def test_macd_signal(self):
target = 'MACD_signal'
result = macd_signal(close=self._df['Close'],
n_slow=12,
n_fast=26,
n_sign=9,
fillna=False)
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def MACD(df, col_name, intervals):
"""
Moving Average Convergence/divergence
Reference: https://www.investopedia.com/terms/m/macd.asp
"""
from ta.trend import macd_signal, macd
from tqdm.auto import tqdm
for interval in tqdm(intervals):
df['macd_' + str(interval)] = macd_signal(df[col_name],
n_fast=min(intervals),
n_slow=max(intervals),
n_sign=interval)
def MACD(df0):
df = df0
df['macd'] = macd(df['close'], n_slow=26, n_fast=12, fillna=False)
df['macd_signal'] = macd_signal(df['close'],
n_slow=26,
n_fast=12,
n_sign=9,
fillna=False)
df['macd_hist'] = macd_diff(df['close'],
n_slow=26,
n_fast=12,
n_sign=9,
fillna=False)
return df
def macd_cb_pressed(stock_analysis_tool: QMainWindow,
cb: QCheckBox) -> None:
"""
:param cb:
:return:
"""
if cb.isChecked():
# Add MACD to Display Graph
stock_analysis_tool.df['MACD'] = macd(close=stock_analysis_tool.df['close'],
n_slow=stock_analysis_tool.macd_slow,
n_fast=stock_analysis_tool.macd_fast) - \
macd_signal(close=stock_analysis_tool.df['close'],
n_slow=stock_analysis_tool.macd_slow,
n_fast=stock_analysis_tool.macd_fast,
n_sign=stock_analysis_tool.macd_sign)
stock_analysis_tool.add_column_to_graph(column_name='MACD')
else:
# Remove MACD from Display Graph
stock_analysis_tool.remove_column_from_graph(
column_name='MACD')
stock_analysis_tool.df = stock_analysis_tool.df.drop("MACD",
axis=1)
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)
def test_macd_signal(self):
target = 'MACD_signal'
result = macd_signal(**self._params)
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def calculate_Trend_Indicators():
JSON_sent = request.get_json()
if len(JSON_sent[0]) > 0:
df = pd.DataFrame(JSON_sent[0])
_, sma, ema, macdDict, macdSignal, adxDict, adxPos, adxNeg, VIpos, VIneg, trixDict, mi, dpoDict = JSON_sent
if sma['displaySMA']:
indicator_sma = sma_indicator(close=df['close'], n=sma['nForSMA'])
df['sma'] = indicator_sma
if ema['displayEMA']:
indicator_ema = ema_indicator(close=df['close'], n=ema['nForEMA'])
df['ema'] = indicator_ema
if macdDict['displayMACD']:
indicator_macd = macd(close=df['close'],
n_slow=macdDict['nSlowForMACD'],
n_fast=macdDict['nFastForMACD'])
df['macd'] = indicator_macd
if macdSignal['displayMACDsignal']:
indicator_macdSignal = macd_signal(
close=df['close'],
n_slow=macdSignal['nSlowForMACDsignal'],
n_fast=macdSignal['nFastForMACDsignal'],
n_sign=macdSignal['nSignForMACDsignal'])
df['macds'] = indicator_macdSignal
if adxDict['displayADX']:
indicator_ADX = adx(high=df['high'],
low=df['low'],
close=df['close'],
n=adxDict['nForADX'])
df['adx'] = indicator_ADX
if adxPos['displayADXP']:
indicator_ADXpositive = adx_pos(high=df['high'],
low=df['low'],
close=df['close'],
n=adxPos['nForADXP'])
df['adxp'] = indicator_ADXpositive
if adxNeg['displayADXN']:
indicator_ADXnegative = adx_neg(high=df['high'],
low=df['low'],
close=df['close'],
n=adxNeg['nForADXN'])
df['adxn'] = indicator_ADXnegative
if VIpos['displayVIPOS']:
indicator_VIpositive = vortex_indicator_pos(high=df['high'],
low=df['low'],
close=df['close'],
n=VIpos['nForVIPOS'])
df['vipos'] = indicator_VIpositive
if VIneg['displayVINEG']:
indicator_VInegative = vortex_indicator_neg(high=df['high'],
low=df['low'],
close=df['close'],
n=VIneg['nForVINEG'])
df['vineg'] = indicator_VInegative
if trixDict['displayTRIX']:
indicator_TRIX = trix(close=df['close'], n=trixDict['nForTRIX'])
df['trix'] = indicator_TRIX
if mi['displayMI']:
indicator_MassIndex = mass_index(high=df['high'],
low=df['low'],
n=mi['nForMI'],
n2=mi['n2ForMI'])
df['mi'] = indicator_MassIndex
if dpoDict['displayDPO']:
indicator_dpo = dpo(close=df['close'], n=dpoDict['nForDPO'])
df['dpo'] = indicator_dpo
df.fillna(0, inplace=True)
return (json.dumps(df.to_dict('records')))
else:
df = pd.DataFrame([])
return (json.dumps(df.to_dict('records')))
def scan(stock_detail,stock_budget):
# scan the macd on the stock
stock_macd = {}
stock_macd_signal = {}
stock_macd_diff = {}
stock_sma = {}
# get the macd of all the stocks
for sym in stock_detail:
# perform a macd on the aggregated data
stock_macd[sym] = macd(
close = stock_detail[sym]['close'].dropna(),
)
stock_macd_signal[sym] = macd_signal(
close = stock_detail[sym]['close'].dropna(),
)
stock_macd_diff[sym] = macd_diff(
close = stock_detail[sym]['close'].dropna(),
)
# get 200 day sma
stock_sma[sym] = sma_indicator(
close = stock_detail[sym]['close'].dropna(),
n = 200
)
for sym in stock_macd:
curr_price = currentClosePrice(sym,'day')
print('PRICE: ' + str(curr_price))
print("MACD: "+ str(stock_macd[sym][-1]))
print("MACD SIGNAL: " + str(stock_macd_signal[sym][-1]))
print("MACD DIFF:" + str(stock_macd_diff[sym][-1]))
print("SMA: " + str(stock_sma[sym][-1])+'\n')
# Buy Signal
if ((stock_macd[sym][-1]<stock_macd_diff[sym][-1] and stock_macd_signal[sym][-1]<stock_macd_diff[sym][-1]) and #check for macd and signal neg ovr hist
not(stock_macd_diff[sym][-3]>0 and stock_macd_diff[sym][-2]>0 and stock_macd_diff[sym][-1]>0) and #hist is not going neg
(curr_price<stock_macd[sym][-1])): #close price below 200 moving average
# check how many share that the budget can afford
shares = determineShare(sym, stock_budget[sym])
# so macd looks good, so maybe want to buy?
if shares>0 and clock.is_open():
stock_shares[sym] = shares #update the shares about to buy
api.submit_order(sym,shares,'buy','market','day')
print("Buying Stock {symbol}".format(symbol=sym))
# Sell Signal
elif((stock_macd[sym][-1]>stock_macd_diff[sym][-1] and stock_macd_signal[sym][-1]>stock_macd_diff[sym][-1]) and #check for macd and signal pos ovr hist
not(stock_macd_diff[sym][-3]<0 and stock_macd_diff[sym][-2]<0 and stock_macd_diff[sym][-1]<0) and #hist is not going pos
(curr_price>stock_macd[sym][-1])): #close price above 200 moving average
# check if we have any shares of this stock
if stock_shares[sym]>0 and clock.is_open():
print(stock_shares[sym])
# sell all the shares
api.submit_order(sym,stock_budget[sym],'sell','market','day')
stock_shares[sym] = 0 #update the shares dictionary to 0, b/c sold all
print("Selling Stock {symbol}".format(symbol=sym))
