Algorithmic Trading quantra-go-algo

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quantra-go-algo / import_libraries_and_price_data.py

Last active February 27, 2020 12:50

We are importing the libraries and price data of General Motors to implement Random Walk

	# Import the necessary libraries

	import yfinance as yf
	import numpy as np
	import matplotlib.pyplot as plt
	import pandas as pd

	# Import the price data of General Motors

	df = yf.download('GM','2008-01-02', '2020-2-27')

quantra-go-algo / simulation_of_random_walk.py

Last active April 28, 2020 20:42

We are creating the simulation of the random walk for General Motors price data from 2019 February onwards

	# Calculate the daily percentage change, mean and sigma
	df['daily_pct_change'] = df['Adj Close'].pct_change()
	mu = df['daily_pct_change'].iloc[:-252].mean()
	sigma = df['daily_pct_change'].iloc[:-252].std()

	# Creating the random walk simulation of the probable price path
	simulation = {}
	simulation['Actual'] = list(df['Adj Close'].iloc[-252:].values)

	for sim in range(1,5): # Taking 5 paths

quantra-go-algo / random_walk_with_5_trials.py

Last active February 28, 2020 14:00

We will plot the random walk for General Motors for 50 simulations

	# Plotting the actual Adjusted Close price of General Motors
	df['Adj Close'][-252:].plot(figsize=(10,7),grid=True,legend=False)
	plt.xlabel('Year')
	plt.ylabel('Price')

	# Plotting the simulation of random walk

	simulation=pd.DataFrame(simulation)
	simulation.index=df[-252:].index
	simulation.plot(figsize=(10,7),grid=True,legend=False)

quantra-go-algo / five_number_summary.py

Created March 2, 2020 20:17

To list the minimum, first Quartile, second quartile, third quartile and maximum of a group

	# import 11 day Tesla data
	import yfinance as yf
	tesla = yf.download('TSLA','2020-01-27', '2020-02-11')
	tesla['Close']

	# Calculating the 5 number summary
	from numpy import percentile

	# calculate quartiles
	All_quartiles = percentile(tesla['Close'], [25, 50, 75])

quantra-go-algo / variance_standard_deviation.py

Created March 2, 2020 20:20

To compute the variance and the standard deviation

	import statistics as st

	print("Variance of the Closing price is % s"
	%(st.variance(tesla['Close'])))
	print("Standard Deviation of Closing Price is % s "
	% (st.stdev(tesla['Close'])))

quantra-go-algo / Plot_closing_price.py

Created March 2, 2020 20:21

To plot the closing price of a stock

	import matplotlib.pyplot as plt
	tesla['Close'].plot(figsize=(10,7))
	plt.legend()
	plt.grid()
	plt.show()

quantra-go-algo / Application of Deep Learning.py

Last active July 28, 2025 07:20

	# Importing the modules
	import yfinance as yf
	import pandas as pd
	import numpy as np
	from sklearn.preprocessing import MinMaxScaler
	from sklearn.metrics import confusion_matrix
	from keras.models import Sequential
	from keras.layers import Dense, Dropout, LSTM

	# Downloading GOOGLE OHLCV using yfinance with adjusted prices

quantra-go-algo / Sample_Stand_alone_HTML.py

Created March 11, 2020 08:11

This creates an html file of the output

	import plotly
	import plotly.graph_objs as go
	plotly.offline.plot({
	"data": [go.Scatter(x=[11, 15, 30, 37, 42], y=[15, 18, 71, 42, 42])],
	"layout": go.Layout(title="HTML file")}, auto_open=True)

quantra-go-algo / Sample_Plotly_In_Notebook.py

Created March 11, 2020 08:13

This code creates a sample plot in the Jupyter Notebook itself

	import plotly
	import plotly.graph_objs as go

	plotly.offline.iplot({
	"data": [go.Scatter(x=[11, 15, 30, 37, 42], y=[15, 18, 71, 42, 42])],
	"layout": go.Layout(title="Line plot")})

quantra-go-algo / Import_OHLC_data.py

Last active March 11, 2020 12:35

This code saves the OHLC data in a csvfile

	# Importing libraries
	import yfinance as yf
	import plotly as py
	import plotly.graph_objects as go
	import pandas as pd
	# Import Tesla data
	tesla = yf.download('TSLA','2020-02-01', '2020-03-03')