简介

在下单函数后调用 write_logs(contest)，将order、trade、position对象，以csv格式输出三张表至研究，新的数据将追加在数据框后，在回测和模拟交易中均可使用。支持每日、分钟策略保存对象，分为每日定时调用保存函数和按order调用保存函数，若分钟级策略按order调用时，可能由于下单数量过多导致回测速度下降，建议使用定时调用保存函数。本篇使用官网回测环境，给出期货每日定时调用保存函数的简单实例，文件输出在研究根目录下，若使用客户端请自行在根目录中寻找文件。

输出对象

Order

Trade

Position

def process_initialize(context):
    ls=['security']
    g.__df1 = pd.DataFrame(columns=ls)
    g.__df2 = pd.DataFrame(columns=ls)
    g.__df3 = pd.DataFrame(columns=ls)

def write_logs(context):
    ########## Orders ##############
    orders = get_orders()
    i=0
    ls1 = ['order_id','status','security','price','amount',\
            'avg_cost','Time','is_buy','filled','side','action']
    df1 = pd.DataFrame(columns=ls1)
    for _orders in orders.values():
        df1.loc[i,'order_id'] = _orders.order_id
        df1.loc[i,'status'] = _orders.status
        df1.loc[i, 'security'] = _orders.security
        df1.loc[i,'price'] = _orders.price
        df1.loc[i,'amount'] = _orders.amount
        df1.loc[i,'avg_cost'] = _orders.avg_cost
        df1.loc[i, 'Time'] = _orders.add_time
        df1.loc[i, 'is_buy'] = _orders.is_buy
        df1.loc[i, 'filled'] = _orders.filled
        df1.loc[i, 'side'] = _orders.side
        df1.loc[i, 'action'] = _orders.action
        i=i 1
    df1.set_index('Time',drop=True,inplace=True)
    g.__df1 = g.__df1.append(df1)
    #print(g._df1)
    write_file('orders.csv', g.__df1.to_csv())
    ############# Trades ####################
    trades = get_trades()
    i=0
    ls2 = ['Time','order_id','trade_id','security','price','amount']
    df2 = pd.DataFrame(columns=ls2)
    for _trades in trades.values():
        df2.loc[i,'Time'] = _trades.time
        df2.loc[i,'order_id'] = _trades.order_id
        df2.loc[i, 'trade_id'] = _trades.trade_id
        df2.loc[i,'security'] = _trades.security
        df2.loc[i,'price'] = _trades.price
        df2.loc[i,'amount'] = _trades.amount
        i=i 1
    df2.set_index('Time',drop=True,inplace=True)
    g.__df2 = g.__df2.append(df2)
    #print(g._df2)
    write_file('trade.csv', g.__df2.to_csv())

    ############### Position ################
    position = context.portfolio.positions.keys()
    i=0
    ls3 = ['security','price','acc_avg_cost','avg_cost','Time','transact_time','total_amount',\
        'closeable_amount','today_amount','locked_amount','value','side']
    df3 = pd.DataFrame(columns=ls3)
    for stock in position:
        df3.loc[i,'security']=context.portfolio.positions[stock].security
        df3.loc[i,'price'] = context.portfolio.positions[stock].price
        df3.loc[i,'acc_avg_cost'] = context.portfolio.positions[stock].acc_avg_cost
        df3.loc[i,'avg_cost'] = context.portfolio.positions[stock].avg_cost
        df3.loc[i, 'Time'] = context.portfolio.positions[stock].init_time
        df3.loc[i,'transact_time'] = context.portfolio.positions[stock].transact_time
        df3.loc[i,'total_amount'] = context.portfolio.positions[stock].total_amount
        df3.loc[i,'closeable_amount'] = context.portfolio.positions[stock].closeable_amount
        df3.loc[i,'today_amount'] = context.portfolio.positions[stock].today_amount
        df3.loc[i,'locked_amount'] = context.portfolio.positions[stock].locked_amount
        df3.loc[i,'value'] = context.portfolio.positions[stock].value
        df3.loc[i,'side'] = context.portfolio.positions[stock].side
        i=i 1
    df3.set_index('Time',drop=True,inplace=True)
    g.__df3 = g.__df3.append(df3)
    #print(g._df3)
    write_file('Position.csv', g.__df3.to_csv())