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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96 views

NLP related finance projects

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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101 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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38 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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69 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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29 views

CART vs Random Forest [migrated]

Considering that individual trees in random forests use the cart algorithm(or can be configured to) , if cart fails to predict anything(empty tree) ,why should random forest perform better on the same ...
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30 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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47 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
46 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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74 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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37 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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1answer
82 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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40 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
123 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
131 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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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
70 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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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 ...
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23 views

Machine-learning (python) non-parametric continuous variables and output

There are various machine-learning techniques available, of which I know there is the (K) NN -> nearest neighbour. However, it seems most non-parametric ML techniques need the input and output to be '...
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1answer
24 views

Mutual fund rating predictions

I am working on a dataset with aim to predict the MF ratings. There are cols like, 10 yr, 7 yr, 5 yr etc returns. I also have commencement date of MFs, the question is there are MFs with commencements ...
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1answer
118 views

Forecasting volatility with machine learning: Performance comparison

ARIMA and GARCH are old news for predicting volatility time series of asset returns. How does the performances of machine learning algorithms compare (such as random forest, support vector ...
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1answer
61 views

What's the meaning of linearity in classical statistics in Prado's book?

I am reading Prado's new book, Machine Learning for Asset Managers. In the page1 of his book, there is this sentence. To a greater extent than other mathematical disciplines, statistics is a ...
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1answer
48 views

How to use multi-periods and mult-factors to predict stock price by linear regression?

Give data in $t_n$ denoted by $[x_1^n, x_2^n, ... x_d^n]$ and label $y_n$ to be predicted. We can just train a $d$-dimensional linear regression $y_n=\sum b_ix_i^n$ to make a prediction. However, I ...
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83 views

Meta Labeling for trading opportunities

In Advances in Financial Machine Learning, Lopez explains how we should build a primary exogenous model (binary classifier) to identify trading opportunities and a secondary meta model to filter out ...
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95 views

Does the non-causal nature of quant models limit their applicability?

I understand that to describe financial data, we build stochastic models and calibrate their parameters to past data. When coming up with new algorithms, we rely on rigorous backtesting to convince ...
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1answer
96 views

Random Forest on financial time-serie?

Is it okay to apply Random Forest to a non-stationary financial serie? Or would it be correct to first difference the serie and then apply Random Forest to the new serie?
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299 views

Backtest overfitting - in-sample vs out-of-sample

Recently, I read a great paper by De Prado et al. on backtest overfitting problem in Quantitative Finance titled Pseudo-Mathematics and Financial Charlatanism: the Effects of Backtest Overfitting on ...
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272 views

How can I approximate Dollar Bars from Minute Data instead of Tick Data?

Having been influenced by de Prado's Advances in Machine learning book, I've set out to build the dollar bars (in which each bar represents a set dollar amount of transactions in the security) that he ...
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1answer
72 views

Appropriate Encoding for Stock Technical Indicators ? RSI

happy new year and i am new to machine learning + python.. so recently i am doing a project on my own to use machine learning models on technical indicators.. I have my technical indicators data ...
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263 views

What's a good resource of book for Python programming in relation to quantitative finance?

I know some of base Python, but I have only briefly used numpy, pandas, etc... I was wondering what's a good resource to learn Python specifically for quantitative finance. I know of plenty of books/...
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1answer
239 views

How to use neural network for technical analysis?

I am working on building a Neural network for technical analysis of stocks. The input I have is the open price and two (so far) technical indicators : RSI and William's R - for the past 2 years. I can ...
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1answer
179 views

Why meta-labeling is is robust?

With all due respect, I saw this technique in the book , Advances in financial machine learning, but I found that it acts like a filter for the trades only. And it seems doing the job of overfitting ...
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62 views

Using news to predict Stock Prices dataset

In order to build Regression or Deep Learning models for predicting the market, we need a bunch of historical data. Prices and technical indicators are easily accessible, but getting news from the ...
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172 views

Machine learning for portfolio optimization

What algorithms from machine learning, supervised learning or unsupervised learning have been recently used for asset allocation models as alternatives to the Markowitz mean-variance optimization ...
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58 views

machine-learning method to predict PCA weights

I have been using certain linear-regression to extract the PCA (top 3) weights relating to a certain data-set. I was wondering, instead of using linear-regression to generate the weights, I can use ...
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1answer
105 views

Dollar bars in Advances in Financial Machine Learning book

Does anyone have use the dollar bars for building a strategy? I would like to know what ways you guys might be interested to set the dollar bars' parameter ( the dollar value ). I have thought of one ...
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1answer
98 views

Sample uniqueness and sample weight in AFML book

With reference to AFML ("Advances in Financial Machine Learning" book by Marcos Lopez de Prado). Are sample uniqueness and sample weight pointing towards to the same thing? I am confused on the term ...
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1answer
71 views

Labeling and excluding specific market conditions

I'm going through "Advances in Financial ML" book and got stuck with something which is not covered there (correct me if I'm wrong). Let's assume I labeled data to 0, 1, 2 according to triple barrier ...
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2answers
196 views

Flexible horizon in Triple Barrier Method

I'm going through "Advances in Financial ML" book and I really like the ideas behind Triple Barrier Method and using a flexible horizontal threshold based on volatility. What bothers me is that an ...
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34 views

Clusters evolution over time

I have a dataset of stock prices and I want to group stocks that share similar characteristics together using cluster analysis. I'm interested in following the evolution of each cluster over time, but ...
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1answer
95 views

Choosing best expressions from all possible combinations on variables, unary operators and binary operators along with hyper parameters

I have a few financial variables of a stock universe like OHLC prices, volume, and other fundamentals with varying time-frequency. Using this set I'm creating an expression that gives the weights to ...
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49 views

Conceptual help - Machine Learning on finance data set [closed]

I am working on Anomaly detection model problem for a finance data set - set of gift card activation transactions. My team member suggested an idea that " First train the model with normal instances ...
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1answer
80 views

Random Forests - Trees vs Predictors

This question relates to the use of random forests in finance and the relationship between the number of features, the observations, and the number of trees. Consider the relation between an RF, the ...
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1answer
127 views

Building a semi-discretionary system

I've been investing for the last 15 years in a weird Buffett/Soros way. For the last few years I've been toying with the idea of modeling myself. I want to build a 'stock screener' that will be able ...
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3answers
214 views

Getting sets of random correlated variables

For the training of a machine learning model I need to add additional features (macro variables), and these features are correlated. I need to run the model N times, and for each time I have to add ...
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1answer
95 views

Modeling mortgage loan defaults

I have a machine learning model trained with a list of mortgage features that include macro variables where the field to predict (the label) is "Mortgage Defaulted" = 1 or 0 (Yes or No). Now, I need ...
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1answer
159 views

How can stationary time series data be used as input in an ML model?

I am halfway through "Advances in Financial Machine Learning" by Marcos Lopez de Prado. I understand that a time series like stock prices can be transformed to make it sufficiently stationary. ...
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1answer
255 views

Which machine learning model rely on the normality assumption?

In the machine learning project, when the target variable is skewed, we need to use box-cox transformation to turn that into a normal distribution. But why do we need to do that? I mean, besides the ...
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1answer
141 views

Calculating PD of commercial bank loan

I have two main options to calculate PD of a loan in a commercial bank; with and without machine learning. On one hand, there are traditional methods such as Merton or KVM. On the other hand, I could ...
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2answers
107 views

Lower MSE results in less profit when using Machine Learning

When using Machine Learning for predicting stocks, can a lower Mean Squared Error result in less profit after Backtesting or is there a mistake in the experiment?