How to Convert Vertically Stored Asset Data into Columnar Format for Cointegration Analysis

#machinelearning #python #programming

Introduction

This piece of code fetches asset information from a table stored vertically.

Dependencies

Install the following package.

conda install pandas
conda install numpy as np
conda install mysql-connector-python
conda install sqlalchemy
conda install pymysql

Hands-on Coding

Connect to the database

def query_df(query):
    try: 
        engine_uri = f"mysql+pymysql://db_user:db_pass_123@localhost:3306/tradewise_pse"
        db_conn = create_engine(engine_uri)        
        df_result = pd.read_sql(query, db_conn)    
        return df_result

    except Exception as e:    
        print(str(e))

Fetching the Dataset

if not load_existing:
    sql_distinct_tickers = "select ticker from candlestick where event_time='2023-12-29' and ticker not like '^%%'"
    df_tickers = query_df(sql_distinct_tickers)

    df = pd.DataFrame(index=['event_time'])

    ### Get the candlesticks
    for ticker in df_tickers['ticker']:
        sql_ticker_col = "select event_time, close from candlestick where ticker='{0}'"
        df_temp = query_df(sql_ticker_col.format(ticker))
        df_temp.set_index('event_time', inplace=True)
        df_temp.rename(columns={'close': ticker}, inplace=True)        
        df = df.add(df_temp, fill_value=0)

    df.to_csv(file_name)

Load the dataset from file

df = pd.read_csv(file_name, index_col=0)
df.drop(index=df.index[-1],axis=0, inplace=True)

Drop NA

df.dropna(axis=1, inplace=True)

Print the Dataset

print(f"Shape: {df.shape}")
print(f"Null values: {df.isnull().values.any()}")
df

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