def willr(self, n: int, array: bool = False) -> Union[float, np.ndarray]:
"""
WILLR.
"""
result = talib.WILLR(self.high, self.low, self.close, n)
if array:
return result
return result[-1]
def willr(prices, signal):
"""
Williams R Oscillator
"""
window = signal['params']['window']
signal['data'] = talib.WILLR(prices['high'], prices['low'],
prices['close'], window).to_numpy()[:, None]
def willr(self, n, array=False):
"""
WILLR.
"""
result = talib.WILLR(self.high, self.low, self.close, n)
if array:
return result
return result[-1]
def GetWR( self, lst ):
C = np.array( self.df[ '收盤' ], dtype = float, ndmin = 1 )
H = np.array( self.df[ '最高' ], dtype = float, ndmin = 1 )
L = np.array( self.df[ '最低' ], dtype = float, ndmin = 1 )
for val in lst:
self.df[ 'WILLR' + str( val ) ] = talib.WILLR( H, L, C, timeperiod = val )
def WR(single_stock_df, col_h='high', col_l='low', col_c='close', timeperiod=10):
"""
计算威廉指标，timeperiod=10
重要参数：high最高价 low最低价 close收盘价,此处算出为负值需要取绝对值
"""
single_stock_df['wr'] = talib.WILLR(single_stock_df[col_h], single_stock_df[col_l],single_stock_df[col_c],timeperiod=timeperiod)
single_stock_df['wr'] = single_stock_df['wr'].abs()
return single_stock_df
def entry(self, df_ohlc, np_emas, tick):
self.master_df_updated = df_ohlc
self.master_df_updated['wr'] = ta.WILLR(
self.master_df_updated['high'],
self.master_df_updated['low'],
self.master_df_updated['close'],
timeperiod=40)
list_r = self.master_df_updated['wr'].tolist()
if self.activated is False:
for i in range(-1, -1 * len(list_r), -1):
if list_r[i] < -80 and self.data['long'] is True:
self.activated = True
break
if list_r[i] > -20 and self.data['long'] is True:
self.activated = False
break
if list_r[i] > -20 and self.data['short'] is True:
self.activated = True
break
if list_r[i] <= -80 and self.data['short'] is True:
self.activated = False
break
elif self.activated is True:
print(self.data['symbol'])
if self.data['long'] is True and tick['last_price'] > np_emas[
0, -1] and tick['last_price'] > np_emas[
1, -1] and list_r[-1] >= -20:
self.long_position = True
self.activated = False
self.buy_price = tick['last_price']
# threading
order_id = self.kite.place_order(
tradingsymbol=self.data['symbol'],
exchange=self.kite.EXCHANGE_NSE,
transaction_type=self.kite.TRANSACTION_TYPE_BUY,
quantity=self.data['quantity'],
variety=self.kite.VARIETY_REGULAR,
order_type=self.kite.ORDER_TYPE_MARKET,
product=self.kite.PRODUCT_MIS)
print(order_id)
if self.data['short'] is True and tick['last_price'] < np_emas[
0, -1] and tick['last_price'] < np_emas[
1, -1] and list_r[-1] <= -80:
self.short_position = True
self.activated = False
self.sell_price = tick['last_price']
order_id = self.kite.place_order(
tradingsymbol=self.data['symbol'],
exchange=self.kite.EXCHANGE_NSE,
transaction_type=self.kite.TRANSACTION_TYPE_SELL,
quantity=self.data['quantity'],
variety=self.kite.VARIETY_REGULAR,
order_type=self.kite.ORDER_TYPE_MARKET,
product=self.kite.PRODUCT_MIS)
print(order_id)
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 run_techicals(df):
'''
Preform technical anlysis calcuaitons and add to a dataframe.
Algorithms stem from the TAlib module other helper functions, including distretizing data,
using alternate series, and time shifting
TAlib docs: https://github.com/mrjbq7/ta-lib/blob/master/docs/func_groups/momentum_indicators.md
'''
days_back = 0
opn = np.roll(df['exp_smooth_open'], days_back)
high = np.roll(df['exp_smooth_high'], 0)
low = np.roll(df['exp_smooth_low'], 0)
close = np.roll(df['exp_smooth_close'], days_back)
volume = np.roll(df['exp_smooth_volume'], 0)
# series = df['close'].values
series = close
# df['roc'] = talib.ROCP(series, timeperiod=1)
df['roc'] = rate_of_change(df['close'], 1) # > 0.04
df['roc_d'] = discrete_series_pos_neg(df['roc']) # > 80.638
df['rsi'] = talib.RSI(series, timeperiod=14) # > 80.638
df['rsi_d'] = continuous_to_discrete_w_bounds(df['rsi'], 30,
70) # > 80.638
df['willr'] = talib.WILLR(high, low, series, timeperiod=14) # > -11
df['willr_d'] = discrete_trend(df['willr']) # > -11
df['cci'] = talib.CCI(high, low, series, timeperiod=14) # > -11
df['cci_d'] = continuous_to_discrete_w_bounds(df['cci'], -200,
200) # > -11
df['obv'] = talib.OBV(series, volume)
df['mom'] = talib.MOM(series)
df['mom_d'] = discrete_series_pos_neg(df['mom'])
df['sma20'] = discrete_series_compare(close, talib.SMA(
series, 20)) # > talib.MA(series, 200)
df['sma50'] = discrete_series_compare(close, talib.SMA(
series, 50)) # > talib.MA(series, 200)
df['sma200'] = discrete_series_compare(close, talib.SMA(
series, 200)) # > talib.MA(series, 200)
df['wma10'] = discrete_series_compare(close, talib.WMA(series, 10))
df['macd'], df['macd_sig'], macdhist = talib.MACD(series,
fastperiod=12,
slowperiod=26,
signalperiod=9)
df['macd_d'] = discrete_trend(df['macd']) # > -11
df['stok'], df['stod'] = talib.STOCH(high,
low,
series,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['stok_d'] = discrete_trend(df['stok']) # > -11
df['stod_d'] = discrete_trend(df['stod']) # > -11
#df['sto'] = slowk #> 80
return df
def calcFeatures(df,indx):
df1 = pd.DataFrame({'A' : []})
#for i in range(0,7):
for i in range(len(indx)):
if (indx[i] and i==0):
s1 = df['Open']
df1 = pd.concat([df1, s1], axis=1)
elif (indx[i] and i==1):
s1 = df['High']
df1 = pd.concat([df1, s1], axis=1)
elif (indx[i] and i==2):
s1 = df['Low']
df1 = pd.concat([df1, s1], axis=1)
elif (indx[i] and i==3):
s1 = df['Close']
df1 = pd.concat([df1, s1], axis=1)
elif (indx[i] and i==4):
s1 = df['Adj Close']
df1 = pd.concat([df1, s1], axis=1)
elif (indx[i] and i==5):
s1 = df['Volume']
df1 = pd.concat([df1, s1], axis=1)
elif (indx[i] and i==6):
s1 = pd.DataFrame( {'RSI': talib.RSI(np.array(df['Close'].values),timeperiod=14) })
df1 = pd.concat([df1, s1], axis=1)
elif (indx[i] and i==7):
s1 = pd.DataFrame( {'ROC': talib.ROC(np.array(df['Close'].values), timeperiod=10) })
df1 = pd.concat([df1, s1], axis=1)
elif (indx[i] and i==8):
s1 = pd.DataFrame( {'BETA': talib.BETA(np.array(df['High'].values), \
np.array(df['Low'].values), timeperiod=5) })
df1 = pd.concat([df1, s1], axis=1)
elif (indx[i] and i==9):
s1 = pd.DataFrame( {'STDDEV': talib.STDDEV(np.array(df['Close'].values), \
timeperiod=5, nbdev=1) })
df1 = pd.concat([df1, s1], axis=1)
elif (indx[i] and i==10):
s1 = pd.DataFrame( {'WILLR': talib.WILLR(np.array(df['Open'].values), \
np.array(df['High'].values),np.array(df['Close'].values),timeperiod=40) })
df1 = pd.concat([df1, s1], axis=1)
elif (indx[i] and i==11):
s1 = pd.DataFrame( {'SMA1': talib.SMA(np.array(df['Close'].values), timeperiod=30) })
df1 = pd.concat([df1, s1], axis=1)
elif (indx[i] and i==12):
s1 = pd.DataFrame( {'SMA2': talib.SMA(np.array(df['Close'].values), timeperiod=60) })
df1 = pd.concat([df1, s1], axis=1)
#Delete 'A' column
df1=df1.drop(columns=['A'])
#Numpy array
out=np.array(df1.values)
out=out[~np.any(np.isnan(out), axis=1)]
return out
def get_feature(a, n):
#1. data gathering & processing
data = get_stock_data(a)
data = data.replace(0, np.nan)
data.dropna(inplace=True)
#2. exponential smoothing
S = X = np.array(data['Close'])
alpha = 0.9
for i in range(1, len(S)):
S[i] = alpha * X[i] + (1 - alpha) * S[i - 1]
data['Close'] = S
#3.data extraction
macd, dea, bar = talib.MACD(data['Close'].values,
fastperiod=12,
slowperiod=26,
signalperiod=9)
fastk, fastd = talib.STOCHF(data['High'],
data['Low'],
data['Close'],
fastk_period=14,
fastd_period=3,
fastd_matype=0)
data['dif'] = data['Close'].diff(-n)
data['MACD'] = macd
data['STOCH'] = fastk
data['WILLR'] = talib.WILLR(data['High'],
data['Low'],
data['Close'],
timeperiod=14)
data['OBV'] = talib.OBV(data['Close'], data['Volume'])
data['RSI'] = talib.RSI(data['Close'], timeperiod=14)
data['ATR'] = talib.ATR(data['High'],
data['Low'],
data['Close'],
timeperiod=14)
data = pd.DataFrame(data)
data.dropna(inplace=True)
#4. Labels are the values we want to predict
diff = np.array(data['dif'])
labels = np.zeros(len(diff))
sum = 0
for i in range(len(diff)):
if diff[i] > 0:
labels[i] = 1
sum += 1
# print (sum/len(diff))
#5. Data for the features
features = data[['MACD', 'STOCH', 'WILLR', 'OBV', 'ATR', 'RSI']]
#6. Saving feature names for later use
feature_list = list(features.columns)
return features, labels, feature_list
def WILLR(self, timeperiod=14):
real_data = np.array([self.df.high, self.df.low, self.df.close], dtype='f8')
willr = talib.WILLR(real_data[0], real_data[1], real_data[2], timeperiod=timeperiod)
# return go.Scatter(
# x=self.df.index,
# y=willr,
# name='WILLR'
# )
return willr
def technical_index(context):
#EMA,RSI指标
context.var.ts['EMA_5min'] = talib.EMA(np.array(context.var.ts.close), timeperiod=5)
context.var.ts['EMA_10min'] = talib.EMA(np.array(context.var.ts.close), timeperiod=10)
context.var.ts['EMA_15min'] = talib.EMA(np.array(context.var.ts.close), timeperiod=15)
context.var.ts['EMA_20min'] = talib.EMA(np.array(context.var.ts.close), timeperiod=20)
context.var.ts['RSI'] = talib.RSI(np.array(context.var.ts.close))
STOCHRSI_usual = talib.STOCHRSI(np.array(context.var.ts.close))
# BOLL-BAND指标
BBANDS_usual = talib.BBANDS(np.array(context.var.ts.close))
upperband, middleband, lowerband = BBANDS_usual
context.var.ts['upperband'] = upperband
context.var.ts['middleband'] = middleband
context.var.ts['lowerband'] = lowerband
arrClose = np.array(context.var.ts.close)
arrHigh = np.array(context.var.ts.high)
arrLow = np.array(context.var.ts.low)
arrVolume = np.array(context.var.ts.volume,dtype=np.float)
# MACD指标
MACD_usual = talib.MACD(arrClose)
macd, macdsignal, macdhist = MACD_usual
context.var.ts['macd'] = macd
context.var.ts['macdsignal'] = macdsignal
context.var.ts['macdhist'] = macdhist
# KDJ指标
KDJ_usual = talib.STOCH(arrHigh, arrLow, arrClose)
slowk, slowd = KDJ_usual
context.var.ts['slowk'] = slowk
context.var.ts['slowd'] = slowd
# ATR指标
ATR_usual = talib.ATR(arrHigh, arrLow, arrClose)
context.var.ts['ATR'] = ATR_usual
# WILLR指标
WILLR_usual = talib.WILLR(arrHigh, arrLow, arrClose)
context.var.ts['WILLR'] = WILLR_usual
# BOV指标
OBV_usual = talib.OBV(arrClose, arrVolume)
context.var.ts['OBV'] = OBV_usual
# SAR指标
SAR_usual = talib.SAR(arrHigh, arrLow)
context.var.ts['SAR'] = SAR_usual
# DEMA指标
DEMA_usual = talib.DEMA(arrClose)
context.var.ts['DEMA'] = DEMA_usual
#MOM指标
MOM_usual = talib.MOM(arrClose)
context.var.ts['MOM'] = MOM_usual
def result_willams(data):
try:
willams = talib.WILLR(data.high.values, data.low.values, data.close.values, timeperiod=14)
if willams[-2] > -20 > willams[-1]:
return "turn to short"
if willams[-2] < -80 < willams[-1]:
return "turn to long"
except:
logging.info("could not get williams values")
return 'undefined'
def MakeMiddleData(self,code,df):
PDI = tl.PLUS_DI(df.high, df.low, df.close, timeperiod=14)
MDI = tl.MINUS_DI(df.high, df.low, df.close, timeperiod=14)
ADX = tl.ADX(df.high, df.low, df.close, timeperiod=14)
R = tl.WILLR(df.high, df.low, df.close, timeperiod=14)
if list(R)[-1] < -80:
if list(PDI)[-1] > list(MDI)[-1]:
print("R : ",R[len(df)-1])
print("Name : ",self.dicStockInfo['info'][code])
def main(df):
highs = df['high'].values
lows = df['low'].values
closes=df['close'].values
dftmp = df[[]]
dftmp["w"] = talib.WILLR(highs,lows,closes,50)
dftmp["w2"] = dftmp["w"].shift(1)
df["ta_sig_ta_WILLR_2_1"] = dftmp.apply(lambda row: adx_signal(row), axis = 1)
return df
def evaluate(self, kite_fetcher, instrument):
period = int(self.period)
df = self.get_small_data(kite_fetcher=kite_fetcher,
instrument=instrument)
result = talib.WILLR(high=df['high'],
low=df['low'],
close=df['close'],
timeperiod=period)
return np.round(result.iloc[-1], 3)
def williamsR(df, n):
"""Calculate William's R using Ta-Lib library's built-in function.
:param df: pandas.DataFrame
:return: pandas.DataFrame
"""
williamsR = talib.WILLR(df['High'], df['Low'], df['Close'], timeperiod=n)
williamsR_series = pd.Series(williamsR, name='WilliamsR_' + str(n))
df = df.join(williamsR_series)
return df
def calculate(self, stock_code, date, time_period1=14, time_period2=6):
wr_14 = 0.0
wr_6 = 0.0
data = su.get_basic_data(stock_code, date,
time_period1 + 1).sort_index(ascending=False)
if data.shape[0] >= time_period1 + 1:
wr_14 = round(
ta.WILLR(data['HIGH'].as_matrix(), data['LOW'].as_matrix(),
data['CLOSE'].as_matrix(), time_period1)[-1] * -1, 3)
wr_6 = round(
ta.WILLR(data['HIGH'].as_matrix(), data['LOW'].as_matrix(),
data['CLOSE'].as_matrix(), time_period2)[-1] * -1, 3)
if np.isnan(wr_14) or np.isinf(wr_14) or np.isneginf(wr_14):
wr_14 = 0.0
if np.isnan(wr_6) or np.isinf(wr_6) or np.isneginf(wr_6):
wr_6 = 0.0
self.save_tech_data(stock_code, date, {'WR_14': wr_14, 'WR_6': wr_6})
return wr_14, wr_6
def main(df):
highs = df['high'].values
lows = df['low'].values
closes=df['close'].values
df["ta_tmp"] = talib.WILLR(highs,lows,closes,2)
df["ta_sig_ta_WILLR_2_2"] = df.apply(lambda row: adx_signal(row), axis = 1)
del df["ta_tmp"]
return df
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 generate_feature(data):
high = data.High.values
low = data.Low.values
close = data.Close.values
# feature_df = pd.DataFrame(index=data.index)
feature_df = data.copy()
feature_df["ADX"] = ADX = talib.ADX(high, low, close, timeperiod=14)
feature_df["ADXR"] = ADXR = talib.ADXR(high, low, close, timeperiod=14)
feature_df["APO"] = APO = talib.APO(close,
fastperiod=12,
slowperiod=26,
matype=0)
feature_df["AROONOSC"] = AROONOSC = talib.AROONOSC(high,
low,
timeperiod=14)
feature_df["CCI"] = CCI = talib.CCI(high, low, close, timeperiod=14)
feature_df["CMO"] = CMO = talib.CMO(close, timeperiod=14)
feature_df["DX"] = DX = talib.DX(high, low, close, timeperiod=14)
feature_df["MINUS_DI"] = MINUS_DI = talib.MINUS_DI(high,
low,
close,
timeperiod=14)
feature_df["MINUS_DM"] = MINUS_DM = talib.MINUS_DM(high,
low,
timeperiod=14)
feature_df["MOM"] = MOM = talib.MOM(close, timeperiod=10)
feature_df["PLUS_DI"] = PLUS_DI = talib.PLUS_DI(high,
low,
close,
timeperiod=14)
feature_df["PLUS_DM"] = PLUS_DM = talib.PLUS_DM(high, low, timeperiod=14)
feature_df["PPO"] = PPO = talib.PPO(close,
fastperiod=12,
slowperiod=26,
matype=0)
feature_df["ROC"] = ROC = talib.ROC(close, timeperiod=10)
feature_df["ROCP"] = ROCP = talib.ROCP(close, timeperiod=10)
feature_df["ROCR100"] = ROCR100 = talib.ROCR100(close, timeperiod=10)
feature_df["RSI"] = RSI = talib.RSI(close, timeperiod=14)
feature_df["ULTOSC"] = ULTOSC = talib.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
feature_df["WILLR"] = WILLR = talib.WILLR(high, low, close, timeperiod=14)
feature_df = feature_df.fillna(0.0)
# Exclude columns you don't want
feature_df = feature_df[feature_df.columns[
~feature_df.columns.isin(['Open', 'High', 'Low', 'Close'])]]
matrix = feature_df.values
return feature_df, matrix
def test_william_r(self):
"""
Test William's %R.
"""
periods = 200
william_qufilab = qufilab.willr(self.close, self.high, self.low,
periods)
william_talib = talib.WILLR(self.high, self.low, self.close, periods)
np.testing.assert_allclose(william_qufilab,
william_talib,
rtol=self.tolerance)
def PipelineEstrategiaWPR(self, timeperiodEstrategy, VectorLimits,
shiftParaComparar, NombreEstrategia):
Est = PreParacionDatosModelo(self.Base)
self.Base['WPR'] = ta.WILLR(np.array(self.Base['High']),
np.array(self.Base['Low']),
np.array(self.Base['Close']),
timeperiod=timeperiodEstrategy)
self.Base = Est.GenerarEstrategiaWPR(VectorLimits, shiftParaComparar,
NombreEstrategia)
precision = self.CalcularPrecision(NombreEstrategia)
return self.Base
def cal_technical_indicators(df):
# Simple Moving Average SMA 简单移动平均
df['SMA5'] = talib.MA(df['close'], timeperiod=5)
df['SMA10'] = talib.MA(df['close'], timeperiod=10)
df['SMA20'] = talib.MA(df['close'], timeperiod=20)
# Williams Overbought/Oversold Index WR 威廉指标
df['WR14'] = talib.WILLR(df['high'], df['low'], df['close'], timeperiod=14)
df['WR18'] = talib.WILLR(df['high'], df['low'], df['close'], timeperiod=18)
df['WR22'] = talib.WILLR(df['high'], df['low'], df['close'], timeperiod=22)
# Moving Average Convergence / Divergence MACD 指数平滑移动平均线
DIFF1, DEA1, df['MACD9'] = talib.MACD(np.array(df['close']), fastperiod=12, slowperiod=26, signalperiod=9)
DIFF2, DEA2, df['MACD10'] = talib.MACD(np.array(df['close']), fastperiod=14, slowperiod=28, signalperiod=10)
df['MACD9'] = df['MACD9'] * 2
df['MACD10'] = df['MACD10'] * 2
# Relative Strength Index RSI 相对强弱指数
df['RSI15'] = talib.RSI(np.array(df['close']), timeperiod=15)
df['RSI20'] = talib.RSI(np.array(df['close']), timeperiod=20)
df['RSI25'] = talib.RSI(np.array(df['close']), timeperiod=25)
df['RSI30'] = talib.RSI(np.array(df['close']), timeperiod=30)
# Stochastic Oscillator Slow STOCH 常用的KDJ指标中的KD指标
df['STOCH'] = \
talib.STOCH(df['high'], df['low'], df['close'], fastk_period=9, slowk_period=3, slowk_matype=0, slowd_period=3,
slowd_matype=0)[1]
# On Balance Volume OBV 能量潮
df['OBV'] = talib.OBV(np.array(df['close']), df['vol'])
# Simple moving average SMA 简单移动平均
df['SMA15'] = talib.SMA(df['close'], timeperiod=15)
df['SMA20'] = talib.SMA(df['close'], timeperiod=20)
df['SMA25'] = talib.SMA(df['close'], timeperiod=25)
df['SMA30'] = talib.SMA(df['close'], timeperiod=30)
# Money Flow Index MFI MFI指标
df['MFI14'] = talib.MFI(df['high'], df['low'], df['close'], df['vol'], timeperiod=14)
df['MFI18'] = talib.MFI(df['high'], df['low'], df['close'], df['vol'], timeperiod=18)
df['MFI22'] = talib.MFI(df['high'], df['low'], df['close'], df['vol'], timeperiod=22)
# Ultimate Oscillator UO 终极指标
df['UO7'] = talib.ULTOSC(df['high'], df['low'], df['close'], timeperiod1=7, timeperiod2=14, timeperiod3=28)
df['UO8'] = talib.ULTOSC(df['high'], df['low'], df['close'], timeperiod1=8, timeperiod2=16, timeperiod3=22)
df['UO9'] = talib.ULTOSC(df['high'], df['low'], df['close'], timeperiod1=9, timeperiod2=18, timeperiod3=26)
# Rate of change Percentage ROCP 价格变化率
df['ROCP'] = talib.ROCP(df['close'], timeperiod=10)
return df
def handle_data(context):
# 获取历史数据
hist = context.data.get_price(context.security,
count=context.user_data.wr_period,
frequency=context.frequency)
if len(hist.index) < context.user_data.wr_period:
context.log.warn("bar的数量不足, 等待下一根bar...")
return
# 收盘价
close = np.array(hist["close"])
# 最高价
high = np.array(hist["high"])
# 最低价
low = np.array(hist["low"])
try:
# talib计算WR值
wr_temp = talib.WILLR(high,
low,
close,
timeperiod=context.user_data.wr_period)
# talib计算的WR为负数，我们在这里变为正值
wr_current_dt = -1 * wr_temp[-1]
except:
context.log.error("计算WR时出现错误...")
return
context.log.info("当前 WR = %s" % wr_current_dt)
# 根据WR值来判断交易：
if wr_current_dt > context.user_data.over_sell:
context.log.info("WR值超过了超卖线，产生买入信号")
if context.account.huobi_cny_cash >= HUOBI_CNY_BTC_MIN_ORDER_CASH_AMOUNT:
# WR大于超卖线且有现金，全仓买入
context.log.info("正在买入 %s" % context.security)
context.log.info("下单金额为 %s 元" % context.account.huobi_cny_cash)
context.order.buy(context.security,
cash_amount=str(context.account.huobi_cny_cash))
else:
context.log.info("现金不足，无法下单")
elif wr_current_dt < context.user_data.over_buy:
context.log.info("WR值超过了超买线，产生卖出信号")
if context.account.huobi_cny_btc >= HUOBI_CNY_BTC_MIN_ORDER_QUANTITY:
# WR小于超买线且持有仓位，全仓卖出
context.log.info("正在卖出 %s" % context.security)
context.log.info("卖出数量为 %s" % context.account.huobi_cny_btc)
context.order.sell(context.security,
quantity=str(context.account.huobi_cny_btc))
else:
context.log.info("仓位不足，无法卖出")
else:
context.log.info("无交易信号，进入下一根bar")
def stock_William(userstock):
stock=TheConstructor(userstock)
ret = pd.DataFrame(talib.WILLR(stock['High'], stock['Low'], stock['Open']), columns= ['Williams'])
### 開始畫圖 ###
ret.plot(color=['#5599FF'], linestyle='dashed')
stock['Close'].plot(secondary_y=True,color='#FF0000')
plt.title("Williams_Overbought") # 標題設定
plt.show()
plt.savefig('Williams_Overbought.png') #存檔
plt. close() # 殺掉記憶體中的圖片
return Imgur.showImgur('Williams_Overbought')#開始利用imgur幫我們存圖片，以便於等等發送到手機
def WILLR(high, low, close, timeperiod=14):
''' Williams' %R 威廉指标
分组: Momentum Indicator 动量指标
简介: WMS表示的是市场处于超买还是超卖状态。
股票投资分析方法主要有如下三种：基本分析、技术分析、演化分析。
在实际应用中，它们既相互联系，又有重要区别。
real = WILLR(high, low, close, timeperiod=14)
'''
return talib.WILLR(high, low, close, timeperiod)
def get_momentum_studies(open, low, high, close, volume, df):
# Momentum studies
# https://mrjbq7.github.io/ta-lib/func_groups/momentum_indicators.html
df['MACD'], df['MACD_SIGN'], df['MACD_HIST'] = talib.MACD(
close, fastperiod=12, slowperiod=26, signalperiod=9)
df['STOCH-SLOW-K'], df['STOCH-SLOW-D'] = talib.STOCH(high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['STOCH-FAST-K'], df['STOCH-FAST-D'] = talib.STOCHF(high,
low,
close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['STOCH-RSI-K'], df['STOCH-RSI-D'] = talib.STOCHRSI(close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['AROON-DOWN'], df['AROON-UP'] = talib.AROON(high,
low,
timeperiod=14)
df["MINUS_DI"] = talib.MINUS_DI(high, low, close, timeperiod=14)
df["MINUS_DM"] = talib.MINUS_DM(high, low, timeperiod=14)
df["PLUS_DI"] = talib.PLUS_DI(high, low, close, timeperiod=14)
df["PLUS_DM"] = talib.PLUS_DM(high, low, timeperiod=14)
df["MOM"] = talib.MOM(close, timeperiod=10)
df["MFI"] = talib.MFI(high, low, close, volume, timeperiod=14)
df["ADX"] = talib.ADX(high, low, close, timeperiod=14)
df["ADXR"] = talib.ADXR(high, low, close, timeperiod=14)
df["APO"] = talib.APO(close, fastperiod=12, slowperiod=26, matype=0)
df["AROONOSC"] = talib.AROONOSC(high, low, timeperiod=14)
df["BOP"] = talib.BOP(open, high, low, close)
df["CCI"] = talib.CCI(high, low, close, timeperiod=14)
df["CMO"] = talib.CMO(close, timeperiod=14)
df["DX"] = talib.DX(high, low, close, timeperiod=14)
df["PPO"] = talib.PPO(close, fastperiod=12, slowperiod=26, matype=0)
df["ROC"] = talib.ROC(close, timeperiod=10)
df["RSI"] = talib.RSI(close, timeperiod=14)
df["TRIX"] = talib.TRIX(close, timeperiod=30)
df["ULT"] = talib.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
df["WILLR"] = talib.WILLR(high, low, close, timeperiod=14)
def get_list_of_willams_deals(test_dateframe):
start_index = ""
buy_or_sell = ""
list_of_deals = list()
for index, row in test_dateframe[14:].iterrows():
temp_slice = test_dateframe.iloc[:test_dateframe.index.searchsorted(
datetime.fromtimestamp(index.timestamp())) + 1]
willams = talib.WILLR(temp_slice.high.values,
temp_slice.low.values,
temp_slice.close.values,
timeperiod=14)
# if willams[-2] > -20 > willams[-1]:
# # "turn to short"
# if start_index == "":
# start_index = index
# buy_or_sell = "sell"
if willams[-2] < -80 < willams[-1]:
# "turn to long"
adx = talib.ADX(temp_slice.high.values,
temp_slice.low.values,
temp_slice.close.values,
timeperiod=14)
if start_index == "" and adx[-2] > adx[-1]:
start_index = index
buy_or_sell = "buy"
if start_index != "":
if buy_or_sell == "buy":
if willams[-1] > -20:
deal = Deal(
test_dateframe.iloc[test_dateframe.index.searchsorted(
datetime.fromtimestamp(start_index.timestamp())
):test_dateframe.index.searchsorted(
datetime.fromtimestamp(index.timestamp())) + 1],
buy_or_sell)
list_of_deals.append(deal)
start_index = ""
buy_or_sell = ""
if buy_or_sell == "sell":
if willams[-1] < -80:
# deal = Deal(
# week_dateframe.iloc[
# week_dateframe.index.searchsorted(datetime.fromtimestamp(start_index.timestamp())):
# week_dateframe.index.searchsorted(datetime.fromtimestamp(index.timestamp())) + 1],
# buy_or_sell)
# list_of_deals.append(deal)
start_index = ""
buy_or_sell = ""
return list_of_deals
def wpr(df, n=14, normalize=False):
h = df['High'].as_matrix()
l = df['Low'].as_matrix()
c = df['Close'].as_matrix()
_wpr = pd.Series(ta.WILLR(h, l, c, timeperiod=n),
index=df.index,
name="WPR_" + str(n))
if normalize:
_wpr = z_score(_wpr)
return _wpr
