Create Stock Price Indicator Dashboard in Power BI

# import python libraries
from pandas_datareader import data
import numpy as np
from datetime import datetime 
from datetime import date, timedelta
import pandas as pd
import talib

# Create Parameters 
Number_of_days = input('Enter Calendar days:')
print(Number_of_days)

Stock_Ticker = input('Enter Stock Ticker:') 
print(Stock_Ticker)

Moving_Average = input('Enter Bollinger Bands Moving Average Type (Simple or Exponential): ')
if Moving_Average == 'Simple':
    print(Moving_Average)
elif Moving_Average == 'Exponential':
    print(Moving_Average)
else: 
    print('error')

Days = int(Number_of_days)

# load stock data from yahoo 

start = date.today() - timedelta(Days)
today = date.today()
df = data.DataReader(Stock_Ticker, start=start, end=today,
                       data_source='yahoo')

# ADD INDICATORS TO MAIN DATAFRAME df

df['Daily_Returns'] = df['Adj Close'].pct_change()  # create column for daily returns
df['Price_Up_or_Down'] = np.where(df['Daily_Returns'] < 0, -1, 1) # create column for price up or down

## add columns for the volatility and volume indicators
from talib import ATR, OBV, ADX, RSI
df['Average_True_Range'] = ATR(df['High'], df['Low'], df['Close'])
df['On_Balance_Volume'] = OBV(df['Adj Close'], df['Volume'])

## add columns for momentum indicators
from talib import ADX, RSI, STOCH, WILLR, MFI
df['ADX'] = ADX(df['High'], df['Low'], df['Close'], timeperiod=14) #create column for ADX assume timeperiod of 14 days
df['RSI'] = RSI(df['Adj Close'],timeperiod=14) #create column for RSI assume timeperiod of 14 days 
df['William_%R'] = WILLR(df['High'],df['Low'], df['Close'], timeperiod=14) #create column for William %R use high, low and close, and assume timeperiod of 14 days
df['MFI'] = MFI(df['High'],df['Low'], df['Close'], df['Volume'], timeperiod=14) #create column for MFI use high, low and close, and assume timeperiod of 14 days

## add columns for statistic functions
from talib import LINEARREG, TSF

adj_close = df['Adj Close'].to_numpy()  # create ndarray from the adj_close prices

df['Linear_Regression'] = LINEARREG(adj_close, timeperiod=14)
df['Time_Series_Forecast'] = TSF(adj_close, timeperiod=14)

## add column for moving averages
from talib import MA, SMA, EMA, WMA    #import the moving average functions
df['Simple_Moving_Average_50'] = SMA(df['Adj Close'], timeperiod=50)
df['Simple_Moving_Average_200'] = SMA(df['Adj Close'], timeperiod=200)
    
## add columns for momentum indicators STOCH_df
slowk, slowd = STOCH(df['High'], df['Low'], df['Close'], fastk_period= 5, slowk_period= 3, slowk_matype= 0, slowd_period = 3, slowd_matype = 0) # uses high, low, close by default
STOCH_array = np.array([slowk, slowd]).transpose() 
STOCH_df = pd.DataFrame(data = STOCH_array, index=df.index, columns=['STOCH_slowk', 'STOCH_slowd'])

df = STOCH_df[['STOCH_slowk', 'STOCH_slowd']].join(df, how='right')  # join STOCH to main dataframe 



# CREATE Bollinger Bands® DATAFRAME WITH Bollinger Bands® INDICATORS

from talib import BBANDS

## Parameters
BBands_periods = 20
SDnumber = 2

if Moving_Average == 'Simple': 
    moving_avg= 0
    
else:
    moving_avg = 1    
 
upperband, middleband, lowerband = BBANDS(adj_close, timeperiod=BBands_periods, nbdevup=SDnumber, nbdevdn = SDnumber, matype=moving_avg)  # calculate the bollinger bands assuming the middle band as the simple moving average

bands = np.array([upperband, middleband, lowerband]).transpose()  # transpose the ndarrays 

stocks_bollingerbands = pd.DataFrame(data = bands, index = df.index, columns=['BB_upperband', 'BB_middleband', 'BB_lowerband']) # create dataframe from the ndarrays

stocks_bollingerbands = df[['Adj Close']].join(stocks_bollingerbands, how='left')  # add Adj Close Column and volume to bollinger bands dataframe 

stocks_bollingerbands['BB_Width'] = (stocks_bollingerbands['BB_upperband'] - stocks_bollingerbands['BB_lowerband']).div(stocks_bollingerbands['BB_middleband']) # add column for Bollinger Band Width

stocks_bollingerbands['Percent_B'] = (stocks_bollingerbands['Adj Close'] - stocks_bollingerbands['BB_lowerband']).div(stocks_bollingerbands['BB_upperband'] - stocks_bollingerbands['BB_lowerband']).mul(100) # add column for Percent B

## create list for Bollinger Bands® buy/sell strategy conditions
buy_or_sell = [
    (stocks_bollingerbands['Adj Close'] > stocks_bollingerbands['BB_upperband']),
    (stocks_bollingerbands['Adj Close'] <  stocks_bollingerbands['BB_lowerband'])
    ]

choices = ['Sell', 'Buy']

stocks_bollingerbands['Action'] = np.select(buy_or_sell, choices, default = 'Neutral') # create new column for action to take based on BollingerBand values

stocks_bollingerbands.drop(columns='Adj Close', inplace=True)  # drop Adj Close column

stocks_bollingerbands.dropna(inplace=True) # drop empty rows


# CREATE MERGED DATAFRAME

# join Bollinger Bands® dataframe to main dataframe to create new

stocks_df = stocks_bollingerbands.join(df, how='left')   
stocks_df

df.info()

stocks_bollingerbands.info()

stocks_df.info()

## send to CSV file

df.to_csv(r"C:\TQQQ_df.csv", sep = ',')
stocks_bollingerbands.to_csv(r"C:\TQQQ_bbands.csv", sep=',')