buy_sell_signals_pandas_ta.py

'''

Author: Utkarsh Singhal
Adapted from: Bruno
Blog: https://www.trading-et-data-analyses.com/p/strategies-dans-tradinginpython.html
This code has implementation for MACD, RSI and Bollinger Bands

'''

import numpy as np
import pandas as pd
import yfinance
import pandas_ta as ta
import matplotlib.pyplot as plt
from datetime import date
plt.style.use('fivethirtyeight')

stocksymbols = ['TATACOMM.NS']
startdate = date(2025,1,1)
end_date = date.today()
print(end_date)
def getMyPortfolio(stocks = stocksymbols ,start = startdate , end = end_date):
    data = yfinance.download( 
        stocks, start=startdate, end=end_date, prepost=True, auto_adjust=False
    )
    return data

data = getMyPortfolio(stocksymbols)

########### Simple Moving Average Implementation #############

data['SMA 30'] = ta.sma(data['Close'],30)
data['SMA 100'] = ta.sma(data['Close'],100)
# Remplir les valeurs manquantes après calcul des indicateurs
data.fillna(0, inplace=True)

#SMA BUY SELL
#Function for buy and sell signal
def buy_sell(data):
    signalBuy = []
    signalSell = []
    position = False 

    for i in range(len(data)):
        if data['SMA 30'][i] > data['SMA 100'][i]:
            if position == False :
                signalBuy.append(data['Adj Close'][i])
                signalSell.append(np.nan)
                position = True
            else:
                signalBuy.append(np.nan)
                signalSell.append(np.nan)
        elif data['SMA 30'][i] < data['SMA 100'][i]:
            if position == True:
                signalBuy.append(np.nan)
                signalSell.append(data['Adj Close'][i])
                position = False
            else:
                signalBuy.append(np.nan)
                signalSell.append(np.nan)
        else:
            signalBuy.append(np.nan)
            signalSell.append(np.nan)
    return pd.Series([signalBuy, signalSell])


data['Buy_Signal_price'], data['Sell_Signal_price'] = buy_sell(data)

fig, ax = plt.subplots(figsize=(14,8))
ax.plot(data['Adj Close'] , label = stocksymbols[0] ,linewidth=0.5, color='blue', alpha = 0.9)
ax.plot(data['SMA 30'], label = 'SMA30', alpha = 0.85)
ax.plot(data['SMA 100'], label = 'SMA100' , alpha = 0.85)
ax.scatter(data.index , data['Buy_Signal_price'] , label = 'Buy' , marker = '^', color = 'green',alpha =1 )
ax.scatter(data.index , data['Sell_Signal_price'] , label = 'Sell' , marker = 'v', color = 'red',alpha =1 )
ax.set_title(stocksymbols[0] + " Price History with buy and sell signals",fontsize=10, backgroundcolor='blue', color='white')
ax.set_xlabel(f'{startdate} - {end_date}' ,fontsize=18)
ax.set_ylabel('Close Price INR (₨)' , fontsize=18)
legend = ax.legend()
ax.grid()
plt.tight_layout()
plt.show()

########### MACD Implementation #############

close = data['Close'].squeeze()   
macd = ta.macd( close )
data = pd.concat([data, macd], axis=1).reindex(data.index)

if 'Adj Close' not in data.columns:
    data['Adj Close'] = close
        
def MACD_Strategy(df, risk):
    MACD_Buy=[]
    MACD_Sell=[]
    position=False

    for i in range(0, len(df)):
        if df['MACD_12_26_9'][i] > df['MACDs_12_26_9'][i] :
            MACD_Sell.append(np.nan)
            if position ==False:
                MACD_Buy.append(df['Adj Close'][i])
                position=True
            else:
                MACD_Buy.append(np.nan)
        elif df['MACD_12_26_9'][i] < df['MACDs_12_26_9'][i] :
            MACD_Buy.append(np.nan)
            if position == True:
                MACD_Sell.append(df['Adj Close'][i])
                position=False
            else:
                MACD_Sell.append(np.nan)
        elif position == True and df['Adj Close'][i] < MACD_Buy[-1] * (1 - risk):
            MACD_Sell.append(df["Adj Close"][i])
            MACD_Buy.append(np.nan)
            position = False
        elif position == True and df['Adj Close'][i] < df['Adj Close'][i - 1] * (1 - risk):
            MACD_Sell.append(df["Adj Close"][i])
            MACD_Buy.append(np.nan)
            position = False
        else:
            MACD_Buy.append(np.nan)
            MACD_Sell.append(np.nan)

    data['MACD_Buy_Signal_price'] = MACD_Buy
    data['MACD_Sell_Signal_price'] = MACD_Sell


MACD_strategy = MACD_Strategy(data, 0.025)

def MACD_color(data):
    MACD_color = []
    for i in range(0, len(data)):
        if data['MACDh_12_26_9'][i] > data['MACDh_12_26_9'][i - 1]:
            MACD_color.append(True)
        else:
            MACD_color.append(False)
    return MACD_color

data['positive'] = MACD_color(data)

plt.rcParams.update({'font.size': 10})
fig, ax1 = plt.subplots(figsize=(14,8))
fig.suptitle(stocksymbols[0], fontsize=10, backgroundcolor='blue', color='white')
ax1 = plt.subplot2grid((14, 8), (0, 0), rowspan=8, colspan=14)
ax2 = plt.subplot2grid((14, 12), (10, 0), rowspan=6, colspan=14)
ax1.set_ylabel('Price in ₨')
ax1.plot('Adj Close',data=data, label='Close Price', linewidth=0.5, color='blue')
ax1.scatter(data.index, data['MACD_Buy_Signal_price'], color='green', marker='^', alpha=1)
ax1.scatter(data.index, data['MACD_Sell_Signal_price'], color='red', marker='v', alpha=1)
ax1.legend()
ax1.grid()
ax1.set_xlabel('Date', fontsize=8)

ax2.set_ylabel('MACD', fontsize=8)
ax2.plot('MACD_12_26_9', data=data, label='MACD', linewidth=0.5, color='blue')
ax2.plot('MACDs_12_26_9', data=data, label='signal', linewidth=0.5, color='red')
ax2.bar(data.index,'MACDh_12_26_9', data=data, label='Volume', color=data.positive.map({True: 'g', False: 'r'}),width=1,alpha=0.8)
ax2.axhline(0, color='black', linewidth=0.5, alpha=0.5)
ax2.grid()
plt.show()


############# Bollinger Bands Implementation #############


def bb_strategy(data):
    bbBuy = []
    bbSell = []
    position = False
    bb = ta.bbands(data['Adj Close'], length=20,std=2)
    data = pd.concat([data, bb], axis=1).reindex(data.index)

    for i in range(len(data)):
        if data['Adj Close'][i] < data['BBL_20_2.0'][i]:
            if position == False :
                bbBuy.append(data['Adj Close'][i])
                bbSell.append(np.nan)
                position = True
            else:
                bbBuy.append(np.nan)
                bbSell.append(np.nan)
        elif data['Adj Close'][i] > data['BBU_20_2.0'][i]:
            if position == True:
                bbBuy.append(np.nan)
                bbSell.append(data['Adj Close'][i])
                position = False #To indicate that I actually went there
            else:
                bbBuy.append(np.nan)
                bbSell.append(np.nan)
        else :
            bbBuy.append(np.nan)
            bbSell.append(np.nan)

    data['bb_Buy_Signal_price'] = bbBuy
    data['bb_Sell_Signal_price'] = bbSell

    return data

#storing the function
data = bb_strategy(data)

#plot
fig, ax1 = plt.subplots(figsize=(14,8))
fig.suptitle(stocksymbols[0], fontsize=10, backgroundcolor='blue', color='white')
ax1 = plt.subplot2grid((14, 8), (0, 0), rowspan=8, colspan=14)
ax2 = plt.subplot2grid((14, 12), (10, 0), rowspan=6, colspan=14)
ax1.set_ylabel('Price in ₨')
ax1.plot(data['Adj Close'],label='Close Price', linewidth=0.5, color='blue')
ax1.scatter(data.index, data['bb_Buy_Signal_price'], color='green', marker='^', alpha=1)
ax1.scatter(data.index, data['bb_Sell_Signal_price'], color='red', marker='v', alpha=1)
ax1.legend()
ax1.grid()
ax1.set_xlabel('Date', fontsize=8)

ax2.plot(data['BBM_20_2.0'], label='Middle', color='blue', alpha=0.35) #middle band
ax2.plot(data['BBU_20_2.0'], label='Upper', color='green', alpha=0.35) #Upper band
ax2.plot(data['BBL_20_2.0'], label='Lower', color='red', alpha=0.35) #lower band
ax2.fill_between(data.index, data['BBL_20_2.0'], data['BBU_20_2.0'], alpha=0.1)
ax2.legend(loc='upper left')
ax2.grid()
plt.show()

################ The End #############