一份用交易数据预测股票代码分享
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ps：非本人代码，来自网络开源分享 分享一篇，科赛网《〈 公开新闻预测A股行业板块动向〉〉比赛第三名的开源方案： 本次比赛使用的tushare免费数据，个人可以复现。
import datetime
import os
import sys
from multiprocessing.pool import Pool
import numpy as np
import pandas as pd
import talib
from loguru import logger
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold
from sklearn.svm import SVC
In [6]:
def channel_index_cross(close_list, lowerband_list, upperband_list):
"""
轨道类指标的交叉序列
:param close_list:日线收盘价
:param lowerband_list:下行轨道
:param upperband_list:上行轨道
:return:
"""
result = []
result.append(0)
for i in range(1, len(close_list)):
if close_list[i - 1] < lowerband_list[i - 1] and close_list[i] > lowerband_list[i]:
result.append(1)
elif close_list[i - 1] > upperband_list[i - 1] and close_list[i] < upperband_list[i]:
result.append(-1)
else:
result.append(0)
return result
def index_cross(short_index_values, long_index_values):
"""
计算指标金叉死叉的序列
:param short_index_values: 短周期指标的值
:param long_index_values: 长周期指标的值
:return:
"""
result = []
result.append(0) # 补齐第一天
for i in range(1, len(short_index_values)):
if short_index_values[i - 1] < long_index_values[i] and short_index_values[i] > long_index_values[i]:
result.append(1)
elif short_index_values[i - 1] > long_index_values[i] and short_index_values[i] < long_index_values[i]:
result.append(-1)
else:
result.append(0)
return result
def index_oversold_overbought(day_index_values, min_index_value=20, max_index_value=80):
"""
计算指标值满足超买超卖情况的序列
:param day_index_values: 日线指标的值
:param min_index_value:超卖状态的指标的值，如果指标小于该值，则记录为1
:param max_index_value:超买状态的指标的值，如果指标大于该值，则记录为-1
:return:
"""
res = []
condition_min_list = (day_index_values < min_index_value).tolist()
condition_max_list = (day_index_values > max_index_value).tolist()
for i in range(len(condition_max_list)):
if condition_min_list[i]:
res.append(1)
elif condition_max_list[i]:
res.append(-1)
else:
res.append(0)
return res
def calc_oversold_overbought_value(n, bins):
"""
计算超卖值，超买值
:param n: hist统计出来的n
:param bins:hist统计出来的bins
:return:(超卖值，超买值)
"""
oversold = 0
overbought = 0
for i in range(len(n)):
if n[i] > 0:
oversold = bins[i].right
break
for i in range(len(n) - 1, -1, -1):
if n[i] > 0:
overbought = bins[i].left
break
return oversold, overbought
def calculate_oversold_overbought(index_values, min_value=0, max_value=100, step=10):
"""
计算超买超卖区域
:param index_values: 指标值
:param min_value: 指标最小值
:param max_value: 指标最大值
:param step: 统计超买超卖区域的步长
:return: (超卖值,超买值)
"""
index_counter_series = pd.Series(index_values).value_counts(normalize=False, sort=False, ascending=True,
bins=[t for t in
range(min_value, max_value + step, step)])
n = index_counter_series.values.tolist()
bins = index_counter_series.index.values
return calc_oversold_overbought_value(n, bins)
In [7]:
pd.set_option('mode.chained_assignment', None)
logger.add("data_hive.log", format="{time} {level} {line} {message}", level='DEBUG', rotation="5 MB")
algorithms = [
[RandomForestClassifier(random_state=1, n_estimators=100, min_samples_split=4, min_samples_leaf=2),
["boll_upper", "boll_middle", "boll_lower", "k", "d", "j", "macd", "macd_signal", "macd_hist",
"cci6", "rsi6", "willr6", "cci10", "rsi10", "willr10",
'boll_cross', 'kd_cross', 'kj_cross', 'cci_cross', 'rsi_cross', 'willr_cross',
'over_cci6', 'over_cci10', 'over_willr6', 'over_willr10', 'over_j']],
[LogisticRegression(random_state=1, solver='liblinear'),
["boll_upper", "boll_middle", "boll_lower", "k", "d", "j", "macd", "macd_signal", "macd_hist",
"cci6", "rsi6", "willr6", "cci10", "rsi10", "willr10",
'boll_cross', 'kd_cross', 'kj_cross', 'cci_cross', 'rsi_cross', 'willr_cross',
'over_cci6', 'over_cci10', 'over_willr6', 'over_willr10', 'over_j']],
[SVC(C=1.0, kernel='linear', probability=True),
["boll_upper", "boll_middle", "boll_lower", "k", "d", "j", "macd", "macd_signal", "macd_hist",
"cci6", "rsi6", "willr6", "cci10", "rsi10", "willr10",
'boll_cross', 'kd_cross', 'kj_cross', 'cci_cross', 'rsi_cross', 'willr_cross',
'over_cci6', 'over_cci10', 'over_willr6', 'over_willr10', 'over_j']]
]
def __get_n_day_str():
"""
获取未来三天日期
:return:
"""
today = datetime.date.today()
today_str = datetime.datetime.strftime(today, "%Y%m%d")
dt = datetime.datetime.strptime(today_str, "%Y%m%d")
delta_day = datetime.timedelta(days=1)
date1 = dt + delta_day
date2 = date1 + delta_day
date3 = date2 + delta_day
return [int(datetime.datetime.strftime(date1, "%Y%m%d")), int(datetime.datetime.strftime(date2, "%Y%m%d")), int(datetime.datetime.strftime(date3, "%Y%m%d"))]
date_list = __get_n_day_str()
def load_data(csv_data_fullname):
day_dataframe = pd.read_csv(csv_data_fullname)
result_list = method_name2(day_dataframe)
if os.path.exists('result.csv'):
os.remove('result.csv')
with open('result.csv', 'w') as f:
f.write('{0},{1},{2}\n'.format('ts_code', 'trade_date', 'p'))
for p_result in result_list:
f.write('{0},{1},{2}\n'.format(p_result[0][0:6], p_result[1], p_result[2]))
def method_name2(dataframe):
result_list = []
pool = Pool()
tuple_list = list()
for stock_id, day_dataframe in dataframe.groupby('ts_code'):
day_dataframe = day_dataframe.sort_values(by=['trade_date'])
tuple_list.append((stock_id, day_dataframe))
temp_result_list_2d = pool.map(task, tuple_list)
for temp_list in temp_result_list_2d:
result_list.extend(temp_list)
pool.close()
pool.join()
return result_list
def task(tuple_stockid_daydataframe):
stock_id = tuple_stockid_daydataframe[0]
day_dataframe = tuple_stockid_daydataframe[1]
logger.debug('板块ID:{0}'.format(stock_id))
result_list = list()
stock_name = day_dataframe.iloc[0]['name']
day_open_series = day_dataframe['open']
day_high_series = day_dataframe['high']
day_low_series = day_dataframe['low']
day_close_series = day_dataframe['close']
logger.debug('初始化结果值开始:{0}'.format(stock_id))
y1, y2, y3 = init_target_values(day_close_series, day_open_series)
day_dataframe['y1'] = y1
day_dataframe['y2'] = y2
day_dataframe['y3'] = y3
logger.debug('初始化结果值完成:{0}'.format(stock_id))
logger.debug('初始化指标值开始:{0}'.format(stock_id))
init_index_values(day_high_series, day_low_series, day_close_series, day_dataframe)
logger.debug('初始化指标值完成:{0}'.format(stock_id))
day_data_dataframe = pd.DataFrame(day_dataframe,
columns=['open', 'low', 'low', 'close', 'change', 'vol', 'amount',
'pe', 'pb',
'y1', 'y2', 'y3',
'boll_upper', 'boll_middle', 'boll_lower',
'k', 'd', 'j',
'macd', 'macd_signal', 'macd_hist',
'cci6', 'rsi6', 'willr6',
'cci10', 'rsi10', 'willr10',
'boll_cross', 'kd_cross', 'kj_cross', 'cci_cross', 'rsi_cross', 'willr_cross',
'over_cci6', 'over_cci10', 'over_willr6', 'over_willr10', 'over_j'
])
logger.debug('开始预测:{0}'.format(stock_id))
pre_value1 = get_pre_value(algorithms=algorithms, data_dataframe=day_data_dataframe, target_label='y1')
pre_value2 = get_pre_value(algorithms=algorithms, data_dataframe=day_data_dataframe, target_label='y2')
pre_value3 = get_pre_value(algorithms=algorithms, data_dataframe=day_data_dataframe, target_label='y3')
logger.debug('预测完成:{0}'.format(stock_id))
result_list.append((stock_id, date_list[0], pre_value1))
result_list.append((stock_id, date_list[1], pre_value2))
result_list.append((stock_id, date_list[2], pre_value3))
return result_list
def get_pre_value(algorithms, data_dataframe, target_label='y1'):
# pre_value = 0.5
pre_value1 = get_pre_value_by_alg(algorithms[0][0], algorithms[0][1], target_label, data_dataframe)
pre_value2 = get_pre_value_by_alg(algorithms[1][0], algorithms[1][1], target_label, data_dataframe)
pre_value3 = get_pre_value_by_alg(algorithms[2][0], algorithms[2][1], target_label, data_dataframe)
logger.debug('alg1:{0}'.format(pre_value1))
logger.debug('alg2:{0}'.format(pre_value2))
logger.debug('alg3:{0}'.format(pre_value3))
if pre_value1 + pre_value2 + pre_value3 > 1.0: # 三种算法举手表决，如果两个算法预测上涨，则为上涨，否则为下跌
pre_value = 1
else:
pre_value = 0
return pre_value
def get_pre_value_by_alg(alg, predictors, target_label, data_dataframe):
train_target1 = data_dataframe[target_label].iloc[:]
alg.fit(data_dataframe[predictors].iloc[:], train_target1)
alg_result1 = alg.predict([data_dataframe[predictors].iloc[-1, :].values.tolist()])
return alg_result1[0]
def init_index_values(high_series, low_series, close_series, group):
lowerband, middleband, upperband = calc_boll_values(close_series)
k, d, j = calc_kdj_values(close_series, high_series, low_series)
macd, macd_hist, macd_signal = calc_macd_values(close_series)
cci_6 = calc_cci_values(close_series, high_series, low_series, timeperiod=6)
rsi_6 = calc_rsi_values(close_series, timeperiod=6)
willr_6 = calc_willr_values(close_series, high_series, low_series, timeperiod=6)
cci_10 = calc_cci_values(close_series, high_series, low_series, timeperiod=10)
rsi_10 = calc_rsi_values(close_series, timeperiod=10)
willr_10 = calc_willr_values(close_series, high_series, low_series, timeperiod=10)
group['boll_upper'] = upperband
group['boll_middle'] = middleband
group['boll_lower'] = lowerband
group['k'] = k
group['d'] = d
group['j'] = j
group['macd'] = macd
group['macd_signal'] = macd_signal
group['macd_hist'] = macd_hist
group['cci6'] = cci_6
group['rsi6'] = rsi_6
group['willr6'] = willr_6
group['cci10'] = cci_10
group['rsi10'] = rsi_10
group['willr10'] = willr_10
boll_cross_list = channel_index_cross(close_series.values.tolist(), lowerband.tolist(), upperband.tolist())
kd_cross = index_cross(k.tolist(), d.tolist())
kj_cross = index_cross(k.tolist(), j.tolist())
cci_cross = index_cross(cci_6.tolist(), cci_10.tolist())
rsi_cross = index_cross(rsi_6.tolist(), rsi_10.tolist())
willr_cross = index_cross(willr_6.tolist(), willr_10.tolist())
group['boll_cross'] = boll_cross_list
group['kd_cross'] = kd_cross
group['kj_cross'] = kj_cross
group['cci_cross'] = cci_cross
group['rsi_cross'] = rsi_cross
group['willr_cross'] = willr_cross
oversold_cci6, overbought_cci6 = calculate_oversold_overbought(cci_6, min_value=-100, max_value=100)
over_cci6 = index_oversold_overbought(cci_6, oversold_cci6, overbought_cci6)
oversold_cci10, overbought_cci10 = calculate_oversold_overbought(cci_10, min_value=-100, max_value=100)
over_cci10 = index_oversold_overbought(cci_10, oversold_cci10, overbought_cci10)
oversold_willr6, overbought_willr6 = calculate_oversold_overbought(willr_6, min_value=-100, max_value=100)
over_willr6 = index_oversold_overbought(willr_6, oversold_willr6, overbought_willr6)
oversold_willr10, overbought_willr10 = calculate_oversold_overbought(willr_10, min_value=-100, max_value=100)
over_willr10 = index_oversold_overbought(willr_10, oversold_willr10, overbought_willr10)
oversold_j, overbought_j = calculate_oversold_overbought(j, min_value=-100, max_value=100)
over_j = index_oversold_overbought(j, oversold_j, overbought_j)
group['over_cci6'] = over_cci6
group['over_cci10'] = over_cci10
group['over_willr6'] = over_willr6
group['over_willr10'] = over_willr10
group['over_j'] = over_j
# deviate_cci_6 = bottom_deviate(close_series.values, cci_6, timeperiod=5)
# deviate_cci_10 = bottom_deviate(close_series.values, cci_10, timeperiod=5)
# deviate_willr_6 = bottom_deviate(close_series.values, willr_6, timeperiod=5)
# deviate_willr_10 = bottom_deviate(close_series.values, willr_10, timeperiod=5)
# deviate_macd = bottom_deviate(close_series.values, macd, timeperiod=5)
# deviate_macd_hist = bottom_deviate(close_series.values, macd_hist, timeperiod=5)
# deviate_macd_signal = bottom_deviate(close_series.values, macd_signal, timeperiod=5)
# group['deviate_cci6'] = deviate_cci_6
# group['deviate_cci10'] = deviate_cci_10
# group['deviate_willr6'] = deviate_willr_6
# group['deviate_willr10'] = deviate_willr_10
# group['deviate_macd'] = deviate_macd
# group['deviate_macd_hist'] = deviate_macd_hist
# group['deviate_macd_signal'] = deviate_macd_signal
def calc_willr_values(close_series, high_series, low_series, timeperiod=6):
willr_series = talib.WILLR(high_series, low_series, close_series, timeperiod=timeperiod)
willr = np.where(np.isnan(willr_series), -50, willr_series)
return willr
def calc_rsi_values(close_series, timeperiod=6):
rsi_series = talib.RSI(close_series, timeperiod=timeperiod)
rsi = np.where(np.isnan(rsi_series), 50, rsi_series)
return rsi
def calc_macd_values(close_series):
macd_series, macd_signal_series, macd_hist_series = talib.MACD(close_series, fastperiod=12, slowperiod=26,
signalperiod=9)
macd = np.where(np.isnan(macd_series), 0, macd_series)
macd_signal = np.where(np.isnan(macd_signal_series), 0, macd_signal_series)
macd_hist = np.where(np.isnan(macd_hist_series), 0, macd_hist_series)
return macd, macd_hist, macd_signal
def calc_kdj_values(close_series, high_series, low_series):
k_series, d_series = talib.STOCH(high_series, low_series, close_series, fastk_period=9, slowk_period=3,
slowk_matype=0, slowd_period=3, slowd_matype=0)
k = np.where(np.isnan(k_series), 50, k_series)
d = np.where(np.isnan(d_series), 50, d_series)
j = 3 * k - 2 * d
return k, d, j
def calc_cci_values(close_series, high_series, low_series, timeperiod=6):
cci_series = talib.CCI(high_series, low_series, close_series, timeperiod=timeperiod)
cci = np.where(np.isnan(cci_series), 0, cci_series)
return cci
def calc_boll_values(close_series):
upperband_series, middleband_series, lowerband_series = talib.BBANDS(close_series, timeperiod=20, nbdevup=2,
nbdevdn=2, matype=0)
upperband = np.where(np.isnan(upperband_series), 0, upperband_series)
middleband = np.where(np.isnan(middleband_series), 0, middleband_series)
lowerband = np.where(np.isnan(lowerband_series), 0, lowerband_series)
return lowerband, middleband, upperband
def init_target_values(close_series, open_series):
y1 = []
y2 = []
y3 = []
for i in range(close_series.shape[0] - 1):
if i + 1 < close_series.shape[0]:
if close_series.iloc[i + 1] > open_series.iloc[i]:
y1.append(1)
else:
y1.append(0)
if i + 2 < close_series.shape[0]:
if close_series.iloc[i + 2] > open_series.iloc[i]:
y2.append(1)
else:
y2.append(0)
if i + 3 < close_series.shape[0]:
if close_series.iloc[i + 3] > open_series.iloc[i]:
y3.append(1)
else:
y3.append(0)
y1.append(0)
y2.append(0)
y2.append(0)
y3.append(0)
y3.append(0)
y3.append(0)
return y1, y2, y3
In [8]:
load_data('/home/kesci/input/input9882/TRAINSET_STOCK.csv')
详情请看连接https://www.kesci.com/home/project/5d353597cf76a60036f579ad 非常欢迎讨论与分享，希望可以在bigquant平台做成策略 QQ2260068285希望可以认识更多大佬
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