def engineer_data_over_single_interval(df: pd.DataFrame,
indicators: list,
ticker: str = "",
rsi_n: int = 14,
cmo_n: int = 7,
macd_fast: int = 12,
macd_slow: int = 26,
macd_sign: int = 9,
roc_n: int = 12,
cci_n: int = 20,
dpo_n: int = 20,
cmf_n: int = 20,
adx_n: int = 14,
mass_index_low: int = 9,
mass_index_high: int = 25,
trix_n: int = 15,
stochastic_oscillator_n: int = 14,
stochastic_oscillator_sma_n: int = 3,
ultimate_oscillator_short_n: int = 7,
ultimate_oscillator_medium_n: int = 14,
ultimate_oscillator_long_n: int = 28,
ao_short_n: int = 5,
ao_long_n: int = 34,
kama_n: int = 10,
tsi_high_n: int = 25,
tsi_low_n: int = 13,
eom_n: int = 14,
force_index_n: int = 13,
ichimoku_low_n: int = 9,
ichimoku_medium_n: int = 26):
from ta.momentum import rsi, wr, roc, ao, stoch, uo, kama, tsi
from ta.trend import macd, macd_signal, cci, dpo, adx, mass_index, trix, ichimoku_a
from ta.volume import chaikin_money_flow, acc_dist_index, ease_of_movement, force_index
# Momentum Indicators
if Indicators.RELATIVE_STOCK_INDEX in indicators:
Logger.console_log(message="Calculating " +
Indicators.RELATIVE_STOCK_INDEX.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.RELATIVE_STOCK_INDEX.value] = rsi(close=df['close'],
n=rsi_n)
if Indicators.WILLIAMS_PERCENT_RANGE in indicators:
Logger.console_log(message="Calculating " +
Indicators.WILLIAMS_PERCENT_RANGE.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.WILLIAMS_PERCENT_RANGE.value] = wr(
df['high'], df['low'], df['close'])
if Indicators.CHANDE_MOMENTUM_OSCILLATOR in indicators:
Logger.console_log(message="Calculating " +
Indicators.CHANDE_MOMENTUM_OSCILLATOR.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.CHANDE_MOMENTUM_OSCILLATOR.
value] = chande_momentum_oscillator(close_data=df['close'],
period=cmo_n)
if Indicators.RATE_OF_CHANGE in indicators:
Logger.console_log(message="Calculating " +
Indicators.RATE_OF_CHANGE.value + " for stock " +
ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.RATE_OF_CHANGE.value] = roc(close=df['close'], n=roc_n)
if Indicators.STOCHASTIC_OSCILLATOR in indicators:
Logger.console_log(message="Calculating " +
Indicators.STOCHASTIC_OSCILLATOR.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.STOCHASTIC_OSCILLATOR.value] = stoch(
high=df['high'],
low=df['low'],
close=df['close'],
n=stochastic_oscillator_n,
d_n=stochastic_oscillator_sma_n)
if Indicators.ULTIMATE_OSCILLATOR in indicators:
Logger.console_log(message="Calculating " +
Indicators.ULTIMATE_OSCILLATOR.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.ULTIMATE_OSCILLATOR.value] = uo(
high=df['high'],
low=df['low'],
close=df['close'],
s=ultimate_oscillator_short_n,
m=ultimate_oscillator_medium_n,
len=ultimate_oscillator_long_n)
if Indicators.AWESOME_OSCILLATOR in indicators:
Logger.console_log(message="Calculating " +
Indicators.AWESOME_OSCILLATOR.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.AWESOME_OSCILLATOR.value] = ao(high=df['high'],
low=df['low'],
s=ao_short_n,
len=ao_long_n)
if Indicators.KAUFMAN_ADAPTIVE_MOVING_AVERAGE in indicators:
Logger.console_log(message="Calculating " +
Indicators.KAUFMAN_ADAPTIVE_MOVING_AVERAGE.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.KAUFMAN_ADAPTIVE_MOVING_AVERAGE.value] = kama(
close=df['close'], n=kama_n)
if Indicators.TRUE_STRENGTH_INDEX in indicators:
Logger.console_log(message="Calculating " +
Indicators.TRUE_STRENGTH_INDEX.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.TRUE_STRENGTH_INDEX.value] = tsi(close=df['close'],
r=tsi_high_n,
s=tsi_low_n)
# Trend Indicator
if Indicators.MOVING_AVERAGE_CONVERGENCE_DIVERGENCE in indicators:
Logger.console_log(
message="Calculating " +
Indicators.MOVING_AVERAGE_CONVERGENCE_DIVERGENCE.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.MOVING_AVERAGE_CONVERGENCE_DIVERGENCE.value] = macd(close=df['close'],
n_slow=macd_slow,
n_fast=macd_fast) - \
macd_signal(close=df['close'],
n_slow=macd_slow,
n_fast=macd_fast,
n_sign=macd_sign)
if Indicators.COMMODITY_CHANNEL_INDEX in indicators:
Logger.console_log(message="Calculating " +
Indicators.COMMODITY_CHANNEL_INDEX.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.COMMODITY_CHANNEL_INDEX.value] = cci(high=df['high'],
low=df['low'],
close=df['close'],
n=cci_n)
if Indicators.DETRENDED_PRICE_OSCILLATOR in indicators:
Logger.console_log(message="Calculating " +
Indicators.DETRENDED_PRICE_OSCILLATOR.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.DETRENDED_PRICE_OSCILLATOR.value] = dpo(
close=df['close'], n=dpo_n)
if Indicators.AVERAGE_DIRECTIONAL_INDEX in indicators:
Logger.console_log(message="Calculating " +
Indicators.AVERAGE_DIRECTIONAL_INDEX.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.AVERAGE_DIRECTIONAL_INDEX.value] = adx(high=df['high'],
low=df['low'],
close=df['close'],
n=adx_n)
if Indicators.MASS_INDEX in indicators:
Logger.console_log(message="Calculating " +
Indicators.MASS_INDEX.value + " for stock " +
ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.MASS_INDEX.value] = mass_index(high=df['high'],
low=df['low'],
n=mass_index_low,
n2=mass_index_high)
if Indicators.TRIPLE_EXPONENTIALLY_SMOOTHED_MOVING_AVERAGE in indicators:
Logger.console_log(
message="Calculating " +
Indicators.TRIPLE_EXPONENTIALLY_SMOOTHED_MOVING_AVERAGE.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.TRIPLE_EXPONENTIALLY_SMOOTHED_MOVING_AVERAGE.
value] = trix(close=df['close'], n=trix_n)
if Indicators.ICHIMOKU_A in indicators:
Logger.console_log(message="Calculating " +
Indicators.ICHIMOKU_A.value + " for stock " +
ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.ICHIMOKU_A.value] = ichimoku_a(high=df['high'],
low=df['low'],
n1=ichimoku_low_n,
n2=ichimoku_medium_n)
# Volume Indicator
if Indicators.CHAIKIN_MONEY_FLOW in indicators:
Logger.console_log(message="Calculating " +
Indicators.CHAIKIN_MONEY_FLOW.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.CHAIKIN_MONEY_FLOW.value] = chaikin_money_flow(
high=df['high'],
low=df['low'],
close=df['close'],
volume=df['volume'],
n=cmf_n)
if Indicators.ACCUMULATION_DISTRIBUTION_INDEX in indicators:
Logger.console_log(message="Calculating " +
Indicators.ACCUMULATION_DISTRIBUTION_INDEX.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.ACCUMULATION_DISTRIBUTION_INDEX.value] = acc_dist_index(
high=df['high'],
low=df['low'],
close=df['close'],
volume=df['volume'])
if Indicators.EASE_OF_MOVEMENT in indicators:
Logger.console_log(message="Calculating " +
Indicators.EASE_OF_MOVEMENT.value + " for stock " +
ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.EASE_OF_MOVEMENT.value] = ease_of_movement(
high=df['high'], low=df['low'], volume=df['volume'], n=eom_n)
if Indicators.FORCE_INDEX in indicators:
Logger.console_log(message="Calculating " +
Indicators.FORCE_INDEX.value + " for stock " +
ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.FORCE_INDEX.value] = force_index(close=df['close'],
volume=df['volume'],
n=force_index_n)
from ta.momentum import stoch # Stochastic Oscilator
from ta.momentum import uo # Ultimate Oscilator
from ta.momentum import wr #William Percent Range
from ta.trend import macd # Moving Average Convergence/Divergence
# *************************** DATA PREPROCESSING ******************************
# Load Data
df = pd.read_csv('^GSPC.csv')
# Data Augmentation
df['Relative_Strength_Index'] = rsi(df['Close'])
df['Money_Flow_Index'] = money_flow_index(df['High'], df['Low'], df['Close'],
df['Volume'])
df['Stoch_Oscilator'] = stoch(df['High'], df['Low'], df['Close'])
df['Ultimate_Oscilator'] = uo(df['High'], df['Low'], df['Close'])
df['William_Percent'] = wr(df['High'], df['Low'], df['Close'])
df['MACD'] = macd(df['Close'])
# Some indicators require many days in advance before they produce any
# values. So the begining rows of our df may have NaNs. Lets drop them:
df = df.dropna()
# Scaling Data
from sklearn.preprocessing import MinMaxScalern
sc = MinMaxScaler(feature_range=(0, 1))
scaled_df = sc.fit_transform(df.iloc[:, 1:].values)
# Creating a data structure with 60 timesteps and 1 output
X_train = np.array(
[scaled_df[i:i + 60, :] for i in range(len(scaled_df) - 60)])
def test_uo2(self):
target = 'Ult_Osc'
result = uo(**self._params)
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def calculate_Momentum_Indicators():
JSON_sent = request.get_json()
df = pd.DataFrame(JSON_sent[0])
_, RSI, TSI, UO, STOCH, STOCH_SIGNAL, WR, AO, KAMA, ROC = JSON_sent
indicator_RSI = RSIIndicator(close=df["close"], n=RSI['N'])
df['rsi'] = indicator_RSI.rsi()
if TSI['displayTSI']:
indicator_TSI = TSIIndicator(close=df["close"],
r=TSI['rTSI'],
s=TSI['sTSI'])
df['tsi'] = indicator_TSI.tsi()
if UO['displayUO']:
indicator_UO = uo(high=df['high'],
low=df['low'],
close=df['close'],
s=UO['sForUO'],
m=UO['mForUO'],
len=UO['lenForUO'],
ws=UO['wsForUO'],
wm=UO['wmForUO'],
wl=UO['wlForUO'])
df['uo'] = indicator_UO
if STOCH['displaySTOCH']:
indicator_Stoch = stoch(high=df['high'],
low=df['low'],
close=df['close'],
n=STOCH['nForSTOCH'],
d_n=STOCH['dnForSTOCH'])
df['stoch'] = indicator_Stoch
if STOCH_SIGNAL['displayStochSignal']:
indicator_StochSignal = stoch_signal(
high=df['high'],
low=df['low'],
close=df['close'],
n=STOCH_SIGNAL['nForStochSignal'],
d_n=STOCH_SIGNAL['dnForStochSignal'])
df['stoch_signal'] = indicator_StochSignal
if WR['displayWR']:
indicator_wr = wr(high=df['high'],
low=df['low'],
close=df['close'],
lbp=WR['lbpForWR'])
df['wr'] = indicator_wr
if AO['displayAO']:
indicator_ao = ao(high=df['high'],
low=df['low'],
s=AO['sForAO'],
len=AO['lenForAO'])
df['ao'] = indicator_ao
if KAMA['displayKama']:
indicator_kama = kama(close=df['close'],
n=KAMA['nForKama'],
pow1=KAMA['pow1ForKama'],
pow2=KAMA['pow2ForKama'])
df['kama'] = indicator_kama
if ROC['displayROC']:
indicator_roc = roc(close=df['close'], n=ROC['nForROC'])
df['roc'] = indicator_roc
df.fillna(0, inplace=True)
export_df = df.drop(columns=['open', 'high', 'low', 'close', 'volume'])
return (json.dumps(export_df.to_dict('records')))