def signal(gpdm, gpmc, start, m1, m, n, in_threshold, de_threshold):
tdxday = Tdxday(gpdm)
ohlc = tdxday.get_qfqdata(start=start)
if ohlc.empty:
return None
ohlc.ta.doublebottom(append=True, m1=m1, m=m, n1=n1, n=n,
in_threshold=in_threshold, de_threshold=de_threshold)
sc = get_gpfl(gpdm)[0].upper()
gpdm = f'{gpdm}.{sc}'
gpmc1 = get_gpmc_fromdb(gpdm)
ohlc = pd.merge(ohlc, gpmc1, on='date', how='outer')
ohlc = ohlc.sort_index()
ohlc['gpmc'].fillna(method='ffill', inplace=True)
# 下句对于新股，数据库中还没有日期对应名称的情况进行处理
ohlc.loc[ohlc['gpmc'].isnull(), 'gpmc'] = gpmc
ohlc = ohlc.assign(date=ohlc.index)
start1 = '20180101'
ohlc = ohlc.loc[(ohlc['date'] >= start1)]
# ohlc1 = ohlc.loc[(ohlc['double_bott'] == 1)]
# ohlc = selefirstsignal(ohlc)
# if ohlc1.empty:
# return None
ohlc = ohlc.assign(gpdm=tdxday.gpdm)
ohlc = ohlc.assign(ssdate=tdxday.ssdate)
df = dfsortcolumns(ohlc, subset='gpdm,gpmc,ssdate,date,close')
# 注意：df.iloc[-1]返回的是pandas.core.series.Series
# 下句-1后面有冒号，返回的是pandas.core.frame.DataFrame。“:”不能少，
# return df.iloc[-1:]
return df
def signal(gpdm, start, m1, m, n, in_threshold, de_threshold):
tdxday = Tdxday(gpdm)
ohlc = tdxday.get_qfqdata(start=start)
if ohlc.empty:
return None
ohlc.ta.doublebottom(append=True,
m1=m1,
m=m,
n1=n1,
n=n,
in_threshold=in_threshold,
de_threshold=de_threshold)
try:
# 新股由于交易天数少，无法计算，会出现没有返回
# double_bott,double_bott1列的情况
# signals = ohlc.loc[(ohlc['double_bott'] == 1)]
# signals = ohlc.loc[(ohlc['double_bott1'] == 1)]
signals = ohlc.loc[(ohlc['double_bott2'] == 1)]
except:
return None
if not signals.empty:
signals = signals.assign(date=signals.index)
signals = signals.assign(gpdm=tdxday.gpdm)
signals = signals.assign(gpmc=tdxday.gpmc)
return dfsortcolumns(signals, subset='gpdm,gpmc,date')
return None
def signal(gpdm):
tdxday = Tdxday(gpdm)
start = '20170101'
ohlc = tdxday.get_qfqdata(start=start)
if ohlc.empty:
return None
ohlc.ta.bottompattern(append=True)
signals = ohlc.loc[(ohlc['BOTTOM_REVERSAL'] == 1)]
if not signals.empty:
signals['date'] = signals.index
signals['gpdm'] = tdxday.gpdm
signals['gpmc'] = tdxday.gpmc
return signals[['date', 'gpdm', 'gpmc']]
return None
def get_macd(df):
columns = []
for col in ['macd_', 'macds_', 'macdh_']:
matches = df.columns.str.match(col, case=False)
match = [i for i, x in enumerate(matches) if x]
if len(match) != 1:
print('macd指标有误')
return None
columns.append(df.columns[match[0]])
return df[columns]
if __name__ == '__main__':
import sys
filename = '603093'
tdxday = Tdxday(filename)
# stock = tdxday.get_data_yahoo(start='20180101')
# stock = tdxday.get_qfqdata(start='20180101', otype='ohlc')
# stock = tdxday.get_data_backtrader(start='20180101')
# scdm = stock.scdm
# stock.to_csv(f"{scdm}{filename}.csv")
stock = tdxday.get_qfqdata(start='20130101')
stock.ta.doublebottom(append=True, in_threshold=0.3, de_threshold=-0.2)
stock.to_csv('tmp1.csv')
sys.exit()
stock.ta.alligator(append=True)
stock.ta.macd(append=True)
stock.ta.fractals(append=True)
stock.ta.bottomreversal(append=True)
# [('Buy', 'buy'), ('Sell', 'sell'), ('Short', 'short'), ('Cover', 'cover')]
for f_int, f_ext in zip(int_mask, ext_mask):
obj = dataobj.get(f_ext)
if isinstance(obj, pd.Series):
df[f_int] = obj
else:
df[f_int] = None
if any([isinstance(x, pd.Series) for x in list(df.values())]):
return pd.DataFrame(df)
return None
if __name__ == '__main__':
filename = '002456'
tdxday = Tdxday(filename)
ohlc = tdxday.get_qfqdata(start='20170101')
sys.exit()
# pybacktest.Backtest通过从python locals()提取交易原始数据
# pandas DataFrame的名称可以命名为'ohlc', 'bars', 'ohlcv'
# 数据格式遵循Amibroker的命名约定，date(index)，O(open),H(high),
# L(low),C(close)
# 信号Series的名称buy,sell,cover(Boy to Cover),short(Sell Short)
ohlc = tdxday.get_data_pybacktest(start='20110101')
ohlc.ta.fractals(high='H', low='L', open='O', close='C', append=True)
ohlc.ta.bottomreversal(high='H', low='L', open='O', close='C', append=True)
ohlc.ta.topreversal(high='H', low='L', open='O', close='C', append=True)
scdm = ohlc.scdm
ohlc.to_csv(f"{scdm}{filename}.csv")
# buy买入，sell卖出，Buy to Cover 空单补回，Sell Short 卖空
author: huangyunbin email: [email protected] QQ: 592440193 """ from stock_pandas.tdx.tdxdayread import Tdxday from stock_pandas.tdx.tdxcfg import Tdx from stock_pandas.tdx.class_func import * import pandas_ta as ta if __name__ == '__main__': filename = '600138' tdxday = Tdxday(filename) df2 = tdxday.get_qfqdata(start='20190101') df2.ta.bottomreversal(append=True) # df2.to_csv('tmp1.csv') # df2.ta.alligator(append=True) # df2 = df2.assign(bias = 100 * (df2['close'] - df2['ALLIGATOR_TEETH_8_5']) / df2['ALLIGATOR_TEETH_8_5']) # df2.ta.fractals(append=True) # df2=df2.assign(A=(df2['bias']>15),B=(df2['bias']<-15)) #if __name__ == '__main__': # tdxday = Tdxday(r'D:\tdx\sz002294.day') # df = tdxday.get_data() # # df2 = get_data(r'D:\tdx\szlday\sz002294.day') #
QQ: 592440193
"""
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from collections import OrderedDict
from stock_pandas.tdx.tdxdayread import Tdxday
from stock_pandas.tdx.tdxcfg import Tdx
from stock_pandas.tdx.class_func import *
import pandas_ta as ta
filename = '600138'
tdxday = Tdxday(filename)
df = tdxday.get_qfqdata('20150101')
rng_date = df.index
df = df.reset_index() #重新按行号建立索引
def tick_category(frequency, step):
'''
刻度分类：根据频率和频数获取刻度的时间轴
frequency 可取值: 'year','month','day','hour','minute','second'
'''
if frequency == 'year':
df['frequency'] = rng_date.strftime('%Y')
elif frequency == 'month':
for t in troughs.index:
for p in peaks.index:
if p > t:
z = (peaks[p] / troughs[t] - 1) * 100
if (zf is None) or zf < z:
zf = z
tt = t
pp = p
return tt.strftime('%Y-%m-%d'), pp.strftime('%Y-%m-%d'), round(zf, 2)
if __name__ == "__main__":
gpdm = '002451'
tdxday = Tdxday(gpdm)
start = '20180101'
end = '20191231'
df = tdxday.get_qfqdata(start=start, end=end)
ds = round(df['close'], 4)
sys.exit()
# 波峰：大于前一，同时大于后一或等于后一且大于后二
p = (ds.diff(1) > 0) & ((ds.diff(-1) > 0) | ((ds.diff(-1) == 0) &
(ds.diff(-2) > 0)))
peaks = ds.loc[p]
# 波谷：小于前一，同时小于后一或等于后一且小于后二
t = (ds.diff(1) < 0) & ((ds.diff(-1) < 0) | ((ds.diff(-1) == 0) &
(ds.diff(-2) < 0)))
troughs = ds.loc[t]
# 起始点
start = pd.Series([ds[0]], index=[ds.index[0]])
for n in maxset:
df1 = hhv(ds, n)
df = df.join(df1)
for n in minset:
df1 = llv(ds, n)
df = df.join(df1)
# df = df.drop(columns=[name])
return df
maxmin.__doc__ = \
'''
思路：假定股价经过较长时间的盘整，近期拉升，最近回调。通过查找n天内和n1天内的高点
如果两个高点重合，即确定该高点为近期高点（位置idxmax）。再从idxmax往前查找m天内和
m1天内点近远两个低点（idxmin,idxmin1），
'''
if __name__ == '__main__':
from stock_pandas.tdx.tdxdayread import Tdxday
from stock_pandas.tdx.class_func import *
gpdm = '600734'
start = '20180101'
tdxday = Tdxday(gpdm)
ohlc = tdxday.get_qfqdata(start=start)
close = ohlc.close
df = maxmin(ohlc.close)
# sys.exit()
data = []
j = 30
k = 20
n = 20
m = 60
ln = len(df)
start_time = time.time()
for i, gpxx in df.iterrows():
print(i, ln, gpxx.gpdm, gpxx.gpmc, gpxx.date)
dm = gpxx.gpdm[:6]
gpmc = gpxx.gpmc
date = gpxx.date.strftime('%Y-%m-%d')
tdxday = Tdxday(dm)
ohlc = tdxday.get_qfqdata(start='20160101')
data.append([gpxx.gpdm, gpmc, date] +
supershort(ohlc, date, j, k, n, m))
if ((i + 1) % 50 == 0) or (i >= ln - 1):
now_time = time.time()
t1 = now_time - start_time
# 每只股票秒数
p = t1 / (i - k + 1)
# 估计剩余时间
t1 = t1 / 60
t2 = (ln - i) * p / 60
print('------已用时%d分钟，估计还需要%d分钟' % (t1, t2))
# -*- coding: utf-8 -*- """ Created on 2019-10-06 20:41:34 author: huangyunbin email: [email protected] QQ: 592440193 """ import pandas as pd import matplotlib.pyplot as plt import numpy as np from collections import OrderedDict from stock_pandas.tdx.tdxdayread import Tdxday from stock_pandas.tdx.tdxcfg import Tdx from stock_pandas.tdx.class_func import * import pandas_ta as ta filename = '600138' tdxday = Tdxday(filename) df = tdxday.get_qfqdata() rng_date = df.index df = df.reset_index()
# list(zip(int_mask, ext_mask))
# [('Buy', 'buy'), ('Sell', 'sell'), ('Short', 'short'), ('Cover', 'cover')]
for f_int, f_ext in zip(int_mask, ext_mask):
obj = dataobj.get(f_ext)
if isinstance(obj, pd.Series):
df[f_int] = obj
else:
df[f_int] = None
if any([isinstance(x, pd.Series) for x in list(df.values())]):
return pd.DataFrame(df)
return None
if __name__ == '__main__':
filename = '600674'
tdxday = Tdxday(filename)
ohlc = tdxday.get_data_pybacktest(start='20180101')
short_ma = 10
long_ma = 60
ms = ohlc['C'].rolling(short_ma).mean()
ml = ohlc['C'].rolling(long_ma).mean()
# buy买入，sell卖出，cover，Short sell做空
buy = (ms > ml) & (ms.shift() < ml.shift()) # ma cross up
sell = (ms < ml) & (ms.shift() > ml.shift()) # ma cross down
# buy = cover = (ms > ml) & (ms.shift() < ml.shift()) # ma cross up
# sell = short = (ms < ml) & (ms.shift() > ml.shift()) # ma cross down
#
