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.

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what exactly is a time bucket? [closed]

I am refferring to kaggle optiver realized volatility prediction competition. In their intro : https://www.kaggle.com/code/jiashenliu/introduction-to-financial-concepts-and-data/notebook there is a ...
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AFML (by Lopez Deprado) Vs ESL by Trevor Hastie

The books "The Elements of Statistical Learning" by Trevor Hastie, and "Advances in Financial Machine Learning" by Lopez De Prado are highly recommended books for ML. They both ...
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Yield curve PCA: levels or daily moves?

I have tried using both yield curve levels as well as daily moves (absolute change) while doing PCA. Using both types of input/dataset gives me roughly the same shape in terms of principal components ...
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Lazypredict using Multiple Tickers

I am trying to use multiple tickers close prices , create some technical indicators and run lazy predict to get multiple results of Machine Learning performance models. I have cleaned the data somehow....
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What does the locality linear coding function do?

I got this code for spectral clustering from this link. This is a landmark-based spectral clustering code. What is the purpose of the "locality linear coding" function in this code? how it ...
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Why is return-weighted (over)sampling making ML result worse?

I'm working on an ML approach to predict trend (binary classification, up/down). I decided to try to emphasize the parts of my dataset where there are big moves by the following sampling scheme: ...
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Bet sizing to actual orders

At chapter 10.2 in Advances in Financial Machine Learning it says: Suppose that one strategy produced a sequence of bet sizes $[m_{1,1}, m_{1,2}, m_{1,3}] = [.5, 1, 0]$, as the market price followed ...
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Calculate core deposits in commercial bank

Given 10 years history of past balances of deposit accounts in a commercial bank, I need to calculate what part of those deposits were core, month by month. This is my thinking: for each account/month ...
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Sampling dollar bars for a machine learning model

I'm trying to understand the rationale behind using information drive bars over traditional time bars and specifically when it comes to practically feeding those in to a machine learning model to run ...
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How to merge ML-based $\alpha$-signal with stochastic control approach?

I'm having a hypothetical situation where I have a set of ML-based alpha signals $\{\alpha_i\}_{i=1}^{N}$ that describe a different states of order book - imbalances, order flow, spread properties etc....
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Is there a Pytorch counterpart of the book Machine Learning for Algorithmic Trading?

As a beginner in fintech, I am reading the book Machine Learning for Algorithmic Trading by Stefan Jansen. I think it is a really helpful book. But most of the codes are written in tensorflow 2. I ...
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Instrument clustering to maximize model prediction accuracy

I've been developing models for crypto trading. Usually, I use a group of about 25 of the larger market cap currencies and train a model on all of them, however, sometimes I'll use fewer while ...
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Machine learning models for sequential truncated time series ahead of a series of events

After some unsuccessful searches, I am turning to the community for the following issue: Assume I am interested in the dynamics of a stock prior to FOMC meetings. I am interested in the 20 days prior ...
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Disecting a log diff transformation for time series analysis and prediction

I have been working in a predictive ML model that uses financial time-series as predictor variables. In one of the academic papers I used as reference, and to do feature engineering for building the ...
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Suggestion on the models to estimate public indeces future returns

I would like to to estimate the future returns of some public indeces. I have several of them so it is a multivariate problem. The series are quarterly and the estimation should be of at least 15-20 ...
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R-squared to be computed on training sample or test sample?

I am currently going through the book Machine Learning For Factor Investing whose online version can be read here: http://www.mlfactor.com In the section on model validation, one can read the ...
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Good (non-random walk) financial time series to perform forecasting on

I would like to start with a brief caveat, namely that I am by no means a domain expert in financial markets. Therefore the question I am asking may sound silly to a practitioner but I am asking it ...
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Hidden Markov Model Stock Prediction Next Level

I was able to fit HMM Model in Python on stocks data. I have completed the training and testing part. The overall fit looks good. However, I have a question, I am not able to predict the next "t+...
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transforming a model to long short instead of long-only

I am currently trying to adapt a model to a long short portfolio strategy. The model is stated here: A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem by Jiang, Xu,...
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What is an appropriate Risk Metric for Portfolio Construction when returns are predicted instead of using mean returns?

I am trying to build a portfolio management system as my college project, and the approach I have chosen is that of combining machine learning and mathematical optimization. I am using weekly data. ...
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Stress testing by Banks

AFAIK typically banks stress test it trading portfolio by assuming stressed value of risk factors or by considering times series corresponding to some historical ...
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How to translate patterns in technical analysis into ML model?

Is there any working example/study that quantifies patterns seen in technical charts into tradeable signals for intraday regime? For example, I want to short a stock as it goes up to a 100-minute ...
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How to identify between Analytical, Numerical and ML Model based option pricing? [closed]

I am new to Quantitiative Finance. Coming from Computer Science domain, I wanted to clear the key distinguishing factor between analytical, numerical and ML based models for option pricing. As far as ...
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What kind of data cleansing/scrubbing are hedge funds doing?

It's a well-known fact that several hedge funds have a handful of PhDs just doing data cleansing. All day. Every day. What kind of data cleansing are they actually doing? Is it really that difficult? ...
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Triple barrier labeling for long-short strategy

Based on this previous question: Flexible horizon in Triple Barrier Method For a long-short strategy, should I develop one model to predict the direction of long or short (binary classification) and ...
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Applying day trading strategy into a quantitative strategy

I have been day trading US equities for a while successfully. I have a set of technical indicators and time frame that works for me plus profit taking and stop loss rules. I want to apply the rules ...
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larger sample weights for larger absolute returns?

In section 4.6 of Advances in Financial Machine Learning, Lopez de Prado writes In the previous section we learned a method to bootstrap samples closer to IID. In this section we will introduce a ...
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Algortihm for distributing volume for 1min candle

Context: I have historical 1min prices for stocks, including premarket. However, when importing real-time data, the standard practice in the financial data industry is to give only OHLC (open, high, ...
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Machine learning in stock price prediction [duplicate]

I am new and thinking to experiment in the stock price predication. There are many way like moving average but I am interested in using machine learning. Anyone can help me here to give pointer?
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stock price trend classification using Random Forest in sklearn

I have created a random forest classification model in skicit-learn, but I am unsure how to finalise my forecast. I have built the model and it is showing good results on the testing data. I get a ...
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How to create a local price index?

I have a set of real estate data; historic sales price, square meters, location (latitude, longitude), neighbourhood, city, sold date and bunch of other features. I have used a boosting model to ...
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Standardizing Sharpe Ratio or not when standardizing Features

I am currently trying to check the Feature Autocorrelation for a Trend Strategy. I am using XGBoost for that purpose. In addition I work with SHAP. In the first run I realized that without ...
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Train/test: why 80:20 split performed better than 90:10 split?

Playing with Random Forest Classifier, I am wondering what could cause in a 80:20 split the test results to perform better than in a 90:10 split? With 2000+ data points and: with 80:20 split, ...
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Target variable for a supervised learning approach for market sentiment index

My goal is to produce a signal going from -1 (negative) to +1 (positive) which corresponds to a sentiment index for USA. The index will be computed both based on headlines (taken from some free ...
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Machine Learning approach for the probability estimation of certain events

I am planning a research project on estimating the probability of corporate takeovers. I think that different variables could be indicators to predict takeover bids. For example, price increases in ...
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Minimize Composite Dispersion

Let's say that we have a composite of 10 fixed income portfolios, each with the same benchmark, the US Aggregate. Additionally, let's say that each portfolio has a position in Corporation ABC. The ...
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Backshifting Price Timeseries with Memory Preservation

In Advances in Financial Machine Learning the author makes a case for fractionally differentiated price returns in chapter 5. The reason is to both maintain memory and to generate a stationary time ...
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Using candlesticks for Stock price direction prediction

I am working on a college project wherein I want my machine learning model to predict the one-day-ahead direction of a given stock (i.e. whether the closing price of the stock would rise or fall as ...
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References on cashflow modelling for private equity

I would like to build a model to predict capital calls and distributions of a private equity fund. The first question is: does any of you can address me towards the state of art for it? also machine ...
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How are the values of the ARMA process linked in python

In the code below, you can see that 'ret' is an ARMA process, and I am trying to see how the ret[0], etc... ret3, ret4, etc. are linked to each other, and although I know the formula for the ARMA ...
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What data should I use for a machine learning model

I would like to ask you for an advice of any of you could help me with this information it would be really helpful. I am trying to build a reinforcement learning trading bot that based on the current ...
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What is the best way to impute missing values for financial data?

I've been tasked with imputing missing values for a dataset of ca. 4000 firms and 225 key metrics (e.g. revenue, net income, EPS, PE etc.). Since I haven't found a thread on here which answers my ...
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Steps to fit a Machine learning model for prediction of up and down market movement

I have around 5 years of data of an index containing many features on a daily basis. I want to classify whether the index will move up or down the next trading day (up or down movement is determined ...
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27 votes
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Why are there no papers about stock prediction with machine learning in leading financial journals?

I'm writing my master's thesis about stock price prediction using machine learning methods. During my literature review, I noticed that a lot of research produced on this topic is of poor quality, ...
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How to use 'purging' in predicting stock price tomorrow based on information today?

Q1. How to create an 'overlap' when we predict a stock price tomorrow based on information today? According to the book 'Advances in Financial Machine Learning' written by Marcos Lopez de Prado, the ...
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In Lopez de Prado's Advances in Financial Machine Learning, what is meant by "unnecessary labels"?

In Lopez de Prado's Advances in Financial Machine Learning, Chapter 3, Prof. Lopez de Padro talks about dropping rare labels: ...
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Computing statistics from historical returns

I'm reading age 35 of "Advances in Machine Learning" by de Prado. Consider an IID multivariate Gaussian process characterized by a vector of means μ, of size Nx1, and a covariance matrix V, ...
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Cannot achieve generalization of machine learning model

I'm working on a balanced, binary classification problem in a time-series (financial) dataset. I am using K-fold cross validation that is adapted for time-series (so that I'm never using future data ...
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1 vote
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House price inflation modelling

I have a data set of house prices and their corresponding features (rooms, meter squared, etc). An additional feature is the sold date of the house. The aim is to create a model that can estimate the ...
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How to use machine learning to generate optimal allocations for an instrument?

What is the idea behind using Machine Learning in finance? Let's assume that we have just one instrument given by its prices. At a given moment of time, we can "compress" the available ...
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