Technical Analysis using embedPy

[hoffmanroni]

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 · Accepted solution]

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]]]