hidden markov model 4 trading (jim simons fav)
Updated: November 20, 2024
Summary
This video introduces the hidden Markov model, a favorite model used in financial trading by Jim Simons. The model's appeal lies in its ability to predict future market behavior by recognizing patterns in noisy financial data. The video provides insights on applying the hidden Markov model in algorithmic trading of cryptocurrencies, including data collection, model training, trading strategy development, and risk management. It also touches on the process of fitting, projecting, and predicting with the model, emphasizing the importance of consistency in learning coding for long-term progress.
TABLE OF CONTENTS
Introduction to Hidden Markov Model for Trading
Overview of Hidden Markov Model
Key Insights of Hidden Markov Model
Applying Hidden Markov Model in Algorithmic Trading
Examples of Hidden Markov Models
Introduction to Model Training
Model Parameters and Components
Convergence and Model Saving
Observation Categorization
Predicting with the Model
Data Acquisition and Exploration
Final Thoughts and Coding Motivation
Encouragement for Learning to Code
Importance of Learning and Coding
Introduction to Hidden Markov Model for Trading
Introduction to Jim Simons' favorite model, the hidden Markov model for trading, explaining its appeal and use in financial trading.
Overview of Hidden Markov Model
Explanation of the hidden Markov model with its hidden states, application to financial markets, and the model's ability to predict future market behavior.
Key Insights of Hidden Markov Model
Insights on the model's ability to recognize patterns in noisy financial data, its quantitative nature, competitive advantage, versatility, and application in predicting future market behavior.
Applying Hidden Markov Model in Algorithmic Trading
Breakdown of steps to apply the hidden Markov model in algorithmic trading of cryptocurrencies, including data collection, model training, state prediction, trading strategy, backtesting, live trading, feature engineering, model tuning, risk management, and validation.
Examples of Hidden Markov Models
Explanation using simple examples like weather, mood, traffic light, seasons, and ocean to illustrate the application of hidden Markov models in algorithmic trading for cryptocurrencies.
Introduction to Model Training
The speaker introduces the concept of training a model where Dynamics remain the same and you fit it to get similar results. They mention using the HMM learn package to build models and monitoring convergence during the fitting process.
Model Parameters and Components
Exploration of model parameters including using Gaussian hidden Markov models or a mixture of Gaussian mixture models. Discussion on defining the number of components, verbosity, and iterations for the expectation maximization algorithm.
Convergence and Model Saving
Explanation of monitoring convergence with the expectation maximization algorithm and saving the hidden states generated by the model. Also, details on saving or loading the model using pickle and plotting results in subplots.
Observation Categorization
Description of categorizing observations based on volatility where highly volatile returns are purple and low volatile returns are red. Explanation of model assigning observations to certain distributions based on data.
Predicting with the Model
Discussion on fitting, projecting, and predicting with the model. Instructions on using the 'predict' method on data and observing the results. Mention of a script called 'hidden Markov model predict.pi'.
Data Acquisition and Exploration
The speaker requests code to use the hidden Markov model on Bitcoin data and displays a large dataset of hourly BTC data. They mention preparing to run the code and future plans for coding sessions.
Final Thoughts and Coding Motivation
The speaker expresses appreciation and motivation for coding. They discuss the challenges and rewards of learning to code, encouraging perseverance for long-term progress in coding skills.
Encouragement for Learning to Code
Continuation of the speaker's discussion on learning to code, emphasizing the initial challenges and eventual enjoyment once coding skills are mastered. Advice on consistent learning and perseverance in coding.
Importance of Learning and Coding
Further encouragement and personal anecdotes from the speaker about the learning process and the transformation from struggling to code to enjoying coding as a game. Emphasis on dedication and consistent effort in learning.
FAQ
Q: What is the hidden Markov model used for in financial trading?
A: The hidden Markov model is used in financial trading to predict future market behavior by recognizing patterns in noisy financial data.
Q: Can you explain the steps involved in applying the hidden Markov model in algorithmic trading of cryptocurrencies?
A: The steps include data collection, model training, state prediction, trading strategy development, backtesting, live trading, feature engineering, model tuning, risk management, and validation.
Q: How does the hidden Markov model categorize observations based on volatility in financial data?
A: The model categorizes highly volatile returns as purple and low volatile returns as red, assigning observations to certain distributions based on the data.
Q: What are some insights into the usage of the hidden Markov model in financial markets?
A: The model's quantitative nature, competitive advantage, versatility, and its ability to predict future market behavior are some key insights into its usage in financial markets.
Q: What are some examples used to illustrate the application of hidden Markov models in algorithmic trading for cryptocurrencies?
A: Simple examples like weather, mood, traffic lights, seasons, and ocean are used to illustrate the application of hidden Markov models in algorithmic trading for cryptocurrencies.
Q: What details are discussed about fitting, projecting, and predicting with the hidden Markov model?
A: The discussion covers topics like exploring model parameters using Gaussian hidden Markov models, defining the number of components, verbosity, iterations for the expectation maximization algorithm, monitoring convergence, and saving the hidden states generated by the model.
Q: How can the hidden Markov model be used with Bitcoin data?
A: The model can be used on Bitcoin data by preparing to run the code, choosing appropriate model parameters, and using the 'predict' method on the data to observe the results.
Get your own AI Agent Today
Thousands of businesses worldwide are using Chaindesk Generative
AI platform.
Don't get left behind - start building your
own custom AI chatbot now!