mirror of
https://gitee.com/fasiondog/hikyuu.git
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112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
#!/usr/bin/python
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# -*- coding: utf8 -*-
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# cp936
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#
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# The MIT License (MIT)
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#
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# Copyright (c) 2010-2017 fasiondog
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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# ===============================================================================
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# History:
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# 1. 20130213, Added by fasiondog
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# ===============================================================================
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from hikyuu.util.slice import list_getitem
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from hikyuu import *
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import numpy as np
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import pandas as pd
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def TradeList_to_np(t_list):
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"""转化为numpy结构数组"""
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t_type = np.dtype(
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{
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'names': [
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'交易日期', '证券代码', '证券名称', '业务名称', '计划交易价格', '实际成交价格', '目标价格', '成交数量', '佣金', '印花税', '过户费', '其它成本',
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'交易总成本', '止损价', '现金余额', '信号来源'
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],
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'formats':
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['datetime64[D]', 'U10', 'U20', 'U10', 'd', 'd', 'd', 'i', 'd', 'd', 'd', 'd', 'd', 'd', 'd', 'U5']
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}
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)
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return np.array(
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[
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(
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t.datetime, t.stock.market_code, t.stock.name, get_business_name(t.business), t.plan_price,
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t.real_price, t.goal_price, t.number, t.cost.commission, t.cost.stamptax, t.cost.transferfee,
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t.cost.others, t.cost.total, t.stoploss, t.cash, get_system_part_name(t.part)
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) for t in t_list
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],
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dtype=t_type
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)
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def TradeList_to_df(t):
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"""转化为pandas的DataFrame"""
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return pd.DataFrame.from_records(TradeList_to_np(t), index='交易日期')
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TradeRecordList.to_np = TradeList_to_np
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TradeRecordList.to_df = TradeList_to_df
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def PositionList_to_np(pos_list):
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"""转化为numpy结构数组"""
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t_type = np.dtype(
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{
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'names': ['证券代码', '证券名称', '买入日期', '已持仓天数', '持仓数量', '投入金额', '当前市值', '盈亏金额', '盈亏比例'],
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'formats': ['U10', 'U20', 'datetime64[D]', 'i', 'i', 'd', 'd', 'd', 'd']
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}
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)
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sm = StockManager.instance()
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query = Query(-1)
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data = []
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for pos in pos_list:
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invest = pos.buy_money - pos.sell_money + pos.total_cost
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k = pos.stock.get_kdata(query)
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cur_val = k[0].close * pos.number
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bonus = cur_val - invest
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date_list = sm.get_trading_calendar(Query(Datetime(pos.take_datetime.date())))
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data.append(
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(
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pos.stock.market_code, pos.stock.name, pos.take_datetime, len(date_list), pos.number, invest,
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cur_val, bonus, 100 * bonus / invest
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)
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)
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return np.array(data, dtype=t_type)
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def PositionList_to_df(pos_list):
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"""转化为pandas的DataFrame"""
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return pd.DataFrame.from_records(PositionList_to_np(pos_list), index='证券代码')
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PositionRecordList.to_np = PositionList_to_np
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PositionRecordList.to_df = PositionList_to_df
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def Performance_to_df(per):
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"""将 Performance 统计结果转换为 DataFrame 格式"""
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return pd.DataFrame(dict(name=per.names(), value=per.values()))
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Performance.to_df = Performance_to_df
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