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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0answers
66 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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1answer
78 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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49 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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15 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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26 views

```RuntimeWarning: invalid value encountered in double_scalars ``` [migrated]

so I got this code from https://github.com/rodler/quantcon2018/blob/master/Quantcon-RL-Example.ipynb the code is written in Jupyter notebook and is uploaded in 2018, so here are the few changes I made....
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3answers
1k views

Where I can find historical earnings dates for stocks?

I'm trying to find all of the historical earnings dates (just the dates is good enough) for certain stocks ranging back to their IPOs. I'm plan to use it for my machine learning project. Yahoo and ...
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72 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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36 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
68 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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1answer
215 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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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 ...
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43 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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1answer
47 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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2answers
210 views

Forecasting volatility farther ahead with autoregressive machine learning

ARIMA and GARCH are old news for predicting volatility time series of asset returns. I am aware of papers that replace ARIMA and GARCH with machine learning algorithms to predict financial volatility ...
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106 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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2answers
122 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
79 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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7answers
914 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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1answer
30 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
149 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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2answers
126 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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1answer
79 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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45 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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0answers
82 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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1answer
410 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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43 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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0answers
54 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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0answers
122 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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3answers
140 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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0answers
44 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
89 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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1answer
57 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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0answers
50 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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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 ...
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72 views

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 ...
2
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1answer
66 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
80 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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0answers
48 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 ...
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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
25 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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4answers
6k views

Is it really possible to create a robust algorithmic trading strategy for intraday trading?

I'm an engineer doing academic research for my master thesis in the area of quantitative finance, basically the purpose is to study the possibility to create an intraday-trading algorithm. I've tried ...
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0answers
141 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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4answers
574 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 ...
4
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1answer
280 views

The “Universal Model” by Justin Sirignano and Rama Cont

In the nicely written article https://arxiv.org/abs/1803.06917 by Justin Sirignano and Rama Cont, they explained that their model is universal and stationary. I am a bit confused about some questions. ...
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1answer
174 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
424 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 ...
5
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1answer
321 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 ...
3
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1answer
128 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
114 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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3answers
337 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/...