def PlotEMA(stock_ticker, close, window_long, window_short):
# Getting values of EMA from the `ta` module
EMA_Long = ema_indicator(close=close,
window=int(window_long),
fillna=False)
EMA_Short = ema_indicator(close=close,
window=int(window_short),
fillna=False)
# Creating DataFrame
df = pd.DataFrame()
# Adding all the required columns to the DataFrame
df['Close'] = close
df['EMA_Long'] = EMA_Long.dropna()
df['EMA_Short'] = EMA_Short.dropna()
df['Signal'] = 0
# Giving buy signals
df.loc[(df['EMA_Short'] > df['EMA_Long']), 'Signal'] = 1
df['Position'] = df['Signal'].diff()
# Plot EMA's wrt Close price
plt.figure(figsize=(7, 6))
df['Close'].plot(color='k', lw=1, label='Close Price')
df['EMA_Short'].plot(color='b',
lw=1,
label=f'{window_short}-period EMA')
df['EMA_Long'].plot(color='g', lw=1, label=f'{window_long}-period EMA')
# Plot Buy/Sell Indicators
plt.plot(df[df['Position'] == 1].index,
df['EMA_Short'][df['Position'] == 1],
'^',
markersize=15,
color='g',
label='buy')
plt.plot(df[df['Position'] == -1].index,
df['EMA_Short'][df['Position'] == -1],
'v',
markersize=15,
color='r',
label='sell')
plt.ylabel('Price in Rupees', fontsize=15)
plt.xlabel('Date', fontsize=15)
plt.title(f'EMA Crossover: {stock_ticker}', fontsize=20)
plt.legend()
plt.grid()
try:
plt.savefig(f'image/{stock_ticker}/EMA_{stock_ticker}.png',
bbox_inches='tight')
except:
directory = f'image/{stock_ticker}'
for f in os.listdir(stock_ticker):
os.remove(os.path.join(directory, f))
plt.savefig(f'image/{stock_ticker}/EMA_{stock_ticker}.png',
bbox_inches='tight')
def CCI(df0, n=160):
df = df0
df['cci'] = ema_indicator(cci(df['high'],
df['low'],
df['close'],
n,
0.015,
fillna=False),
4,
fillna=False)
return df
def EMAvg(close, window_long, window_short):
# Getting values of EMA from the `ta` module
EMA_Long = ema_indicator(close=close, window=int(window_long), fillna=False)
EMA_Short = ema_indicator(close=close, window=int(window_short), fillna=False)
EMA_Long = EMA_Long.dropna()
EMA_Short = EMA_Short.dropna()
# Creating DataFrame
df = pd.DataFrame()
# Adding all the required columns to the DataFrame
df['Close'] = close
df['EMA_Long'] = EMA_Long
df['EMA_Short'] = EMA_Short
df['Signal'] = 0
# Giving buy signals
df.loc[(df['EMA_Short'] > df['EMA_Long']), 'Signal'] = 1
df['Position'] = df['Signal'].diff()
return df['Close'], df['Position']
def EMA(df0, n=4):
df = df0
df['ema'] = ema_indicator(df['close'], n, fillna=False)
return df
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')))
df["ADX_indicator"] = "0"
print("The ADX error is coming for SYMBOL" + f'{i}')
# df['ADX'] = indicator.adx()
#print(df)
#print(df)
"""OBV"""
indicator = on_balance_volume(close=df["close_price"],
volume=df["volume"],
fillna=True)
#print(indicator)
df["obv_indicator"] = indicator
#print(df)
"""EMA"""
indicator = ema_indicator(close=df["close_price"], n=12, fillna=True)
#print(indicator)
df["EMA_indicator"] = indicator
#print(df)
df2 = df2.append(df)
df2.to_csv('MACD_price.csv', mode='w', header=False)
table_name1 = 'TECHNICAL_ANALYSIS1'
def pg_load_table(file_path, table_name1, dbname, host, port, user, pwd):
'''
This function upload csv to a target table
'''
try:
conn = psycopg2.connect(dbname=dbname,
def __init__(self, N_day):
super().__init__(indicator=lambda x: ema_indicator(x, N_day))
self.lookback_window = N_day