143 lines
5.8 KiB
Python
143 lines
5.8 KiB
Python
# -*- encoding:utf-8 -*-
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"""
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梦想中的机器学习股票数据环境
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"""
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import numpy as np
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from abupy import ABuSymbolPd
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import sklearn.preprocessing as preprocessing
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"""
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是否开启date_week噪音
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"""
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g_with_date_week_noise = True
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def _gen_another_word_price(kl_another_word):
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"""
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生成股票在另一个世界中的价格
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:param kl_another_word:
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:return:
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"""
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for ind in np.arange(2, kl_another_word.shape[0]):
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# 前天数据
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bf_yesterday = kl_another_word.iloc[ind - 2]
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# 昨天
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yesterday = kl_another_word.iloc[ind - 1]
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# 今天
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today = kl_another_word.iloc[ind]
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# 生成今天的收盘价格
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kl_another_word.close[ind] = _gen_another_word_price_rule(yesterday.close, yesterday.volume,
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bf_yesterday.close, bf_yesterday.volume,
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today.volume, today.date_week)
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def _gen_another_word_price_rule(yesterday_close, yesterday_volume, bf_yesterday_close, bf_yesterday_volume,
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today_volume, date_week):
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"""
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通过前天收盘量价，昨天收盘量价，今天的量，构建另一个世界中的价格模型
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"""
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price_change = yesterday_close - bf_yesterday_close
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volume_change = yesterday_volume - bf_yesterday_volume
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# 如果量和价变动一致，今天价格涨，否则跌
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sign = 1.0 if price_change * volume_change > 0 else -1.0
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# 通过date_week生成噪音，否则之后分类100%分对
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if g_with_date_week_noise:
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# 噪音的先决条件是今天的量是这三天最大的
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gen_noise = today_volume > np.max([yesterday_volume, bf_yesterday_volume])
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# 如果是周五，下跌
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if gen_noise and date_week == 4:
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sign = -1.0
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# 如果是周一，上涨
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elif gen_noise and date_week == 0:
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sign = 1.0
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# 今天的涨跌幅度基础是price_change（昨天前天的价格变动）
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price_base = abs(price_change)
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# 今天的涨跌幅度变动因素
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price_factor = np.mean([today_volume / yesterday_volume, today_volume / bf_yesterday_volume])
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# 如果涨跌幅度超过10%，限制上限，下限为10%
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if abs(price_base * price_factor) < yesterday_close * 0.10:
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today_price = yesterday_close + sign * price_base * price_factor
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else:
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today_price = yesterday_close + sign * yesterday_close * 0.10
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return today_price
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def change_real_to_another_word(symbol):
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"""
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将原始真正的股票数据只保留价格的头两个，量，周几，将其它价格使用_gen_another_word_price变成另一个世界价格
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:param symbol:
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:return:
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"""
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kl_pd = ABuSymbolPd.make_kl_df(symbol)
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if kl_pd is not None:
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kl_dream = kl_pd.filter(['close', 'date_week', 'volume'])
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# 只保留原始头两天的交易收盘价格
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kl_dream['close'][2:] = np.nan
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# 将其它价格变成另一个世界中价格
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_gen_another_word_price(kl_dream)
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return kl_dream
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def gen_pig_three_feature(kl_another_word):
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"""
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猪老三构建特征模型函数
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"""
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# 回顾预测的y值
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kl_another_word['regress_y'] = kl_another_word.close.pct_change()
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# 前天收盘价格
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kl_another_word['bf_yesterday_close'] = 0
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# 昨天收盘价格
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kl_another_word['yesterday_close'] = 0
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# 昨天收盘成交量
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kl_another_word['yesterday_volume'] = 0
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# 前天收盘成交量
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kl_another_word['bf_yesterday_volume'] = 0
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# 今天收盘成交量, 不用了用了之后更接近完美，但也算是使用了未来数据，虽然可以狡辩说为快收盘时候买入
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# kl_deram['feature_today_volume'] = kl_deram['volume']
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# 对其特征
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kl_another_word['bf_yesterday_close'][2:] = kl_another_word['close'][:-2]
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kl_another_word['bf_yesterday_volume'][2:] = kl_another_word['volume'][:-2]
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kl_another_word['yesterday_close'][1:] = kl_another_word['close'][:-1]
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kl_another_word['yesterday_volume'][1:] = kl_another_word['volume'][:-1]
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# 特征1: 价格差
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kl_another_word['feature_price_change'] = kl_another_word['yesterday_close'] - kl_another_word['bf_yesterday_close']
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# 特征2: 成交量差
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kl_another_word['feature_volume_Change'] = kl_another_word['yesterday_volume'] - kl_another_word[
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'bf_yesterday_volume']
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# 特征3: 涨跌sign
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kl_another_word['feature_sign'] = np.sign(
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kl_another_word['feature_price_change'] * kl_another_word['feature_volume_Change'])
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# 为之后kmena实例准备数据
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kmean_date_week = kl_another_word['date_week']
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# 构建噪音特征, 因为猪老三也不可能全部分析正确真实的特征因素，这里引入一些噪音特征
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# 成交量乘积
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kl_another_word['feature_volume_noise'] = kl_another_word['yesterday_volume'] * kl_another_word[
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'bf_yesterday_volume']
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# 价格乘积
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kl_another_word['feature_price_noise'] = kl_another_word['yesterday_close'] * kl_another_word['bf_yesterday_close']
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# 将数据标准化
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scaler = preprocessing.StandardScaler()
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kl_another_word['feature_price_change'] = scaler.fit_transform(
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kl_another_word['feature_price_change'].values.reshape(-1, 1))
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kl_another_word['feature_volume_Change'] = scaler.fit_transform(
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kl_another_word['feature_volume_Change'].values.reshape(-1, 1))
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kl_another_word['feature_volume_noise'] = scaler.fit_transform(
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kl_another_word['feature_volume_noise'].values.reshape(-1, 1))
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kl_another_word['feature_price_noise'] = scaler.fit_transform(
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kl_another_word['feature_price_noise'].values.reshape(-1, 1))
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# 只筛选feature_开头的特征和regress_y
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kl_pig_three_feature = kl_another_word.filter(regex='regress_y|feature_*')[2:]
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return kl_pig_three_feature, kmean_date_week[2:]
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