def analyze(self, historical_data, signal=["obv"], hot_thresh=None, cold_thresh=None):
"""Performs OBV analysis on the historical data
Args:
historical_data (list): A matrix of historical OHCLV data.
signal (list, optional): Defaults to obv. The indicator line to check hot/cold
against.
hot_thresh (float, optional): Defaults to None. The threshold at which this might be
good to purchase.
cold_thresh (float, optional): Defaults to None. The threshold at which this might be
good to sell.
Returns:
pandas.DataFrame: A dataframe containing the indicators and hot/cold values.
"""
dataframe = self.convert_to_dataframe(historical_data)
obv_values = abstract.OBV(dataframe).to_frame()
obv_values.dropna(how="all", inplace=True)
obv_values.rename(columns={obv_values.columns[0]: "obv"}, inplace=True)
if obv_values[signal[0]].shape[0]:
obv_values["is_hot"] = obv_values[signal[0]] > hot_thresh
obv_values["is_cold"] = obv_values[signal[0]] < cold_thresh
return obv_values
def calculator_talib(data):
ETF = {
'open': data[OHLCV_columns[0]].dropna().astype(float),
'high': data[OHLCV_columns[1]].dropna().astype(float),
'low': data[OHLCV_columns[2]].dropna().astype(float),
'close': data[OHLCV_columns[3]].dropna().astype(float),
'volume': data[OHLCV_columns[4]].dropna().astype(float)
}
def talib2df(talib_output):
if type(talib_output) == list:
ret = pd.DataFrame(talib_output).transpose()
else:
ret = pd.Series(talib_output)
ret.index = data['收盤價'].index
return ret
KD = talib2df(abstract.STOCH(ETF, fastk_period=9))
#計算MACD#
MACD = talib2df(abstract.MACD(ETF))
#計算OBV#
OBV = talib2df(abstract.OBV(ETF))
#計算威廉指數#
WILLR = talib2df(abstract.WILLR(ETF))
#ATR 計算#
ATR = talib2df(abstract.ATR(ETF))
ETF = pd.DataFrame()
ETF = pd.concat([data, KD, MACD, OBV, WILLR, ATR], axis=1)
return ETF
def get_data(self, ticker):
#取得資料和RSI,要先整理成Talib接受的小寫格式...
self.df = pdr.DataReader(ticker, 'yahoo')
self.df.drop(columns=['Adj Close'], inplace=True)
self.df.rename(columns={
'Open': 'open',
'High': 'high',
'Low': 'low',
'Close': 'close',
'Volume': 'volume'
},
inplace=True)
self.df['RSI'] = abstract.RSI(self.df)
self.df['OBV'] = abstract.OBV(self.df)
self.df['short_ma'] = self.df['close'].rolling(window=5).mean()
self.df['mid_ma'] = self.df['close'].rolling(window=10).mean()
self.df['long_ma'] = self.df['close'].rolling(
window=self.monitor_days).mean()
self.df.dropna(inplace=True) #drop掉nan的部分
#因為是RSI，所以把收盤價以外的drop掉
self.df_analysis = self.df.drop(
columns=['open', 'high', 'low', 'volume'])
self.df_analysis['RSI_check'] = np.nan
self.df_analysis['OBV_check'] = np.nan
self.df_analysis['both_check'] = np.nan
self.df_analysis['direction'] = np.nan
self.df_analysis['direction_switch'] = np.nan
def apply_indicators(df: pd.DataFrame):
# ADX
df['adx'] = ta.ADX(df)
# EMA
df['ema_5'] = ta.EMA(df, 5)
df['ema_10'] = ta.EMA(df, 10)
df['ema_20'] = ta.EMA(df, 20)
df['ema_50'] = ta.EMA(df, 50)
df['ema_100'] = ta.EMA(df, 100)
df['ema_200'] = ta.EMA(df, 200)
# MACD
macd = ta.MACD(df)
df['macd'] = macd['macd']
df['macdsignal'] = macd['macdsignal']
df['macdhist'] = macd['macdhist']
# inverse Fisher rsi/ RSI
df['rsi'] = ta.RSI(df)
rsi = 0.1 - (df['rsi'] - 50)
df['i_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# Stoch fast
stoch_fast = ta.STOCHF(df)
df['fastd'] = stoch_fast['fastd']
df['fastk'] = stoch_fast['fastk']
# Stock slow
stoch_slow = ta.STOCH(df)
df['slowd'] = stoch_slow['slowd']
df['slowk'] = stoch_slow['slowk']
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(df),
window=20,
stds=2)
df['bb_lowerband'] = bollinger['lower']
df['bb_middleband'] = bollinger['mid']
df['bb_upperband'] = bollinger['upper']
# ROC
df['roc'] = ta.ROC(df, 10)
# CCI
df['cci'] = ta.CCI(df, 14)
# on balance volume
df['obv'] = ta.OBV(df)
# Average True Range
df['atr'] = ta.ATR(df, 14)
df = ichimoku(df)
return df
def __init__(self, ticker, start_day=None, length=730):
self.ticker = ticker
if (start_day == None):
today = datetime.today()
start_day = today - timedelta(days=length)
start_str = str(start_day.strftime('%Y-%m-%d'))
end_str = str(today.strftime('%Y-%m-%d'))
else:
start_str = start_day
#start = time.strptime(start_day, "%Y-%m-%d")
start = datetime.strptime(start_str, "%Y-%m-%d")
end_day = start + timedelta(days=length)
end_str = str(end_day.strftime('%Y-%m-%d'))
i = y.get_historical_prices(ticker, start_str, end_str)
#Lag Pandas DataFrame
self.df = pd.DataFrame(i)
#Snu Dataframe
self.df = self.df.transpose()
#endre datatype til float
self.df = self.df.astype(float)
self.df = self.df.rename(
columns={
'Close': 'close',
'High': 'high',
'Open': 'open',
'Low': 'low',
'Volume': 'volume'
})
stoch = abstract.STOCH(self.df, 14, 1, 3)
macd = abstract.MACD(self.df)
atr = abstract.ATR(self.df)
obv = abstract.OBV(self.df)
rsi = abstract.RSI(self.df)
self.df['atr'] = pd.DataFrame(atr)
self.df['obv'] = pd.DataFrame(obv)
self.df['rsi'] = pd.DataFrame(rsi)
#kombinerer to dataframes
self.df = pd.merge(self.df,
pd.DataFrame(macd),
left_index=True,
right_index=True,
how='outer')
self.df = pd.merge(self.df,
stoch,
left_index=True,
right_index=True,
how='outer')
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['sar'] = ta.SAR(dataframe)
dataframe['adx'] = ta.ADX(dataframe)
stoch = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2,
nbdevdn=2)['lowerband']
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['cci'] = ta.CCI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['mom'] = ta.MOM(dataframe)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
dataframe['ao'] = awesome_oscillator(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# add volatility indicators
dataframe['natr'] = ta.NATR(dataframe)
# add volume indicators
dataframe['obv'] = ta.OBV(dataframe)
# add more momentum indicators
dataframe['rocp'] = ta.ROCP(dataframe)
# add some pattern recognition
dataframe['CDL2CROWS'] = ta.CDL2CROWS(dataframe)
dataframe['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(dataframe)
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe)
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe)
dataframe['CDL3STARSINSOUTH'] = ta.CDL3STARSINSOUTH(dataframe)
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe)
dataframe['CDLADVANCEBLOCK'] = ta.CDLADVANCEBLOCK(dataframe)
dataframe['CDLBELTHOLD'] = ta.CDLBELTHOLD(dataframe)
dataframe['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(dataframe)
dataframe['CDLDOJI'] = ta.CDLDOJI(dataframe)
dataframe['CDLDOJISTAR'] = ta.CDLDOJISTAR(dataframe)
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe)
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
dataframe['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(dataframe)
dataframe['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(dataframe)
# enter categorical time
hour = datetime.strptime(str(dataframe['date'][len(dataframe) - 1]),
"%Y-%m-%d %H:%M:%S").hour
for h in range(24):
dataframe['hour_{0:02}'.format(h)] = int(h == hour)
return dataframe
def cal_obv(self):
self.analysis_data['obv'] = ta.OBV(self.close, self.volume)
ret = pd.DataFrame(talib_output).transpose()
else :
ret = pd.Series(talib_output)
ret.index = taini['close'].index
ret.fillna(0)
return ret
#ax.set_xlim(, right)
#KD
talib2df(talib.abstract.STOCH(taini)).plot()
taini['close'].plot(secondary_y=True)
plt.savefig("kd.png")
plt.show()
plt.cla()
#RSI
talib2df(talib.abstract.RSI(taini)).plot()
taini['close'].plot(secondary_y=True)
plt.savefig("rsi.png")
plt.cla()
#MACD
talib2df(abstract.STOCH(taini, fastk_period=9)).plot()
taini['close'].plot(secondary_y=True)
plt.savefig("macd.png")
plt.show()
plt.cla()
#OBV
talib2df(abstract.OBV(taini)).plot()
taini['close'].plot(secondary_y=True)
plt.savefig("obv.png")
plt.cla()