def handle_data(context, data):
portfolio = context.portfolio
for ticker, position in portfolio.positions.items():
if not data.can_trade(ticker):
continue
ma_fast = abstract.MA(data.history(ticker, 'D', length=fast + 1),
timeperiod=fast,
price='close').dropna()
ma_slow = abstract.MA(data.history(ticker, 'D', length=slow + 1),
timeperiod=slow,
price='close').dropna()
if ma_slow[0] < ma_fast[0] and ma_slow[1] > ma_fast[1]:
portfolio.send_order(ticker, -position['quantity'])
for ticker in context.tickers:
if not data.can_trade(ticker):
continue
ma_fast = abstract.MA(data.history(ticker, 'D', length=fast + 1),
timeperiod=fast,
price='close').dropna()
ma_slow = abstract.MA(data.history(ticker, 'D', length=slow + 1),
timeperiod=slow,
price='close').dropna()
if ma_slow[0] > ma_fast[0] and ma_slow[1] < ma_fast[1]:
portfolio.send_order(ticker, 1000)
def handle_data(context, data):
portfolio = context.portfolio
for ticker in portfolio.positions.keys():
ticker = sid(ticker).symbol
if not data.can_trade(ticker):
continue
ma_fast = abstract.MA(data.history(ticker, 'D', length=fast + 1),
timeperiod=fast,
price='close').dropna()
ma_slow = abstract.MA(data.history(ticker, 'D', length=slow + 1),
timeperiod=slow,
price='close').dropna()
if ma_slow[0] < ma_fast[0] and ma_slow[1] > ma_fast[1]:
order(ticker, -1000)
for ticker in context.tickers:
ticker = symbol(ticker).symbol
if not data.can_trade(ticker):
continue
ma_fast = abstract.MA(data.history(ticker, 'D', length=fast + 1),
timeperiod=fast,
price='close').dropna()
ma_slow = abstract.MA(data.history(ticker, 'D', length=slow + 1),
timeperiod=slow,
price='close').dropna()
if ma_slow[0] > ma_fast[0] and ma_slow[1] < ma_fast[1]:
order(ticker, 1000)
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
macd = ta.MACD(dataframe)
dataframe['maShort'] = ta.MA(dataframe, timeperiod=50)
dataframe['maMedium'] = ta.MA(dataframe, timeperiod=200)
return dataframe
def execute(self, pool, context, data):
out = []
for code in pool:
candle = data.history(code,
frequency=self.frequency,
length=self.slow + 1)
try:
fast = abstract.MA(candle, timeperiod=self.fast)
slow = abstract.MA(candle, timeperiod=self.slow)
if fast[-1] > slow[-1] and (fast[-2] < slow[-2]):
out.append(code)
except Exception as e:
print e
print candle
print 'MA', out
return out
def updateMA(Client, symbol, mas, interval, ma_type):
if ma_type == MA_Type.SMA:
folder = "ma"
elif ma_type == MA_Type.WMA:
folder = "wma"
else:
raise UnknownMATypeException()
#check files and directories
filename = f"{Client.MAIN_PATH}/data/candles/{interval}"
filename2 = f"{Client.MAIN_PATH}/data/{folder}/{interval}"
if not os.path.exists(filename):
os.makedirs(filename)
if not os.path.exists(filename2):
os.makedirs(filename2)
fn = f"{filename}/{symbol}.feather"
fn2 = f"{filename2}/{symbol}.feather"
if os.path.isfile(fn):
df_cdl = feather.read_feather(fn)
if not df_cdl["OpenTime"].empty:
if os.path.isfile(fn2):
df_ma = feather.read_feather(fn2)
else:
df_ma = pd.DataFrame(columns=["OpenTime","ClosePrice"])
feather.write_feather(df_ma, fn2)
length_old = len(df_ma.index)
if not df_ma.empty:
if length_old >= mas[-1]:
ma_lastDate = df_ma["OpenTime"].iloc[-(mas[-1] - 1)]
length_old = mas[-1] - 1
else:
ma_lastDate = df_ma["OpenTime"].iloc[0]
else:
ma_lastDate = 0
df_add = df_cdl.loc[df_cdl["OpenTime"] >= ma_lastDate, ["OpenTime", "ClosePrice"]]
if not df_add.empty:
for num in mas:
name = f"MA{num}"
df_add[name] = abstract.MA(df_add["ClosePrice"], timeperiod=num, matype=ma_type)
df_add = df_add[length_old:]
df_ma = df_ma.append(df_add, ignore_index=True)
feather.write_feather(df_ma, fn2)
else:
print("Nothing to do")
else:
print("file is empty")
def signal(data):
data['RSI'] = abstract.RSI(data.close,2)
data['MA'] = abstract.MA(data.close, timeperiod=7, matype=0)
#RVI
#Nem = data.close-data.open + 2*(data.iloc[-1:,:].close - data.iloc[-1:,:].open) + 2*(data.iloc[-2:,:].close - data.iloc[-1:,:].open) + data.iloc[-3:,:].close - data.iloc[-3:,:].open
Nem =(data.close-data.open)+2*(data.close.shift(1) - data.open.shift(1))+2*(data.close.shift(2) - data.open.shift(2))+(data.close.shift(3) - data.open.shift(3))
Dem =data.high-data.low+2*(data.high.shift(1) - data.low.shift(1)) +2*(data.high.shift(2) - data.low.shift(2)) +(data.high.shift(3) - data.low.shift(3))
#Dem = data.high-data.low + 2*(data.iloc[-1:,:].high - data.iloc[-1:,:].low) + 2*(data.iloc[-2:,:].high - data.iloc[-1:,:].low) + data.iloc[-3:,:].high - data.iloc[-3:,:].low
data['RVI'] = RVI = (Nem/6)/(Dem/6)
data['RVIR'] = (RVI + 2*RVI.shift(1) + 2*RVI.shift(2) + RVI.shift(3))/6
if data.iloc[-1:,:].RSI <=RSILo | data.iloc[-2:-1,:].RSI <=RSILo | data.iloc[-3:-2,:].RSI <=RSILo:
if data.iloc[-1:,:].RVI >= data.iloc[-1:,:].RVIR & data.iloc[-2:-1,:].RVI <= data.iloc[-2:-1,:].RVIR:
now = datetime.now()
if (now > today930 and now < today11) or (now > today13 and now < today15):
ret_code, info_data = trd_ctx.accinfo_query() #get ac info
if info_data.cash > data.close[-1]*size:
print('place order')
buy()
if size != 0:
if data.iloc[-1:,:].RSI >=RSIHi | data.iloc[-2:-1,:].RSI <=RSIHi | data.iloc[-3:-1,:].RSI <=RSIHi:
if data.iloc[-1:,:].RVI <= data.iloc[-1:,:].RVIR:
if data.iloc[-1:,:].MA <= price:
print('sell')
sell()
def MA(ohlcv, kw):
""" :return Moving Average (ma) """
params = {'timeperiod': 30, 'matype': 0}
timeperiod, matype = _get_params(kw, params, ['timeperiod', 'matype'])
result = talib.MA(ohlcv, timeperiod, matype)
return {
'ma': result
}
def KELCH(self, df, length, mult):
ma = tb.MA(df, timeperiod=length)
range = tb.TRANGE(df)
rangema = talib.MA(np.array(range), timeperiod=length)
upperKC = ma + rangema * mult
lowerKC = ma - rangema * mult
return upperKC[len(upperKC) - 1], lowerKC[len(lowerKC) - 1], upperKC[len(upperKC) - 2], lowerKC[
len(lowerKC) - 2]
def ma_200(self):
ma_200 = abstract.MA(self.df, 200)
self.df["ma_200"] = 0
for i in range(199, len(self.df)):
try:
ma = ma_200[i]
close = self.df.iloc[i]['close']
#If Close pirce is higher tha MA200
if ma < close:
self.df['ma_200'][i] = 1
except:
pass
return self.df
def _get_statistics(n=1, start_time=None, end_time=None):
ohlc = _get_ohlc(3, start_time, end_time)
# TODO: maybe handle nan differently
limit = n * -1
stoch = np.nan_to_num(abstract.STOCH(ohlc))
macd = np.nan_to_num(abstract.MACD(ohlc))
output = dict(
macd=macd[0][limit:].tolist(),
macd_signal=macd[1][limit:].tolist(),
macd_hist=macd[2][limit:].tolist(),
stoch_slowk=stoch[0][limit:].tolist(),
stoch_slowd=stoch[1][limit:].tolist(),
vave=np.nan_to_num(abstract.MA(ohlc)[limit:]).tolist(),
sma=np.nan_to_num(abstract.SMA(ohlc)[limit:]).tolist(),
rsi=np.nan_to_num(abstract.RSI(ohlc)[limit:]).tolist(),
stddev=np.nan_to_num(abstract.STDDEV(ohlc)[limit:]).tolist())
return output
print('data ok')
else:
print('error:', data)
continue
#get snap data
ret, snapdata = quote_ctx.get_market_snapshot(['HK.' + code])
if ret == RET_OK:
print('snap ok')
else:
print('error:', data)
continue
#check lot size and price per lot
#print('lot_size')
#print(snapdata.iloc[0].lot_size * snapdata.iloc[0].last_price)
if snapdata.iloc[0].lot_size * snapdata.iloc[0].last_price > 10000:
continue
#calculate bias
if len(data) == 0:
continue
MA = abstract.MA(data.close, timeperiod=12, matype=0)
print('MA:' + str(MA))
bias = (data.iloc[-1].close - MA[len(MA)-1])/(MA[len(MA)-1])
if bias < 0:
df = df.append({'Stock number':code}, ignore_index=True)
sleep(15)
quote_ctx.close() #close connection
df.to_csv('filter.csv', encoding='utf-8', index=False)
def signal(data):
global NumPos,openprice,indicator
trd_ctx = OpenHKTradeContext(host='127.0.0.1', port=11111) #make connection
data['RSI'] = abstract.RSI(data.close,RSIP)
data['MA'] = abstract.MA(data.close, timeperiod=7, matype=0)
#RVI
#Nem = data.close-data.open + 2*(data.iloc[-1:,:].close - data.iloc[-1:,:].open) + 2*(data.iloc[-2:,:].close - data.iloc[-1:,:].open) + data.iloc[-3:,:].close - data.iloc[-3:,:].open
data['Nem'] =((data.close-data.open)+2*(data.close.shift(1) - data.open.shift(1))+2*(data.close.shift(2) - data.open.shift(2))+(data.close.shift(3) - data.open.shift(3)))/6
data['Dem'] =((data.high-data.low)+2*(data.high.shift(1) - data.low.shift(1)) +2*(data.high.shift(2) - data.low.shift(2)) +(data.high.shift(3) - data.low.shift(3)))/6
#Dem = data.high-data.low + 2*(data.iloc[-1:,:].high - data.iloc[-1:,:].low) + 2*(data.iloc[-2:,:].high - data.iloc[-1:,:].low) + data.iloc[-3:,:].high - data.iloc[-3:,:].low
for j in range(1,RVIper+3): #calculate RVI value
maNEM = 0
maDEM = 0
for i in range (j,RVIper+j):
maNEM = maNEM + data.iloc[-i].Nem
maDEM = maDEM + data.iloc[-i].Dem
#data.iloc[-j].at['RVI'] = (maNEM/RVIper)/(maDEM/RVIper)
data.at[30-j,'RVI'] = (maNEM/RVIper)/(maDEM/RVIper)
data.at[29,'RVIR'] = (data.iloc[-1].RVI + 2*data.iloc[-2].RVI + 2*data.iloc[-3].RVI + data.iloc[-4].RVI)/6
print(data.iloc[-3:])
#data['RVI'] = (maNEM/RVIper)/(maDEM/RVIper)
#data.iloc[-1].RVI = (maNEM/RVIper)/(maDEM/RVIper)
#new_row = {'RVI':'', 'RVIR':''}
#indicator = indicator.append(new_row,ignore_index=True)
#indicator.at[len(indicator)-1,'RVI'] = (maNEM/RVIper)/(maDEM/RVIper)
#data['RVIR'] = (RVI + 2*RVI.shift(1) + 2*RVI.shift(2) + RVI.shift(3))/6
#data.iloc[-1].RVIR = (data.iloc[-1].RVI + 2*data.iloc[-2].RVI + 2*data.iloc[-3].RVI + data.iloc[-4].RVI)/6
#data.at[-1,'RVIR'] = (data.iloc[-1].RVI + 2*data.iloc[-2].RVI + 2*data.iloc[-3].RVI + data.iloc[-4].RVI)/6
#indicator.at[len(indicator)-1,'RVIR'] = (data.iloc[-1].RVI + 2*data.iloc[-2].RVI + 2*data.iloc[-3].RVI + data.iloc[-4].RVI)/6
#print(indicator)
if (data.iloc[-1].RSI <=RSILo) | (data.iloc[-2].RSI <=RSILo) | (data.iloc[-3].RSI <=RSILo):
print('RSI match')
if (data.iloc[-1].RVI > data.iloc[-1].RVIR) & (data.iloc[-2].RVI < data.iloc[-2].RVIR):
print('RVI match')
print('-----buy signal-----')
print(size)
notify("AutoTrade.py", "!!!!!!!Buy Signal!!!!!!!")
now = datetime.now()
print(now)
print(data.iloc[-4].time_key)
time_object = datetime.strptime(data.iloc[-4].time_key, '%Y-%m-%d %H:%M:%S')
print(time_object)
if (time_object >= today930):
if (now > today930 and now < today11) or (now > today13 and now < today15):
ret_code, info_data = trd_ctx.accinfo_query(trd_env = TrdEnv.SIMULATE) #get ac info
if ret_code == RET_OK:
print('info data ok')
else:
while ret_code != RET_OK:
ret_code, info_data = trd_ctx.accinfo_query(trd_env = TrdEnv.SIMULATE)
if info_data.iloc[-1].cash > ((data.iloc[-1].close)*(size)):
print('place order')
buy(data.iloc[-1].close) #buy stock
if NumPos > 0:
print('RSI:')
print(data.iloc[-1].RSI)
print(data.iloc[-2].RSI)
print(data.iloc[-3].RSI)
print('RVI')
print(data.iloc[-1].RVI - data.iloc[-1].RVIR)
print('MA')
print(data.iloc[-1].MA)
print('close')
print(data.iloc[-1].close)
if (data.iloc[-1].RSI >=RSIHi) | (data.iloc[-2].RSI >=RSIHi) | (data.iloc[-3].RSI >=RSIHi):
if (data.iloc[-1].RVI <= data.iloc[-1].RVIR):
if data.iloc[-1].close <= data.iloc[-1].MA:
notify("AutoTrade.py", "!!!!!!!SELL SELL SELL!!!!!!!")
print('~~~sell~~~') #sell stock
sell(data.iloc[-1].close)
if data.iloc[-1].close >= openprice*1.1: #sell if profit >10%
sell(data.iloc[-1].close)
trd_ctx.close()
def set_ma(frame):
close = pd.DataFrame(frame['close'].dropna(), dtype=numpy.float64)
frame['ma'] = abstract.MA(close, timeperiod=50)
return frame
def signal(data):
global NumPos
trd_ctx = OpenHKTradeContext(host='127.0.0.1',
port=11111) #make connection
data['RSI'] = abstract.RSI(data.close, 2)
data['MA'] = abstract.MA(data.close, timeperiod=7, matype=0)
#RVI
data['Nem'] = ((data.close - data.open) + 2 *
(data.close.shift(1) - data.open.shift(1)) + 2 *
(data.close.shift(2) - data.open.shift(2)) +
(data.close.shift(3) - data.open.shift(3))) / 6
data['Dem'] = (data.high - data.low + 2 *
(data.high.shift(1) - data.low.shift(1)) + 2 *
(data.high.shift(2) - data.low.shift(2)) +
(data.high.shift(3) - data.low.shift(3))) / 6
maNEM = 0
maDEM = 0
for i in range(1, RVIper):
maNEM = maNEM + data.iloc[-i].Nem
maDEM = maDEM + data.iloc[-i].Dem
data['RVI'] = RVI = (maNEM / RVIper) / (maDEM / RVIper)
data['RVIR'] = (data.iloc[-1].RVI + 2 * data.iloc[-2].RVI +
2 * data.iloc[-3].RVI + data.iloc[-4].RVI) / 6
if (data.iloc[-1].RSI <= RSILo) | (data.iloc[-2].RSI <=
RSILo) | (data.iloc[-3].RSI <= RSILo):
print('RSI match')
if (data.iloc[-1].RVI > data.iloc[-1].RVIR) & (data.iloc[-2].RVI <
data.iloc[-2].RVIR):
print('RVI match')
print('-----buy signal-----')
print(size)
notify("AutoTrade.py", "!!!!!!!Buy Signal!!!!!!!")
now = datetime.now()
print(now)
print(data.iloc[-4].time_key)
time_object = datetime.strptime(data.iloc[-4].time_key,
'%Y-%m-%d %H:%M:%S')
print(time_object)
if (time_object >= today930):
if (now > today930 and now < today11) or (now > today13
and now < today15):
ret_code, info_data = trd_ctx.accinfo_query(
trd_env=TrdEnv.REAL) #get ac info
if ret_code == RET_OK:
print('info data ok')
else:
while ret_code != RET_OK:
ret_code, info_data = trd_ctx.accinfo_query(
trd_env=TrdEnv.REAL)
if info_data.iloc[-1].cash > ((data.iloc[-1].close) *
(size)):
print('place order')
buy(data.iloc[-1].close) #buy stock
if NumPos > 0:
print('RSI:')
print(data.iloc[-1].RSI)
print(data.iloc[-2].RSI)
print(data.iloc[-3].RSI)
print('RVI')
print(data.iloc[-1].RVI)
print(data.iloc[-1].RVIR)
print(data.iloc[-1].RVI - data.iloc[-1].RVIR)
print('MA')
print(data.iloc[-1].MA)
print('close')
print(data.iloc[-1].close)
if (data.iloc[-1].RSI >= RSIHi) | (data.iloc[-2].RSI >= RSIHi) | (
data.iloc[-3].RSI >= RSIHi):
if (data.iloc[-1].RVI <= data.iloc[-1].RVIR):
if data.iloc[-1].close <= data.iloc[-1].MA:
notify("AutoTrade.py", "!!!!!!!SELL SELL SELL!!!!!!!")
print('~~~sell~~~') #sell stock
sell(data.iloc[-1].close)
if data.iloc[-1].close >= openprice * 1.1: #sell if profit >10%
sell(data.iloc[-1].close)
trd_ctx.close()
def MA(data, period, interest, ma_type):
ret = abstract.MA(data, timeperiod=period, price=interest, matype=ma_type)
return ret
def signal(data):
global NumPos, openprice, indicator
trd_ctx = OpenHKTradeContext(host='127.0.0.1',
port=11111) #make connection
data['RSI'] = abstract.RSI(data.close, RSIP)
data['MA'] = abstract.MA(data.close, timeperiod=7, matype=0)
#RVI Original
data['Nem_Ori'] = ((data.close - data.open) + 2 *
(data.close.shift(1) - data.open.shift(1)) + 2 *
(data.close.shift(2) - data.open.shift(2)) +
(data.close.shift(3) - data.open.shift(3))) / 6
data['Dem_Ori'] = ((data.high - data.low) + 2 *
(data.high.shift(1) - data.low.shift(1)) + 2 *
(data.close.shift(2) - data.open.shift(2)) +
(data.close.shift(3) - data.open.shift(3))) / 6
data['RVI_Ori'] = data.Nem_Ori / data.Dem_Ori
data['RVIR_Ori'] = (data.RVI_Ori + 2 * data.shift(1).RVI_Ori +
2 * data.shift(2).RVI_Ori + data.shift(3).RVI_Ori) / 6
#RVI
data['Nem'] = ((data.close - data.open) + 2 *
(data.close.shift(1) - data.open.shift(1)) + 2 *
(data.close.shift(2) - data.open.shift(2)) +
(data.close.shift(3) - data.open.shift(3))) / 6
data['Dem'] = ((data.high - data.low) + 2 *
(data.high.shift(1) - data.low.shift(1)) + 2 *
(data.high.shift(2) - data.low.shift(2)) +
(data.high.shift(3) - data.low.shift(3))) / 6
for j in range(1, RVIper + 3): #calculate RVI value with period
maNEM = 0
maDEM = 0
for i in range(j, RVIper + j):
maNEM = maNEM + data.iloc[-i].Nem
maDEM = maDEM + data.iloc[-i].Dem
data.at[30 - j, 'RVI'] = (maNEM / RVIper) / (maDEM / RVIper)
data.at[29, 'RVIR'] = (data.iloc[-1].RVI + 2 * data.iloc[-2].RVI +
2 * data.iloc[-3].RVI + data.iloc[-4].RVI) / 6
print(data.iloc[-3:])
#get the newest stock price
ret, stock = quote_ctx.get_cur_kline('HK.' + str(code), 3, SubType.K_3M,
AuType.QFQ)
print(stock.iloc[-3:])
#when to in
if NumPos == 0:
if (data.iloc[-1].RSI <= RSILo) | (data.iloc[-2].RSI <= RSILo) | (
data.iloc[-3].RSI <= RSILo):
print('RSI match')
if (data.iloc[-1].RVI_Ori > data.iloc[-1].RVIR_Ori) & (
data.iloc[-2].RVI_Ori < data.iloc[-2].RVIR_Ori):
print('RVI match')
print('-----buy signal-----')
print(size)
notify("AutoTrade.py", "!!!!!!!Buy Signal!!!!!!!" + str(code))
print('\007')
print('\007')
print('\007')
now = datetime.now()
print(now)
print(data.iloc[-4].time_key)
time_object = datetime.strptime(data.iloc[-4].time_key,
'%Y-%m-%d %H:%M:%S')
print(time_object)
if (time_object >= today930):
if (now > today940
and now < today11) or (now > today13
and now < today15):
ret_code, info_data = trd_ctx.accinfo_query(
trd_env=TrdEnv.REAL) #get ac info
if ret_code == RET_OK:
print('info data ok')
else:
while ret_code != RET_OK:
ret_code, info_data = trd_ctx.accinfo_query(
trd_env=TrdEnv.REAL)
'''
ret, stock = quote_ctx.get_cur_kline('HK.' + str(code), 3, SubType.K_3M, AuType.QFQ)
if ret == RET_OK:
print('stock price get')
else:
while ret != RET_OK:
print('stock price again loop')
ret, stock = quote_ctx.get_cur_kline('HK.' + str(code), 3, SubType.K_3M, AuType.QFQ)
'''
if info_data.iloc[-1].cash > ((stock.iloc[-1].close) *
(size)):
print('place order')
notify("AutoTrade.py", "Buy Signal" + str(code))
buy(data.iloc[-1].close, False) #buy stock
if NumPos > 0:
#second in
'''
ret, stock = quote_ctx.get_cur_kline('HK.' + str(code), 3, SubType.K_3M, AuType.QFQ) #refresh stock price
if ret == RET_OK:
print('stock price get')
else:
while ret != RET_OK:
print('stock price again loop')
ret, stock = quote_ctx.get_cur_kline('HK.' + str(code), 3, SubType.K_3M, AuType.QFQ)
'''
if (stock.iloc[-1].close - openprice) > 0.003:
buy(stock.iloc[-1].close, True)
#sell
print('RSI:')
print(data.iloc[-1].RSI)
print(data.iloc[-2].RSI)
print(data.iloc[-3].RSI)
print('RVI')
print(data.iloc[-1].RVI - data.iloc[-1].RVIR)
print('MA')
print(data.iloc[-1].MA)
print('close')
print(stock.iloc[-1].close)
print('openprice')
print(openprice)
if NumPos == size * hand:
if (stock.iloc[-1].close - openprice) < -0.002:
print(' ')
print(stock.iloc[-1].close)
print(openprice)
print(' ')
notify("AutoTrade.py",
"!!!!!!!SELL SELL SELL!!!!!!! first hand")
print('\007')
print('\007')
print('\007')
print('~~~sell~~~') #sell stock
sell(stock.iloc[-1].close)
elif (stock.iloc[-1].close - openprice) < -0.001:
print(' ')
print(stock.iloc[-1].close)
print(openprice)
print(' ')
notify("AutoTrade.py",
"!!!!!!!SELL SELL SELL!!!!!!! multi hand" + str(code))
print('~~~sell~~~') #sell stock
print('\007')
print('\007')
print('\007')
sell(stock.iloc[-1].close)
'''
if (data.iloc[-1].RSI >=RSIHi) | (data.iloc[-2].RSI >=RSIHi) | (data.iloc[-3].RSI >=RSIHi):
if (data.iloc[-1].RVI <= data.iloc[-1].RVIR):
if data.iloc[-1].close <= data.iloc[-1].MA:
notify("AutoTrade.py", "!!!!!!!SELL SELL SELL!!!!!!!")
print('~~~sell~~~') #sell stock
sell(data.iloc[-1].close)
if data.iloc[-1].close >= openprice*1.1: #sell if profit >10%
sell(data.iloc[-1].close)
'''
trd_ctx.close()
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Load the pre-built regressor
dataframe['historical_mean'] = ta.MA(dataframe, timeperiod=33)
return dataframe
print('one_dif ', c)
print('two_dif ', c)
# # 3. pandas数据处理试题
# 3.1 如何查看列名、怎么对数据转置
# 3.2 读取data里的600029这只股票的DataFrame,将其收盘价转换成用Numpy的Array格式，并用talib计算10日EMA值，返回ndarray的最后五个值
# In[4]:
import talib.abstract as ta
df_ma = pd.DataFrame({name: ta.MA(value, 10) for name, value in PN.iteritems()})
print(df_ma.tail())
# In[5]:
# 3.3 读取sz50.xlsx的['600029.XSHG','600050.XSHG','601318.XSHG']的全数据做成Panel
# In[6]:
from datetime import datetime