stock_trading_strategy

In [1]:

# Algorithmic Stock Trading Strategy Using Python
# compare the simple moving average wi th a 30/100 day to determine the buy/sell price
import math
import pandas_datareader as web
import numpy as np
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')

In [22]:

AAPL = web.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2021-04-25')
AAPL

Out[22]:

	High	Low	Open	Close	Volume	Adj Close
Date
2012-01-03	14.732143	14.607143	14.621429	14.686786	302220800.0	12.650659
2012-01-04	14.810000	14.617143	14.642857	14.765714	260022000.0	12.718646
2012-01-05	14.948214	14.738214	14.819643	14.929643	271269600.0	12.859850
2012-01-06	15.098214	14.972143	14.991786	15.085714	318292800.0	12.994284
2012-01-09	15.276786	15.048214	15.196429	15.061786	394024400.0	12.973674
…	…	…	…	…	…	…
2021-04-19	135.470001	133.339996	133.509995	134.839996	94264200.0	134.839996
2021-04-20	135.529999	131.809998	135.020004	133.110001	94812300.0	133.110001
2021-04-21	133.750000	131.300003	132.360001	133.500000	68847100.0	133.500000
2021-04-22	134.149994	131.410004	133.039993	131.940002	84566500.0	131.940002
2021-04-23	135.119995	132.160004	132.160004	134.320007	78657500.0	134.320007

2342 rows × 6 columns

In [23]:

plt.figure(figsize=(16,8))
plt.title('Close Price History')
plt.plot(AAPL['Adj Close'])
plt.xlabel('Date', fontsize=18)
plt.ylabel('Close Price USD$', fontsize=18)
plt.show()

In [24]:

# create the simple moving average with a 30 day window
SMA30 = pd.DataFrame()
SMA30['Adj Close'] = AAPL['Adj Close'].rolling(window=30).mean()
SMA30

Out[24]:

	Adj Close
Date
2012-01-03	NaN
2012-01-04	NaN
2012-01-05	NaN
2012-01-06	NaN
2012-01-09	NaN
…	…
2021-04-19	125.136333
2021-04-20	125.694666
2021-04-21	126.108333
2021-04-22	126.507000
2021-04-23	126.919000

2342 rows × 1 columns

In [25]:

# create the simple moving average with a 100 day window
SMA100 = pd.DataFrame()
SMA100['Adj Close'] = AAPL['Adj Close'].rolling(window=100).mean()
SMA100

Out[25]:

	Adj Close
Date
2012-01-03	NaN
2012-01-04	NaN
2012-01-05	NaN
2012-01-06	NaN
2012-01-09	NaN
…	…
2021-04-19	127.688672
2021-04-20	127.882971
2021-04-21	128.067989
2021-04-22	128.228820
2021-04-23	128.407860

2342 rows × 1 columns

In [26]:

plt.figure(figsize=(16,8))
plt.title('Close Price History')
plt.plot(AAPL['Adj Close'], label = 'AAPL')
plt.plot(SMA30['Adj Close'], label = 'SMA30')
plt.plot(SMA100['Adj Close'], label = 'SMA100')
plt.xlabel('Date', fontsize=18)
plt.ylabel('Close Price USD$', fontsize=18)
plt.legend(loc='upper left')
plt.show()

In [27]:

# create a new data frame to store all the data
data = pd.DataFrame()
data['AAPL'] = AAPL['Adj Close']
data['SMA30'] = SMA30['Adj Close']
data['SMA100'] = SMA100['Adj Close']
data

Out[27]:

	AAPL	SMA30	SMA100
Date
2012-01-03	12.650659	NaN	NaN
2012-01-04	12.718646	NaN	NaN
2012-01-05	12.859850	NaN	NaN
2012-01-06	12.994284	NaN	NaN
2012-01-09	12.973674	NaN	NaN
…	…	…	…
2021-04-19	134.839996	125.136333	127.688672
2021-04-20	133.110001	125.694666	127.882971
2021-04-21	133.500000	126.108333	128.067989
2021-04-22	131.940002	126.507000	128.228820
2021-04-23	134.320007	126.919000	128.407860

2342 rows × 3 columns

In [41]:

#create a function to signal when to buy and sell 
def buy_sell(data):
  sigPriceBuy = []
  sigPriceSell = []
  flag = -1
  
  for i in range(len(data)):
    if data['SMA30'][i] > data['SMA100'][i]:
      if flag != 1:
        sigPriceBuy.append(data['AAPL'][i])
        sigPriceSell.append(np.nan)
        flag = 1
      else:
        sigPriceBuy.append(np.nan)
        sigPriceSell.append(np.nan)
    elif data['SMA30'][i] < data['SMA100'][i]:
      if flag != 0:
        sigPriceBuy.append(np.nan)
        sigPriceSell.append(data['AAPL'][i])
        flag = 0
      else:
        sigPriceBuy.append(np.nan)
        sigPriceSell.append(np.nan)
    else:
        sigPriceBuy.append(np.nan)
        sigPriceSell.append(np.nan)
  return(sigPriceBuy, sigPriceSell)

In [42]:

#store the buy and sell data into a variable
buy_sell = buy_sell(data)
data['Buy_Signal_Price'] = buy_sell[0]
data['Sell_Signal_Price'] = buy_sell[1]

In [43]:

data

Out[43]:

	AAPL	SMA30	SMA100	Buy_Signal_Price	Sell_Signal_Price
Date
2012-01-03	12.650659	NaN	NaN	NaN	NaN
2012-01-04	12.718646	NaN	NaN	NaN	NaN
2012-01-05	12.859850	NaN	NaN	NaN	NaN
2012-01-06	12.994284	NaN	NaN	NaN	NaN
2012-01-09	12.973674	NaN	NaN	NaN	NaN
…	…	…	…	…	…
2021-04-19	134.839996	125.136333	127.688672	NaN	NaN
2021-04-20	133.110001	125.694666	127.882971	NaN	NaN
2021-04-21	133.500000	126.108333	128.067989	NaN	NaN
2021-04-22	131.940002	126.507000	128.228820	NaN	NaN
2021-04-23	134.320007	126.919000	128.407860	NaN	NaN

2342 rows × 5 columns

In [47]:

#visualize the data and the strategy to buy and sell
plt.figure(figsize=(16,8))
plt.title('Close Price History')
plt.plot(data['AAPL'], label = 'AAPL', alpha = 0.35)
plt.plot(data['SMA30'], label = 'SMA30', alpha = 0.35)
plt.plot(data['SMA100'], label = 'SMA100', alpha = 0.35)
plt.scatter(data.index, data['Buy_Signal_Price'], label = 'Buy', marker = '^', color = 'green')
plt.scatter(data.index, data['Sell_Signal_Price'], label = 'Sell', marker = 'v', color = 'red')
plt.xlabel('Date', fontsize=18)
plt.ylabel('Close Price USD$', fontsize=18)
plt.legend(loc='upper left')
plt.show()

In [ ]:

2 Replies to “Algorithmic Stock Trading Strategy Using Python”

I Fashion Styles says:

July 13, 2021 at 11:57 pm

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Hairstyles says:

December 16, 2021 at 12:40 pm

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2 Replies to “Algorithmic Stock Trading Strategy Using Python”

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