In [1]:
import numpy as np
import pandas as pd
import talib
def fix_data(path):
tmp = pd.read_csv(path, encoding="gbk", engine='python')
tmp.rename(columns={'Unnamed: 0':'trading_time'}, inplace=True)
tmp['trading_point'] = pd.to_datetime(tmp.trading_time)
del tmp['trading_time']
tmp.set_index(tmp.trading_point, inplace=True)
return tmp
def High_2_Low(tmp, freq):
"""处理从RiceQuant下载的分钟线数据，
从分钟线数据合成低频数据
2017-08-11
"""
# 分别处理bar数据
tmp_open = tmp['open'].resample(freq).ohlc()
tmp_open = tmp_open['open'].dropna()
tmp_high = tmp['high'].resample(freq).ohlc()
tmp_high = tmp_high['high'].dropna()
tmp_low = tmp['low'].resample(freq).ohlc()
tmp_low = tmp_low['low'].dropna()
tmp_close = tmp['close'].resample(freq).ohlc()
tmp_close = tmp_close['close'].dropna()
tmp_price = pd.concat([tmp_open, tmp_high, tmp_low, tmp_close], axis=1)
# 处理成交量
tmp_volume = tmp['volume'].resample(freq).sum()
tmp_volume.dropna(inplace=True)
return pd.concat([tmp_price, tmp_volume], axis=1)
def get_factors(index,
Open,
Close,
High,
Low,
Volume,
rolling = 26,
drop=False,
normalization=True):
tmp = pd.DataFrame()
tmp['tradeTime'] = index
#累积/派发线（Accumulation / Distribution Line，该指标将每日的成交量通过价格加权累计，
#用以计算成交量的动量。属于趋势型因子
tmp['AD'] = talib.AD(High, Low, Close, Volume)
# 佳庆指标（Chaikin Oscillator），该指标基于AD曲线的指数移动均线而计算得到。属于趋势型因子
tmp['ADOSC'] = talib.ADOSC(High, Low, Close, Volume, fastperiod=3, slowperiod=10)
# 平均动向指数，DMI因子的构成部分。属于趋势型因子
tmp['ADX'] = talib.ADX(High, Low, Close,timeperiod=14)
# 相对平均动向指数，DMI因子的构成部分。属于趋势型因子
tmp['ADXR'] = talib.ADXR(High, Low, Close,timeperiod=14)
# 绝对价格振荡指数
tmp['APO'] = talib.APO(Close, fastperiod=12, slowperiod=26)
# Aroon通过计算自价格达到近期最高值和最低值以来所经过的期间数，帮助投资者预测证券价格从趋势到区域区域或反转的变化，
#Aroon指标分为Aroon、AroonUp和AroonDown3个具体指标。属于趋势型因子
tmp['AROONDown'], tmp['AROONUp'] = talib.AROON(High, Low,timeperiod=14)
tmp['AROONOSC'] = talib.AROONOSC(High, Low,timeperiod=14)
# 均幅指标（Average TRUE Ranger），取一定时间周期内的股价波动幅度的移动平均值，
#是显示市场变化率的指标，主要用于研判买卖时机。属于超买超卖型因子。
tmp['ATR14']= talib.ATR(High, Low, Close, timeperiod=14)
tmp['ATR6']= talib.ATR(High, Low, Close, timeperiod=6)
# 布林带
tmp['Boll_Up'],tmp['Boll_Mid'],tmp['Boll_Down']= talib.BBANDS(Close, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
# 均势指标
tmp['BOP'] = talib.BOP(Open, High, Low, Close)
#5日顺势指标（Commodity Channel Index），专门测量股价是否已超出常态分布范围。属于超买超卖型因子。
tmp['CCI5'] = talib.CCI(High, Low, Close, timeperiod=5)
tmp['CCI10'] = talib.CCI(High, Low, Close, timeperiod=10)
tmp['CCI20'] = talib.CCI(High, Low, Close, timeperiod=20)
tmp['CCI88'] = talib.CCI(High, Low, Close, timeperiod=88)
# 钱德动量摆动指标（Chande Momentum Osciliator），与其他动量指标摆动指标如相对强弱指标（RSI）和随机指标（KDJ）不同，
# 钱德动量指标在计算公式的分子中采用上涨日和下跌日的数据。属于超买超卖型因子
tmp['CMO_Close'] = talib.CMO(Close,timeperiod=14)
tmp['CMO_Open'] = talib.CMO(Close,timeperiod=14)
# DEMA双指数移动平均线
tmp['DEMA6'] = talib.DEMA(Close, timeperiod=6)
tmp['DEMA12'] = talib.DEMA(Close, timeperiod=12)
tmp['DEMA26'] = talib.DEMA(Close, timeperiod=26)
# DX 动向指数
tmp['DX'] = talib.DX(High, Low, Close,timeperiod=14)
# EMA 指数移动平均线
tmp['EMA6'] = talib.EMA(Close, timeperiod=6)
tmp['EMA12'] = talib.EMA(Close, timeperiod=12)
tmp['EMA26'] = talib.EMA(Close, timeperiod=26)
# KAMA 适应性移动平均线
tmp['KAMA'] = talib.KAMA(Close, timeperiod=30)
# MACD
tmp['MACD_DIF'],tmp['MACD_DEA'],tmp['MACD_bar'] = talib.MACD(Close, fastperiod=12, slowperiod=24, signalperiod=9)
# 中位数价格 不知道是什么意思
tmp['MEDPRICE'] = talib.MEDPRICE(High, Low)
# 负向指标 负向运动
tmp['MiNUS_DI'] = talib.MINUS_DI(High, Low, Close,timeperiod=14)
tmp['MiNUS_DM'] = talib.MINUS_DM(High, Low,timeperiod=14)
# 动量指标（Momentom Index），动量指数以分析股价波动的速度为目的，研究股价在波动过程中各种加速，
#减速，惯性作用以及股价由静到动或由动转静的现象。属于趋势型因子
tmp['MOM'] = talib.MOM(Close, timeperiod=10)
# 归一化平均值范围
tmp['NATR'] = talib.NATR(High, Low, Close,timeperiod=14)
# OBV 能量潮指标（On Balance Volume，OBV），以股市的成交量变化来衡量股市的推动力，
#从而研判股价的走势。属于成交量型因子
tmp['OBV'] = talib.OBV(Close, Volume)
# PLUS_DI 更向指示器
tmp['PLUS_DI'] = talib.PLUS_DI(High, Low, Close,timeperiod=14)
tmp['PLUS_DM'] = talib.PLUS_DM(High, Low, timeperiod=14)
# PPO 价格振荡百分比
tmp['PPO'] = talib.PPO(Close, fastperiod=6, slowperiod= 26, matype=0)
# ROC 6日变动速率（Price Rate of Change），以当日的收盘价和N天前的收盘价比较，
#通过计算股价某一段时间内收盘价变动的比例，应用价格的移动比较来测量价位动量。属于超买超卖型因子。
tmp['ROC6'] = talib.ROC(Close, timeperiod=6)
tmp['ROC20'] = talib.ROC(Close, timeperiod=20)
#12日量变动速率指标（Volume Rate of Change），以今天的成交量和N天前的成交量比较，
#通过计算某一段时间内成交量变动的幅度，应用成交量的移动比较来测量成交量运动趋向，
#达到事先探测成交量供需的强弱，进而分析成交量的发展趋势及其将来是否有转势的意愿，
#属于成交量的反趋向指标。属于成交量型因子
tmp['VROC6'] = talib.ROC(Volume, timeperiod=6)
tmp['VROC20'] = talib.ROC(Volume, timeperiod=20)
# ROC 6日变动速率（Price Rate of Change），以当日的收盘价和N天前的收盘价比较，
#通过计算股价某一段时间内收盘价变动的比例，应用价格的移动比较来测量价位动量。属于超买超卖型因子。
tmp['ROCP6'] = talib.ROCP(Close, timeperiod=6)
tmp['ROCP20'] = talib.ROCP(Close, timeperiod=20)
#12日量变动速率指标（Volume Rate of Change），以今天的成交量和N天前的成交量比较，
#通过计算某一段时间内成交量变动的幅度，应用成交量的移动比较来测量成交量运动趋向，
#达到事先探测成交量供需的强弱，进而分析成交量的发展趋势及其将来是否有转势的意愿，
#属于成交量的反趋向指标。属于成交量型因子
tmp['VROCP6'] = talib.ROCP(Volume, timeperiod=6)
tmp['VROCP20'] = talib.ROCP(Volume, timeperiod=20)
# RSI
tmp['RSI'] = talib.RSI(Close, timeperiod=14)
# SAR 抛物线转向
tmp['SAR'] = talib.SAR(High, Low, acceleration=0.02, maximum=0.2)
# TEMA
tmp['TEMA6'] = talib.TEMA(Close, timeperiod=6)
tmp['TEMA12'] = talib.TEMA(Close, timeperiod=12)
tmp['TEMA26'] = talib.TEMA(Close, timeperiod=26)
# TRANGE 真实范围
tmp['TRANGE'] = talib.TRANGE(High, Low, Close)
# TYPPRICE 典型价格
tmp['TYPPRICE'] = talib.TYPPRICE(High, Low, Close)
# TSF 时间序列预测
tmp['TSF'] = talib.TSF(Close, timeperiod=14)
# ULTOSC 极限振子
tmp['ULTOSC'] = talib.ULTOSC(High, Low, Close, timeperiod1=7, timeperiod2=14, timeperiod3=28)
# 威廉指标
tmp['WILLR'] = talib.WILLR(High, Low, Close, timeperiod=14)
# 标准化
if normalization:
factors_list = tmp.columns.tolist()[1:]
if rolling >= 26:
for i in factors_list:
tmp[i] = (tmp[i] - tmp[i].rolling(window=rolling, center=False).mean())\
/tmp[i].rolling(window=rolling, center=False).std()
elif rolling < 26 & rolling > 0:
print ('Recommended rolling range greater than 26')
elif rolling <=0:
for i in factors_list:
tmp[i] = (tmp[i] - tmp[i].mean())/tmp[i].std()
if drop:
tmp.dropna(inplace=True)
tmp.set_index('tradeTime', inplace=True)
return tmp
In [2]:
tmp = fix_data('HS300.csv')
tmp = High_2_Low(tmp, '5min')
Dtmp = High_2_Low(tmp, '1d')
Index = tmp.index
High = tmp.high.values
Low = tmp.low.values
Close = tmp.close.values
Open = tmp.open.values
Volume = tmp.volume.values
factors = get_factors(Index, Open, Close, High, Low, Volume, rolling = 188, drop=True)
Dtmp['returns'] = np.log(Dtmp['close'].shift(-1)/Dtmp['close'])
Dtmp.dropna(inplace=True)
start_date = pd.to_datetime('2011-01-12')
end_date = pd.to_datetime('2016-12-29')
Dtmp = Dtmp.loc[start_date:end_date]
Dtmp = Dtmp.iloc[5:]
factors = factors.loc[start_date:end_date]
flist = []
for i in range(len(Dtmp)):
s = i * 50
e = (i + 5) * 50
f = np.array(factors.iloc[s:e])
flist.append(np.expand_dims(f, axis=0))
fac_array = np.concatenate(flist, axis=0)
shape = [fac_array.shape[0], 5, 50, fac_array.shape[2]]
fac_array = fac_array.reshape(shape)
fac_array = np.transpose(fac_array, [0,2,3,1])
data_quotes = Dtmp
data_fac = fac_array
In [3]:
class Account(object):
def __init__(self, data_quotes, data_fac):
self.data_close = data_quotes['close']
self.data_open = data_quotes['open']
self.data_observation = data_fac
self.action_space = ['long', 'short', 'close']
self.free = 1e-4
self.reset()
def reset(self):
self.step_counter = 0
self.cash = 1e5
self.position = 0
self.total_value = self.cash + self.position
self.flags = 0
def get_initial_state(self):
return np.expand_dims(self.data_observation[0],axis=0)
def get_action_space(self):
return self.action_space
def long(self):
self.flags = 1
quotes = self.data_open[self.step_counter] * 10
self.cash -= quotes * (1 + self.free)
self.position = quotes
def short(self):
self.flags = -1
quotes = self.data_open[self.step_counter] * 10
self.cash += quotes * (1 - self.free)
self.position = - quotes
def keep(self):
quotes = self.data_open[self.step_counter] * 10
self.position = quotes * self.flags
def close_long(self):
self.flags = 0
quotes = self.data_open[self.step_counter] * 10
self.cash += quotes * (1 - self.free)
self.position = 0
def close_short(self):
self.flags = 0
quotes = self.data_open[self.step_counter] * 10
self.cash -= quotes * (1 + self.free)
self.position = 0
def step_op(self, action):
if action == 'long':
if self.flags == 0:
self.long()
elif self.flags == -1:
self.close_short()
self.long()
else:
self.keep()
elif action == 'close':
if self.flags == 1:
self.close_long()
elif self.flags == -1:
self.close_short()
else:
pass
elif action == 'short':
if self.flags == 0:
self.short()
elif self.flags == 1:
self.close_long()
self.short()
else:
self.keep()
else:
raise ValueError("action should be elements of ['long', 'short', 'close']")
position = self.data_close[self.step_counter] * 10 * self.flags
reward = self.cash + position - self.total_value
self.step_counter += 1
self.total_value = position + self.cash
next_observation = self.data_observation[self.step_counter]
done = False
if self.total_value < 4000:
done = True
if self.step_counter > 600:
done = True
return reward, np.expand_dims(next_observation, axis=0), done
def step(self, action):
if action == 0:
return self.step_op('long')
elif action == 1:
return self.step_op('short')
elif action == 2:
return self.step_op('close')
else:
raise ValueError("action should be one of [0,1,2]")
In [4]:
env = Account(data_quotes, data_fac)
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