def STOCH(self):
STOCH = tb.STOCH(self.dataframe,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
slowk = STOCH["slowk"][len(STOCH) - 1] #main
slowd = STOCH["slowd"][len(STOCH) - 1] #signal
oldSlowk = STOCH["slowk"][len(STOCH) - 2] #main
oldSlowd = STOCH["slowd"][len(STOCH) - 2] #signal
#sell when main line > upper band (80) and main line crosses the signal line from above-down
#buy when main line < lower band (20) and main line crosses the signal line from bottom-up
if (slowk > 80):
#print("RSI: " + str(value) + " overbought")
return "overbought"
elif (slowk > 60):
#print("RSI: " + str(value) + " buy")
return "buy"
elif (slowk < 20):
#print("RSI: " + str(value) + " oversold")
return "oversold"
elif (slowk < 40):
#print("RSI: " + str(value) + " sell")
return "sell"
else:
#print("RSI: " + str(value) + " neutral")
return "neutral"
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowk'] = stoch['slowk']
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) -
1) / (numpy.exp(2 * rsi) + 1)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
return dataframe
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 KD_signal(self, fastk=9):
# KD指標，呈現過去一段時間股價強弱趨勢
# 用K和D判斷目前價格相對過去一段時間的高低變化
# KD指標呈現「最新股價的相對高低位置」，估股價目前處於相對高點或低點。
# RSV= (今日收盤價 – 最近n天的最低價) ÷ (最近n天的最高價 – 最近n天最低價) × 100
# 今日K值(快線)= 昨日K值 × (2/3 ) +今日RSV × ( 1/3)
# 今日D值(慢線)= 昨日D值 × (2/3) +今日K值 × ( 1/3)
# ※RSV中的n天，常見天數有以9天、14天等來比較，依標的屬性來調整。
# fastk : RSV以 fastk 天為期
df = self.stock_data
df.columns = [col.lower() for col in df.columns]
df_KD = abstract.STOCH(df, fastk_period=fastk)
if (df_KD.iloc[-1, 0] > df_KD.iloc[-1, 1]) & (df_KD.iloc[-2, 0] < df_KD.iloc[-2, 1])\
& (df_KD.iloc[-2, 0] < 20) & (df_KD.iloc[-2, 1] < 20):
print('k值轉為大於d值，黃金交叉')
return 1
elif (df_KD.iloc[-1, 0] < df_KD.iloc[-1, 1]) & (df_KD.iloc[-2, 0] > df_KD.iloc[-2, 1])\
& (df_KD.iloc[-2, 0] > 80) & (df_KD.iloc[-2, 1] > 80):
print('k值轉為小於d值，死亡交叉')
return -1
# if (df_KD.iloc[-2, 0] < 80) & (df_KD.iloc[-1, 0] >= 80):
# return 1
# elif (df_KD.iloc[-2, 0] > 30) & (df_KD.iloc[-1, 0] <= 30):
# return -1
return 0
def do_populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
"""
Adds multiple TA indicators to MoniGoMani's DataFrame per pair.
Should be called with 'informative_pair' (1h candles) during backtesting/hyperopting with TimeFrame-Zoom!
Performance Note: For the best performance be frugal on the number of indicators you are using.
Only add in indicators that you are using in your weighted signal configuration for MoniGoMani,
otherwise you will waste your memory and CPU usage.
:param dataframe: (DataFrame) DataFrame with data from the exchange
:param metadata: (dict) Additional information, like the currently traded pair
:return DataFrame: DataFrame for MoniGoMani with all mandatory indicator data populated
"""
# Momentum Indicators (timeperiod is expressed in candles)
# -------------------
# Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe)
# Stochastic Slow
stoch = ta.STOCH(dataframe)
dataframe['slowk'] = stoch['slowk']
# MACD - Moving Average Convergence Divergence
macd = ta.MACD(dataframe)
dataframe['macd'] = macd[
'macd'] # MACD - Blue TradingView Line (Bullish if on top)
dataframe['macdsignal'] = macd[
'macdsignal'] # Signal - Orange TradingView Line (Bearish if on top)
# MFI - Money Flow Index (Under bought / Over sold & Over bought / Under sold / volume Indicator)
dataframe['mfi'] = ta.MFI(dataframe)
# Overlap Studies
# ---------------
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_middleband'] = bollinger['mid']
# SMA's & EMA's are trend following tools (Should not be used when line goes sideways)
# SMA - Simple Moving Average (Moves slower compared to EMA, price trend over X periods)
dataframe['sma9'] = ta.SMA(dataframe, timeperiod=9)
dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Volume Indicators
# -----------------
# Rolling VWAP - Volume Weighted Average Price
dataframe['rolling_vwap'] = qtpylib.rolling_vwap(dataframe)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
dataframe['gap'] = dataframe['close'].shift(1) - (
(dataframe['high'].shift(1) - dataframe['low'].shift(1)) * 0.1)
dataframe['adx'] = ta.ADX(dataframe)
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stochastic Fast
stoch_fast = ta.STOCH(dataframe)
dataframe['slowd'] = stoch_fast['slowd']
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
return dataframe
def KDJ(self): #(933)
kd = abstract.STOCH(self.company_stock, fastk_period=9)
self.company_stock['K'] = kd['slowk']
self.company_stock['D'] = kd['slowd']
self.company_stock[
'J'] = 3 * self.company_stock['D'] - 2 * self.company_stock['K']
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Dynamic TA indicators
Used so hyperopt can optimized around the period of various indicators
"""
for rsip in range(rsiStart, (rsiEnd + 1)):
dataframe[f'rsi({rsip})'] = ta.RSI(dataframe, timeperiod=rsip)
for temap in range(temaStart, (temaEnd + 1)):
dataframe[f'tema({temap})'] = ta.TEMA(dataframe, timeperiod=temap)
"""
Static TA indicators.
RSI and TEMA Only used when --spaces does not include buy or sell
"""
# Stochastic Slow
# fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
stoch_slow = ta.STOCH(dataframe, fastk_period=fastkPeriod, slowk_period=slowkPeriod, slowd_period=slowdPeriod)
dataframe['stoch-slowk'] = stoch_slow['slowk']
dataframe['stoch-slowd'] = stoch_slow['slowd']
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# WAVETREND
try:
ap = (dataframe['high'] + dataframe['low'] +
dataframe['close']) / 3
esa = ta.EMA(ap, 10)
d = ta.EMA((ap - esa).abs(), 10)
ci = (ap - esa).div(0.0015 * d)
tci = ta.EMA(ci, 21)
wt1 = tci
wt2 = ta.SMA(np.nan_to_num(wt1), 4)
dataframe['wt1'], dataframe['wt2'] = wt1, wt2
stoch = ta.STOCH(dataframe, 14)
slowk = stoch['slowk']
dataframe['slowk'] = slowk
# print(dataframe.iloc[:, 6:].keys())
x = dataframe.iloc[:, 6:].values # returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
dataframe.iloc[:, 6:] = pd.DataFrame(x_scaled)
# print('wt:\t', dataframe['wt'].min(), dataframe['wt'].max())
# print('stoch:\t', dataframe['stoch'].min(), dataframe['stoch'].max())
dataframe['def'] = dataframe['slowk'] - dataframe['wt1']
# print('def:\t', dataframe['def'].min(), "\t", dataframe['def'].max())
except:
dataframe['wt1'], dataframe['wt2'], dataframe['def'], dataframe[
'slowk'] = 0, 10, 100, 1000
return dataframe
def __init__(self, equityDataFrame, tickerCode):
"""
Create the STOCH-specific logic. Everything else lives in the Indicator Base Class.
:param equityDataFrame: A Pandas DataFrame from the Equity Class.
:param tickerCode: Ticker Code (String).
"""
tableName = "Indicator_STOCH"
tickerCode = tickerCode
insertQuery = "insert or replace into %s (Date, STOCH_K, STOCH_D, STOCH_K_ROC, STOCH_D_ROC, Code) values (?,?,?,?,?,?)" % (
tableName)
# Stick the Indicator Values into the new DataFrame
indicatorDataFrame = abstract.STOCH(equityDataFrame,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0,
prices=['High', 'Low', 'Close'])
indicatorDataFrame['STOCH_K_ROC'] = abstract.ROC(indicatorDataFrame,
timeperiod=5,
price='slowk')
indicatorDataFrame['STOCH_D_ROC'] = abstract.ROC(indicatorDataFrame,
timeperiod=5,
price='slowd')
indicatorDataFrame['Code'] = tickerCode
Indicator.__init__(self, tableName, tickerCode, insertQuery,
equityDataFrame, indicatorDataFrame)
def parse_data(self, response):
"""Parse data."""
data = json.loads(response.text)
date = response.meta['date']
if data['hits']['total']['value'] == 0:
stock_ids = []
self.logger.info(f'{date} doesn\'t have data')
else:
stock_ids = [
hit['_source']['stock_id'] for hit in data['hits']['hits']
]
for stock_id in stock_ids:
df = self.get_history_pirce_by_id(stock_id, date)
s_item = items.TechnicalIndexItem()
s_item['date'] = date
s_item['stock_id'] = stock_id
sma5 = self.filter_nan(abstract.SMA(df, timeperiod=5)[0])
if sma5 is not None:
s_item['sma5'] = sma5
sma10 = self.filter_nan(abstract.SMA(df, timeperiod=10)[0])
if sma10 is not None:
s_item['sma10'] = sma10
sma20 = self.filter_nan(abstract.SMA(df, timeperiod=20)[0])
if sma20 is not None:
s_item['sma20'] = sma20
sma60 = self.filter_nan(abstract.SMA(df, timeperiod=60)[0])
if sma60 is not None:
s_item['sma60'] = sma60
sma100 = self.filter_nan(abstract.SMA(df, timeperiod=100)[0])
if sma100 is not None:
s_item['sma100'] = sma100
rsi = self.filter_nan(abstract.RSI(df, timeperiod=14)[0])
if rsi is not None:
s_item['rsi'] = rsi
stoch_df = abstract.STOCH(df)
slowk = self.filter_nan(stoch_df['slowk'][0])
if slowk is not None:
s_item['slowk'] = slowk
slowd = self.filter_nan(stoch_df['slowd'][0])
if slowd is not None:
s_item['slowd'] = slowd
if df.shape[0] >= 2:
s_item['double_volume'] = df['trade_volume'][0] > (
df['trade_volume'][1] * 2)
s_item['triple_volume'] = df['trade_volume'][0] > (
df['trade_volume'][1] * 3)
if df['close'][1] != 0:
quote_change = (df['close'][0] -
df['close'][1]) / df['close'][1] * 100
# TODO: 取小數後兩位
s_item['quote_change'] = float(
'{:.2f}'.format(quote_change))
s_item['id'] = f"{s_item['date']}-{s_item['stock_id']}"
yield s_item
self.logger.info(f'complete crawl \"{date}\"')
def update_basic_info(n_clicks, year, input_value):
data = Data()
conn = sqlite3.connect('dataBase.db')
strid = str(input_value)
sPrice = data.get('收盤價', 1)[strid].values[0]
try:
sName = data.get('公司名稱', 1)[strid]
except:
sName = data.get('公司名稱', -1)[strid].values[0]
sNumber = format(data.get('成交股數', 1)[strid].values[0], ',')
sPER = data.get('本益比', 1)[strid].values[0]
sClose = data.dates['price'].iloc[-1].name.strftime("%Y/%m/%d")
sPBR = getPB(strid)
sEPS = data.get('基本每股盈餘合計', 1)[strid].values[0]
#基本圖figure
revenueFig = getMonthRevenue(strid)
epsFigure = getOneSeasonEPS(strid)
chasflowFigure = getCashFlow(strid)
incometableFigure = getIncomeTable(strid)
#技術圖
date = str(year[0]) + '-01-01'
df = pdr.DataReader(str(strid) + '.TW', 'yahoo', start=date)
fig = go.Figure(data=go.Candlestick(x=df.index,
open=df['Open'],
high=df['High'],
low=df['Low'],
close=df['Close']))
fig.update_layout(xaxis_rangeslider_visible=False)
alldf = pdr.DataReader(str(strid) + '.TW', 'yahoo', start=date)
alldf = alldf.rename(columns={
'High': 'high',
'Low': 'low',
'OPen': 'open',
'Close': 'close'
})
#RSI
RSI = abstract.RSI(alldf)
#rsifig = px.line(RSI)
rsifig = go.Scatter(x=RSI.index, y=RSI.values)
tf = make_subplots(specs=[[{"secondary_y": True}]])
#STOCH
STOCH = abstract.STOCH(alldf)
stochfig = px.line(STOCH)
#SMA
SMA = abstract.SMA(alldf)
SMAfig = px.line(SMA)
return sName, sPrice, sNumber, sPER, sPBR, sEPS, sClose, revenueFig, epsFigure, chasflowFigure, incometableFigure, fig, stochfig, SMAfig, year[
0]
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 generate_indicators(dataset,
timeperiod=5,
generate_target=True,
reset_index=False):
# To avoid changes from original dataset
df = pd.DataFrame(dataset).copy()
# To prevent index from being Date
if (reset_index):
df = df.reset_index()
df.columns = df.columns.str.lower()
# check the given dataset has necessary_columns
__check_columns(df)
df = df[necessary_columns]
# df_indicators has all columns except date, this is for talib to produces other indicators
df_indicators = df.iloc[:, 1:]
# Produce other indicators
RSI = abstract.RSI(df_indicators.close, timeperiod)
RSI = pd.DataFrame({'RSI': RSI})
MOM = abstract.MOM(df_indicators.close, timeperiod)
MOM = pd.DataFrame({'MOM': MOM})
KD = abstract.STOCH(df_indicators)
KD = pd.DataFrame(KD) # KD has slowd and slowk
MACD = abstract.MACD(df_indicators)
MACD = pd.DataFrame(MACD) # MACD has its own column names
ADX = abstract.ADX(df_indicators)
ADX = pd.DataFrame({'ADX': ADX})
SMA = abstract.SMA(df_indicators.close)
SMA = pd.DataFrame({'SMA': SMA})
upper, middle, lower = talib.BBANDS(df_indicators.close, matype=MA_Type.T3)
bb_df = pd.DataFrame({ \
'upper_bb' : upper,
'middel_bb' : middle,
'lower_bb' : lower})
# Combine all metrix
frames = [df, RSI, MOM, KD, MACD, ADX, SMA, bb_df]
combined = pd.concat(frames, axis=1)
if (generate_target):
target_name = 'next_' + str(timeperiod) + 'day_trend'
combined[target_name] = np.where(
combined.close.shift(-timeperiod) > combined.close, 1, 0)
return combined
def add_indicators(self, df: pd.DataFrame = None) -> pd.DataFrame:
"""Add indicators."""
cols = ['high', 'low', 'open', 'close', 'volume']
HLOCV = {key: df[key].values for key in df if key in cols}
try:
df['volume'] = df['volumeto']
except:
pass
# Moving Averages
df['sma'] = abstract_ta.SMA(df, timeperiod=25)
df['ema20'] = abstract_ta.EMA(df, timeperiod=20)
df['ema50'] = abstract_ta.EMA(df, timeperiod=50)
df['ema100'] = abstract_ta.EMA(df, timeperiod=100)
df['ema200'] = abstract_ta.EMA(df, timeperiod=200)
df['ema300'] = abstract_ta.EMA(df, timeperiod=300)
# Bollinger Bands
u, m, l = abstract_ta.BBANDS(HLOCV,
timeperiod=24,
nbdevup=2.5,
nbdevdn=2.5,
matype=MA_Type.T3)
df['upper'] = u
df['middle'] = m
df['lower'] = l
# Stochastic
# uses high, low, close (default)
slowk, slowd = abstract_ta.STOCH(HLOCV, 5, 3, 0, 3,
0) # uses high, low, close by default
df['slowk'] = slowk
df['slowd'] = slowd
df['slow_stoch'] = (slowk + slowd) / 2
df['slow_stoch_sma14'] = df.slow_stoch.rolling(window=14).mean()
df['slow_stoch_sma26'] = df.slow_stoch.rolling(window=26).mean()
# Relative Strength Index
rsi = abstract_ta.RSI(df, timeperiod=14)
df['rsi'] = rsi
# Money Flow Index
mfi = abstract_ta.MFI(df, timeperiod=14)
df['mfi'] = mfi
# Medivh Relative Flow Index
mrfi_df = MRFI(df)
df['mrfi'] = mrfi_df['mrfi'].astype(float)
df['smrfi'] = mrfi_df['smrfi'].astype(float)
df['mrfi_basis'] = mrfi_df['mrfi_basis'].astype(float)
df['mrfi_inverse'] = mrfi_df['mrfi_inverse'].astype(float)
return df
def main():
engine = db.create_engine('sqlite:///sql_pratice_db')
conn = engine.connect()
data = {}
n_days = 16
date = datetime.datetime.now()
fail_count = 0
allow_continuous_fail_count = 5
while len(data) < n_days:
table_name = date.date().__str__()
if table_exists(table_name, engine):
try:
data[date.date()] = pd.read_sql_table(table_name=table_name, con=conn)
data[date.date()] = data[date.date()].set_index('靡ㄩ腹')
fail_count = 0
print("parsing ", date, " success")
except:
print("parsing ", date, " failed")
fail_count += 1
if fail_count >= allow_continuous_fail_count:
raise
break
date -= datetime.timedelta(days=1)
close_price = pd.DataFrame({k: d['Μ絃基'] for k, d in data.items()}).transpose()
close_price.index = pd.to_datetime(close_price.index)
open_price = pd.DataFrame({k: d['秨絃基'] for k, d in data.items()}).transpose()
open_price.index = pd.to_datetime(open_price.index)
high_price = pd.DataFrame({k: d['程蔼基'] for k, d in data.items()}).transpose()
high_price.index = pd.to_datetime(high_price.index)
low_price = pd.DataFrame({k: d['程基'] for k, d in data.items()}).transpose()
low_price.index = pd.to_datetime(low_price.index)
volume = pd.DataFrame({k: d['Θユ计'] for k, d in data.items()}).transpose()
volume.index = pd.to_datetime(volume.index)
tsmc = pd.DataFrame({
'close': close_price['2330']['2019'].dropna().astype(float),
'open': open_price['2330']['2019'].dropna().astype(float),
'high': high_price['2330']['2019'].dropna().astype(float),
'low': low_price['2330']['2019'].dropna().astype(float),
'volume': volume['2330']['2019'].dropna().astype(float),
}).sort_index()
print(tsmc)
kd = abstract.STOCH(tsmc)
print(kd)
kd.plot()
tsmc['close'].plot(secondary_y=True)
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(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stochastic Slow
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
return dataframe
def TA_processing(dataframe):
bias(dataframe, days=[3, 6, 10, 25])
moving_average(dataframe, days=[5, 10, 20])
dataframe['ROC'] = abstract.ROC(dataframe, timeperiod=10)
dataframe['MACD'] = abstract.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)['macd']
dataframe['MACD_signal'] = abstract.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)['macdsignal']
dataframe['UBBANDS'] = abstract.BBANDS(dataframe, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)['upperband']
dataframe['MBBANDS'] = abstract.BBANDS(dataframe, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)['middleband']
dataframe['LBBANDS'] = abstract.BBANDS(dataframe, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)['lowerband']
dataframe['%K'] = abstract.STOCH(dataframe, fastk_period=9)['slowk']/100
dataframe['%D'] = abstract.STOCH(dataframe, fastk_period=9)['slowd']/100
dataframe['W%R'] = abstract.WILLR(dataframe, timeperiod=14)/100
dataframe['RSI9'] = abstract.RSI(dataframe, timeperiod = 9)/100
dataframe['RSI14'] = abstract.RSI(dataframe, timeperiod = 14)/100
dataframe['CCI'] = abstract.CCI(dataframe, timeperiod=14)/100
counter_daily_potential(dataframe)
dataframe['MOM'] = abstract.MOM(dataframe, timeperiod=10)
dataframe['DX'] = abstract.DX(dataframe, timeperiod=14)/100
psy_line(dataframe)
volumn_ratio(dataframe, d=26)
on_balance_volume(dataframe)
def populate_indicators(self, dataframe, metadata):
stoch = ta.STOCH(dataframe)
rsi = ta.RSI(dataframe)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe))
dataframe["slowk"] = stoch["slowk"]
dataframe["rsi"] = rsi
rsi = 0.1 * (rsi - 50)
dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
dataframe["bb_lowerband"] = bollinger["lower"]
dataframe["sar"] = ta.SAR(dataframe)
dataframe["CDLHAMMER"] = ta.CDLHAMMER(dataframe)
return dataframe
def _get_statistics(n=1, start_time=None, end_time=None):
ohlc = _get_ohlc(3, start_time, end_time)
# TODO: maybe handle nan differently
limit = n * -1
stoch = np.nan_to_num(abstract.STOCH(ohlc))
macd = np.nan_to_num(abstract.MACD(ohlc))
output = dict(
macd=macd[0][limit:].tolist(),
macd_signal=macd[1][limit:].tolist(),
macd_hist=macd[2][limit:].tolist(),
stoch_slowk=stoch[0][limit:].tolist(),
stoch_slowd=stoch[1][limit:].tolist(),
vave=np.nan_to_num(abstract.MA(ohlc)[limit:]).tolist(),
sma=np.nan_to_num(abstract.SMA(ohlc)[limit:]).tolist(),
rsi=np.nan_to_num(abstract.RSI(ohlc)[limit:]).tolist(),
stddev=np.nan_to_num(abstract.STDDEV(ohlc)[limit:]).tolist())
return output
def taCalcSTOCH(df):
df = copy.deepcopy(df)
inputs = {
'open': df['OPEN'].values.astype(float),
'high': df['HIGH'].values.astype(float),
'low': df['LOW'].values.astype(float),
'close': df['CLOSE'].values.astype(float),
'volume': df['VOLUME'].values.astype(float)
}
slowk, slowd = abstract.STOCH(inputs, 5, 3, 0, 3, 0)
df['K'] = slowk
df['D'] = slowd
return df['K'], df['D']
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
for rsip in range(self.rsiStart, (self.rsiEnd + 1)):
dataframe[f'rsi({rsip})'] = ta.RSI(dataframe, timeperiod=rsip)
for temap in range(self.temaStart, (self.temaEnd + 1)):
dataframe[f'tema({temap})'] = ta.TEMA(dataframe, timeperiod=temap)
# Stochastic Slow
# fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
stoch_slow = ta.STOCH(dataframe,
fastk_period=self.fastkPeriod,
slowk_period=self.slowkPeriod,
slowd_period=self.slowdPeriod)
dataframe['stoch-slowk'] = stoch_slow['slowk']
dataframe['stoch-slowd'] = stoch_slow['slowd']
return dataframe
def test_ta_lib_abstract_api(self):
"""Test the abstract API of TA-Lib"""
with IQFeedHistoryProvider() as provider:
df = provider.request_data(BarsFilter(ticker="AAPL",
interval_len=300,
interval_type='s',
max_bars=1000),
sync_timestamps=False)
close = df['close']
output = abstract.SMA(df)
self.assertTrue(isinstance(output, pd.Series))
self.assertFalse(output.empty)
self.assertTrue(pd.isna(output[0]))
self.assertFalse(pd.isna(output[-1]))
self.assertEqual(close.shape, output.shape)
self.assertEqual(close.dtype, output.dtype)
assert_index_equal(close.index, output.index)
bbands = abstract.BBANDS(df, matype=talib.MA_Type.T3)
self.assertTrue(isinstance(bbands, pd.DataFrame))
assert_index_equal(close.index, bbands.index)
for _, bband in bbands.iteritems():
self.assertTrue(isinstance(bband, pd.Series))
self.assertFalse(bband.empty)
self.assertEqual(close.shape, bband.shape)
self.assertEqual(close.dtype, bband.dtype)
self.assertTrue(pd.isna(bband[0]))
self.assertFalse(pd.isna(bband[-1]))
stoch = abstract.STOCH(df, 5, 3, 0, 3, 0)
self.assertTrue(isinstance(stoch, pd.DataFrame))
assert_index_equal(close.index, stoch.index)
for _, s in stoch.iteritems():
self.assertTrue(isinstance(s, pd.Series))
self.assertFalse(s.empty)
self.assertEqual(close.shape, s.shape)
self.assertEqual(close.dtype, s.dtype)
self.assertTrue(pd.isna(s[0]))
self.assertFalse(pd.isna(s[-1]))
def stoch(self):
stoch = tb.STOCH(self.dataframe,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
slowk = stoch["slowk"][len(stoch) - 1]
if slowk > 80:
return "overbought"
elif slowk > 60:
return "buy"
elif slowk < 20:
return "oversold"
elif slowk < 40:
return "sell"
else:
return "neutral"
def _process(self, v, param):
data = {
"open" : v["open"].astype(float),
"high" : v["high"].astype(float),
"low" : v["low"].astype(float),
"close" : v["close"].astype(float),
"volume" : v["jdiff_vol"].astype(float)
}
# 이동평균선
if "SMA" in param:
for p in param["SMA"]:
v["SMA" + str(p)] = Series(abstract.SMA(data, p), index=v.index)
# Bollinger Bands
if "BBANDS" in param:
temp = abstract.BBANDS(data, param["BBANDS"][0], param["BBANDS"][1], param["BBANDS"][1])
v["BBANDS-UPPER"] = temp[0]
v["BBANDS-MIDDLE"] = temp[1]
v["BBANDS-LOWER"] = temp[2]
# Slow stochastic
if "STOCH" in param:
temp = abstract.STOCH(data, param["STOCH"][0], param["STOCH"][1], param["STOCH"][2])
v["STOCH-K"] = temp[0]
v["STOCH-D"] = temp[1]
# ATR (Average True Range)
if "ATR" in param:
v["ATR"] = Series(abstract.ATR(data, param["ATR"]), index=v.index)
# MACD (Moving Average Convergence/Divergence)
if "MACD" in param:
temp = abstract.MACD(data, param["MACD"][0], param["MACD"][1], param["MACD"][2])
v["MACD-OUT"] = temp[0]
v["MACD-SIGNAL"] = temp[1]
v["MACD-HIST"] = temp[2]
# RSI (Relative Strength Index)
if "RSI" in param:
v["RSI"] = Series(abstract.RSI(data, param["RSI"]), index=v.index)
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Stochastic Slow
# fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
stoch_slow = ta.STOCH(
dataframe,
fastk_period=self.stoch_params['stoch-fastk-period'],
slowk_period=self.stoch_params['stoch-slowk-period'],
slowd_period=self.stoch_params['stoch-slowd-period'])
dataframe['stoch-slowk'] = stoch_slow['slowk']
dataframe['stoch-slowd'] = stoch_slow['slowd']
# RSI
dataframe['rsi'] = ta.RSI(dataframe,
timeperiod=self.buy_params['rsi-period'])
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe,
timeperiod=self.sell_params['tema-period'])
return dataframe
def STOCH(self, window_size):
from talib import abstract
input_arrays = {
'open': self.data['Open'].values,
'high': self.data['High'].values,
'low': self.data['Low'].values,
'close': self.data['Close'].values,
'volume': self.data['Volume'].values
}
#slowk, slowd = STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
#return abstract.STOCH(input_arrays, fastk_period=window_size * self.no_of_intervals_per_day, slowk_period=window_size * self.no_of_intervals_per_day, slowk_matype=0, slowd_period=window_size * self.no_of_intervals_per_day, slowd_matype=0)
return abstract.STOCH(input_arrays,
fastk_period=window_size *
self.no_of_intervals_per_day,
slowk_period=3 * self.no_of_intervals_per_day,
slowk_matype=0,
slowd_period=3 * self.no_of_intervals_per_day,
slowd_matype=0)
def feature_add(df):
ma_period = 10
# rsi_period
fastk = 9
macd_fast = 6
macd_slow = 12
macd_period = 9
df['MA' + str(ma_period)] = talib.SMA(df['close'], timeperiod=ma_period)
df['RSI_14'] = talib.RSI(df['close'])
_, _, df['MACDhist'] = talib.MACD(df['close'])
vix_close = yf.Ticker('^VIX').history(period='max')['Close']
tw_weighted = yf.Ticker('^TWII').history(period='max')['Close']
sp500 = yf.Ticker('^GSPC').history(period='max').rename(
columns={'Close': 'sp500'})['sp500']
dowj = yf.Ticker('^DJI').history(period='max').rename(
columns={'Close': 'Dowj'})['Dowj']
# idx50 = yf.Tikcer('0050.TW').rename(columns={'Close': '0050'})['0050']
# idx56 = yf.Tikcer('0056.TW').rename(columns={'Close': '0056'})['0056']
df = pd.concat([
df,
abstract.STOCH(df, fastk_period=fastk), vix_close, tw_weighted, sp500,
dowj
],
axis=1).dropna()
try:
df.drop(columns=['adj close'], inplace=True)
except:
pass
normalizer = MinMaxScaler()
scaled_features = normalizer.fit_transform(df.values)
df_scaled = pd.DataFrame(scaled_features, index=df.index)
data = [list(row.values) for idx, row in df_scaled.iterrows()]
max_price = max(df['close'])
min_price = min(df['close'])
return data, [max_price, min_price]
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stochastic Slow
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
# Bollinger Bands
bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=1)
dataframe['bb_lowerband1'] = bollinger1['lower']
dataframe['bb_middleband1'] = bollinger1['mid']
dataframe['bb_upperband1'] = bollinger1['upper']
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband2'] = bollinger2['lower']
dataframe['bb_middleband2'] = bollinger2['mid']
dataframe['bb_upperband2'] = bollinger2['upper']
bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=3)
dataframe['bb_lowerband3'] = bollinger3['lower']
dataframe['bb_middleband3'] = bollinger3['mid']
dataframe['bb_upperband3'] = bollinger3['upper']
bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=4)
dataframe['bb_lowerband4'] = bollinger4['lower']
dataframe['bb_middleband4'] = bollinger4['mid']
dataframe['bb_upperband4'] = bollinger4['upper']
return dataframe
