217 lines
8.9 KiB
Python
217 lines
8.9 KiB
Python
# -*- encoding:utf-8 -*-
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"""
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交易可视化模块
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"""
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from __future__ import print_function
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from __future__ import absolute_import
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from __future__ import division
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import copy
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import logging
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import matplotlib.pyplot as plt
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import numpy as np
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import seaborn as sns
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from ..CoreBu import ABuEnv
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from ..UtilBu import ABuDateUtil
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from ..UtilBu.ABuProgress import AbuProgress
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# noinspection PyUnresolvedReferences
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from ..CoreBu.ABuFixes import range
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from ..TradeBu.ABuCapital import AbuCapital
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g_enable_his_corr = True
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g_enable_his_trade = True
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__author__ = '阿布'
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__weixin__ = 'abu_quant'
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def plot_his_trade(orders, kl_pd):
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"""
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可视化绘制AbuOrder对象，绘制交易买入时间，卖出时间，价格，生效因子等
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:param orders: AbuOrder对象序列
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:param kl_pd: 金融时间序列，pd.DataFrame对象
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:return:
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"""
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if not g_enable_his_trade:
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return
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# 拿出时间序列中最后一个，做为当前价格
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now_price = kl_pd.iloc[-1].close
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all_pd = kl_pd
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# ipython环境绘制在多个子画布上，普通python环境绘制一个show一个
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draw_multi_ax = ABuEnv.g_is_ipython
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# 根据绘制环境设置子画布数量
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ax_cnt = 1 if not draw_multi_ax else len(orders)
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# 根据子画布数量设置画布大小
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plt.figure(figsize=(14, 8 * ax_cnt))
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fig_dims = (ax_cnt, 1)
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with AbuProgress(len(orders), 0) as pg:
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for index, order in enumerate(orders):
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pg.show(index + 1)
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# 迭代所有orders，对每一个AbuOrder对象绘制交易细节
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mask_date = all_pd['date'] == order.buy_date
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st_key = all_pd[mask_date]['key']
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if order.sell_type == 'keep':
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rv_pd = all_pd.iloc[st_key.values[0]:, :]
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else:
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mask_sell_date = all_pd['date'] == order.sell_date
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st_sell_key = all_pd[mask_sell_date]['key']
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rv_pd = all_pd.iloc[st_key.values[0]:st_sell_key.values[0], :]
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if draw_multi_ax:
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# ipython环境绘制在多个子画布上
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plt.subplot2grid(fig_dims, (index, 0))
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# 绘制价格曲线
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plt.plot(all_pd.index, all_pd['close'], label='close')
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try:
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# 填充透明blue, 针对用户一些版本兼容问题进行处理
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plt.fill_between(all_pd.index, 0, all_pd['close'], color='blue', alpha=.18)
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if order.sell_type == 'keep':
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# 如果单子还没卖出，是否win使用now_price代替sell_price，需＊单子期望的盈利方向
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order_win = (now_price - order.buy_price) * order.expect_direction > 0
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elif order.sell_type == 'win':
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order_win = True
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else:
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order_win = False
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if order_win:
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# 盈利的使用红色
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plt.fill_between(rv_pd.index, 0, rv_pd['close'], color='red', alpha=.38)
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else:
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# 亏损的使用绿色
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plt.fill_between(rv_pd.index, 0, rv_pd['close'], color='green', alpha=.38)
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except:
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logging.debug('fill_between numpy type not safe!')
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# 格式化买入信息标签
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buy_date_fmt = ABuDateUtil.str_to_datetime(str(order.buy_date), '%Y%m%d')
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buy_tip = 'buy_price:{:.2f}'.format(order.buy_price)
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# 写买入tip信息
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plt.annotate(buy_tip, xy=(buy_date_fmt, all_pd['close'].asof(buy_date_fmt) * 2 / 5),
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xytext=(buy_date_fmt, all_pd['close'].asof(buy_date_fmt)),
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arrowprops=dict(facecolor='red'),
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horizontalalignment='left', verticalalignment='top')
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if order.sell_price is not None:
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# 如果单子卖出，卖出入信息标签使用，收益使用sell_price计算，需＊单子期望的盈利方向
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sell_date_fmt = ABuDateUtil.str_to_datetime(str(order.sell_date), '%Y%m%d')
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pft = (order.sell_price - order.buy_price) * order.buy_cnt * order.expect_direction
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sell_tip = 'sell price:{:.2f}, profit:{:.2f}'.format(order.sell_price, pft)
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else:
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# 如果单子未卖出，卖出入信息标签使用，收益使用now_price计算，需＊单子期望的盈利方向
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sell_date_fmt = ABuDateUtil.str_to_datetime(str(all_pd[-1:]['date'][0]), '%Y%m%d')
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pft = (now_price - order.buy_price) * order.buy_cnt * order.expect_direction
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sell_tip = 'now price:{:.2f}, profit:{:.2f}'.format(now_price, pft)
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# 写卖出tip信息
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plt.annotate(sell_tip, xy=(sell_date_fmt, all_pd['close'].asof(sell_date_fmt) * 2 / 5),
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xytext=(sell_date_fmt, all_pd['close'].asof(sell_date_fmt)),
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arrowprops=dict(facecolor='green'),
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horizontalalignment='left', verticalalignment='top')
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# 写卖出因子信息
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plt.annotate(order.sell_type_extra, xy=(buy_date_fmt, all_pd['close'].asof(sell_date_fmt) / 4),
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xytext=(buy_date_fmt, all_pd['close'].asof(sell_date_fmt) / 4),
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arrowprops=dict(facecolor='yellow'),
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horizontalalignment='left', verticalalignment='top')
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# 写买入因子信息
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if order.buy_factor is not None:
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plt.annotate(order.buy_factor, xy=(buy_date_fmt, all_pd['close'].asof(sell_date_fmt) / 3),
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xytext=(buy_date_fmt, all_pd['close'].asof(sell_date_fmt) / 3),
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arrowprops=dict(facecolor='yellow'),
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horizontalalignment='left', verticalalignment='top')
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# title使用时间序列symbol
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plt.title(order.buy_symbol)
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if not draw_multi_ax:
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# ipython环境绘制在多个子画布上，普通python环境绘制一个show一个
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plt.show()
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plt.show()
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def plot_capital_info(capital_pd, init_cash=-1):
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"""
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资金信息可视化
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:param capital_pd: AbuCapital对象或者AbuCapital对象的capital_pd
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:param init_cash: 初始化cash，如果capital_pd为AbuCapital对象，即从capital_pd获取
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"""
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if isinstance(capital_pd, AbuCapital):
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# 如果是AbuCapital对象进行转换
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init_cash = capital_pd.read_cash
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capital_pd = capital_pd.capital_pd
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plt.figure(figsize=(14, 8))
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if init_cash != -1:
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cb_earn = capital_pd['capital_blance'] - init_cash
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try:
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# 从有资金变化开始的loc开始绘制
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# noinspection PyUnresolvedReferences
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cb_earn = cb_earn.loc[cb_earn[cb_earn != 0].index[0]:]
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cb_earn.plot()
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plt.title('capital_blance earn from none zero point')
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plt.show()
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sns.regplot(x=np.arange(0, cb_earn.shape[0]), y=cb_earn.values, marker='+')
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plt.show()
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except Exception as e:
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logging.exception(e)
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capital_pd['capital_blance'].plot()
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plt.title('capital blance')
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plt.show()
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# 为了画出平滑的曲线，取有值的
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cap_cp = copy.deepcopy(capital_pd)
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cap_cp['stocks_blance'][cap_cp['stocks_blance'] <= 0] = np.nan
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cap_cp['stocks_blance'].fillna(method='pad', inplace=True)
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cap_cp['stocks_blance'].dropna(inplace=True)
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cap_cp['stocks_blance'].plot()
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plt.title('stocks blance')
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plt.show()
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try:
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sns.distplot(capital_pd['capital_blance'], kde_kws={"lw": 3, "label": "capital blance kde"})
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plt.show()
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except Exception as e:
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logging.debug(e)
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capital_pd['capital_blance'].plot(kind='kde')
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plt.title('capital blance kde')
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plt.show()
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def plot_bk_xd(bk_summary, kl_pd_xd_mean, title=None):
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"""根据有bk_summary属性的bk交易因子进行可视化，暂时未迁移完成"""
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plt.figure()
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plt.plot(list(range(0, len(kl_pd_xd_mean))), kl_pd_xd_mean['close'])
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for bk in bk_summary.bk_xd_obj_list:
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plt.hold(True)
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pc = 'r' if bk.break_sucess is True else 'g'
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plt.plot(bk.break_index, kl_pd_xd_mean['close'][bk.break_index], 'ro', markersize=12, markeredgewidth=1.5,
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markerfacecolor='None', markeredgecolor=pc)
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if title is not None:
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plt.title(title)
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plt.grid(True)
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def plot_kp_xd(kp_summary, kl_pd_xd_mean, title=None):
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"""根据有bk_summary属性的kp交易因子进行可视化，暂时未迁移完成"""
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plt.figure()
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plt.plot(list(range(0, len(kl_pd_xd_mean))), kl_pd_xd_mean['close'])
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for kp in kp_summary.kp_xd_obj_list:
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plt.hold(True)
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plt.plot(kp.break_index, kl_pd_xd_mean['close'][kp.break_index], 'ro', markersize=8, markeredgewidth=1.5,
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markerfacecolor='None', markeredgecolor='r')
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if title is not None:
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plt.title(title)
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plt.grid(True)
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