quantra-go-algo

# Define a list of the tickers we are interested to observe
ticker_list = ['RELIANCE.NS','ADANIPOWER.NS','TCS.NS', 'HDFCBANK.NS', 'WIPRO.NS', 'HCLTECH.NS', 'KOTAKBANK.NS','INFY.NS','M&M.NS', 'BAJFINANCE.NS','ICICIBANK.NS']

# Define function to pull data from yahoo
def get_prices(tickers,start):
    prices = yf.download(tickers, start=start, auto_adjust=False)['Adj Close']
    return prices

prices_df = get_prices(ticker_list,start)
prices_df.shape

quantra-go-algo

# To work with the date
import datetime 
# We will be working with pandas
import pandas as pd
# To pull the data from yahoo
import yfinance as yf
# To visualize the results 
import matplotlib.pyplot as plt
import seaborn as sns

quantra-go-algo

# Download the GBPUSD data from 2003 to 2013
data = yf.download('GBPUSD=X', start='2003-12-01', end='2023-10-06', auto_adjust=True, group_by='ticker')['GBPUSD=X']

# Compute the log returns
data['returns'] = np.log(data['Close']/data['Close'].shift(1))

# Print the data
data

quantra-go-algo

# !pip install pyfolio-reloaded

# For data manipulation
import numpy as np
import pandas as pd
import yfinance as yf
import pyfolio as pf
from hmmlearn import hmm
from sklearn.utils import check_random_state

quantra-go-algo

# Set the for loop 
for i in range(1,len(monthly_index)):
    print('='*100)
    # Set the current month-end variable
    current_month_end = monthly_index[(i-1)]
    # Set the next month-end variable
    next_month_end = monthly_index[i]

    # Set the span from the previous to the next month end
    span = len(data.loc[current_month_end:next_month_end,:].index)

quantra-go-algo

from statsmodels.tsa.arima.model import ARIMA

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pip install pandas numpy matplotlib statsmodels yfinance

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pip install pandas statsmodels matplotlib

quantra-go-algo

wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz \
  && sudo tar -xzf ta-lib-0.4.0-src.tar.gz \
  && sudo rm ta-lib-0.4.0-src.tar.gz \
  && cd ta-lib/ \
  && sudo ./configure --prefix=/usr \
  && sudo make \
  && sudo make install \
  && cd ~ \
  && sudo rm -rf ta-lib/ \
  && pip install ta-lib

quantra-go-algo

# Predicting the Gold ETF prices
predicted_price = linear.predict(X_test)
predicted_price = pd.DataFrame(
    predicted_price, index=y_test.index, columns=['price'])
predicted_price.plot(figsize=(10, 7))
y_test.plot()
plt.legend(['predicted_price', 'actual_price'])
plt.ylabel("Gold ETF Price")
plt.show()