def show(args, klines, kline_column_names, display_count, disp_ic_keys):
for index, value in enumerate(kline_column_names):
if value == "high":
highindex = index
if value == "low":
lowindex = index
if value == "open":
openindex = index
if value == "close":
closeindex = index
if value == "volume":
volumeindex = index
if value == "open_time":
opentimeindex = index
klines_df = pd.DataFrame(klines, columns=kline_column_names)
open_times = [
datetime.fromtimestamp((float(open_time) / 1000))
for open_time in klines_df["open_time"][-display_count:]
]
close_times = [
datetime.fromtimestamp((float(close_time) / 1000))
for close_time in klines_df["close_time"][-display_count:]
]
fig, axes = plt.subplots(len(disp_ic_keys) + 1, 1, sharex=True)
fig.subplots_adjust(left=0.05,
bottom=0.04,
right=1,
top=1,
wspace=0,
hspace=0)
fig.suptitle(args.s + ' ' + args.i)
quotes = []
for k in klines[-display_count:]:
d = datetime.fromtimestamp(k[0] / 1000)
quote = (dts.date2num(d), float(k[1]), float(k[4]), float(k[2]),
float(k[3]))
quotes.append(quote)
i = -1
# kine
i += 1
ax = axes[i]
mpf.candlestick_ochl(axes[i],
quotes,
width=0.02,
colorup='g',
colordown='r')
ax.set_ylabel('price')
ax.grid(True)
ax.autoscale_view()
ax.xaxis_date()
handle_overlap_studies(args, ax, klines_df, close_times, display_count)
"""
if args.EBANDS: # BANDS
name = 'EBANDS'
upperband, middleband, lowerband = tal.EBANDS(klines_df, timeperiod=26)
ax.plot(close_times, middleband[-display_count:], "b--", label=name)
ax.plot(close_times, upperband[-display_count:], 'y--', label=name+' upperband')
ax.plot(close_times, lowerband[-display_count:], 'y--', label=name+' lowerband')
"""
ic_key = 'macd'
if ic_key in disp_ic_keys:
i += 1
ax = axes[i]
ax.set_ylabel('macd')
ax.grid(True)
klines_df = ic.pd_macd(klines_df)
difs = [round(a, 2) for a in klines_df["dif"]]
deas = [round(a, 2) for a in klines_df["dea"]]
macds = [round(a, 2) for a in klines_df["macd"]]
ax.plot(close_times, difs[-display_count:], "y", label="dif")
ax.plot(close_times, deas[-display_count:], "b", label="dea")
ax.plot(close_times,
macds[-display_count:],
"r",
drawstyle="steps",
label="macd")
ic_key = 'RSI'
if ic_key in disp_ic_keys:
i += 1
ax = axes[i]
ax.set_ylabel(ic_key)
ax.grid(True)
rsis = talib.RSI(klines_df["close"], timeperiod=14)
rsis = [round(a, 2) for a in rsis][-display_count:]
ax.plot(close_times, rsis, "r", label=ic_key)
ax.plot(close_times, [70] * len(rsis), '-', color='r')
ax.plot(close_times, [30] * len(rsis), '-', color='r')
"""
rs2 = ic.py_rsis(klines, closeindex, period=14)
rs2 = [round(a, 2) for a in rs2][-display_count:]
axes[i].plot(close_times, rs2, "y", label="rsi2")
"""
"""
fastk, fastd = talib.STOCHRSI(klines_df["close"], timeperiod=14)
rsifks = [round(a, 2) for a in fastk][-display_count:]
rsifds = [round(a, 2) for a in fastd][-display_count:]
axes[i].plot(close_times, rsifks, "b", label="rsi")
axes[i].plot(close_times, rsifds, "y", label="rsi")
"""
ic_key = 'KDJ'
if ic_key in disp_ic_keys:
i += 1
ax = axes[i]
ax.set_ylabel(ic_key)
ax.grid(True)
"""
ks, ds = talib.STOCH(klines_df["high"], klines_df["low"], klines_df["close"],
fastk_period=9, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
js = ks - ds
"""
ks, ds, js = ic.pd_kdj(klines_df)
ks = [round(a, 2) for a in ks][-display_count:]
ds = [round(a, 2) for a in ds][-display_count:]
js = [round(a, 2) for a in js][-display_count:]
ax.plot(close_times, ks, "b", label="K")
ax.plot(close_times, ds, "y", label="D")
ax.plot(close_times, js, "m", label="J")
# Volume Indicator
ic_key = 'AD'
if ic_key in disp_ic_keys:
real = talib.AD(klines_df["high"], klines_df["low"],
klines_df["close"], klines_df["volume"])
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'ADOSC'
if ic_key in disp_ic_keys:
real = talib.ADOSC(klines_df["high"],
klines_df["low"],
klines_df["close"],
klines_df["volume"],
fastperiod=3,
slowperiod=10)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'OBV'
if ic_key in disp_ic_keys:
real = talib.OBV(klines_df["close"], klines_df["volume"])
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
# Volatility Indicator
ic_key = 'ATR'
if ic_key in disp_ic_keys:
real = talib.ATR(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'NATR'
if ic_key in disp_ic_keys:
real = talib.NATR(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'TRANGE'
if ic_key in disp_ic_keys:
real = talib.TRANGE(klines_df["high"], klines_df["low"],
klines_df["close"])
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
# Price Transform
ic_key = 'AVGPRICE'
if ic_key in disp_ic_keys:
real = talib.AVGPRICE(klines_df["open"], klines_df["high"],
klines_df["low"], klines_df["close"])
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'MEDPRICE'
if ic_key in disp_ic_keys:
real = talib.MEDPRICE(klines_df["high"], klines_df["low"])
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'TYPPRICE'
if ic_key in disp_ic_keys:
real = talib.TYPPRICE(klines_df["high"], klines_df["low"],
klines_df["close"])
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'WCLPRICE'
if ic_key in disp_ic_keys:
real = talib.WCLPRICE(klines_df["high"], klines_df["low"],
klines_df["close"])
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
# Cycle Indicator
ic_key = 'HT_DCPERIOD'
if ic_key in disp_ic_keys:
real = talib.HT_DCPERIOD(klines_df["close"])
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'HT_DCPHASE'
if ic_key in disp_ic_keys:
real = talib.HT_DCPHASE(klines_df["close"])
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'HT_PHASOR'
if ic_key in disp_ic_keys:
real = talib.HT_PHASOR(klines_df["close"])
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'HT_SINE'
if ic_key in disp_ic_keys:
real = talib.HT_SINE(klines_df["close"])
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'HT_TRENDMODE'
if ic_key in disp_ic_keys:
real = talib.HT_TRENDMODE(klines_df["close"])
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
# Statistic
ic_key = 'BETA'
if ic_key in disp_ic_keys:
real = talib.BETA(klines_df["high"], klines_df["low"], timeperiod=5)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'CORREL'
if ic_key in disp_ic_keys:
real = talib.CORREL(klines_df["high"], klines_df["low"], timeperiod=30)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'LINEARREG'
if ic_key in disp_ic_keys:
real = talib.LINEARREG(klines_df["close"], timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'LINEARREG_ANGLE'
if ic_key in disp_ic_keys:
real = talib.LINEARREG_ANGLE(klines_df["close"], timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'LINEARREG_INTERCEPT'
if ic_key in disp_ic_keys:
real = talib.LINEARREG_INTERCEPT(klines_df["close"], timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'LINEARREG_SLOPE'
if ic_key in disp_ic_keys:
real = talib.LINEARREG_SLOPE(klines_df["close"], timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'STDDEV' # Standard Deviation
if ic_key in disp_ic_keys:
real = talib.STDDEV(klines_df["close"], timeperiod=5, nbdev=1)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'TSF' # Time Series Forecast
if ic_key in disp_ic_keys:
real = talib.TSF(klines_df["close"], timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'VAR' # Variance
if ic_key in disp_ic_keys:
real = talib.VAR(klines_df["close"], timeperiod=5, nbdev=1)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
# Momentum Indicator
ic_key = 'DX'
if ic_key in disp_ic_keys:
dxs = talib.DX(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=14)
adxs = talib.ADX(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=14)
adxrs = talib.ADXR(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, dxs[-display_count:], "r:")
ts.ax(axes[i], ic_key, close_times, adxs[-display_count:], "y:")
ts.ax(axes[i], ic_key, close_times, adxrs[-display_count:], "k:")
ic_key = 'APO'
if ic_key in disp_ic_keys:
real = talib.APO(klines_df["close"],
fastperiod=12,
slowperiod=26,
matype=0)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'AROON'
if ic_key in disp_ic_keys:
aroondown, aroonup = talib.AROON(klines_df["high"],
klines_df["low"],
timeperiod=14)
i += 1
ts.ax(axes[i], ic_key + ' DOWN', close_times,
aroondown[-display_count:], "y:")
ts.ax(axes[i], ic_key + ' UP', close_times, aroonup[-display_count:],
"y:")
ic_key = 'AROONOSC'
if ic_key in disp_ic_keys:
real = talib.AROONOSC(klines_df["high"],
klines_df["low"],
timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'BOP'
if ic_key in disp_ic_keys:
real = talib.BOP(klines_df["open"], klines_df["high"],
klines_df["low"], klines_df["close"])
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'CCI'
if ic_key in disp_ic_keys:
real = talib.CCI(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'CMO'
if ic_key in disp_ic_keys:
real = talib.CMO(klines_df["close"], timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'MACD'
if ic_key in disp_ic_keys:
macd, macdsignal, macdhist = talib.MACD(klines_df["close"],
fastperiod=12,
slowperiod=26,
signalperiod=9)
i += 1
ts.ax(axes[i], ic_key, close_times, macd[-display_count:], "y")
ts.ax(axes[i], ic_key, close_times, macdsignal[-display_count:], "b")
ts.ax(axes[i],
ic_key,
close_times,
macdhist[-display_count:],
"r",
drawstyle="steps")
ic_key = 'MACDEXT'
if ic_key in disp_ic_keys:
macd, macdsignal, macdhist = talib.MACDEXT(klines_df["close"],
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
i += 1
ts.ax(axes[i], ic_key, close_times, macd[-display_count:], "y")
ts.ax(axes[i], ic_key, close_times, macdsignal[-display_count:], "b")
ts.ax(axes[i],
ic_key,
close_times,
macdhist[-display_count:],
"r",
drawstyle="steps")
ic_key = 'MACDFIX'
if ic_key in disp_ic_keys:
macd, macdsignal, macdhist = talib.MACDFIX(klines_df["close"],
signalperiod=9)
i += 1
ts.ax(axes[i], ic_key, close_times, macd[-display_count:], "y")
ts.ax(axes[i], ic_key, close_times, macdsignal[-display_count:], "b")
ts.ax(axes[i],
ic_key,
close_times,
macdhist[-display_count:],
"r",
drawstyle="steps")
ic_key = 'MFI'
if ic_key in disp_ic_keys:
real = talib.MFI(klines_df["high"], klines_df["low"],
klines_df["close"], klines_df["volume"])
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'MINUS_DI'
if ic_key in disp_ic_keys:
real = talib.MINUS_DI(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'MINUS_DM'
if ic_key in disp_ic_keys:
real = talib.MINUS_DM(klines_df["high"],
klines_df["low"],
timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'MOM'
if ic_key in disp_ic_keys:
real = talib.MOM(klines_df["close"], timeperiod=10)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'PLUS_DI'
if ic_key in disp_ic_keys:
real = talib.PLUS_DI(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'PLUS_DM'
if ic_key in disp_ic_keys:
real = talib.PLUS_DM(klines_df["high"],
klines_df["low"],
timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'PPO'
if ic_key in disp_ic_keys:
real = talib.PPO(klines_df["close"],
fastperiod=12,
slowperiod=26,
matype=0)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'ROC'
if ic_key in disp_ic_keys:
real1 = talib.ROC(klines_df["close"], timeperiod=10)
real2 = talib.ROCP(klines_df["close"], timeperiod=10)
real3 = talib.ROCR(klines_df["close"], timeperiod=10)
real4 = talib.ROCR100(klines_df["close"], timeperiod=10)
i += 1
ts.ax(axes[i], ic_key, close_times, real1[-display_count:], "y:")
ts.ax(axes[i], ic_key, close_times, real2[-display_count:], "k:")
ts.ax(axes[i], ic_key, close_times, real3[-display_count:], "m:")
ts.ax(axes[i], ic_key, close_times, real4[-display_count:], "b:")
ic_key = 'STOCH'
if ic_key in disp_ic_keys:
slowk, slowd = talib.STOCH(klines_df["high"],
klines_df["low"],
klines_df["close"],
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
i += 1
slowj = 3 * slowk - 2 * slowd
ts.ax(axes[i], ic_key, close_times, slowk[-display_count:], "b")
ts.ax(axes[i], ic_key, close_times, slowd[-display_count:], "y")
ts.ax(axes[i], ic_key, close_times, slowj[-display_count:], "m")
ic_key = 'STOCHF'
if ic_key in disp_ic_keys:
fastk, fastd = talib.STOCHF(klines_df["high"],
klines_df["low"],
klines_df["close"],
fastk_period=5,
fastd_period=3,
fastd_matype=0)
i += 1
fastj = 3 * fastk - 2 * fastd
ts.ax(axes[i], ic_key, close_times, fastk[-display_count:], "y:")
ts.ax(axes[i], ic_key, close_times, fastd[-display_count:], "b:")
ts.ax(axes[i], ic_key, close_times, fastj[-display_count:], "m")
ic_key = 'STOCHRSI'
if ic_key in disp_ic_keys:
fastk, fastd = talib.STOCHRSI(klines_df["close"],
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
i += 1
ts.ax(axes[i], ic_key, close_times, fastk[-display_count:], "y:")
ts.ax(axes[i], ic_key, close_times, fastd[-display_count:], "b:")
ic_key = 'TRIX'
if ic_key in disp_ic_keys:
real = talib.TRIX(klines_df["close"], timeperiod=30)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'ULTOSC'
if ic_key in disp_ic_keys:
real = talib.ULTOSC(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
ic_key = 'WILLR'
if ic_key in disp_ic_keys:
real = talib.WILLR(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=14)
i += 1
ts.ax(axes[i], ic_key, close_times, real[-display_count:], "y:")
plt.show()
def ROCR(data, **kwargs):
_check_talib_presence()
prices = _extract_series(data)
return talib.ROCR(prices, **kwargs)
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.STOCHRSI(close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_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 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 talib_023(self):
data_ROCR = copy.deepcopy(self.close)
for symbol in symbols:
data_ROCR[symbol] = ta.ROCR(self.close[symbol].values, timeperiod=10)
return data_ROCR
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
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
def main(df):
opens = df['open'].values
highs = df['high'].values
lows = df['low'].values
closes = df['close'].values
volumes = df['volume'].values
#Cycle Indicators': {{{
df['ta_HT_DCPERIOD'] = talib.HT_DCPERIOD(closes)
df['ta_HT_DCPHASE'] = talib.HT_DCPHASE(closes)
#df['ta_HT_PHASOR'] = talib.HT_PHASOR(closes)
#df['ta_HT_SINE'] = talib.HT_SINE(closes)
#df['ta_HT_TRENDMODE'] = talib.HT_TRENDLINE(closes)
#}}}
#'Momentum Indicators': {{{
for i in [7, 10, 14, 28]:
df['ta_ADX_%d' % i] = talib.ADX(highs, lows, closes, i)
df['ta_ADXR_%d' % i] = talib.ADXR(highs, lows, closes, i)
#for couple in [(6, 13), (12, 26), (24, 52)]:
# df['ta_APO_%d_%d' % (couple[0], couple[1])] = talib.APO(closes, couple[0], couple[1])
#for i in range(7, 52):
# df['ta_AROON_%d' % i] = talib.AROON(highs, lows, i)
# df['ta_AROONOSC_%d' % i] = talib.AROONOSC(highs, lows, i)
df['ta_BOP'] = talib.BOP(opens, highs, lows, closes)
df['ta_CCI'] = talib.CCI(highs, lows, closes)
for i in [7, 10, 14, 28]:
df['ta_CMO_%d' % i] = talib.CMO(closes, i)
#df['ta_DX_%d' %i] = talib.DX(closes, i)
#for tri in [(6,13,8),(12, 26, 9), (24, 52, 18)]:
# macd = talib.MACD(closes, tri[0], tri[1], tri[2])
# #df['ta_MACD_macd_%d_%d_%d'%(tri[0],tri[1],tri[2])] = macd[0]
# #df['ta_MACD_macdsignal_%d_%d_%d'%(tri[0],tri[1],tri[2])] = macd[1]
# #df['ta_MACD_macdhist_%d_%d_%d'%(tri[0],tri[1],tri[2])] = macd[2]
#
#for i in range(6,18):
# macd = talib.MACDFIX(closes,i)
# df['ta_MACDFIX_macd_%d'% i]= macd[0]
# df['ta_MACDFIX_macdsignal_%d'% i]= macd[1]
# df['ta_MACDFIX_macdhist_%d'% i]= macd[2]
for i in [7, 10, 14, 28]:
df['ta_MFI_%d' % i] = talib.MFI(highs, lows, closes, volumes)
for i in [7, 10, 14, 28]:
df['ta_MINUS_DI_%d' % i] = talib.MINUS_DI(highs, lows, closes, i)
df['ta_MINUS_DM_%d' % i] = talib.MINUS_DM(highs, lows, i)
#df['ta_MOM_%d' % i] = talib.MOM(closes, i)
df['ta_PLUS_DI_%d' % i] = talib.PLUS_DI(highs, lows, closes, i)
df['ta_PLUS_DM_%d' % i] = talib.PLUS_DM(highs, lows, i)
for couple in [(6, 13), (12, 26)]:
df['ta_PPO_%d_%d' % (couple[0], couple[1])] = talib.PPO(
closes, couple[0], couple[1])
for i in [2, 5, 7, 10, 14, 28]:
df['ta_ROC_%d' % i] = talib.ROC(closes, i)
df['ta_ROCP_%d' % i] = talib.ROCP(closes, i)
df['ta_ROCR_%d' % i] = talib.ROCR(closes, i)
df['ta_ROCR100_%d' % i] = talib.ROCR100(closes, i)
df['ta_RSI_%d' % i] = talib.RSI(closes, i)
for tri in [(5, 3, 3), (10, 6, 6), (20, 12, 12)]:
stoch = talib.STOCH(highs,
lows,
closes,
fastk_period=tri[0],
slowk_period=tri[1],
slowd_period=tri[2])
df['ta_STOCH_slowk_%d_%d_%d' % (tri[0], tri[1], tri[2])] = stoch[0]
df['ta_STOCH_slowd_%d_%d_%d' % (tri[0], tri[1], tri[2])] = stoch[1]
for i in [2, 5, 7, 10, 14, 28]:
for c in [(5, 3), (10, 6), (20, 12)]:
stochrsi = talib.STOCHRSI(closes, i, c[0], c[1])
df['ta_STOCHRSI_slowk_%d_%d_%d' % (i, c[0], c[1])] = stochrsi[0]
df['ta_STOCHRSI_slowd_%d_%d_%d' % (i, c[0], c[1])] = stochrsi[1]
for i in [2, 5, 7, 10, 14, 28]:
df['ta_TRIX_%d' % i] = talib.TRIX(closes, i)
for tr in [(7, 14, 28)]:
df['ta_ULTOSC_%d_%d_%d' % (tr[0], tr[1], tr[2])] = talib.ULTOSC(
highs, lows, closes, tr[0], tr[1], tr[2])
for i in [2, 5, 7, 10, 14, 28]:
df['ta_WILLR_%d' % i] = talib.WILLR(highs, lows, closes, i)
#}}}
# 'Overlap Studies': {{{
#for i in range(7,52):
#df['ta_DEMA_%d'%i] = talib.DEMA(closes, i)
#df['ta_EMA_%d'%i] = talib.EMA(closes,i)
#df['ta_HT_TRENDLINE'] = talib.HT_TRENDLINE(closes)
#for i in range(7,52):
# df['ta_KAMA_%d'%i] = talib.KAMA(closes, i)
#for c in [(2,30),(4,30),(4,50)]:
# df['ta_MAVP_%d_%d'%(c[0],c[1])] = talib.MAVP(closes, 14, c[0],c[1])
#for i in range(7,52):
# df['ta_MIDPOINT_%d'%i] = talib.MIDPOINT(closes, i)
# df['ta_MIDPRICE_%d'%i] = talib.MIDPRICE(highs, lows,i)
#df['ta_SAR'] = talib.SAR(highs,lows, 0.02, 0.2)
#for i in range(7,52):
# df['ta_SMA_%d'%i] = talib.SMA(closes, i)
#for i in range(2,21):
# df['ta_T3_%d'%i] = talib.T3(closes, i, 0.7)
#for i in range(7,52):
# df['ta_TEMA_%d'%i] = talib.TEMA(closes, i)
# df['ta_TRIMA_%d'%i] = talib.TRIMA(closes, i)
# df['ta_WMA_%d'%i] = talib.WMA(closes, i)
# 'Pattern Recognition': [
#df = cdl(df)
# 'Volatility Indicators': [
for i in [7, 10, 14, 28]:
#df['ta_ATR_%d'%i] = talib.ATR(highs, lows, closes, i)
df['ta_NATR_%d' % i] = talib.NATR(highs, lows, closes, i)
#df['ta_TRANGE'] = talib.TRANGE(highs, lows, closes)
# 'Volume Indicators': [
#df['ta_AD'] = talib.AD(highs, lows, closes, volumes)
#for c in [(3,10), (6,20),(12,40)]:
# df['ta_ADOSC_%d_%d'%(c[0],c[1])] = talib.ADOSC(highs, lows, closes, volumes,c[0],c[1])
#df['ta_OBV'] = talib.OBV(closes, volumes)
return df
def techIndi(self, gubun):
try:
file = open("C:/Users/jini/PycharmProjects/stock/sql/sqlOra116Real.sql", 'r')
sqlOra116 = ''
line = file.readline()
while line:
sqlOra116 += ' ' + line.strip('\n').strip('\t')
line = file.readline()
file.close()
summaryG = self.selectDB(sqlOra116)
except Exception as e:
e.msg = "techIndi Sql 에러"
print(e)
print("-----------------------------------------------------------------")
print("["+ datetime.today().strftime("%Y-%m-%d %H:%M:%S") + "] " + "보조지표 만들기 시작!" )
try:
if len(summaryG) > 0:
for row_data in summaryG:
print("[" + datetime.today().strftime("%Y-%m-%d %H:%M:%S") + "] " + "종목코드 : " + row_data[0])
try:
sql = "SELECT RN, STOCK_CODE, TRADE_TIME, START_PRICE, HIGH_PRICE, LOW_PRICE, CURRENT_PRICE, VOLUME " \
"FROM ( " \
" SELECT A.*, ROW_NUMBER() OVER(PARTITION BY A.STOCK_CODE ORDER BY TRADE_TIME DESC) RN" \
" , ROW_NUMBER() OVER(PARTITION BY A.STOCK_CODE ORDER BY TRADE_TIME ASC) RN2 " \
" , ROW_NUMBER() over (PARTITION BY A.STOCK_CODE, A.TRADE_TIME ORDER BY VOLUME DESC) RN3" \
" FROM TRADE_CRR_BACK A " \
" , (SELECT DISTINCT YYYYMMDD, STOCK_CODE FROM TRADE_LIST WHERE YYYYMMDD BETWEEN '20201001' AND '20201231' UNION SELECT DISTINCT YYYYMMDD, STOCK_CODE FROM TRADE_LIST_TMP) B " \
" WHERE 1=1 " \
" AND A.STOCK_CODE = '%s' " \
" AND A.STOCK_CODE = B.STOCK_CODE " \
" AND A.STOCK_CODE NOT IN ('155960','900090') " \
" AND A.TRADE_TIME >= '20200915' || '090000' " \
" AND A.TRADE_TIME <= '20201231' || '160000' " \
" ) X " \
"WHERE RN3 = 1 " % row_data[0]
sql2 = "ORDER BY RN2"
summaryGc = self.selectDB(sql + sql2)
except Exception as e:
print(e)
df = pd.DataFrame(summaryGc)
try:
if len(df) > 0:
df.columns = ["rn", "stockCd", "tradeTime", "open", "high", "low", "close", "volume"]
# cloF = pd.Series(df.iloc[len(df) - 1]['open'], name='close') # 3분봉 종가 만들어주는 로직
# clo = pd.Series(df.iloc[1:len(df)]['open'], name='close')
# clo = pd.DataFrame(clo.append(cloF))
# clo = clo.reset_index(drop=True)
#
# df = df.join(clo)
macd, macdsignal, macdhist = talib.MACD(df['close'], fastperiod=12, slowperiod=26,
signalperiod=9)
slowk, slowd = talib.STOCH(df['high'], df['low'], df['close'], fastk_period=5, slowk_period=3,
slowk_matype=3,
slowd_period=3, slowd_matype=0)
fastk, fastd = talib.STOCHF(df['high'], df['low'], df['close'], fastk_period=20,
fastd_period=10)
sma5 = talib.SMA(df['close'], 5)
sma10 = talib.SMA(df['close'], 10)
sma20 = talib.SMA(df['close'], 20)
sma60 = talib.SMA(df['close'], 60)
sma120 = talib.SMA(df['close'], 120)
cci = talib.CCI(df['high'], df['low'], df['close'], 20)
rsi = talib.RSI(df['close'], 14)
roc1 = talib.ROC(df['close'], 1)
roc5 = talib.ROC(df['close'], 5)
rocr1 = talib.ROCR(df['close'], 1)
rocr5 = talib.ROCR(df['close'], 5)
df = df.join(pd.DataFrame(pd.Series(macd, name="MACD")))
df = df.join(pd.DataFrame(pd.Series(macdsignal, name="MACDSIG")))
df = df.join(pd.DataFrame(pd.Series(slowd, name="SLOWD")))
df = df.join(pd.DataFrame(pd.Series(fastd, name="FASTD")))
df = df.join(pd.DataFrame(pd.Series(sma5, name="SMA5")))
df = df.join(pd.DataFrame(pd.Series(sma10, name="SMA10")))
df = df.join(pd.DataFrame(pd.Series(sma20, name="SMA20")))
df = df.join(pd.DataFrame(pd.Series(sma60, name="SMA60")))
df = df.join(pd.DataFrame(pd.Series(sma120, name="SMA120")))
df = df.join(pd.DataFrame(pd.Series(cci, name="CCI")))
df = df.join(pd.DataFrame(pd.Series(rsi, name="RSI")))
df = df.join(pd.DataFrame(pd.Series(roc1, name="ROC1")))
df = df.join(pd.DataFrame(pd.Series(roc5, name="ROC5")))
df = df.join(pd.DataFrame(pd.Series(rocr1, name="ROCR1")))
df = df.join(pd.DataFrame(pd.Series(rocr5, name="ROCR5")))
df = df.fillna(0)
try:
for i in range(4, len(df)):
if df.iloc[i]['SMA5'] == 0:
dis5 = 0
else:
dis5 = float(df.iloc[i]['close']) / float(df.iloc[i]['SMA5']) * 100
if df.iloc[i]['SMA10'] == 0:
dis10 = 0
else:
dis10 = float(df.iloc[i]['close']) / float(df.iloc[i]['SMA10']) * 100
if df.iloc[i]['SMA20'] == 0:
dis20 = 0
else:
dis20 = float(df.iloc[i]['close']) / float(df.iloc[i]['SMA20']) * 100
if df.iloc[i]['SMA60'] == 0:
dis60 = 0
else:
dis60 = float(df.iloc[i]['close']) / float(df.iloc[i]['SMA60']) * 100
file = open("C:/Users/jini/PycharmProjects/stock/sql/sqlOra72.sql", 'r') # TRADE_TECH에 데이터 넣기
sqlOra72 = ''
line = file.readline()
while line:
sqlOra72 += ' ' + line.strip('\n').strip('\t')
line = file.readline()
file.close()
sqlOra72 = sqlOra72 % (
df.iloc[i]['tradeTime'], df.iloc[i]['stockCd']
, df.iloc[i - 3]['high'], df.iloc[i - 2]['high'], df.iloc[i - 1]['high']
, df.iloc[i]['high'], df.iloc[i - 3]['low'], df.iloc[i - 2]['low'],
df.iloc[i - 1]['low']
, df.iloc[i]['low'], df.iloc[i - 3]['open'], df.iloc[i - 2]['open'],
df.iloc[i - 1]['open']
, df.iloc[i]['open'], df.iloc[i - 3]['close'], df.iloc[i - 2]['close']
, df.iloc[i - 1]['close'], df.iloc[i]['close'], df.iloc[i - 3]['volume']
, df.iloc[i - 2]['volume'], df.iloc[i - 1]['volume'],
df.iloc[i]['volume']
, (df.iloc[i - 2]['high'] - df.iloc[i - 3]['high']) / df.iloc[i - 3][
'high']
, (df.iloc[i - 1]['high'] - df.iloc[i - 2]['high']) / df.iloc[i - 2][
'high']
, (df.iloc[i]['high'] - df.iloc[i - 1]['high']) / df.iloc[i - 1]['high']
, (df.iloc[i]['high'] - df.iloc[i - 3]['high']) / df.iloc[i - 3]['high']
, (df.iloc[i]['high'] - df.iloc[i - 2]['high']) / df.iloc[i - 2]['high']
, (df.iloc[i - 2]['low'] - df.iloc[i - 3]['low']) / df.iloc[i - 3]['low']
, (df.iloc[i - 1]['low'] - df.iloc[i - 2]['low']) / df.iloc[i - 2]['low']
, (df.iloc[i]['low'] - df.iloc[i - 1]['low']) / df.iloc[i - 1]['low']
, (df.iloc[i]['low'] - df.iloc[i - 3]['low']) / df.iloc[i - 3]['low']
, (df.iloc[i]['low'] - df.iloc[i - 2]['low']) / df.iloc[i - 2]['low']
, df.iloc[i]['SMA5'], df.iloc[i]['SMA10'], df.iloc[i]['SMA20'], df.iloc[i]['SMA60']
, df.iloc[i]['SMA120'], df.iloc[i]['RSI'], df.iloc[i]['CCI']
, float(dis10)
, float(dis20)
, df.iloc[i]['MACD'], df.iloc[i]['MACDSIG'], df.iloc[i]['SLOWD'], df.iloc[i]['FASTD']
, (max(df.iloc[i]['high'],df.iloc[i-1]['high'],df.iloc[i-2]['high']) - min(df.iloc[i]['low'],df.iloc[i-1]['low'],df.iloc[i-2]['low']))
/ min(df.iloc[i]['low'],df.iloc[i-1]['low'],df.iloc[i-2]['low']) * 100
, df.iloc[i]['MACD'] - df.iloc[i]['MACDSIG']
, (df.iloc[i]['close'] - df.iloc[i]['high']) * 100 / df.iloc[i]['high']
, (df.iloc[i]['close'] - max(df.iloc[i]['high'],df.iloc[i-1]['high'],df.iloc[i-2]['high'],df.iloc[i-3]['high'])) * 100
/ max(df.iloc[i]['high'],df.iloc[i-1]['high'],df.iloc[i-2]['high'],df.iloc[i-3]['high'])
, (df.iloc[i]['open'] - max(df.iloc[i]['high'],df.iloc[i-1]['high'],df.iloc[i-2]['high'],df.iloc[i-3]['high'])) * 100
/ max(df.iloc[i]['high'],df.iloc[i-1]['high'],df.iloc[i-2]['high'],df.iloc[i-3]['high'])
, (df.iloc[i]['close'] - min(df.iloc[i]['low'],df.iloc[i-1]['low'],df.iloc[i-2]['low'])) * 100
/ min(df.iloc[i]['low'],df.iloc[i-1]['low'],df.iloc[i-2]['low'])
, float(dis5)
, float(dis60)
, df.iloc[i]['tradeTime']
)
try:
self.updateDB(sqlOra72)
except Exception as e:
print(e)
except Exception as e:
print(e)
except Exception as e:
print(e)
except Exception as e:
print(e)
print("["+ datetime.today().strftime("%Y-%m-%d %H:%M:%S") + "] " + "보조지표 만들기 끝!" )
def get_factors(high,
low,
close,
volume,
BBANDS=False,
DEMA=False,
EMA=False,
HT_TRENDLINE=False,
KAMA=False,
MA=False,
MAMA=False,
MAVP=False,
MIDPOINT=False,
MIDPRICE=False,
SAR=False,
SAREXT=False,
SMA=False,
T3=False,
TEMA=False,
TRIMA=False,
WMA=False,
AD=False,
ADOSC=False,
OBV=False,
HT_DCPERIOD=False,
HT_DCPHASE=False,
HT_PHASOR=False,
HT_SINE=False,
HT_TRENDMODE=False,
AVGPRICE=False,
MEDPRICE=False,
TYPPRICE=False,
WCLPRICE=False,
ATR=False,
NATR=False,
TRANGE=False,
ADX=False,
ADXR=False,
APO=False,
AROON=False,
AROONOSC=False,
BOP=False,
CCI=False,
CMO=False,
DX=False,
MACD=False,
ivergence=False,
MACDEXT=False,
MACDFIX=False,
MFI=False,
MINUS_DI=False,
MINUS_DM=False,
MOM=False,
PLUS_DI=False,
PLUS_DM=False,
PPO=False,
ROC=False,
ROCP=False,
ROCR=False,
ROCR100=False,
RSI=False,
STOCH=False,
STOCHF=False,
STOCHRSI=False,
TRIX=False,
ULTOSC=False,
WILLR=False):
fators = [
'BBANDS', 'DEMA', 'EMA', 'HT_TRENDLINE', 'KAMA', 'MA', 'MAMA',
'MIDPOINT', 'MIDPRICE', 'SAR', 'SAREXT', 'SMA', 'T3', 'TEMA', 'TRIMA',
'WMA', 'AD', 'ADOSC', 'OBV', 'HT_DCPERIOD', 'HT_DCPHASE', 'HT_PHASOR',
'HT_SINE', 'HT_TRENDMODE', 'AVGPRICE', 'MEDPRICE', 'TYPPRICE',
'WCLPRICE', 'ATR', 'NATR', 'TRANGE', 'ADX', 'ADXR', 'APO', 'AROON',
'AROONOSC', 'BOP', 'CCI', 'CMO', 'DX', 'MACD', 'ivergence', 'MACDEXT',
'MACDFIX', 'MFI,MINUS_DI', 'MINUS_DM', 'MOM', 'PLUS_DI', 'PLUS_DM',
'PPO', 'ROC', 'ROCP', 'ROCR', 'ROCR100', 'RSI', 'STOCH', 'STOCHF',
'STOCHRSI', 'TRIX', 'ULTOSC', 'WILLR'
]
data_sets = np.zeros([len(high), 1])
if BBANDS == True:
upperband, middleband, lowerband = ta.BBANDS(close,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
upperband = array_process(upperband)
middleband = array_process(middleband)
lowerband = array_process(lowerband)
data_sets = np.hstack([data_sets, upperband, middleband, lowerband])
if DEMA == True:
dema = ta.DEMA(close, timeperiod=30)
dema = array_process(dema)
data_sets = np.hstack([data_sets, dema])
if EMA == True:
ema = ta.EMA(close, timeperiod=30)
ema = array_process(ema)
data_sets = np.hstack([data_sets, ema])
if HT_TRENDLINE == True:
trendline = ta.HT_TRENDLINE(close)
trendline = array_process(trendline)
data_sets = np.hstack([data_sets, trendline])
if KAMA == True:
kama = ta.KAMA(close, timeperiod=30)
kama = array_process(kama)
data_sets = np.hstack([data_sets, kama])
if MA == True:
ma = ta.MA(close, timeperiod=30, matype=0)
ma = array_process(ma)
data_sets = np.hstack([data_sets, ma])
if MAMA == True:
mama, fama = ta.MAMA(close, fastlimit=0, slowlimit=0)
mama = array_process(mama)
fama = array_process(fama)
data_sets = np.hstack([data_sets, mama, fama])
if MIDPOINT == True:
midpoint = ta.MIDPOINT(close, timeperiod=14)
midpoint = array_process(midpoint)
data_sets = np.hstack([data_sets, midpoint])
if MIDPRICE == True:
midprice = ta.MIDPRICE(high, low, timeperiod=14)
midprice = array_process(midprice)
data_sets = np.hstack([data_sets, midprice])
if SAR == True:
sar = ta.SAR(high, low, acceleration=0, maximum=0)
sar = array_process(sar)
data_sets = np.hstack([data_sets, sar])
if SAREXT == True:
sarext = ta.SAREXT(high,
low,
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
sarext = array_process(sarext)
data_sets = np.hstack([data_sets, sarext])
if SMA == True:
sma = ta.SMA(close, timeperiod=30)
sma = array_process(sma)
data_sets = np.hstack([data_sets, sma])
if T3 == True:
t3 = ta.T3(close, timeperiod=5, vfactor=0)
t3 = array_process(t3)
data_sets = np.hstack([data_sets, t3])
if TEMA == True:
tema = ta.TEMA(close, timeperiod=30)
tema = array_process(tema)
data_sets = np.hstack([data_sets, tema])
if TRIMA == True:
trima = ta.TRIMA(close, timeperiod=30)
trima = array_process(trima)
data_sets = np.hstack([data_sets, trima])
if WMA == True:
wma = ta.WMA(close, timeperiod=30)
wma = array_process(wma)
data_sets = np.hstack([data_sets, wma])
if AD == True:
ad = ta.AD(high, low, close, volume)
ad = array_process(ad)
data_sets = np.hstack([data_sets, ad])
if ADOSC == True:
adosc = ta.ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10)
adosc = array_process(adosc)
data_sets = np.hstack([data_sets, adosc])
if OBV == True:
obv = ta.OBV(close, volume)
obv = array_process(obv)
data_sets = np.hstack([data_sets, obv])
if HT_DCPERIOD == True:
dcperiod = ta.HT_DCPERIOD(close)
dcperiod = array_process(dcperiod)
data_sets = np.hstack([data_sets, dcperiod])
if HT_DCPHASE == True:
dcphase = ta.HT_DCPHASE(close)
dcphase = array_process(dcphase)
data_sets = np.hstack([data_sets, dcphase])
if HT_PHASOR == True:
inphase, quadrature = ta.HT_PHASOR(close)
inphase = array_process(inphase)
data_sets = np.hstack([data_sets, inphase])
quadrature = array_process(quadrature)
data_sets = np.hstack([data_sets, quadrature])
if HT_SINE == True:
sine, leadsine = ta.HT_SINE(close)
sine = array_process(sine)
data_sets = np.hstack([data_sets, sine])
leadsine = array_process(leadsine)
data_sets = np.hstack([data_sets, leadsine])
if HT_TRENDMODE == True:
integer = ta.HT_TRENDMODE(close)
integer = array_process(integer)
data_sets = np.hstack([data_sets, integer])
if AVGPRICE == True:
avgprice = ta.AVGPRICE(open, high, low, close)
avgprice = array_process(avgprice)
data_sets = np.hstack([data_sets, avgprice])
if MEDPRICE == True:
medprice = ta.MEDPRICE(high, low)
medprice = array_process(medprice)
data_sets = np.hstack([data_sets, medprice])
if TYPPRICE == True:
typprice = ta.TYPPRICE(high, low, close)
typprice = array_process(typprice)
data_sets = np.hstack([data_sets, typprice])
if WCLPRICE == True:
wclprice = ta.WCLPRICE(high, low, close)
wclprice = array_process(wclprice)
data_sets = np.hstack([data_sets, wclprice])
if ATR == True:
atr = ta.ATR(high, low, close, timeperiod=14)
atr = array_process(atr)
data_sets = np.hstack([data_sets, atr])
if NATR == True:
natr = ta.NATR(high, low, close, timeperiod=14)
natr = array_process(natr)
data_sets = np.hstack([data_sets, natr])
if TRANGE == True:
trange = ta.TRANGE(high, low, close)
natr = array_process(trange)
data_sets = np.hstack([data_sets, trange])
if ADX == True:
adx = ta.ADX(high, low, close, timeperiod=14)
adx = array_process(adx)
data_sets = np.hstack([data_sets, adx])
if ADXR == True:
adxr = ta.ADXR(high, low, close, timeperiod=14)
adxr = array_process(adxr)
data_sets = np.hstack([data_sets, adxr])
if APO == True:
apo = ta.APO(close, fastperiod=12, slowperiod=26, matype=0)
apo = array_process(apo)
data_sets = np.hstack([data_sets, apo])
if AROON == True:
aroondown, aroonup = ta.AROON(high, low, timeperiod=14)
aroondown = array_process(aroondown)
data_sets = np.hstack([data_sets, aroondown])
aroonup = array_process(aroonup)
data_sets = np.hstack([data_sets, aroonup])
if AROONOSC == True:
aroonosc = ta.AROONOSC(high, low, timeperiod=14)
aroonosc = array_process(aroonosc)
data_sets = np.hstack([data_sets, aroonosc])
if BOP == True:
bop = ta.BOP(open, high, low, close)
bop = array_process(bop)
data_sets = np.hstack([data_sets, bop])
if CCI == True:
cci = ta.CCI(high, low, close, timeperiod=14)
cci = array_process(cci)
data_sets = np.hstack([data_sets, cci])
if CMO == True:
cmo = ta.CMO(close, timeperiod=14)
cmo = array_process(cmo)
data_sets = np.hstack([data_sets, cmo])
if DX == True:
dx = ta.DX(high, low, close, timeperiod=14)
dx = array_process(dx)
data_sets = np.hstack([data_sets, dx])
if MACD == True:
macd, macdsignal, macdhist = ta.MACD(close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
macd = array_process(macd)
data_sets = np.hstack([data_sets, macd])
macdhist = array_process(macdhist)
data_sets = np.hstack([data_sets, macdhist])
macdsignal = array_process(macdsignal)
data_sets = np.hstack([data_sets, macdsignal])
if MACDEXT == True:
macd, macdsignal, macdhist = ta.MACDEXT(close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
macd = array_process(macd)
data_sets = np.hstack([data_sets, macd])
macdhist = array_process(macdhist)
data_sets = np.hstack([data_sets, macdhist])
macdsignal = array_process(macdsignal)
data_sets = np.hstack([data_sets, macdsignal])
if MACDFIX == True:
macd, macdsignal, macdhist = ta.MACDFIX(close, signalperiod=9)
macd = array_process(macd)
data_sets = np.hstack([data_sets, macd])
macdhist = array_process(macdhist)
data_sets = np.hstack([data_sets, macdhist])
macdsignal = array_process(macdsignal)
data_sets = np.hstack([data_sets, macdsignal])
if MFI == True:
mfi = ta.MFI(high, low, close, volume, timeperiod=14)
mfi = array_process(mfi)
data_sets = np.hstack([data_sets, mfi])
if MINUS_DI == True:
minus_di = ta.MINUS_DI(high, low, close, timeperiod=14)
minus_di = array_process(minus_di)
data_sets = np.hstack([data_sets, minus_di])
if MINUS_DM == True:
minus_dm = ta.MINUS_DM(high, low, timeperiod=14)
minus_dm = array_process(minus_dm)
data_sets = np.hstack([data_sets, minus_dm])
if MOM == True:
mom = ta.MOM(close, timeperiod=10)
mom = array_process(mom)
data_sets = np.hstack([data_sets, mom])
if PLUS_DI == True:
plus_di = ta.PLUS_DI(high, low, close, timeperiod=14)
plus_di = array_process(plus_di)
data_sets = np.hstack([data_sets, plus_di])
if PLUS_DM == True:
plus_dm = ta.PLUS_DM(high, low, timeperiod=14)
plus_dm = array_process(plus_dm)
data_sets = np.hstack([data_sets, plus_dm])
if PPO == True:
ppo = ta.PPO(close, fastperiod=12, slowperiod=26, matype=0)
ppo = array_process(ppo)
data_sets = np.hstack([data_sets, ppo])
if ROC == True:
roc = ta.ROC(close, timeperiod=10)
roc = array_process(roc)
data_sets = np.hstack([data_sets, roc])
if ROCP == True:
rocp = ta.ROCP(close, timeperiod=10)
rocp = array_process(rocp)
data_sets = np.hstack([data_sets, rocp])
if ROCR == True:
rocr = ta.ROCR(close, timeperiod=10)
rocr = array_process(rocr)
data_sets = np.hstack([data_sets, rocr])
if ROCR100 == True:
rocr100 = ta.ROCR100(close, timeperiod=10)
rocr100 = array_process(rocr100)
data_sets = np.hstack([data_sets, rocr100])
if RSI == True:
rsi = ta.RSI(close, timeperiod=14)
rsi = array_process(rsi)
data_sets = np.hstack([data_sets, rsi])
if STOCH == True:
slowk, slowd = ta.STOCH(high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
slowd = array_process(slowd)
data_sets = np.hstack([data_sets, slowd])
slowk = array_process(slowk)
data_sets = np.hstack([data_sets, slowk])
if STOCHF == True:
fastk, fastd = ta.STOCHF(high,
low,
close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
fastd = array_process(fastd)
data_sets = np.hstack([data_sets, fastd])
fastk = array_process(fastk)
data_sets = np.hstack([data_sets, fastk])
if STOCHRSI == True:
fastk, fastd = ta.STOCHRSI(close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
fastk = array_process(fastk)
data_sets = np.hstack([data_sets, fastk])
fastd = array_process(fastd)
data_sets = np.hstack([data_sets, fastd])
if TRIX == True:
trix = ta.TRIX(close, timeperiod=30)
trix = array_process(trix)
data_sets = np.hstack([data_sets, trix])
if ULTOSC == True:
ultosc = ta.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
ultosc = array_process(ultosc)
data_sets = np.hstack([data_sets, ultosc])
if WILLR == True:
willr = ta.WILLR(high, low, close, timeperiod=14)
willr = array_process(willr)
data_sets = np.hstack([data_sets, willr])
return data_sets[:, 1:]
def handle_momentum_indicators(args, axes, i, klines_df, close_times,
display_count):
if args.macd: # macd
name = 'macd'
klines_df = ic.pd_macd(klines_df)
difs = [round(a, 2) for a in klines_df["dif"]]
deas = [round(a, 2) for a in klines_df["dea"]]
macds = [round(a, 2) for a in klines_df["macd"]]
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, difs[-display_count:], "y", label="dif")
axes[i].plot(close_times, deas[-display_count:], "b", label="dea")
axes[i].plot(close_times,
macds[-display_count:],
"r",
drawstyle="steps",
label="macd")
if args.mr: # macd rate
name = 'macd rate'
klines_df = ic.pd_macd(klines_df)
closes = klines_df["close"][-display_count:]
closes = pd.to_numeric(closes)
mrs = [
round(a, 4) for a in (klines_df["macd"][-display_count:] / closes)
]
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, mrs, "r--", label="mr")
if args.kdj: # kdj
name = 'kdj'
ks, ds, js = ic.pd_kdj(klines_df)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, ks[-display_count:], "b", label="k")
axes[i].plot(close_times, ds[-display_count:], "y", label="d")
axes[i].plot(close_times, js[-display_count:], "m", label="j")
# talib
if args.ADX: # ADX
name = 'ADX'
adxs = talib.ADX(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=14)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, adxs[-display_count:], "b", label=name)
if args.ADXR: # ADXR
name = 'ADXR'
adxrs = talib.ADXR(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=14)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, adxrs[-display_count:], "r", label=name)
if args.APO: # APO
name = 'APO'
real = talib.APO(klines_df["close"],
fastperiod=12,
slowperiod=26,
matype=0)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.AROON: # AROON
name = 'AROON'
aroondown, aroonup = talib.AROON(klines_df["high"],
klines_df["low"],
timeperiod=14)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, aroondown[-display_count:], "y:", label=name)
axes[i].plot(close_times, aroonup[-display_count:], "y:", label=name)
if args.AROONOSC: # AROONOSC
name = 'AROONOSC'
real = talib.AROONOSC(klines_df["high"],
klines_df["low"],
timeperiod=14)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.BOP: # BOP
name = 'BOP'
real = talib.BOP(klines_df["open"], klines_df["high"],
klines_df["low"], klines_df["close"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.CCI: # CCI
name = 'CCI'
real = talib.CCI(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=14)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.CMO: # CMO
name = 'CMO'
real = talib.CMO(klines_df["close"], timeperiod=14)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.DX: # DX
name = 'DX'
dxs = talib.DX(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=14)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, dxs[-display_count:], "y", label=name)
if args.MACD: # MACD
name = 'MACD'
fastperiod = 12
slowperiod = 26
signalperiod = 9
macd, macdsignal, macdhist = talib.MACD(klines_df["close"],
fastperiod=fastperiod,
slowperiod=slowperiod,
signalperiod=signalperiod)
i += 1
axes[i].set_ylabel("%s(%s,%s,%s)" %
(name, fastperiod, slowperiod, signalperiod))
axes[i].grid(True)
axes[i].plot(close_times, macd[-display_count:], "y", label="dif")
axes[i].plot(close_times,
macdsignal[-display_count:],
"b",
label="dea")
axes[i].plot(close_times,
macdhist[-display_count:],
"r",
drawstyle="steps",
label="macd")
if args.MACDEXT: # MACDEXT
name = 'MACDEXT'
fastperiod = 12
slowperiod = 26
signalperiod = 9
macd, macdsignal, macdhist = talib.MACDEXT(klines_df["close"],
fastperiod=fastperiod,
fastmatype=0,
slowperiod=slowperiod,
slowmatype=0,
signalperiod=signalperiod,
signalmatype=0)
i += 1
axes[i].set_ylabel("%s(%s,%s,%s)" %
(name, fastperiod, slowperiod, signalperiod))
axes[i].grid(True)
axes[i].plot(close_times, macd[-display_count:], "y", label="dif")
axes[i].plot(close_times,
macdsignal[-display_count:],
"b",
label="dea")
axes[i].plot(close_times,
macdhist[-display_count:],
"r",
drawstyle="steps",
label="macd")
if args.MACDFIX: # MACDFIX
name = 'MACDFIX'
signalperiod = 9
macd, macdsignal, macdhist = talib.MACDFIX(klines_df["close"],
signalperiod=signalperiod)
i += 1
axes[i].set_ylabel("%s(%s)" % (name, signalperiod))
axes[i].grid(True)
axes[i].plot(close_times, macd[-display_count:], "y", label="dif")
axes[i].plot(close_times,
macdsignal[-display_count:],
"b",
label="dea")
axes[i].plot(close_times,
macdhist[-display_count:],
"r",
drawstyle="steps",
label="macd")
if args.MFI: #
name = 'MFI'
real = talib.MFI(klines_df["high"], klines_df["low"],
klines_df["close"], klines_df["volume"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.MINUS_DI: #
name = 'MINUS_DI'
real = talib.MINUS_DI(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=14)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.MINUS_DM: #
name = 'MINUS_DM'
real = talib.MINUS_DM(klines_df["high"],
klines_df["low"],
timeperiod=14)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.MOM: #
name = 'MOM'
real = talib.MOM(klines_df["close"], timeperiod=10)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.PLUS_DI: #
name = 'PLUS_DI'
real = talib.PLUS_DI(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=14)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.PLUS_DM: #
name = 'PLUS_DM'
real = talib.PLUS_DM(klines_df["high"],
klines_df["low"],
timeperiod=14)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.PPO: #
name = 'PPO'
real = talib.PPO(klines_df["close"],
fastperiod=12,
slowperiod=26,
matype=0)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.ROC: #
name = 'ROC'
real = talib.ROC(klines_df["close"], timeperiod=10)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.ROCP: #
name = 'ROCP'
real = talib.ROCP(klines_df["close"], timeperiod=10)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "k:", label=name)
if args.ROCR: #
name = 'ROCR'
real = talib.ROCR(klines_df["close"], timeperiod=10)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "m:", label=name)
if args.ROCR100: #
name = 'ROCR100'
real = talib.ROCR100(klines_df["close"], timeperiod=10)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "b:", label=name)
if args.RSI is not None: # RSI
name = 'RSI'
i += 1
axes[i].grid(True)
if len(args.RSI) == 0:
tps = [14]
else:
tps = args.RSI
cs = ["r", "y", "b"]
axes[i].set_ylabel("%s %s" % (name, tps))
for idx, tp in enumerate(tps):
rsis = talib.RSI(klines_df["close"], timeperiod=tp)
rsis = [round(a, 3) for a in rsis][-display_count:]
axes[i].plot(close_times, rsis, cs[idx], label="rsi")
linetype = "."
axes[i].plot(close_times, [90] * len(rsis), linetype, color='r')
axes[i].plot(close_times, [80] * len(rsis), linetype, color='r')
axes[i].plot(close_times, [50] * len(rsis), linetype, color='r')
axes[i].plot(close_times, [40] * len(rsis), linetype, color='r')
linetype = "."
axes[i].plot(close_times, [65] * len(rsis), linetype, color='b')
axes[i].plot(close_times, [55] * len(rsis), linetype, color='b')
axes[i].plot(close_times, [30] * len(rsis), linetype, color='b')
axes[i].plot(close_times, [20] * len(rsis), linetype, color='b')
if args.STOCH: # STOCH
name = 'STOCH'
slowk, slowd = talib.STOCH(klines_df["high"],
klines_df["low"],
klines_df["close"],
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, slowk[-display_count:], "b", label="slowk")
axes[i].plot(close_times, slowd[-display_count:], "y", label="slowd")
if args.STOCHF: #
name = 'STOCHF'
fastk, fastd = talib.STOCHF(klines_df["high"],
klines_df["low"],
klines_df["close"],
fastk_period=5,
fastd_period=3,
fastd_matype=0)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, fastk[-display_count:], "b", label="fastk")
axes[i].plot(close_times, fastd[-display_count:], "y", label="fastd")
if args.STOCHRSI: #
name = 'STOCHRSI'
fastk, fastd = talib.STOCHRSI(klines_df["close"],
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, fastk[-display_count:], "b", label="fastk")
axes[i].plot(close_times, fastd[-display_count:], "y", label="fastd")
if args.TRIX is not None: #
name = 'TRIX'
i += 1
axes[i].grid(True)
if len(args.TRIX) == 0:
tps = [30]
else:
tps = args.TRIX
axes[i].set_ylabel("%s %s" % (name, tps))
for idx, tp in enumerate(tps):
real = talib.TRIX(klines_df["close"], timeperiod=tp)
axes[i].plot(close_times,
real[-display_count:],
plot_colors[idx],
label=name)
if args.ULTOSC: #
name = 'ULTOSC'
real = talib.ULTOSC(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.WILLR:
tp = 14
name = 'WILLR %s' % tp
real = talib.WILLR(klines_df["high"],
klines_df["low"],
klines_df["close"],
timeperiod=tp)
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
def rocr(stockData, point):
return talib.ROCR(stockData['close'][:point], timeperiod=10)[-1]
data["PPO"] = talib.PPO(data.Close, fastperiod=12, slowperiod=26, matype=0) #Learn more about the Percentage Price Oscillator at tadoc.org. #ROC - Rate of change : ((price/prevPrice)-1)*100 data["ROC"] = talib.ROC(data.Close, timeperiod=10) #Learn more about the Rate of change : ((price/prevPrice)-1)*100 at tadoc.org. #ROCP - Rate of change Percentage: (price-prevPrice)/prevPrice data["ROCP"] = talib.ROCP(data.Close, timeperiod=10) #Learn more about the Rate of change Percentage: (price-prevPrice)/prevPrice at tadoc.org. #ROCR - Rate of change ratio: (price/prevPrice) data["ROCR"] = talib.ROCR(data.Close, timeperiod=10) #Learn more about the Rate of change ratio: (price/prevPrice) at tadoc.org. #ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100 data["ROCR100"] = talib.ROCR100(data.Close, timeperiod=10) #Learn more about the Rate of change ratio 100 scale: (price/prevPrice)*100 at tadoc.org. #RSI - Relative Strength Index #NOTE: The RSI function has an unstable period. data["RSI"] = talib.RSI(data.Close, timeperiod=14) #Learn more about the Relative Strength Index at tadoc.org. #STOCH - Stochastic
def ROCR_factor(self, df, timeperiod=10):
return talib.ROCR(df.loc[:, self.map_dict['default']].values,
timeperiod)
def rocr(col, timeperiod=10):
return {"rocr": ta.ROCR(col, timeperiod)}
def ROCR(raw_df, timeperiod=10):
# extract necessary data from raw dataframe (close)
return ta.ROCR(raw_df.Close.values, timeperiod)
def add_ta_features(df):
pr = df.price
HL = (df.high, df.low)
HLC = (df.high, df.low, df.close)
OHLC = (df.open, df.high, df.low, df.close)
HLCV = (df.high, df.low, df.close, df.vol)
PV = (pr, df.vol)
initial_col_count = df.columns.shape[0]
df = (
df.assign(SMA4=(ta.SMA(pr, timeperiod=4) - pr)).assign(SMA12=(
ta.SMA(pr, timeperiod=12) -
pr)).assign(SMA24=(ta.SMA(pr, timeperiod=24) - pr)).assign(
BBAND20_HI=(ta.BBANDS(pr, timeperiod=20)[0] - pr)).assign(
BBAND20_LO=(pr -
ta.BBANDS(pr, timeperiod=20)[2])).assign(
SAREXT=(ta.SAREXT(*HL) -
pr)).assign(ADX=ta.ADX(*HLC)).
assign(ADOSC=ta.ADOSC(*HLCV)).assign(
CDL3STARSINSOUTH=(ta.CDL3STARSINSOUTH(*OHLC))).assign(
HT_TRENDLINE=ta.HT_TRENDLINE(pr) -
pr).assign(KAMA=ta.KAMA(pr) - pr).assign(
MIDPOINT=ta.MIDPOINT(pr) -
pr).assign(T3=ta.T3(pr) - pr).assign(
TEMA=ta.TEMA(pr) - pr).assign(
TRIMA=ta.TRIMA(pr) - pr).assign(ADXR=ta.ADXR(
*HLC)).assign(APO=ta.APO(pr)).assign(
AROON1=ta.AROON(*HL)[0]).assign(
AROON2=ta.AROON(*HL)[1]).
assign(AROONOSC=ta.AROONOSC(*HL)).assign(BOP=ta.BOP(
*OHLC)).assign(CMO=ta.CMO(pr)).assign(DX=ta.DX(*HLC)).assign(
MACD1=ta.MACD(pr)[0]).assign(MACD2=ta.MACD(pr)[1]).assign(
MACDEXT1=ta.MACDEXT(pr)[0]).assign(
MACDEXT2=ta.MACDEXT(pr)[1]).assign(MFI=ta.MFI(
*HLCV)).assign(MINUS_DI=ta.MINUS_DI(
*HLC)).assign(MINUS_DM=ta.MINUS_DM(
*HL)).assign(CMOMCI=ta.MOM(pr)).assign(
PLUS_DI=ta.PLUS_DI(*HLC)).assign(
PLUS_DM=ta.PLUS_DM(*HL)).
assign(PPO=ta.PPO(pr)).assign(ROC=ta.ROC(pr)).assign(
ROCP=ta.ROCP(pr)).assign(ROCR=ta.ROCR(pr)).assign(
RSI=ta.RSI(pr)).assign(STOCH1=ta.STOCH(
*HLC)[0]).assign(STOCH2=ta.STOCH(*HLC)[1]).assign(
STOCHF1=ta.STOCHF(*HLC)[0]).assign(
STOCHF2=ta.STOCHF(*HLC)[1]).assign(
STOCHRSI1=ta.STOCHRSI(pr)[0]).assign(
STOCHRSI2=ta.STOCHRSI(pr)[1]).assign(
ULTOSC=ta.ULTOSC(*HLC)).
assign(WILLR=ta.WILLR(*HLC)).assign(AD=ta.AD(
*HLCV)).assign(ADOSC=ta.ADOSC(*HLCV)).assign(OBV=ta.OBV(
*PV)).assign(HT_DCPERIOD=ta.HT_DCPERIOD(pr)).assign(
HT_DCPHASE=ta.HT_DCPHASE(pr)).assign(
HT_PHASOR1=ta.HT_PHASOR(pr)[0]).assign(
HT_PHASOR2=ta.HT_PHASOR(pr)[1]).assign(
HT_SINE1=ta.HT_SINE(pr)[0]).assign(
HT_SINE2=ta.HT_SINE(pr)[1]).assign(
HT_TRENDMODE=ta.HT_TRENDMODE(pr)).
assign(ATR14=ta.ATR(*HLC, timeperiod=14)).assign(
NATR14=ta.NATR(*HLC, timeperiod=14)).assign(TRANGE=ta.TRANGE(
*HLC)).assign(ATR7=ta.ATR(*HLC, timeperiod=7)).assign(
NATR7=ta.NATR(*HLC, timeperiod=7)).assign(
ATR21=ta.ATR(*HLC, timeperiod=21)).assign(
NATR21=ta.NATR(*HLC, timeperiod=21))
# .astype(int, errors='ignore')
)
# adding difference
for col in df.columns[initial_col_count:]:
df[col + '_change'] = df[col] - df[col].shift(1)
candle_methods = [
method_name for method_name in dir(ta)
if method_name.startswith('CDL')
]
for method in candle_methods:
df[method] = getattr(ta, method)(*OHLC)
# n_before = df.shape[0]
df = df.dropna()
# l.debug(f'TA indicators do require {n_before - df.shape[0]} steps of history')
return df
def extract_features(stock_df: pd.DataFrame) -> pd.DataFrame:
# stock_df = stock_df.reindex(pd.date_range(stock_df.first_valid_index(), stock_df.last_valid_index()))
# mask = stock_df.isna().as_matrix(['Open']).reshape(-1)
stock_df = stock_df.fillna(method='pad')
stock_raw = {
'open': stock_df.as_matrix(['Open']).reshape(-1),
'high': stock_df.as_matrix(['High']).reshape(-1),
'low': stock_df.as_matrix(['Low']).reshape(-1),
'close': stock_df.as_matrix(['Adj Close']).reshape(-1),
'volume':
stock_df.as_matrix(['Volume']).reshape(-1).astype(np.float64),
}
feat_data = {
'Adj Close':
stock_raw['close'],
'OBV':
talib.OBV(stock_raw['close'], stock_raw['volume']),
'Volume':
stock_raw['close'],
'RSI6':
talib.RSI(stock_raw['close'], timeperiod=6),
'RSI12':
talib.RSI(stock_raw['close'], timeperiod=12),
'SMA3':
talib.SMA(stock_raw['close'], timeperiod=3),
'EMA6':
talib.EMA(stock_raw['close'], timeperiod=6),
'EMA12':
talib.EMA(stock_raw['close'], timeperiod=12),
'ATR14':
talib.ATR(stock_raw['high'],
stock_raw['low'],
stock_raw['close'],
timeperiod=14),
'MFI14':
talib.MFI(stock_raw['high'],
stock_raw['low'],
stock_raw['close'],
stock_raw['volume'],
timeperiod=14),
'ADX14':
talib.ADX(stock_raw['high'],
stock_raw['low'],
stock_raw['close'],
timeperiod=14),
'ADX20':
talib.ADX(stock_raw['high'],
stock_raw['low'],
stock_raw['close'],
timeperiod=20),
'MOM1':
talib.MOM(stock_raw['close'], timeperiod=1),
'MOM3':
talib.MOM(stock_raw['close'], timeperiod=3),
'CCI12':
talib.CCI(stock_raw['high'],
stock_raw['low'],
stock_raw['close'],
timeperiod=12),
'CCI20':
talib.CCI(stock_raw['high'],
stock_raw['low'],
stock_raw['close'],
timeperiod=20),
'ROCR3':
talib.ROCR(stock_raw['close'], timeperiod=3),
'ROCR12':
talib.ROCR(stock_raw['close'], timeperiod=12),
'outMACD':
talib.MACD(stock_raw['close'])[0],
'outMACDSignal':
talib.MACD(stock_raw['close'])[1],
'outMACDHist':
talib.MACD(stock_raw['close'])[2],
'WILLR':
talib.WILLR(stock_raw['high'], stock_raw['low'], stock_raw['close']),
'TSF10':
talib.TSF(stock_raw['close'], timeperiod=10),
'TSF20':
talib.TSF(stock_raw['close'], timeperiod=20),
'TRIX':
talib.TRIX(stock_raw['close']),
'BBANDSUPPER':
talib.BBANDS(stock_raw['close'])[0],
'BBANDSMIDDLE':
talib.BBANDS(stock_raw['close'])[1],
'BBANDSLOWER':
talib.BBANDS(stock_raw['close'])[2],
}
for colname, colval in feat_data.items():
stock_df[colname] = colval
# stock_df = stock_df.loc[np.logical_not(mask)]
return stock_df
def get_rocr(ohlc):
rocr = ta.ROCR(ohlc['4_close'], timeperiod=10)
ohlc['rocr'] = rocr
return ohlc
def ROCR(matrix, timeperiod):
matrix['ROCR' + str(timeperiod)] = talib.ROCR(
real=matrix['1-Close'].values, timeperiod=timeperiod)
return matrix
df['MINUS_DM'] = talib.MINUS_DM(df["High"], df["Low"], timeperiod=14)
# PLUS_DI - Plus Directional Indicator
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 ############################
def ROCR(self, window=10):
real = talib.ROCR(self.close, timeperiod=window)
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
# MOM
ti_MOM = talib.MOM(bars['price'].values, time_period)
# OBV
ti_OBV = talib.OBV(bars['price'].values, vol['volume'].values)
# PLUS_DI
ti_PLUS_DI = talib.PLUS_DI(bars['high'].values, bars['low'].values,\
bars['close'].values, time_period)
# ROCP
ti_ROCP = talib.ROCP(bars['price'].values, time_period)
# ROCR
ti_ROCR = talib.ROCR(bars['price'].values, time_period)
# RSI
ti_RSI = talib.RSI(bars['price'].values, time_period)
# SMA
ti_SMA = talib.SMA(bars['price'].values, time_period)
# STOCHRSI
ti_FASTK, ti_FASTD = talib.STOCHRSI(bars['price'].values, time_period)
# TRANGE
ti_TRANGE = talib.TRANGE(bars['high'].values, bars['low'].values,\
bars['close'].values)
# TYPPRICE
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 techIndi(self, gubun):
try:
daySqlOra = "SELECT NVL(MAX(SUBSTR(TRADE_TIME,1,8)),TO_CHAR(SYSDATE,'YYYYMMDD')) YMD FROM TRADE_TECH " \
"WHERE SUBSTR(TRADE_TIME,1,8) <> '%s' " \
% datetime.today().strftime("%Y%m%d")
dateD = self.selectDB(daySqlOra)
dateS = dateD[0][0]
except Exception as e:
e.msg = "techindi daySql 에러"
print(e)
try:
sqlOra71 = "SELECT DISTINCT TRIM(A.STOCK_CODE) STOCK_CODE " \
"FROM TRADE_HOLD A " \
"WHERE A.YMD = '%s' " \
" AND A.REQ_VOLUME <> 0" \
" AND (A.EARNING_RATE >= 3 AND A.CURR_RATE >= 15)" \
"UNION " \
"SELECT DISTINCT TRIM(B.STOCK_CODE) STOCK_CODE " \
"FROM TRADE_LIST B " \
"WHERE B.YMD BETWEEN '%s' AND '%s' " \
" AND B.BUY_AMT < %s * 0.9" % (datetime.today().strftime("%Y%m%d")
, dateS
, datetime.today().strftime("%Y%m%d")
, self.unitPrice)
summaryG = self.selectDB(sqlOra71)
except Exception as e:
e.msg = "techIndi Sql 에러"
print(e)
print("-----------------------------------------------------------------")
print("["+ datetime.today().strftime("%Y-%m-%d %H:%M:%S") + "] " + "Process2 : 보조지표 만들기 시작!" )
try:
if len(summaryG) > 0:
for row_data in summaryG:
try:
if gubun == "장중":
sql = "SELECT RN, STOCK_CODE, TRADE_TIME, START_PRICE, HIGH_PRICE, LOW_PRICE, CURRENT_PRICE, VOLUME " \
"FROM ( " \
" SELECT A.*, ROW_NUMBER() OVER(PARTITION BY STOCK_CODE ORDER BY TRADE_TIME DESC) RN" \
" , ROW_NUMBER() OVER(PARTITION BY STOCK_CODE ORDER BY TRADE_TIME ASC) RN2" \
" FROM TRADE_CRR A" \
" WHERE A.TRADE_TIME BETWEEN '%s' AND '%s' " \
" AND A.STOCK_CODE = '%s' " \
" AND A.STOCK_CODE NOT IN ('155960','900090') " \
" ) X " % (
dateS + "090000"
, datetime.today().strftime("%Y%m%d") + "160000", row_data[0])
sql2 = "WHERE RN BETWEEN 1 AND 130 " \
"ORDER BY RN2"
else:
sql = "SELECT RN, STOCK_CODE, TRADE_TIME, START_PRICE, HIGH_PRICE, LOW_PRICE, CURRENT_PRICE, VOLUME " \
"FROM ( " \
" SELECT A.*, ROW_NUMBER() OVER(PARTITION BY STOCK_CODE ORDER BY TRADE_TIME DESC) RN" \
" , ROW_NUMBER() OVER(PARTITION BY STOCK_CODE ORDER BY TRADE_TIME ASC) RN2" \
" FROM TRADE_CRR A" \
" WHERE 1=1 " \
" AND A.STOCK_CODE = '%s' " \
" AND A.STOCK_CODE NOT IN ('155960','900090') " \
" ) X " % row_data[0]
sql2 = "ORDER BY RN2"
summaryG = self.selectDB(sql + sql2)
except Exception as e:
print(e)
df = pd.DataFrame(summaryG)
try:
if len(df) > 0:
df.columns = ["rn", "stockCd", "tradeTime", "open", "high", "low", "close", "volume"]
# cloF = pd.Series(df.iloc[len(df) - 1]['open'], name='close') # 3분봉 종가 만들어주는 로직
# clo = pd.Series(df.iloc[1:len(df)]['open'], name='close')
# clo = pd.DataFrame(clo.append(cloF))
# clo = clo.reset_index(drop=True)
#
# df = df.join(clo)
macd, macdsignal, macdhist = talib.MACD(df['close'], fastperiod=12, slowperiod=26,
signalperiod=9)
slowk, slowd = talib.STOCH(df['high'], df['low'], df['close'], fastk_period=5, slowk_period=3,
slowk_matype=3,
slowd_period=3, slowd_matype=0)
fastk, fastd = talib.STOCHF(df['high'], df['low'], df['close'], fastk_period=20,
fastd_period=10)
sma5 = talib.SMA(df['close'], 5)
sma10 = talib.SMA(df['close'], 10)
sma20 = talib.SMA(df['close'], 20)
sma60 = talib.SMA(df['close'], 60)
sma120 = talib.SMA(df['close'], 120)
cci = talib.CCI(df['high'], df['low'], df['close'], 20)
rsi = talib.RSI(df['close'], 14)
roc1 = talib.ROC(df['close'], 1)
roc5 = talib.ROC(df['close'], 5)
rocr1 = talib.ROCR(df['close'], 1)
rocr5 = talib.ROCR(df['close'], 5)
df = df.join(pd.DataFrame(pd.Series(macd, name="MACD")))
df = df.join(pd.DataFrame(pd.Series(macdsignal, name="MACDSIG")))
df = df.join(pd.DataFrame(pd.Series(slowd, name="SLOWD")))
df = df.join(pd.DataFrame(pd.Series(fastd, name="FASTD")))
df = df.join(pd.DataFrame(pd.Series(sma5, name="SMA5")))
df = df.join(pd.DataFrame(pd.Series(sma10, name="SMA10")))
df = df.join(pd.DataFrame(pd.Series(sma20, name="SMA20")))
df = df.join(pd.DataFrame(pd.Series(sma60, name="SMA60")))
df = df.join(pd.DataFrame(pd.Series(sma120, name="SMA120")))
df = df.join(pd.DataFrame(pd.Series(cci, name="CCI")))
df = df.join(pd.DataFrame(pd.Series(rsi, name="RSI")))
df = df.join(pd.DataFrame(pd.Series(roc1, name="ROC1")))
df = df.join(pd.DataFrame(pd.Series(roc5, name="ROC5")))
df = df.join(pd.DataFrame(pd.Series(rocr1, name="ROCR1")))
df = df.join(pd.DataFrame(pd.Series(rocr5, name="ROCR5")))
df = df.fillna(0)
try:
for i in range(4, len(df)):
if df.iloc[i]['SMA10'] == 0:
dis10 = 0
else:
dis10 = float(df.iloc[i]['close']) / float(df.iloc[i]['SMA10']) * 100
if df.iloc[i]['SMA20'] == 0:
dis20 = 0
else:
dis20 = float(df.iloc[i]['close']) / float(df.iloc[i]['SMA20']) * 100
file = open("C:/Users/jini/sqlOra72.sql", 'r') # TRADE_TECH에 데이터 넣기
sqlOra72 = ''
line = file.readline()
while line:
sqlOra72 += ' ' + line.strip('\n').strip('\t')
line = file.readline()
file.close()
sqlOra72 = sqlOra72 % (
df.iloc[i]['tradeTime'], df.iloc[i]['stockCd']
, df.iloc[i - 3]['high'], df.iloc[i - 2]['high'], df.iloc[i - 1]['high']
, df.iloc[i]['high'], df.iloc[i - 3]['low'], df.iloc[i - 2]['low'],
df.iloc[i - 1]['low']
, df.iloc[i]['low'], df.iloc[i - 3]['open'], df.iloc[i - 2]['open'],
df.iloc[i - 1]['open']
, df.iloc[i]['open'], df.iloc[i - 3]['close'], df.iloc[i - 2]['close']
, df.iloc[i - 1]['close'], df.iloc[i]['close'], df.iloc[i - 3]['volume']
, df.iloc[i - 2]['volume'], df.iloc[i - 1]['volume'],
df.iloc[i]['volume']
, (df.iloc[i - 2]['high'] - df.iloc[i - 3]['high']) / df.iloc[i - 3][
'high']
, (df.iloc[i - 1]['high'] - df.iloc[i - 2]['high']) / df.iloc[i - 2][
'high']
, (df.iloc[i]['high'] - df.iloc[i - 1]['high']) / df.iloc[i - 1]['high']
, (df.iloc[i]['high'] - df.iloc[i - 3]['high']) / df.iloc[i - 3]['high']
, (df.iloc[i]['high'] - df.iloc[i - 2]['high']) / df.iloc[i - 2]['high']
, (df.iloc[i - 2]['low'] - df.iloc[i - 3]['low']) / df.iloc[i - 3]['low']
, (df.iloc[i - 1]['low'] - df.iloc[i - 2]['low']) / df.iloc[i - 2]['low']
, (df.iloc[i]['low'] - df.iloc[i - 1]['low']) / df.iloc[i - 1]['low']
, (df.iloc[i]['low'] - df.iloc[i - 3]['low']) / df.iloc[i - 3]['low']
, (df.iloc[i]['low'] - df.iloc[i - 2]['low']) / df.iloc[i - 2]['low']
, df.iloc[i]['SMA5'], df.iloc[i]['SMA10'], df.iloc[i]['SMA20'], df.iloc[i]['SMA60']
, df.iloc[i]['SMA120'], df.iloc[i]['RSI'], df.iloc[i]['CCI']
, float(dis10)
, float(dis20)
, df.iloc[i]['MACD'], df.iloc[i]['MACDSIG'], df.iloc[i]['SLOWD'], df.iloc[i]['FASTD']
, (max(df.iloc[i]['high'],df.iloc[i-1]['high'],df.iloc[i-2]['high']) - min(df.iloc[i]['low'],df.iloc[i-1]['low'],df.iloc[i-2]['low']))
/ min(df.iloc[i]['low'],df.iloc[i-1]['low'],df.iloc[i-2]['low']) * 100
, df.iloc[i]['MACD'] - df.iloc[i]['MACDSIG']
, (df.iloc[i]['close'] - df.iloc[i]['high']) * 100 / df.iloc[i]['high']
, (df.iloc[i]['close'] - max(df.iloc[i]['high'],df.iloc[i-1]['high'],df.iloc[i-2]['high'],df.iloc[i-3]['high'])) * 100
/ max(df.iloc[i]['high'],df.iloc[i-1]['high'],df.iloc[i-2]['high'],df.iloc[i-3]['high'])
, (df.iloc[i]['open'] - max(df.iloc[i]['high'],df.iloc[i-1]['high'],df.iloc[i-2]['high'],df.iloc[i-3]['high'])) * 100
/ max(df.iloc[i]['high'],df.iloc[i-1]['high'],df.iloc[i-2]['high'],df.iloc[i-3]['high'])
, (df.iloc[i]['close'] - min(df.iloc[i]['low'],df.iloc[i-1]['low'],df.iloc[i-2]['low'])) * 100
/ min(df.iloc[i]['low'],df.iloc[i-1]['low'],df.iloc[i-2]['low'])
, df.iloc[i]['tradeTime'])
try:
self.updateDB(sqlOra72)
except Exception as e:
print(e)
except Exception as e:
print(e)
except Exception as e:
print(e)
except Exception as e:
print(e)
print("["+ datetime.today().strftime("%Y-%m-%d %H:%M:%S") + "] " + "Process2 : 보조지표 만들기 끝!" )
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
resorted['close'])
mfi_real = talib.MFI(resorted['high'], resorted['low'],
resorted['close'], resorted['volume'])
minus_di_real = talib.MINUS_DI(resorted['high'],
resorted['low'],
resorted['close'])
minus_dm_real = talib.MINUS_DM(resorted['high'],
resorted['low'])
mom_real = talib.MOM(resorted['close'])
plus_di_real = talib.PLUS_DI(resorted['high'], resorted['low'],
resorted['close'])
plus_dm_real = talib.PLUS_DM(resorted['high'], resorted['low'])
ppo_real = talib.PPO(resorted['close'])
roc_real = talib.ROC(resorted['close'])
rocp_real = talib.ROCP(resorted['close'])
rocr_real = talib.ROCR(resorted['close'])
rocr100_real = talib.ROCR100(resorted['close'])
rsi_real = talib.RSI(resorted['close'])
slowk_real, slowd_real = talib.STOCH(resorted['high'],
resorted['low'],
resorted['close'])
fastk_real, fastd_real = talib.STOCHF(resorted['high'],
resorted['low'],
resorted['close'])
fastk_rsi_real, fastd_rsi_real = talib.STOCHRSI(
resorted['close'])
trix_real = talib.TRIX(resorted['close'])
ultosc_real = talib.ULTOSC(resorted['high'], resorted['low'],
resorted['close'])
willr_real = talib.WILLR(resorted['high'], resorted['low'],
resorted['close'])
timeperiod=n)
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)
def ROCR(Close, timeperiod=10):
return Close.apply(lambda col: ta.ROCR(col, timeperiod), axis=0)
