Questions tagged [algorithmic-trading]
For questions about algorithmic trading (stocks, commodities bonds, etc.) and predictive financial algorithms in general.
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How do Deep Momentum Networks work?
Here is a paper about Deep Momentum Networks: https://arxiv.org/pdf/1904.04912.pdf
From what I understand, they are a neural network that's used for stock trading, that uses the Sharpe Ratio as a loss ...
user55176
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Do I need to normalize all state-space variables? If so, how?
I am playing around with a DRL agent in a stock-trading environment.
I have normalized all the external input data (the features that my agent will use). However, what about characteristics that don't ...
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Does $S_{t+1}$ denote the future information in Q-learning?
In Q-learning, $Q(S_t,a)$ is updated by the Bellman equation. $Q(S_t,a) = r + \max_{a'}(Q(S_{t+1},a'))$ where $S_{t+1}$ is the future state.
Let's say $S$ denotes the stock price, does it mean we are ...
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How should I define the reward function for a stock trading-like game?
Problem setting
Consider a game like trading a stock
At each step, the agent can buy / sell a stock.
Trade is a pair of ...
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Is there a way to parallelise the RL training on multiple stocks to avoid the memory issue?
I have some plans in working with Reinforcement Learning in order to predict the stock price movement. For a stock like TSLA
some training features might be the pivot price values and the set of the ...
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Selecting features for a neural network: is it redundant to have a feature that is an average (or max, or min) of some other features
I'm trying to create a neural network that would able to look at the current price of a crypto asset and classify between a "BUY", "SELL" or "HOLD". So far for my input ...
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Given the daily stock prices of the last 3 years, how should I sample the training data for episodic RL?
I am playing around with a stock trading agent trained via (deep) reinforcement learning, including memory replay. The agent is trained for 1000 episodes, where each episode consists of 180 timesteps (...
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Looping over Sarsa algorithm for better Q values
Let's say an RL trading system places trades based on pricing data.
Each episode represents 1 hour of trading, and there are 24 hours of data available. The Q table represents for a given state, what ...
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What are the most popular and effective approaches to leveraging AI for stock price prediction?
Currently, what are the most popular and effective approaches to leveraging AI for stock price prediction?
It seems like there could be several approaches and problem formulations:
Supervised ...
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What are the pros and cons of deep learning and machine learning to develop a trading system?
As I want to start coding a new Trading AI this year (first based on Python and later maybe in C++) I stumbled over the following question:
Today, I would like to make a pro/contra list with you in ...
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How can we make sure how well the reinforcement learning agent works on a stock dataset?
I read a paper, which is about Deep Reinforcement Learning and it tries to use this method on stock data set. It has been shown that it reaches the maximum return (profit). It has been implemented in ...
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How do you game an automatic trading system by messing with data, as opposed to hacking the algorithm itself?
There was a recent question on adversarial AI applications, which led me to start digging around.
Here my interest is not general, but specific:
How do you game an automatic trading system by messing ...
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Suitable reward function for trading buy and sell orders
I am working to build a deep reinforcement learning agent which can place orders (i.e. limit buy and limit sell orders). The actions are ...
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