• kdb+

  Technical Analysis using embedPy

  Posted by hoffmanroni on June 28, 2021 at 12:00 am

  Hello All,

  I’m just starting out with embedPy and wondering if aoneone can help.  I’m trying to use a module in python for some technical analysis.

  I was trying to use this module

  https://technical-analysis-library-in-python.readthedocs.io/en/latest/

  I’ve just got some bar data something like

  t:([]sym:30#`A;open:30?50f;high:30?50f;low:30?50f;close:30?50f;vol:30?10000)

  I’m having some difficulty passing the dataframe back and forth and can’t seem to get it to work.  Wondering if someone could help me through this first example on their website, making this work properly from q.

  import pandas as pd from ta.utils 
  import dropna from ta.volatility 
  import BollingerBands 
  # Load datas 
  df = pd.read_csv('ta/tests/data/datas.csv', sep=',') 
  # Clean NaN values 
  df = dropna(df) 
  # Initialize Bollinger Bands Indicator 
  indicator_bb = BollingerBands(close=df["Close"], window=20, window_dev=2) 
  # Add Bollinger Bands features 
  df['bb_bbm'] = indicator_bb.bollinger_mavg() 
  df['bb_bbh'] = indicator_bb.bollinger_hband() 
  df['bb_bbl'] = indicator_bb.bollinger_lband() 
  # Add Bollinger Band high indicator 
  df['bb_bbhi'] = indicator_bb.bollinger_hband_indicator() 
  # Add Bollinger Band low indicator 
  df['bb_bbli'] = indicator_bb.bollinger_lband_indicator()

   

  Many thanks!

   

  hoffmanroni replied 8 months ago 1 Member · 1 Reply
• 1 Reply

• 

  hoffmanroni

  Member

  June 29, 2021 at 12:00 am

  FYI if anyone is interested I was able to get this working now

  py script

  import pandas as pd from ta.utils 
  import dropna from ta.volatility 
  import BollingerBands 
  def returnBB(df, window=20, window_dev=2): 
      indicator_bb = BollingerBands(close=df["close"], window=20, window_dev=2) 
      # Add Bollinger Bands features 
      df['bb_bbm'] = indicator_bb.bollinger_mavg() 
      df['bb_bbh'] = indicator_bb.bollinger_hband() 
      df['bb_bbl'] = indicator_bb.bollinger_lband() 
      # Add Bollinger Band high indicator 
      df['bb_bbhi'] = indicator_bb.bollinger_hband_indicator() 
      # Add Bollinger Band low indicator 
      df['bb_bbli'] = indicator_bb.bollinger_lband_indicator() 
  return df

  and then q script

  //load above .p script l bb.p

  //make returnBB py func callable in q

  func:.p.get`returnBB

  //conv t to dataframe, pass to the py func and then conv back to qtable .ml.df2tab[func[.ml.tab2df[t]]]

Log In to Reply