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1.2 Structured Data: Information Driven Bars

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This post will describe how to process & store High Frequency financial data to support your Machine Learning Algorithm, based on how much information is contained in the data The post is directly based on content from the book "Advances in Financial Machine Learning" from Marcos Lopez de Prado Physical meaning: This approach is inspired by Claude Shannon's Information Theory, that is the basis of most compression algorithms. Shannon argued that only departure from the norm is new information. A famous example, is that the sun has risen in the morning. Yes, the event has happened, but because the probability of this event was 1.0, this event does not represent information. In information driven bars, we attempt to use the same insight, to compress the financial data. Here, we know that financial data is inherently noisy, and put guardrails around what level of variance we expect for a particular behavior. It is only when the variance exceeds these guardrails, that we
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2.2 Labeling: Triple barrier method

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 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
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2.1 Labeling: Fixed Horizon Method

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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
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