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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106 views

How to assign n day target variables in machine learning

I am trying to forecast future price using supervised machine learning. My logic is to take open and close price from t, t-1, t-2 and t-3 period to predict future close price in the period t+1,t+3 ...
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1answer
264 views

Machine learning to build top 3 price scenarios over n days

I have a time series of closing prices for a given stock. I would like to formulate possible future scenarios for the price. My intention is not to use these "likely" scenarios to take any position. ...
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2answers
459 views

Feature Selection Effect on Deep Multi-Layer-Perceptron for Financial Applications

I am trying to build a machine learning system for financial price prediction. I am using a 3 layer MLP (a deep network) with 3 outputs (buy,hold,sell). I am using different features such as price ...
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23 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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39 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
224 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
64 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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61 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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160 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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93 views

Machine Learnign for Factor Model python [closed]

I have read several articles about Factor Model using Deep Learning or machine learning, but none of them post the code. Where can I find the python code for anything similar?
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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 ...
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83 views

Uniqueness of data metric [closed]

Is there a metric that calculates "uniqueness of data"? For example if i have two sets of 200 observations, DataSet 1 has 70 unique values but 4 values take up the next 130 observations. DataSet 2 ...
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31 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, ...
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2answers
660 views

Machine Learning on matlab 2010

I am trying to develop a trading model. It uses certain technical and fundamental features and the model learns from the past. I have a 3-class output - bullish, neutral and bearish. On trying neural ...
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1answer
83 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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2answers
2k views

What’s the derivative of the sharpe ratio for one asset? Trying to optimize on it for a model

It seems most Sharpe ratio derivations seem to be for portfolios but I am just tracking a single asset? $SR = (r_p - r_f) / \sigma_p$ but what would I derive with respect to for an optimization/ ...
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1answer
147 views

Market, Limit and Cancellation orders

From the paper https://web.stanford.edu/class/msande448/2017/Final/Reports/gr4.pdf page 8, I need at least the limit and market order. I can easily find the full depth from dxfeed or algoseek, but I ...
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3answers
709 views

Appropriate way to normalize Bollinger Bands?

I am playing around with using neural nets to make predictions on market trends. I am currently feeding in a portfolio of historical data of many stocks, and am now implementing several technical ...
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1answer
422 views

ML classification algorithms give random profit [closed]

I use backtrader python framework to backtest ML classification algorithms to make decision ...
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2answers
138 views

Imputation of missing returns

I'm trying to calculate a historical VaR for a portfolio of futures, however there are certain days for which some assets are missing prices. Since the portfolio consists of many spread positions, the ...
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2answers
438 views

Can someone please share examples of machine learning in quantitative finance? [closed]

There has been a lot said about the application of AI, ML and Neural Networks in trading for predictive modelling. I was unable to find any relevant examples that prove a credible output based on ...
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1answer
84 views

Subset selection to identify independent variables that impact the market?

Given a lot of market-related features (~100 independent variables such as emerging market, developed market, s&p 500, tech sector returns, etc), I need to select a subset of them that are ideally ...
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3answers
197 views

Combining Quantitative data with fundamental data

These day, there is relatively new phenomena of combining quantitative data and fundamental data called 'Quantamentals'. In this regards, I was wondering how to combine Four Essential Types of ...
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1answer
78 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
54 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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1answer
94 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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2answers
639 views

Hedging with machine learning

I’ve been thinking about an interesting problem lately: Suppose I have a position in an exotic derivative. How can I automate the hedging process? Traditionally, one build a pricing model and ...
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1answer
119 views

Find a reasonable h

The mid-price at time $t$ is denoted by $$p_t = \frac{s_t^{a,1} + s_t^{b,1}}{2}.$$ This mid-price can evolve in minimum increments of half a tick but is almost always observed to move at ...
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1answer
82 views

How to properly classify rate of change?

I am working in a Machine Learning Model for Bitcoin Price. I am attempting to predict how much the price changes in the next day. I am approaching this as a classification problem instead of ...
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1answer
141 views

Unsupervised learning and in out of sample

Assume we are given $N$ samples, let's say small timeseries of 1 hour resolution daily exchange rates - for the sake of argument. Each sample is a $24$ element vector $x$. Then we proceed to do ...
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1answer
152 views

How to normalize various indicators into one column?

I've seen this video which talks about how to compress different indicators into a sin https://www.youtube.com/watch?v=sDu6CudKa0Q I tried to do the same by this way: ...
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2answers
260 views

Scaling the data to train, then how to scale the input data?

I'm somewhat new into the world of trading algo's, so bare with me. I've made a dataframe with 5 features say. I used preprocessing.scale to scale it. I checked the csv dump of it and it looks fine ...
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1answer
237 views

Choosing a weak learner

I want to compare different error rates of different classifiers with the error rate from a weak learner (better than random guessing). So, my question is, what are a few choices for a simple, easy to ...
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0answers
25 views

Accurate day trading environment model using model-base methods

There are several different angles we can classify Reinforcement Learning methods from. We can distinguished three main aspects : Value-based and policy-based On-policy and off-policy Model-free and ...
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0answers
20 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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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
141 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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33 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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0answers
302 views

How Machine Learning model addresses adverse action concerns -credit scorcard?

How to find the variables involved in the decision to report adverse action when the origination scorecard is developed using Machine Learning - XGBOOST with monotonic constraints (80 variables) ...
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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 ...
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1answer
126 views

Markov chain downgrades in loan book modelling

I am working on a personal loan dataset. For each loan, we recorded its credit status monthly after it was drawn by the borrower. Let's say there were 6 status coded by A-F. My project is to use ...
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1answer
124 views

Separate market and limit orders from market depth/tick data

From the website https://www.algoseek.com/equities/, we can get a sample of the full depth market/tick data. From the paper https://arxiv.org/pdf/1710.03870.pdf page 8, I would like to extract the ...
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1answer
485 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 ...
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1answer
492 views

z-score versus log standardisation of stock prices for calculating correlation; which to use (in ML clustering, distance measure)?

I need to compare (get correlation between) different financial instruments (stocks). The problem is that different stocks will have different price scales. I was thinking of using z-score ...
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1answer
313 views

python: How use the S&P 500 index to predict japan stock,namely timezone issue

I want to use American stock index, such as S&P 500 index(open, close...) to predict japan stock daily close price or other with machine learning. I found that there is timezone between japan and ...

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