Questions tagged [machine-learning]
Algorithms that allow computers to evolve behaviors based on empirical data. Approaches include genetic programming, artificial neural networks, decision trees, support vector machines, and cluster analysis.
259 questions
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Challenges and Open-Source Solutions for Practical Quantum Algorithm Integration in Financial Applications [closed]
Hello Quant Stack Exchange community,
I'm keen to understand the community's perspective on the current state and practical challenges of integrating quantum algorithms into real-world financial ...
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Stock clustering as a part of portfolio diversification
I have a research hypothesis and now I'am trying to look at it from different angles.Now I am a bit puzzled.Maybe someone is also interested in machine learning application(especially clustering) in ...
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How can I robustly detect dynamic support and resistance levels programmatically in Python?
I am working on a Python project to programmatically detect dynamic support and resistance levels in historical price data, particularly for forex instruments such as EUR/USD. My primary goal is to ...
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Associative rule mining in quant finance
Has anyone seen/used associative rule mining in quant finance? I'm particularly curious in seeing if it has applications in differentiating between manager skill vs. luck but other applications are ...
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What model(s) can be used to simulate the joint dynamics of the 2y forward curve and implied volatility surface?
I am trying to train a reinforcement learning model for dynamic hedging like Cao et al. 2023. Their model uses SABR to generate joint dynamics of implied volatility and underlying equity asset prices.
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Metric for volatility time series similarity - European swaptions
I'm trying to estimate the volatility surface of illiquid swaptions (say CHF) given hourly data (atm vol, skew, for different strikes) of other liquid swaptions (EUR, USD, etc.). Having the underlying ...
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Training models for production
Not entirely sure which workflow is considered correct.
Say for both workflows, I have 10 splits for my test data and perform walk-forward validation during training.
Is it better use the last model ...
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Sell Logic When Using Triple Barrier method for Buy Signals Only
I have defined a rules based primary indicator that does a decent job predicting future uptrends. For better results, I want to take all records where the primary indicator occurs and feed it through ...
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How to represent dividend schedule to ML Model for barrier Options
I am looking at creating an ML model to price an exotic equity option which has a barrier where the buyer is paid out if the barrier is crossed, and multiple observation dates where the price is ...
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Quantification of Complexity in Trading Systems
I am trying to define and quantify the complexity of a trading system, where complexity is measured by degrees of freedom as the sum of its rules and unique data points.
where:
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Signal update frequency and predicting directional return one step ahead
I am trying to get some insights on this specific sort of problem from experienced people, as I do not have much experience in this field.
I have a family of features that for simplicity I will just ...
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Train-test split configuration on timeseries data for machine learning optimization
I have a strategy (running in the seconds scale) which parameters I would like to optimize. The thing is I'm relatively new to financial machine learning and I'm not quite sure how to split the data ...
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How do I predict future earnings dates if I have a database of all prior earnings dates?
So I have a database of all earnings announcements for all US stocks down to the millisecond for the past 10 years, and I want to make reasonable predictions on when exactly next earnings will be ...
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Weak Stationarity for Neural Network Input?
I am taking a course that detailed that input data into neural networks should be at least weakly predictive and weakly stationary (stable mean).
Does this principle apply to other ML models like tree-...
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Proper Use of CPCV for Hyperparameter Tuning and Backtesting in a Trading Strategy
I'm working on a binary classification model for a month-end trading strategy with 6 months of data. Initially, I split the data by using the last month for evaluation and backtesting, but this left ...
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