def TKE(dataframe, *, length=14, emaperiod=5):
"""
Source: https://www.tradingview.com/script/Pcbvo0zG/
Author: Dr Yasar ERDINC
The calculation is simple:
TKE=(RSI+STOCHASTIC+ULTIMATE OSCILLATOR+MFI+WIILIAMS %R+MOMENTUM+CCI)/7
Buy signal: when TKE crosses above 20 value
Oversold region: under 20 value
Overbought region: over 80 value
Another usage of TKE is with its EMA ,
the default value is defined as 5 bars of EMA of the TKE line,
Go long: when TKE crosses above EMALine
Go short: when TKE crosses below EMALine
Usage:
`dataframe['TKE'], dataframe['TKEema'] = TKE1(dataframe)`
"""
import talib.abstract as ta
df = dataframe.copy()
# TKE=(RSI+STOCHASTIC+ULTIMATE OSCILLATOR+MFI+WIILIAMS %R+MOMENTUM+CCI)/7
df["rsi"] = ta.RSI(df, timeperiod=length)
df['stoch'] = (100 * (df['close'] - df['low'].rolling(window=length).min()) /
(df['high'].rolling(window=length).max()
- df['low'].rolling(window=length).min()))
df["ultosc"] = ta.ULTOSC(df, timeperiod1=7, timeperiod2=14, timeperiod3=28)
df["mfi"] = ta.MFI(df, timeperiod=length)
df["willr"] = ta.WILLR(df, timeperiod=length)
df["mom"] = ta.MOM(df, timeperiod=length)
df["cci"] = ta.CCI(df, timeperiod=length)
df['TKE'] = df[['rsi', 'stoch', 'ultosc', 'mfi', 'willr', 'mom', 'cci']].mean(axis='columns')
df["TKEema"] = ta.EMA(df["TKE"], timeperiod=emaperiod)
return df["TKE"], df["TKEema"]
def compMFI(self):
mfi = talib.MFI(self.high,self.low,self.close,self.tickVol,timeperiod=self.lookback)
self.removeNullID(mfi)
self.rawFeatures['MFI'] = mfi
FEATURE_SIZE_DICT['MFI'] = 1
return
def extract_features(data):
high = data['High']
low = data['Low']
close = data['Close']
volume = data['Volume']
open_ = data['Open']
data['ADX'] = ta.ADX(high, low, close, timeperiod=19)
data['CCI'] = ta.CCI(high, low, close, timeperiod=19)
data['CMO'] = ta.CMO(close, timeperiod=14)
data['MACD'], X, Y = ta.MACD(close, fastperiod=10, slowperiod=30, signalperiod=9)
data['MFI'] = ta.MFI(high, low, close, volume, timeperiod=19)
data['MOM'] = ta.MOM(close, timeperiod=9)
data['ROCR'] = ta.ROCR(close, timeperiod=12)
data['RSI'] = ta.RSI(close, timeperiod=19)
data['STOCHSLOWK'], data['STOCHSLOWD'] = ta.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
data['TRIX'] = ta.TRIX(close, timeperiod=30)
data['WILLR'] = ta.WILLR(high, low, close, timeperiod=14)
data['OBV'] = ta.OBV(close, volume)
data['TSF'] = ta.TSF(close, timeperiod=14)
data['NATR'] = ta.NATR(high, low, close)#, timeperiod=14)
data['ULTOSC'] = ta.ULTOSC(high, low, close)
data['AROONOSC'] = ta.AROONOSC(high, low, timeperiod=14)
data['BOP'] = ta.BOP(open_, high, low, close)
data['LINEARREG'] = ta.LINEARREG(close)
data['AP0'] = ta.APO(close, fastperiod=9, slowperiod=23, matype=1)
data['TEMA'] = ta.TRIMA(close, 29)
return data
def MFI_factor(self, df, timeperiod):
return talib.MFI(
df.loc[:, self.map_dict['high']].values,
df.loc[:, self.map_dict['low']].values,
df.loc[:, self.map_dict['close']].values,
df.loc[:, self.map_dict['volume']].values,
timeperiod)
def momentum(self): adx = talib.ADX(self.high,self.low,self.close,self.period) adxr = talib.ADXR(self.high,self.low,self.close,self.period) apo = talib.APO(self.high,self.low,self.close,self.period) aroondown, aroonup = talib.AROON(self.high, self.low, period) aroonosc = talib.AROONOSC(self.high,self.low,self.period) bop = talib.BOP(self.opens,self.high,self.low,self.close) cci = talib.CCI(self.high,self.low,self.close,self.period) cmo = talib.CMO(self.close,self.period) dx = talib.DX(self.high,self.low,self.close,self.period) macd, macdsignal, macdhist = talib.MACD(self.close, fastperiod=period, slowperiod=period*5, signalperiod=period*2) macd1, macdsignal1, macdhist1 = talib.MACDEXT(self.close, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0) macd2, macdsignal2, macdhist2 = talib.MACDFIX(self.close, signalperiod=9) mfi = talib.MFI(self.high, self.low, self.close, self.volume, timeperiod=14) minus_di = talib.MINUS_DI(self.high, self.low, self.close, timeperiod=14) minus_dm = talib.MINUS_DM(self.high, self.low, timeperiod=14) mom = talib.MOM(self.close, timeperiod=10) plus_di = talib.PLUS_DI(self.high, self.low, self.close, timeperiod=14) plus_dm = talib.PLUS_DM(self.high, self.low, timeperiod=14) ppo = talib.PPO(self.close, fastperiod=12, slowperiod=26, matype=0) roc = talib.ROC(self.close, timeperiod=10) rocp = talib.ROCP(self.close, timeperiod=10) rocr = talib.ROCR(self.close, timeperiod=10) rocr100 = talib.ROCR100(self.close, timeperiod=10) rsi = talib.RSI(self.close, timeperiod=14) slowk, slowd = talib.STOCH(self.high, self.low, self.close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) fastk, fastd = talib.STOCHF(self.high, self.low, self.close, fastk_period=5, fastd_period=3, fastd_matype=0) fastk1, fastd1 = talib.STOCHRSI(self.close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0) trix = talib.TRIX(self.close, timeperiod=30) ultosc = talib.ULTOSC(self.high, self.low, self.close, timeperiod1=7, timeperiod2=14, timeperiod3=28) willr = talib.WILLR(self.high, self.low, self.close, timeperiod=14)
def getMomentumIndicators(df):
high = df['High']
low = df['Low']
close = df['Close']
open = df['Open']
volume = df['Volume']
df['ADX'] = ta.ADX(high, low, close, timeperiod=14)
df['SMA'] = ta.ADXR(high, low, close, timeperiod=14)
df['APO'] = ta.APO(close, fastperiod=12, slowperiod=26, matype=0)
df['AROONDOWN'], df['AROOONUP'] = ta.AROON(high, low, timeperiod=14)
df['AROONOSC'] = ta.AROONOSC(high, low, timeperiod=14)
df['BOP'] = ta.BOP(open, high, low, close)
df['CCI'] = ta.CCI(high, low, close, timeperiod=14)
df['CMO'] = ta.CMO(close, timeperiod=14)
df['DX'] = ta.DX(high, low, close, timeperiod=14)
df['MACD'], df['MACDSIGNAL'], df['MACDHIST'] = ta.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
df['MFI'] = ta.MFI(high, low, close, volume, timeperiod=14)
df['MINUS_DI'] = ta.MINUS_DI(high, low, close, timeperiod=14)
df['MINUS_DM']= ta.MINUS_DM(high, low, timeperiod=14)
df['MOM'] = ta.MOM(close, timeperiod=10)
df['PLUS_DM'] =ta.PLUS_DM(high, low, timeperiod=14)
df['PPO'] = ta.PPO(close, fastperiod=12, slowperiod=26, matype=0)
df['ROC'] = ta.ROC(close, timeperiod=10)
df['ROCP'] = ta.ROCP(close, timeperiod=10)
df['ROCR'] = ta.ROCR(close, timeperiod=10)
df['ROCR100'] = ta.ROCR100(close, timeperiod=10)
df['RSI'] = ta.RSI(close, timeperiod=14)
df['SLOWK'], df['SLOWD'] = ta.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
df['FASTK'], df['FASTD'] = ta.STOCHF(high, low, close, fastk_period=5, fastd_period=3, fastd_matype=0)
df['FASTK2'], df['FASTD2'] = ta.STOCHRSI(close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)
df['TRIX'] = ta.TRIX(close, timeperiod=30)
df['ULTOSC'] = ta.ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28)
df['WILLR'] = ta.WILLR(high, low, close, timeperiod=14)
def mfi(candles: np.ndarray,
period=14,
sequential=False) -> Union[float, np.ndarray]:
"""
MFI - Money Flow Index
:param candles: np.ndarray
:param period: int - default=14
:param sequential: bool - default=False
:return: float | np.ndarray
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
res = talib.MFI(candles[:, 3],
candles[:, 4],
candles[:, 2],
candles[:, 5],
timeperiod=period)
if sequential:
return res
else:
return None if np.isnan(res[-1]) else res[-1]
def mfi():
for i in range(len(ForexTraderSwitch.curr_pair_list)):
close=(ForexTraderSwitch.curr_pair_history_data[i]['closeAsk'].values+\
ForexTraderSwitch.curr_pair_history_data[i]['closeBid'].values)/2
high=(ForexTraderSwitch.curr_pair_history_data[i]['highAsk'].values+\
ForexTraderSwitch.curr_pair_history_data[i]['highBid'].values)/2
low=(ForexTraderSwitch.curr_pair_history_data[i]['lowAsk'].values+\
ForexTraderSwitch.curr_pair_history_data[i]['lowBid'].values)/2
#open=(history['openAsk']+history['openBid'])/2
mfi = talib.MFI(high,
low,
close,
history['volume'].values.astype(float),
timeperiod=14)
mfi_signal = mfi[-1]
#print(mfi_signal)
ForexTraderSwitch.signal[i, 12] = mfi_signal
if mfi_signal > 70:
#trader.create_buy_order(ticker,units)
ForexTraderSwitch.order[i, 5, 1] = 1
elif mfi_signal < 30:
#trader.create_sell_order(ticker,units)
ForexTraderSwitch.order[i, 5, 2] = 1
else:
#print('No trade made')
ForexTraderSwitch.order[i, 5, 0] = 1
def on_preparing_data(self, data, **kwargs):
"""附加计算顺势指标"""
real = 'mfi_{}'.format(self.timeperiod)
data[real] = talib.MFI(data[self.col_high].values,
data[self.col_low].values,
data[self.col_close].values,
data[self.col_vol].values, self.timeperiod)
def MFI(matrix, timeperiod=14):
matrix['MFI' + str(timeperiod)] = talib.MFI(high=matrix['High'].values,
low=matrix['Low'].values,
close=matrix['1-Close'].values,
volume=matrix['Volume'].values,
timeperiod=timeperiod)
return matrix
def get(self, idx):
"""Get the prediction for given idx.
"""
# No Information yet
if (idx <= (self.lag + self.timeperiod)):
return (0) # No Signal
# Calculate mfi
mfi = talib.MFI(
self.series_high[(idx - self.lag -
self.timeperiod):(idx - self.lag + 1)],
self.series_low[(idx - self.lag - self.timeperiod):(idx -
self.lag + 1)],
self.series_close[(idx - self.lag -
self.timeperiod):(idx - self.lag + 1)],
self.series_volume[(idx - self.lag -
self.timeperiod):(idx - self.lag + 1)],
timeperiod=self.timeperiod)[-1]
# Result
if (mfi >= self.sell_threshold):
result = -1
elif (mfi <= self.buy_thershold):
result = 1
else:
result = 0
# Return
return (result)
def MFI(high=None,
low=None,
close=None,
volume=None,
timeperiod=None,
verbose=False):
"""MFI
Wrapper for talib.MFI for running unittests
on ci/cd tools that do not provide talib
.. code-block:: python
real = MFI(
high,
low,
close,
volume,
timeperiod=14)
:param high: high list
:param low: low list
:param close: close list
:param timeperiod: number of values
in ``high``, ``low`` and ``close``
:param verbose: show logs
"""
if verbose:
log.info('mfi - start')
return talib.MFI(high, low, close, volume, timeperiod)
def _mfi(data):
result = talib.MFI(data.highest_price.values,
data.lowest_price.values,
data.close_price,
data.turnover_vol,
timeperiod=14)
return result.iloc[-1]
def test_mfi(self):
"""
Test Money Flow Index.
"""
q = qufilab.mfi(self.high, self.low, self.close, self.volume, 200)
t = talib.MFI(self.high, self.low, self.close, self.volume, 200)
np.testing.assert_allclose(q, t, rtol=self.tolerance)
def get_mfi(high, low, close, volume, period, max_period):
mfi = talib.MFI(np.array(high),
np.array(low),
np.array(close),
np.array(volume, dtype=np.double),
timeperiod=period)
return mfi[max_period:]
def buildTAindicators(stock_df):
stock_df['REDORGREEN'] = stock_df.loc[:, 'Open'].subtract(
stock_df.loc[:, 'Close']).apply(lambda x: 1 if x > 0 else 0).values
stock_df['CANDLE_HEIGHT'] = stock_df.loc[:, 'Open'].subtract(
stock_df.loc[:, 'Close']).values
stock_df['NATR'] = ta.NATR(stock_df.loc[:, 'High'].values,
stock_df.loc[:, 'Low'].values,
stock_df.loc[:, 'Close'].values, 5)
close = stock_df.loc[:, 'Close'].values
high = stock_df.loc[:, 'High'].values
low = stock_df.loc[:, 'Low'].values
volume = pd.Series.astype(stock_df.loc[:, 'Volume'], dtype='double').values
adx = ta.ADX(high, low, close, timeperiod=14)
macd, macdsignal, macdhist = ta.MACD(close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
mfi = ta.MFI(high, low, close, volume, timeperiod=14)
rsi = ta.RSI(close, timeperiod=14)
beta = ta.BETA(high, low, timeperiod=5)
stock_df['ADX'] = pd.Series(adx).values
stock_df['MFI'] = pd.Series(mfi).values
stock_df['RSI'] = pd.Series(rsi).values
stock_df['MACD'] = pd.Series(macd).values
stock_df['BETA'] = pd.Series(beta).values
stock_df = stock_df.iloc[33:, :]
return stock_df
def test_feature(date_after,bar,count):
d1 = dt.datetime.now()
engine = sa.create_engine('mysql+pymysql://gupiao:[email protected]/stocktool?charset=utf8')
stock_df = pd.read_sql_query("select DISTINCT code from StockPrediction_stock_price;",engine)
stock_s = pd.Series(stock_df['code']).sort_values()
stock_list = stock_s.tolist()
counter = 0
for i in stock_list:
counter += 1
sql_price = "SELECT * FROM StockPrediction_stock_price WHERE code = "+i+" and date > '2018-04-01' ORDER BY date;"# 4月1日只是为了缩短预测时获取的数据量，应该是随着时间增加而增加的
price_data = pd.read_sql(sql_price,engine)
if(price_data.empty):
print(i)
else:
macd,macds,price_data['macdh'] =ta.MACD(price_data['close'].values, fastperiod=12, slowperiod=26, signalperiod=9)
price_data['stoK'],price_data['stoD'] =ta.STOCH(price_data['high'].values,price_data['low'].values,price_data['close'].values)
price_data['rsi'] =ta.RSI(price_data['close'].values)
price_data['willR'] =ta.WILLR(price_data['high'].values,price_data['low'].values,price_data['close'].values)
price_data['ultosc'] =ta.ULTOSC(price_data['high'].values,price_data['low'].values,price_data['close'].values)
price_data['mfi'] =ta.MFI(price_data['high'].values,price_data['low'].values,price_data['close'].values,price_data['volume'].values)
price_data.pop('id')# pandas算出结果后会多出一列 存入数据库会报错就pop掉
""" 判断是否下跌"""
iD = pd.DataFrame(np.zeros((price_data.shape[0], 1)), columns=['down'])
iD['down'] = False
row_number = price_data.shape[0]
for j in range(0,row_number):
if(j<2):
continue
elif(price_data.iloc[j-2].high*0.95>price_data.iloc[j].open):#FIXME:两天前的最高价*0.95>预测日的开盘价
iD.iloc[j].down = True
else:
continue
price_data.insert(price_data.shape[1],'down',iD)
print(price_data[price_data.date>=dt.datetime.strptime(date_after,'%Y-%m-%d').date()])
price_data[price_data.date>=dt.datetime.strptime(date_after,'%Y-%m-%d').date()].to_sql('StockPrediction_stock_pred',engine,if_exists='append',index=False)
bar.update(count+counter)
# 最终手机提示
if(Notifi):
d2 = dt.datetime.now()
print("time used: ")
print(d2 - d1)
print_str = "\ntime used: " + str(d2 - d1)
if(result is not None):
msg = "done ！\n" + print_str+"\nresult: "+str(result)
else:
msg = "done ！\n" + print_str
conn = http.client.HTTPSConnection("api.pushover.net:443")
conn.request("POST", "/1/messages.json",
urllib.parse.urlencode({
"token": "a22vgia8q7vszh4zromzgd2jgikxeh",
"user": "******",
"message": msg,
"title": os.path.basename(__file__),
}), {"Content-type": "application/x-www-form-urlencoded"})
conn.getresponse()
return len(stock_list)
def run_indicators(symbol, file):
try:
if os.path.exists(file):
# read file
df = np.genfromtxt(file, delimiter=',')
# run talib indicators using dataframe
# create additional columns in the datafarame
closePrice = df[:, 4]
openPrice = df[:, 1]
highPrice = df[:, 2]
lowPrice = df[:, 3]
volume = df[:, 5]
sma = talib.SMA(closePrice)
atr = talib.ATR(highPrice, lowPrice, closePrice)
natr = talib.NATR(highPrice, lowPrice, closePrice)
adx = talib.ADX(highPrice, lowPrice, closePrice)
mfi = talib.MFI(highPrice, lowPrice, closePrice, volume)
ppo = talib.PPO(closePrice, fastperiod=12, slowperiod=26, matype=0)
rsi = talib.RSI(closePrice)
slowk, slowd = talib.STOCH(highPrice,
lowPrice,
closePrice,
fastk_period=14,
slowk_period=3,
slowd_period=3)
macd, macdsignal, macdhist = talib.MACD(closePrice,
fastperiod=12,
slowperiod=26,
signalperiod=9)
# get ticker current price
client = Client(os.environ.get('BINANCE_API_KEY'),
os.environ.get('BINANCE_SECRET_KEY'),
{"timeout": 100})
closing_price = client.get_symbol_ticker(symbol=symbol)
# create crypto object
crypto = {}
crypto['ticker'] = symbol
crypto['price'] = closing_price['price']
crypto['SMA14'] = sma[-1]
crypto['ATR'] = atr[-1]
crypto['NATR'] = natr[-1]
crypto['ADX'] = adx[-1]
crypto['MFI'] = mfi[-1]
crypto['RSI'] = rsi[-1]
crypto['STO'] = slowk[-1], slowd[-1]
crypto['MACD'] = macd[-1]
crypto['PPO'] = ppo[-1]
else:
crypto = {}
crypto['ticker'] = symbol
return crypto
except Exception as e:
print(str(e))
def calc_mfi(self, timeperiod=14):
column = 'mfi' + str(timeperiod)
self.stock[column] = ta.MFI(self.high,
self.low,
self.close,
self.volume,
timeperiod=timeperiod)
return self.stock
def mfi(self, n: int, array: bool = False) -> Union[float, np.ndarray]:
"""
Money Flow Index.
"""
result = talib.MFI(self.high, self.low, self.close, self.volume, n)
if array:
return result
return result[-1]
def ta_mfi(n, k_data):
mfi = pd.DataFrame()
mfi['mfi'] = ta.MFI(k_data.high,
k_data.low,
k_data.close,
k_data.volume,
timeperiod=n)
return (mfi.round(2))
def test_mfi():
'''test TA.MFI'''
mfi = TA.MFI(ohlc, 9)
talib_mfi = talib.MFI(ohlc['high'], ohlc['low'], ohlc['close'],
ohlc['volume'], 9)
assert int(talib_mfi[-1]) == int(mfi.values[-1])
def mfi(self, n, array=False):
"""
Money Flow Index.
"""
result = talib.MFI(self.high, self.low, self.close, self.volume, n)
if array:
return result
return result[-1]
def add_mfi(df):
mfi = talib.MFI(df['high'].values,
df['low'].values,
df['close'].values,
df['volume'].values,
timeperiod=14)
df['mfi'] = mfi
return df
def calculate_mfi(df, period=14):
df, data = reset_index(df)
df['MFI'] = ta.MFI(df['High'],
df['Low'],
df['Close'],
df['Volume'],
timeperiod=period)
return df
def MFI(self, timeperiod=14):
real_data = np.array([self.df.high, self.df.low, self.df.close, self.df.volume], dtype='f8')
mfi = talib.MFI(real_data[0], real_data[1], real_data[2], real_data[3], timeperiod=timeperiod)
# return go.Scatter(
# x=self.df.index,
# y=mfi,
# name='MFI'
# )
return mfi
def mfiIndicator(self, am, paraDict):
mfiPeriod = paraDict['bandPeriod']
mfiThreshold = paraDict['bPctThreshold']
mfiSignal = ta.MFI(am.high, am.low, am.close, am.volume, mfiPeriod)
mfiStatus = 0
if mfiSignal[-1] > mfiThreshold:
mfiStatus = 1
if mfiSignal[-1] < (100 - mfiThreshold):
mfiStatus = -1
return mfiStatus, mfiSignal
def MFI(high, low, close, volume, timeperiod=14):
''' Money Flow Index 资金流量指标
分组: Momentum Indicator 动量指标
简介: 属于量价类指标，反映市场的运行趋势
real = MFI(high, low, close, volume, timeperiod=14)
'''
return talib.MFI(high, low, close, volume, timeperiod)
def _get_indicators(security, open_name, close_name, high_name, low_name,
volume_name):
"""
expand the features of the data through technical analysis across 26 different signals
:param security: data which features are going to be expanded
:param open_name: open price column name
:param close_name: close price column name
:param high_name: high price column name
:param low_name: low price column name
:param volume_name: traded volumn column name
:return: expanded and extracted data
"""
open_price = security[open_name].values
close_price = security[close_name].values
low_price = security[low_name].values
high_price = security[high_name].values
volume = security[volume_name].values if volume_name else None
security['MOM'] = talib.MOM(close_price)
security['HT_DCPERIOD'] = talib.HT_DCPERIOD(close_price)
security['HT_DCPHASE'] = talib.HT_DCPHASE(close_price)
security['SINE'], security['LEADSINE'] = talib.HT_SINE(close_price)
security['INPHASE'], security['QUADRATURE'] = talib.HT_PHASOR(
close_price)
security['ADXR'] = talib.ADXR(high_price, low_price, close_price)
security['APO'] = talib.APO(close_price)
security['AROON_UP'], _ = talib.AROON(high_price, low_price)
security['CCI'] = talib.CCI(high_price, low_price, close_price)
security['PLUS_DI'] = talib.PLUS_DI(high_price, low_price, close_price)
security['PPO'] = talib.PPO(close_price)
security['MACD'], security['MACD_SIG'], security[
'MACD_HIST'] = talib.MACD(close_price)
security['CMO'] = talib.CMO(close_price)
security['ROCP'] = talib.ROCP(close_price)
security['FASTK'], security['FASTD'] = talib.STOCHF(
high_price, low_price, close_price)
security['TRIX'] = talib.TRIX(close_price)
security['ULTOSC'] = talib.ULTOSC(high_price, low_price, close_price)
security['WILLR'] = talib.WILLR(high_price, low_price, close_price)
security['NATR'] = talib.NATR(high_price, low_price, close_price)
security['RSI'] = talib.RSI(close_price)
security['EMA'] = talib.EMA(close_price)
security['SAREXT'] = talib.SAREXT(high_price, low_price)
# security['TEMA'] = talib.EMA(close_price)
security['RR'] = security[close_name] / security[close_name].shift(
1).fillna(1)
security['LOG_RR'] = np.log(security['RR'])
if volume_name:
security['MFI'] = talib.MFI(high_price, low_price, close_price,
volume)
# security['AD'] = talib.AD(high_price, low_price, close_price, volume)
# security['OBV'] = talib.OBV(close_price, volume)
security[volume_name] = np.log(security[volume_name])
security.drop([open_name, close_name, high_name, low_name], axis=1)
security = security.dropna().astype(np.float32)
return security
def get_SP500_4hrStateEvery1hour(self):
while self.running:
monthAgo = time.time() - 2419200
sp_df = self.get_Yahoo_Data('%5EGSPC',
str(monthAgo).split('.')[0], '60m')
moneyFlow = talib.MFI(sp_df, 14)
print('last 10 values of latest 1hr Money Flow:')
Trade = Query()
self.db.update({'SP500_4hrMF': moneyFlow.values[-1:][0]},
Trade.type == 'current_state')
time.sleep(3600)
