TA_pandas.py

# FUNK from https://colab.research.google.com/drive/12rWn1enSZP6OTgYgRT1OYFhFnt7QFfIu#scrollTo=dwe_prwCn2HH

def moving_average(df, n):
  MA = pd.Series(df['Close'].rolling(n, min_periods=n).mean(), name='MA_' + str(n))
  df = df.join(MA)
  return df

def exponential_moving_average(df, n):
  EMA = pd.Series(df['Close'].ewm(span=n, min_periods=n).mean(), name="EMA_" + str(n))
  df = df.join(EMA)
  return df

def momentum(df, n):
  M = pd.Series(df['Close'].diff(n), name="Momentum_"+str(n))
  df = df.join(M)
  return df

def bollinger_bands(df, n):
  MA = pd.Series(df['Close'].rolling(n, min_periods=n).mean())
  MSD = pd.Series(df['Close'].rolling(n, min_periods=n).std())
  b1 = 4 * MSD / MA
  B1 = pd.Series(b1, name = "BollingerB_" + str(n))
  df = df.join(B1)
  b2 = (df['Close'] - MA  + 2 * MSD) / (4 * MSD)
  B2 = pd.Series(b2, name='Bollinger%b_'+str(n))
  df = df.join(B2)
  return df

def stochastic_oscillator(df ,n):
  S0K = pd.Series((df['Close']- df['Low']) / (df['High']-df['Low']), name='S0k_' + str(n))
  df = df.join(S0K)
  return df

def relative_strength_index(df, n):
  delta = df['Close'].diff()
  up = delta.clip(lower=0)
  down = -1*delta.clip(upper=0)
  ema_up = up.ewm(com=n, adjust=False).mean()
  ema_down = down.ewm(com=n, adjust=False).mean()
  rs = ema_up/ema_down
  rsi = 100 - (100/(1 + rs))
  df['rsi_' + str(n)] = rsi
  return df

def common_channel_index(df, n):
  PP = (df['High'] + df['Low'] + df['Close']) / 3
  CCI = pd.Series((PP - PP.rolling(n, min_periods=n).mean()) / PP.rolling(n, min_periods=n).std(), name='CCI_' + str(n))
  df = df.join(CCI)
  return df

def standart_deviation(df, n):
  df = df.join(pd.Series(df['Close'].rolling(n, min_periods=n).std(), name='STD_' + str(n)))
  return df