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.

31 questions with no upvoted or accepted answers
Filter by
Sorted by
Tagged with
4
votes
0answers
104 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 ...
4
votes
0answers
579 views

Algorithms for predicting a couple points in the future

I'm familiar with supervised learning algorithms like regression and neural networks which look at a bunch of input points and learn a function which outputs a value (the value varying depending on ...
3
votes
2answers
120 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 ...
2
votes
0answers
43 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 ...
2
votes
0answers
47 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 ...
2
votes
0answers
118 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 ...
2
votes
1answer
76 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 ...
2
votes
0answers
60 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 ...
2
votes
0answers
425 views

Information Driven Bars (Advances in Financial Machine Learning)

My team and I are busy coding up a python implementation of the information driven bars (imbalance and run bars) mentioned in Chapter 2 of the text book Advances in Financial Machine Learning. There ...
2
votes
0answers
616 views

Getting over bid-ask bounce

One property of High-Frequency data is it's subject to bid-ask bounce. Description : Unlike traditional data based on just closing prices, tick data carry additional supply-and-demand information in ...
2
votes
0answers
412 views

Differential Sortino Ratio

I'm attempting to optimize a reinforcement learning system to maximize risk adjusted returns. I have currently defined the reward as the differential Sharpe ratio at each step: the influence of the ...
2
votes
0answers
305 views

Good books on predictive modeling (for alpha signal research)

In terms of books on predictive models, I find ESL (elements of statistical learning) trying to cover too much and serves more like a reference, instead of explaining and developing the theories for ...
2
votes
0answers
174 views

What are examples of boosting, bagging, stacking or subspace method in quantitative finance?

The above ensemble methods appear useful in several machine learning competitions, like Netflix prize or KDD. They work by diversifying between several model variants. Are they also useful in ...
1
vote
0answers
30 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 ...
1
vote
0answers
81 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 ...
1
vote
0answers
37 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 ...
1
vote
0answers
49 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 ...
1
vote
0answers
24 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 ...
1
vote
0answers
67 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 ...
1
vote
0answers
186 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 ...
1
vote
0answers
29 views

Given historical performance of a financial index, how to categorise different historical periods depending on the market regime at the time?

We are trying to work on a Machine Learning application to attempt to predict market regime changes (bull, bear, stale?). Generally a ML algorithm needs well defined training data for establishing its ...
1
vote
0answers
32 views

Machine Learning Munging - order of transforms? + adding in econometric tests?

I have a list of possible transforms, and I've read some confusing/contradictory stuff about the preferred order in which these operations are performed. Maybe 1) the order is sometimes amorphous, ...
0
votes
0answers
29 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 ...
0
votes
0answers
38 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 ...
0
votes
0answers
44 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 ...
0
votes
0answers
53 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 ...
0
votes
0answers
120 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 ...
0
votes
0answers
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 '...
0
votes
0answers
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 ...
0
votes
0answers
54 views

Can Q-table learned by specific stock be applied to only that stock?

Let say I develop Q-learning strategy to predict IBM's stock price. So, it means that Q-table is created based on past IBM stock price data. In this case, this Q-table could be applied only to ...
-1
votes
1answer
491 views

How to use exponential smoothing for trading?

I was wondering if there's a rule of thumb regarding the value of alpha used when performing exponential smoothing. I plan to use this technique to preprocess my data before feeding them into my ...