up_fractal_exists = {} #判断有效向上碎形
    g.down_fractal_exists = {} #判断有效向下碎形
    g.AO_index = {} #存放连续的AO指标数据
    g.cal_AC_index = {} #计算AC指标中转存储
    g.AC_index = {} #存放连续的AC指标数据
    g.amount = {} #满仓仓位
    g.buy_stock = []

def initial_stock_global(stock):
    g.up_price[stock] = 0
    g.low_price[stock] = 0
    g.up_fractal_exists[stock] = False
    g.down_fractal_exists[stock] = False #判断有效向下碎形
    g.AO_index[stock] = [0] #存放连续的AO指标数据
    g.cal_AC_index[stock] = [0]  #计算AC指标中转存储
    g.AC_index[stock] = [0] #存放连续的AC指标数据
    g.amount[stock] = 0 #满仓仓位

#轮换选股后清空持仓
def reset_position(context):
    for stock in g.buy_stock:
        order_target(stock,0)
        log.info("sell %s for reset position"%stock)

#选股
def select_universe(context):
    #每三个月操作一次
    month = context.current_dt.month
    if month%6 != g.month%6:
        return
    #清空全局变量
    reset_position(context)
    reset_global()
    hist = history(30,'1d','close',g.stock,df = False)
    for stock in g.stock:
        if is_sleeping_alligator(stock,hist,20):
            g.buy_stock.append(stock)
            #初始化该股票全局变量
            initial_stock_global(stock)
    print g.buy_stock
    return None
    
#睡着的鳄鱼
def is_sleeping_alligator(stock,hist,nday):
    for i in range(nday):
        if is_struggle(stock,hist,i) == False:
            return False
    return True

#均线纠缠，BRG三线非常接近
def is_struggle(stock,hist,delta):
    blue_line = hist[stock][-21-delta:-8-delta].mean()
    red_line = hist[stock][-13-delta:-5-delta].mean()
    green_line = hist[stock][-8-delta:-3-delta].mean()
    if abs(blue_line/red_line-1)<0.02 and abs(red_line/green_line-1)<0.02:
        return True
    else:
        return False
        
#判断 向上 或 向下 碎形
def is_fractal(stock,direction):
    hist = attribute_history(stock, 5, fields=[direction])
    if direction == 'high':
        if np.all(hist.iloc[:2] < hist.iloc[2]) and np.all(hist.iloc[3:] < hist.iloc[2]):
            g.up_price[stock] = hist.iloc[2].values
            return True
    elif direction == 'low':
        if np.all(hist.iloc[:2] > hist.iloc[2]) and np.all(hist.iloc[3:] > hist.iloc[2]):
            g.low_price[stock] = hist.iloc[2].values
            return True
    return False
    
#通过比较碎形与红线位置，判断碎形是否有效
def is_effective_fractal(stock, direction):
    if is_fractal(stock,direction):
        hist = attribute_history(stock, 11)
        red_line = hist['close'][:-3].mean()
        close_price = hist['close'][-1]
        if direction == 'high':
            if close_price > red_line:
                g.up_fractal_exists[stock] = True
            else:
                g.up_fractal_exists[stock] = False
        elif direction == 'low':
            if close_price < red_line:
                g.down_fractal_exists[stock] = True
            else:
                g.down_fractal_exists[stock] = False
    

#N日内最高价格的N日线
def nday_high_point(stock,n):
    hist = history(2*n,'1d','high',[stock],df = False)[stock]
    high_point = []
    for i in range(n):
        high_point.append(max(hist[-5-i:-1-i]))
    return np.array(high_point).mean()

#N日内最低价格的N日线
def nday_low_point(stock,n):
    hist = history(2*n,'1d','low',[stock],df = False)[stock]
    low_point = []
    for i in range(n):
        low_point.append(max(hist[-5-i:-1-i]))
    return np.array(low_point).mean()

#AO=5日内（最高-最低）/2的5日移动平均-34日内（最高-最低）/2的34日移动平均
def AO_index(stock):
    g.AO_index[stock].append(nday_high_point(stock,5)/2 + nday_low_point(stock,5)/2
                      - nday_high_point(stock,34)/2 - nday_low_point(stock,34)/2)
    return None

#AO-AO的5日平均值的5日平均
def AC_index(stock):
    AO_index(stock)
    if len(g.AO_index[stock]) >= 5:
        g.cal_AC_index[stock].append(g.AO_index[stock][-1] - np.array(g.AO_index[stock][-5:]).mean())
        if len(g.cal_AC_index[stock]) >=5:
            g.AC_index[stock].append(np.array(g.cal_AC_index[stock][-5:]).mean())

#判断序列n日上行
def is_up_going(alist,n):
    if len(alist) < n:
        return False
    for i in range(n-1):
        if alist[-(1+i)] <= alist[-(2+i)]:
            return False
    return True

#判断序列n日下行
def is_down_going(alist,n):
    if len(alist) < n:
        return False
    for i in range(n-1):
        if alist[-(1+i)] >= alist[-(2+i)]:
            return False
    return True

#碎形被突破
def active_fractal(stock,direction):
    close_price = history(1,'1d','close',[stock],df=False)[stock][0]
    if direction == 'up' and close_price > g.up_price[stock]:
        return True
    elif direction == 'down' and close_price < g.low_price[stock]:
        return True
    return False

#进场，初始仓位
def set_initial_position(stock,context):
    close_price = history(1,'1d','close',[stock],df=False)[stock][0]
    g.amount[stock] = context.portfolio.cash/close_price/len(g.buy_stock)*3
    order(stock, g.amount[stock])
    log.info("buying %s 股数为 %s"%(stock,g.amount[stock]))
    g.down_fractal_exists[stock] = False

#卖出
def sell_all_stock(stock,context):
    order_target(stock,0)
    log.info("selling %s"%stock)
    g.up_fractal_exists[stock] = False

#加仓
def adjust_position(stock,context,position):
    order(stock,g.amount[stock]*position)
    log.info("adjust position buying %s 股数为 %s"%(stock,g.amount[stock]*position))

# 计算股票前n日收益率
def security_return(days,security_code):
    hist1 = attribute_history(security_code, days + 1, '1d', 'close',df=False)
    security_returns = (hist1['close'][-1]-hist1['close'][0])/hist1['close'][0]
    return security_returns

# 止损，根据前n日收益率
def conduct_nday_stoploss(context,security_code,days,bench):
    if  security_return(days,security_code)<= bench:
        for stock in g.buy_stock:
            order_target_value(stock,0)
            log.info("Sell %s for stoploss" %stock)
        return True
    else:
        return False

# 计算股票累计收益率（从建仓至今）
def security_accumulate_return(context,data,stock):
    current_price = data[stock].price
    cost = context.portfolio.positions[stock].avg_cost
    if cost != 0:
        return (current_price-cost)/cost
    else:
        return None

# 个股止损，根据累计收益
def conduct_accumulate_stoploss(context,data,stock,bench):
    if security_accumulate_return(context,data,stock) != None
    and security_accumulate_return(context,data,stock) < bench:
        order_target_value(stock,0)
        log.info("Sell %s for stoploss" %stock)
        return True
    else:
        return False

# 个股止盈，根据累计收益
def conduct_accumulate_stopwin(context,data,stock,bench):
    if security_accumulate_return(context,data,stock) != None
    and security_accumulate_return(context,data,stock) > bench:
        order_target_value(stock,0)
        log.info("Sell %s for stopwin" %stock)
        return True
    else:
        return False

def handle_data(context,data):
    #大盘止损
    if conduct_nday_stoploss(context,'000300.XSHG',3,-0.03):
        return
    for stock in g.buy_stock:
        #个股止损
        if conduct_accumulate_stopwin(context,data,stock,0.3)
        or conduct_accumulate_stoploss(context,data,stock,-0.1):
            return
        #计算AO，AC指标
        AC_index(stock)
        #空仓时，寻找机会入场
        if context.portfolio.positions[stock].amount == 0:
            #计算向上碎形
            is_effective_fractal(stock,'high')
            #有效向上碎形存在，并被突破，买入
            if g.up_fractal_exists and active_fractal(stock,'up'):
                close_price = history(5, '1d', 'close', [stock],df = False)
                if is_up_going(g.AO_index[stock],5)
                and is_up_going(g.AC_index[stock],3)
                and is_up_going(close_price[stock],2):
                    set_initial_position(stock,context)
        #有持仓时，加仓或离场
        else:
            #计算向下碎形
            is_effective_fractal(stock,'low')
            #出场条件1：有效向下碎形存在，并被突破，卖出
            if g.down_fractal_exists and active_fractal(stock,'down'):
                sell_all_stock(stock,context)
                return