def rocr_100(self,
n: int,
array: bool = False) -> Union[float, np.ndarray]:
"""
ROCR100.
"""
result = talib.ROCR100(self.close, n)
if array:
return result
return result[-1]
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 rate_of_change(close, n):
try:
df = pd.DataFrame()
df['rate_of_change'] = talib.ROC(close, timeperiod=n)
df['rate_of_change_precentage'] = talib.ROCP(close, timeperiod=n)
df['rate_of_change_ratio'] = talib.ROCR(close, timeperiod=n)
df['rate_of_change_ratio_100_scale'] = talib.ROCR100(close,
timeperiod=n)
return df
except Exception as e:
rasie(e)
def add_ROCR100(self, timeperiod=10, type='line', color='tertiary', **kwargs):
"""Rate of Change (Ratio * 100)."""
if not self.has_close:
raise Exception()
utils.kwargs_check(kwargs, VALID_TA_KWARGS)
if 'kind' in kwargs:
type = kwargs['kind']
name = 'ROCR100({})'.format(str(timeperiod))
self.sec[name] = dict(type=type, color=color)
self.ind[name] = talib.ROCR100(self.df[self.cl].values, timeperiod)
def rocr100(client, symbol, timeframe="6m", col="close", period=14):
"""This will return a dataframe of
Rate of change ratio 100 scale: (price/prevPrice)*100
for the given symbol across the given timeframe
Args:
client (pyEX.Client): Client
symbol (string): Ticker
timeframe (string): timeframe to use, for pyEX.chart
col (string): column to use to calculate
period (int): period to calculate across
Returns:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
return pd.DataFrame(
{col: df[col].values, "rocr100": t.ROCR100(df[col].values, period)}
)
def rocr100(candles: np.ndarray,
period: int = 10,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100
:param candles: np.ndarray
:param period: int - default: 10
:param source_type: str - default: "close"
:param sequential: bool - default: False
:return: float | np.ndarray
"""
candles = slice_candles(candles, sequential)
source = get_candle_source(candles, source_type=source_type)
res = talib.ROCR100(source, timeperiod=period)
return res if sequential else res[-1]
def get_df(filename):
tech = pd.read_csv(filename,index_col=0)
dclose = np.array(tech.close)
volume = np.array(tech.volume)
tech['RSI'] = ta.RSI(np.array(tech.close))
tech['OBV'] = ta.OBV(np.array(tech.close),np.array(tech.volume))
tech['NATR'] = ta.NATR(np.array(tech.high),np.array(tech.low),np.array(tech.close))
tech['upper'],tech['middle'],tech['lower'] = ta.BBANDS(np.array(tech.close), timeperiod=10, nbdevup=2, nbdevdn=2, matype=0)
tech['DEMA'] = ta.DEMA(dclose, timeperiod=30)
tech['EMA'] = ta.EMA(dclose, timeperiod=30)
tech['HT_TRENDLINE'] = ta.HT_TRENDLINE(dclose)
tech['KAMA'] = ta.KAMA(dclose, timeperiod=30)
tech['MA'] = ta.MA(dclose, timeperiod=30, matype=0)
# tech['mama'], tech['fama'] = ta.MAMA(dclose, fastlimit=0, slowlimit=0)
tech['MIDPOINT'] = ta.MIDPOINT(dclose, timeperiod=14)
tech['SMA'] = ta.SMA(dclose, timeperiod=30)
tech['T3'] = ta.T3(dclose, timeperiod=5, vfactor=0)
tech['TEMA'] = ta.TEMA(dclose, timeperiod=30)
tech['TRIMA'] = ta.TRIMA(dclose, timeperiod=30)
tech['WMA'] = ta.WMA(dclose, timeperiod=30)
tech['APO'] = ta.APO(dclose, fastperiod=12, slowperiod=26, matype=0)
tech['CMO'] = ta.CMO(dclose, timeperiod=14)
tech['macd'], tech['macdsignal'], tech['macdhist'] = ta.MACD(dclose, fastperiod=12, slowperiod=26, signalperiod=9)
tech['MOM'] = ta.MOM(dclose, timeperiod=10)
tech['PPO'] = ta.PPO(dclose, fastperiod=12, slowperiod=26, matype=0)
tech['ROC'] = ta.ROC(dclose, timeperiod=10)
tech['ROCR'] = ta.ROCR(dclose, timeperiod=10)
tech['ROCP'] = ta.ROCP(dclose, timeperiod=10)
tech['ROCR100'] = ta.ROCR100(dclose, timeperiod=10)
tech['RSI'] = ta.RSI(dclose, timeperiod=14)
tech['fastk'], tech['fastd'] = ta.STOCHRSI(dclose, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)
tech['TRIX'] = ta.TRIX(dclose, timeperiod=30)
tech['OBV'] = ta.OBV(dclose,volume)
tech['HT_DCPHASE'] = ta.HT_DCPHASE(dclose)
tech['inphase'], tech['quadrature'] = ta.HT_PHASOR(dclose)
tech['sine'], tech['leadsine'] = ta.HT_SINE(dclose)
tech['HT_TRENDMODE'] = ta.HT_TRENDMODE(dclose)
df = tech.fillna(method='bfill')
return df
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.ROCR100(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 create_Indicators(data):
dfs = data
df = dfs.copy()
df['ADX'] = talib.ADX(df.high, df.low, df.close, timeperiod=14)
df['ADXR'] = talib.ADXR(df.high, df.low, df.close, timeperiod=14)
df['APO'] = talib.APO(df.close, fastperiod=12, slowperiod=26, matype=0)
df['AROONOSC'] = talib.AROONOSC(df.high, df.low, timeperiod=14)
df['BOP'] = talib.BOP(
df.open, df.high, df.low,
df.close) # (Close price – Open price) / (High price – Low price)
df["CCI"] = talib.CCI(df.high, df.low, df.close, timeperiod=14)
df["CMO"] = talib.CMO(df.close, timeperiod=14)
df["DX"] = talib.DX(df.high, df.low, df.close, timeperiod=14)
#df["MFI"] = talib.MFI(df.high, df.low, df.close, df.volume, timeperiod=14).pct_change()
df["MINUS_DI"] = talib.MINUS_DI(df.high, df.low, df.close, timeperiod=14)
df["MINUS_DM"] = talib.MINUS_DM(df.high, df.low, timeperiod=14)
df["MOM"] = talib.MOM(df.close, timeperiod=10)
df["PLUS_DI"] = talib.PLUS_DI(df.high, df.low, df.close, timeperiod=14)
df["PLUS_DM"] = talib.PLUS_DM(df.high, df.low, timeperiod=14)
#X_corr["PPO"] = PPO(df.close, fastperiod=12, sdf.lowperiod=26, matype=0)
df["ROC"] = talib.ROC(df.close, timeperiod=10)
df["ROCP"] = talib.ROCP(df.close, timeperiod=10)
df["ROCR"] = talib.ROCR(df.close, timeperiod=10)
df["ROCR100"] = talib.ROCR100(df.close, timeperiod=10)
df["RSI"] = talib.RSI(df.close, timeperiod=14)
#sdf.lowk, sdf.lowd = STOCH(df.high, df.low, df.close, fastk_period=5, sdf.lowk_period=3, sdf.lowk_matype=0, sdf.lowd_period=3, sdf.lowd_matype=0)
df["TRIX"] = talib.TRIX(df.close, timeperiod=30)
df["ULTOSC"] = talib.ULTOSC(df.high,
df.low,
df.close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
df["WILLR"] = talib.WILLR(df.high, df.low, df.close, timeperiod=14)
return df
def rocr100(candles: np.ndarray, period: int = 10, source_type: str = "close", sequential: bool = False) -> Union[
float, np.ndarray]:
"""
ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100
:param candles: np.ndarray
:param period: int - default=10
:param source_type: str - default: "close"
:param sequential: bool - default=False
:return: float | np.ndarray
"""
warmup_candles_num = get_config('env.data.warmup_candles_num', 240)
if not sequential and len(candles) > warmup_candles_num:
candles = candles[-warmup_candles_num:]
source = get_candle_source(candles, source_type=source_type)
res = talib.ROCR100(source, timeperiod=period)
if sequential:
return res
else:
return None if np.isnan(res[-1]) else res[-1]
def TALIB_ROCR100(close, timeperiod=10):
'''00376,2,1'''
return talib.ROCR100(close, timeperiod)
def load_stocks_calculate_short_corr(only_latest=False):
"""
only_latest: boolean; if True, will keep only the latest 50 days to speed up procesing
"""
dfs, sh_int, fin_sh = dp.load_stocks(stocks=None)
if only_latest:
earliest = sh_int.index[-50]
sh_int = sh_int[sh_int.index >= earliest]
latest_stocks = []
all_sh_stocks = []
all_sh_stocks_full = []
latest_date = sh_int['SPY'].index[-1]
# TODO: make parallel
for s in tqdm(sh_int.keys()):
if sh_int[s].shape[0] == 0:
print(s, 'is empty')
continue
if latest_date != sh_int[s].index[-1]:
print(s, 'is old')
continue
if verbose:
print(s)
df = sh_int[s].copy()
# create future price changes
for i in [5, 10, 20, 40]:
df['{}d_future_price_change'.format(
i)] = df['Adj_Close'].pct_change(i).shift(-i)
df['ticker'] = s
# create short-close correlations -- TODO: deal with -1s
# if short % is all -1 or 0, won't work. if less than 20 samples, rolling corr with 20 period window won't work
# also broke on DF with 22 samples
if df['Short_%_of_Float'].mean() in [-1, 0] or df.shape[0] < 30:
df['Short_%_of_Float_10d_EMA'] = -np.inf
df['Adj_Close_10d_EMA'] = talib.EMA(df['Adj_Close'].values,
timeperiod=10)
df['short_close_corr_10d_EMA'] = -np.inf
df['short_close_corr_rocr_20d'] = -np.inf
df['short_%_rocr_20d'] = -np.inf
else:
df['Short_%_of_Float_10d_EMA'] = talib.EMA(
df['Short_%_of_Float'].values, timeperiod=10)
df['Adj_Close_10d_EMA'] = talib.EMA(df['Adj_Close'].values,
timeperiod=10)
# essentially, we want to take an arbitrary number of points, calculate correlation, and find where the correlations are largest
# take 10 points at a time and get correlations first, then take parts that have largest correlations, and keep expanding by 5 points at a time until correlation decreases
corr = df[['Short_%_of_Float_10d_EMA',
'Adj_Close_10d_EMA']].rolling(window=20).corr()
df['short_close_corr_10d_EMA'] = corr.unstack(
1)['Short_%_of_Float_10d_EMA']['Adj_Close_10d_EMA']
df['short_close_corr_10d_EMA'].replace(np.inf, 1, inplace=True)
df['short_close_corr_10d_EMA'].replace(-np.inf, -1, inplace=True)
df['short_close_corr_10d_EMA'].clip(lower=-1,
upper=1,
inplace=True)
# WARNING: things with small (< 1%) Short % of float will result in huge rocr...maybe do something about this
df['short_close_corr_rocr_20d'] = talib.ROCR100(
df['short_close_corr_10d_EMA'].values, timeperiod=20)
df['short_%_rocr_20d'] = talib.ROCR100(
df['Short_%_of_Float_10d_EMA'].values, timeperiod=20)
# auto-detect long stretches of negative and positive correlation
thresh = 0.7
rolling = df['short_close_corr_10d_EMA'].rolling(window=20).min()
df['Short_%_positive_corr_detection'] = rolling > thresh
df['Short_%_positive_corr_detection'] = df[
'Short_%_positive_corr_detection'].astype('int16')
# sh_int[ticker]['Short_%_positive_corr_detection'].plot()
# plt.show()
df['Short_%_negative_corr_detection'] = rolling < -thresh
df['Short_%_negative_corr_detection'] = df[
'Short_%_negative_corr_detection'].astype('int16')
latest_stocks.append(df.iloc[-1])
all_sh_stocks_full.append(df)
all_sh_stocks.append(df.dropna())
latest_stocks_df = pd.concat(latest_stocks, axis=1).T
latest_stocks_df.set_index('ticker', inplace=True)
all_sh_stocks_df = pd.concat(all_sh_stocks)
all_sh_stocks_df['market_cap'] = all_sh_stocks_df[
'Shares_Outstanding'] * all_sh_stocks_df['Adj_Close']
all_sh_stocks_full_df = pd.concat(all_sh_stocks_full)
return all_sh_stocks_df, all_sh_stocks_full_df, latest_stocks_df, sh_int
def momentum_process(event):
print(event.widget.get())
momentum = event.widget.get()
upperband, middleband, lowerband = ta.BBANDS(close,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
fig, axes = plt.subplots(2, 1, sharex=True)
ax1, ax2 = axes[0], axes[1]
axes[0].plot(close, 'rd-', markersize=3)
axes[0].plot(upperband, 'y-')
axes[0].plot(middleband, 'b-')
axes[0].plot(lowerband, 'y-')
axes[0].set_title(momentum, fontproperties='SimHei')
if momentum == '绝对价格振荡器':
real = ta.APO(close, fastperiod=12, slowperiod=26, matype=0)
axes[1].plot(real, 'r-')
elif momentum == '钱德动量摆动指标':
real = ta.CMO(close, timeperiod=14)
axes[1].plot(real, 'r-')
elif momentum == '移动平均收敛/散度':
macd, macdsignal, macdhist = ta.MACD(close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
axes[1].plot(macd, 'r-')
axes[1].plot(macdsignal, 'g-')
axes[1].plot(macdhist, 'b-')
elif momentum == '带可控MA类型的MACD':
macd, macdsignal, macdhist = ta.MACDEXT(close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
axes[1].plot(macd, 'r-')
axes[1].plot(macdsignal, 'g-')
axes[1].plot(macdhist, 'b-')
elif momentum == '移动平均收敛/散度 固定 12/26':
macd, macdsignal, macdhist = ta.MACDFIX(close, signalperiod=9)
axes[1].plot(macd, 'r-')
axes[1].plot(macdsignal, 'g-')
axes[1].plot(macdhist, 'b-')
elif momentum == '动量':
real = ta.MOM(close, timeperiod=10)
axes[1].plot(real, 'r-')
elif momentum == '比例价格振荡器':
real = ta.PPO(close, fastperiod=12, slowperiod=26, matype=0)
axes[1].plot(real, 'r-')
elif momentum == '变化率':
real = ta.ROC(close, timeperiod=10)
axes[1].plot(real, 'r-')
elif momentum == '变化率百分比':
real = ta.ROCP(close, timeperiod=10)
axes[1].plot(real, 'r-')
elif momentum == '变化率的比率':
real = ta.ROCR(close, timeperiod=10)
axes[1].plot(real, 'r-')
elif momentum == '变化率的比率100倍':
real = ta.ROCR100(close, timeperiod=10)
axes[1].plot(real, 'r-')
elif momentum == '相对强弱指数':
real = ta.RSI(close, timeperiod=14)
axes[1].plot(real, 'r-')
elif momentum == '随机相对强弱指标':
fastk, fastd = ta.STROCHRSI(close,
timeperiod=14,
fastk_period=5,
fasted_period=3,
fasted_matype=0)
axes[1].plot(fastk, 'r-')
axes[1].plot(fastd, 'r-')
elif momentum == '三重光滑EMA的日变化率':
real = ta.TRIX(close, timeperiod=30)
axes[1].plot(real, 'r-')
plt.show()
def get_technical_indicators(dataset):
# Create 7 and 21 days Moving Average
dataset['ma7'] = dataset['Adj Close'].rolling(window=7).mean()
dataset['ma21'] = dataset['Adj Close'].rolling(window=21).mean()
# Create Exponential moving average
dataset['ema'] = dataset['Adj Close'].ewm(com=0.5).mean()
# Create MACD
dataset['26ema'] = dataset['Adj Close'].ewm(span=26).mean()
dataset['12ema'] = dataset['Adj Close'].ewm(span=12).mean()
dataset['MACD'] = (dataset['12ema'] - dataset['26ema'])
# Create Momentum
dataset['momentum'] = dataset['Adj Close'] - 1
# Create Bollinger Bands
dataset['20sd'] = dataset['Adj Close'].rolling(20).std()
dataset['upper_band'] = dataset['ma21'] + (dataset['20sd'] * 2)
dataset['lower_band'] = dataset['ma21'] - (dataset['20sd'] * 2)
# Create RSI indicator
dataset['RSI'] = ta.RSI(np.array(dataset['Adj Close']))
#Part I: Create Cycle Indicators
#Create HT_DCPERIOD - Hilbert Transform - Dominant Cycle Period
dataset['HT_DCPERIOD'] = ta.HT_DCPERIOD(np.array(dataset['Adj Close']))
#Create HT_DCPHASE - Hilbert Transform - Dominant Cycle Phase
dataset['HT_DCPHASE'] = ta.HT_DCPHASE(np.array(dataset['Adj Close']))
#HT_TRENDMODE - Hilbert Transform - Trend vs Cycle Mode
dataset['HT_TRENDMODE'] = ta.HT_TRENDMODE(np.array(dataset['Adj Close']))
#Part II: Create Volatility Indicators
#Create Average True Range
dataset['ATR'] = ta.ATR(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Create NATR - Normalized Average True Range
dataset['NATR'] = ta.NATR(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Create TRANGE - True Range
dataset['TRANGE'] = ta.TRANGE(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Part III Overlap Studies
#Create DEMA - Double Exponential Moving Average
dataset['DEMA'] = ta.DEMA(np.array(dataset['Adj Close']), timeperiod=30)
#Create HT_TRENDLINE - Hilbert Transform - Instantaneous Trendline
dataset['HT_TRENDLINE'] = ta.HT_TRENDLINE(np.array(dataset['Adj Close']))
#Create KAMA - Kaufman Adaptive Moving Average
dataset['KAMA'] = ta.KAMA(np.array(dataset['Adj Close']), timeperiod=30)
#Create MIDPOINT - MidPoint over period
dataset['MIDPOINT'] = ta.MIDPOINT(np.array(dataset['Adj Close']),
timeperiod=14)
#Create MIDPRICE - Midpoint Price over period
dataset['MIDPRICE'] = ta.MIDPRICE(np.array(dataset['High']),
np.array(dataset['Low']),
timeperiod=14)
#Create SAR - Parabolic SAR
dataset['SAR'] = ta.SAR(np.array(dataset['High']),
np.array(dataset['Low']),
acceleration=0,
maximum=0)
#Create SMA - Simple Moving Average
dataset['SMA10'] = ta.SMA(np.array(dataset['Adj Close']), timeperiod=10)
#Create T3 - Triple Exponential Moving Average (T3)
dataset['T3'] = ta.T3(np.array(dataset['Adj Close']),
timeperiod=5,
vfactor=0)
#Create TRIMA - Triangular Moving Average
dataset['TRIMA'] = ta.TRIMA(np.array(dataset['Adj Close']), timeperiod=30)
#Create WMA - Weighted Moving Average
dataset['WMA'] = ta.WMA(np.array(dataset['Adj Close']), timeperiod=30)
#PART IV Momentum Indicators
#Create ADX - Average Directional Movement Index
dataset['ADX14'] = ta.ADX(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
dataset['ADX20'] = ta.ADX(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=20)
#Create ADXR - Average Directional Movement Index Rating
dataset['ADXR'] = ta.ADXR(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Create APO - Absolute Price Oscillator
dataset['APO'] = ta.APO(np.array(dataset['Adj Close']),
fastperiod=12,
slowperiod=26,
matype=0)
#Create AROONOSC - Aroon Oscillator
dataset['AROONOSC'] = ta.AROONOSC(np.array(dataset['High']),
np.array(dataset['Low']),
timeperiod=14)
#Create BOP - Balance Of Power
dataset['BOP'] = ta.BOP(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CCI - Commodity Channel Index
dataset['CCI3'] = ta.CCI(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=3)
dataset['CCI5'] = ta.CCI(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=5)
dataset['CCI10'] = ta.CCI(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=10)
dataset['CCI14'] = ta.CCI(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Create CMO - Chande Momentum Oscillator
dataset['CMO'] = ta.CMO(np.array(dataset['Adj Close']), timeperiod=14)
#Create DX - Directional Movement Index
dataset['DX'] = ta.DX(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Create MINUS_DI - Minus Directional Indicator
dataset['MINUS_DI'] = ta.MINUS_DI(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Create MINUS_DM - Minus Directional Movement
dataset['MINUS_DM'] = ta.MINUS_DM(np.array(dataset['High']),
np.array(dataset['Low']),
timeperiod=14)
#Create MOM - Momentum
dataset['MOM3'] = ta.MOM(np.array(dataset['Adj Close']), timeperiod=3)
dataset['MOM5'] = ta.MOM(np.array(dataset['Adj Close']), timeperiod=5)
dataset['MOM10'] = ta.MOM(np.array(dataset['Adj Close']), timeperiod=10)
#Create PLUS_DI - Plus Directional Indicator
dataset['PLUS_DI'] = ta.PLUS_DI(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Create PLUS_DM - Plus Directional Movement
dataset['PLUS_DM'] = ta.PLUS_DM(np.array(dataset['High']),
np.array(dataset['Low']),
timeperiod=14)
#Create PPO - Percentage Price Oscillator
dataset['PPO'] = ta.PPO(np.array(dataset['Adj Close']),
fastperiod=12,
slowperiod=26,
matype=0)
#Create ROC - Rate of change : ((price/prevPrice)-1)*100
dataset['ROC'] = ta.ROC(np.array(dataset['Adj Close']), timeperiod=10)
#Create ROCP - Rate of change Percentage: (price-prevPrice)/prevPrice
dataset['ROCP'] = ta.ROCP(np.array(dataset['Adj Close']), timeperiod=10)
#Create ROCR - Rate of change ratio: (price/prevPrice)
dataset['ROCR'] = ta.ROCR(np.array(dataset['Adj Close']), timeperiod=10)
#Create ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100
dataset['ROCR100'] = ta.ROCR100(np.array(dataset['Adj Close']),
timeperiod=10)
#Create RSI - Relative Strength Index
dataset['RSI5'] = ta.RSI(np.array(dataset['Adj Close']), timeperiod=5)
dataset['RSI10'] = ta.RSI(np.array(dataset['Adj Close']), timeperiod=10)
dataset['RSI14'] = ta.RSI(np.array(dataset['Adj Close']), timeperiod=14)
#Create TRIX - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
dataset['TRIX'] = ta.TRIX(np.array(dataset['Adj Close']), timeperiod=30)
#Create ULTOSC - Ultimate Oscillator
dataset['ULTOSC'] = ta.ULTOSC(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
#Create WILLR - Williams' %R
dataset['WILLR'] = ta.WILLR(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Part V Pattern Recognition
#Create CDL2CROWS - Two Crows
dataset['CDL2CROWS'] = ta.CDL2CROWS(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDL3BLACKCROWS - Three Black Crows
dataset['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDL3INSIDE - Three Inside Up/Down
dataset['CDL3INSIDE'] = ta.CDL3INSIDE(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDL3LINESTRIKE - Three-Line Strike
dataset['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDL3OUTSIDE - Three Outside Up/Down
dataset['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDL3STARSINSOUTH - Three Stars In The South
dataset['CDL3STARSINSOUTH '] = ta.CDL3STARSINSOUTH(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDL3WHITESOLDIERS - Three Advancing White Soldiers
dataset['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLABANDONEDBABY - Abandoned Baby
dataset['CDLABANDONEDBABY'] = ta.CDLABANDONEDBABY(
np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
penetration=0)
#Create CDLADVANCEBLOCK - Advance Block
dataset['CDLADVANCEBLOCK'] = ta.CDLADVANCEBLOCK(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLBELTHOLD - Belt-hold
dataset['CDLBELTHOLD'] = ta.CDLBELTHOLD(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLBREAKAWAY - Breakaway
dataset['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLCLOSINGMARUBOZU - Closing Marubozu
dataset['CDLCLOSINGMARUBOZU'] = ta.CDLCLOSINGMARUBOZU(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLCONCEALBABYSWALL - Concealing Baby Swalnp.array(dataset['Low'])
dataset['CDLCONCEALBABYSWALL'] = ta.CDLCONCEALBABYSWALL(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLCOUNTERATTACK - Counterattack
dataset['CDLCOUNTERATTACK'] = ta.CDLCOUNTERATTACK(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLDARKCLOUDCOVER - Dark Cloud Cover
dataset['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(
np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
penetration=0)
#Create CDLDOJI - Doji
dataset['CDLDOJI'] = ta.CDLDOJI(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLDOJISTAR - Doji Star
dataset['CDLDOJISTAR'] = ta.CDLDOJISTAR(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLDRAGONFLYDOJI - Dragonfly Doji
dataset['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLENGULFING - Engulfing Pattern
dataset['CDLENGULFING'] = ta.CDLENGULFING(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLEVENINGDOJISTAR - Evening Doji Star
dataset['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(
np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
penetration=0)
#Create CDLEVENINGSTAR - Evening Star
dataset['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(
dataset['Adj Close']),
penetration=0)
#Create CDLGAPSIDESIDEWHITE - Up/Down-gap side-by-side white lines
dataset['CDLGAPSIDESIDEWHITE'] = ta.CDLGAPSIDESIDEWHITE(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLGRAVESTONEDOJI - Gravestone Doji
dataset['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLHAMMER - Hammer
dataset['CDLHAMMER'] = ta.CDLHAMMER(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLHANGINGMAN - Hanging Man
dataset['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLHARAMI - Harami Pattern
dataset['CDLHARAMI'] = ta.CDLHARAMI(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLHARAMICROSS - Harami Cross Pattern
dataset['CDLHARAMICROSS'] = ta.CDLHARAMICROSS(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLHIGHWAVE - High-Wave Candle
dataset['CDLHIGHWAVE'] = ta.CDLHIGHWAVE(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLHIKKAKE - Hikkake Pattern
dataset['CDLHIKKAKE'] = ta.CDLHIKKAKE(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLHIKKAKEMOD - Modified Hikkake Pattern
dataset['CDLHIKKAKEMOD'] = ta.CDLHIKKAKEMOD(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLHOMINGPIGEON - Homing Pigeon
dataset['CDLHOMINGPIGEON'] = ta.CDLHOMINGPIGEON(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLIDENTICAL3CROWS - Identical Three Crows
dataset['CDLIDENTICAL3CROWS'] = ta.CDLIDENTICAL3CROWS(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLINNECK - In-Neck Pattern
dataset['CDLINNECK'] = ta.CDLINNECK(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLINVERTEDHAMMER - Inverted Hammer
dataset['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLKICKING - Kicking
dataset['CDLKICKING'] = ta.CDLKICKING(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLKICKINGBYLENGTH - Kicking - bull/bear determined by the longer marubozu
dataset['CDLKICKINGBYLENGTH'] = ta.CDLKICKINGBYLENGTH(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLLADDERBOTTOM - Ladder Bottom
dataset['CDLLADDERBOTTOM'] = ta.CDLLADDERBOTTOM(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLLONGLEGGEDDOJI - Long Legged Doji
dataset['CDLLONGLEGGEDDOJI'] = ta.CDLLONGLEGGEDDOJI(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLLONGLINE - Long Line Candle
dataset['CDLLONGLINE'] = ta.CDLLONGLINE(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLMARUBOZU - Marubozu
dataset['CDLMARUBOZU'] = ta.CDLMARUBOZU(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLMATCHINGLOW - Matching Low
dataset['CDLMATCHINGLOW'] = ta.CDLMATCHINGLOW(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLMATHOLD - Mat Hold
dataset['CDLMATHOLD'] = ta.CDLMATHOLD(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
penetration=0)
#Create CDLMORNINGDOJISTAR - Morning Doji Star
dataset['CDLMORNINGDOJISTAR'] = ta.CDLMORNINGDOJISTAR(
np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
penetration=0)
#Create CDLMORNINGSTAR - Morning Star
dataset['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(
dataset['Adj Close']),
penetration=0)
#Create CDLONNECK - On-Neck Pattern
dataset['CDLONNECK'] = ta.CDLONNECK(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLPIERCING - Piercing Pattern
dataset['CDLPIERCING'] = ta.CDLPIERCING(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLRICKSHAWMAN - Rickshaw Man
dataset['CDLRICKSHAWMAN'] = ta.CDLRICKSHAWMAN(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLRISEFALL3METHODS - Rising/Falling Three Methods
dataset['CDLRISEFALL3METHODS'] = ta.CDLRISEFALL3METHODS(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLSEPARATINGLINES - Separating Lines
dataset['CDLSEPARATINGLINES'] = ta.CDLSEPARATINGLINES(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLSHOOTINGSTAR - Shooting Star
dataset['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLSHORTLINE - Short Line Candle
dataset['CDLSHORTLINE'] = ta.CDLSHORTLINE(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLSPINNINGTOP - Spinning Top
dataset['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLSTALLEDPATTERN - Stalled Pattern
dataset['CDLSTALLEDPATTERN'] = ta.CDLSTALLEDPATTERN(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLSTICKSANDWICH - Stick Sandwich
dataset['CDLSTICKSANDWICH'] = ta.CDLSTICKSANDWICH(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLTAKURI - Takuri (Dragonfly Doji with very long np.array(dataset['Low'])er shadow)
dataset['CDLTAKURI'] = ta.CDLTAKURI(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLTASUKIGAP - Tasuki Gap
dataset['CDLTASUKIGAP'] = ta.CDLTASUKIGAP(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLTHRUSTING - Thrusting Pattern
dataset['CDLTHRUSTING'] = ta.CDLTHRUSTING(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLTRISTAR - Tristar Pattern
dataset['CDLTRISTAR'] = ta.CDLTRISTAR(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLUNIQUE3RIVER - Unique 3 River
dataset['CDLUNIQUE3RIVER'] = ta.CDLUNIQUE3RIVER(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLUPSIDEGAP2CROWS - Upside Gap Two Crows
dataset['CDLUPSIDEGAP2CROWS'] = ta.CDLUPSIDEGAP2CROWS(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLXSIDEGAP3METHODS - Upside/Downside Gap Three Methods
dataset['CDLXSIDEGAP3METHODS'] = ta.CDLXSIDEGAP3METHODS(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
return dataset
df['MOM'] = ta.MOM(np.array(df['Adj Close'].shift(1)), timeperiod=n)
df['PLUS_DI'] = ta.PLUS_DI(np.array(df['High'].shift(1)),
np.array(df['Low'].shift(1)),
np.array(df['Adj Close'].shift(1)),
timeperiod=n)
df['PLUS_DM'] = ta.PLUS_DM(np.array(df['High'].shift(1)),
np.array(df['Low'].shift(1)),
timeperiod=n)
df['PPO'] = ta.PPO(np.array(df['Adj Close'].shift(1)),
fastperiod=12,
slowperiod=26,
matype=0)
df['ROC'] = ta.ROC(np.array(df['Adj Close'].shift(1)), timeperiod=n)
df['ROCP'] = ta.ROCP(np.array(df['Adj Close'].shift(1)), timeperiod=n)
df['ROCR'] = ta.ROCR(np.array(df['Adj Close'].shift(1)), timeperiod=n)
df['ROCR100'] = ta.ROCR100(np.array(df['Adj Close'].shift(1)), timeperiod=n)
df['slowk'], df['slowd'] = ta.STOCH(np.array(df['High'].shift(1)),
np.array(df['Low'].shift(1)),
np.array(df['Adj Close']),
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['fastk'], df['fastd'] = ta.STOCHF(np.array(df['High'].shift(1)),
np.array(df['Low'].shift(1)),
np.array(df['Adj Close']),
fastk_period=5,
fastd_period=3,
fastd_matype=0)
# df['fastk'], df['fastd'] =ta.STOCHRIS(np.array(df['Adj Close'].shift(1)), timeperiod=N, fastk_period=5, fastd_period=3, fastd_matype=0)
def Set_indicators(data, period):
"""
:param data: dataframe containing ohlcv prices and indexed with date
:param period: period used to calculate indicators
:return: dataframe of Technical indicators of specefic timeframe
"""
df = pd.DataFrame(index=data.index)
df["mom" + str(period)] = talib.MOM(data[USED_CLOSE_PRICE],
timeperiod=period)
#change it later
df["slowk" + str(period)], df["slowd" + str(period)] = talib.STOCH(
data["High"],
data["Low"],
data[USED_CLOSE_PRICE],
fastk_period=period,
slowk_period=period,
slowk_matype=0,
slowd_period=period,
slowd_matype=0)
#WILLR
df["willr" + str(period)] = talib.WILLR(data["High"],
data["Low"],
data[USED_CLOSE_PRICE],
timeperiod=period)
#MACDFIX - Moving Average Convergence/Divergence Fix 12/26
df["macd" + str(period)], df["macdsignal" +
str(period)], df["macdhist" +
str(period)] = talib.MACDFIX(
data[USED_CLOSE_PRICE],
signalperiod=period)
#CCI
df["cci" + str(period)] = talib.CCI(data["High"],
data["Low"],
data[USED_CLOSE_PRICE],
timeperiod=period)
#Bollinger Bands
df["upperband" +
str(period)], df["middleband" +
str(period)], df["lowerband" +
str(period)] = talib.BBANDS(
data[USED_CLOSE_PRICE],
timeperiod=period,
nbdevup=2,
nbdevdn=2,
matype=0)
#HIGH SMA
df["smaHigh" + str(period)] = talib.SMA(data["High"], timeperiod=period)
# SMA Adj Prices
df["sma" + str(period)] = talib.SMA(data[USED_CLOSE_PRICE],
timeperiod=period)
df["smaHighLow" + str(period)] = talib.SMA(talib.MEDPRICE(
data["High"], data["Low"]),
timeperiod=period)
#DEMA - Double Exponential Moving Average
df["DEMA" + str(period)] = talib.DEMA(data[USED_CLOSE_PRICE],
timeperiod=period)
#EMA - Exponential Moving Average
df["EMA" + str(period)] = talib.EMA(data[USED_CLOSE_PRICE],
timeperiod=period)
#HT_TRENDLINE - Hilbert Transform - Instantaneous Trendline
df["HT_TRENDLINE" + str(period)] = talib.HT_TRENDLINE(
data[USED_CLOSE_PRICE])
#KAMA - Kaufman Adaptive Moving Average
df["KAMA" + str(period)] = talib.KAMA(data[USED_CLOSE_PRICE],
timeperiod=period)
#T3 - Triple Exponential Moving Average (T3)
df["T3-" + str(period)] = talib.T3(data[USED_CLOSE_PRICE],
timeperiod=period,
vfactor=0)
#TEMA - Triple Exponential Moving Average
df["TEMA" + str(period)] = talib.TEMA(data[USED_CLOSE_PRICE],
timeperiod=period)
#TRIMA - Triangular Moving Average
df["TRIMA" + str(period)] = talib.TRIMA(data[USED_CLOSE_PRICE],
timeperiod=period)
#WMA - Weighted Moving Average
df["TRIMA" + str(period)] = talib.WMA(data[USED_CLOSE_PRICE],
timeperiod=period)
##########
#ADX - Average Directional Movement Index
df["ADX" + str(period)] = talib.ADX(data["High"],
data["Low"],
data[USED_CLOSE_PRICE],
timeperiod=period)
#ADXR - Average Directional Movement Index Rating
df["ADXR" + str(period)] = talib.ADXR(data["High"],
data["Low"],
data[USED_CLOSE_PRICE],
timeperiod=period)
#AROON - Aroon
df["aroondown" + str(period)], df["aroonup" + str(period)] = talib.AROON(
data["High"], data["Low"], timeperiod=period)
#AROONOSC - Aroon Oscillator
df["aroondown" + str(period)] = talib.AROONOSC(data["High"],
data["Low"],
timeperiod=period)
#CMO - Chande Momentum Oscillator
df["CMO" + str(period)] = talib.CMO(data[USED_CLOSE_PRICE],
timeperiod=period)
#DX - Directional Movement Index
df["DX" + str(period)] = talib.DX(data["High"],
data["Low"],
data[USED_CLOSE_PRICE],
timeperiod=period)
#MINUS_DI - Minus Directional Indicator
df["MINUS_DI" + str(period)] = talib.MINUS_DI(data["High"],
data["Low"],
data[USED_CLOSE_PRICE],
timeperiod=period)
#MINUS_DM - Minus Directional Movement
df["MINUS_DM" + str(period)] = talib.MINUS_DM(data["High"],
data["Low"],
timeperiod=period)
#PLUS_DI - Plus Directional Indicator
df["PLUS_DI" + str(period)] = talib.PLUS_DI(data["High"],
data["Low"],
data[USED_CLOSE_PRICE],
timeperiod=period)
#PLUS_DM - Plus Directional Movement
df["PLUS_DM" + str(period)] = talib.PLUS_DM(data["High"],
data["Low"],
timeperiod=period)
#ROC - Rate of change : ((price/prevPrice)-1)*100
df["roc" + str(period)] = talib.ROC(data[USED_CLOSE_PRICE],
timeperiod=period)
#ROCP - Rate of change Percentage: (price-prevPrice)/prevPrice
df["ROCP" + str(period)] = talib.ROCP(data[USED_CLOSE_PRICE],
timeperiod=period)
#ROCR - Rate of change ratio: (price/prevPrice)
df["ROCR" + str(period)] = talib.ROCR(data[USED_CLOSE_PRICE],
timeperiod=period)
#ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100
df["ROCR100-" + str(period)] = talib.ROCR100(data[USED_CLOSE_PRICE],
timeperiod=period)
#RSI - Relative Strength Index
df["RSI-" + str(period)] = talib.RSI(data[USED_CLOSE_PRICE],
timeperiod=period)
#TRIX - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
df["TRIX" + str(period)] = talib.TRIX(data[USED_CLOSE_PRICE],
timeperiod=period)
#MFI - Money Flow Index
df["MFI" + str(period)] = talib.MFI(data["High"],
data["Low"],
data[USED_CLOSE_PRICE],
data["Volume"],
timeperiod=period)
#ADOSC - Chaikin A/D Oscillator set periods later please
df["ADOSC" + str(period)] = talib.ADOSC(data["High"],
data["Low"],
data[USED_CLOSE_PRICE],
data["Volume"],
fastperiod=np.round(period / 3),
slowperiod=period)
return df
df['PLUS_DI'] = talib.PLUS_DI(df["High"],
df["Low"],
df["Close"],
timeperiod=14)
# PLUS_DM - Plus Directional Movement
df['PLUS_DM'] = talib.PLUS_DM(df["High"], df["Low"], timeperiod=14)
print('Time #7 batch TI: ' + str(time.time() - start_time) + ' seconds')
start_time = time.time()
# PPO - Percentage Price Oscillator
df['PPO'] = talib.PPO(df["Close"], fastperiod=12, slowperiod=26, matype=0)
# ROCP - Rate of change Percentage: (price-prevPrice)/prevPrice
df['ROCP'] = talib.ROCP(df["Close"], timeperiod=10)
# ROCR - Rate of change ratio: (price/prevPrice)
df['ROCR'] = talib.ROCR(df["Close"], timeperiod=10)
# ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100
df['ROCR100'] = talib.ROCR100(df["Close"], timeperiod=10)
# RSI - Relative Strength Index
df['RSI'] = talib.RSI(df["Close"], timeperiod=14)
# TRIX - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
df['TRIX'] = talib.TRIX(df["Close"], timeperiod=30)
# ULTOSC - Ultimate Oscillator
df['ULTOSC'] = talib.ULTOSC(df["High"],
df["Low"],
df["Close"],
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
# ######################## Volume Indicators ############################
# AD - Chaikin A/D Line
df['AD'] = talib.AD(df["High"], df["Low"], df["Close"], df["Volume"])
B=ta.abstract.MA(data, 2, price='600036.SH').tail() ''' data['SMA'] = ta.abstract.MA(data, 20, price='600036.SH') #普通均线与ta.abstract.MA一样 #data['SMA2'] = ta.abstract.SMA(data, 20, price='600036.SH') #与上面完全一样,普通均线 data['WMA'] = ta.abstract.WMA(data, 20, price='600036.SH') #权重均线(突出中间,用于周期分析) data['TRIMA'] = ta.abstract.TRIMA(data, 20, price='600036.SH') #指数移动平均线 data['EMA'] = ta.abstract.EMA(data, 20, price='600036.SH') #指数移动平均线 data['DEMA'] = ta.abstract.DEMA(data, 20, price='600036.SH') #突出EMA(减小了EMA的滞后) data['KAMA'] = ta.abstract.KAMA(data, 20, price='600036.SH') #KAMA表示自适应均线 data_B= ta.abstract.BBANDS(data, timeperiod=20, price='600036.SH') #ta.abstract.BBANDS计算布林带通道 upperband = ta.abstract.MAX(stock, 20, price='high') #求20日最高价(针对high列) lowerband = ta.abstract.MIN(stock, 20, price='low') #求20日最低价(针对low列) A=ta.ROCR100(value.values,20) #表示计算动量 macd = ta.abstract.MACD(data, price='600036.SH') #蓝色线代表macd,红色线代表macdsignal(均线),两者相减就是macdhist表示macd的变化速度 RSI= ta.abstract.RSI(data,20, price='600036.SH') RSI = talib.RSI(price, 20) #一样,好像都可以 k,d = ta.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) #KDJ的计算 KDJ = pd.concat([pd.Series(k,index=data.index), pd.Series(d,index=data.index)], axis=1, keys=['slowk','slowd']) #KDJ的计算 adv10 = ta.abstract.MA(volume, 10, price='600036.SH') #观察下这种计算方式！求成交量均线 adv20 = ta.abstract.MA(volume, 20, price='600036.SH') OBV= pd.Series(ta.OBV(Close, Volume), index=close.index) AD = pd.Series(ta.AD(High, Low, Close, Volume), index=close.index) VWAP = talib.SUM(denominator,context.PERIOD)/talib.SUM(volume,context.PERIOD) #talib.SUM是加总函数 MOM_MOM = Momentum(MOM_RS) #Momentum是计算变化率的函数 slope = ta.abstract.LINEARREG_SLOPE(data, 60, price='000001.SZ') #线性回归求K值
#1.计算并作图
#step1 Momentum: ROCR100的计算并作图
def change_index(df):
df.index = pd.Index(
map(lambda x: datetime.strptime(str(x), "%Y%m%d"), df.index))
return df
data = change_index(dv.get_ts('close_adj').loc[20170105:])
#！！！注意下这个表达,通过字典生成式读取数据 最后一行是典型的字典读取数据并转换为dataframe格式
symbol = ['000001.SZ', '600036.SH', '600050.SH', '000008.SZ', '000009.SZ']
price_dict = {name: data[name]
for name in symbol} #name成为字典的key,data[name]成为key对应的元素
data_mom = pd.DataFrame(
{item: ta.ROCR100(value.values, 20)
for item, value in price_dict.items()},
index=data.index).dropna(axis=0)
#dropna()表示删除为空的行,ta.ROCR100(value.values,20)表示计算动量,原式可为：A=ta.ROCR100(price_dict['000001.SZ'],20) (ta.ROCR100在talib中比较特殊)
fig = plt.figure(figsize=(15, 7)) #图片大小
plt.plot(data_mom)
plt.hlines(100,
data_mom.index[0],
data_mom.index[-1],
linestyles='dashed',
alpha=0.5)
# ！！！ plt.hlines表示画一条虚线,100为虚线的位置(高度),data_mom.index[0],data_mom.index[-1]表示宽度,linestyles='dashed'表示虚线
plt.legend(data_mom.columns,
loc='upper left') #data_mom.columns为列名,plt.legend表示给线条依次命名
plt.show()
def get_rocr100(ohlc):
rocr100 = ta.ROCR100(ohlc['4_close'], timeperiod=10)
ohlc['rocr100'] = rocr100
return ohlc
def get_talib_stock_daily(
stock_code,
s,
e,
append_ori_close=False,
norms=['volume', 'amount', 'ht_dcphase', 'obv', 'adosc', 'ad', 'cci']):
"""获取经过talib处理后的股票日线数据"""
stock_data = QA.QA_fetch_stock_day_adv(stock_code, s, e)
stock_df = stock_data.to_qfq().data
if append_ori_close:
stock_df['o_close'] = stock_data.data['close']
# stock_df['high_qfq'] = stock_data.to_qfq().data['high']
# stock_df['low_hfq'] = stock_data.to_hfq().data['low']
close = np.array(stock_df['close'])
high = np.array(stock_df['high'])
low = np.array(stock_df['low'])
_open = np.array(stock_df['open'])
_volume = np.array(stock_df['volume'])
stock_df['dema'] = talib.DEMA(close)
stock_df['ema'] = talib.EMA(close)
stock_df['ht_tradeline'] = talib.HT_TRENDLINE(close)
stock_df['kama'] = talib.KAMA(close)
stock_df['ma'] = talib.MA(close)
stock_df['mama'], stock_df['fama'] = talib.MAMA(close)
# MAVP
stock_df['midpoint'] = talib.MIDPOINT(close)
stock_df['midprice'] = talib.MIDPRICE(high, low)
stock_df['sar'] = talib.SAR(high, low)
stock_df['sarext'] = talib.SAREXT(high, low)
stock_df['sma'] = talib.SMA(close)
stock_df['t3'] = talib.T3(close)
stock_df['tema'] = talib.TEMA(close)
stock_df['trima'] = talib.TRIMA(close)
stock_df['wma'] = talib.WMA(close)
stock_df['adx'] = talib.ADX(high, low, close)
stock_df['adxr'] = talib.ADXR(high, low, close)
stock_df['apo'] = talib.APO(close)
stock_df['aroondown'], stock_df['aroonup'] = talib.AROON(high, low)
stock_df['aroonosc'] = talib.AROONOSC(high, low)
stock_df['bop'] = talib.BOP(_open, high, low, close)
stock_df['cci'] = talib.CCI(high, low, close)
stock_df['cmo'] = talib.CMO(close)
stock_df['dx'] = talib.DX(high, low, close)
# MACD
stock_df['macd'], stock_df['macdsignal'], stock_df[
'macdhist'] = talib.MACDEXT(close)
# MACDFIX
stock_df['mfi'] = talib.MFI(high, low, close, _volume)
stock_df['minus_di'] = talib.MINUS_DI(high, low, close)
stock_df['minus_dm'] = talib.MINUS_DM(high, low)
stock_df['mom'] = talib.MOM(close)
stock_df['plus_di'] = talib.PLUS_DI(high, low, close)
stock_df['plus_dm'] = talib.PLUS_DM(high, low)
stock_df['ppo'] = talib.PPO(close)
stock_df['roc'] = talib.ROC(close)
stock_df['rocp'] = talib.ROCP(close)
stock_df['rocr'] = talib.ROCR(close)
stock_df['rocr100'] = talib.ROCR100(close)
stock_df['rsi'] = talib.RSI(close)
stock_df['slowk'], stock_df['slowd'] = talib.STOCH(high, low, close)
stock_df['fastk'], stock_df['fastd'] = talib.STOCHF(high, low, close)
# STOCHRSI - Stochastic Relative Strength Index
stock_df['trix'] = talib.TRIX(close)
stock_df['ultosc'] = talib.ULTOSC(high, low, close)
stock_df['willr'] = talib.WILLR(high, low, close)
stock_df['ad'] = talib.AD(high, low, close, _volume)
stock_df['adosc'] = talib.ADOSC(high, low, close, _volume)
stock_df['obv'] = talib.OBV(close, _volume)
stock_df['ht_dcperiod'] = talib.HT_DCPERIOD(close)
stock_df['ht_dcphase'] = talib.HT_DCPHASE(close)
stock_df['inphase'], stock_df['quadrature'] = talib.HT_PHASOR(close)
stock_df['sine'], stock_df['leadsine'] = talib.HT_PHASOR(close)
stock_df['ht_trendmode'] = talib.HT_TRENDMODE(close)
stock_df['avgprice'] = talib.AVGPRICE(_open, high, low, close)
stock_df['medprice'] = talib.MEDPRICE(high, low)
stock_df['typprice'] = talib.TYPPRICE(high, low, close)
stock_df['wclprice'] = talib.WCLPRICE(high, low, close)
stock_df['atr'] = talib.ATR(high, low, close)
stock_df['natr'] = talib.NATR(high, low, close)
stock_df['trange'] = talib.TRANGE(high, low, close)
stock_df['beta'] = talib.BETA(high, low)
stock_df['correl'] = talib.CORREL(high, low)
stock_df['linearreg'] = talib.LINEARREG(close)
stock_df['linearreg_angle'] = talib.LINEARREG_ANGLE(close)
stock_df['linearreg_intercept'] = talib.LINEARREG_INTERCEPT(close)
stock_df['linearreg_slope'] = talib.LINEARREG_SLOPE(close)
stock_df['stddev'] = talib.STDDEV(close)
stock_df['tsf'] = talib.TSF(close)
stock_df['var'] = talib.VAR(close)
stock_df = stock_df.reset_index().set_index('date')
if norms:
x = stock_df[norms].values # returns a numpy array
x_scaled = MinMaxScaler().fit_transform(x)
stock_df = stock_df.drop(columns=norms).join(
pd.DataFrame(x_scaled, columns=norms, index=stock_df.index))
# stock_df = stock_df.drop(columns=['code', 'open', 'high', 'low'])
stock_df = stock_df.dropna()
stock_df = stock_df.drop(columns=['code'])
return stock_df
def add_ta_features(df, ta_settings):
"""Add technial analysis features from typical financial dataset that
typically include columns such as "open", "high", "low", "price" and
"volume".
http://mrjbq7.github.io/ta-lib/
Args:
df(pandas.DataFrame): original DataFrame.
ta_settings(dict): configuration.
Returns:
pandas.DataFrame: DataFrame with new features included.
"""
open = df['open']
high = df['high']
low = df['low']
close = df['price']
volume = df['volume']
if ta_settings['overlap']:
df['ta_overlap_bbands_upper'], df['ta_overlap_bbands_middle'], df[
'ta_overlap_bbands_lower'] = ta.BBANDS(close,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
df['ta_overlap_dema'] = ta.DEMA(
close, timeperiod=15) # NOTE: Changed to avoid a lot of Nan values
df['ta_overlap_ema'] = ta.EMA(close, timeperiod=30)
df['ta_overlap_kama'] = ta.KAMA(close, timeperiod=30)
df['ta_overlap_ma'] = ta.MA(close, timeperiod=30, matype=0)
df['ta_overlap_mama_mama'], df['ta_overlap_mama_fama'] = ta.MAMA(close)
period = np.random.randint(10, 20, size=len(close)).astype(float)
df['ta_overlap_mavp'] = ta.MAVP(close,
period,
minperiod=2,
maxperiod=30,
matype=0)
df['ta_overlap_midpoint'] = ta.MIDPOINT(close, timeperiod=14)
df['ta_overlap_midprice'] = ta.MIDPRICE(high, low, timeperiod=14)
df['ta_overlap_sar'] = ta.SAR(high, low, acceleration=0, maximum=0)
df['ta_overlap_sarext'] = ta.SAREXT(high,
low,
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
df['ta_overlap_sma'] = ta.SMA(close, timeperiod=30)
df['ta_overlap_t3'] = ta.T3(close, timeperiod=5, vfactor=0)
df['ta_overlap_tema'] = ta.TEMA(
close, timeperiod=12) # NOTE: Changed to avoid a lot of Nan values
df['ta_overlap_trima'] = ta.TRIMA(close, timeperiod=30)
df['ta_overlap_wma'] = ta.WMA(close, timeperiod=30)
# NOTE: Commented to avoid a lot of Nan values
# df['ta_overlap_ht_trendline'] = ta.HT_TRENDLINE(close)
if ta_settings['momentum']:
df['ta_momentum_adx'] = ta.ADX(high, low, close, timeperiod=14)
df['ta_momentum_adxr'] = ta.ADXR(high, low, close, timeperiod=14)
df['ta_momentum_apo'] = ta.APO(close,
fastperiod=12,
slowperiod=26,
matype=0)
df['ta_momentum_aroondown'], df['ta_momentum_aroonup'] = ta.AROON(
high, low, timeperiod=14)
df['ta_momentum_aroonosc'] = ta.AROONOSC(high, low, timeperiod=14)
df['ta_momentum_bop'] = ta.BOP(open, high, low, close)
df['ta_momentum_cci'] = ta.CCI(high, low, close, timeperiod=14)
df['ta_momentum_cmo'] = ta.CMO(close, timeperiod=14)
df['ta_momentum_dx'] = ta.DX(high, low, close, timeperiod=14)
df['ta_momentum_macd_macd'], df['ta_momentum_macd_signal'], df[
'ta_momentum_macd_hist'] = ta.MACD(close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
df['ta_momentum_macdext_macd'], df['ta_momentum_macdext_signal'], df[
'ta_momentum_macdext_hist'] = ta.MACDEXT(close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
df['ta_momentum_macdfix_macd'], df['ta_momentum_macdfix_signal'], df[
'ta_momentum_macdfix_hist'] = ta.MACDFIX(close, signalperiod=9)
df['ta_momentum_mfi'] = ta.MFI(high, low, close, volume, timeperiod=14)
df['ta_momentum_minus_di'] = ta.MINUS_DI(high,
low,
close,
timeperiod=14)
df['ta_momentum_minus_dm'] = ta.MINUS_DM(high, low, timeperiod=14)
df['ta_momentum_mom'] = ta.MOM(close, timeperiod=10)
df['ta_momentum_plus_di'] = ta.PLUS_DI(high, low, close, timeperiod=14)
df['ta_momentum_plus_dm'] = ta.PLUS_DM(high, low, timeperiod=14)
df['ta_momentum_ppo'] = ta.PPO(close,
fastperiod=12,
slowperiod=26,
matype=0)
df['ta_momentum_roc'] = ta.ROC(close, timeperiod=10)
df['ta_momentum_rocp'] = ta.ROCP(close, timeperiod=10)
df['ta_momentum_rocr'] = ta.ROCR(close, timeperiod=10)
df['ta_momentum_rocr100'] = ta.ROCR100(close, timeperiod=10)
df['ta_momentum_rsi'] = ta.RSI(close, timeperiod=14)
df['ta_momentum_slowk'], df['ta_momentum_slowd'] = ta.STOCH(
high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['ta_momentum_fastk'], df['ta_momentum_fastd'] = ta.STOCHF(
high, low, close, fastk_period=5, fastd_period=3, fastd_matype=0)
df['ta_momentum_fastk'], df['ta_momentum_fastd'] = ta.STOCHRSI(
close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['ta_momentum_trix'] = ta.TRIX(
close, timeperiod=12) # NOTE: Changed to avoid a lot of Nan values
df['ta_momentum_ultosc'] = ta.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
df['ta_momentum_willr'] = ta.WILLR(high, low, close, timeperiod=14)
if ta_settings['volume']:
df['ta_volume_ad'] = ta.AD(high, low, close, volume)
df['ta_volume_adosc'] = ta.ADOSC(high,
low,
close,
volume,
fastperiod=3,
slowperiod=10)
df['ta_volume_obv'] = ta.OBV(close, volume)
if ta_settings['volatility']:
df['ta_volatility_atr'] = ta.ATR(high, low, close, timeperiod=14)
df['ta_volatility_natr'] = ta.NATR(high, low, close, timeperiod=14)
df['ta_volatility_trange'] = ta.TRANGE(high, low, close)
if ta_settings['price']:
df['ta_price_avgprice'] = ta.AVGPRICE(open, high, low, close)
df['ta_price_medprice'] = ta.MEDPRICE(high, low)
df['ta_price_typprice'] = ta.TYPPRICE(high, low, close)
df['ta_price_wclprice'] = ta.WCLPRICE(high, low, close)
if ta_settings['cycle']:
df['ta_cycle_ht_dcperiod'] = ta.HT_DCPERIOD(close)
df['ta_cycle_ht_phasor_inphase'], df[
'ta_cycle_ht_phasor_quadrature'] = ta.HT_PHASOR(close)
df['ta_cycle_ht_trendmode'] = ta.HT_TRENDMODE(close)
# NOTE: Commented to avoid a lot of Nan values
# df['ta_cycle_ht_dcphase'] = ta.HT_DCPHASE(close)
# df['ta_cycle_ht_sine_sine'], df['ta_cycle_ht_sine_leadsine'] = ta.HT_SINE(close)
if ta_settings['pattern']:
df['ta_pattern_cdl2crows'] = ta.CDL2CROWS(open, high, low, close)
df['ta_pattern_cdl3blackrows'] = ta.CDL3BLACKCROWS(
open, high, low, close)
df['ta_pattern_cdl3inside'] = ta.CDL3INSIDE(open, high, low, close)
df['ta_pattern_cdl3linestrike'] = ta.CDL3LINESTRIKE(
open, high, low, close)
df['ta_pattern_cdl3outside'] = ta.CDL3OUTSIDE(open, high, low, close)
df['ta_pattern_cdl3starsinsouth'] = ta.CDL3STARSINSOUTH(
open, high, low, close)
df['ta_pattern_cdl3whitesoldiers'] = ta.CDL3WHITESOLDIERS(
open, high, low, close)
df['ta_pattern_cdlabandonedbaby'] = ta.CDLABANDONEDBABY(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdladvanceblock'] = ta.CDLADVANCEBLOCK(
open, high, low, close)
df['ta_pattern_cdlbelthold'] = ta.CDLBELTHOLD(open, high, low, close)
df['ta_pattern_cdlbreakaway'] = ta.CDLBREAKAWAY(open, high, low, close)
df['ta_pattern_cdlclosingmarubozu'] = ta.CDLCLOSINGMARUBOZU(
open, high, low, close)
df['ta_pattern_cdlconcealbabyswall'] = ta.CDLCONCEALBABYSWALL(
open, high, low, close)
df['ta_pattern_cdlcounterattack'] = ta.CDLCOUNTERATTACK(
open, high, low, close)
df['ta_pattern_cdldarkcloudcover'] = ta.CDLDARKCLOUDCOVER(
open, high, low, close, penetration=0)
df['ta_pattern_cdldoji'] = ta.CDLDOJI(open, high, low, close)
df['ta_pattern_cdldojistar'] = ta.CDLDOJISTAR(open, high, low, close)
df['ta_pattern_cdldragonflydoji'] = ta.CDLDRAGONFLYDOJI(
open, high, low, close)
df['ta_pattern_cdlengulfing'] = ta.CDLENGULFING(open, high, low, close)
df['ta_pattern_cdleveningdojistar'] = ta.CDLEVENINGDOJISTAR(
open, high, low, close, penetration=0)
df['ta_pattern_cdleveningstar'] = ta.CDLEVENINGSTAR(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdlgapsidesidewhite'] = ta.CDLGAPSIDESIDEWHITE(
open, high, low, close)
df['ta_pattern_cdlgravestonedoji'] = ta.CDLGRAVESTONEDOJI(
open, high, low, close)
df['ta_pattern_cdlhammer'] = ta.CDLHAMMER(open, high, low, close)
df['ta_pattern_cdlhangingman'] = ta.CDLHANGINGMAN(
open, high, low, close)
df['ta_pattern_cdlharami'] = ta.CDLHARAMI(open, high, low, close)
df['ta_pattern_cdlharamicross'] = ta.CDLHARAMICROSS(
open, high, low, close)
df['ta_pattern_cdlhighwave'] = ta.CDLHIGHWAVE(open, high, low, close)
df['ta_pattern_cdlhikkake'] = ta.CDLHIKKAKE(open, high, low, close)
df['ta_pattern_cdlhikkakemod'] = ta.CDLHIKKAKEMOD(
open, high, low, close)
df['ta_pattern_cdlhomingpigeon'] = ta.CDLHOMINGPIGEON(
open, high, low, close)
df['ta_pattern_cdlidentical3crows'] = ta.CDLIDENTICAL3CROWS(
open, high, low, close)
df['ta_pattern_cdlinneck'] = ta.CDLINNECK(open, high, low, close)
df['ta_pattern_cdlinvertedhammer'] = ta.CDLINVERTEDHAMMER(
open, high, low, close)
df['ta_pattern_cdlkicking'] = ta.CDLKICKING(open, high, low, close)
df['ta_pattern_cdlkickingbylength'] = ta.CDLKICKINGBYLENGTH(
open, high, low, close)
df['ta_pattern_cdlladderbottom'] = ta.CDLLADDERBOTTOM(
open, high, low, close)
df['ta_pattern_cdllongleggeddoji'] = ta.CDLLONGLEGGEDDOJI(
open, high, low, close)
df['ta_pattern_cdllongline'] = ta.CDLLONGLINE(open, high, low, close)
df['ta_pattern_cdlmarubozu'] = ta.CDLMARUBOZU(open, high, low, close)
df['ta_pattern_cdlmatchinglow'] = ta.CDLMATCHINGLOW(
open, high, low, close)
df['ta_pattern_cdlmathold'] = ta.CDLMATHOLD(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdlmorningdojistar'] = ta.CDLMORNINGDOJISTAR(
open, high, low, close, penetration=0)
df['ta_pattern_cdlmorningstar'] = ta.CDLMORNINGSTAR(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdllonneck'] = ta.CDLONNECK(open, high, low, close)
df['ta_pattern_cdlpiercing'] = ta.CDLPIERCING(open, high, low, close)
df['ta_pattern_cdlrickshawman'] = ta.CDLRICKSHAWMAN(
open, high, low, close)
df['ta_pattern_cdlrisefall3methods'] = ta.CDLRISEFALL3METHODS(
open, high, low, close)
df['ta_pattern_cdlseparatinglines'] = ta.CDLSEPARATINGLINES(
open, high, low, close)
df['ta_pattern_cdlshootingstar'] = ta.CDLSHOOTINGSTAR(
open, high, low, close)
df['ta_pattern_cdlshortline'] = ta.CDLSHORTLINE(open, high, low, close)
df['ta_pattern_cdlspinningtop'] = ta.CDLSPINNINGTOP(
open, high, low, close)
df['ta_pattern_cdlstalledpattern'] = ta.CDLSTALLEDPATTERN(
open, high, low, close)
df['ta_pattern_cdlsticksandwich'] = ta.CDLSTICKSANDWICH(
open, high, low, close)
df['ta_pattern_cdltakuri'] = ta.CDLTAKURI(open, high, low, close)
df['ta_pattern_cdltasukigap'] = ta.CDLTASUKIGAP(open, high, low, close)
df['ta_pattern_cdlthrusting'] = ta.CDLTHRUSTING(open, high, low, close)
df['ta_pattern_cdltristar'] = ta.CDLTRISTAR(open, high, low, close)
df['ta_pattern_cdlunique3river'] = ta.CDLUNIQUE3RIVER(
open, high, low, close)
df['ta_pattern_cdlupsidegap2crows'] = ta.CDLUPSIDEGAP2CROWS(
open, high, low, close)
df['ta_pattern_cdlxsidegap3methods'] = ta.CDLXSIDEGAP3METHODS(
open, high, low, close)
if ta_settings['statistic']:
df['ta_statistic_beta'] = ta.BETA(high, low, timeperiod=5)
df['ta_statistic_correl'] = ta.CORREL(high, low, timeperiod=30)
df['ta_statistic_linearreg'] = ta.LINEARREG(close, timeperiod=14)
df['ta_statistic_linearreg_angle'] = ta.LINEARREG_ANGLE(close,
timeperiod=14)
df['ta_statistic_linearreg_intercept'] = ta.LINEARREG_INTERCEPT(
close, timeperiod=14)
df['ta_statistic_linearreg_slope'] = ta.LINEARREG_SLOPE(close,
timeperiod=14)
df['ta_statistic_stddev'] = ta.STDDEV(close, timeperiod=5, nbdev=1)
df['ta_statistic_tsf'] = ta.TSF(close, timeperiod=14)
df['ta_statistic_var'] = ta.VAR(close, timeperiod=5, nbdev=1)
if ta_settings['math_transforms']:
df['ta_math_transforms_atan'] = ta.ATAN(close)
df['ta_math_transforms_ceil'] = ta.CEIL(close)
df['ta_math_transforms_cos'] = ta.COS(close)
df['ta_math_transforms_floor'] = ta.FLOOR(close)
df['ta_math_transforms_ln'] = ta.LN(close)
df['ta_math_transforms_log10'] = ta.LOG10(close)
df['ta_math_transforms_sin'] = ta.SIN(close)
df['ta_math_transforms_sqrt'] = ta.SQRT(close)
df['ta_math_transforms_tan'] = ta.TAN(close)
if ta_settings['math_operators']:
df['ta_math_operators_add'] = ta.ADD(high, low)
df['ta_math_operators_div'] = ta.DIV(high, low)
df['ta_math_operators_min'], df['ta_math_operators_max'] = ta.MINMAX(
close, timeperiod=30)
df['ta_math_operators_minidx'], df[
'ta_math_operators_maxidx'] = ta.MINMAXINDEX(close, timeperiod=30)
df['ta_math_operators_mult'] = ta.MULT(high, low)
df['ta_math_operators_sub'] = ta.SUB(high, low)
df['ta_math_operators_sum'] = ta.SUM(close, timeperiod=30)
return df
def Momentum_Indicators(dataframe):
"""
Momentum Indicators:
ADX Average Directional Movement Index
ADXR Average Directional Movement Index Rating
APO Absolute Price Oscillator
AROON Aroon
AROONOSC Aroon Oscillator
BOP Balance Of Power
CCI Commodity Channel Index
CMO Chande Momentum Oscillator
DX Directional Movement Index
MACD Moving Average Convergence/Divergence
MACDEXT MACD with controllable MA type
MACDFIX Moving Average Convergence/Divergence Fix 12/26
MFI Money FLow Index
MINUS_DI Minus Directional Indicator
MINUS_DM Minus Directional Movement
MOM Momentum
PLUS_DI Plus Directional Indicator
PLUS_DM Plus Directional Movement
PPO Percentage Price Oscillator
ROC Rate of change : ((price/prevPrice)-1)*100
ROCP Rate of change Percentage: (price-prevPrice)/prevPrice
ROCR Rate of change ratio: (price/prevPrice)
ROCR100 Rate of change ratio 100 scale: (price/prevPrice)*100
RSI Relative Strength Index
STOCH Stochastic
STOCHF Stochastic Fast
STOCHRSI Stochastic Relative Strength Index
TRIX 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
ULTOSC Ultimate Oscillator
WILLR Williams' %R
"""
#ADX - Average Directional Movement Index
df[f'{ratio}_ADX'] = talib.ADX(High, Low, Close, timeperiod=14)
#ADXR - Average Directional Movement Index Rating
df[f'{ratio}_ADXR'] = talib.ADXR(High, Low, Close, timeperiod=14)
#APO - Absolute Price Oscillator
#df[f'{ratio}_APO'] = talib.APO(Close, fastperiod=12, sLowperiod=26, matype=0)
#AROON - Aroon
aroondown, aroonup = talib.AROON(High, Low, timeperiod=14)
#AROONOSC - Aroon Oscillator
df[f'{ratio}_AROONOSC'] = talib.AROONOSC(High, Low, timeperiod=14)
#BOP - Balance Of Power
df[f'{ratio}_BOP'] = talib.BOP(Open, High, Low, Close)
#CCI - Commodity Channel Index
df[f'{ratio}_CCI'] = talib.CCI(High, Low, Close, timeperiod=14)
#CMO - Chande Momentum Oscillator
df[f'{ratio}_CMO'] = talib.CMO(Close, timeperiod=14)
#DX - Directional Movement Index
df[f'{ratio}_DX'] = talib.DX(High, Low, Close, timeperiod=14)
#MACD - Moving Average Convergence/Divergence
#macd, macdsignal, macdhist = talib.MACD(Close, fastperiod=12, sLowperiod=26, signalperiod=9)
#MACDEXT - MACD with controllable MA type
#macd, macdsignal, macdhist = talib.MACDEXT(Close, fastperiod=12, fastmatype=0, sLowperiod=26, sLowmatype=0, signalperiod=9, signalmatype=0)
#MACDFIX - Moving Average Convergence/Divergence Fix 12/26
#macd, macdsignal, macdhist = talib.MACDFIX(Close, signalperiod=9)
#MFI - Money FLow Index
#df[f'{ratio}_MFI'] = talib.MFI(High, Low, Close, volume, timeperiod=14)
#MINUS_DI - Minus Directional Indicator
df[f'{ratio}_MINUS_DI'] = talib.MINUS_DI(High, Low, Close, timeperiod=14)
#MINUS_DM - Minus Directional Movement
df[f'{ratio}_MINUS_DM'] = talib.MINUS_DM(High, Low, timeperiod=14)
#Minus Directional Movement
#MOM - Momentum
df[f'{ratio}_MOM'] = talib.MOM(Close, timeperiod=10)
#PLUS_DI - Plus Directional Indicator
df[f'{ratio}_PLUS_DI'] = talib.PLUS_DI(High, Low, Close, timeperiod=14)
#PLUS_DM - Plus Directional Movement
df[f'{ratio}_PLUS_DM'] = talib.PLUS_DM(High, Low, timeperiod=14)
#PPO - Percentage Price Oscillator
#df[f'{ratio}_PPO'] = talib.PPO(Close, fastperiod=12, sLowperiod=26, matype=0)
#ROC - Rate of change : ((price/prevPrice)-1)*100
df[f'{ratio}_ROC'] = talib.ROC(Close, timeperiod=10)
#ROCP - Rate of change Percentage: (price-prevPrice)/prevPrice
df[f'{ratio}_ROCP'] = talib.ROCP(Close, timeperiod=10)
#ROCR - Rate of change ratio: (price/prevPrice)
df[f'{ratio}_ROCR'] = talib.ROCR(Close, timeperiod=10)
#ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100
df[f'{ratio}_ROCR100'] = talib.ROCR100(Close, timeperiod=10)
#RSI - Relative Strength Index
#NOTE: The RSI function has an unstable period.
df[f'{ratio}_RSI'] = talib.RSI(Close, timeperiod=14)
#STOCH - Stochastic
#sLowk, sLowd = talib.STOCH(High, Low, Close, fastk_period=5, sLowk_period=3, sLowk_matype=0, sLowd_period=3, sLowd_matype=0)
#STOCHF - Stochastic Fast
fastk, fastd = talib.STOCHF(High, Low, Close, fastk_period=5, fastd_period=3, fastd_matype=0)
#STOCHRSI - Stochastic Relative Strength Index
fastk, fastd = talib.STOCHRSI(Close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)
#TRIX - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
df[f'{ratio}_TRIX'] = talib.TRIX(Close, timeperiod=30)
#ULTOSC - Ultimate Oscillator
df[f'{ratio}_ULTOSC'] = talib.ULTOSC(High, Low, Close, timeperiod1=7, timeperiod2=14, timeperiod3=28)
#WILLR - Williams' %R
df[f'{ratio}_ULTOSC'] = talib.WILLR(High, Low, Close, timeperiod=14)
return
def ta(name, price_h, price_l, price_c, price_v, price_o):
# function 'MAX'/'MAXINDEX'/'MIN'/'MININDEX'/'MINMAX'/'MINMAXINDEX'/'SUM' is missing
if name == 'AD':
return talib.AD(np.array(price_h), np.array(price_l),
np.array(price_c), np.asarray(price_v, dtype='float'))
if name == 'ADOSC':
return talib.ADOSC(np.array(price_h),
np.array(price_l),
np.array(price_c),
np.asarray(price_v, dtype='float'),
fastperiod=2,
slowperiod=10)
if name == 'ADX':
return talib.ADX(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'ADXR':
return talib.ADXR(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'APO':
return talib.APO(np.asarray(price_c, dtype='float'),
fastperiod=12,
slowperiod=26,
matype=0)
if name == 'AROON':
AROON_DWON, AROON2_UP = talib.AROON(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=90)
return (AROON_DWON, AROON2_UP)
if name == 'AROONOSC':
return talib.AROONOSC(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=14)
if name == 'ATR':
return talib.ATR(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'AVGPRICE':
return talib.AVGPRICE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'BBANDS':
BBANDS1, BBANDS2, BBANDS3 = talib.BBANDS(np.asarray(price_c,
dtype='float'),
matype=MA_Type.T3)
return BBANDS1
if name == 'BETA':
return talib.BETA(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=5)
if name == 'BOP':
return talib.BOP(np.array(price_o), np.array(price_h),
np.array(price_l), np.asarray(price_c, dtype='float'))
if name == 'CCI':
return talib.CCI(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'CDL2CROWS':
return talib.CDL2CROWS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3BLACKCROWS':
return talib.CDL3BLACKCROWS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3INSIDE':
return talib.CDL3INSIDE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3LINESTRIKE':
return talib.CDL3LINESTRIKE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3OUTSIDE':
return talib.CDL3OUTSIDE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3STARSINSOUTH':
return talib.CDL3STARSINSOUTH(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3WHITESOLDIERS':
return talib.CDL3WHITESOLDIERS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLABANDONEDBABY':
return talib.CDLABANDONEDBABY(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLADVANCEBLOCK':
return talib.CDLADVANCEBLOCK(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLBELTHOLD':
return talib.CDLBELTHOLD(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLBREAKAWAY':
return talib.CDLBREAKAWAY(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLCLOSINGMARUBOZU':
return talib.CDLCLOSINGMARUBOZU(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLCONCEALBABYSWALL':
return talib.CDLCONCEALBABYSWALL(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLCOUNTERATTACK':
return talib.CDLCOUNTERATTACK(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLDARKCLOUDCOVER':
return talib.CDLDARKCLOUDCOVER(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLDOJI':
return talib.CDLDOJI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLDOJISTAR':
return talib.CDLDOJISTAR(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLDRAGONFLYDOJI':
return talib.CDLDRAGONFLYDOJI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLENGULFING':
return talib.CDLENGULFING(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLEVENINGDOJISTAR':
return talib.CDLEVENINGDOJISTAR(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLEVENINGSTAR':
return talib.CDLEVENINGSTAR(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLGAPSIDESIDEWHITE':
return talib.CDLGAPSIDESIDEWHITE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLGRAVESTONEDOJI':
return talib.CDLGRAVESTONEDOJI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHAMMER':
return talib.CDLHAMMER(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHANGINGMAN':
return talib.CDLHANGINGMAN(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHARAMI':
return talib.CDLHARAMI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHARAMICROSS':
return talib.CDLHARAMICROSS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHIGHWAVE':
return talib.CDLHIGHWAVE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHIKKAKE':
return talib.CDLHIKKAKE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHIKKAKEMOD':
return talib.CDLHIKKAKEMOD(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHOMINGPIGEON':
return talib.CDLHOMINGPIGEON(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLIDENTICAL3CROWS':
return talib.CDLIDENTICAL3CROWS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLINNECK':
return talib.CDLINNECK(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLINVERTEDHAMMER':
return talib.CDLINVERTEDHAMMER(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLKICKING':
return talib.CDLKICKING(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLKICKINGBYLENGTH':
return talib.CDLKICKINGBYLENGTH(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLLADDERBOTTOM':
return talib.CDLLADDERBOTTOM(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLLONGLEGGEDDOJI':
return talib.CDLLONGLEGGEDDOJI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLLONGLINE':
return talib.CDLLONGLINE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLMARUBOZU':
return talib.CDLMARUBOZU(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLMATCHINGLOW':
return talib.CDLMATCHINGLOW(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLMATHOLD':
return talib.CDLMATHOLD(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLMORNINGDOJISTAR':
return talib.CDLMORNINGDOJISTAR(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLMORNINGSTAR':
return talib.CDLMORNINGSTAR(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLONNECK':
return talib.CDLONNECK(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLPIERCING':
return talib.CDLPIERCING(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLRICKSHAWMAN':
return talib.CDLRICKSHAWMAN(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLRISEFALL3METHODS':
return talib.CDLRISEFALL3METHODS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSEPARATINGLINES':
return talib.CDLSEPARATINGLINES(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSHOOTINGSTAR':
return talib.CDLSHOOTINGSTAR(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSHORTLINE':
return talib.CDLSHORTLINE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSPINNINGTOP':
return talib.CDLSPINNINGTOP(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSTALLEDPATTERN':
return talib.CDLSTALLEDPATTERN(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSTICKSANDWICH':
return talib.CDLSTICKSANDWICH(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLTAKURI':
return talib.CDLTAKURI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLTASUKIGAP':
return talib.CDLTASUKIGAP(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLTHRUSTING':
return talib.CDLTHRUSTING(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLTRISTAR':
return talib.CDLTRISTAR(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLUNIQUE3RIVER':
return talib.CDLUNIQUE3RIVER(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLUPSIDEGAP2CROWS':
return talib.CDLUPSIDEGAP2CROWS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLXSIDEGAP3METHODS':
return talib.CDLXSIDEGAP3METHODS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CMO':
return talib.CMO(np.asarray(price_c, dtype='float'), timeperiod=14)
if name == 'CORREL':
return talib.CORREL(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=30)
if name == 'DEMA':
return talib.DEMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'DX':
return talib.DX(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'EMA':
return talib.EMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'HT_DCPERIOD':
return talib.HT_DCPERIOD(np.asarray(price_c, dtype='float'))
if name == 'HT_DCPHASE':
return talib.HT_DCPHASE(np.asarray(price_c, dtype='float'))
if name == 'HT_PHASOR':
HT_PHASOR1, HT_PHASOR2 = talib.HT_PHASOR(
np.asarray(price_c, dtype='float')
) # use HT_PHASOR1 for the indication of up and down
return HT_PHASOR1
if name == 'HT_SINE':
HT_SINE1, HT_SINE2 = talib.HT_SINE(np.asarray(price_c, dtype='float'))
return HT_SINE1
if name == 'HT_TRENDLINE':
return talib.HT_TRENDLINE(np.asarray(price_c, dtype='float'))
if name == 'HT_TRENDMODE':
return talib.HT_TRENDMODE(np.asarray(price_c, dtype='float'))
if name == 'KAMA':
return talib.KAMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'LINEARREG':
return talib.LINEARREG(np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'LINEARREG_ANGLE':
return talib.LINEARREG_ANGLE(np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'LINEARREG_INTERCEPT':
return talib.LINEARREG_INTERCEPT(np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'LINEARREG_SLOPE':
return talib.LINEARREG_SLOPE(np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'MA':
return talib.MA(np.asarray(price_c, dtype='float'),
timeperiod=30,
matype=0)
if name == 'MACD':
MACD1, MACD2, MACD3 = talib.MACD(np.asarray(price_c, dtype='float'),
fastperiod=12,
slowperiod=26,
signalperiod=9)
return MACD1
if nam == 'MACDEXT':
return talib.MACDEXT(np.asarray(price_c, dtype='float'),
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
if name == 'MACDFIX':
MACDFIX1, MACDFIX2, MACDFIX3 = talib.MACDFIX(np.asarray(price_c,
dtype='float'),
signalperiod=9)
return MACDFIX1
if name == 'MAMA':
MAMA1, MAMA2 = talib.MAMA(np.asarray(price_c, dtype='float'),
fastlimit=0,
slowlimit=0)
return MAMA1
if name == 'MEDPRICE':
return talib.MEDPRICE(np.array(price_h),
np.asarray(price_l, dtype='float'))
if name == 'MINUS_DI':
return talib.MINUS_DI(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'MINUS_DM':
return talib.MINUS_DM(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=14)
if name == 'MOM':
return talib.MOM(np.asarray(price_c, dtype='float'), timeperiod=10)
if name == 'NATR':
return talib.NATR(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'OBV':
return talib.OBV(np.array(price_c), np.asarray(price_v, dtype='float'))
if name == 'PLUS_DI':
return talib.PLUS_DI(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'PLUS_DM':
return talib.PLUS_DM(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=14)
if name == 'PPO':
return talib.PPO(np.asarray(price_c, dtype='float'),
fastperiod=12,
slowperiod=26,
matype=0)
if name == 'ROC':
return talib.ROC(np.asarray(price_c, dtype='float'), timeperiod=10)
if name == 'ROCP':
return talib.ROCP(np.asarray(price_c, dtype='float'), timeperiod=10)
if name == 'ROCR100':
return talib.ROCR100(np.asarray(price_c, dtype='float'), timeperiod=10)
if name == 'RSI':
return talib.RSI(np.asarray(price_c, dtype='float'), timeperiod=14)
if name == 'SAR':
return talib.SAR(np.array(price_h),
np.asarray(price_l, dtype='float'),
acceleration=0,
maximum=0)
if name == 'SAREXT':
return talib.SAREXT(np.array(price_h),
np.asarray(price_l, dtype='float'),
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
if name == 'SMA':
return talib.SMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'STDDEV':
return talib.STDDEV(np.asarray(price_c, dtype='float'),
timeperiod=5,
nbdev=1)
if name == 'STOCH':
STOCH1, STOCH2 = talib.STOCH(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
return STOCH1
if name == 'STOCHF':
STOCHF1, STOCHF2 = talib.STOCHF(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
fastk_period=5,
fastd_period=3,
fastd_matype=0)
return STOCHF1
if name == 'STOCHRSI':
STOCHRSI1, STOCHRSI2 = talib.STOCHRSI(np.asarray(price_c,
dtype='float'),
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
return STOCHRSI1
if name == 'T3':
return talib.T3(np.asarray(price_c, dtype='float'),
timeperiod=5,
vfactor=0)
if name == 'TEMA':
return talib.TEMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'TRANGE':
return talib.TRANGE(np.array(price_h), np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'TRIMA':
return talib.TRIMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'TRIX':
return talib.TRIX(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'TSF':
return talib.TSF(np.asarray(price_c, dtype='float'), timeperiod=14)
if name == 'TYPPRICE':
return talib.TYPPRICE(np.array(price_h), np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'ULTOSC':
return talib.ULTOSC(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
if name == 'VAR':
return talib.VAR(np.asarray(price_c, dtype='float'),
timeperiod=5,
nbdev=1)
if name == 'WCLPRICE':
return talib.WCLPRICE(np.array(price_h), np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'WILLR':
return talib.WILLR(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'WMA':
return talib.WMA(np.asarray(price_c, dtype='float'), timeperiod=30)
plus_dm = ta.PLUS_DM(high, low, timeperiod=14)
#PPO - Percentage Price Oscillator
ppo = ta.PPO(close, fastperiod=12, slowperiod=26, matype=0)
#ROC - Rate of change : ((price/prevPrice)-1)*100
roc = ta.ROC(close, timeperiod=10)
#ROCP - Rate of change Percentage: (price-prevPrice)/prevPrice
rocp = ta.ROCP(close, timeperiod=10)
#ROCR - Rate of change ratio: (price/prevPrice)
rocr = ta.ROCR(close, timeperiod=10)
#ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100
rocr100 = ta.ROCR100(close, timeperiod=10)
#RSI - Relative Strength Index
rsi = ta.RSI(close, timeperiod=14)
#STOCH - Stochastic
slowk, slowd = ta.STOCH(high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
#STOCHF - Stochastic Fast
def ROCR100(data, **kwargs):
_check_talib_presence()
prices = _extract_series(data)
return talib.ROCR100(prices, **kwargs)
def calculate(self, para):
self.t = self.inputdata[:, 0]
self.op = self.inputdata[:, 1]
self.high = self.inputdata[:, 2]
self.low = self.inputdata[:, 3]
self.close = self.inputdata[:, 4]
#adjusted close
self.close1 = self.inputdata[:, 5]
self.volume = self.inputdata[:, 6]
#Overlap study
#Overlap Studies
#Overlap Studies
if para is 'BBANDS': #Bollinger Bands
upperband, middleband, lowerband = ta.BBANDS(self.close,
timeperiod=self.tp,
nbdevup=2,
nbdevdn=2,
matype=0)
self.output = [upperband, middleband, lowerband]
elif para is 'DEMA': #Double Exponential Moving Average
self.output = ta.DEMA(self.close, timeperiod=self.tp)
elif para is 'EMA': #Exponential Moving Average
self.output = ta.EMA(self.close, timeperiod=self.tp)
elif para is 'HT_TRENDLINE': #Hilbert Transform - Instantaneous Trendline
self.output = ta.HT_TRENDLINE(self.close)
elif para is 'KAMA': #Kaufman Adaptive Moving Average
self.output = ta.KAMA(self.close, timeperiod=self.tp)
elif para is 'MA': #Moving average
self.output = ta.MA(self.close, timeperiod=self.tp, matype=0)
elif para is 'MAMA': #MESA Adaptive Moving Average
mama, fama = ta.MAMA(self.close, fastlimit=0, slowlimit=0)
elif para is 'MAVP': #Moving average with variable period
self.output = ta.MAVP(self.close,
periods=10,
minperiod=self.tp,
maxperiod=self.tp1,
matype=0)
elif para is 'MIDPOINT': #MidPoint over period
self.output = ta.MIDPOINT(self.close, timeperiod=self.tp)
elif para is 'MIDPRICE': #Midpoint Price over period
self.output = ta.MIDPRICE(self.high, self.low, timeperiod=self.tp)
elif para is 'SAR': #Parabolic SAR
self.output = ta.SAR(self.high,
self.low,
acceleration=0,
maximum=0)
elif para is 'SAREXT': #Parabolic SAR - Extended
self.output = ta.SAREXT(self.high,
self.low,
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
elif para is 'SMA': #Simple Moving Average
self.output = ta.SMA(self.close, timeperiod=self.tp)
elif para is 'T3': #Triple Exponential Moving Average (T3)
self.output = ta.T3(self.close, timeperiod=self.tp, vfactor=0)
elif para is 'TEMA': #Triple Exponential Moving Average
self.output = ta.TEMA(self.close, timeperiod=self.tp)
elif para is 'TRIMA': #Triangular Moving Average
self.output = ta.TRIMA(self.close, timeperiod=self.tp)
elif para is 'WMA': #Weighted Moving Average
self.output = ta.WMA(self.close, timeperiod=self.tp)
#Momentum Indicators
elif para is 'ADX': #Average Directional Movement Index
self.output = ta.ADX(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'ADXR': #Average Directional Movement Index Rating
self.output = ta.ADXR(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'APO': #Absolute Price Oscillator
self.output = ta.APO(self.close,
fastperiod=12,
slowperiod=26,
matype=0)
elif para is 'AROON': #Aroon
aroondown, aroonup = ta.AROON(self.high,
self.low,
timeperiod=self.tp)
self.output = [aroondown, aroonup]
elif para is 'AROONOSC': #Aroon Oscillator
self.output = ta.AROONOSC(self.high, self.low, timeperiod=self.tp)
elif para is 'BOP': #Balance Of Power
self.output = ta.BOP(self.op, self.high, self.low, self.close)
elif para is 'CCI': #Commodity Channel Index
self.output = ta.CCI(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'CMO': #Chande Momentum Oscillator
self.output = ta.CMO(self.close, timeperiod=self.tp)
elif para is 'DX': #Directional Movement Index
self.output = ta.DX(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'MACD': #Moving Average Convergence/Divergence
macd, macdsignal, macdhist = ta.MACD(self.close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
self.output = [macd, macdsignal, macdhist]
elif para is 'MACDEXT': #MACD with controllable MA type
macd, macdsignal, macdhist = ta.MACDEXT(self.close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
self.output = [macd, macdsignal, macdhist]
elif para is 'MACDFIX': #Moving Average Convergence/Divergence Fix 12/26
macd, macdsignal, macdhist = ta.MACDFIX(self.close, signalperiod=9)
self.output = [macd, macdsignal, macdhist]
elif para is 'MFI': #Money Flow Index
self.output = ta.MFI(self.high,
self.low,
self.close,
self.volume,
timeperiod=self.tp)
elif para is 'MINUS_DI': #Minus Directional Indicator
self.output = ta.MINUS_DI(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'MINUS_DM': #Minus Directional Movement
self.output = ta.MINUS_DM(self.high, self.low, timeperiod=self.tp)
elif para is 'MOM': #Momentum
self.output = ta.MOM(self.close, timeperiod=10)
elif para is 'PLUS_DI': #Plus Directional Indicator
self.output = ta.PLUS_DI(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'PLUS_DM': #Plus Directional Movement
self.output = ta.PLUS_DM(self.high, self.low, timeperiod=self.tp)
elif para is 'PPO': #Percentage Price Oscillator
self.output = ta.PPO(self.close,
fastperiod=12,
slowperiod=26,
matype=0)
elif para is 'ROC': #Rate of change : ((price/prevPrice)-1)*100
self.output = ta.ROC(self.close, timeperiod=10)
elif para is 'ROCP': #Rate of change Percentage: (price-prevPrice)/prevPrice
self.output = ta.ROCP(self.close, timeperiod=10)
elif para is 'ROCR': #Rate of change ratio: (price/prevPrice)
self.output = ta.ROCR(self.close, timeperiod=10)
elif para is 'ROCR100': #Rate of change ratio 100 scale: (price/prevPrice)*100
self.output = ta.ROCR100(self.close, timeperiod=10)
elif para is 'RSI': #Relative Strength Index
self.output = ta.RSI(self.close, timeperiod=self.tp)
elif para is 'STOCH': #Stochastic
slowk, slowd = ta.STOCH(self.high,
self.low,
self.close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
self.output = [slowk, slowd]
elif para is 'STOCHF': #Stochastic Fast
fastk, fastd = ta.STOCHF(self.high,
self.low,
self.close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
self.output = [fastk, fastd]
elif para is 'STOCHRSI': #Stochastic Relative Strength Index
fastk, fastd = ta.STOCHRSI(self.close,
timeperiod=self.tp,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
self.output = [fastk, fastd]
elif para is 'TRIX': #1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
self.output = ta.TRIX(self.close, timeperiod=self.tp)
elif para is 'ULTOSC': #Ultimate Oscillator
self.output = ta.ULTOSC(self.high,
self.low,
self.close,
timeperiod1=self.tp,
timeperiod2=self.tp1,
timeperiod3=self.tp2)
elif para is 'WILLR': #Williams' %R
self.output = ta.WILLR(self.high,
self.low,
self.close,
timeperiod=self.tp)
# Volume Indicators : #
elif para is 'AD': #Chaikin A/D Line
self.output = ta.AD(self.high, self.low, self.close, self.volume)
elif para is 'ADOSC': #Chaikin A/D Oscillator
self.output = ta.ADOSC(self.high,
self.low,
self.close,
self.volume,
fastperiod=3,
slowperiod=10)
elif para is 'OBV': #On Balance Volume
self.output = ta.OBV(self.close, self.volume)
# Volatility Indicators: #
elif para is 'ATR': #Average True Range
self.output = ta.ATR(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'NATR': #Normalized Average True Range
self.output = ta.NATR(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'TRANGE': #True Range
self.output = ta.TRANGE(self.high, self.low, self.close)
#Price Transform : #
elif para is 'AVGPRICE': #Average Price
self.output = ta.AVGPRICE(self.op, self.high, self.low, self.close)
elif para is 'MEDPRICE': #Median Price
self.output = ta.MEDPRICE(self.high, self.low)
elif para is 'TYPPRICE': #Typical Price
self.output = ta.TYPPRICE(self.high, self.low, self.close)
elif para is 'WCLPRICE': #Weighted Close Price
self.output = ta.WCLPRICE(self.high, self.low, self.close)
#Cycle Indicators : #
elif para is 'HT_DCPERIOD': #Hilbert Transform - Dominant Cycle Period
self.output = ta.HT_DCPERIOD(self.close)
elif para is 'HT_DCPHASE': #Hilbert Transform - Dominant Cycle Phase
self.output = ta.HT_DCPHASE(self.close)
elif para is 'HT_PHASOR': #Hilbert Transform - Phasor Components
inphase, quadrature = ta.HT_PHASOR(self.close)
self.output = [inphase, quadrature]
elif para is 'HT_SINE': #Hilbert Transform - SineWave #2
sine, leadsine = ta.HT_SINE(self.close)
self.output = [sine, leadsine]
elif para is 'HT_TRENDMODE': #Hilbert Transform - Trend vs Cycle Mode
self.integer = ta.HT_TRENDMODE(self.close)
#Pattern Recognition : #
elif para is 'CDL2CROWS': #Two Crows
self.integer = ta.CDL2CROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDL3BLACKCROWS': #Three Black Crows
self.integer = ta.CDL3BLACKCROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDL3INSIDE': #Three Inside Up/Down
self.integer = ta.CDL3INSIDE(self.op, self.high, self.low,
self.close)
elif para is 'CDL3LINESTRIKE': #Three-Line Strike
self.integer = ta.CDL3LINESTRIKE(self.op, self.high, self.low,
self.close)
elif para is 'CDL3OUTSIDE': #Three Outside Up/Down
self.integer = ta.CDL3OUTSIDE(self.op, self.high, self.low,
self.close)
elif para is 'CDL3STARSINSOUTH': #Three Stars In The South
self.integer = ta.CDL3STARSINSOUTH(self.op, self.high, self.low,
self.close)
elif para is 'CDL3WHITESOLDIERS': #Three Advancing White Soldiers
self.integer = ta.CDL3WHITESOLDIERS(self.op, self.high, self.low,
self.close)
elif para is 'CDLABANDONEDBABY': #Abandoned Baby
self.integer = ta.CDLABANDONEDBABY(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLBELTHOLD': #Belt-hold
self.integer = ta.CDLBELTHOLD(self.op, self.high, self.low,
self.close)
elif para is 'CDLBREAKAWAY': #Breakaway
self.integer = ta.CDLBREAKAWAY(self.op, self.high, self.low,
self.close)
elif para is 'CDLCLOSINGMARUBOZU': #Closing Marubozu
self.integer = ta.CDLCLOSINGMARUBOZU(self.op, self.high, self.low,
self.close)
elif para is 'CDLCONCEALBABYSWALL': #Concealing Baby Swallow
self.integer = ta.CDLCONCEALBABYSWALL(self.op, self.high, self.low,
self.close)
elif para is 'CDLCOUNTERATTACK': #Counterattack
self.integer = ta.CDLCOUNTERATTACK(self.op, self.high, self.low,
self.close)
elif para is 'CDLDARKCLOUDCOVER': #Dark Cloud Cover
self.integer = ta.CDLDARKCLOUDCOVER(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLDOJI': #Doji
self.integer = ta.CDLDOJI(self.op, self.high, self.low, self.close)
elif para is 'CDLDOJISTAR': #Doji Star
self.integer = ta.CDLDOJISTAR(self.op, self.high, self.low,
self.close)
elif para is 'CDLDRAGONFLYDOJI': #Dragonfly Doji
self.integer = ta.CDLDRAGONFLYDOJI(self.op, self.high, self.low,
self.close)
elif para is 'CDLENGULFING': #Engulfing Pattern
self.integer = ta.CDLENGULFING(self.op, self.high, self.low,
self.close)
elif para is 'CDLEVENINGDOJISTAR': #Evening Doji Star
self.integer = ta.CDLEVENINGDOJISTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLEVENINGSTAR': #Evening Star
self.integer = ta.CDLEVENINGSTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLGAPSIDESIDEWHITE': #Up/Down-gap side-by-side white lines
self.integer = ta.CDLGAPSIDESIDEWHITE(self.op, self.high, self.low,
self.close)
elif para is 'CDLGRAVESTONEDOJI': #Gravestone Doji
self.integer = ta.CDLGRAVESTONEDOJI(self.op, self.high, self.low,
self.close)
elif para is 'CDLHAMMER': #Hammer
self.integer = ta.CDLHAMMER(self.op, self.high, self.low,
self.close)
elif para is 'CDLHANGINGMAN': #Hanging Man
self.integer = ta.CDLHANGINGMAN(self.op, self.high, self.low,
self.close)
elif para is 'CDLHARAMI': #Harami Pattern
self.integer = ta.CDLHARAMI(self.op, self.high, self.low,
self.close)
elif para is 'CDLHARAMICROSS': #Harami Cross Pattern
self.integer = ta.CDLHARAMICROSS(self.op, self.high, self.low,
self.close)
elif para is 'CDLHIGHWAVE': #High-Wave Candle
self.integer = ta.CDLHIGHWAVE(self.op, self.high, self.low,
self.close)
elif para is 'CDLHIKKAKE': #Hikkake Pattern
self.integer = ta.CDLHIKKAKE(self.op, self.high, self.low,
self.close)
elif para is 'CDLHIKKAKEMOD': #Modified Hikkake Pattern
self.integer = ta.CDLHIKKAKEMOD(self.op, self.high, self.low,
self.close)
elif para is 'CDLHOMINGPIGEON': #Homing Pigeon
self.integer = ta.CDLHOMINGPIGEON(self.op, self.high, self.low,
self.close)
elif para is 'CDLIDENTICAL3CROWS': #Identical Three Crows
self.integer = ta.CDLIDENTICAL3CROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDLINNECK': #In-Neck Pattern
self.integer = ta.CDLINNECK(self.op, self.high, self.low,
self.close)
elif para is 'CDLINVERTEDHAMMER': #Inverted Hammer
self.integer = ta.CDLINVERTEDHAMMER(self.op, self.high, self.low,
self.close)
elif para is 'CDLKICKING': #Kicking
self.integer = ta.CDLKICKING(self.op, self.high, self.low,
self.close)
elif para is 'CDLKICKINGBYLENGTH': #Kicking - bull/bear determined by the longer marubozu
self.integer = ta.CDLKICKINGBYLENGTH(self.op, self.high, self.low,
self.close)
elif para is 'CDLLADDERBOTTOM': #Ladder Bottom
self.integer = ta.CDLLADDERBOTTOM(self.op, self.high, self.low,
self.close)
elif para is 'CDLLONGLEGGEDDOJI': #Long Legged Doji
self.integer = ta.CDLLONGLEGGEDDOJI(self.op, self.high, self.low,
self.close)
elif para is 'CDLLONGLINE': #Long Line Candle
self.integer = ta.CDLLONGLINE(self.op, self.high, self.low,
self.close)
elif para is 'CDLMARUBOZU': #Marubozu
self.integer = ta.CDLMARUBOZU(self.op, self.high, self.low,
self.close)
elif para is 'CDLMATCHINGLOW': #Matching Low
self.integer = ta.CDLMATCHINGLOW(self.op, self.high, self.low,
self.close)
elif para is 'CDLMATHOLD': #Mat Hold
self.integer = ta.CDLMATHOLD(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLMORNINGDOJISTAR': #Morning Doji Star
self.integer = ta.CDLMORNINGDOJISTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLMORNINGSTAR': #Morning Star
self.integer = ta.CDLMORNINGSTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLONNECK': #On-Neck Pattern
self.integer = ta.CDLONNECK(self.op, self.high, self.low,
self.close)
elif para is 'CDLPIERCING': #Piercing Pattern
self.integer = ta.CDLPIERCING(self.op, self.high, self.low,
self.close)
elif para is 'CDLRICKSHAWMAN': #Rickshaw Man
self.integer = ta.CDLRICKSHAWMAN(self.op, self.high, self.low,
self.close)
elif para is 'CDLRISEFALL3METHODS': #Rising/Falling Three Methods
self.integer = ta.CDLRISEFALL3METHODS(self.op, self.high, self.low,
self.close)
elif para is 'CDLSEPARATINGLINES': #Separating Lines
self.integer = ta.CDLSEPARATINGLINES(self.op, self.high, self.low,
self.close)
elif para is 'CDLSHOOTINGSTAR': #Shooting Star
self.integer = ta.CDLSHOOTINGSTAR(self.op, self.high, self.low,
self.close)
elif para is 'CDLSHORTLINE': #Short Line Candle
self.integer = ta.CDLSHORTLINE(self.op, self.high, self.low,
self.close)
elif para is 'CDLSPINNINGTOP': #Spinning Top
self.integer = ta.CDLSPINNINGTOP(self.op, self.high, self.low,
self.close)
elif para is 'CDLSTALLEDPATTERN': #Stalled Pattern
self.integer = ta.CDLSTALLEDPATTERN(self.op, self.high, self.low,
self.close)
elif para is 'CDLSTICKSANDWICH': #Stick Sandwich
self.integer = ta.CDLSTICKSANDWICH(self.op, self.high, self.low,
self.close)
elif para is 'CDLTAKURI': #Takuri (Dragonfly Doji with very long lower shadow)
self.integer = ta.CDLTAKURI(self.op, self.high, self.low,
self.close)
elif para is 'CDLTASUKIGAP': #Tasuki Gap
self.integer = ta.CDLTASUKIGAP(self.op, self.high, self.low,
self.close)
elif para is 'CDLTHRUSTING': #Thrusting Pattern
self.integer = ta.CDLTHRUSTING(self.op, self.high, self.low,
self.close)
elif para is 'CDLTRISTAR': #Tristar Pattern
self.integer = ta.CDLTRISTAR(self.op, self.high, self.low,
self.close)
elif para is 'CDLUNIQUE3RIVER': #Unique 3 River
self.integer = ta.CDLUNIQUE3RIVER(self.op, self.high, self.low,
self.close)
elif para is 'CDLUPSIDEGAP2CROWS': #Upside Gap Two Crows
self.integer = ta.CDLUPSIDEGAP2CROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDLXSIDEGAP3METHODS': #Upside/Downside Gap Three Methods
self.integer = ta.CDLXSIDEGAP3METHODS(self.op, self.high, self.low,
self.close)
#Statistic Functions : #
elif para is 'BETA': #Beta
self.output = ta.BETA(self.high, self.low, timeperiod=5)
elif para is 'CORREL': #Pearson's Correlation Coefficient (r)
self.output = ta.CORREL(self.high, self.low, timeperiod=self.tp)
elif para is 'LINEARREG': #Linear Regression
self.output = ta.LINEARREG(self.close, timeperiod=self.tp)
elif para is 'LINEARREG_ANGLE': #Linear Regression Angle
self.output = ta.LINEARREG_ANGLE(self.close, timeperiod=self.tp)
elif para is 'LINEARREG_INTERCEPT': #Linear Regression Intercept
self.output = ta.LINEARREG_INTERCEPT(self.close,
timeperiod=self.tp)
elif para is 'LINEARREG_SLOPE': #Linear Regression Slope
self.output = ta.LINEARREG_SLOPE(self.close, timeperiod=self.tp)
elif para is 'STDDEV': #Standard Deviation
self.output = ta.STDDEV(self.close, timeperiod=5, nbdev=1)
elif para is 'TSF': #Time Series Forecast
self.output = ta.TSF(self.close, timeperiod=self.tp)
elif para is 'VAR': #Variance
self.output = ta.VAR(self.close, timeperiod=5, nbdev=1)
else:
print('You issued command:' + para)
def third_making_indicator_5min(dataframe):
Open_lis = np.array(dataframe['open'], dtype='float')
High_lis = np.array(dataframe['high'], dtype='float')
Low_lis = np.array(dataframe['low'], dtype='float')
Clz_lis = np.array(dataframe['weigted_price'], dtype='float')
Vol_lis = np.array(dataframe['volume'], dtype='float')
##지표##
SMA_3_C = ta.SMA(Clz_lis, timeperiod=3)
SMA_5_H = ta.SMA(High_lis, timeperiod=5)
SMA_5_L = ta.SMA(Low_lis, timeperiod=5)
SMA_5_C = ta.SMA(Clz_lis, timeperiod=5)
SMA_10_H = ta.SMA(High_lis, timeperiod=10)
SMA_10_L = ta.SMA(Low_lis, timeperiod=10)
SMA_10_C = ta.SMA(Clz_lis, timeperiod=10)
RSI_2_C = ta.RSI(Clz_lis, timeperiod=2)
RSI_3_C = ta.RSI(Clz_lis, timeperiod=3)
RSI_5_C = ta.RSI(Clz_lis, timeperiod=5)
RSI_7_H = ta.RSI(High_lis, timeperiod=7)
RSI_7_L = ta.RSI(Low_lis, timeperiod=7)
RSI_7_C = ta.RSI(Clz_lis, timeperiod=7)
RSI_14_H = ta.RSI(High_lis, timeperiod=14)
RSI_14_L = ta.RSI(Low_lis, timeperiod=14)
RSI_14_C = ta.RSI(Clz_lis, timeperiod=14)
ADX = ta.ADX(High_lis, Low_lis, Clz_lis, timeperiod=14)
ADXR = ta.ADXR(High_lis, Low_lis, Clz_lis, timeperiod=14)
Aroondown, Aroonup = ta.AROON(High_lis, Low_lis, timeperiod=14)
Aroonosc = ta.AROONOSC(High_lis, Low_lis, timeperiod=14)
BOP = ta.BOP(Open_lis, High_lis, Low_lis, Clz_lis)
CMO = ta.CMO(Clz_lis, timeperiod=14)
DX = ta.DX(High_lis, Low_lis, Clz_lis, timeperiod=14)
MFI = ta.MFI(High_lis, Low_lis, Clz_lis, Vol_lis, timeperiod=14)
MINUS_DI = ta.MINUS_DI(High_lis, Low_lis, Clz_lis, timeperiod=14)
PLUSDI = ta.PLUS_DI(High_lis, Low_lis, Clz_lis, timeperiod=14)
PPO = ta.PPO(Clz_lis, fastperiod=12, slowperiod=26, matype=0)
ROC = ta.ROC(Clz_lis, timeperiod=10)
ROCP = ta.ROCP(Clz_lis, timeperiod=10)
ROCR = ta.ROCR(Clz_lis, timeperiod=10)
ROCR100 = ta.ROCR100(Clz_lis, timeperiod=10)
Slowk, Slowd = ta.STOCH(High_lis,
Low_lis,
Clz_lis,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
STOCHF_f, STOCHF_d = ta.STOCHF(High_lis,
Low_lis,
Clz_lis,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
Fastk, Fastd = ta.STOCHRSI(Clz_lis,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
TRIX = ta.TRIX(Clz_lis, timeperiod=30)
ULTOSC = ta.ULTOSC(High_lis,
Low_lis,
Clz_lis,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
WILLR = ta.WILLR(High_lis, Low_lis, Clz_lis, timeperiod=14)
ADOSC = ta.ADOSC(High_lis,
Low_lis,
Clz_lis,
Vol_lis,
fastperiod=3,
slowperiod=10)
NATR = ta.NATR(High_lis, Low_lis, Clz_lis, timeperiod=14)
HT_DCPERIOD = ta.HT_DCPERIOD(Clz_lis)
sine, leadsine = ta.HT_SINE(Clz_lis)
integer = ta.HT_TRENDMODE(Clz_lis)
########
#append
dataframe['SMA_3_C'] = SMA_3_C
dataframe['SMA_5_H'] = SMA_5_H
dataframe['SMA_5_L'] = SMA_5_L
dataframe['SMA_5_C'] = SMA_5_C
dataframe['SMA_10_H'] = SMA_10_H
dataframe['SMA_10_L'] = SMA_10_L
dataframe['SMA_10_C'] = SMA_10_C
dataframe['RSI_2_C'] = (RSI_2_C / 100.) * 2 - 1.
dataframe['RSI_3_C'] = (RSI_3_C / 100.) * 2 - 1.
dataframe['RSI_5_C'] = (RSI_5_C / 100.) * 2 - 1.
dataframe['RSI_7_H'] = (RSI_7_H / 100.) * 2 - 1.
dataframe['RSI_7_L'] = (RSI_7_L / 100.) * 2 - 1.
dataframe['RSI_7_C'] = (RSI_7_C / 100.) * 2 - 1.
dataframe['RSI_14_H'] = (RSI_14_H / 100.) * 2 - 1.
dataframe['RSI_14_L'] = (RSI_14_L / 100.) * 2 - 1.
dataframe['RSI_14_C'] = (RSI_14_C / 100.) * 2 - 1.
dataframe['ADX'] = (ADX / 100.) * 2 - 1.
dataframe['ADXR'] = (ADXR / 100.) * 2 - 1.
dataframe['Aroondown'] = (Aroondown / 100.) * 2 - 1
dataframe['Aroonup'] = (Aroonup / 100.) * 2 - 1
dataframe['Aroonosc'] = Aroonosc / 100.
dataframe['BOP'] = BOP
dataframe['CMO'] = CMO / 100.
dataframe['DX'] = (DX / 100.) * 2 - 1
dataframe['MFI'] = (MFI / 100.) * 2 - 1
dataframe['MINUS_DI'] = (MINUS_DI / 100.) * 2 - 1
dataframe['PLUSDI'] = (PLUSDI / 100.) * 2 - 1
dataframe['PPO'] = PPO / 10.
dataframe['ROC'] = ROC / 10.
dataframe['ROCP'] = ROCP * 10
dataframe['ROCR'] = (ROCR - 1.0) * 100.
dataframe['ROCR100'] = ((ROCR100 / 100.) - 1.0) * 100.
dataframe['Slowk'] = (Slowk / 100.) * 2 - 1
dataframe['Slowd'] = (Slowd / 100.) * 2 - 1
dataframe['STOCHF_f'] = (STOCHF_f / 100.) * 2 - 1
dataframe['STOCHF_d'] = (STOCHF_d / 100.) * 2 - 1
dataframe['Fastk'] = (Fastk / 100.) * 2 - 1
dataframe['Fastd'] = (Fastd / 100.) * 2 - 1
dataframe['TRIX'] = TRIX * 10.
dataframe['ULTOSC'] = (ULTOSC / 100.) * 2 - 1
dataframe['WILLR'] = (WILLR / 100.) * 2 + 1
dataframe['ADOSC'] = ADOSC / 100.
dataframe['NATR'] = NATR * 2 - 1
dataframe['HT_DCPERIOD'] = (HT_DCPERIOD / 100.) * 2 - 1
dataframe['sine'] = sine
dataframe['leadsine'] = leadsine
dataframe['integer'] = integer
return dataframe
def talib_024(self):
data_ROCR100 = copy.deepcopy(self.close)
for symbol in symbols:
data_ROCR100[symbol] = ta.ROCR100(self.close[symbol].values, timeperiod=10)
return data_ROCR100
def _extract_feature(candle, params, candle_type, target_dt):
'''
前に余分に必要なデータ量: {(stockf_fastk_period_l + stockf_fastk_period_l) * 最大分足 (min)} + window_size
= (12 + 12) * 5 + 5 = 125 (min)
'''
o = candle.open
h = candle.high
l = candle.low
c = candle.close
v = candle.volume
# OHLCV
features = pd.DataFrame()
features['open'] = o
features['high'] = h
features['low'] = l
features['close'] = c
features['volume'] = v
####################################
#
# Momentum Indicator Functions
#
####################################
# ADX = SUM((+DI - (-DI)) / (+DI + (-DI)), N) / N
# N — 計算期間
# SUM (..., N) — N期間の合計
# +DI — プラスの価格変動の値（positive directional index)
# -DI — マイナスの価格変動の値（negative directional index）
# rsi_timeperiod_l=30の場合、30分足で、(30 * 30 / 60(min)) = 15時間必要
features['adx_s'] = ta.ADX(h, l, c, timeperiod=params['adx_timeperiod_s'])
features['adx_m'] = ta.ADX(h, l, c, timeperiod=params['adx_timeperiod_m'])
features['adx_l'] = ta.ADX(h, l, c, timeperiod=params['adx_timeperiod_l'])
features['adxr_s'] = ta.ADXR(h, l, c, timeperiod=params['adxr_timeperiod_s'])
features['adxr_m'] = ta.ADXR(h, l, c, timeperiod=params['adxr_timeperiod_m'])
features['adxr_l'] = ta.ADXR(h, l, c, timeperiod=params['adxr_timeperiod_l'])
# APO = Shorter Period EMA – Longer Period EMA
features['apo_s'] = ta.APO(c, fastperiod=params['apo_fastperiod_s'], slowperiod=params['apo_slowperiod_s'], matype=ta.MA_Type.EMA)
features['apo_m'] = ta.APO(c, fastperiod=params['apo_fastperiod_m'], slowperiod=params['apo_slowperiod_m'], matype=ta.MA_Type.EMA)
# AroonUp = (N － 過去N日間の最高値からの経過期間) ÷ N × 100
# AroonDown = (N － 過去N日間の最安値からの経過期間) ÷ N × 100
# aroon_timeperiod_l=30の場合、30分足で、(30 * 30 / 60(min)) = 15時間必要
#features['aroondown_s'], features['aroonup_s'] = ta.AROON(h, l, timeperiod=params['aroon_timeperiod_s'])
#features['aroondown_m'], features['aroonup_m'] = ta.AROON(h, l, timeperiod=params['aroon_timeperiod_m'])
#features['aroondown_l'], features['aroonup_l'] = ta.AROON(h, l, timeperiod=params['aroon_timeperiod_l'])
# Aronnオシレーター = AroonUp － AroonDown
# aroonosc_timeperiod_l=30の場合、30分足で、(30 * 30 / 60(min)) = 15時間必要
features['aroonosc_s'] = ta.AROONOSC(h, l, timeperiod=params['aroonosc_timeperiod_s'])
features['aroonosc_m'] = ta.AROONOSC(h, l, timeperiod=params['aroonosc_timeperiod_m'])
features['aroonosc_l'] = ta.AROONOSC(h, l, timeperiod=params['aroonosc_timeperiod_l'])
# BOP = (close - open) / (high - low)
features['bop'] = ta.BOP(o, h, l, c)
# CCI = (TP - MA) / (0.015 * MD)
# TP: (高値+安値+終値) / 3
# MA: TPの移動平均
# MD: 平均偏差 = ((MA - TP1) + (MA - TP2) + ...) / N
features['cci_s'] = ta.CCI(h, l, c, timeperiod=params['cci_timeperiod_s'])
features['cci_m'] = ta.CCI(h, l, c, timeperiod=params['cci_timeperiod_m'])
features['cci_l'] = ta.CCI(h, l, c, timeperiod=params['cci_timeperiod_l'])
# CMO - Chande Momentum Oscillator
#features['cmo_s'] = ta.CMO(c, timeperiod=params['cmo_timeperiod_s'])
#features['cmo_m'] = ta.CMO(c, timeperiod=params['cmo_timeperiod_m'])
#features['cmo_l'] = ta.CMO(c, timeperiod=params['cmo_timeperiod_l'])
# DX - Directional Movement Index
features['dx_s'] = ta.DX(h, l, c, timeperiod=params['dx_timeperiod_s'])
features['dx_m'] = ta.DX(h, l, c, timeperiod=params['dx_timeperiod_m'])
features['dx_l'] = ta.DX(h, l, c, timeperiod=params['dx_timeperiod_l'])
# MACD＝基準線－相対線
# 基準線（EMA）：過去12日（週・月）間の終値指数平滑平均
# 相対線（EMA）：過去26日（週・月）間の終値指数平滑平均
# https://www.sevendata.co.jp/shihyou/technical/macd.html
# macd_slowperiod_m = 30 の場合30分足で（(30 + macd_signalperiod_m) * 30）/ 60 = 16.5時間必要（macd_signalperiod_m=3の時）
macd, macdsignal, macdhist = ta.MACDEXT(c, fastperiod=params['macd_fastperiod_s'],
slowperiod=params['macd_slowperiod_s'],
signalperiod=params['macd_signalperiod_s'],
fastmatype=ta.MA_Type.EMA, slowmatype=ta.MA_Type.EMA,
signalmatype=ta.MA_Type.EMA)
change_macd = calc_change(macd, macdsignal)
change_macd.index = macd.index
features['macd_s'] = macd
features['macdsignal_s'] = macdsignal
features['macdhist_s'] = macdhist
features['change_macd_s'] = change_macd
macd, macdsignal, macdhist = ta.MACDEXT(c, fastperiod=params['macd_fastperiod_m'],
slowperiod=params['macd_slowperiod_m'],
signalperiod=params['macd_signalperiod_m'],
fastmatype=ta.MA_Type.EMA, slowmatype=ta.MA_Type.EMA,
signalmatype=ta.MA_Type.EMA)
change_macd = calc_change(macd, macdsignal)
change_macd.index = macd.index
features['macd_m'] = macd
features['macdsignal_m'] = macdsignal
features['macdhist_m'] = macdhist
features['change_macd_m'] = change_macd
# MFI - Money Flow Index
features['mfi_s'] = ta.MFI(h, l, c, v, timeperiod=params['mfi_timeperiod_s'])
features['mfi_m'] = ta.MFI(h, l, c, v, timeperiod=params['mfi_timeperiod_m'])
features['mfi_l'] = ta.MFI(h, l, c, v, timeperiod=params['mfi_timeperiod_l'])
# MINUS_DI - Minus Directional Indicator
features['minus_di_s'] = ta.MINUS_DI(h, l, c, timeperiod=params['minus_di_timeperiod_s'])
features['minus_di_m'] = ta.MINUS_DI(h, l, c, timeperiod=params['minus_di_timeperiod_m'])
features['minus_di_l'] = ta.MINUS_DI(h, l, c, timeperiod=params['minus_di_timeperiod_l'])
# MINUS_DM - Minus Directional Movement
features['minus_dm_s'] = ta.MINUS_DM(h, l, timeperiod=params['minus_dm_timeperiod_s'])
features['minus_dm_m'] = ta.MINUS_DM(h, l, timeperiod=params['minus_dm_timeperiod_m'])
features['minus_dm_l'] = ta.MINUS_DM(h, l, timeperiod=params['minus_dm_timeperiod_l'])
# MOM - Momentum
features['mom_s'] = ta.MOM(c, timeperiod=params['mom_timeperiod_s'])
features['mom_m'] = ta.MOM(c, timeperiod=params['mom_timeperiod_m'])
features['mom_l'] = ta.MOM(c, timeperiod=params['mom_timeperiod_l'])
# PLUS_DI - Minus Directional Indicator
features['plus_di_s'] = ta.PLUS_DI(h, l, c, timeperiod=params['plus_di_timeperiod_s'])
features['plus_di_m'] = ta.PLUS_DI(h, l, c, timeperiod=params['plus_di_timeperiod_m'])
features['plus_di_l'] = ta.PLUS_DI(h, l, c, timeperiod=params['plus_di_timeperiod_l'])
# PLUS_DM - Minus Directional Movement
features['plus_dm_s'] = ta.PLUS_DM(h, l, timeperiod=params['plus_dm_timeperiod_s'])
features['plus_dm_m'] = ta.PLUS_DM(h, l, timeperiod=params['plus_dm_timeperiod_m'])
features['plus_dm_l'] = ta.PLUS_DM(h, l, timeperiod=params['plus_dm_timeperiod_l'])
# PPO - Percentage Price Oscillator
#features['ppo_s'] = ta.PPO(c, fastperiod=params['ppo_fastperiod_s'], slowperiod=params['ppo_slowperiod_s'], matype=ta.MA_Type.EMA)
#features['ppo_m'] = ta.PPO(c, fastperiod=params['ppo_fastperiod_m'], slowperiod=params['ppo_slowperiod_m'], matype=ta.MA_Type.EMA)
# ROC - Rate of change : ((price/prevPrice)-1)*100
features['roc_s'] = ta.ROC(c, timeperiod=params['roc_timeperiod_s'])
features['roc_m'] = ta.ROC(c, timeperiod=params['roc_timeperiod_m'])
features['roc_l'] = ta.ROC(c, timeperiod=params['roc_timeperiod_l'])
# ROCP = (price-prevPrice) / prevPrice
# http://www.tadoc.org/indicator/ROCP.htm
# rocp_timeperiod_l = 30 の場合、30分足で(30 * 30) / 60 = 15時間必要
rocp = ta.ROCP(c, timeperiod=params['rocp_timeperiod_s'])
change_rocp = calc_change(rocp, pd.Series(np.zeros(len(candle)), index=candle.index))
change_rocp.index = rocp.index
features['rocp_s'] = rocp
features['change_rocp_s'] = change_rocp
rocp = ta.ROCP(c, timeperiod=params['rocp_timeperiod_m'])
change_rocp = calc_change(rocp, pd.Series(np.zeros(len(candle)), index=candle.index))
change_rocp.index = rocp.index
features['rocp_m'] = rocp
features['change_rocp_m'] = change_rocp
rocp = ta.ROCP(c, timeperiod=params['rocp_timeperiod_l'])
change_rocp = calc_change(rocp, pd.Series(np.zeros(len(candle)), index=candle.index))
change_rocp.index = rocp.index
features['rocp_l'] = rocp
features['change_rocp_l'] = change_rocp
# ROCR - Rate of change ratio: (price/prevPrice)
features['rocr_s'] = ta.ROCR(c, timeperiod=params['rocr_timeperiod_s'])
features['rocr_m'] = ta.ROCR(c, timeperiod=params['rocr_timeperiod_m'])
features['rocr_l'] = ta.ROCR(c, timeperiod=params['rocr_timeperiod_l'])
# ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100
features['rocr100_s'] = ta.ROCR100(c, timeperiod=params['rocr100_timeperiod_s'])
features['rocr100_m'] = ta.ROCR100(c, timeperiod=params['rocr100_timeperiod_m'])
features['rocr100_l'] = ta.ROCR100(c, timeperiod=params['rocr100_timeperiod_l'])
# RSI = (100 * a) / (a + b) (a: x日間の値上がり幅の合計, b: x日間の値下がり幅の合計)
# https://www.sevendata.co.jp/shihyou/technical/rsi.html
# rsi_timeperiod_l=30の場合、30分足で、(30 * 30 / 60(min)) = 15時間必要
#features['rsi_s'] = ta.RSI(c, timeperiod=params['rsi_timeperiod_s'])
#features['rsi_m'] = ta.RSI(c, timeperiod=params['rsi_timeperiod_m'])
#features['rsi_l'] = ta.RSI(c, timeperiod=params['rsi_timeperiod_l'])
# FASTK(KPeriod) = 100 * (Today's Close - LowestLow) / (HighestHigh - LowestLow)
# FASTD(FastDperiod) = MA Smoothed FASTK over FastDperiod
# http://www.tadoc.org/indicator/STOCHF.htm
# stockf_fastk_period_l=30の場合30分足で、(((30 + 30) * 30) / 60(min)) = 30時間必要 (LowestLowが移動平均の30分余分に必要なので60period余分に計算する)
fastk, fastd = ta.STOCHF(h, l, c, fastk_period=params['stockf_fastk_period_s'], fastd_period=params['stockf_fastd_period_s'], fastd_matype=ta.MA_Type.EMA)
change_stockf = calc_change(fastk, fastd)
change_stockf.index = fastk.index
features['fastk_s'] = fastk
features['fastd_s'] = fastd
features['fast_change_s'] = change_stockf
fastk, fastd = ta.STOCHF(h, l, c, fastk_period=params['stockf_fastk_period_m'], fastd_period=params['stockf_fastd_period_m'], fastd_matype=ta.MA_Type.EMA)
change_stockf = calc_change(fastk, fastd)
change_stockf.index = fastk.index
features['fastk_m'] = fastk
features['fastd_m'] = fastd
features['fast_change_m'] = change_stockf
fastk, fastd = ta.STOCHF(h, l, c, fastk_period=params['stockf_fastk_period_l'], fastd_period=params['stockf_fastk_period_l'], fastd_matype=ta.MA_Type.EMA)
change_stockf = calc_change(fastk, fastd)
change_stockf.index = fastk.index
features['fastk_l'] = fastk
features['fastd_l'] = fastd
features['fast_change_l'] = change_stockf
# TRIX - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
features['trix_s'] = ta.TRIX(c, timeperiod=params['trix_timeperiod_s'])
features['trix_m'] = ta.TRIX(c, timeperiod=params['trix_timeperiod_m'])
features['trix_l'] = ta.TRIX(c, timeperiod=params['trix_timeperiod_l'])
# ULTOSC - Ultimate Oscillator
features['ultosc_s'] = ta.ULTOSC(h, l, c, timeperiod1=params['ultosc_timeperiod_s1'], timeperiod2=params['ultosc_timeperiod_s2'], timeperiod3=params['ultosc_timeperiod_s3'])
# WILLR = (当日終値 - N日間の最高値) / (N日間の最高値 - N日間の最安値)× 100
# https://inet-sec.co.jp/study/technical-manual/williamsr/
# willr_timeperiod_l=30の場合30分足で、(30 * 30 / 60) = 15時間必要
features['willr_s'] = ta.WILLR(h, l, c, timeperiod=params['willr_timeperiod_s'])
features['willr_m'] = ta.WILLR(h, l, c, timeperiod=params['willr_timeperiod_m'])
features['willr_l'] = ta.WILLR(h, l, c, timeperiod=params['willr_timeperiod_l'])
####################################
#
# Volume Indicator Functions
#
####################################
# Volume Indicator Functions
# slowperiod_adosc_s = 10の場合、30分足で(10 * 30) / 60 = 5時間必要
features['ad'] = ta.AD(h, l, c, v)
features['adosc_s'] = ta.ADOSC(h, l, c, v, fastperiod=params['fastperiod_adosc_s'], slowperiod=params['slowperiod_adosc_s'])
features['obv'] = ta.OBV(c, v)
####################################
#
# Volatility Indicator Functions
#
####################################
# ATR - Average True Range
features['atr_s'] = ta.ATR(h, l, c, timeperiod=params['atr_timeperiod_s'])
features['atr_m'] = ta.ATR(h, l, c, timeperiod=params['atr_timeperiod_m'])
features['atr_l'] = ta.ATR(h, l, c, timeperiod=params['atr_timeperiod_l'])
# NATR - Normalized Average True Range
#features['natr_s'] = ta.NATR(h, l, c, timeperiod=params['natr_timeperiod_s'])
#features['natr_m'] = ta.NATR(h, l, c, timeperiod=params['natr_timeperiod_m'])
#features['natr_l'] = ta.NATR(h, l, c, timeperiod=params['natr_timeperiod_l'])
# TRANGE - True Range
features['trange'] = ta.TRANGE(h, l, c)
####################################
#
# Price Transform Functions
#
####################################
features['avgprice'] = ta.AVGPRICE(o, h, l, c)
features['medprice'] = ta.MEDPRICE(h, l)
#features['typprice'] = ta.TYPPRICE(h, l, c)
#features['wclprice'] = ta.WCLPRICE(h, l, c)
####################################
#
# Cycle Indicator Functions
#
####################################
#features['ht_dcperiod'] = ta.HT_DCPERIOD(c)
#features['ht_dcphase'] = ta.HT_DCPHASE(c)
#features['inphase'], features['quadrature'] = ta.HT_PHASOR(c)
#features['sine'], features['leadsine'] = ta.HT_SINE(c)
features['integer'] = ta.HT_TRENDMODE(c)
####################################
#
# Statistic Functions
#
####################################
# BETA - Beta
features['beta_s'] = ta.BETA(h, l, timeperiod=params['beta_timeperiod_s'])
features['beta_m'] = ta.BETA(h, l, timeperiod=params['beta_timeperiod_m'])
features['beta_l'] = ta.BETA(h, l, timeperiod=params['beta_timeperiod_l'])
# CORREL - Pearson's Correlation Coefficient (r)
#features['correl_s'] = ta.CORREL(h, l, timeperiod=params['correl_timeperiod_s'])
#features['correl_m'] = ta.CORREL(h, l, timeperiod=params['correl_timeperiod_m'])
#features['correl_l'] = ta.CORREL(h, l, timeperiod=params['correl_timeperiod_l'])
# LINEARREG - Linear Regression
#features['linearreg_s'] = ta.LINEARREG(c, timeperiod=params['linearreg_timeperiod_s'])
#features['linearreg_m'] = ta.LINEARREG(c, timeperiod=params['linearreg_timeperiod_m'])
#features['linearreg_l'] = ta.LINEARREG(c, timeperiod=params['linearreg_timeperiod_l'])
# LINEARREG_ANGLE - Linear Regression Angle
features['linearreg_angle_s'] = ta.LINEARREG_ANGLE(c, timeperiod=params['linearreg_angle_timeperiod_s'])
features['linearreg_angle_m'] = ta.LINEARREG_ANGLE(c, timeperiod=params['linearreg_angle_timeperiod_m'])
features['linearreg_angle_l'] = ta.LINEARREG_ANGLE(c, timeperiod=params['linearreg_angle_timeperiod_l'])
# LINEARREG_INTERCEPT - Linear Regression Intercept
features['linearreg_intercept_s'] = ta.LINEARREG_INTERCEPT(c, timeperiod=params['linearreg_intercept_timeperiod_s'])
features['linearreg_intercept_m'] = ta.LINEARREG_INTERCEPT(c, timeperiod=params['linearreg_intercept_timeperiod_m'])
features['linearreg_intercept_l'] = ta.LINEARREG_INTERCEPT(c, timeperiod=params['linearreg_intercept_timeperiod_l'])
# LINEARREG_SLOPE - Linear Regression Slope
features['linearreg_slope_s'] = ta.LINEARREG_SLOPE(c, timeperiod=params['linearreg_slope_timeperiod_s'])
features['linearreg_slope_m'] = ta.LINEARREG_SLOPE(c, timeperiod=params['linearreg_slope_timeperiod_m'])
features['linearreg_slope_l'] = ta.LINEARREG_SLOPE(c, timeperiod=params['linearreg_slope_timeperiod_l'])
# STDDEV - Standard Deviation
features['stddev_s'] = ta.STDDEV(c, timeperiod=params['stddev_timeperiod_s'], nbdev=1)
features['stddev_m'] = ta.STDDEV(c, timeperiod=params['stddev_timeperiod_m'], nbdev=1)
features['stddev_l'] = ta.STDDEV(c, timeperiod=params['stddev_timeperiod_l'], nbdev=1)
# TSF - Time Series Forecast
features['tsf_s'] = ta.TSF(c, timeperiod=params['tsf_timeperiod_s'])
features['tsf_m'] = ta.TSF(c, timeperiod=params['tsf_timeperiod_m'])
features['tsf_l'] = ta.TSF(c, timeperiod=params['tsf_timeperiod_l'])
# VAR - Variance
#features['var_s'] = ta.VAR(c, timeperiod=params['var_timeperiod_s'], nbdev=1)
#features['var_m'] = ta.VAR(c, timeperiod=params['var_timeperiod_m'], nbdev=1)
#features['var_l'] = ta.VAR(c, timeperiod=params['var_timeperiod_l'], nbdev=1)
# ボリンジャーバンド
# bbands_timeperiod_l = 30の場合、30分足で(30 * 30) / 60 = 15時間必要
bb_upper, bb_middle, bb_lower = ta.BBANDS(c, timeperiod=params['bbands_timeperiod_s'],
nbdevup=params['bbands_nbdevup_s'], nbdevdn=params['bbands_nbdevdn_s'],
matype=ta.MA_Type.EMA)
bb_trend1 = pd.Series(np.zeros(len(candle)), index=candle.index)
bb_trend1[c > bb_upper] = 1
bb_trend1[c < bb_lower] = -1
bb_trend2 = pd.Series(np.zeros(len(candle)), index=candle.index)
bb_trend2[c > bb_middle] = 1
bb_trend2[c < bb_middle] = -1
features['bb_upper_s'] = bb_upper
features['bb_middle_s'] = bb_middle
features['bb_lower_s'] = bb_lower
features['bb_trend1_s'] = bb_trend1
features['bb_trend2_s'] = bb_trend2
bb_upper, bb_middle, bb_lower = ta.BBANDS(c, timeperiod=params['bbands_timeperiod_m'],
nbdevup=params['bbands_nbdevup_m'], nbdevdn=params['bbands_nbdevdn_m'],
matype=ta.MA_Type.EMA)
bb_trend1 = pd.Series(np.zeros(len(candle)), index=candle.index)
bb_trend1[c > bb_upper] = 1
bb_trend1[c < bb_lower] = -1
bb_trend2 = pd.Series(np.zeros(len(candle)), index=candle.index)
bb_trend2[c > bb_middle] = 1
bb_trend2[c < bb_middle] = -1
features['bb_upper_m'] = bb_upper
features['bb_middle_m'] = bb_middle
features['bb_lower_m'] = bb_lower
features['bb_trend1_m'] = bb_trend1
features['bb_trend2_m'] = bb_trend2
bb_upper, bb_middle, bb_lower = ta.BBANDS(c, timeperiod=params['bbands_timeperiod_l'],
nbdevup=params['bbands_nbdevup_l'], nbdevdn=params['bbands_nbdevdn_l'],
matype=ta.MA_Type.EMA)
bb_trend1 = pd.Series(np.zeros(len(candle)), index=candle.index)
bb_trend1[c > bb_upper] = 1
bb_trend1[c < bb_lower] = -1
bb_trend2 = pd.Series(np.zeros(len(candle)), index=candle.index)
bb_trend2[c > bb_middle] = 1
bb_trend2[c < bb_middle] = -1
features['bb_upper_l'] = bb_upper
features['bb_middle_l'] = bb_middle
features['bb_lower_l'] = bb_lower
features['bb_trend1_l'] = bb_trend1
features['bb_trend2_l'] = bb_trend2
# ローソク足
features['CDL2CROWS'] = ta.CDL2CROWS(o, h, l, c)
features['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(o, h, l, c)
features['CDL3INSIDE'] = ta.CDL3INSIDE(o, h, l, c)
features['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(o, h, l, c)
features['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(o, h, l, c)
features['CDL3STARSINSOUTH'] = ta.CDL3STARSINSOUTH(o, h, l, c)
features['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(o, h, l, c)
features['CDLABANDONEDBABY'] = ta.CDLABANDONEDBABY(o, h, l, c, penetration=0)
features['CDLADVANCEBLOCK'] = ta.CDLADVANCEBLOCK(o, h, l, c)
features['CDLBELTHOLD'] = ta.CDLBELTHOLD(o, h, l, c)
features['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(o, h, l, c)
features['CDLCLOSINGMARUBOZU'] = ta.CDLCLOSINGMARUBOZU(o, h, l, c)
features['CDLCONCEALBABYSWALL'] = ta.CDLCONCEALBABYSWALL(o, h, l, c)
features['CDLCOUNTERATTACK'] = ta.CDLCOUNTERATTACK(o, h, l, c)
features['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(o, h, l, c, penetration=0)
#features['CDLDOJI'] = ta.CDLDOJI(o, h, l, c)
features['CDLDOJISTAR'] = ta.CDLDOJISTAR(o, h, l, c)
features['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(o, h, l, c)
features['CDLENGULFING'] = ta.CDLENGULFING(o, h, l, c)
features['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(o, h, l, c, penetration=0)
features['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(o, h, l, c, penetration=0)
#features['CDLGAPSIDESIDEWHITE'] = ta.CDLGAPSIDESIDEWHITE(o, h, l, c)
features['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(o, h, l, c)
features['CDLHAMMER'] = ta.CDLHAMMER(o, h, l, c)
#features['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(o, h, l, c)
features['CDLHARAMI'] = ta.CDLHARAMI(o, h, l, c)
features['CDLHARAMICROSS'] = ta.CDLHARAMICROSS(o, h, l, c)
features['CDLHIGHWAVE'] = ta.CDLHIGHWAVE(o, h, l, c)
#features['CDLHIKKAKE'] = ta.CDLHIKKAKE(o, h, l, c)
features['CDLHIKKAKEMOD'] = ta.CDLHIKKAKEMOD(o, h, l, c)
features['CDLHOMINGPIGEON'] = ta.CDLHOMINGPIGEON(o, h, l, c)
#features['CDLIDENTICAL3CROWS'] = ta.CDLIDENTICAL3CROWS(o, h, l, c)
features['CDLINNECK'] = ta.CDLINNECK(o, h, l, c)
#features['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(o, h, l, c)
features['CDLKICKING'] = ta.CDLKICKING(o, h, l, c)
features['CDLKICKINGBYLENGTH'] = ta.CDLKICKINGBYLENGTH(o, h, l, c)
features['CDLLADDERBOTTOM'] = ta.CDLLADDERBOTTOM(o, h, l, c)
#features['CDLLONGLEGGEDDOJI'] = ta.CDLLONGLEGGEDDOJI(o, h, l, c)
features['CDLMARUBOZU'] = ta.CDLMARUBOZU(o, h, l, c)
#features['CDLMATCHINGLOW'] = ta.CDLMATCHINGLOW(o, h, l, c)
features['CDLMATHOLD'] = ta.CDLMATHOLD(o, h, l, c, penetration=0)
features['CDLMORNINGDOJISTAR'] = ta.CDLMORNINGDOJISTAR(o, h, l, c, penetration=0)
features['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(o, h, l, c, penetration=0)
features['CDLONNECK'] = ta.CDLONNECK(o, h, l, c)
features['CDLPIERCING'] = ta.CDLPIERCING(o, h, l, c)
features['CDLRICKSHAWMAN'] = ta.CDLRICKSHAWMAN(o, h, l, c)
features['CDLRISEFALL3METHODS'] = ta.CDLRISEFALL3METHODS(o, h, l, c)
features['CDLSEPARATINGLINES'] = ta.CDLSEPARATINGLINES(o, h, l, c)
#features['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(o, h, l, c)
features['CDLSHORTLINE'] = ta.CDLSHORTLINE(o, h, l, c)
#features['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(o, h, l, c)
features['CDLSTALLEDPATTERN'] = ta.CDLSTALLEDPATTERN(o, h, l, c)
features['CDLSTICKSANDWICH'] = ta.CDLSTICKSANDWICH(o, h, l, c)
features['CDLTAKURI'] = ta.CDLTAKURI(o, h, l, c)
features['CDLTASUKIGAP'] = ta.CDLTASUKIGAP(o, h, l, c)
features['CDLTHRUSTING'] = ta.CDLTHRUSTING(o, h, l, c)
features['CDLTRISTAR'] = ta.CDLTRISTAR(o, h, l, c)
features['CDLUNIQUE3RIVER'] = ta.CDLUNIQUE3RIVER(o, h, l, c)
features['CDLUPSIDEGAP2CROWS'] = ta.CDLUPSIDEGAP2CROWS(o, h, l, c)
features['CDLXSIDEGAP3METHODS'] = ta.CDLXSIDEGAP3METHODS(o, h, l, c)
'''
# トレンドライン
for dt in datetimerange(candle.index[0], candle.index[-1] + timedelta(minutes=1)):
start_dt = (dt - timedelta(minutes=130)).strftime('%Y-%m-%d %H:%M:%S')
end_dt = dt.strftime('%Y-%m-%d %H:%M:%S')
tmp = candle.loc[(start_dt <= candle.index) & (candle.index <= end_dt)]
for w_size, stride in [(15, 5), (30, 10), (60, 10), (120, 10)]:
# for w_size, stride in [(120, 10)]:
trendlines = calc_trendlines(tmp, w_size, stride)
if len(trendlines) == 0:
continue
trendline_feature = calc_trendline_feature(tmp, trendlines)
print('{}-{} {} {} {}'.format(dt - timedelta(minutes=130), dt, trendline_feature['high_a'], trendline_feature['high_b'], trendline_feature['high_diff']))
features.loc[features.index == end_dt, 'trendline_high_a_{}'.format(w_size)] = trendline_feature['high_a']
features.loc[features.index == end_dt, 'trendline_high_b_{}'.format(w_size)] = trendline_feature['high_b']
features.loc[features.index == end_dt, 'trendline_high_diff_{}'.format(w_size)] = trendline_feature['high_diff']
features.loc[features.index == end_dt, 'trendline_low_a_{}'.format(w_size)] = trendline_feature['low_a']
features.loc[features.index == end_dt, 'trendline_low_b_{}'.format(w_size)] = trendline_feature['low_b']
features.loc[features.index == end_dt, 'trendline_low_diff_{}'.format(w_size)] = trendline_feature['low_diff']
'''
window = 5
features_ext = features
for w in range(window):
tmp = features.shift(periods=60 * (w + 1), freq='S')
tmp.columns = [c + '_' + str(w + 1) + 'w' for c in features.columns]
features_ext = pd.concat([features_ext, tmp], axis=1)
if candle_type == '5min':
features_ext = features_ext.shift(periods=300, freq='S')
features_ext = features_ext.fillna(method='ffill')
features_ext = features_ext[features_ext.index == target_dt]
return features_ext
