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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Technical Analysis Indicators as input of a LSTM Neural Network ? Need advices
I'm trying to make a trading strategy by training a LSTM neural network with input features being typical technical analysis metrics: RSI, MACD, ema ratio (EMA 50 divided by EMA 200, so that the NN ...
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Advances in financial machine learning (Marcos López de Prado): explanation of snippet 3.1
I have been reading AFML ( Marcos López de Prado
) and I am having trouble understanding snippet 3.1 which provides the following code:
...
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Derivatives without analytic expressions? [closed]
I was wondering if there exist options or other derivatives that do not have a known closed-form analytic expression (i.e., some sort of Black-Scholes PDE) and are usually priced using Monte Carlo ...
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Having a hard time getting sentiment analysis bot to calibrate bet probabilities well
Training a language model for taking bets in the stock market based on news. For each news release I have three categories for each stock: long bet, short bet, or neither. The NN outputs probabilities ...
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Test significance of Sharpe ratio using machine learning
I am trying to create forecasts for ETF returns using machine learning tools and I am creating mean-variance portfolios based on these forecasts. I want to compare the Sharpe ratios of these different ...
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How would I find correlation / association of different time series datapoints with a target variable?
the title is a bit confusing.
Functionally, I have a dataset of N stocks containing options information, short information, and earnings information for each of the N stocks.
For each unique stock in ...
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Should we split data into several periods before calculating class weight? (Advances in Financial Machine Learning)
In the book, section 4.8 class weights, Marcos suggests applying class weight, which I agree because sometimes you have more bullish price action than bearish price action e.g. 52% of the time is ...
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Interpretation of Chu-Stinchcombe-White CUSUM Test results
Context:
I am new to quant finance. I am doing some structural break analysis on a future price time series. I applied the Chu-Stinchcombe-White CUSUM Test from Chap 17 (Advances in Financial Machine ...
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ML/AI in fixed income vs equity
From my perception of learning different ML/AI applications to finance I found there are lots of them in equity and not as many in fixed income. I wonder if the markets are different in some ways and ...
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Machine learning on NASDAQ [duplicate]
I want to perform an ML analysis on NASDAQ, based on historical fundamentals.
Suppose I want to take a temporal window of 10 year and quarterly data.
I have a question:
I suppose I should know the ...
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Using regression for binomial prediction of tomorrow's return
I completed a challenge which asks the user to predict tomorrow's market return. The data available is prices data and the model must be logistic regression. They call it "machine learning" ...
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Lopez de Prado's Triple Barrier Method - do you reset the barriers on every timestep, or only when you reach a hi/lo/no barrier hit?
I've used de Prado's trend scanning labels but now I want to try the triple barrier method idea.
I understand the rough set up, and I understand the logic behind it. However, what I don't understand ...
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ML/DS in fixed income asset management
I am new to the topic but I would like to read papers/books/anything interesting to learn more how ML and data science is used in buy side Fixed income Asset management firms. Factor investing/signals/...
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Suggestions for backtesting machine learning 'model'/strategy in Python
I have coded a machine learning algo (sklearn) in Python, that uses different 'look back periods' for training a model, which is then used to predict future prices of a stock.
It has a 52% accuracy in ...
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What is industry best practice to combine alphas?
Say I have 100 different alphas that all have statistically significant returns in-sample.
Is the best practice to use historical covariance matrix plus Markowitz portfolio theory to create an optimal ...
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What are some interesting recent machine learning related developments in the QF domain?
In 2020 I wrote a MSc thesis on the hedging of exotic options using recurrent neural networks (loosely based on the paper Deep Hedging (2018)by Buehler et al.).
Since then I have been interested in ...
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Differences vs ratios
High, I am working on an exercise which involves performing a regression analysis to predict market direction (e.g. up or down). I am using daily OHLCV data. I've created various factors from the ...
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AFML (by Lopez De Prado) 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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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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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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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 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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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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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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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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