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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46 views

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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46 views

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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Understanding additive profit of risky and risk free asset

I was going through paper "Learning to Trade via Direct Reinforcement" by Moody and Saffell. It explains additive profit as follows Additive profits are appropriate to consider if each ...
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25 views

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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1answer
54 views

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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1answer
61 views

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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46 views

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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40 views

Leakage and bias in XGBoost trading strategy

I apologize for my persistence, i'm on a course of study and doubts increase every day. My goal is "just" to code a profitable forex trading strategy with machine learning. I'm trying to ...
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133 views

Bug found in Optimal Number of Clusters algorithm - from de Prado and Lewis (2018)

I believe I have found a bug in Optimal Number of Clusters (ONC) from the paper "Detection of False Investment Strategies Using Unsupervised Learning Methods". ...
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55 views

Machine/Deep Learning for Exotic Option Pricing - Reference Request

Exotic options, in general, have very time-consuming valuation models. I believe in recent years there has been some research done on using supervised machine/deep learning to predict the valuation ...
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294 views

Deep Reinforcement Learning in Quant Finance?

I've been struggling to find engaging papers on the application of deep reinforcement learning in quantitative risk analysis, portfolio management, algorithmic trading and/or options pricing. What are ...
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219 views

Forecasting non-maturity deposits with machine learning

I need to forecast non-maturity deposits in a bank. My intent is to use Recurrent Neural Networks (aka deep learning) to model time series. The model will learn from past bank data and macroeconomic ...
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1answer
95 views

Fair Value Regression Methods

Recently we had an invited talk at our university (I'm Ph.D. student in ML department, so I'm sorry if my question is stupid, since I do not have quantitative finance background), where one researcher ...
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How to deal with outliers when normalizing price related input in algorithmic trading

I am training a CNN model for trading using indicator and MA lines to compose a 2D array as input. I want to normalize MA data(ema, sma...) into range between -1 and 1, I have tried several techniques ...
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55 views

Optimising returns weighted by Sharpe ratio in the context of Supervised Learning

In the Kaggle Jane Street market prediction competition we are put in a Supervised Learning Framework to deal with 'trade opportunities'. That is, we are given instances of previous trade ...
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175 views

Markov Property in Optimal Execution?

After reading papers on reinforcement learning with respect to the problem of optimal execution (Nevmyvaka et al (2006), Ning et al (2018), etc), I was wondering if the Markov property assumed in all ...
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209 views

Limit order book modeling based on computational statistics

Is someone aware of publications that try to model limit order book (and market mircostructure) in general using CS tools (such as online machine learning, game theory ecc...) and not stochastic ...
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90 views

Machine Learning model forecasting on real time data in python

I’m building a Forex trading system based on machine learning with Python and brokers API. I get price time series data + fundamental data and then i train the model on that. Model means SVM, RF, ...
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194 views

Reinforcement learning in finance

In brief, what are some mainstream and recent applications of reinforcement learning in finance that fall outside of the usual scope of agent-based modeling?
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82 views

Spectral clustering in finance

What are some examples of applying spectral clustering to financial times series data or other areas of finance? Why spectral clustering was used for each application rather than other types of ...
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1answer
86 views

How can deep learning methods measure implied volatility?

Why and how should we utilize deep learning methods to calculate implied vol of options? I've also heard that finding the fair price of the option is not nearly as important as finding a numerical ...
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96 views

D-Limit and Crumbling Quote Indicator

I've been following the development of the D-Limit order at IEX for some time. In the last couple of days I see the SEC has been sued by Citadel Securities for approving this order type. Can anyone ...
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16 views

Models that can improve FHS (with possible residuals manipulation)

The Filtered Historical Simulation (FHS) is a tough benchmark. By: choosing among the most complicated ARMA-GARCH variants with automatic model and lag selection, manipulating standardized residuals ...
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70 views

How often to tune the regularisation parameter in LASSO?

I'm trying to implement the following paper: Avellaneda & Lee (2010), Statistical Arbitrage in the US equities market. To build the strategy, the idea is to trade a stock and hedge using a basket ...
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111 views

Machine learning algorithms that generate trading models (literature)?

Is there any academic literature on machine learning algorithms that are able to generate functioning trading models? Would this even be feasible at all, now or in the future? Could you point me to ...
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48 views

Methods for feature selection in quant finance dataset

I want to perform features selection on my dataset. I've split my data into train, test and out-of-sample set. The dataset is time-series based, so the split is sequenced in the order that train set ...
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1answer
83 views

Feature engineering for mid-price prediction - quickly changing features

I'm training a fully-connected feed-forward neural network on HFT (limit order book) data to predict the midprice at timepoint $t+\Delta t$ (assuming that $t$ is the current moment, and $\Delta t$ is ...
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31 views

Reliable metric to predict out of sample performance of trading strategy

How can one estimate the performance of a trading strategy on out of sample dataset? Yes, the good old model selection problem. Everyone knows sharpe ratio of your in-sample dataset by itself is a ...
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57 views

Isn't portfolio optimization basically just feature selection?

Statistical learning has a large assortment of tools for conducting feature selection such as PCA analysis, ridge regression, LASSO, SVM and almost every other machine learning algorithm. In portfolio ...
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71 views

How to combine different strategies in a backtest (and IRL)

I am trying to combine long and short strategies into an L/S strategy in my backtesting program. The way I have my backtester set up is it takes a signals object (...
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113 views

Machine learning - assigning a value to each tradable moment

I've been looking at machine learning trading strategies for some time and realized recently that I've been neglecting a very important part of the equation in terms of training an effective model. In ...
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1k views

Learning and applying Quantitative Finance successfully as an individual instead of a team

In the past few months, I became really interested in using machine learning techniques in the realm of quantitative finance and trading. I made a few rudimentary models and I immediately realized how ...
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36 views

In your experience, when trying to predict something that occurs, do you model with a fixed time period?

Let's say you are building a simple model (like the classroom examples) of trying to predict, given past information, if the stock goes up or down in the future. One could, like in classroom examples, ...
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2answers
235 views

NLP related finance projects [closed]

fist of all I do apologize if my question is not fit for this forum, but after much research I didn't find a better place to ask this question. I am a PhD student in mathematics. I do know some ML and ...
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187 views

What are important statistical concepts used as a quant?

I'm interviewing for some quantitative researcher positions at some hedge funds, and I've been told that there will be one interview session focused on stats, and one focused on ML, among others. This ...
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49 views

Is non-linear correlation problematic in financial time series prediction?

Many traditional finance models assume linear relationships between variables and features. Aren't linear correlations/covariances unable to capture financial processes empirically since they actually ...
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84 views

Proof of variance reduction of bagging

In Lecture 4 of the following course: Advances in Financial Machine Learning: 10 Lectures by Marcos Lopez de Prado link in the proof of variance reduction for a ...
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96 views

Using unsupervised classification to find support and resistance levels

I do not have a specific question, it's more of a general & conceptual one. What would be the optimal approach to finding support and resistance levels? Have you approached this problem ...
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57 views

What is the differential Value-at-Risk?

I am currently working on a Machine Learning Project, implementing portfolio optimization algorithms according to different risk measures. I have found sufficient information on Sharpe Ratio ...
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1answer
92 views

Unsupervised learning for portfolio construction

Are there techniques or models in finance that (unlike supervised learning where input data such as returns and volatility is estimated making the asset allocation data-driven) allow for portfolio ...
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128 views

Optimal predictors for 1-month returns

I am implementing a Random Forest classifier algorithm on Python for predicting future stock returns (one month). My goal is to foresee whether the cumulative returns in a month will be negative or ...
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56 views

The discontinuity when applying the combinatorial purged cross-validation

In Marcos Lopez de Prado's book, Advances in financial machine learning, he recommends using the combinatorial purged cross-validation(CPCV) for backtesting. His motivation is sensible. Through the ...
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104 views

Which metric is most predictive: Mean, Sharpe, Calmar, …?

Suppose you have created a new trading algorithm: by varying the params of the algorithm, you get a large number of similar trading strategies (e.g. slightly different trigger thresholds, stop loss ...
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58 views

Looking for references on reinforcement learning in finance

I plan on using reinforcement learning for a research project. To be specific, I plan to define learning environments using market microstructure models whose solutions are well known and see if I can ...
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1answer
300 views

LSTM for trend prediction

Been wanting to get my hands dirty with ML for a while now and since I'm interested in finance and trading as well, I figured this would be a good project to get started after reading Deep LSTM with ...
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3answers
143 views

Dealing with stochastic results of Machine Learning Models

I'm building stock selection models, and pick top 5 and bottom 5 stocks. Given the variability in Stochastic gradient decent results, they keep changing. One way to get consistent results is to use ...
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27 views

What is the correct order of operations when cleaning and structuring financial time series?

I'm studying Lopez' Advances in Financial Machine Learning where he talks about how to sample and structure financial data, as well as how to apply machine learning models to the data. I am also ...
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What machine learning algorithms are important for quant interviews? [closed]

I'm not sure if this question is appropriate for this SE board. If not, I can definitely remove it. FWIW, I saw a few other interview-related questions posted on here. Anyways, I will be ...
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1answer
111 views

How can I combine traditional trading patterns and machine learning algorithms to produce a trading system?

Traditionally, retail traders have leveraged on price patterns discovered by applying graphical tools such as flags, fractals, pennants, heads, shoulders, etc. However, while this method has been ...
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56 views

What benefits do using log returns for model training provide?

I came across a paper that uses Support Vector Machines to classify a buy/sell/hold decision each hour at the $\pm$0.5% threshold. The paper can bee seen here. The ...