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stock_dream_ml.py
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stock_dream_ml.py
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# -*- encoding:utf-8 -*-
"""
梦想中的机器学习股票数据环境
"""
import numpy as np
from abupy import ABuSymbolPd
import sklearn.preprocessing as preprocessing
"""
是否开启date_week噪音
"""
g_with_date_week_noise = True
def _gen_another_word_price(kl_another_word):
"""
生成股票在另一个世界中的价格
:param kl_another_word:
:return:
"""
for ind in np.arange(2, kl_another_word.shape[0]):
# 前天数据
bf_yesterday = kl_another_word.iloc[ind - 2]
# 昨天
yesterday = kl_another_word.iloc[ind - 1]
# 今天
today = kl_another_word.iloc[ind]
# 生成今天的收盘价格
kl_another_word.close[ind] = _gen_another_word_price_rule(yesterday.close, yesterday.volume,
bf_yesterday.close, bf_yesterday.volume,
today.volume, today.date_week)
def _gen_another_word_price_rule(yesterday_close, yesterday_volume, bf_yesterday_close, bf_yesterday_volume,
today_volume, date_week):
"""
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
"""
price_change = yesterday_close - bf_yesterday_close
volume_change = yesterday_volume - bf_yesterday_volume
# 如果量和价变动一致，今天价格涨，否则跌
sign = 1.0 if price_change * volume_change > 0 else -1.0
# 通过date_week生成噪音，否则之后分类100%分对
if g_with_date_week_noise:
# 噪音的先决条件是今天的量是这三天最大的
gen_noise = today_volume > np.max([yesterday_volume, bf_yesterday_volume])
# 如果是周五，下跌
if gen_noise and date_week == 4:
sign = -1.0
# 如果是周一，上涨
elif gen_noise and date_week == 0:
sign = 1.0
# 今天的涨跌幅度基础是price_change（昨天前天的价格变动）
price_base = abs(price_change)
# 今天的涨跌幅度变动因素
price_factor = np.mean([today_volume / yesterday_volume, today_volume / bf_yesterday_volume])
# 如果涨跌幅度超过10%，限制上限，下限为10%
if abs(price_base * price_factor) < yesterday_close * 0.10:
today_price = yesterday_close + sign * price_base * price_factor
else:
today_price = yesterday_close + sign * yesterday_close * 0.10
return today_price
def change_real_to_another_word(symbol):
"""
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
:param symbol:
:return:
"""
kl_pd = ABuSymbolPd.make_kl_df(symbol)
if kl_pd is not None:
kl_dream = kl_pd.filter(['close', 'date_week', 'volume'])
# 只保留原始头两天的交易收盘价格
kl_dream['close'][2:] = np.nan
# 将其它价格变成另一个世界中价格
_gen_another_word_price(kl_dream)
return kl_dream
def gen_pig_three_feature(kl_another_word):
"""
猪老三构建特征模型函数
"""
# 回顾预测的y值
kl_another_word['regress_y'] = kl_another_word.close.pct_change()
# 前天收盘价格
kl_another_word['bf_yesterday_close'] = 0
# 昨天收盘价格
kl_another_word['yesterday_close'] = 0
# 昨天收盘成交量
kl_another_word['yesterday_volume'] = 0
# 前天收盘成交量
kl_another_word['bf_yesterday_volume'] = 0
# 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
# kl_deram['feature_today_volume'] = kl_deram['volume']
# 对其特征
kl_another_word['bf_yesterday_close'][2:] = kl_another_word['close'][:-2]
kl_another_word['bf_yesterday_volume'][2:] = kl_another_word['volume'][:-2]
kl_another_word['yesterday_close'][1:] = kl_another_word['close'][:-1]
kl_another_word['yesterday_volume'][1:] = kl_another_word['volume'][:-1]
# 特征1: 价格差
kl_another_word['feature_price_change'] = kl_another_word['yesterday_close'] - kl_another_word['bf_yesterday_close']
# 特征2: 成交量差
kl_another_word['feature_volume_Change'] = kl_another_word['yesterday_volume'] - kl_another_word[
'bf_yesterday_volume']
# 特征3: 涨跌sign
kl_another_word['feature_sign'] = np.sign(
kl_another_word['feature_price_change'] * kl_another_word['feature_volume_Change'])
# 为之后kmena实例准备数据
kmean_date_week = kl_another_word['date_week']
# 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
# 成交量乘积
kl_another_word['feature_volume_noise'] = kl_another_word['yesterday_volume'] * kl_another_word[
'bf_yesterday_volume']
# 价格乘积
kl_another_word['feature_price_noise'] = kl_another_word['yesterday_close'] * kl_another_word['bf_yesterday_close']
# 将数据标准化
scaler = preprocessing.StandardScaler()
kl_another_word['feature_price_change'] = scaler.fit_transform(
kl_another_word['feature_price_change'].values.reshape(-1, 1))
kl_another_word['feature_volume_Change'] = scaler.fit_transform(
kl_another_word['feature_volume_Change'].values.reshape(-1, 1))
kl_another_word['feature_volume_noise'] = scaler.fit_transform(
kl_another_word['feature_volume_noise'].values.reshape(-1, 1))
kl_another_word['feature_price_noise'] = scaler.fit_transform(
kl_another_word['feature_price_noise'].values.reshape(-1, 1))
# 只筛选feature_开头的特征和regress_y
kl_pig_three_feature = kl_another_word.filter(regex='regress_y|feature_*')[2:]
return kl_pig_three_feature, kmean_date_week[2:]