Here's a simple code for downloading daily stock data and computing Bollinger band indicator, but what I am not able to do is set up a logic for generating a buy and sell signal. Can someone help me with that.

What i want is for the system to check if previous close price is less than Bollinger Band low and last close price should be above the Bollinger Band low. if yes then the system should show it as a buy and vice versa.

PS: I am only using Pandas, numpy, matplotlib and Quandl. Code:

import quandldownload_source = (r'F:\Trading\download.xlsx')df = quandl.get('NSE/RELIANCE', api_key = '*Quandl Api key*')
sma20 = df['Close'].rolling(window=20, min_periods=20 - 1).mean()
std = df['Close'].rolling(window=20, min_periods=20 - 1).std()df['bbMid'] = sma20
df['bbUp'] = (sma20 + (std * 2))
df['bblower'] = (sma20 - (std * 2))df.to_excel(download_source)

    previous_close = df['Close'].shift(1).valueslast_close = df['Close'].valuesbband_low = df['bblower'].valuesbband_up = df['bbUp'].valuescond_buy1 = previous_close < bband_lowcond_buy2 = last_close > bband_lowdf['BUY'] = np.where((cond_buy1 & cond_buy2), True, False)cond_sell1 = previous_close > bband_upcond_sell2 = last_close < bband_updf['SELL'] = np.where((cond_sell1 & cond_sell2), True, False)

I think this is what you are looking for.

Put these few lines of codes in your script before "df.to_excel(download_source)" and it should work.