def backtest(acts, o_R):
pacts = np.asarray(acts).copy()
for i in range(1, pacts.shape[0]):
if pacts[i] == 0:
if pacts[i - 1] == -1:
# from short
pacts[i] = 0.5
elif pacts[i - 1] == 1:
pacts[i] = -0.5
# plt.plot(pacts, '.')
data = o_R[dep - 1:-3]
buy = cover = pd.Series(pacts == 1, index=data.index)
short = sell = pd.Series(pacts == -1, index=data.index)
cover = pd.Series(pacts > 0, index=data.index)
sell = pd.Series(pacts < 0, index=data.index)
# print(np.sum(buy-sell))
OHLC = data[[('DealPrice', 'close'), ('DealPrice', 'close'), ('DealPrice', 'close'), ('DealPrice', 'close')]]
OHLC.columns = ['O', 'H', 'L', 'C']
bt = pb.Backtest(locals())
return bt
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
# 由于融资融券风险较大，在此不做融券策略，也就是不配置cover和short
# 只配置buy和sell
# buy
buy = ohlc['BOTTOM_REVERSAL']
sell = ohlc['TOP_REVERSAL']
# locals() 函数会以字典类型返回当前位置的全部局部变量。
bt = pybacktest.Backtest(locals(), 'hyb_reversal')
newlist = list(filter(lambda x: not x.startswith('_'), dir(bt)))
print(newlist)
print('\n> bt.signals\n%s' % bt.signals.tail())
print('\n> bt.trades\n%s' % bt.trades.tail())
print('\n> bt.positions\n%s' % bt.positions.tail())
print('\n> bt.equity\n%s' % bt.equity.tail())
print('\n> bt.trade_price\n%s' % bt.trade_price.tail())
bt.summary()
fig = plt.figure(figsize=(24, 12))
ax1 = plt.subplot2grid((6, 4), (0, 0), rowspan=3, colspan=4)
bt.plot_trades(ax=ax1)
ax1.grid(True)
plt.ylabel('Price')
ml1 = pandas.rolling_mean(hl_diff, lips_sma_period) mt1 = pandas.rolling_mean(hl_diff, teeth_sma_period) mj1 = pandas.rolling_mean(hl_diff, jaw_sma_period) ml = ml1.shift(2) mt = mt1.shift(4) mj = mj1.shift(7) buy = cover = (ml > mt) & (mt > mj) & ((ml.shift() < mt.shift()) | (mt.shift() < mj.shift())) sell = short = (ml < mt) & (mt < mj) & ((ml.shift() > mt.shift()) | (mt.shift() > mj.shift())) del cover del short bt = pybacktest.Backtest(locals(), 'alligator') # df = pandas.DataFrame() # df['ml'] = ml # df['mt'] = mt # df['mj'] = mj # location = '/home/sigmasoft6/Desktop/alligator_test.xlsx' # df = ohlc.copy() # df['ml'] = ml # df['mt'] = mt # df['mj'] = mj # df['buy'] = bt.signals['Buy'] # df['sell'] = bt.signals['Sell'] # df.to_excel(location,sheet_name='testing',index=True) #print bt.signals.tail()
weekclose,
fastperiod=Week_fastLength,
slowperiod=Week_slowLength,
signalperiod=Week_signalLength)
entry_Rsi = ta.RSI(close, rsi_entry_length)
exit_Rsi = ta.RSI(close, rsi_exit_length)
Week_Macdhist_resampled = Week_Macdhist.resample('B').ffill().shift(5)
weekbool = (Week_Macdhist_resampled >= week_macd_filter)
df['W'] = weekbool
weekbool = df['W']
sell = (exit_Macd <
exit_Macdsignal) & (exit_Macd.shift() >= exit_Macdsignal.shift()) & (
exit_Macd >= top_Long_filter) & (exit_Rsi > rsi_exit_filter)
buy = (Macd > Macdsignal) & (Macd.shift() <= Macdsignal.shift()) & (
Macd <= -bot_Long_filter) & (entry_Rsi <= rsi_entry_filter) & weekbool
bt = pybacktest.Backtest(locals(), 'test1')
bt.summary()
matplotlib.rcParams['figure.figsize'] = (15.0, 8.0)
bt.plot_equity()
try:
bt.plot_trades()
except ValueError:
pass
# finally:
# plt.legend(loc='upper right')
def test_1():
#### 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
# print('login respond error_code:' + lg.error_code)
# print('login respond error_msg:' + lg.error_msg)
#### 获取沪深A股历史K线数据 ####
# 详细指标参数，参见“历史行情指标参数”章节；“分钟线”参数与“日线”参数不同。
# 分钟线指标：date,time,code,open,high,low,close,volume,amount,adjustflag
rs = bs.query_history_k_data_plus(
"sz.000001",
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,isST",
start_date='2018-04-01',
end_date='2020-04-18',
frequency="d",
adjustflag="3")
# print('query_history_k_data_plus respond error_code:' + rs.error_code)
# print('query_history_k_data_plus respond error_msg:' + rs.error_msg)
#### 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
#### 结果集输出到csv文件 ####
# result.to_csv("D:\\history_A_stock_k_data.csv", index=False)
# print(result)
#### 登出系统 ####
bs.logout()
ohlc = result.rename(
columns={
'date': 'Date',
'open': 'O',
'high': 'H',
'low': 'L',
'close': 'C', #'Adj Close': 'AC',
'volume': 'V'
})
ohlc['Date'] = pd.to_datetime(ohlc['Date'])
ohlc.set_index("Date", inplace=True)
ohlc["O"] = ohlc["O"].astype("float")
ohlc["H"] = ohlc["H"].astype("float")
ohlc["L"] = ohlc["L"].astype("float")
ohlc["C"] = ohlc["C"].astype("float")
ohlc["V"] = ohlc["V"].astype("int")
ohlc.info()
# ohlc = ohlc.set_index('Date', drop=False)
# ohlc = pybacktest.load_from_yahoo('GOOG')
ohlc.tail()
short_ma = 10
long_ma = 20
ms = ohlc.C.rolling(short_ma).mean()
ml = ohlc.C.rolling(long_ma).mean()
buy = cover = (ms > ml) & (ms.shift() < ml.shift()) # ma cross up
sell = short = (ms < ml) & (ms.shift() > ml.shift()) # ma cross down
print('> Short MA\n%s\n' % ms.tail())
print('> Long MA\n%s\n' % ml.tail())
print('> Buy/Cover signals\n%s\n' % buy.tail())
print('> Short/Sell signals\n%s\n' % sell.tail())
bt = pybacktest.Backtest(locals(), 'ma_cross')
print(list(filter(lambda x: not x.startswith('_'), dir(bt))))
print('\n> bt.signals\n%s' % bt.signals.tail())
print('\n> bt.trades\n%s' % bt.trades.tail())
print('\n> bt.positions\n%s' % bt.positions.tail())
print('\n> bt.equity\n%s' % bt.equity.tail())
print('\n> bt.trade_price\n%s' % bt.trade_price.tail())
bt.summary()
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['figure.figsize'] = (15.0, 8.0)
bt.plot_equity()
bt.plot_trades()
ohlc.C.rolling(short_ma).mean().plot(c='green')
ohlc.C.rolling(long_ma).mean().plot(c='blue')
plt.legend(loc='upper left')
# bt.trdplot['2018':'2020']
# ohlc.C['2018':'2020'].rolling(short_ma).mean().plot(c='green')
# ohlc.C['2018':'2020'].rolling(long_ma).mean().plot(c='blue')
#
plt.show()
import pybacktest # obviously, you should install pybacktest before importing it
import pandas as pd
ohlc = pybacktest.load_from_csv('btc_etc.csv')
ohlc.tail()
short_ma = 50
long_ma = 200
ms = ohlc.C.rolling(short_ma).mean()
ml = ohlc.C.rolling(long_ma).mean()
buy = cover = (ms > ml) & (ms.shift() < ml.shift()) # ma cross up
sell = short = (ms < ml) & (ms.shift() > ml.shift()) # ma cross down
bt = pybacktest.Backtest(locals(), 'ma_cross')
bt.summary()
ohlc1.index = ohlc1['Date']
del ohlc1['Date']
start = ohlc1.index.searchsorted(dt.datetime(2007, 01, 01))
ohlc = ohlc1[start:]
#supertrand===========================
moving_small = sptd.supertrand(ohlc)
buy = cover = (ohlc.C > moving_small) & (ohlc.C.shift() < moving_small.shift())
sell = short = (ohlc.C < moving_small) & (ohlc.C.shift() >
moving_small.shift())
del cover
del short
#=====================================
#===========excel export=============
bt = pbt.Backtest(locals(), 'suprtrand')
location = '/home/sigmasoft6/Desktop/traded_sheet_output/suptndss.xlsx'
df = ohlc.copy()
df['ms'] = moving_small
# df['ml'] = moving_long
df['buy'] = bt.signals['Buy']
df['sell'] = bt.signals['Sell']
df.to_excel(location, sheet_name='testing', index=True)
#====================================
#==========graph ploat===============
# del cover
# del short
# bt = pbt.Backtest(locals(), 'aj')
# import json
# json.dump(bt.report, open(script + '-output.json', 'w'))
