Machine Learning in Finance

 This post will describe how to label a financial dataset to support supervised learning The post is directly based on content from the book "Advances in Financial Machine Learning" from Marcos Lopez de Prado Physical meaning: Algorithm description: Python code: # Import libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt import math # Import data df = pd.read_csv( r'C:\Users\josde\OneDrive\Denny\Deep-learning\Data-sets\Trade-data\ES_Trades.csv' ) df = df.iloc[: , 0 : 5 ] df[ 'Dollar' ] = df[ 'Price' ] * df[ 'Volume' ] print (df.columns) # Generate thresholds d = pd.DataFrame(pd.pivot_table(df , values = 'Dollar' , aggfunc = 'sum' , index = 'Date' )) DOLLAR_THRESHOLD = ( 1 / 50 ) * np.average(d[ 'Dollar' ]) # Generate bars def bar_gen (df , DOLLAR_THRESHOLD): collector , dollarbar_tmp = [] , [] dollar_cusum = 0 for i , (price , dollar) in enumerate ( zip (df[ 'Price

This post will describe how to assign labels to bars (financial features) to support supervised learning. Upon successful training, the algorithm will be able to predict the probability of a label once a bar (financial feature) has been observed. The post is directly based on content from the book "Advances in Financial Machine Learning" from Marcos Lopez de Prado Physical meaning: Algorithm description: Python code: import numpy as np import pandas as pd import matplotlib.pyplot as plt # Import data df = pd.read_csv( r'C:\Users\josde\OneDrive\Denny\Deep-learning\Data-sets\Trade-data\ES_Trades.csv' ) df = df.iloc[: , 0 : 5 ] df[ 'Dollar' ] = df[ 'Price' ]*df[ 'Volume' ] # Generate thresholds d = pd.DataFrame(pd.pivot_table(df , values = 'Dollar' , aggfunc = 'sum' , index = 'Date' )) DOLLAR_THRESHOLD = ( 1 / 50 )*np.average(d[ 'Dollar' ]) # Generate bars def bar_gen (df , DOLLAR_THRESHOLD): collector , dollarb