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Trading Strategy with Python

Developing an Algorithmic Trading Strategy with Python is something that goes through a couple of phases, just like when you build machine learning models: you formulate a strategy and specify it in a form that you can test on your computer, you do some preliminary testing or back testing, you optimize your strategy and lastly, you evaluate the performance and robustness of your strategy.

Let’s Build an Algorithmic Trading Strategy with Python and Machine Learning.

Let's import the necessary Libraries:

In [1]:
import pandas as pd
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
plt.style.use("ggplot")

This program uses the dual moving average crossover to determine when to buy and sell stocks. Let's read the dataset:

In [2]:
apple = pd.read_csv("AAPL.csv")apple
Out[2]:
Date Open Price High Price Low Price Close Price Adj Close Price Volume
0 5/27/2014 87.982857 89.408569 87.947144 89.375717 80.948952 87216500
1 5/28/2014 89.431427 89.975716 89.111427 89.144287 80.739334 78870400
2 5/29/2014 89.692856 90.981430 89.681427 90.768570 82.210480 94118500
3 5/30/2014 91.139999 92.024284 89.842857 90.428574 81.902557 141005200
4 6/2/2014 90.565712 90.690002 88.928574 89.807144 81.339699 92337700
... ... ... ... ... ... ... ...
1505 5/18/2020 313.170013 316.500000 310.320007 314.959991 314.959991 33843100
1506 5/19/2020 315.029999 318.519989 313.010010 313.140015 313.140015 25432400
1507 5/20/2020 316.679993 319.519989 316.519989 319.230011 319.230011 27876200
1508 5/21/2020 318.660004 320.890015 315.869995 316.850006 316.850006 25672200
1509 5/22/2020 315.769989 319.230011 315.350006 318.890015 318.890015 20430600

1510 rows × 7 columns

Let’s Visualize the data

In [3]:
plt.figure(figsize=(12, 5))
plt.plot(apple['Adj Close Price'], label='Apple')
plt.title('Apple Adj Close Price History')
plt.xlabel("May 27,2014 - May 25,2020 ")
plt.ylabel("Adj Close Price USD ($)")
plt.legend(loc="upper left")
plt.show()
Apple Close Price History

Create a Simple moving average with a 30 day window

In [4]:
sma30 = pd.DataFrame()
sma30['Adj Close Price'] = apple['Adj Close Price'].rolling(window=30).mean()
sma30
Out[4]:
Adj Close Price
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
... ...
1505 287.864806
1506 289.577426
1507 291.594106
1508 293.310716
1509 295.031499

1510 rows × 1 columns

To create a Simple moving average 100 day window

In [5]:
sma100 = pd.DataFrame()
sma100['Adj Close Price'] = apple['Adj Close Price'].rolling(window=100).mean()
sma100
Out[5]:
Adj Close Price
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
... ...
1505 289.734840
1506 290.037927
1507 290.345799
1508 290.630966
1509 290.919420

1510 rows × 1 columns

Now let’s Visualize the new data

In [6]:
plt.figure(figsize=(12,5))
plt.plot(apple['Adj Close Price'], label='Apple')
plt.plot(sma30['Adj Close Price'], label='SMA30')
plt.plot(sma100['Adj Close Price'], label='SMA100')
plt.title("Apple Adj. Close Price History")
plt.xlabel('May 27,2014 - May 25,2020')
plt.ylabel('Adj. Close Price USD($)')
plt.legend(loc='upper left')
plt.show()
Apple Close Price History
Now create a new Data Frame to store all the data
In [7]:
data = pd.DataFrame()
data['apple'] = apple['Adj Close Price']
data['SMA30'] = sma30['Adj Close Price']
data['SMA100'] = sma100['Adj Close Price']
data
Out[7]:
apple SMA30 SMA100
0 80.948952 NaN NaN
1 80.739334 NaN NaN
2 82.210480 NaN NaN
3 81.902557 NaN NaN
4 81.339699 NaN NaN
... ... ... ...
1505 314.959991 287.864806 289.734840
1506 313.140015 289.577426 290.037927
1507 319.230011 291.594106 290.345799
1508 316.850006 293.310716 290.630966
1509 318.890015 295.031499 290.919420

1510 rows × 3 columns

Create a function to signal when to buy or sell stock

In [8]:
def buySell(data):
  sigPriceBuy = []
  sigPriceSell = []
  flag = -1
  for i in range(len(data)):
    if data ['SMA30'][i] > data['SMA100'][i]:
      if flag != 1:
        sigPriceBuy.append(data['apple'][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['apple'][i])
        flag = 0
      else:
        sigPriceBuy.append(np.nan)
        sigPriceSell.append(np.nan)
    else:
      sigPriceBuy.append(np.nan)
      sigPriceSell.append(np.nan)
  return(sigPriceBuy, sigPriceSell)

To store the buy and sell data into a variable

In [9]:
buySell = buySell(data)
data['Buy Signal Price'] = buySell[0]
data['Sell Signal Price'] = buySell[1]
# To show the data
data
Out[9]:
apple SMA30 SMA100 Buy Signal Price Sell Signal Price
0 80.948952 NaN NaN NaN NaN
1 80.739334 NaN NaN NaN NaN
2 82.210480 NaN NaN NaN NaN
3 81.902557 NaN NaN NaN NaN
4 81.339699 NaN NaN NaN NaN
... ... ... ... ... ...
1505 314.959991 287.864806 289.734840 NaN NaN
1506 313.140015 289.577426 290.037927 NaN NaN
1507 319.230011 291.594106 290.345799 319.230011 NaN
1508 316.850006 293.310716 290.630966 NaN NaN
1509 318.890015 295.031499 290.919420 NaN NaN

1510 rows × 5 columns

Now let’s Visualize the data and strategy to buy and sell stock

In [10]:
plt.style.use('classic')
plt.figure(figsize=(12,5))
plt.plot(data['apple'], label='Apple', 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.title('Apple Adj. Close Price History Buy and Sell Signals')
plt.xlabel("May 27,2014 - May 25,2020")
plt.ylabel("Adj Close Price USD($)")
plt.legend(loc='upper left')
plt.show()
Apple Close Price History

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