The Process

This post will describe the overall process to craft an investment strategy. It is a 10 step process. Each step will be described separately. I will update this page with an overview, after I have completed all steps. The posts describe the work of Marcos De Prado

  1. Structured Data
    1. Standard Bars
      1. Time bars
      2. Tick bars
      3. Volume bars
      4. Dollar/Dynamic Dollar bars
    2. Information Driven Bars
      1. Tick imbalance bars
      2. Volume/Dollar Imbalance bars
      3. Tick run bars
    3. Event driven bars
      1. Structural breaks
        1. Cusum Tests
        2. Explosiveness Tests
        3. Right Tail Unit root tests
        4. Sub/super Martingale Tests
      2. Entropy features
      3. Microstructural features
        1. Tick rule
        2. Roll model
        3. High/Low volatility estimator
        4. VPIN
        5. Distribution of Order Sizes
        6. Cancellation Rates, Limit Orders, Market Orders
  2. Labeling
    1. Fixed Horizon Method
    2. Triple Barrier Method
    3. Trend Scanning Method
    4. Meta Labeling
  3. Weighting
    1. Fixed-width Window fracdiff
  4. Ensembles
    1. Bagging
    2. Random Forest
    3. Ada Boost
  5. Cross Validation
    1. K-Fold Cross Validation
  6. Feature Importance
    1. Mean Decrease Impurity
    2. Mean Decrease Accuracy
    3. Single Feature Importance
  7. Hyper Parameter Tuning
    1. Grid Search Cross Validation
    2. Randomised Search Cross Validation
  8. Bet Sizing
    1. Bet sizing from predicted probabilities
    2. Averaging Active Bets
    3. Size discretisation
    4. Dynamic bet sizes & limit prices
  9. Backtesting
    1. Walk forward method
    2. Cross validation method
    3. Combinatorial
    4. Backtesting on synthetic data
  10. Portfolio Construction
    1. Nested clustered optimisation algorithm

Comments

Post a Comment

Popular posts from this blog

1.2 Structured Data: Information Driven Bars

Image
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
Read more

2.1 Labeling: Fixed Horizon Method

Image
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
Read more

2.2 Labeling: Triple barrier method

Image
 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
Read more